@article {pmid39814210, year = {2025}, author = {Kyoda, Y and Shibamori, K and Tachikawa, K and Nofuji, S and Saito, Y and Tabata, H and Shindo, T and Hashimoto, K and Kobayashi, K and Tanaka, T and Masumori, N}, title = {The change of detrusor contractility at 5 years after transurethral resection of the prostate: a single center prospective observational study.}, journal = {Urology}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.urology.2024.12.038}, pmid = {39814210}, issn = {1527-9995}, abstract = {OBJECTIVE: To prospectively assess the impact of transurethral resection of the prostate (TURP) on detrusor function using pressure flow study (PFS) at 5 years after surgery in a single center prospective non-randomized observational study.<br><br>METHODS: Sixty consecutive male patients were prospectively enrolled and underwent TURP from November 2014 to November 2018. A questionnaire survey, free uroflowmetry and PFS were performed at baseline, and 6, 24 and 60 months after surgery. We divided the age groups at 70 years and defined the younger group as those younger than 70 years old, and the elderly group as those aged 70 years or older. The primary endpoint was the change of the bladder contractility index (BCI).<br><br>RESULTS: Of the 60 patients, 39 completed the protocol. Regardless of age, the bladder outlet obstruction indices at 6, 24, and 60 months after surgery were significantly lower than before surgery (all, p<0.01). Although the BCI did not significantly change during 60 months for the entire group of 39 patients, it was significantly decreased at 60 months (85.6) after surgery compared to before surgery (102) in the elderly group (p=0.02).<br><br>CONCLUSION: We prospectively evaluated detrusor contractility up to 5 years after TURP. It was significantly reduced in the elderly, in spite of which the relief of bladder outlet obstruction was maintained for 5 years after surgery.}, }
@article {pmid39814172, year = {2025}, author = {Wei, Q and Fan, W and Li, HF and Wanga, PS and Xu, M and Dong, HL and Yu, H and Lyu, J and Luo, WJ and Chen, DF and Ge, W and Wu, ZY}, title = {Biallelic variants in SREBF2 cause autosomal recessive spastic paraplegia.}, journal = {Journal of genetics and genomics = Yi chuan xue bao}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.jgg.2025.01.004}, pmid = {39814172}, issn = {1673-8527}, abstract = {Hereditary spastic paraplegias (HSPs) refer to a genetically and clinically heterogeneous group of neurodegenerative disorders characterized by the degeneration of motor neurons. To date, a significant number of patients still have not received a definite genetic diagnosis. Therefore, identifying unreported causative genes continues to be of great importance. Here, we perform whole exome sequencing in a cohort of Chinese HSP patients. Three homozygous variants (p.L604W, p.S517F, and p.T984A) within the sterol regulatory element-binding factor 2 (SREBF2) gene are identified in one autosomal recessive family and two sporadic patients, respectively. Co-segregation is confirmed by Sanger sequencing in all available members. The three variants are rare in the public or in-house database and are predicted to be damaging. The biological impacts of variants in SREBF2 are examined by functional experiments in patient-derived fibroblasts and Drosophila. We find that the variants upregulate cellular cholesterol due to the overactivation of SREBP2, eventually impairing the autophagosomal and lysosomal functions. The overexpression of the mature form of SREBP2 leads to locomotion defects in Drosophila. Our findings identify SREBF2 as a causative gene for HSP and highlight the impairment of cholesterol as a critical pathway for HSP.}, }
@article {pmid39813939, year = {2025}, author = {Chen, L and Yin, Z and Gu, X and Zhang, X and Cao, X and Zhang, C and Li, X}, title = {Neurophysiological data augmentation for EEG-fNIRS multimodal features based on a denoising diffusion probabilistic model.}, journal = {Computer methods and programs in biomedicine}, volume = {261}, number = {}, pages = {108594}, doi = {10.1016/j.cmpb.2025.108594}, pmid = {39813939}, issn = {1872-7565}, abstract = {BACKGROUND AND OBJECTIVE: The hybrid brain computer interfaces (BCI) combining electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have attracted extensive attention for overcoming the decoding limitations of the single-modality BCI. With the deepening application of deep learning approaches in BCI systems, its significant performance improvement has become apparent. However, the scarcity of brain signal data limits the performance of deep learning models.<br><br>METHODS: In this study, we proposed an EEG-fNIRS data augmentation framework based on the combination of denoising diffusion probabilistic model (DDPM) and adding Gaussian noise (EFDA-CDG), for enhancing the performance of hybrid BCI systems. Firstly, we unified the temporal and spatial dimensions of EEG and fNIRS by manually extracting features and spatial mapping interpolation to create EEG-fNIRS joint distribution samples. Then, the DDPM generative model was combined with the traditional method of adding Gaussian noise to provide richer training data for the classifier. Finally, we constructed a classification module that applies EEG feature attention and fNIRS terrain attention to improve classification accuracy.<br><br>RESULTS: In order to evaluate the effectiveness of EFDA-CDG framework, experiments were conducted and fully validated on three publicly available databases and one self-collected database. In the context of a participant-dependent training approach, our method achieves accuracy rates of 82.02% for motor imagery, 91.93% for mental arithmetic, and 90.54% for n-back tasks on public databases. Additionally, our method boasts an accuracy rate of 97.82% for drug addiction discrimination task on the self-collected database.<br><br>CONCLUSIONS: EFDA-CDG framework successfully facilitates data augmentation, thereby enhancing the performance of EEG-fNIRS hybrid BCI systems.}, }
@article {pmid39811472, year = {2024}, author = {Maibam, PC and Pei, D and Olikkal, P and Vinjamuri, RK and Kakoty, NM}, title = {Enhancing prosthetic hand control: A synergistic multi-channel electroencephalogram.}, journal = {Wearable technologies}, volume = {5}, number = {}, pages = {e18}, pmid = {39811472}, issn = {2631-7176}, abstract = {Electromyogram (EMG) has been a fundamental approach for prosthetic hand control. However it is limited by the functionality of residual muscles and muscle fatigue. Currently, exploring temporal shifts in brain networks and accurately classifying noninvasive electroencephalogram (EEG) for prosthetic hand control remains challenging. In this manuscript, it is hypothesized that the coordinated and synchronized temporal patterns within the brain network, termed as brain synergy, contain valuable information to decode hand movements. 32-channel EEGs were acquired from 10 healthy participants during hand grasp and open. Synergistic spatial distribution pattern and power spectra of brain activity were investigated using independent component analysis of EEG. Out of 32 EEG channels, 15 channels spanning the frontal, central and parietal regions were strategically selected based on the synergy of spatial distribution pattern and power spectrum of independent components. Time-domain and synergistic features were extracted from the selected 15 EEG channels. These features were employed to train a Bayesian optimizer-based support vector machine (SVM). The optimized SVM classifier could achieve an average testing accuracy of 94.39 .84% using synergistic features. The paired t-test showed that synergistic features yielded significantly higher area under curve values (p < .05) compared to time-domain features in classifying hand movements. The output of the classifier was employed for the control of the prosthetic hand. This synergistic approach for analyzing temporal activities in motor control and control of prosthetic hands have potential contributions to future research. It addresses the limitations of EMG-based approaches and emphasizes the effectiveness of synergy-based control for prostheses.}, }
@article {pmid38934637, year = {2025}, author = {Tankus, A and Stern, E and Klein, G and Kaptzon, N and Nash, L and Marziano, T and Shamia, O and Gurevitch, G and Bergman, L and Goldstein, L and Fahoum, F and Strauss, I}, title = {A Speech Neuroprosthesis in the Frontal Lobe and Hippocampus: Decoding High-Frequency Activity into Phonemes.}, journal = {Neurosurgery}, volume = {96}, number = {2}, pages = {356-364}, doi = {10.1227/neu.0000000000003068}, pmid = {38934637}, issn = {1524-4040}, support = {17630//Ministry of Science and Technology, Israel/ ; }, mesh = {Humans ; Male ; Adult ; *Hippocampus/surgery/physiology ; *Frontal Lobe/surgery/physiology ; Speech/physiology ; Electrodes, Implanted ; Brain-Computer Interfaces ; Neural Prostheses ; }, abstract = {BACKGROUND AND OBJECTIVES: Loss of speech due to injury or disease is devastating. Here, we report a novel speech neuroprosthesis that artificially articulates building blocks of speech based on high-frequency activity in brain areas never harnessed for a neuroprosthesis before: anterior cingulate and orbitofrontal cortices, and hippocampus.<br><br>METHODS: A 37-year-old male neurosurgical epilepsy patient with intact speech, implanted with depth electrodes for clinical reasons only, silently controlled the neuroprosthesis almost immediately and in a natural way to voluntarily produce 2 vowel sounds.<br><br>RESULTS: During the first set of trials, the participant made the neuroprosthesis produce the different vowel sounds artificially with 85% accuracy. In the following trials, performance improved consistently, which may be attributed to neuroplasticity. We show that a neuroprosthesis trained on overt speech data may be controlled silently.<br><br>CONCLUSION: This may open the way for a novel strategy of neuroprosthesis implantation at earlier disease stages (eg, amyotrophic lateral sclerosis), while speech is intact, for improved training that still allows silent control at later stages. The results demonstrate clinical feasibility of direct decoding of high-frequency activity that includes spiking activity in the aforementioned areas for silent production of phonemes that may serve as a part of a neuroprosthesis for replacing lost speech control pathways.}, }
@article {pmid39809040, year = {2025}, author = {Xu, R and Allison, BZ and Zhao, X and Liang, W and Wang, X and Cichocki, A and Jin, J}, title = {Multi-Scale Pyramid Squeeze Attention Similarity Optimization Classification Neural Network for ERP Detection.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {184}, number = {}, pages = {107124}, doi = {10.1016/j.neunet.2025.107124}, pmid = {39809040}, issn = {1879-2782}, abstract = {Event-related potentials (ERPs) can reveal brain activity elicited by external stimuli. Innovative methods to decode ERPs could enhance the accuracy of brain-computer interface (BCI) technology and promote the understanding of cognitive processes. This paper proposes a novel Multi-Scale Pyramid Squeeze Attention Similarity Optimization Classification Neural Network (MS-PSA-SOC) for ERP Detection. The model integrates a multi-scale architecture, self-attention mechanism, and deep metric learning to achieve a more comprehensive, refined, and discriminative feature representation. The MS module aggregates fine-grained local features and global features with a larger receptive field within a multi-scale architecture, effectively capturing the dynamic characteristics of complex oscillatory activities in the brain at different levels of abstraction. This preserves complementary spatiotemporal representation information. The PSA module continues the multi-scale contextual modeling from the previous module and achieves adaptive recalibration of multi-scale features. By employing effective aggregation and selection mechanisms, it highlights key features while suppressing redundant information. The SOC module jointly optimizes similarity metric loss and classification loss, maintaining the feature space distribution while focusing on sample class labels. This optimization of similarity relationships between samples improves the model's generalization ability and robustness. Results from public and self-collected datasets demonstrate that the command recognition accuracy of the MS-PSA-SOC model is at least 3.1% and 2.8% higher than other advanced algorithms, achieving superior performance. Additionally, the method demonstrates a lower standard deviation across both datasets. This study also validated the network parameters based on Shannon's sampling theorem and EEG "microstates" through relevant experiments.}, }
@article {pmid39808940, year = {2025}, author = {Gu, M and Pei, W and Gao, X and Wang, Y}, title = {Optimizing the proportion of stimulation area in a grid stimulus for user-friendly SSVEP-based BCIs.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/adaa1e}, pmid = {39808940}, issn = {1741-2552}, abstract = {OBJECTIVE: Steady-state visual evoked potentials (SSVEPs) rely on the photic driving response to encode electroencephalogram (EEG) signals stably and efficiently. However, the user experience of the traditional stimulation with high-contrast flickers urgently needs to be improved. In this study, we introduce a novel paradigm of grid stimulation with weak flickering perception, distinguished by a markedly lower proportion of stimulation area in the overall pattern.<br><br>APPROACH: In an offline single-target experiment, we investigated the unique characteristics of SSVEPs evoked by varying proportions in grid stimuli within low and medium frequency bands. Based on the analysis of simulation performance across a four-class brain-computer interface (BCI) task and the evaluation of user experience questionnaires, a subset of paradigms that balance performance and comfort were selected for implementation in four-target online BCI systems.<br><br>MAIN RESULTS: Our results demonstrate that even ultra-low stimulation proportion paradigms can still evoke strong responses within specific frequency bands, effectively enhancing user experience with low and middle frequency stimuli. Notably, proportions of 0.94% and 2.10% within the 3-5 Hz range provide an optimal balance between performance and user experience. For frequencies extending up to 15 Hz, a 2.10% proportion remains ideal. At 20 Hz, slightly higher proportions of 3.75% and 8.43% maintain these benefits.<br><br>SIGNIFICANCE: These findings are crucial for advancing the development of effective and user-friendly SSVEP-based BCI systems.}, }
@article {pmid39808939, year = {2025}, author = {Prakash, P and Lei, T and Flint, RD and Hsieh, JK and Fitzgerald, Z and Mugler, EM and Templer, J and Goldrick, MA and Tate, MC and Rosenow, JM and Glaser, JI and Slutzky, MW}, title = {Decoding speech intent from non-frontal cortical areas.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/adaa20}, pmid = {39808939}, issn = {1741-2552}, abstract = {Brain-machine interfaces (BMIs) have advanced greatly in decoding speech signals originating from the speech motor cortices. Primarily, these BMIs target individuals with intact speech motor cortices but who are paralyzed by disrupted connections between frontal cortices and their articulators due to brainstem stroke or motor neuron diseases such as amyotrophic lateral sclerosis. A few studies have shown some information outside the speech motor cortices, such as in parietal and temporal lobes, that also may be useful for BMIs. The ability to use information from outside the frontal lobes could be useful not only for people with locked-in syndrome but also to people with frontal lobe damage, which can cause nonfluent aphasia or apraxia of speech. However, temporal and parietal lobes are predominantly involved in perceptive speech processing and comprehension. Therefore, to be able to use signals from these areas in a speech BMI, it is important to ascertain that they are related to production. Here, using intracranial recordings, we sought evidence for whether, when and where neural information related to speech intention could be found in the temporal and parietal cortices. Causal information enabled us to distinguish speech intent from resting state and other processes involved in language processing or working memory. Information related to speech intent was distributed widely across the temporal and parietal lobes, including superior temporal, medial temporal, angular, and supramarginal gyri. This provides evidence of a decodable production-related signal in these areas. This insight may help in designing speech brain-machine interfaces that could benefit people with locked-in syndrome, aphasia or apraxia of speech.}, }
@article {pmid39808931, year = {2025}, author = {Russo, JS and Shiels, TA and Lin, CS and John, SE and Grayden, DB}, title = {Decoding imagined movement in people with multiple sclerosis for brain-computer interface translation.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/adaa1d}, pmid = {39808931}, issn = {1741-2552}, abstract = {Multiple Sclerosis (MS) is a heterogeneous autoimmune-mediated disorder affecting the central nervous system, commonly manifesting as fatigue and progressive limb impairment. This can significantly impact quality of life due to weakness or paralysis in the upper and lower limbs. A Brain-Computer Interface (BCI) aims to restore quality of life through control of an external device, such as a wheelchair. However, the limited BCI research in people with MS has been confined to exploring the P300 response and brain signals associated with attempted movement. The current study aims to expand the MS-BCI literature by highlighting the feasibility of decoding MS imagined movement. Approach. We collected electroencephalography (EEG) data from eight participants with various symptoms of MS and ten neurotypical control participants. Participants made imagined movements of the hands and feet as directed by a go no-go protocol. Binary regularised linear discriminant analysis was used to classify imagined movement vs. rest and vs. movement at individual time-frequency points. The frequency bands which provided the maximal accuracy, and the associated latency, were compared. Main Results. In all MS participants, the classification algorithm achieved above 70% accuracy in at least one imagined movement vs. rest classification and most movement vs. movement classifications. There was no significant difference between classification of limbs with weakness or paralysis to neurotypical controls. Both the MS and control groups possessed decodable information within the alpha (7-13 Hz) and beta (16-30 Hz) bands at similar latency. Significance. This study is the first to demonstrate the feasibility of decoding imagined movements in people with MS. As an alternative to the P300 response, motor imagery-based control of a BCI may also be combined with existing motor imagery therapy to supplement MS rehabilitation. These promising results merit further long term BCI studies to investigate the effect of MS progression on classification performance. .}, }
@article {pmid39808922, year = {2025}, author = {Dekleva, BM and Collinger, J}, title = {Using transient, effector-specific neural responses to gate decoding for brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/adaa1f}, pmid = {39808922}, issn = {1741-2552}, abstract = {Real-world implementation of brain-computer interfaces (BCI) for continuous control of devices should ideally rely on fully asynchronous decoding approaches. That is, the decoding algorithm should continuously update its output by estimating the user's intended actions from real-time neural activity, without the need for any temporal alignment to an external cue. This kind of open-ended temporal flexibility is necessary to achieve naturalistic and intuitive control, but presents a challenge: how do we know when it is appropriate to decode anything at all? Activity in motor cortex is dynamic and modulates with many different types of actions (proximal arm control, hand control, speech, etc.), which can interfere with each other. Additionally, the "decodability" of any given action type (amount of relevant information present in the neural activity) fluctuates over time based on motor intent as well as intrinsic network dynamics. Here we present a method for simplifying the problem of continual decoding that uses transient, end effector-specific neural responses to identify periods of effector engagement. For example, we have observed unique neural signatures at the onset and offset of hand-related actions. Only after detecting the period of engagement do we then decode specific action features (e.g. digit movement or force). By using this gated approach, decoding models can be simpler (owing to local linearities) and are less sensitive to interference from cross-effector interference such as combined reaching and grasping actions. Clinical Trial ID: NCT01894802.}, }
@article {pmid39805390, year = {2025}, author = {Xu, A and Huang, Y and Wu, B and Zhang, J and Deng, B and Cai, M and Cao, J and Wang, J and Yang, B and Shao, X and He, Q and Ying, M}, title = {Phase separation-based screening identifies arsenic trioxide as the N-Myc-DNA interaction inhibitor for neuroblastoma therapy.}, journal = {Cancer letters}, volume = {}, number = {}, pages = {217449}, doi = {10.1016/j.canlet.2025.217449}, pmid = {39805390}, issn = {1872-7980}, }
@article {pmid39805258, year = {2025}, author = {Liu, DH and Kumar, S and Alawieh, H and Racz, FS and Millan, JDR}, title = {Personalized μ-transcranial alternating current stimulation improves online brain-computer interface control.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ada9c0}, pmid = {39805258}, issn = {1741-2552}, abstract = {OBJECTIVE: A motor imagery (MI)-based brain-computer interface (BCI) enables users to engage with external environments by capturing and decoding electroencephalography (EEG) signals associated with the imagined movement of specific limbs. Despite significant advancements in BCI technologies over the past 40 years, a notable challenge remains: many users lack BCI proficiency, unable to produce sufficiently distinct and reliable MI brain patterns, hence leading to low classification rates in their BCIs. The objective of this study is to enhance the online performance of MI-BCIs in a personalized, biomarker-driven approach using transcranial alternating current stimulation (tACS).<br><br>APPROACH: Previous studies have identified that the peak power spectral density (PSD) value in sensorimotor idling rhythms is a neural correlate of participants' upper limb MI-BCI performances. In this active-controlled, single-blind study, we applied 20 minutes of tACS at the participant-specific, peak &#xb5; frequency in resting-state sensorimotor rhythms (SMRs), with the goal of enhancing resting-state &#xb5; SMRs.<br><br>MAIN RESULTS: After tACS, we observed significant improvements in event-related desynchronizations (ERDs) of &#xb5; sensorimotor rhythms (SMRs), and in the performance of an online MI-BCI that decodes left versus right hand commands in healthy participants (N=10) -but not in an active control-stimulation control group (N=10). Lastly, we showed a significant correlation between the resting-state &#xb5; SMRs and &#xb5; ERD, offering a mechanistic interpretation behind the observed changes in online BCI performances.<br><br>SIGNIFICANCE: Our research lays the groundwork for future non-invasive interventions designed to enhance BCI performances, thereby improving the independence and interactions of individuals who rely on these systems.}, }
@article {pmid39803338, year = {2024}, author = {Kim, KH and Jeong, JH and Ko, MJ and Lee, S and Kwon, WK and Lee, BJ}, title = {Using Artificial Intelligence in the Comprehensive Management of Spinal Cord Injury.}, journal = {Korean journal of neurotrauma}, volume = {20}, number = {4}, pages = {215-224}, pmid = {39803338}, issn = {2234-8999}, abstract = {Spinal cord injury (SCI) frequently results in persistent motor, sensory, or autonomic dysfunction, and the outcomes are largely determined by the location and severity of the injury. Despite significant technological progress, the intricate nature of the spinal cord anatomy and the difficulties associated with neuroregeneration make full recovery from SCI uncommon. This review explores the potential of artificial intelligence (AI), with a particular focus on machine learning, to enhance patient outcomes in SCI management. The application of AI, specifically machine learning, has revolutionized the diagnosis, treatment, prognosis, and rehabilitation of patients with SCI. By leveraging large datasets and identifying complex patterns, AI contributes to improved diagnostic accuracy, optimizes surgical procedures, and enables the personalization of therapeutic interventions. AI-driven prognostic models provide accurate predictions of recovery, facilitating improved planning and resource allocation. Additionally, AI-powered rehabilitation systems, including robotic devices and brain-computer interfaces, increase the effectiveness and accessibility of therapy. However, realizing the full potential of AI in SCI care requires ongoing research, interdisciplinary collaboration, and the development of comprehensive datasets. As AI continues to evolve, it is expected to play an increasingly vital role in enhancing the outcomes of patients with SCI.}, }
@article {pmid39801915, year = {2025}, author = {Gu, C and Jin, X and Zhu, L and Yi, H and Liu, H and Yang, X and Babiloni, F and Kong, W}, title = {Cross-session SSVEP brainprint recognition using attentive multi-sub-band depth identity embedding learning network.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {15}, pmid = {39801915}, issn = {1871-4080}, abstract = {Brainprint recognition technology, regarded as a promising biometric technology, encounters challenges stemming from the time-varied, low signal-to-noise ratio of brain signals, such as electroencephalogram (EEG). Steady-state visual evoked potentials (SSVEP) exhibit high signal-to-noise ratio and frequency locking, making them a promising paradigm for brainprint recognition. Consequently, the extraction of time-invariant identity information from SSVEP EEG signals is essential. In this paper, we propose an Attentive Multi-sub-band Depth Identity Embedding Learning Network for stable cross-session SSVEP brainprint recognition. To address the issue of low recognition accuracy across sessions, we introduce the Sub-band Attentive Frequency mechanism, which integrates the frequency-domain relevant characteristics of the SSVEP paradigm and focuses on exploring depth-frequency identity embedding information. Also, we employ Attentive Statistic Pooling to enhance the stability of frequency domain feature distributions across sessions. Extensive experimentation and validation were conducted on two multi-session SSVEP benchmark datasets. The experimental results show that our approach outperforms other state-of-art models on 2-second samples across sessions and has the potential to serve as a benchmark in multi-subject biometric recognition systems.}, }
@article {pmid39801913, year = {2025}, author = {Zhou, Y and Wang, P and Gong, P and Wan, P and Wen, X and Zhang, D}, title = {Cross-subject mental workload recognition using bi-classifier domain adversarial learning.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {16}, pmid = {39801913}, issn = {1871-4080}, abstract = {To deploy Electroencephalogram (EEG) based Mental Workload Recognition (MWR) systems in the real world, it is crucial to develop general models that can be applied across subjects. Previous studies have utilized domain adaptation to mitigate inter-subject discrepancies in EEG data distributions. However, they have focused on reducing global domain discrepancy, while neglecting local workload-categorical domain divergence. This degrades the workload-discriminating ability of subject-invariant features. To deal with this problem, we propose a novel joint category-wise and domain-wise alignment Domain Adaptation (cdaDA) algorithm, using bi-classifier learning and domain discriminative adversarial learning. The bi-classifier learning approach is adopted to address the similarities and differences between categories, helping to align EEG data within the same mental workload categories. Additionally, the domain discriminative adversarial learning technique is adopted to consider global domain information by minimizing global domain discrepancy. By integrating both local category information and global domain information, the cdaDA model performs a coarse-to-fine alignment and achieves promising cross-subject MWR results.}, }
@article {pmid39801910, year = {2025}, author = {Wang, J and Zhang, L and Chen, S and Xue, H and Du, M and Xu, Y and Liu, S and Ming, D}, title = {Individuals with high autistic traits exhibit altered interhemispheric brain functional connectivity patterns.}, journal = {Cognitive neurodynamics}, volume = {19}, number = {1}, pages = {9}, pmid = {39801910}, issn = {1871-4080}, abstract = {Individuals with high autistic traits (AT) encounter challenges in social interaction, similar to autistic persons. Precise screening and focused interventions positively contribute to improving this situation. Functional connectivity analyses can measure information transmission and integration between brain regions, providing neurophysiological insights into these challenges. This study aimed to investigate the patterns of brain networks in high AT individuals to offer theoretical support for screening and intervention decisions. EEG data were collected during a 4-min resting state session with eyes open and closed from 48 participants. Using the Autism Spectrum Quotient (AQ) scale, participants were categorized into the high AT group (HAT, n = 15) and low AT groups (LAT, n = 15). We computed the interhemispheric and intrahemispheric alpha coherence in two groups. The correlation between physiological indices and AQ scores was also examined. Results revealed that HAT exhibited significantly lower alpha coherence in the homologous hemispheres of the occipital cortex compared to LAT during the eyes-closed resting state. Additionally, significant negative correlations were observed between the degree of AT (AQ scores) and the alpha coherence in the occipital cortex, as well as in the right frontal and left occipital regions. The findings indicated that high AT individuals exhibit decreased connectivity in the occipital region, potentially resulting in diminished ability to process social information from visual inputs. Our discovery contributes to a deeper comprehension of the neural underpinnings of social challenges in high AT individuals, providing neurophysiological signatures for screening and intervention strategies for this population.}, }
@article {pmid39798828, year = {2025}, author = {Wu, L and Jiang, M and Zhao, M and Hu, X and Wang, J and Zhang, K and Jia, K and Ren, F and Gao, F}, title = {Right Inferior Frontal Cortex and preSMA in Response Inhibition: An Investigation Based on PTC Model.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {121004}, doi = {10.1016/j.neuroimage.2025.121004}, pmid = {39798828}, issn = {1095-9572}, abstract = {Response inhibition is an essential component of cognitive function. A large body of literature has used neuroimaging data to uncover the neural architecture that regulates inhibitory control in general and movement cancelation. The presupplementary motor area (preSMA) and the right inferior frontal cortex (rIFC) are the key nodes in the inhibitory control network. However, how these two regions contribute to response inhibition remains controversial. Based on the Pause-then-Cancel Model (PTC), this study employed functional magnetic resonance imaging (fMRI) to investigate the functional specificity of two regions in the stopping process. The Go/No-Go task (GNGT) and the Stop Signal Task (SST) were administered to the same group of participants. We used the GNGT to dissociate the pause process and both the GNGT and the SST to investigate the inhibition mechanism. Imaging data revealed that response inhibition produced by both tasks activated the preSMA and rIFC. Furthermore, an across-participants analysis showed that increased activation in the rIFC was associated with a delay in the go response in the GNGT. In contrast, increased activation in the preSMA was associated with good inhibition efficiency via the striatum in both GNGT and SST. These behavioral and imaging findings support the PTC model of the role of rIFC and preSMA, that the former is involved in a pause process to delay motor responses, whereas the preSMA is involved in the stopping of motor responses.}, }
@article {pmid39796947, year = {2024}, author = {Chio, N and Quiles-Cucarella, E}, title = {A Bibliometric Review of Brain-Computer Interfaces in Motor Imagery and Steady-State Visually Evoked Potentials for Applications in Rehabilitation and Robotics.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {1}, pages = {}, doi = {10.3390/s25010154}, pmid = {39796947}, issn = {1424-8220}, mesh = {*Brain-Computer Interfaces ; Humans ; *Robotics/methods ; *Evoked Potentials, Visual/physiology ; *Bibliometrics ; Imagination/physiology ; Rehabilitation/methods ; Electroencephalography/methods ; }, abstract = {In this paper, a bibliometric review is conducted on brain-computer interfaces (BCI) in non-invasive paradigms like motor imagery (MI) and steady-state visually evoked potentials (SSVEP) for applications in rehabilitation and robotics. An exploratory and descriptive approach is used in the analysis. Computational tools such as the biblioshiny application for R-Bibliometrix and VOSViewer are employed to generate data on years, sources, authors, affiliation, country, documents, co-author, co-citation, and co-occurrence. This article allows for the identification of different bibliometric indicators such as the research process, evolution, visibility, volume, influence, impact, and production in the field of brain-computer interfaces for MI and SSVEP paradigms in rehabilitation and robotics applications from 2000 to August 2024.}, }
@article {pmid39796911, year = {2024}, author = {Özkahraman, A and Ölmez, T and Dokur, Z}, title = {Performance Improvement with Reduced Number of Channels in Motor Imagery BCI System.}, journal = {Sensors (Basel, Switzerland)}, volume = {25}, number = {1}, pages = {}, doi = {10.3390/s25010120}, pmid = {39796911}, issn = {1424-8220}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Electrooculography/methods ; Deep Learning ; Signal Processing, Computer-Assisted ; Algorithms ; }, abstract = {Classifying Motor Imaging (MI) Electroencephalogram (EEG) signals is of vital importance for Brain-Computer Interface (BCI) systems, but challenges remain. A key challenge is to reduce the number of channels to improve flexibility, portability, and computational efficiency, especially in multi-class scenarios where more channels are needed for accurate classification. This study demonstrates that combining Electrooculogram (EOG) channels with a reduced set of EEG channels is more effective than relying on a large number of EEG channels alone. EOG channels provide useful information for MI signal classification, countering the notion that they only introduce eye-related noise. The study uses advanced deep learning techniques, including multiple 1D convolution blocks and depthwise-separable convolutions, to optimize classification accuracy. The findings in this study are tested on two datasets: dataset 1, the BCI Competition IV Dataset IIa (4-class MI), and dataset 2, the Weibo dataset (7-class MI). The performance for dataset 1, utilizing 3 EEG and 3 EOG channels (6 channels total), is of 83% accuracy, while dataset 2, with 3 EEG and 2 EOG channels (5 channels total), achieves an accuracy of 61%, demonstrating the effectiveness of the proposed channel reduction method and deep learning model.}, }
@article {pmid39793200, year = {2025}, author = {Peters, B and Celik, B and Gaines, D and Galvin-McLaughlin, D and Imbiriba, T and Kinsella, M and Klee, D and Lawhead, M and Memmott, T and Smedemark-Margulies, N and Wiedrick, J and Erdogmus, D and Oken, B and Vertanen, K and Fried-Oken, M}, title = {RSVP Keyboard with Inquiry Preview: mixed performance and user experience with an adaptive, multimodal typing interface combining EEG and switch input.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ada8e0}, pmid = {39793200}, issn = {1741-2552}, abstract = {OBJECTIVE: The RSVP Keyboard is a non-implantable, event-related potential-based brain-computer interface (BCI) system designed to support communication access for people with severe speech and physical impairments. Here we introduce Inquiry Preview, a new RSVP Keyboard interface incorporating switch input for users with some voluntary motor function, and describe its effects on typing performance and other outcomes.<br><br>APPROACH: Four individuals with disabilities participated in the collaborative design of possible switch input applications for the RSVP Keyboard, leading to the development of Inquiry Preview and a method of fusing switch input with language model and electroencephalography (EEG) evidence for typing. Twenty-four participants without disabilities and one potential end user with incomplete locked-in syndrome took part in two experiments investigating the effects of Inquiry Preview and two modes of switch input on typing accuracy and speed during a copy-spelling task.<br><br>MAIN RESULTS: For participants without disabilities, Inquiry Preview and switch input tended to worsen typing performance compared to the standard RSVP Keyboard condition, with more consistent effects across participants for speed than for accuracy. However, there was considerable variability, with some participants demonstrating improved typing performance and better user experience with Inquiry Preview and switch input. Typing performance for the potential end user was comparable to that of participants without disabilities. He typed most quickly and accurately with Inquiry Preview and switch input and gave favorable user experience ratings to those conditions, but preferred standard RSVP Keyboard.<br><br>SIGNIFICANCE: Inquiry Preview is a novel multimodal interface for the RSVP Keyboard BCI, incorporating switch input as an additional control signal. Typing performance and user experience and preference varied widely across participants, reinforcing the need for flexible, customizable BCI systems that can adapt to individual users.<br><br>CLINICALTRIALS: gov Identifier: NCT04468919.}, }
@article {pmid39794127, year = {2025}, author = {Chang, A and Poeppel, D and Teng, X}, title = {Temporally dissociable neural representations of pitch height and chroma.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1523/JNEUROSCI.1567-24.2024}, pmid = {39794127}, issn = {1529-2401}, abstract = {The extraction and analysis of pitch underpin speech and music recognition, sound segregation, and other auditory tasks. Perceptually, pitch can be represented as a helix composed of two factors: height monotonically aligns with frequency, while chroma cyclically repeats at doubled frequencies. Although the early perceptual and neurophysiological mechanisms for extracting pitch from acoustic signals have been extensively investigated, the equally essential subsequent stages that bridge to high-level auditory cognition remain less well understood. How does the brain represent perceptual attributes of pitch at higher-order processing stages and how are the neural representations formed over time? We used a machine learning approach to decode time-resolved neural responses of human listeners (10 females and 7 males) measured by magnetoencephalography across different pitches, hypothesizing that different pitches sharing similar neural representations would result in reduced decoding performance. We show that pitch can be decoded from lower-frequency neural responses within auditory-frontal cortical regions. Specifically, linear mixed-effect modeling reveals that height and chroma explain decoding performance of delta band (0.5-4 Hz) neural activity at distinct latencies: a long-lasting height effect precedes a transient chroma effect, followed by a recurrence of height after chroma, indicating sequential processing stages associated with unique perceptual and neural characteristics. Furthermore, the localization analyses of the decoder demonstrate that height and chroma are associated with overlapping cortical regions, with differences observed in the right orbital and polar frontal cortex. The data provide a perspective motivating new hypotheses on the mechanisms of pitch representation.Significance Statement Pitch is fundamental to various facets of human hearing, including music appreciation, speech comprehension, vocal learning, and sound source differentiation. How does the brain encode the perceptual features of pitch? By applying machine learning techniques to time-resolved neuroimaging data of individuals listening to different pitches, our findings reveal that pitch height and chroma-two distinct features of pitch-are associated with different neural dynamics within the auditory-frontal cortical network, with height playing a more prominent role. This offers a unified theoretical framework for understanding the perceptual and neural characteristics of pitch perception and opens new avenues for noninvasively decoding human auditory perception to develop brain-computer interfaces.}, }
@article {pmid39793091, year = {2025}, author = {Zhang, F and Pu, Y and Kong, XZ}, title = {Parallel vector memories or single memory updating?.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {122}, number = {1}, pages = {e2422788121}, doi = {10.1073/pnas.2422788121}, pmid = {39793091}, issn = {1091-6490}, support = {32171031//MOST | National Natural Science Foundation of China (NSFC)/ ; 32400882//MOST | National Natural Science Foundation of China (NSFC)/ ; 2021ZD0200409//STI 2030-Major Projects/ ; }, }
@article {pmid39793054, year = {2025}, author = {Chivukula, S and Aflalo, T and Zhang, C and Rosario, ER and Bari, A and Pouratian, N and Andersen, RA}, title = {Population encoding of observed and actual somatosensations in the human posterior parietal cortex.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {122}, number = {1}, pages = {e2316012121}, doi = {10.1073/pnas.2316012121}, pmid = {39793054}, issn = {1091-6490}, support = {R01EY015545//HHS | NIH | National Eye Institute (NEI)/ ; P50MH094258//CIT | Caltech Conte Center for Social Decision Making (Caltech Conte Center for Neuroscience)/ ; }, mesh = {Humans ; *Parietal Lobe/physiology ; Female ; Touch/physiology ; Cognition/physiology ; Adult ; Neurons/physiology ; Mirror Neurons/physiology ; Touch Perception/physiology ; Brain-Computer Interfaces ; }, abstract = {Cognition relies on transforming sensory inputs into a generalizable understanding of the world. Mirror neurons have been proposed to underlie this process, mapping visual representations of others' actions and sensations onto neurons that mediate our own, providing a conduit for understanding. However, this theory has limitations. Here, we hypothesize that mirror-like responses represent one facet of a broader framework in which our brains engage internal models for cognition. We recorded populations of single neurons in the human posterior parietal cortex (PPC) of a brain-machine interface clinical trial participant implanted with a microelectrode array while she either experienced actual touch, or observed diverse tactile stimuli applied to other individuals. Two body locations were tested, on each of the participant and other individuals. Some neurons exhibited mirror-like properties, consistent with earlier literature. However, they were fragile, breaking with increased task complexity. Population responses were better characterized by generalizable and compositional basic-level features encoded within neural subspaces. These features enable the population to respond to diverse actual and observed touch stimuli and are recruited similarly for similar forms of touch. Mirror-like neurons belong within these subspaces, contributing more globally to compositionality and generalizability. We speculate that at a population-level, human PPC manifests an internal model for touch, and that cognition unfolds in the high-level human cortex by versatility in its representational building blocks. In a broad sense, we speculate that the population features we demonstrate support a broad mechanism by which the high-level human cortex enables understanding.}, }
@article {pmid39789264, year = {2025}, author = {Lin, N and Wang, S and Li, Y and Wang, B and Shi, S and He, Y and Zhang, W and Yu, Y and Zhang, Y and Zhang, X and Wong, K and Wang, S and Chen, X and Jiang, H and Zhang, X and Lin, P and Xu, X and Qi, X and Wang, Z and Shang, D and Liu, Q and Liu, M}, title = {Resistive memory-based zero-shot liquid state machine for multimodal event data learning.}, journal = {Nature computational science}, volume = {}, number = {}, pages = {}, pmid = {39789264}, issn = {2662-8457}, support = {62422004//National Natural Science Foundation of China (National Science Foundation of China)/ ; Z210006//Natural Science Foundation of Beijing Municipality (Beijing Natural Science Foundation)/ ; }, abstract = {The human brain is a complex spiking neural network (SNN) capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains minimal power consumption through event-based signal propagation. However, replicating the human brain in neuromorphic hardware presents both hardware and software challenges. Hardware limitations, such as the slowdown of Moore's law and Von Neumann bottleneck, hinder the efficiency of digital computers. In addition, SNNs are characterized by their software training complexities. Here, to this end, we propose a hardware-software co-design on a 40 nm 256 kB in-memory computing macro that physically integrates a fixed and random liquid state machine SNN encoder with trainable artificial neural network projections. We showcase the zero-shot learning of multimodal events on the N-MNIST and N-TIDIGITS datasets, including visual and audio data association, as well as neural and visual data alignment for brain-machine interfaces. Our co-design achieves classification accuracy comparable to fully optimized software models, resulting in a 152.83- and 393.07-fold reduction in training costs compared with state-of-the-art spiking recurrent neural network-based contrastive learning and prototypical networks, and a 23.34- and 160-fold improvement in energy efficiency compared with cutting-edge digital hardware, respectively. These proof-of-principle prototypes demonstrate zero-shot multimodal events learning capability for emerging efficient and compact neuromorphic hardware.}, }
@article {pmid39788124, year = {2025}, author = {An, D and You, Y and Ma, Q and Xu, Z and Liu, Z and Liao, R and Chen, H and Wang, Y and Wang, Y and Dai, H and Li, H and Jiang, L and Chen, Z and Hu, W}, title = {Deficiency of histamine H2 receptors in parvalbumin-positive neurons leads to hyperactivity, impulsivity, and impaired attention.}, journal = {Neuron}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neuron.2024.12.002}, pmid = {39788124}, issn = {1097-4199}, abstract = {Attention deficit hyperactivity disorder (ADHD), affecting 4% of the population, is characterized by inattention, hyperactivity, and impulsivity; however, its neurophysiological mechanisms remain unclear. Here, we discovered that deficiency of histamine H2 receptor (H2R) in parvalbumin-positive neurons in substantia nigra pars recticulata (PV[SNr]) attenuates PV[+] neuronal activity and induces hyperactivity, impulsivity, and inattention in mice. Moreover, decreased H2R expression was observed in PV[SNr] in patients with ADHD symptoms and dopamine-transporter-deficient mice, whose behavioral phenotypes were alleviated by H2R agonist treatment. Dysfunction of PV[SNr] efferents to the substantia nigra pars compacta dopaminergic neurons and superior colliculus differently contributes to H2R-deficiency-induced behavioral disorders. Collectively, our results demonstrate that H2R deficiency in PV[+] neurons contributes to hyperactivity, impulsivity, and inattention by dampening PV[SNr] activity and involving different efferents in mice. It may enhance understanding of the molecular and circuit-level basis of ADHD and afford new potential therapeutic targets for ADHD-like psychiatric diseases.}, }
@article {pmid39787896, year = {2025}, author = {Li, L and Menendez-Lustri, DM and Hartzler, A and Pogharian, A and Zaorski, B and Chen, A and Palen, J and Traylor, B and Quill, E and Pawlowski, CL and Bruckman, MA and Gupta, AS and Capadona, JR and Shoffstall, AJ}, title = {Systemically administered platelet-inspired nanoparticles to reduce inflammation surrounding intracortical microelectrodes.}, journal = {Biomaterials}, volume = {317}, number = {}, pages = {123082}, doi = {10.1016/j.biomaterials.2025.123082}, pmid = {39787896}, issn = {1878-5905}, abstract = {Intracortical microelectrodes (IMEs) are essential for neural signal acquisition in neuroscience and brain-machine interface (BMI) systems, aiding patients with neurological disorders, paralysis, and amputations. However, IMEs often fail to maintain robust signal quality over time, partly due to neuroinflammation caused by vascular damage during insertion. Platelet-inspired nanoparticles (PIN), which possess injury-targeting functions, mimic the adhesion and aggregation of active platelets through conjugated collagen-binding peptides (CBP), von Willebrand Factor-binding peptides (VBP), and fibrinogen-mimetic peptides (FMP). Systemically administered PINs can potentially enhance hemostasis and promote the resealing of IME insertion-induced leaky blood-brain barrier (BBB), thereby attenuating the influx of blood-derived proteins into the brain parenchyma that trigger neuroinflammation. This study explores the potential of PINs to mitigate neuroinflammation at implant sites. Male Sprague Dawley rats underwent craniotomies and IME implantations, followed by a single dose of Cy5 labeled PINs (2 mg/kg). Rats were sacrificed at intervals from 0 to 4 days post-implantation (DPI) for biodistribution analysis using an in vivo live imaging system (IVIS) and immunohistochemistry (IHC) to assess neuroinflammation, BBB permeability, and active platelet distribution. Another cohort of rats received weekly PINs, trehalose buffer (TH, diluent control), or control nanoparticles (CP, PEG-coated liposomes) for 4 weeks, with similar endpoint analyses. Results indicated that PIN concentrations were significantly elevated near IME interfaces acutely (0-4 DPI) and after 4 weeks of repeated dosing. At 3 DPI, peak intensities of active platelets (CD62P), activated microglia/macrophages (CD68), and PINs were observed. Immunoglobulin G (IgG) was upregulated during the first 24 h near implant sites but declined thereafter. At 4 weeks, the PINs group exhibited higher intensities of active platelets and PINs, and reduced CD68 and IgG levels compared to controls. PINs effectively targeted the IME-tissue interface, alongside endogenous activated platelets, resulting in reduced neuroinflammatory and BBB-leakage markers compared to the diluent-only-infused control group. Repeated dosing of PINs presents a promising approach for enhancing the quality of neural recordings in future studies.}, }
@article {pmid39787745, year = {2025}, author = {Elsohaby, I and Kostoulas, P and Fayez, M and Elmoslemany, A and Alkafafy, ME and Bahhary, AM and Alzahrani, R and Morsi, AEKM and Arango-Sabogal, JC}, title = {Bayesian estimation of diagnostic accuracy of fecal smears, fecal PCR and serum ELISA for detecting Mycobacterium avium subsp. paratuberculosis infections in four domestic ruminant species in Saudi Arabia.}, journal = {Veterinary microbiology}, volume = {301}, number = {}, pages = {110377}, doi = {10.1016/j.vetmic.2025.110377}, pmid = {39787745}, issn = {1873-2542}, abstract = {Paratuberculosis, a chronic wasting disease affecting domestic and wild ruminants worldwide, is caused by Mycobacterium avium subsp. paratuberculosis (MAP). Various diagnostic tests exist for detecting MAP infection; however, none of them possess perfect accuracy to be qualified as a reference standard test, particularly due to their notably low sensitivity. Therefore, we used Bayesian latent class models (BLCMs) to estimate diagnostic accuracy of fecal smears (FS), fecal PCR and serum ELISA for detecting MAP infections in sheep, goats, cattle, and camels older than 2 years in Saudi Arabia. Data from a cross-sectional study conducted in the Eastern Province of Saudi Arabia on 31 different farms with a history of MAP infection were analyzed. Fecal and blood samples from all animals older than 2 years in each farm were collected, resulting in a total of 220 sheep, 123 goats, 66 cattle, and 240 camels sampled. FS and IS900-PCR were performed on fecal samples to detect acid-fast bacilli and MAP DNA, respectively. The IDEXX ELISA kit was used to detect MAP antibodies in serum samples. For each ruminant species population, a BLCM was fitted to obtain posterior estimates [medians and 95 % Bayesian credible intervals (95 % BCI)] for sensitivity (Se) and specificity (Sp) of the three tests. We assumed FS and PCR to be conditionally dependent on the true animal MAP status. Prior distributions for test accuracy were used if available. FS had the highest Se among all tests and across all species with median values around 80 % in sheep, goats and camels, and near 50 % in cattle. Median Sp estimates of ELISA and PCR were higher than 90 % for all species. FS yielded the lowest Sp of the study when applied in camels, sheep, and goats. Using the prevalence observed in this study, median positive predictive value (PPV) was higher for PCR and ELISA than FS for camels, sheep, and goats. In cattle, PPV of all tests was similar with median estimates > 95 %. In camels, sheep, and goats, median negative predicative value (NPV) of all tests were > 60 %. The lowest median NPV for all tests were observed in cattle (< 30 %). Our results suggest that ELISA is a suitable option to identify MAP infected animals in farms with previous history of MAP in the Eastern region of Saudi Arabia.}, }
@article {pmid39787440, year = {2024}, author = {Alkawadri, CI and Yan, Q and Kocuglu Kinal, AG and Spencer, DD and Alkawadri, R}, title = {Comparison of EEG Signal Characteristics of Subdural and Depth Electrodes.}, journal = {Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society}, volume = {}, number = {}, pages = {}, doi = {10.1097/WNP.0000000000001139}, pmid = {39787440}, issn = {1537-1603}, abstract = {OBJECTIVES: Our study aimed to compare signal characteristics of subdural electrodes (SDE) and depth stereo EEG placed within a 5-mm vicinity in patients with drug-resistant epilepsy. We report how electrode design and placement collectively affect signal content from a shared source between these electrode types.<br><br>METHODS: In subjects undergoing invasive intracranial EEG evaluation at a surgical epilepsy center from 2012 to 2018, stereo EEG and SDE electrode contacts placed within a 5-mm vicinity were identified. Of these, 24 contacts (12 pairs) met our criteria for signal-to-noise ratio and data availability for final analysis. We used Welch method to analyze the correlation of power spectral densities of EEG segments, root mean square of 1-second windows, and fast-Fourier transform to calculate coherence across conventional frequency bands.<br><br>RESULTS: We observed a median distance of 3.7 mm between the electrode contact pairs. Time-aware analysis highlighted the coherence's strength primarily in the high-gamma band, where the median (r) was 0.889. In addition, the median power ratios between the SDE and stereo EEG signal was 1.99. This ratio decreased from high-gamma to infra-low frequencies, with medians of 2.07 and 0.97, respectively. The power spectral densities for the stereo EEG and SDE electrodes demonstrated a strong correlation, with a median correlation coefficient (r) of 0.99 and an interquartile range from 0.915 to 0.996.<br><br>CONCLUSIONS: Signals captured by standard subdural and depth (intracranial EEG) electrodes within a 5-mm radius exhibit band-specific coherence and are not identical. The association was most pronounced in the high-gamma band, with coherence decreasing with lower frequencies. Our findings underscore the combined effects of electrode size, design, placement, preferred bandwidth, and the nature of the activity source on signal recording. Particularly, SDE employed herein may offer advantages for high-frequency signals, but the impact of electrode size on recordings necessitates careful consideration in context-specific situations.<br><br>SIGNIFICANCE: The findings relate to surgical epilepsy care and may inform the design of brain-computer interface.}, }
@article {pmid39781056, year = {2025}, author = {Cai, Z and Li, P and Cheng, L and Yuan, D and Li, M and Li, H}, title = {A high performance heterogeneous hardware architecture for brain computer interface.}, journal = {Biomedical engineering letters}, volume = {15}, number = {1}, pages = {217-227}, pmid = {39781056}, issn = {2093-985X}, abstract = {Brain-computer interface (BCI) has been widely used in human-computer interaction. The introduction of artificial intelligence has further improved the performance of BCI system. In recent years, the development of BCI has gradually shifted from personal computers to embedded devices, which boasts lower power consumption and smaller size, but at the cost of limited device resources and computing speed, thus can hardly improve the support of complex algorithms. This paper proposes a heterogeneous BCI architecture based on ARM + FPGA, enabling real-time processing of electroencephalogram (EEG) signals. Adopting data quantization, layer fusion and data augmentation to optimize the compact neural network model EEGNet, and design dedicated hardware engines to accelerate the network. Experimental results show that the system achieves 93.3% classification accuracy for steady-state visual evoked potential signals, with a time delay of 0.2 ms per trail, and a power consumption of approximately (1.91 W). That is 31.5 times faster acceleration is realized at the cost of only 0.7% lower accuracy compared with the conventional processor. The results show that the BCI architecture proposed in this study has strong practicability and high research significance.}, }
@article {pmid39779796, year = {2025}, author = {Shelishiyah, R and Thiyam, DB and Margaret, MJ and Banu, NMM}, title = {A hybrid CNN model for classification of motor tasks obtained from hybrid BCI system.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {1360}, pmid = {39779796}, issn = {2045-2322}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Neural Networks, Computer ; Deep Learning ; Adult ; Male ; Movement/physiology ; }, abstract = {The Hybrid-Brain Computer Interface (BCI) has shown improved performance, especially in classifying multi-class data. Two non-invasive BCI modules are combined to achieve an improved classification which are Electroencephalogram (EEG) and functional Near Infra-red Spectroscopy (fNIRS). Classifying contralateral and ipsilateral motor movements is found challenging among the other mental activity signals. The current work focuses on the performance of deep learning methods like - Convolutional Neural Networks (CNN) and Bidirectional Long-Short term memory (Bi-LSTM) in classifying a four-class motor execution of Right Hand, Left Hand, Right Arm and Left Arm taken from the CORE dataset. The model performance was evaluated using metrics such as Accuracy, F1 - score, Precision, Recall, AUC and ROC curve. The CNN and Hybrid CNN models have resulted in 98.3% and 99% accuracy respectively.}, }
@article {pmid39776784, year = {2024}, author = {Ahmadi, S and Desain, P and Thielen, J}, title = {A Bayesian dynamic stopping method for evoked response brain-computer interfacing.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1437965}, pmid = {39776784}, issn = {1662-5161}, abstract = {INTRODUCTION: As brain-computer interfacing (BCI) systems transition fromassistive technology to more diverse applications, their speed, reliability, and user experience become increasingly important. Dynamic stopping methods enhance BCI system speed by deciding at any moment whether to output a result or wait for more information. Such approach leverages trial variance, allowing good trials to be detected earlier, thereby speeding up the process without significantly compromising accuracy. Existing dynamic stopping algorithms typically optimize measures such as symbols per minute (SPM) and information transfer rate (ITR). However, these metrics may not accurately reflect system performance for specific applications or user types. Moreover, many methods depend on arbitrary thresholds or parameters that require extensive training data.<br><br>METHODS: We propose a model-based approach that takes advantage of the analytical knowledge that we have about the underlying classification model. By using a risk minimization approach, our model allows precise control over the types of errors and the balance between precision and speed. This adaptability makes it ideal for customizing BCI systems to meet the diverse needs of various applications.<br><br>RESULTS AND DISCUSSION: We validate our proposed method on a publicly available dataset, comparing it with established static and dynamic stopping methods. Our results demonstrate that our approach offers a broad range of accuracy-speed trade-offs and achieves higher precision than baseline stopping methods.}, }
@article {pmid39775950, year = {2025}, author = {Zeng, X and Kang, T and Huang, W and Jin, T}, title = {How Should the BCI and BOOI Index be Correctly Applied in Patients With Low-Compliance Bladder?.}, journal = {Neurourology and urodynamics}, volume = {}, number = {}, pages = {}, doi = {10.1002/nau.25663}, pmid = {39775950}, issn = {1520-6777}, }
@article {pmid39771903, year = {2024}, author = {Alzahrani, S and Banjar, H and Mirza, R}, title = {Systematic Review of EEG-Based Imagined Speech Classification Methods.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {24}, pages = {}, pmid = {39771903}, issn = {1424-8220}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Speech/physiology ; Machine Learning ; Signal Processing, Computer-Assisted ; Imagination/physiology ; Neural Networks, Computer ; Brain/physiology ; }, abstract = {This systematic review examines EEG-based imagined speech classification, emphasizing directional words essential for development in the brain-computer interface (BCI). This study employed a structured methodology to analyze approaches using public datasets, ensuring systematic evaluation and validation of results. This review highlights the feature extraction techniques that are pivotal to classification performance. These include deep learning, adaptive optimization, and frequency-specific decomposition, which enhance accuracy and robustness. Classification methods were explored by comparing traditional machine learning with deep learning and emphasizing the role of brain lateralization in imagined speech for effective recognition and classification. This study discusses the challenges of generalizability and scalability in imagined speech recognition, focusing on subject-independent approaches and multiclass scalability. Performance benchmarking across various datasets and methodologies revealed varied classification accuracies, reflecting the complexity and variability of EEG signals. This review concludes that challenges remain despite progress, particularly in classifying directional words. Future research directions include improved signal processing techniques, advanced neural network architectures, and more personalized, adaptive BCI systems. This review is critical for future efforts to develop practical communication tools for individuals with speech and motor impairments using EEG-based BCIs.}, }
@article {pmid39771862, year = {2024}, author = {Khabti, J and AlAhmadi, S and Soudani, A}, title = {Enhancing Deep-Learning Classification for Remote Motor Imagery Rehabilitation Using Multi-Subject Transfer Learning in IoT Environment.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {24}, pages = {}, pmid = {39771862}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; *Deep Learning ; *Electroencephalography/methods ; Internet of Things ; Signal Processing, Computer-Assisted ; }, abstract = {One of the most promising applications for electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is motor rehabilitation through motor imagery (MI) tasks. However, current MI training requires physical attendance, while remote MI training can be applied anywhere, facilitating flexible rehabilitation. Providing remote MI training raises challenges to ensuring an accurate recognition of MI tasks by healthcare providers, in addition to managing computation and communication costs. The MI tasks are recognized through EEG signal processing and classification, which can drain sensor energy due to the complexity of the data and the presence of redundant information, often influenced by subject-dependent factors. To address these challenges, we propose in this paper a multi-subject transfer-learning approach for an efficient MI training framework in remote rehabilitation within an IoT environment. For efficient implementation, we propose an IoT architecture that includes cloud/edge computing as a solution to enhance the system's efficiency and reduce the use of network resources. Furthermore, deep-learning classification with and without channel selection is applied in the cloud, while multi-subject transfer-learning classification is utilized at the edge node. Various transfer-learning strategies, including different epochs, freezing layers, and data divisions, were employed to improve accuracy and efficiency. To validate this framework, we used the BCI IV 2a dataset, focusing on subjects 7, 8, and 9 as targets. The results demonstrated that our approach significantly enhanced the average accuracy in both multi-subject and single-subject transfer-learning classification. In three-subject transfer-learning classification, the FCNNA model achieved up to 79.77% accuracy without channel selection and 76.90% with channel selection. For two-subject and single-subject transfer learning, the application of transfer learning improved the average accuracy by up to 6.55% and 12.19%, respectively, compared to classification without transfer learning. This framework offers a promising solution for remote MI rehabilitation, providing both accurate task recognition and efficient resource usage.}, }
@article {pmid39771843, year = {2024}, author = {Mikhaylov, D and Saeed, M and Husain Alhosani, M and F Al Wahedi, Y}, title = {Comparison of EEG Signal Spectral Characteristics Obtained with Consumer- and Research-Grade Devices.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {24}, pages = {}, pmid = {39771843}, issn = {1424-8220}, mesh = {Humans ; *Electroencephalography/methods/instrumentation ; *Signal Processing, Computer-Assisted ; Adult ; Male ; Female ; Brain-Computer Interfaces ; Brain/physiology ; Wearable Electronic Devices ; Electrodes ; Young Adult ; }, abstract = {Electroencephalography (EEG) has emerged as a pivotal tool in both research and clinical practice due to its non-invasive nature, cost-effectiveness, and ability to provide real-time monitoring of brain activity. Wearable EEG technology opens new avenues for consumer applications, such as mental health monitoring, neurofeedback training, and brain-computer interfaces. However, there is still much to verify and re-examine regarding the functionality of these devices and the quality of the signal they capture, particularly as the field evolves rapidly. In this study, we recorded the resting-state brain activity of healthy volunteers via three consumer-grade EEG devices, namely PSBD Headband Pro, PSBD Headphones Lite, and Muse S Gen 2, and compared the spectral characteristics of the signal obtained with that recorded via the research-grade Brain Product amplifier (BP) with the mirroring montages. The results showed that all devices exhibited higher mean power in the low-frequency bands, which are characteristic of dry-electrode technology. PSBD Headband proved to match BP most precisely among the other examined devices. PSBD Headphones displayed a moderate correspondence with BP and signal quality issues in the central group of electrodes. Muse demonstrated the poorest signal quality, with extremely low alignment with BP. Overall, this study underscores the importance of considering device-specific design constraints and emphasizes the need for further validation to ensure the reliability and accuracy of wearable EEG devices.}, }
@article {pmid39771785, year = {2024}, author = {Novičić, M and Djordjević, O and Miler-Jerković, V and Konstantinović, L and Savić, AM}, title = {Improving the Performance of Electrotactile Brain-Computer Interface Using Machine Learning Methods on Multi-Channel Features of Somatosensory Event-Related Potentials.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {24}, pages = {}, pmid = {39771785}, issn = {1424-8220}, support = {6066223//Science Fund of the Republic of Serbia/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Somatosensory/physiology ; Male ; *Machine Learning ; Adult ; Female ; Algorithms ; Touch/physiology ; Young Adult ; Signal Processing, Computer-Assisted ; Support Vector Machine ; }, abstract = {Traditional tactile brain-computer interfaces (BCIs), particularly those based on steady-state somatosensory-evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a promising alternative for generating tactile BCI control signals: somatosensory event-related potentials (sERPs). This study aimed to optimize the performance of a novel electrotactile BCI by employing advanced feature extraction and machine learning techniques on sERP signals for the classification of users' selective tactile attention. The experimental protocol involved ten healthy subjects performing a tactile attention task, with EEG signals recorded from five EEG channels over the sensory-motor cortex. We employed sequential forward selection (SFS) of features from temporal sERP waveforms of all EEG channels. We systematically tested classification performance using machine learning algorithms, including logistic regression, k-nearest neighbors, support vector machines, random forests, and artificial neural networks. We explored the effects of the number of stimuli required to obtain sERP features for classification and their influence on accuracy and information transfer rate. Our approach indicated significant improvements in classification accuracy compared to previous studies. We demonstrated that the number of stimuli for sERP generation can be reduced while increasing the information transfer rate without a statistically significant decrease in classification accuracy. In the case of the support vector machine classifier, we achieved a mean accuracy over 90% for 10 electrical stimuli, while for 6 stimuli, the accuracy decreased by less than 7%, and the information transfer rate increased by 60%. This research advances methods for tactile BCI control based on event-related potentials. This work is significant since tactile stimulation is an understudied modality for BCI control, and electrically induced sERPs are the least studied control signals in reactive BCIs. Exploring and optimizing the parameters of sERP elicitation, as well as feature extraction and classification methods, is crucial for addressing the accuracy versus speed trade-off in various assistive BCI applications where the tactile modality may have added value.}, }
@article {pmid39771721, year = {2024}, author = {Víg, L and Zátonyi, A and Csernyus, B and Horváth, &#xc1;C and Bojtár, M and Kele, P and Madarász, M and Rózsa, B and Fürjes, P and Hermann, P and Hakkel, O and Péter, L and Fekete, Z}, title = {Optically Controlled Drug Delivery Through Microscale Brain-Machine Interfaces Using Integrated Upconverting Nanoparticles.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {24}, pages = {}, pmid = {39771721}, issn = {1424-8220}, support = {TKP2021-EGA-42//National Research, Development and Innovation Office/ ; TKP2021-EGA-04//National Research, Development and Innovation Office/ ; VKE-2018-00032//National Research, Development and Innovation Office/ ; KFI-2018-00097//National Research, Development and Innovation Office/ ; 2020-2.1.1-ED-2022-00208//National Research, Development and Innovation Office/ ; (NAP2022I-8/2022//Hungarian Academy of Sciences/ ; Bolyai Janos Scholarship//Hungarian Academy of Sciences/ ; NKFIH FK 134403//National Research, Development and Innovation Office/ ; }, mesh = {*Nanoparticles/chemistry ; *Brain-Computer Interfaces ; *Drug Delivery Systems/methods/instrumentation ; Animals ; Brain/physiology ; Rats ; }, abstract = {The aim of this work is to incorporate lanthanide-cored upconversion nanoparticles (UCNP) into the surface of microengineered biomedical implants to create a spatially controlled and optically releasable model drug delivery device in an integrated fashion. Our approach enables silicone-based microelectrocorticography (ECoG) implants holding platinum/iridium recording sites to serve as a stable host of UCNPs. Nanoparticles excitable in the near-infrared (lower energy) regime and emitting visible (higher energy) light are utilized in a study. With the upconverted higher energy photons, we demonstrate the induction of photochemical (cleaving) reactions that enable the local release of specific dyes as a model system near the implant. The modified ECoG electrodes can be implanted in brain tissue to act as an uncaging system that releases small amounts of substance while simultaneously measuring the evoked neural response upon light activation. In this paper, several technological challenges like the surface modification of UCNPs, the immobilization of particles on the implantable platform, and measuring the stability of integrated UCNPs in in vitro and in vivo conditions are addressed in detail. Besides the chemical, mechanical, and optical characterization of the ready-to-use devices, the effect of nanoparticles on the original electrophysiological function is also evaluated. The results confirm that silicone-based brain-machine interfaces can be efficiently complemented with UCNPs to facilitate local model drug release.}, }
@article {pmid39771689, year = {2024}, author = {Fang, F and Gao, T and Wu, J}, title = {Humanity Test-EEG Data Mediated Artificial Intelligence Multi-Person Interactive System.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {24}, pages = {}, pmid = {39771689}, issn = {1424-8220}, mesh = {Humans ; *Electroencephalography/methods ; *Artificial Intelligence ; *Emotions/physiology ; Male ; Female ; Adult ; Young Adult ; }, abstract = {Artificial intelligence (AI) systems are widely applied in various industries and everyday life, particularly in fields such as virtual assistants, healthcare, and education. However, this paper highlights that existing research has often overlooked the philosophical and media aspects. To address this, we developed an interactive system called "Human Nature Test". In this context, "human nature" refers to emotion and consciousness, while "test" involves a critical analysis of AI technology and an exploration of the differences between humanity and technicality. Additionally, through experimental research and literature analysis, we found that the integration of electroencephalogram (EEG) data with AI systems is becoming a significant trend. The experiment involved 20 participants, with two conditions: C1 (using EEG data) and C2 (without EEG data). The results indicated a significant increase in immersion under the C1 condition, along with a more positive emotional experience. We summarized three design directions: enhancing immersion, creating emotional experiences, and expressing philosophical concepts. Based on these findings, there is potential for further developing EEG data as a medium to enrich interactive experiences, offering new insights into the fusion of technology and human emotion.}, }
@article {pmid39771656, year = {2024}, author = {Khalil, AEK and Perez-Diaz, JA and Cantoral-Ceballos, JA and Antelis, JM}, title = {Unlocking Security for Comprehensive Electroencephalogram-Based User Authentication Systems.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {24}, pages = {}, pmid = {39771656}, issn = {1424-8220}, support = {N/A//Tecnol&#xf3;gico de Monterrey/ ; }, mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; Computer Security ; Signal Processing, Computer-Assisted ; Algorithms ; Brain-Computer Interfaces ; Brain/physiology ; }, abstract = {With recent significant advancements in artificial intelligence, the necessity for more reliable recognition systems has rapidly increased to safeguard individual assets. The use of brain signals for authentication has gained substantial interest within the scientific community over the past decade. Most previous efforts have focused on identifying distinctive information within electroencephalogram (EEG) recordings. In this study, an EEG-based user authentication scheme is presented, employing a multi-layer perceptron feedforward neural network (MLP FFNN). The scheme utilizes P300 potentials derived from EEG signals, focusing on the user's intent to select specific characters. This approach involves two phases: user identification and user authentication. Both phases utilize EEG recordings of brain signals, data preprocessing, a database to store and manage these recordings for efficient retrieval and organization, and feature extraction using mutual information (MI) from selected EEG data segments, specifically targeting power spectral density (PSD) across five frequency bands. The user identification phase employs multi-class classifiers to predict the identity of a user from a set of enrolled users. The user authentication phase associates the predicted user identities with user labels using probability assessments, verifying the claimed identity as either genuine or an impostor. This scheme combines EEG data segments with user mapping, confidence calculations, and claimed user verification for robust authentication. It also accommodates new users by transforming EEG data into feature vectors without the need for retraining. The model extracts selected features to identify users and to classify the input based on these features to authenticate the user. The experiments show that the proposed scheme can achieve 97% accuracy in EEG-based user identification and authentication.}, }
@article {pmid39768099, year = {2024}, author = {Mahmoud, TSM and Munawar, A and Nawaz, MZ and Chen, Y}, title = {Enhancing Multispectral Breast Imaging Quality Through Frame Accumulation and Hybrid GA-CPSO Registration.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {11}, number = {12}, pages = {}, pmid = {39768099}, issn = {2306-5354}, abstract = {Multispectral transmission imaging has emerged as a promising technique for imaging breast tissue with high resolution. However, the method encounters challenges such as low grayscale, noisy transmission images with weak signals, primarily due to the strong absorption and scattering of light in breast tissue. A common approach to improve the signal-to-noise ratio (SNR) and overall image quality is frame accumulation. However, factors such as camera jitter and respiratory motion during image acquisition can cause frame misalignment, degrading the quality of the accumulated image. To address these issues, this study proposes a novel image registration method. A hybrid approach combining a genetic algorithm (GA) and a constriction factor-based particle swarm optimization (CPSO), referred to as GA-CPSO, is applied for image registration before frame accumulation. The efficiency of this hybrid method is enhanced by incorporating a squared constriction factor (SCF), which speeds up the registration process and improves convergence towards optimal solutions. The GA identifies potential solutions, which are then refined by CPSO to expedite convergence. This methodology was validated on the sequence of breast frames taken at 600 nm, 620 nm, 670 nm, and 760 nm wavelength of light and proved the enhancement of accuracy by various mathematical assessments. It demonstrated high accuracy (99.93%) and reduced registration time. As a result, the GA-CPSO approach significantly improves the effectiveness of frame accumulation and enhances overall image quality. This study explored the groundwork for precise multispectral transmission image segmentation and classification.}, }
@article {pmid39768034, year = {2024}, author = {Geravanchizadeh, M and Shaygan Asl, A and Danishvar, S}, title = {Selective Auditory Attention Detection Using Combined Transformer and Convolutional Graph Neural Networks.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {11}, number = {12}, pages = {}, pmid = {39768034}, issn = {2306-5354}, abstract = {Attention is one of many human cognitive functions that are essential in everyday life. Given our limited processing capacity, attention helps us focus only on what matters. Focusing attention on one speaker in an environment with many speakers is a critical ability of the human auditory system. This paper proposes a new end-to-end method based on the combined transformer and graph convolutional neural network (TraGCNN) that can effectively detect auditory attention from electroencephalograms (EEGs). This approach eliminates the need for manual feature extraction, which is often time-consuming and subjective. Here, the first EEG signals are converted to graphs. We then extract attention information from these graphs using spatial and temporal approaches. Finally, our models are trained with these data. Our model can detect auditory attention in both the spatial and temporal domains. Here, the EEG input is first processed by transformer layers to obtain a sequential representation of EEG based on attention onsets. Then, a family of graph convolutional layers is used to find the most active electrodes using the spatial position of electrodes. Finally, the corresponding EEG features of active electrodes are fed into the graph attention layers to detect auditory attention. The Fuglsang 2020 dataset is used in the experiments to train and test the proposed and baseline systems. The new TraGCNN approach, as compared with state-of-the-art attention classification methods from the literature, yields the highest performance in terms of accuracy (80.12%) as a classification metric. Additionally, the proposed model results in higher performance than our previously graph-based model for different lengths of EEG segments. The new TraGCNN approach is advantageous because attenuation detection is achieved from EEG signals of subjects without requiring speech stimuli, as is the case with conventional auditory attention detection methods. Furthermore, examining the proposed model for different lengths of EEG segments shows that the model is faster than our previous graph-based detection method in terms of computational complexity. The findings of this study have important implications for the understanding and assessment of auditory attention, which is crucial for many applications, such as brain-computer interface (BCI) systems, speech separation, and neuro-steered hearing aid development.}, }
@article {pmid39766488, year = {2024}, author = {Ma, Y and Huang, Z and Yang, Y and Zhang, S and Dong, Q and Wang, R and Hu, L}, title = {Emotion Recognition Model of EEG Signals Based on Double Attention Mechanism.}, journal = {Brain sciences}, volume = {14}, number = {12}, pages = {}, pmid = {39766488}, issn = {2076-3425}, abstract = {BACKGROUND: Emotions play a crucial role in people's lives, profoundly affecting their cognition, decision-making, and interpersonal communication. Emotion recognition based on brain signals has become a significant challenge in the fields of affective computing and human-computer interaction.<br><br>METHODS: Addressing the issue of inaccurate feature extraction and low accuracy of existing deep learning models in emotion recognition, this paper proposes a multi-channel automatic classification model for emotion EEG signals named DACB, which is based on dual attention mechanisms, convolutional neural networks, and bidirectional long short-term memory networks. DACB extracts features in both temporal and spatial dimensions, incorporating not only convolutional neural networks but also SE attention mechanism modules for learning the importance of different channel features, thereby enhancing the network's performance. DACB also introduces dot product attention mechanisms to learn the importance of spatial and temporal features, effectively improving the model's accuracy.<br><br>RESULTS: The accuracy of this method in single-shot validation tests on the SEED-IV and DREAMER (Valence-Arousal-Dominance three-classification) datasets is 99.96% and 87.52%, 90.06%, and 89.05%, respectively. In 10-fold cross-validation tests, the accuracy is 99.73% and 84.26%, 85.40%, and 85.02%, outperforming other models.<br><br>CONCLUSIONS: This demonstrates that the DACB model achieves high accuracy in emotion classification tasks, demonstrating outstanding performance and generalization ability and providing new directions for future research in EEG signal recognition.}, }
@article {pmid39766471, year = {2024}, author = {Adolf, A and Köllőd, CM and Márton, G and Fadel, W and Ulbert, I}, title = {The Effect of Processing Techniques on the Classification Accuracy of Brain-Computer Interface Systems.}, journal = {Brain sciences}, volume = {14}, number = {12}, pages = {}, pmid = {39766471}, issn = {2076-3425}, support = {KDP-2021-12, 1022428/001//National Research, Development and Innovation Office/ ; FK146115//National Research, Development and Innovation Office/ ; NAP2022-I-2/2022//Hungarian Academy of Sciences/ ; RRF-2.3.1-21-2022-00015//National Research, Development and Innovation Office/ ; }, abstract = {Background/Objectives: Accurately classifying Electroencephalography (EEG) signals is essential for the effective operation of Brain-Computer Interfaces (BCI), which is needed for reliable neurorehabilitation applications. However, many factors in the processing pipeline can influence classification performance. The objective of this study is to assess the effects of different processing steps on classification accuracy in EEG-based BCI systems. Methods: This study explores the impact of various processing techniques and stages, including the FASTER algorithm for artifact rejection (AR), frequency filtering, transfer learning, and cropped training. The Physionet dataset, consisting of four motor imagery classes, was used as input due to its relatively large number of subjects. The raw EEG was tested with EEGNet and Shallow ConvNet. To examine the impact of adding a spatial dimension to the input data, we also used the Multi-branch Conv3D Net and developed two new models, Conv2D Net and Conv3D Net. Results: Our analysis showed that classification accuracy can be affected by many factors at every stage. Applying the AR method, for instance, can either enhance or degrade classification performance, depending on the subject and the specific network architecture. Transfer learning was effective in improving the performance of all networks for both raw and artifact-rejected data. However, the improvement in classification accuracy for artifact-rejected data was less pronounced compared to unfiltered data, resulting in reduced precision. For instance, the best classifier achieved 46.1% accuracy on unfiltered data, which increased to 63.5% with transfer learning. In the filtered case, accuracy rose from 45.5% to only 55.9% when transfer learning was applied. An unexpected outcome regarding frequency filtering was observed: networks demonstrated better classification performance when focusing on lower-frequency components. Higher frequency ranges were more discriminative for EEGNet and Shallow ConvNet, but only when cropped training was applied. Conclusions: The findings of this study highlight the complex interaction between processing techniques and neural network performance, emphasizing the necessity for customized processing approaches tailored to specific subjects and network architectures.}, }
@article {pmid39766454, year = {2024}, author = {Beck, S and Liberman, Y and Dubljević, V}, title = {Media Representation of the Ethical Issues Pertaining to Brain-Computer Interface (BCI) Technology.}, journal = {Brain sciences}, volume = {14}, number = {12}, pages = {}, pmid = {39766454}, issn = {2076-3425}, abstract = {BACKGROUND/OBJECTIVES: Brain-computer interfaces (BCIs) are a rapidly developing technology that captures and transmits brain signals to external sources, allowing the user control of devices such as prosthetics. BCI technology offers the potential to restore physical capabilities in the body and change how we interact and communicate with computers and each other. While BCI technology has existed for decades, recent developments have caused the technology to generate a host of ethical issues and discussions in both academic and public circles. Given that media representation has the potential to shape public perception and policy, it is necessary to evaluate the space that these issues take in public discourse.<br><br>METHODS: We conducted a rapid review of media articles in English discussing ethical issues of BCI technology from 2013 to 2024 as indexed by LexisNexis. Our searches yielded 675 articles, with a final sample containing 182 articles. We assessed the themes of the articles and coded them based on the ethical issues discussed, ethical frameworks, recommendations, tone, and application of technology.<br><br>RESULTS: Our results showed a marked rise in interest in media articles over time, signaling an increased focus on this topic. The majority of articles adopted a balanced or neutral tone when discussing BCIs and focused on ethical issues regarding privacy, autonomy, and regulation.<br><br>CONCLUSIONS: Current discussion of ethical issues reflects growing news coverage of companies such as Neuralink, and reveals a mounting distrust of BCI technology. The growing recognition of ethical considerations in BCI highlights the importance of ethical discourse in shaping the future of the field.}, }
@article {pmid39760490, year = {2025}, author = {Zhai, H and Li, P and Wang, H and Wang, X}, title = {DMSO-promoted α-bromination of α-aryl ketones for the construction of 2-aryl-2-bromo-cycloketones.}, journal = {Organic &amp; biomolecular chemistry}, volume = {}, number = {}, pages = {}, doi = {10.1039/d4ob01937g}, pmid = {39760490}, issn = {1477-0539}, abstract = {A DMSO-promoted practical one-step α-bromination reaction of α-aryl ketones with NBS has been developed for the construction of 2-aryl-2-bromo-cycloketones. The desired regioselective α-bromination products were isolated in moderate to good yields, with a maximum tested scale of 15 mmol. Notably, ketamine derivatives could be smoothly synthesized in two steps.}, }
@article {pmid39760422, year = {2025}, author = {Zhu, L and Wang, Y and Huang, A and Tan, X and Zhang, J}, title = {A multi-branch, multi-scale, and multi-view CNN with lightweight temporal attention mechanism for EEG-based motor imagery decoding.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-15}, doi = {10.1080/10255842.2024.2448576}, pmid = {39760422}, issn = {1476-8259}, abstract = {Convolutional neural networks (CNNs) have been widely utilized for decoding motor imagery (MI) from electroencephalogram (EEG) signals. However, extracting discriminative spatial-temporal-spectral features from low signal-to-noise ratio EEG signals remains challenging. This paper proposes MBMSNet , a multi-branch, multi-scale, and multi-view CNN with a lightweight temporal attention mechanism for EEG-Based MI decoding. Specifically, MBMSNet first extracts multi-view representations from raw EEG signals, followed by independent branches to capture spatial, spectral, temporal-spatial, and temporal-spectral features. Each branch includes a domain-specific convolutional layer, a variance layer, and a temporal attention layer. Finally, the features derived from each branch are concatenated with weights and classified through a fully connected layer. Experiments demonstrate MBMSNet outperforms state-of-the-art models, achieving accuracies of 84.60% on BCI Competition IV 2a, 87.80% on 2b, and 74.58% on OpenBMI, showcasing its potential for robust BCI applications.}, }
@article {pmid39759760, year = {2024}, author = {Cisotto, G and Zancanaro, A and Zoppis, IF and Manzoni, SL}, title = {hvEEGNet: a novel deep learning model for high-fidelity EEG reconstruction.}, journal = {Frontiers in neuroinformatics}, volume = {18}, number = {}, pages = {1459970}, pmid = {39759760}, issn = {1662-5196}, abstract = {INTRODUCTION: Modeling multi-channel electroencephalographic (EEG) time-series is a challenging tasks, even for the most recent deep learning approaches. Particularly, in this work, we targeted our efforts to the high-fidelity reconstruction of this type of data, as this is of key relevance for several applications such as classification, anomaly detection, automatic labeling, and brain-computer interfaces.<br><br>METHODS: We analyzed the most recent works finding that high-fidelity reconstruction is seriously challenged by the complex dynamics of the EEG signals and the large inter-subject variability. So far, previous works provided good results in either high-fidelity reconstruction of single-channel signals, or poor-quality reconstruction of multi-channel datasets. Therefore, in this paper, we present a novel deep learning model, called hvEEGNet, designed as a hierarchical variational autoencoder and trained with a new loss function. We tested it on the benchmark Dataset 2a (including 22-channel EEG data from 9 subjects).<br><br>RESULTS: We show that it is able to reconstruct all EEG channels with high-fidelity, fastly (in a few tens of epochs), and with high consistency across different subjects. We also investigated the relationship between reconstruction fidelity and the training duration and, using hvEEGNet as an anomaly detector, we spotted some data in the benchmark dataset that are corrupted and never highlighted before.<br><br>DISCUSSION: Thus, hvEEGNet could be very useful in several applications where automatic labeling of large EEG dataset is needed and time-consuming. At the same time, this work opens new fundamental research questions about (1) the effectiveness of deep learning models training (for EEG data) and (2) the need for a systematic characterization of the input EEG data to ensure robust modeling.}, }
@article {pmid39759080, year = {2024}, author = {Zhao, X and Xu, S and Geng, K and Zhou, T and Xu, T and Wang, Z and Feng, S and Hu, H}, title = {MP: A steady-state visual evoked potential dataset based on multiple paradigms.}, journal = {iScience}, volume = {27}, number = {11}, pages = {111030}, pmid = {39759080}, issn = {2589-0042}, abstract = {In the field of steady-state visual evoked potential (SSVEP), stimulus paradigms are regularly arranged or mimic the style of a keyboard with the same size. However, stimulation paradigms have important effects on the performance of SSVEP systems, which correlate with the electroencephalogram (EEG) signal amplitude and recognition accuracy. This paper provides MP dataset that was acquired using a 12-target BCI speller. MP dataset contains 9-channel EEG signals from the occipital region of 24 subjects under 5 stimulation paradigms with different stimulus sizes and arrangements. Stimuli were encoded using joint frequency and phase modulation (JFPM) method. Subjects completed an offline prompted spelling task using a speller under 5 paradigms. Each experiment contains 8 blocks, and each block contains 12 trials. Designers can use this dataset to test the performance of algorithms considering "stimulus size" and "stimulus arrangement". EEG data showed SSVEP features through amplitude-frequency analysis. FBCCA and TRCA confirmed its suitability.}, }
@article {pmid39758181, year = {2025}, author = {Ammar, A and Salem, A and Simak, ML and Horst, F and Schöllhorn, WI}, title = {Acute effects of motor learning models on technical efficiency in strength-coordination exercises: a comparative analysis of Olympic snatch biomechanics in beginners.}, journal = {Biology of sport}, volume = {42}, number = {1}, pages = {151-161}, pmid = {39758181}, issn = {0860-021X}, abstract = {Despite the development of various motor learning models over many decades, the question of which model is most effective under which conditions to optimize the acquisition of skills remains a heated and recurring debate. This is particularly important in connection with learning sports movements with a high strength component. This study aims to examine the acute effects of various motor learning models on technical efficiency and force production during the Olympic snatch movement. In a within-subject design, sixteen highly active male participants (mean age: 23.13 ± 2.09 years), who were absolute beginners regarding the learning task, engaged in randomized snatch learning bouts, consisting of 36 trials across different learning models: differential learning (DL), contextual interference (serial, sCI; and blocked, bCI), and repetitive learning (RL). Kinematic and kinetic data were collected from three snatch trials executed following each learning bout. Discrete data from the most commonly monitored biomechanical parameters in Olympic weightlifting were analyzed using inferential statistics to identify differences between learning models. The statistical analysis revealed no significant differences between the learning models across all tested parameters, with p-values ranging from 0.236 to 0.99. However, it was observed that only the bouts with an exercise sequence following the DL model resulted in an average antero-posterior displacement of the barbell that matched the optimal displacement. This was characterized by a mean positive displacement towards the lifter during the pulling phases, a negative displacement away from the lifter in the turnover phase, and a return to positive displacement in the catch phase. These findings indicate the limited acute impact of the exercise sequences based on the three motor learning models on Olympic snatch technical efficiency in beginners, yet they hint at a possible slight advantage for the DL model. Coaches might therefore consider incorporating the DL model to potentially enhance technical efficiency, especially during the early stages of skill acquisition. Future research, involving even bigger amounts of exercise noise, longer learning periods, or a greater number of total learning trials and sessions, is essential to verify the potential advantages of the DL model for weightlifting technical efficiency.}, }
@article {pmid39758128, year = {2025}, author = {Wang, B and Zhang, Y and Li, H and Dou, H and Guo, Y and Deng, Y}, title = {Biologically inspired heterogeneous learning for accurate, efficient and low-latency neural network.}, journal = {National science review}, volume = {12}, number = {1}, pages = {nwae301}, pmid = {39758128}, issn = {2053-714X}, abstract = {The pursuit of artificial neural networks that mirror the accuracy, efficiency and low latency of biological neural networks remains a cornerstone of artificial intelligence (AI) research. Here, we incorporated recent neuroscientific findings of self-inhibiting autapse and neuron heterogeneity for innovating a spiking neural network (SNN) with enhanced learning and memorizing capacities. A bi-level programming paradigm was formulated to respectively learn neuron-level biophysical variables and network-level synapse weights for nested heterogeneous learning. We successfully demonstrated that our biologically inspired neuron model could reproduce neural statistics at both individual and group levels, contributing to the effective decoding of brain-computer interface data. Furthermore, the heterogeneous SNN showed higher accuracy (1%-10% improvement), superior efficiency (maximal 17.83-fold reduction in energy) and lower latency (maximal 5-fold improvement) in performing several AI tasks. For the first time, we benchmarked SNN for conducting cell type identification from scRNA-seq data. The proposed model correctly identified very rare cell types associated with severe brain diseases where typical SNNs failed.}, }
@article {pmid39757218, year = {2025}, author = {Kim, MS and Park, H and Kwon, I and An, KO and Kim, H and Park, G and Hyung, W and Im, CH and Shin, JH}, title = {Efficacy of brain-computer interface training with motor imagery-contingent feedback in improving upper limb function and neuroplasticity among persons with chronic stroke: a double-blinded, parallel-group, randomized controlled trial.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {22}, number = {1}, pages = {1}, pmid = {39757218}, issn = {1743-0003}, support = {NRCTR-IN20001//Translational Research Program for Rehabilitation Robots, National Rehabilitation Center, Ministry of Health and Welfare, Republic of Korea/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Neuronal Plasticity/physiology ; Male ; Female ; *Stroke Rehabilitation/methods ; Middle Aged ; *Upper Extremity/physiopathology ; Double-Blind Method ; Stroke/physiopathology ; Aged ; Electroencephalography ; Recovery of Function/physiology ; Adult ; Imagery, Psychotherapy/methods ; Chronic Disease ; Treatment Outcome ; Imagination/physiology ; }, abstract = {BACKGROUND: Brain-computer interface (BCI) technology can enhance neural plasticity and motor recovery in persons with stroke. However, the effects of BCI training with motor imagery (MI)-contingent feedback versus MI-independent feedback remain unclear. This study aimed to investigate whether the contingent connection between MI-induced brain activity and feedback influences functional and neural plasticity outcomes. We hypothesized that BCI training, with MI-contingent feedback, would result in greater improvements in upper limb function and neural plasticity compared to BCI training, with MI-independent feedback.<br><br>METHODS: This randomized controlled trial included persons with chronic stroke who underwent BCI training involving functional electrical stimulation feedback on the affected wrist extensor. Primary outcomes included the Medical Research Council (MRC) scale score for muscle strength in the wrist extensor (MRC-WE) and active range of motion in wrist extension (AROM-WE). Resting-state electroencephalogram recordings were used to assess neural plasticity.<br><br>RESULTS: Compared to the MI-independent feedback BCI group, the MI-contingent feedback BCI group showed significantly greater improvements in MRC-WE scores (mean difference&#x2009;=&#x2009;0.52, 95% CI&#x2009;=&#x2009;0.03-1.00, p&#x2009;=&#x2009;0.036) and demonstrated increased AROM-WE at 4 weeks post-intervention (p&#x2009;=&#x2009;0.019). Enhanced functional connectivity in the affected hemisphere was observed in the MI-contingent feedback BCI group, correlating with MRC-WE and Fugl-Meyer assessment-distal scores. Improvements were also observed in the unaffected hemisphere's functional connectivity.<br><br>CONCLUSIONS: BCI training with MI-contingent feedback is more effective than MI-independent feedback in improving AROM-WE, MRC, and neural plasticity in individuals with chronic stroke. BCI technology could be a valuable addition to conventional rehabilitation for stroke survivors, enhancing recovery outcomes.<br><br>TRIAL REGISTRATION: CRIS (KCT0009013).}, }
@article {pmid39755291, year = {2025}, author = {Vogeley, AO and Livinski, AA and Varnosfaderani, SD and Javaheripour, N and Jamalabadi, H and Kotoula, V and Henter, ID and Hejazi, NS and Price, RB and Yavi, M and Walter, M and Zarate, CA and Kheirkhah, M}, title = {Temporal Dynamics of Affective Scene Processing in the Healthy Adult Human Brain.}, journal = {Neuroscience and biobehavioral reviews}, volume = {}, number = {}, pages = {106003}, doi = {10.1016/j.neubiorev.2025.106003}, pmid = {39755291}, issn = {1873-7528}, abstract = {Understanding how the brain distinguishes emotional from neutral scenes is crucial for advancing brain-computer interfaces, enabling real-time emotion detection for faster, more effective responses, and improving treatments for emotional disorders like depression and anxiety. However, inconsistent research findings have arisen from differences in study settings, such as variations in the time windows, brain regions, and emotion categories examined across studies. This review sought to compile the existing literature on the timing at which the adult brain differentiates basic affective from neutral scenes in less than one second, as previous studies have consistently shown that the brain can begin recognizing emotions within just a few milliseconds. The review includes studies that used electroencephalography (EEG) or magnetoencephalography (MEG) in healthy adults to examine brain responses to emotional versus neutral images within one second. Articles of interest were limited to the English language but not to any publication year. Excluded studies involved only patients (of any diagnosis), participants under age 18 (since emotional processing can differ between adults and younger individuals), non-passive tasks, low temporal resolution techniques, time intervals over one second, and animals. Of the 3,045 screened articles, 19 met these criteria. Despite the variations between studies, the earliest onset for heightened brain responses to basic affective scenes compared to neutral ones was most commonly observed within the 250-300 ms time window. To the best of our knowledge, this review is the first to synthesize data on the timing of brain differentiation between emotional and neutral scenes in healthy adults.}, }
@article {pmid39755229, year = {2025}, author = {Ma, X and Xue, S and Ma, H and Saeed, S and Zhang, Y and Meng, Y and Chen, H and Yu, H and Wang, H and Hu, S and Cai, M}, title = {Esketamine alleviates LPS-induced depression-like behavior by activating Nrf2-mediated anti-inflammatory response in adolescent mice.}, journal = {Neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neuroscience.2024.12.062}, pmid = {39755229}, issn = {1873-7544}, abstract = {BACKGROUND: The mechanisms underlying esketamine's therapeutic effects remain elusive. The study aimed to explore the impact of single esketamine treatment on LPS-induced adolescent depressive-like behaviors and the role of Nrf2 regulated neuroinflammatory response in esketamine-produced rapid antidepressant efficacy.<br><br>METHODS: Adolescent male C57BL/6J mice were randomly assigned to three groups: control, LPS, and LPS&#x202f;+&#x202f;esketamine (15&#x202f;mg/kg, i.p.). Depressive-like behaviors were evaluated via the OFT, NFST, and TST. Protein expression of Nrf2 and inflammatory cytokines, including TNF-&#x3b1;, IL-1&#x3b2;, and iNOS in the hippocampus and mPFC, were measured by western blot. Moreover, the Nrf2 inhibitor, ML385, was also applied in the current study. The depressive-like behaviors and the protein expression of Nrf2, TNF-&#x3b1;, IL-1&#x3b2;, and iNOS in mPFC and hippocampus were also measured. Additionally, the plasma's pro-inflammatory cytokines and anti-inflammatory cytokines were assessed using ELISA methods with or without ML385.<br><br>RESULTS: A single administration of esketamine treatment alleviated the LPS-induced depressive-like behaviors. Esketamine increased the expression of Nrf2 and reduced the expression of the inflammatory cytokines, including TNF-&#x3b1;, IL-1&#x3b2;, and iNOS, in the mPFC and hippocampus. Notably, pharmacological inhibition of Nrf2 via ML385 administration abrogated the antidepressive-like behaviors and anti-inflammatory effects induced by esketamine. In the periphery, esketamine mitigated the LPS-induced elevation of pro-inflammatory cytokines, and the reduction of anti-inflammatory cytokines, and this effect was reversed by Nrf2 inhibition.<br><br>CONCLUSION: Esketamine treatment exerts rapid antidepressant effects and attenuates neuroinflammation in LPS-induced adolescent depressive-like behaviors, potentially through the activation of Nrf2-mediated anti-inflammatory signaling.}, }
@article {pmid39755222, year = {2025}, author = {Huang, J and Wei, S and Gao, Z and Jiang, S and Wang, M and Sun, L and Ding, W and Zhang, D}, title = {Local structural-functional coupling with counterfactual explanations for epilepsy prediction.}, journal = {NeuroImage}, volume = {306}, number = {}, pages = {120978}, doi = {10.1016/j.neuroimage.2024.120978}, pmid = {39755222}, issn = {1095-9572}, abstract = {The structural-functional brain connections coupling (SC-FC coupling) describes the relationship between white matter structural connections (SC) and the corresponding functional activation or functional connections (FC). It has been widely used to identify brain disorders. However, the existing research on SC-FC coupling focuses on global and regional scales, and few studies have investigated the impact of brain disorders on this relationship from the perspective of multi-brain region cooperation (i.e., local scale). Here, we propose the local SC-FC coupling pattern for brain disorders prediction. Compared with previous methods, the proposed patterns quantify the relationship between SC and FC in terms of subgraphs rather than whole connections or single brain regions. Specifically, we first construct structural and functional connections using diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) data, subsequently organizing them into a multimodal brain network. Then, we extract subgraphs from these multimodal brain networks and select them based on their frequencies to generate local SC-FC coupling patterns. Finally, we employ these patterns to identify brain disorders while refining abnormal patterns to generate counterfactual explanations. Results on a real epilepsy dataset suggest that the proposed method not only outperforms existing methods in accuracy but also provides insights into the local SC-FC coupling pattern and their changes in brain disorders. Code available at https://github.com/UAIBC-Brain/Local-SC-FC-coupling-pattern.}, }
@article {pmid39755127, year = {2025}, author = {Chu, T and Si, X and Song, X and Che, K and Dong, F and Guo, Y and Chen, D and Yao, W and Zhao, F and Xie, H and Shi, Y and Ma, H and Ming, D and Mao, N}, title = {Understanding structural-functional connectivity coupling in patients with major depressive disorder: A white matter perspective.}, journal = {Journal of affective disorders}, volume = {373}, number = {}, pages = {219-226}, doi = {10.1016/j.jad.2024.12.082}, pmid = {39755127}, issn = {1573-2517}, abstract = {PURPOSE: To elucidate the structural-functional connectivity (SC-FC) coupling in white matter (WM) tracts in patients with major depressive disorder (MDD).<br><br>METHODS: A total of 178 individuals diagnosed with MDD and 173 healthy controls (HCs) were recruited for this study. The Euclidean distance was calculated to assess SC-FC coupling. The primary analyses focused on investigating alterations in SC-FC coupling in WM tracts of individuals with MDD. Additionally, we explored the association between coupling and clinical symptoms. Secondary analyses examined differences among three subgroups of MDD: those with suicidal ideation (SI), those with a history of suicidal attempts (SA), and those non-suicidal (NS).<br><br>RESULTS: The study revealed increased SC-FC coupling mainly in the middle cerebellar peduncle and bilateral corticospinal tract (PFDR&#xa0;<&#xa0;0.05) in patients with MDD compared with HCs. Additionally, right cerebral peduncle coupling strength exhibited a significant positive correlation with Hamilton Anxiety Scale scores (r&#xa0;=&#xa0;0.269, PFDR&#xa0;=&#xa0;0.041), while right cingulum (hippocampus) coupling strength showed a significant negative correlation with Nurses' Global Assessment of Suicide Risk scores (r&#xa0;=&#xa0;-0.159, PFDR&#xa0;=&#xa0;0.036). An increase in left anterior limb of internal capsule (PBonferroni&#xa0;<&#xa0;0.01) and left corticospinal tract (PBonferroni&#xa0;<&#xa0;0.05) coupling has been observed in MDD with SI. Additionally, a decrease in right posterior limb of internal capsule coupling has been found in MDD with SA (PBonferroni&#xa0;<&#xa0;0.05).<br><br>CONCLUSIONS: This study emphasizes the variations in SC-FC coupling in WM tracts in individuals with MDD and its subgroups, highlighting the crucial role of WM networks in the pathophysiology of MDD.}, }
@article {pmid39754303, year = {2025}, author = {Qiu, C and Zhang, D and Wang, M and Mei, X and Chen, W and Yu, H and Yin, W and Peng, G and Hu, S}, title = {Peripheral Single-Cell Immune Characteristics Contribute to the Diagnosis of Alzheimer's Disease and Dementia With Lewy Bodies.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {31}, number = {1}, pages = {e70204}, pmid = {39754303}, issn = {1755-5949}, support = {2022YFC3602604//National Key Research and Development Program of China/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation Ten Thousand Talents Program of Zhejiang Province/ ; 2020R01001//Innovation team for precision diagnosis and treatment of major brain diseases/ ; JNL 2023001B//Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2021C03107//Zhejiang Provincial Key Research and Development Program/ ; 2022KTZ004//Chinese Medical Education Association/ ; 2022 F28//NINGBO Medical Health Leading Academic Discipline Project/ ; }, mesh = {Humans ; *Alzheimer Disease/immunology/diagnosis ; *Lewy Body Disease/immunology/diagnosis ; Aged ; Female ; Male ; *Leukocytes, Mononuclear/immunology/metabolism ; Aged, 80 and over ; Single-Cell Analysis/methods ; Middle Aged ; Biomarkers/blood ; T-Lymphocytes/immunology ; }, abstract = {OBJECTIVE: Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) are common neurodegenerative diseases with distinct but overlapping pathogenic mechanisms. The clinical similarities between these diseases often result in high misdiagnosis rates, leading to serious consequences. Peripheral blood mononuclear cells (PBMCs) are easy to collect and can accurately reflect the immune characteristics of both DLB and AD.<br><br>METHODS: We utilized time-of-flight mass cytometry (CyTOF) with single-cell resolution to quantitatively analyze peripheral PBMCs, identifying 1228 immune characteristics. Based on the top-selected immune features, we constructed immunological elastic net (iEN) models.<br><br>RESULTS: These models demonstrated high diagnostic efficacy in distinguishing diseased samples from healthy donors as well as distinguishing AD and DLB cases. The selected features reveal that the primary peripheral immune characteristic of AD is a decrease in total T cells, while DLB is characterized by low expression of I-kappa-B-alpha (IKB&#x3b1;) in the classical monocyte subset.<br><br>CONCLUSIONS: These findings suggest that peripheral immune characteristics could serve as potential biomarkers, facilitating the diagnosis of neurodegenerative diseases.}, }
@article {pmid39753090, year = {2024}, author = {Pang, Y and Zhao, S and Zhang, Z and Xu, J and Gao, L and Zhang, R and Li, Z and Lu, F and Chen, H and Wu, H and Chen, M and Chen, K and Wang, J}, title = {Individual structural covariance connectome reveals aberrant brain developmental trajectories associated with childhood maltreatment.}, journal = {Journal of psychiatric research}, volume = {181}, number = {}, pages = {709-715}, doi = {10.1016/j.jpsychires.2024.12.032}, pmid = {39753090}, issn = {1879-1379}, abstract = {BACKGROUND: The long-term impact of childhood maltreatment (CM) on an individual's physical and mental health is suggested to be mediated by altered neurodevelopment. However, the exact neurobiological consequences of CM remain unclear.<br><br>METHODS: The present study aimed to investigate the relationship between CM and brain age based on structural magnetic resonance imaging data from a sample of 214 adults. The participants were divided into CM and non_CM groups according to Childhood Trauma Questionnaire. For each participant, brain connectome age was estimated from a large-scale structural covariance network through relevance vector regression. Brain predicted age difference (brain_PAD) was then calculated for each participant.<br><br>RESULTS: The brain connectome age matched well with chronological age in young adults (age range: 18-25 years) and adults (age range: 26-44 years) without CM, but not in individuals with CM. Compared with non_CM group, CM group was characterized by higher brain_PAD scores in young adults, whereas lower brain_PAD scores in adults. The finding revealed that brain development in individuals with CM seems to be accelerated in younger adults but retardation with increasing age. Moreover, individuals who suffered child abuse (but not neglect) showed higher brain_PAD scores than non_CM group, suggesting the different influence of abuse and neglect on neurodevelopment. Finally, the brain_PAD was positively correlated with attentional impulsivity in CM group.<br><br>CONCLUSIONS: CM affects different stages of adult brain development differently, and abuse and neglect have different influenced patterns, which may provide new evidence for the impact of CM on structural brain development.}, }
@article {pmid39752412, year = {2025}, author = {Ding, C and Kim Geok, S and Sun, H and Roslan, S and Cao, S and Zhao, Y}, title = {Does music counteract mental fatigue? A systematic review.}, journal = {PloS one}, volume = {20}, number = {1}, pages = {e0316252}, pmid = {39752412}, issn = {1932-6203}, mesh = {*Mental Fatigue/prevention &amp; control ; Humans ; *Music/psychology ; *Cognition/physiology ; Music Therapy/methods ; }, abstract = {INTRODUCTION: Mental fatigue, a psychobiological state induced by prolonged and sustained cognitive tasks, impairs both cognitive and physical performance. Several studies have investigated strategies to counteract mental fatigue. However, potential health risks and contextual restrictions often limit these strategies, which hinder their practical application. Due to its noninvasive and portable nature, music has been proposed as a promising strategy to counteract mental fatigue. However, the effects of music on performance decrements vary with different music styles. Synthesizing studies that systematically report music style and its impact on counteracting performance decrements is crucial for theoretical and practical applications.<br><br>OBJECTIVES: This review aims to provide a comprehensive systematic analysis of different music styles in counteracting mental fatigue and their effects on performance decrements induced by mental fatigue. Additionally, the mechanisms by which music counteracts mental fatigue will be discussed.<br><br>METHODS: A comprehensive search was conducted across five databases-Web of Science, PubMed, SCOPUS, SPORTDiscus via EBSCOhost, and the Psychological and Behavioral Sciences Collection via EBSCOhost-up to November 18, 2023. The selected studies focused solely on music interventions, with outcomes including subjective feelings of mental fatigue, physiological markers, and both cognitive and behavioral performance.<br><br>RESULTS: Nine studies met the predetermined criteria for inclusion in this review. The types of music interventions that counteract mental fatigue include relaxing, exciting, and personal preference music, all of which were associated with decreased subjective feelings of mental fatigue and changes in objective physiological markers. Cognitive performance, particularly in inhibition and working memory tasks impaired by mental fatigue, was countered by both relaxing and exciting music. Exciting music was found to decrease reaction time more effectively than relaxing music in working memory tasks. The physiological marker of steady-state visually evoked potential-based brain-computer interface (SSVEP-BCI) amplitude increased, confirming that exciting music counteracts mental fatigue more effectively than relaxing music. Behavioral performance in tasks such as arm-pointing, the Yo-Yo intermittent test, and the 5 km time-trial, which were impaired by mental fatigue, were counteracted by personal preference music.<br><br>CONCLUSION: Relaxing music, exciting music, and personal preference music effectively counteract mental fatigue by reducing feelings of fatigue and mitigating performance decrements. Individuals engaged in mentally demanding tasks can effectively counteract concurrent or subsequent cognitive performance decrements by simultaneously listening to relaxing or exciting music without lyrics or by using music during recovery from mental fatigue. Exciting music is more effective than relaxing music in counteracting mental fatigue. Personal preference music is effective in counteracting behavioral performance decrements in motor control and endurance tasks. Mentally fatigued individuals could apply personal preference music to counteract subsequent motor control performance decrements or simultaneously listen to it to counteract endurance performance decrements. Future studies should specify and examine the effects of different music genres, tempos, and intensities in counteracting mental fatigue. Additionally, the role of music in counteracting mental fatigue in contexts such as work productivity, traffic accident risk, and sports requires further investigation, along with the underlying mechanisms.}, }
@article {pmid39752045, year = {2025}, author = {Xin, Q and Hu, H}, title = {To Attack or Not: A Neural Circuit Coding Sexually Dimorphic Aggression.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {39752045}, issn = {1995-8218}, }
@article {pmid39748261, year = {2025}, author = {Wu, JY and Zhang, JY and Xia, WQ and Kang, YN and Liao, RY and Chen, YL and Li, XM and Wen, Y and Meng, FX and Xu, LL and Wen, SH and Liu, HF and Li, YQ and Gu, JR and Lv, Q and Ren, Y}, title = {Predicting autoimmune thyroiditis in primary Sjogren's syndrome patients using a random forest classifier: a retrospective study.}, journal = {Arthritis research &amp; therapy}, volume = {27}, number = {1}, pages = {1}, pmid = {39748261}, issn = {1478-6362}, support = {No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; }, mesh = {Humans ; Retrospective Studies ; *Sjogren's Syndrome/immunology/diagnosis ; Female ; Male ; Middle Aged ; *Thyroiditis, Autoimmune/immunology/diagnosis ; Adult ; *Autoantibodies/immunology/blood ; *Machine Learning ; Algorithms ; Aged ; Random Forest ; }, abstract = {BACKGROUND: Primary Sjogren's syndrome (pSS) and autoimmune thyroiditis (AIT) share overlapping genetic and immunological profiles. This retrospective study evaluates the efficacy of machine learning algorithms, with a focus on the Random Forest Classifier, to predict the presence of thyroid-specific autoantibodies (TPOAb and TgAb) in pSS patients.<br><br>METHODS: A total of 96 patients with pSS were included in the retrospective study. All participants underwent a complete clinical and laboratory evaluation. All participants underwent thyroid function tests, including TPOAb and TgAb, and were accordingly divided into positive and negative thyroid autoantibody groups. Four machine learning algorithms were then used to analyze the risk factors affecting patients with pSS with positive and negative for thyroid autoantibodies.<br><br>RESULTS: The results indicated that the Random Forest Classifier algorithm (AUC&#x2009;=&#x2009;0.755) outperformed the other three machine learning algorithms. The random forest classifier indicated Age, IgG, C4 and dry mouth were the main factors influencing the prediction of positive thyroid autoantibodies in pSS patients. It is feasible to predict AIT in pSS using machine learning algorithms.<br><br>CONCLUSIONS: Analyzing clinical and laboratory data from 96 pSS patients, the Random Forest model demonstrated superior performance (AUC&#x2009;=&#x2009;0.755), identifying age, IgG levels, complement component 4 (C4), and absence of dry mouth as primary predictors. This approach offers a promising tool for early identification and management of AIT in pSS patients.<br><br>TRIAL REGISTRATION: This retrospective study was approved and monitored by the Ethics Committee of The Third Affiliated Hospital of Sun Yat-sen University (No.II2023-254-02).}, }
@article {pmid39748063, year = {2025}, author = {Wei, Q and Li, C and Wang, Y and Gao, X}, title = {Enhancing the performance of SSVEP-based BCIs by combining task-related component analysis and deep neural network.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {365}, pmid = {39748063}, issn = {2045-2322}, support = {62066028//National Natural Science Foundation of China/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Neural Networks, Computer ; *Algorithms ; *Electroencephalography/methods ; Deep Learning ; Signal Processing, Computer-Assisted ; }, abstract = {Steady-State Visually Evoked Potential (SSVEP) signals can be decoded by either a traditional machine learning algorithm or a deep learning network. Combining the two methods is expected to enhance the performance of an SSVEP-based brain-computer interface (BCI) by exploiting their advantages. However, an efficient strategy for integrating the two methods has not yet been established. To address this issue, we propose a classification framework named eTRCA + sbCNN that combines an ensemble task-related component analysis (eTRCA) algorithm and a sub-band convolutional neural network (sbCNN) for recognizing the frequency of SSVEP signals. The two models are first trained separately, then their classification score vectors are added together, and finally the frequency corresponding to the maximal summed score is decided as the frequency of SSVEP signals. The proposed framework can effectively exploit the complementarity between the two kinds of feature signals and significantly improve the classification performance of SSVEP-based BCIs. The performance of the proposed method is validated on two SSVEP BCI datasets and compared with that of eTRCA, sbCNN and other state-of-the-art models. Experimental results indicate that the proposed method significantly outperform the compared algorithms, and thus helps to promote the practical application of SSVEP- BCI systems.}, }
@article {pmid39747930, year = {2025}, author = {Anderson, L and De Ridder, D and Glue, P and Mani, R and van Sleeuwen, C and Smith, M and Adhia, DB}, title = {A safety and feasibility randomized placebo controlled trial exploring electroencephalographic effective connectivity neurofeedback treatment for fibromyalgia.}, journal = {Scientific reports}, volume = {15}, number = {1}, pages = {209}, pmid = {39747930}, issn = {2045-2322}, mesh = {Humans ; *Fibromyalgia/therapy/physiopathology ; *Neurofeedback/methods ; Female ; Middle Aged ; *Electroencephalography ; Adult ; *Feasibility Studies ; Male ; Treatment Outcome ; Gyrus Cinguli/physiopathology/diagnostic imaging ; Chronic Pain/therapy/physiopathology ; }, abstract = {Fibromyalgia is a chronic pain condition contributing to significant disability worldwide. Neuroimaging studies identify abnormal effective connectivity between cortical areas responsible for descending pain modulation (pregenual anterior cingulate cortex, pgACC) and sensory components of pain experience (primary somatosensory cortex, S1). Neurofeedback, a brain-computer interface technique, can normalise dysfunctional brain activity, thereby improving pain and function. This study evaluates the safety, feasibility, and acceptability of a novel electroencephalography-based neurofeedback training, targeting effective alpha-band connectivity from the pgACC to S1 and exploring its effect on pain and function. Participants with fibromyalgia (N = 30; 15 = active, 15 = placebo) received 12 sessions of neurofeedback. Feasibility and outcome measures of pain (Brief Pain Inventory) and function (Revised Fibromyalgia Impact Questionnaire) were collected at baseline and immediately, ten-days, and one-month post-intervention. Descriptive statistics demonstrate effective connectivity neurofeedback training is feasible (recruitment rate: 6 participants per-month, mean adherence: 80.5%, dropout rate: 20%), safe (no adverse events) and highly acceptable (average 8.0/10) treatment approach for fibromyalgia. Active and placebo groups were comparable in their decrease in pain and functional impact. Future fully powered clinical trial is warranted to test the efficacy of the effective connectivity neurofeedback training in people with fibromyalgia with versus without chronic fatigue.}, }
@article {pmid39745941, year = {2025}, author = {Stavisky, SD}, title = {Restoring Speech Using Brain-Computer Interfaces.}, journal = {Annual review of biomedical engineering}, volume = {}, number = {}, pages = {}, doi = {10.1146/annurev-bioeng-110122-012818}, pmid = {39745941}, issn = {1545-4274}, abstract = {People who have lost the ability to speak due to neurological injuries would greatly benefit from assistive technology that provides a fast, intuitive, and naturalistic means of communication. This need can be met with brain-computer interfaces (BCIs): medical devices that bypass injured parts of the nervous system and directly transform neural activity into outputs such as text or sound. BCIs for restoring movement and typing have progressed rapidly in recent clinical trials; speech BCIs are the next frontier. This review covers the clinical need for speech BCIs, surveys foundational studies that point to where and how speech can be decoded in the brain, describes recent progress in both discrete and continuous speech decoding and closed-loop speech BCIs, provides metrics for assessing these systems' performance, and highlights key remaining challenges on the road toward clinically useful speech neuroprostheses.}, }
@article {pmid39745924, year = {2025}, author = {Trevelyan, AJ and Marks, VS and Graham, RT and Denison, T and Jackson, A and Smith, EH}, title = {On brain stimulation in epilepsy.}, journal = {Brain : a journal of neurology}, volume = {}, number = {}, pages = {}, doi = {10.1093/brain/awae385}, pmid = {39745924}, issn = {1460-2156}, abstract = {Brain stimulation has, for many decades, been considered as a potential solution for the unmet needs of the many people living with drug-resistant epilepsy. Clinically, there are several different approaches in use, including vagus nerve stimulation (VNS), deep brain stimulation of the thalamus, and responsive neurostimulation (RNS). Across populations of patients, all deliver reductions in seizure load and SUDEP risk, yet do so variably, and the improvements seem incremental rather than transformative. In contrast, within the field of experimental neuroscience, the transformational impact of optogenetic stimulation is evident; by providing a means to control subsets of neurons in isolation, it has revolutionized our ability to dissect out the functional relations within neuronal microcircuits. It is worth asking, therefore, how pre-clinical optogenetics research could advance clinical practice in epilepsy? Here, we review the state of the clinical field, and the recent progress in pre-clinical animal research. We report various breakthrough results, including the development of new models of seizure initiation, its use for seizure prediction, and for fast, closed-loop control of pathological brain rhythms, and what these experiments tell us about epileptic pathophysiology. Finally, we consider how these pre-clinical research advances may be translated into clinical practice.}, }
@article {pmid39745545, year = {2025}, author = {Romano, V and Manto, M}, title = {How and where Effectively Apply Cerebellum Stimulation: The frequency-dependent Modulation of Cerebellar Output by Transcranial Alternating Current Stimulation.}, journal = {Cerebellum (London, England)}, volume = {24}, number = {1}, pages = {22}, pmid = {39745545}, issn = {1473-4230}, mesh = {*Transcranial Direct Current Stimulation/methods ; *Cerebellum/physiology ; Animals ; Humans ; Rats ; }, abstract = {As brain-machine interfaces (BMI) are growingly used in clinical settings, understanding how to apply brain stimulation is increasingly important. Despite the emergence of optogenetic techniques, ethical and medical concerns suggest that interventions that are safe and non-invasive, such as Transcranial Alternating Current Stimulation (tACS), are more likely to be employed in human in the near future. Consequently, the question of how and where to apply current stimulation is becoming increasingly important for the efficient neuromodulation of both neurological and psychiatric disorders. In this edition of The Cerebellum, Mourra et al. demonstrate how ctACS influences cerebellar output at both single-cell and population levels by stimulating Crus I in rats. As the neuron generating this output serves as a crucial convergence and divergence center in the nervous system, it can be leveraged as a strategic hub to target multiple brain structures and influence various behaviors. Accordingly, the discovery that neurons in this relatively deep brain region can be indirectly entrained through Purkinje neuron activation and optimal frequency around 80 Hz could be highly relevant for future medical interventions. In light of these findings, high-γ-tACS might be more effective in humans compared to the more commonly used low-γ (50 Hz) or θ-tACS (5 Hz). This could enhance the chance of cerebellar tACS being utilized in clinical settings and BMI.}, }
@article {pmid39744695, year = {2025}, author = {Li, C and Miao, C and Ge, Y and Wu, J and Gao, P and Yin, S and Zhang, P and Yang, H and Tian, B and Chen, W and Chen, XQ}, title = {A molecularly distinct cell type in the midbrain regulates intermale aggression behaviors in mice.}, journal = {Theranostics}, volume = {15}, number = {2}, pages = {707-725}, pmid = {39744695}, issn = {1838-7640}, mesh = {Animals ; *Aggression/physiology ; Mice ; *Periaqueductal Gray/metabolism/physiology ; Male ; *Neurons/metabolism/physiology ; Mice, Inbred C57BL ; Tachykinins/metabolism/genetics ; Behavior, Animal/physiology ; Mesencephalon/metabolism/physiology ; Serotonin/metabolism ; }, abstract = {Rationale: The periaqueductal gray (PAG) is a central hub for the regulation of aggression, whereas the circuitry and molecular mechanisms underlying this regulation remain uncharacterized. In this study, we investigate the role of a distinct cell type, Tachykinin 2-expressing (Tac2[+]) neurons, located in the dorsomedial PAG (dmPAG) and their modulation of aggressive behavior in mice. Methods: We combined activity mapping, in vivo Ca[2+] recording, chemogenetic and pharmacological manipulation, and a viral-based translating ribosome affinity purification (TRAP) profiling using a mouse resident-intruder model. Results: We revealed that dmPAG[Tac2] neurons are selectively activated by fighting behaviors. Chemogenetic activation of these neurons evoked fighting behaviors, while inhibition or genetic ablation of dmPAG[Tac2] neurons attenuated fighting behaviors. TRAP profiling of dmPAG[Tac2] neurons revealed an enrichment of serotonin-associated transcripts in response to fighting behaviors. Finally, we validated these effects by selectively administering pharmacological agents to the dmPAG, reversing the behavioral outcomes induced by chemogenetic manipulation. Conclusions: We identify dmPAG[Tac2] neurons as critical modulators of aggressive behavior in mouse and thus suggest a distinct molecular target for the treatment of exacerbated aggressive behaviors in populations that exhibit high-level of violence.}, }
@article {pmid39743292, year = {2024}, author = {Huang, T and Guo, X and Huang, X and Yi, C and Cui, Y and Dong, Y}, title = {Input-output specific orchestration of aversive valence in lateral habenula during stress dynamics.}, journal = {Journal of Zhejiang University. Science. B}, volume = {25}, number = {12}, pages = {1055-1065}, pmid = {39743292}, issn = {1862-1783}, support = {2022ZD0211700//the STI2030-Major Projects/ ; 32371057, 31922031, 32071017, 81971309, 32170980, 82201707 and 82200562//the National Natural Science Foundation of China/ ; LDQ24C090001//the Zhejiang Provincial Natural Science Foundation of China/ ; 2023-PT310-01//the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; JCTD-2022-11//the CAS Youth Interdisciplinary Team/ ; BX20230319//the China Postdoctoral Science Foundation/ ; 2022B1515020012 and 2021A1515110121//the Guangdong Basic and Applied Basic Research Foundation/ ; 2023B1212060018//the Science and Technology Planning Project of Guangdong Province/ ; JCYJ20210324123212035, RCYX20200714114644167, ZDSYS20220606100801003, JCYJ20210324122809025 and JCYJ20230807110315031//the Shenzhen Fundamental Research Program/ ; }, mesh = {*Habenula/physiology ; Animals ; *Stress, Psychological/physiopathology ; Humans ; Neurons/physiology ; Neuronal Plasticity/physiology ; Depression/physiopathology ; Neural Pathways/physiology ; }, abstract = {Stress has been considered as a major risk factor for depressive disorders, triggering depression onset via inducing persistent dysfunctions in specialized brain regions and neural circuits. Among various regions across the brain, the lateral habenula (LHb) serves as a critical hub for processing aversive information during the dynamic process of stress accumulation, thus having been implicated in the pathogenesis of depression. LHb neurons integrate aversive valence conveyed by distinct upstream inputs, many of which selectively innervate the medial part (LHbM) or lateral part (LHbL) of LHb. LHb subregions also separately assign aversive valence via dissociable projections to the downstream targets in the midbrain which provides feedback loops. Despite these strides, the spatiotemporal dynamics of LHb-centric neural circuits remain elusive during the progression of depression-like state under stress. In this review, we attempt to describe a framework in which LHb orchestrates aversive valence via the input-output specific neuronal architecture. Notably, a physiological form of Hebbian plasticity in LHb under multiple stressors has been unveiled to incubate neuronal hyperactivity in an input-specific manner, which causally encodes chronic stress experience and drives depression onset. Collectively, the recent progress and future efforts in elucidating LHb circuits shed light on early interventions and circuit-specific antidepressant therapies.}, }
@article {pmid39743145, year = {2024}, author = {Hao, X and Ma, M and Meng, F and Liang, H and Liang, C and Liu, X and Zhang, B and Ju, Y and Liu, S and Ming, D}, title = {Diminished attention network activity and heightened salience-default mode transitions in generalized anxiety disorder: Evidence from resting-state EEG microstate analysis.}, journal = {Journal of affective disorders}, volume = {373}, number = {}, pages = {227-236}, doi = {10.1016/j.jad.2024.12.095}, pmid = {39743145}, issn = {1573-2517}, abstract = {Generalized anxiety disorder (GAD) is a common anxiety disorder characterized by excessive, uncontrollable worry and physical symptoms such as difficulty concentrating and sleep disturbances. Although functional magnetic resonance imaging (fMRI) studies have reported aberrant network-level activity related to cognition and emotion in GAD, its low temporal resolution restricts its ability to capture the rapid neural activity in mental processes. EEG microstate analysis offers millisecond-resolution for tracking the dynamic changes in brain electrical activity, thereby illuminating the neurophysiological mechanisms underlying the cognitive and emotional dysfunctions in GAD. This study collected 64-channel resting-state EEG data from 28 GAD patients and 28 healthy controls (HC), identifying five microstate classes (A-E) in both groups. Results showed that GAD patients exhibited significantly lower duration (p < 0.01), occurrence (p < 0.05), and coverage (p < 0.01) of microstate class D, potentially reflecting deficits in attention-related networks. Such alterations may contribute to the impairments in attention maintenance and cognitive control. Additionally, GAD patients displayed reduced transition probabilities in A → D, B → D, C → D, and E → D (all corrected p < 0.05), but increased in C → E (corrected p < 0.05) and E → C (corrected p < 0.01). These results highlight a significant reduction in the brain's ability to transition into microstate class D, alongside overactivity in switching between the default mode network and the salience network. Such neurophysiological changes may underlie cognitive control deficits, increased spontaneous rumination, and emotional regulation challenges observed in GAD. Together, these insights provide a new perspective for understanding the neurophysiological and pathological mechanisms underlying GAD.}, }
@article {pmid39742538, year = {2024}, author = {Cao, L and Zhao, W and Sun, B}, title = {Emotion recognition using multi-scale EEG features through graph convolutional attention network.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {184}, number = {}, pages = {107060}, doi = {10.1016/j.neunet.2024.107060}, pmid = {39742538}, issn = {1879-2782}, abstract = {Emotion recognition via electroencephalogram (EEG) signals holds significant promise across various domains, including the detection of emotions in patients with consciousness disorders, assisting in the diagnosis of depression, and assessing cognitive load. This process is critically important in the development and research of brain-computer interfaces, where precise and efficient recognition of emotions is paramount. In this work, we introduce a novel approach for emotion recognition employing multi-scale EEG features, denominated as the Dynamic Spatial-Spectral-Temporal Network (DSSTNet). DSSTNet includes three main parts, the first is spatial features extractor, which converts EEG signal into graph structure data, and uses graph convolutional network (GCN) to dynamically optimize the adjacency matrix during the training process to obtain the spatial features between the channels. Next, band attention module is composed of semi-global pooling, localized cross-band interaction and adaptive weighting, which further extracts frequency information. Finally, through the temporal features extractor, the deep temporal information is extracted by stacking several one-dimensional convolutional layers. In addition, in order to improve the performance of emotion recognition and filter valid channels, we add a ℓ2,1-norm regularization term to the loss function to make the adjacency matrix constraint sparse. This makes it easier to preserve emotionally relevant channels and eliminate noise in irrelevant channel. Combined with the channel selection method of graph theory, a small number of optimal channels are selected. We used a self-constructed dataset TJU-EmoEEG and a publicly available SEED dataset to evaluate DSSTNet. The experimental results demonstrate that DSSTNet outperforms current state-of-the-art (SOTA) methods in emotional recognition tasks.}, }
@article {pmid39741784, year = {2024}, author = {Degirmenci, M and Yuce, YK and Perc, M and Isler, Y}, title = {EEG channel and feature investigation in binary and multiple motor imagery task predictions.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1525139}, pmid = {39741784}, issn = {1662-5161}, abstract = {INTRODUCTION: Motor Imagery (MI) Electroencephalography (EEG) signals are non-stationary and dynamic physiological signals which have low signal-to-noise ratio. Hence, it is difficult to achieve high classification accuracy. Although various machine learning methods have already proven useful to that effect, the use of many features and ineffective EEG channels often leads to a complex structure of classifier algorithms. State-of-the-art studies were interested in improving classification performance with complex feature extraction and classification methods by neglecting detailed EEG channel and feature investigation in predicting MI tasks from EEGs. Here, we investigate the effects of the statistically significant feature selection method on four different feature domains (time-domain, frequency-domain, time-frequency domain, and non-linear domain) and their two different combinations to reduce the number of features and classify MI-EEG features by comparing low-dimensional matrices with well-known machine learning algorithms.<br><br>METHODS: Our main goal is not to find the best classifier performance but to perform feature and channel investigation in MI task classification. Therefore, the detailed investigation of the effect of EEG channels and features is implemented using a statistically significant feature distribution on 22 EEG channels for each feature set separately. We used the BCI Competition IV Dataset IIa and 288 samples per person. A total of 1,364 MI-EEG features were analyzed in this study. We tested nine distinct classifiers: Decision tree, Discriminant analysis, Logistic regression, Naive Bayes, Support vector machine, k-Nearest neighbor, Ensemble learning, Neural networks, and Kernel approximation.<br><br>RESULTS: Among all feature sets considered, classifications performed with non-linear and combined feature sets resulted in a maximum accuracy of 63.04% and 47.36% for binary and multiple MI task predictions, respectively. The ensemble learning classifier achieved the maximum accuracy in almost all feature sets for binary and multiple MI task classifications.<br><br>DISCUSSION: Our research thus shows that the statistically significant feature-based feature selection method significantly improves the classification performance with fewer features in almost all feature sets, enabling detailed and effective EEG channel and feature investigation.}, }
@article {pmid39741250, year = {2024}, author = {Liang, S and Li, L and Zu, W and Feng, W and Hang, W}, title = {Adaptive deep feature representation learning for cross-subject EEG decoding.}, journal = {BMC bioinformatics}, volume = {25}, number = {1}, pages = {393}, pmid = {39741250}, issn = {1471-2105}, support = {61902197//National Natural Science Foundation of China/ ; KYCX23_1073//Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province/ ; 23KJB520012//Natural Science Research of Jiangsu Higher Education Institutions of China/ ; }, mesh = {*Electroencephalography/methods ; Humans ; Deep Learning ; Algorithms ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; }, abstract = {BACKGROUND: The collection of substantial amounts of electroencephalogram (EEG) data is typically time-consuming and labor-intensive, which adversely impacts the development of decoding models with strong generalizability, particularly when the available data is limited. Utilizing sufficient EEG data from other subjects to aid in modeling the target subject presents a potential solution, commonly referred to as domain adaptation. Most current domain adaptation techniques for EEG decoding primarily focus on learning shared feature representations through domain alignment strategies. Since the domain shift cannot be completely removed, target EEG samples located near the edge of clusters are also susceptible to misclassification.<br><br>METHODS: We propose a novel adaptive deep feature representation (ADFR) framework to improve the cross-subject EEG classification performance through learning transferable EEG feature representations. Specifically, we first minimize the distribution discrepancy between the source and target domains by employing maximum mean discrepancy (MMD) regularization, which aids in learning the shared feature representations. We then utilize the instance-based discriminative feature learning (IDFL) regularization to make the learned feature representations more discriminative. Finally, the entropy minimization (EM) regularization is further integrated to adjust the classifier to pass through the low-density region between clusters. The synergistic learning between above regularizations during the training process enhances EEG decoding performance across subjects.<br><br>RESULTS: The effectiveness of the ADFR framework was evaluated on two public motor imagery (MI)-based EEG datasets: BCI Competition III dataset 4a and BCI Competition IV dataset 2a. In terms of average accuracy, ADFR achieved improvements of 3.0% and 2.1%, respectively, over the state-of-the-art methods on these datasets.<br><br>CONCLUSIONS: The promising results highlight the effectiveness of the ADFR algorithm for EEG decoding and show its potential for practical applications.}, }
@article {pmid39738346, year = {2024}, author = {Le, TT and Luong, DAQ and Joo, H and Kim, D and Woo, J}, title = {Differences in spatiotemporal dynamics for processing specific semantic categories: An EEG study.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {31918}, pmid = {39738346}, issn = {2045-2322}, support = {NRF-2020R1A2C2003319//National Research Foundation of Korea/ ; }, mesh = {Humans ; *Electroencephalography ; *Semantics ; Male ; Female ; Adult ; *Brain Mapping ; Brain/physiology ; Young Adult ; Comprehension/physiology ; }, abstract = {Semantic processing is an essential mechanism in human language comprehension and has profound implications for speech brain-computer interface technologies. Despite recent advances in brain imaging techniques and data analysis algorithms, the mechanisms underlying human brain semantic representations remain a topic of debate, specifically whether this occurs through the activation of selectively separated cortical regions or via a network of distributed and overlapping regions. This study investigates spatiotemporal neural representation during the perception of semantic words related to faces, numbers, and animals using electroencephalography. Source-level analysis focuses on contrasting neural responses to different semantic categories. Critical intervals used in the source contrast analysis are defined using the peak duration of global field power. Effective connectivity, determined through a causality analysis of brain regions activated for semantic processing, is explored. The findings reveal the necessity of a distributed network of regions for processing specific semantic categories and provide evidence suggesting the existence of a neural substrate for semantic representations.}, }
@article {pmid39738221, year = {2024}, author = {Wolde, HF and Clements, ACA and Gilmour, B and Alene, KA}, title = {Spatial co-distribution of tuberculosis prevalence and low BCG vaccination coverage in Ethiopia.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {31561}, pmid = {39738221}, issn = {2045-2322}, support = {APP1196549//Australian National Health and Medical Research Council/ ; }, mesh = {Humans ; Ethiopia/epidemiology ; *BCG Vaccine/administration &amp; dosage ; Prevalence ; *Vaccination Coverage/statistics &amp; numerical data ; Female ; *Tuberculosis/epidemiology/prevention &amp; control ; Male ; *Bayes Theorem ; Infant ; Child, Preschool ; Adult ; Adolescent ; Vaccination/statistics &amp; numerical data ; Young Adult ; Child ; Spatial Analysis ; }, abstract = {While bacille-calmette-guerin (BCG) vaccination is one of the recommended strategies for preventing tuberculosis (TB), its coverage is low in several countries, including Ethiopia. This study investigated the spatial co-distribution and drivers of TB prevalence and low BCG coverage in Ethiopia. This ecological study was conducted using data from a national TB prevalence survey and the Ethiopian demographic and health survey (EDHS) to map the spatial co-distribution of BCG vaccination coverage and TB prevalence. A Bayesian geostatistical model was built to identify the drivers for the spatial distribution of TB prevalence and low BCG vaccination coverage. BCG vaccination coverage was defined as the number of children who received the vaccine divided by the total number of children born within five years preceding the EDHS surveys. Parameter estimation was done using binary logistic regression. Prediction maps for the co-distribution of high TB prevalence and low BCG vaccination coverage were created by overlying spatial prediction surfaces of the two outcomes. Posterior means and a 95% Bayesian credible interval (CrI) were used to summarize the parameters of the model. The national prevalence was 0.40% (95% confidence interval (CI) 0.34%, 0.47%) for TB and 47% (95% CI 46%, 48%) for vaccination coverage. Substantial spatial variation in TB prevalence and low BCG coverage was observed at a regional and local level, particularly in border areas of the country, including the Somali, Afar, and Oromia regions. Approximately 58% of the pixels (i.e., geographical area or spatial units) with high TB prevalence exhibited low BCG coverage in the same location. While travel time to cities (Mean = 0.28, 95% BCI: 0.15, 0.41) and distance to health facilities (Mean = 0.43, 95% CI 0.22, 0.63), were positively associated, population density (Mean = -0.04, 95% BCI -0.05, -0.02) was negatively associated, with the proportion of unvaccinated children for BCG indicating areas near health facilities and cities have better BCG coverage. However, there were no significant predictors for TB prevalence. Substantial spatial co-distribution between high TB prevalence and low BCG coverage was observed in some parts of the country, indicating that there are areas where the TB burden is not being adequately managed through the provision of vaccines in Ethiopia. Scaling up BCG vaccination coverage and TB diagnosis and treatment through improving access to health services in border regions such as Somalia and Afar would be important to reduce the prevalence of TB in Ethiopia.}, }
@article {pmid39738213, year = {2024}, author = {Liu, Y and Huang, S and Xu, W and Wang, Z and Ming, D}, title = {An fMRI study on the generalization of motor learning after brain actuated supernumerary robot training.}, journal = {NPJ science of learning}, volume = {9}, number = {1}, pages = {80}, pmid = {39738213}, issn = {2056-7936}, support = {62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81925020//National Natural Science Foundation of China (National Science Foundation of China)/ ; 21JCYBJC00520//Natural Science Foundation of Tianjin City (Natural Science Foundation of Tianjin)/ ; MSV202418//State Key Laboratory of Mechanical System and Vibration/ ; }, abstract = {Generalization is central to motor learning. However, few studies are on the learning generalization of BCI-actuated supernumerary robotic finger (BCI-SRF) for human-machine interaction training, and no studies have explored its longitudinal neuroplasticity mechanisms. Here, 20 healthy right-handed participants were recruited and randomly assigned to BCI-SRF group or inborn finger group (Finger) for 4-week training and measured by novel SRF-finger opposition sequences and multimodal MRI. After training, the BCI-SRF group showed 350% times compared to the Finger group in the improvement of sequence opposition accuracy before and after training, and accompanied by significant functional connectivity increases in the sensorimotor region and prefrontal cortex, as well as in the intra- and inter-hemisphere of the sensorimotor network. Moreover, Granger Causality Analysis identified causal effect main transfer within the sensorimotor cortex-cerebellar-thalamus loop and frontal-parietal loop. The findings suggest that BCI-SRF training enhances motor sequence learning ability by influencing the functional reorganization of sensorimotor network.}, }
@article {pmid39735964, year = {2024}, author = {Luo, TJ and Li, J and Li, R and Zhang, X and Wu, SR and Peng, H}, title = {Motion Cognitive Decoding of Cross-Subject Motor Imagery Guided on Different Visual Stimulus Materials.}, journal = {Journal of integrative neuroscience}, volume = {23}, number = {12}, pages = {218}, doi = {10.31083/j.jin2312218}, pmid = {39735964}, issn = {0219-6352}, support = {24JCXK01YB//Planning Project of Philosophy and Social Science of Zhejiang Province/ ; 24JCXK02YB//Planning Project of Philosophy and Social Science of Zhejiang Province/ ; 62106049//National Natural Science Foundation of China/ ; 61662025//National Natural Science Foundation of China/ ; 61871289//National Natural Science Foundation of China/ ; 62007016//National Natural Science Foundation of China/ ; 2022J01655//Natural Science Foundation of Fujian Province of China/ ; }, mesh = {Humans ; *Imagination/physiology ; *Electroencephalography/methods ; Adult ; Young Adult ; Male ; *Brain-Computer Interfaces ; Female ; Motor Activity/physiology ; Psychomotor Performance/physiology ; Motion Perception/physiology ; }, abstract = {BACKGROUND: Motor imagery (MI) plays an important role in brain-computer interfaces, especially in evoking event-related desynchronization and synchronization (ERD/S) rhythms in electroencephalogram (EEG) signals. However, the procedure for performing a MI task for a single subject is subjective, making it difficult to determine the actual situation of an individual's MI task and resulting in significant individual EEG response variations during motion cognitive decoding.<br><br>METHODS: To explore this issue, we designed three visual stimuli (arrow, human, and robot), each of which was used to present three MI tasks (left arm, right arm, and feet), and evaluated differences in brain response in terms of ERD/S rhythms. To compare subject-specific variations of different visual stimuli, a novel cross-subject MI-EEG classification method was proposed for the three visual stimuli. The proposed method employed a covariance matrix centroid alignment for preprocessing of EEG samples, followed by a model agnostic meta-learning method for cross-subject MI-EEG classification.<br><br>RESULTS AND CONCLUSION: The experimental results showed that robot stimulus materials were better than arrow or human stimulus materials, with an optimal cross-subject motion cognitive decoding accuracy of 79.04%. Moreover, the proposed method produced robust classification of cross-subject MI-EEG signal decoding, showing superior results to conventional methods on collected EEG signals.}, }
@article {pmid39733553, year = {2024}, author = {Jain, A and Kumar, L}, title = {ESI-GAL: EEG source imaging-based trajectory estimation for grasp and lift task.}, journal = {Computers in biology and medicine}, volume = {186}, number = {}, pages = {109608}, doi = {10.1016/j.compbiomed.2024.109608}, pmid = {39733553}, issn = {1879-0534}, abstract = {BACKGROUND: Electroencephalogram (EEG) signals-based motor kinematics prediction (MKP) has been an active area of research to develop Brain-computer interface (BCI) systems such as exosuits, prostheses, and rehabilitation devices. However, EEG source imaging (ESI) based kinematics prediction is sparsely explored in the literature.<br><br>METHOD: In this study, pre-movement EEG features are utilized to predict three-dimensional (3D) hand kinematics for the grasp-and-lift motor task. A public dataset, WAY-EEG-GAL, is utilized for MKP analysis. In particular, sensor-domain (EEG data) and source-domain (ESI data) based features from the frontoparietal region are explored for MKP. Deep learning-based models are explored to achieve efficient kinematics decoding. Various time-lagged and window sizes are analyzed for hand kinematics prediction. Subsequently, intra-subject and inter-subject MKP analysis is performed to investigate the subject-specific and subject-independent motor-learning capabilities of the neural decoders. The Pearson correlation coefficient (PCC) is used as the performance metric for kinematics trajectory decoding.<br><br>RESULTS: The rEEGNet neural decoder achieved the best performance with sensor-domain and source-domain features with the time lag and window size of 100ms and 450ms, respectively. The highest mean PCC values of 0.790, 0.795, and 0.637 are achieved using sensor-domain features, while 0.769, 0.777, and 0.647 are achieved using source-domain features in x, y, and z-directions, respectively.<br><br>CONCLUSION: This study explores the feasibility of trajectory prediction using EEG sensor-domain and source-domain features for the grasp-and-lift task. Furthermore, inter-subject trajectory estimation is performed using the proposed deep learning decoder with EEG source domain features.}, }
@article {pmid39733023, year = {2024}, author = {Cheng, Z and Bu, X and Wang, Q and Yang, T and Tu, J}, title = {EEG-based emotion recognition using multi-scale dynamic CNN and gated transformer.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {31319}, pmid = {39733023}, issn = {2045-2322}, support = {2020CB-34//the Science and Technology Plan Project of Jingzhou City/ ; 2022BCE009//Key Plan of Science and Technology Department of Hubei Province/ ; }, mesh = {*Electroencephalography/methods ; *Emotions/physiology ; Humans ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Algorithms ; }, abstract = {Emotions play a crucial role in human thoughts, cognitive processes, and decision-making. EEG has become a widely utilized tool in emotion recognition due to its high temporal resolution, real-time monitoring capabilities, portability, and cost-effectiveness. In this paper, we propose a novel end-to-end emotion recognition method from EEG signals, called MSDCGTNet, which is based on the Multi-Scale Dynamic 1D CNN and the Gated Transformer. First, the Multi-Scale Dynamic CNN is used to extract complex spatial and spectral features from raw EEG signals, which not only avoids information loss but also reduces computational costs associated with the time-frequency conversion of signals. Then, the Gated Transformer Encoder is utilized to capture global dependencies of EEG signals. This encoder focuses on specific regions of the input sequence while reducing computational resources through parallel processing with the improved multi-head self-attention mechanisms. Third, the Temporal Convolution Network is used to extract temporal features from the EEG signals. Finally, the extracted abstract features are fed into a classification module for emotion recognition. The proposed method was evaluated on three publicly available datasets: DEAP, SEED, and SEED_IV. Experimental results demonstrate the high accuracy and efficiency of the proposed method for emotion recognition. This approach proves to be robust and suitable for various practical applications. By addressing challenges posed by existing methods, the proposed method provides a valuable and effective solution for the field of Brain-Computer Interface (BCI).}, }
@article {pmid39732819, year = {2024}, author = {Jin, L and Hu, J and Li, Y and Zhu, Y and He, X and Bai, R and Wang, L}, title = {Altered neurovascular coupling and structure-function coupling in Moyamoya disease affect postoperative collateral formation.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {31324}, pmid = {39732819}, issn = {2045-2322}, support = {81870910//National Natural Science Foundation of China/ ; 2022C03133//Key Research and Development Program of Zhejiang Province/ ; }, mesh = {Humans ; *Moyamoya Disease/physiopathology/surgery/diagnostic imaging ; Female ; Male ; Adult ; *Cerebrovascular Circulation/physiology ; *Neurovascular Coupling/physiology ; *Magnetic Resonance Imaging ; Diffusion Tensor Imaging ; Middle Aged ; Collateral Circulation/physiology ; Case-Control Studies ; Brain/physiopathology/diagnostic imaging/pathology ; Young Adult ; }, abstract = {Chronic ischemia in moyamoya disease (MMD) impaired white matter microstructure and neural functional network. However, the coupling between cerebral blood flow (CBF) and functional connectivity and the association between structural and functional network are largely unknown. 38 MMD patients and 20 sex/age-matched healthy controls (HC) were included for T1-weighted imaging, arterial spin labeling imaging, resting-state functional MRI and diffusion tensor imaging. All patients had preoperative and postoperative digital subtraction angiography. Upon constructing the structural connectivity (SC) and functional connectivity (FC) networks, the SC-FC coupling was calculated. After obtaining the graph theoretical parameters, neurovascular coupling represented the spatial correlation between node degree centrality (DC) of functional networks and CBF. The CBF-DC coupling and SC-FC coupling were compared between MMD and HC groups. We further analyzed the correlation between coupling indexes and cognitive scores, as well as postoperative collateral formation. Compared with HC, CBF-DC coupling was decreased in MMD (p = 0.021), especially in the parietal lobe (p = 0.047). SC-FC coupling in MMD decreased in frontal, occipital, and subcortical regions. Cognitive scores were correlated with the CBF-DC coupling in frontal lobes (r = 0.394, p = 0.029) and SC-FC coupling (r = 0.397, p = 0.027). The CBF-DC coupling of patients with good postoperative collateral formation was higher (p = 0.041). Overall, neurovascular decoupling and structure-functional decoupling at the cortical level may be the underlying neuropathological mechanisms of MMD.}, }
@article {pmid39731856, year = {2024}, author = {Guo, X and Feng, Y and Ji, X and Jia, N and Maimaiti, A and Lai, J and Wang, Z and Yang, S and Hu, S}, title = {Shared genetic architecture and bidirectional clinical risks within the psycho-metabolic nexus.}, journal = {EBioMedicine}, volume = {111}, number = {}, pages = {105530}, doi = {10.1016/j.ebiom.2024.105530}, pmid = {39731856}, issn = {2352-3964}, abstract = {BACKGROUND: Increasing evidence suggests a complex interplay between psychiatric disorders and metabolic dysregulations. However, most research has been limited to specific disorder pairs, leaving a significant gap in our understanding of the broader psycho-metabolic nexus.<br><br>METHODS: This study leveraged large-scale cohort data and genome-wide association study (GWAS) summary statistics, covering 8 common psychiatric disorders and 43 metabolic traits. We introduced a comprehensive analytical strategy to identify shared genetic bases sequentially, from key genetic correlation regions to local pleiotropy and pleiotropic genes. Finally, we developed polygenic risk score (PRS) models to translate these findings into clinical applications.<br><br>FINDINGS: We identified significant bidirectional clinical risks between psychiatric disorders and metabolic dysregulations among 310,848 participants from the UK Biobank. Genetic correlation analysis confirmed 104 robust trait pairs, revealing 1088 key genomic regions, including critical hotspots such as chr3: 47588462-50387742. Cross-trait meta-analysis uncovered 388 pleiotropic single nucleotide variants (SNVs) and 126 shared causal variants. Among variants, 45 novel SNVs were associated with psychiatric disorders and 75 novel SNVs were associated with metabolic traits, shedding light on new targets to unravel the mechanism of comorbidity. Notably, RBM6, a gene involved in alternative splicing and cellular stress response regulation, emerged as a key pleiotropic gene. When psychiatric and metabolic genetic information were integrated, PRS models demonstrated enhanced predictive power.<br><br>INTERPRETATION: The study highlights the intertwined genetic and clinical relationships between psychiatric disorders and metabolic dysregulations, emphasising the need for integrated approaches in diagnosis and treatment.<br><br>FUNDING: The National Key Research and Development Program of China (2023YFC2506200, SHH). The National Natural Science Foundation of China (82273741, SY).}, }
@article {pmid39730087, year = {2024}, author = {Kong, S and Zhang, J and Wang, L and Li, W and Guo, H and Weng, Q and He, Q and Lou, H and Ding, L and Yang, B}, title = {Mechanisms of low MHC I expression and strategies for targeting MHC I with small molecules in cancer immunotherapy.}, journal = {Cancer letters}, volume = {611}, number = {}, pages = {217432}, doi = {10.1016/j.canlet.2024.217432}, pmid = {39730087}, issn = {1872-7980}, abstract = {Major histocompatibility complex (MHC) class I load antigens and present them on the cell surface, which transduces the tumor-associated antigens to CD8[+] T cells, activating the acquired immune system. However, many tumors downregulate MHC I expression to evade immune surveillance. The low expression of MHC I not only reduce recognition by- and cytotoxicity of CD8[+] T cells, but also seriously weakens the anti-tumor effect of immunotherapy by restoring CD8[+] T cells, such as immune checkpoint inhibitors (ICIs). Accumulated evidence suggested that restoring MHC I expression is an effective strategy for enhancing tumor immunotherapy. This review focuses on mechanisms underlying MHC I downregulation include gene deletion and mutation, transcriptional inhibition, reduced mRNA stability, increased protein degradation, and disruption of endocytic trafficking. We also provide a comprehensive review of small molecules that restore or upregulate MHC I expression, as well as clinical trials involving the combination of ICIs and these small molecule drugs.}, }
@article {pmid39729483, year = {2024}, author = {Gao, S and Sun, Y and Wu, F and Jiang, J and Peng, T and Zhang, R and Ling, C and Han, Y and Xu, Q and Zou, L and Liao, Y and Liang, C and Zhang, D and Qi, S and Tang, J and Xu, X}, title = {Effects on Multimodal Connectivity Patterns in Female Schizophrenia During 8 Weeks of Antipsychotic Treatment.}, journal = {Schizophrenia bulletin}, volume = {}, number = {}, pages = {}, doi = {10.1093/schbul/sbae176}, pmid = {39729483}, issn = {1745-1701}, support = {82172061//National Natural Science Foundation of China/ ; BE2022677//Key Research and Development Plan in Jiangsu/ ; WSN-166//16th Batch of Six Talent Peak Projects in Jiangsu/ ; YKK23138//Nanjing Health Technology Development Project/ ; 23-25-289//Training and Management of Young Talents in Nanjing Brain Hospital/ ; 2016YFC1306900//National Key R&amp;D Program of China/ ; }, abstract = {BACKGROUND AND HYPOTHESIS: Respective abnormal structural connectivity (SC) and functional connectivity (FC) have been reported in individuals with schizophrenia. However, transmodal associations between SC and FC following antipsychotic treatment, especially in female schizophrenia, remain unclear. We hypothesized that increased SC-FC coupling may be found in female schizophrenia, and could be normalized after antipsychotic treatment.<br><br>STUDY DESIGN: Sixty-four female drug-na&#xef;ve patients with first-diagnosed schizophrenia treated with antipsychotic drugs for 8 weeks, and 55 female healthy controls (HCs) were enrolled. Magnetic resonance imaging (MRI) data were collected from HCs at baseline and from patients at baseline and after treatment. SC and FC were analyzed by network-based statistics, calculating nonzero SC-FC coupling of the whole brain and altered connectivity following treatment. Finally, an Elastic-net logistic regression analysis was employed to establish a predictive model for evaluating the clinical efficacy treatment.<br><br>STUDY RESULTS: At baseline, female schizophrenia patients exhibited abnormal SC in cortico-cortical, frontal-limbic, frontal-striatal, limbic-striatal, and limbic-cerebellar connectivity compared to HCs, while FC showed no abnormalities. Following treatment, cortico-cortical, frontal-limbic, frontal-striatal, limbic-striatal, temporal-cerebellar, and limbic-cerebellar connectivity were altered in both SC and FC. Additionally, SC-FC coupling of altered connectivity was higher in patients at baseline than in HC, trending toward normalization after treatment. Furthermore, identified FC or/and SC predicted changes in psychopathological symptoms and cognitive impairment among female schizophrenia following treatment.<br><br>CONCLUSIONS: SC-FC coupling may be a potential predictive biomarker of treatment response. Cortico-cortical, frontal-limbic, frontal-striatal, limbic-striatal, temporal-cerebellar, and limbic-cerebellar could represent major targets for antipsychotic drugs in female schizophrenia.}, }
@article {pmid39727765, year = {2024}, author = {Yousefipour, B and Rajabpour, V and Abdoljabbari, H and Sheykhivand, S and Danishvar, S}, title = {An Ensemble Deep Learning Approach for EEG-Based Emotion Recognition Using Multi-Class CSP.}, journal = {Biomimetics (Basel, Switzerland)}, volume = {9}, number = {12}, pages = {}, pmid = {39727765}, issn = {2313-7673}, abstract = {In recent years, significant advancements have been made in the field of brain-computer interfaces (BCIs), particularly in the area of emotion recognition using EEG signals. The majority of earlier research in this field has missed the spatial-temporal characteristics of EEG signals, which are critical for accurate emotion recognition. In this study, a novel approach is presented for classifying emotions into three categories, positive, negative, and neutral, using a custom-collected dataset. The dataset used in this study was specifically collected for this purpose from 16 participants, comprising EEG recordings corresponding to the three emotional states induced by musical stimuli. A multi-class Common Spatial Pattern (MCCSP) technique was employed for the processing stage of the EEG signals. These processed signals were then fed into an ensemble model comprising three autoencoders with Convolutional Neural Network (CNN) layers. A classification accuracy of 99.44 ± 0.39% for the three emotional classes was achieved by the proposed method. This performance surpasses previous studies, demonstrating the effectiveness of the approach. The high accuracy indicates that the method could be a promising candidate for future BCI applications, providing a reliable means of emotion detection.}, }
@article {pmid39727293, year = {2024}, author = {Gefen, N and Mazer, B and Krasovsky, T and Weiss, PL}, title = {Novel rehabilitation technologies in pediatric rehabilitation: knowledge towards translation.}, journal = {Disability and rehabilitation. Assistive technology}, volume = {}, number = {}, pages = {1-10}, doi = {10.1080/17483107.2024.2445017}, pmid = {39727293}, issn = {1748-3115}, abstract = {Purpose: Knowledge translation (KT) refers to the process of applying the most promising research outcomes into practice to ensure that new discoveries and innovations improve healthcare accessibility, effectiveness, and accountability. The objective of this perspective paper is to discuss and illustrate via examples how the KT process can be implemented in an era of rapid advancement in rehabilitation technologies that have the potential to significantly impact pediatric healthcare. Methods: Using Graham et al.'s (2006) Knowledge-to-Action cycle, which includes the knowledge creation funnel and the action cycle, we illustrate its application in implementing novel technologies into clinical practice and informing healthcare policy changes. We explore three successful applications of technology research: powered mobility, head support systems, and telerehabilitation. Additionally, we examine less clinically mature technologies such as brain-computer interfaces and robotic assistive devices, which are hindered by cost, robustness, and ease-of-use issues. Conclusions: The paper concludes by discussing how technology acceptance and usage in clinical settings are influenced by various barriers and facilitators at different stakeholder levels, including clients, families, clinicians, management, researchers, developers, and society. Recommendations include focusing on early and ongoing design partnerships, transitioning from research to real-life implementation, and identifying optimal timing for clinical adoption of new technologies.}, }
@article {pmid39726882, year = {2024}, author = {Afrah, R and Amini, Z and Kafieh, R}, title = {An Unsupervised Feature Extraction Method based on CLSTM-AE for Accurate P300 Classification in Brain-Computer Interface Systems.}, journal = {Journal of biomedical physics &amp; engineering}, volume = {14}, number = {6}, pages = {579-592}, pmid = {39726882}, issn = {2251-7200}, abstract = {BACKGROUND: The P300 signal, an endogenous component of event-related potentials, is extracted from an electroencephalography signal and employed in Brain-computer Interface (BCI) devices.<br><br>OBJECTIVE: The current study aimed to address challenges in extracting useful features from P300 components and detecting P300 through a hybrid unsupervised manner based on Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM).<br><br>MATERIAL AND METHODS: In this cross-sectional study, CNN as a useful method for the P300 classification task emphasizes spatial characteristics of data. However, CNN and LSTM networks are combined to modify the classification system by extracting both spatial and temporal features. Then, the CNN-LSTM network was trained in an unsupervised learning method based on an autoencoder to improve Signal-to-noise Ratio (SNR) by extracting main components from latent space. To deal with imbalanced data, an Adaptive Synthetic Sampling Approach (ADASYN) is used and augmented without any duplication.<br><br>RESULTS: The trained model, tested on the BCI competition III dataset, including two normal subjects, with an accuracy of 95% and 94% for subjects A and B in P300 detection, respectively.<br><br>CONCLUSION: CNN-LSTM, was embedded into an autoencoder and introduced to simultaneously extract spatial and temporal features and manage the computational complexity of the method. Further, ADASYN as an augmentation method was proposed to deal with the imbalanced nature of data, which not only maintained feature space as before but also preserved anatomical features of P300. High-quality results highlight the suitable efficiency of the proposed method.}, }
@article {pmid39726001, year = {2024}, author = {Sun, Q and Merino, EC and Yang, L and Van Hulle, MM}, title = {Unraveling EEG correlates of unimanual finger movements: insights from non-repetitive flexion and extension tasks.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {21}, number = {1}, pages = {228}, pmid = {39726001}, issn = {1743-0003}, support = {202206050022//China Scholarship Council/ ; 101118964//Horizon Europe's Marie Sklodowska-Curie Action/ ; 857375//Horizon 2020 research and innovation programme/ ; C24/18/098//special research fund of the KU Leuven/ ; G0A4118N, G0A4321N, G0C1522N//Belgian Fund for Scientific Research - Flanders/ ; AKUL 043//Hercules Foundation/ ; }, mesh = {Humans ; *Fingers/physiology ; Male ; *Electroencephalography/methods ; Female ; Adult ; *Movement/physiology ; Young Adult ; Brain-Computer Interfaces ; }, abstract = {BACKGROUND: The loss of finger control in individuals with neuromuscular disorders significantly impacts their quality of life. Electroencephalography (EEG)-based brain-computer interfaces that actuate neuroprostheses directly via decoded motor intentions can help restore lost finger mobility. However, the extent to which finger movements exhibit distinct and decodable EEG correlates remains unresolved. This study aims to investigate the EEG correlates of unimanual, non-repetitive finger flexion and extension.<br><br>METHODS: Sixteen healthy, right-handed participants completed multiple sessions of right-hand finger movement experiments. These included five individual (Thumb, Index, Middle, Ring, and Pinky) and four coordinated (Pinch, Point, ThumbsUp, and Fist) finger flexions and extensions, along with a rest condition (None). High-density EEG and finger trajectories were simultaneously recorded and analyzed. We examined low-frequency (0.3-3&#xa0;Hz) time series and movement-related cortical potentials (MRCPs), and event-related desynchronization/synchronization (ERD/S) in the alpha- (8-13&#xa0;Hz) and beta (13-30&#xa0;Hz) bands. A clustering approach based on Riemannian distances was used to chart similarities between the broadband EEG responses (0.3-70&#xa0;Hz) to the different finger scenarios. The contribution of different state-of-the-art features was identified across sub-bands, from low-frequency to low gamma (30-70&#xa0;Hz), and an ensemble approach was used to pairwise classify single-trial finger movements and rest.<br><br>RESULTS: A significant decrease in EEG amplitude in the low-frequency time series was observed in the contralateral frontal-central regions during finger flexion and extension. Distinct MRCP patterns were found in the pre-, ongoing-, and post-movement stages. Additionally, strong ERD was detected in the contralateral central brain regions in both alpha and beta bands during finger flexion and extension, with the beta band showing a stronger rebound (ERS) post-movement. Within the finger movement repertoire, the Thumb was most distinctive, followed by the Fist. Decoding results indicated that low-frequency time-domain amplitude better differentiates finger movements, while alpha and beta band power and Riemannian features better detect movement versus rest. Combining these features yielded over 80% finger movement detection accuracy, while pairwise classification accuracy exceeded 60% for the Thumb versus the other fingers.<br><br>CONCLUSION: Our findings confirm that non-repetitive finger movements, whether individual or coordinated, can be precisely detected from EEG. However, differentiating between specific movements is challenging due to highly overlapping neural correlates in time, spectral, and spatial domains. Nonetheless, certain finger movements, such as those involving the Thumb, exhibit distinct EEG responses, making them prime candidates for dexterous finger neuroprostheses.}, }
@article {pmid39725763, year = {2024}, author = {Fu, R and Niu, S and Feng, X and Shi, Y and Jia, C and Zhao, J and Wen, G}, title = {Performance investigation of MVMD-MSI algorithm in frequency recognition for SSVEP-based brain-computer interface and its application in robotic arm control.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {39725763}, issn = {1741-0444}, support = {62073282//the National Natural Science Foundation of China/ ; F2022203092//Natural Science Foundation of Hebei Province/ ; F2020203070//Natural Science Foundation of Hebei Province/ ; 206Z0301G//the Central Guidance on Local Science and Technology Development Fund of Hebei Province/ ; }, abstract = {This study focuses on improving the performance of steady-state visual evoked potential (SSVEP) in brain-computer interfaces (BCIs) for robotic control systems. The challenge lies in effectively reducing the impact of artifacts on raw data to enhance the performance both in quality and reliability. The proposed MVMD-MSI algorithm combines the advantages of multivariate variational mode decomposition (MVMD) and multivariate synchronization index (MSI). Compared to widely used algorithms, the novelty of this method is its capability of decomposing nonlinear and non-stationary EEG signals into intrinsic mode functions (IMF) across different frequency bands with the best center frequency and bandwidth. Therefore, SSVEP decoding performance can be improved by this method, and the effectiveness of MVMD-MSI is evaluated by the robot with 6 degrees-of-freedom. Offline experiments were conducted to optimize the algorithm's parameters, resulting in significant improvements. Additionally, the algorithm showed good performance even with fewer channels and shorter data lengths. In online experiments, the algorithm achieved an average accuracy of 98.31% at 1.8 s, confirming its feasibility and effectiveness for real-time SSVEP BCI-based robotic arm applications. The MVMD-MSI algorithm, as proposed, represents a significant advancement in SSVEP analysis for robotic control systems. It enhances decoding performance and shows promise for practical application in this field.}, }
@article {pmid39723643, year = {2024}, author = {Tao, T and Liu, S and He, M and Zhao, M and Chen, C and Peng, J and Wang, Y and Cai, J and Xiong, J and Lai, C and Gu, W and Ying, M and Mao, J and Li, L and Jia, X and Wu, X and Peng, W and Zhang, X and Li, Y and Li, T and Wang, J and Shu, Q}, title = {Synchronous bilateral Wilms tumors are prone to develop independently and respond differently to preoperative chemotherapy.}, journal = {International journal of cancer}, volume = {}, number = {}, pages = {}, doi = {10.1002/ijc.35297}, pmid = {39723643}, issn = {1097-0215}, support = {32270853//National Natural Science Foundation of China/ ; U20A20137//National Natural Science Foundation of China/ ; LHDMZ23H160005//Joint Funds of the Zhejiang Provincial Natural Science Foundation of China/ ; 2024C03181//"Pioneer" and "Leading Goose" R&amp;D Program of Zhejiang Province/ ; }, abstract = {Wilms tumor (WT) is the most common kidney cancer in infants and young children. The determination of the clonality of bilateral WTs is critical to the treatment, because lineage-independent and metastatic tumors may require different treatment strategies. Here we found synchronous bilateral WT (n = 24 tumors from 12 patients) responded differently to preoperative chemotherapy. Transcriptome, whole-exome and whole-genome analysis (n = 12 tumors from 6 patients) demonstrated that each side of bilateral WT was clonally independent in terms of somatic driver mutations, copy number variations and transcriptomic profile. Molecular timing analysis revealed distinct timing and patterns of chromosomal evolution and mutational processes between the two sides of WT. Mutations in WT1, CTNNB1 and copy-neutral loss of heterozygosity of 11p15.5 provide possible genetic predisposition for the early initiation of bilateral WT. Our results provide comprehensive evidence and new insights regarding the separate initiation and early embryonic development of bilateral WT, which may benefit clinical practices in treating metastatic or refractory bilateral WT.}, }
@article {pmid39720868, year = {2024}, author = {Downey, JE and Schone, HR and Foldes, ST and Greenspon, C and Liu, F and Verbaarschot, C and Biro, D and Satzer, D and Moon, CH and Coffman, BA and Youssofzadeh, V and Fields, D and Hobbs, TG and Okorokova, E and Tyler-Kabara, EC and Warnke, PC and Gonzalez-Martinez, J and Hatsopoulos, NG and Bensmaia, SJ and Boninger, ML and Gaunt, RA and Collinger, JL}, title = {A Roadmap for Implanting Electrode Arrays to Evoke Tactile Sensations Through Intracortical Stimulation.}, journal = {Human brain mapping}, volume = {45}, number = {18}, pages = {e70118}, pmid = {39720868}, issn = {1097-0193}, support = {N66001-10-C-4056//Defense Advanced Research Projects Agency/ ; UH3 NS107714/NH/NIH HHS/United States ; }, mesh = {Humans ; Male ; Adult ; *Spinal Cord Injuries/physiopathology ; *Electrodes, Implanted ; Female ; *Somatosensory Cortex/physiology/diagnostic imaging ; *Brain-Computer Interfaces ; Middle Aged ; Touch Perception/physiology ; Electric Stimulation/methods ; Brain Mapping/methods ; Hand/physiology ; Magnetic Resonance Imaging ; }, abstract = {Intracortical microstimulation (ICMS) is a method for restoring sensation to people with paralysis as part of a bidirectional brain-computer interface (BCI) to restore upper limb function. Evoking tactile sensations of the hand through ICMS requires precise targeting of implanted electrodes. Here we describe the presurgical imaging procedures used to generate functional maps of the hand area of the somatosensory cortex and subsequent planning that guided the implantation of intracortical microelectrode arrays. In five participants with cervical spinal cord injury, across two study locations, this procedure successfully enabled ICMS-evoked sensations localized to at least the first four digits of the hand. The imaging and planning procedures developed through this clinical trial provide a roadmap for other BCI studies to ensure the successful placement of stimulation electrodes.}, }
@article {pmid39720668, year = {2024}, author = {Li, X and Chu, Y and Wu, X}, title = {3D convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery EEG signal.}, journal = {Frontiers in neurorobotics}, volume = {18}, number = {}, pages = {1485640}, pmid = {39720668}, issn = {1662-5218}, abstract = {Non-invasive brain-computer interfaces (BCI) hold great promise in the field of neurorehabilitation. They are easy to use and do not require surgery, particularly in the area of motor imagery electroencephalography (EEG). However, motor imagery EEG signals often have a low signal-to-noise ratio and limited spatial and temporal resolution. Traditional deep neural networks typically only focus on the spatial and temporal features of EEG, resulting in relatively low decoding and accuracy rates for motor imagery tasks. To address these challenges, this paper proposes a 3D Convolutional Neural Network (P-3DCNN) decoding method that jointly learns spatial-frequency feature maps from the frequency and spatial domains of the EEG signals. First, the Welch method is used to calculate the frequency band power spectrum of the EEG, and a 2D matrix representing the spatial topology distribution of the electrodes is constructed. These spatial-frequency representations are then generated through cubic interpolation of the temporal EEG data. Next, the paper designs a 3DCNN network with 1D and 2D convolutional layers in series to optimize the convolutional kernel parameters and effectively learn the spatial-frequency features of the EEG. Batch normalization and dropout are also applied to improve the training speed and classification performance of the network. Finally, through experiments, the proposed method is compared to various classic machine learning and deep learning techniques. The results show an average decoding accuracy rate of 86.69%, surpassing other advanced networks. This demonstrates the effectiveness of our approach in decoding motor imagery EEG and offers valuable insights for the development of BCI.}, }
@article {pmid39720230, year = {2024}, author = {Hu, F and He, K and Qian, M and Liu, X and Qiao, Z and Zhang, L and Xiong, J}, title = {STAFNet: an adaptive multi-feature learning network via spatiotemporal fusion for EEG-based emotion recognition.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1519970}, pmid = {39720230}, issn = {1662-4548}, abstract = {INTRODUCTION: Emotion recognition using electroencephalography (EEG) is a key aspect of brain-computer interface research. Achieving precision requires effectively extracting and integrating both spatial and temporal features. However, many studies focus on a single dimension, neglecting the interplay and complementarity of multi-feature information, and the importance of fully integrating spatial and temporal dynamics to enhance performance.<br><br>METHODS: We propose the Spatiotemporal Adaptive Fusion Network (STAFNet), a novel framework combining adaptive graph convolution and temporal transformers to enhance the accuracy and robustness of EEG-based emotion recognition. The model includes an adaptive graph convolutional module to capture brain connectivity patterns through spatial dynamic evolution and a multi-structured transformer fusion module to integrate latent correlations between spatial and temporal features for emotion classification.<br><br>RESULTS: Extensive experiments were conducted on the SEED and SEED-IV datasets to evaluate the performance of STAFNet. The model achieved accuracies of 97.89% and 93.64%, respectively, outperforming state-of-the-art methods. Interpretability analyses, including confusion matrices and t-SNE visualizations, were employed to examine the influence of different emotions on the model's recognition performance. Furthermore, an investigation of varying GCN layer depths demonstrated that STAFNet effectively mitigates the over-smoothing issue in deeper GCN architectures.<br><br>DISCUSSION: In summary, the findings validate the effectiveness of STAFNet in EEG-based emotion recognition. The results emphasize the critical role of spatiotemporal feature extraction and introduce an innovative framework for feature fusion, advancing the state of the art in emotion recognition.}, }
@article {pmid39719191, year = {2024}, author = {Rudroff, T}, title = {Decoding thoughts, encoding ethics: A narrative review of the BCI-AI revolution.}, journal = {Brain research}, volume = {1850}, number = {}, pages = {149423}, doi = {10.1016/j.brainres.2024.149423}, pmid = {39719191}, issn = {1872-6240}, abstract = {OBJECTIVES: This narrative review aims to analyze mechanisms underlying Brain-Computer Interface (BCI) and Artificial Intelligence (AI) integration, evaluate recent advances in signal acquisition and processing techniques, and assess AI-enhanced neural decoding strategies. The review identifies critical research gaps and examines emerging solutions across multiple domains of BCI-AI integration.<br><br>METHODS: A narrative review was conducted using major biomedical and scientific databases including PubMed, Web of Science, IEEE Xplore, and Scopus (2014-2024). Literature was analyzed to identify key developments in BCI-AI integration, with particular emphasis on recent advances (2019-2024). The review process involved thematic analysis of selected publications focusing on practical applications, technical innovations, and emerging challenges.<br><br>RESULTS: Recent advances demonstrate significant improvements in BCI-AI systems: 1) High-density electrode arrays achieve spatial resolution up to 5&#xa0;mm, with stable recordings over 15&#xa0;months; 2) Deep learning decoders show 40&#xa0;% improvement in information transfer rates compared to traditional methods; 3) Adaptive algorithms maintain >90&#xa0;% success rates in motor control tasks over 200-day periods without recalibration; 4) Novel closed-loop optimization frameworks reduce user training time by 55&#xa0;% while improving accuracy. Latest developments in flexible neural interfaces and self-supervised learning approaches show promise in addressing long-term stability and cross-user generalization challenges.<br><br>CONCLUSIONS: BCI-AI integration shows remarkable progress in improving signal quality, decoding accuracy, and user adaptation. While challenges remain in long-term stability and user training, advances in adaptive algorithms and feedback mechanisms demonstrate the technology's growing viability for clinical applications. Recent innovations in electrode technology, AI architectures, and closed-loop systems, combined with emerging standardization frameworks, suggest accelerating progress toward widespread therapeutic use and human augmentation applications.}, }
@article {pmid39719121, year = {2025}, author = {Li, K and Chen, P and Chen, Q and Li, X}, title = {A hybrid network using transformer with modified locally linear embedding and sliding window convolution for EEG decoding.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ada30b}, pmid = {39719121}, issn = {1741-2552}, mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; *Neural Networks, Computer ; Imagination/physiology ; Algorithms ; Linear Models ; Signal Processing, Computer-Assisted ; }, abstract = {Objective. Brain-computer interface(BCI) is leveraged by artificial intelligence in EEG signal decoding, which makes it possible to become a new means of human-machine interaction. However, the performance of current EEG decoding methods is still insufficient for clinical applications because of inadequate EEG information extraction and limited computational resources in hospitals. This paper introduces a hybrid network that employs a transformer with modified locally linear embedding and sliding window convolution for EEG decoding.Approach. This network separately extracts channel and temporal features from EEG signals, subsequently fusing these features using a cross-attention mechanism. Simultaneously, manifold learning is employed to lower the computational burden of the model by mapping the high-dimensional EEG data to a low-dimensional space by its dimension reduction function.Main results. The proposed model achieves accuracy rates of 84.44%, 94.96%, and 82.79% on the BCI Competition IV dataset 2a, high gamma dataset, and a self-constructed motor imagery (MI) dataset from the left and right hand fist-clenching tests respectively. The results indicate our model outperforms the baseline models by EEG-channel transformer with dimension-reduced EEG data and window attention with sliding window convolution. Additionally, to enhance the interpretability of the model, features preceding the temporal feature extraction network were visualized. This visualization promotes the understanding of how the model prefers task-related channels.Significance. The transformer-based method makes the MI-EEG decoding more practical for further clinical applications.}, }
@article {pmid39718409, year = {2024}, author = {Ma, X and Chen, W and Pei, Z and Zhang, J}, title = {An attention-based motor imagery brain-computer interface system for lower limb exoskeletons.}, journal = {The Review of scientific instruments}, volume = {95}, number = {12}, pages = {}, doi = {10.1063/5.0243337}, pmid = {39718409}, issn = {1089-7623}, mesh = {*Brain-Computer Interfaces ; Humans ; *Lower Extremity/physiology ; *Exoskeleton Device ; *Electroencephalography/instrumentation ; Attention/physiology ; Neural Networks, Computer ; Adult ; Male ; Imagination/physiology ; }, abstract = {Lower-limb exoskeletons have become increasingly popular in rehabilitation to help patients with disabilities regain mobility and independence. Brain-computer interface (BCI) offers a natural control method for these exoskeletons, allowing users to operate them through their electroencephalogram (EEG) signals. However, the limited EEG decoding performance of the BCI system restricts its application for lower limb exoskeletons. To address this challenge, we propose an attention-based motor imagery BCI system for lower limb exoskeletons. The decoding module of the proposed BCI system combines the convolutional neural network (CNN) with a lightweight attention module. The CNN aims to extract meaningful features from EEG signals, while the lightweight attention module aims to capture global dependencies among these features. The experiments are divided into offline and online experiments. The offline experiment is conducted to evaluate the effectiveness of different decoding methods, while the online experiment is conducted on a customized lower limb exoskeleton to evaluate the proposed BCI system. Eight subjects are recruited for the experiments. The experimental results demonstrate the great classification performance of the decoding method and validate the feasibility of the proposed BCI system. Our approach establishes a promising BCI system for the lower limb exoskeleton and is expected to achieve a more effective and user-friendly rehabilitation process.}, }
@article {pmid39715900, year = {2024}, author = {Robinson, JT and Norman, SL and Angle, MR and Constandinou, TG and Denison, T and Donoghue, JP and Field, RM and Forsland, A and Kouider, S and Millán, JDR and Michaels, JA and Orsborn, AL and Pandarinath, C and Pruszynski, JA and Rozell, CJ and Shah, NP and Shanechi, MM and Shoaran, M and Sheth, SA and Stavisky, SD and Trautmann, E and Vachicouras, N and Xie, C}, title = {An application-based taxonomy for brain-computer interfaces.}, journal = {Nature biomedical engineering}, volume = {}, number = {}, pages = {}, pmid = {39715900}, issn = {2157-846X}, }
@article {pmid39712143, year = {2024}, author = {Bai, G and Jin, J and Xu, R and Wang, X and Cichocki, A}, title = {A novel dual-step transfer framework based on domain selection and feature alignment for motor imagery decoding.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {6}, pages = {3549-3563}, pmid = {39712143}, issn = {1871-4080}, abstract = {In brain-computer interfaces (BCIs) based on motor imagery (MI), reducing calibration time is gradually becoming an urgent issue in practical applications. Recently, transfer learning (TL) has demonstrated its effectiveness in reducing calibration time in MI-BCI. However, the different data distribution of subjects greatly affects the application effect of TL in MI-BCI. Therefore, this paper combines data alignment, source domain selection, and feature alignment into the MI-TL. We propose a novel dual-step transfer framework based on source domain selection and feature alignment. First, the source and target domains are aligned using a pre-calibration strategy (PS), and then a sequential reverse selection method is proposed to match the optimal source domain for each target domain with the designed dual model selection strategy. We use filter bank regularization common space pattern (FBRCSP) to obtain more features and introduce manifold embedded distribution alignment (MEDA) to correct the prediction results of the support vector machine (SVM). The experimental results on two competition public datasets (BCI competition IV Dataset 1 and Dataset 2a) and our dataset show that the average classification accuracy of the proposed framework is higher than the baseline method (no domain selection and no feature alignment), which reaches 84.12%, 79.91%, and 78.45%, respectively. And the computational cost is reduced by half compared with the baseline method.}, }
@article {pmid39712134, year = {2024}, author = {Zhang, J and Shen, C and Chen, W and Ma, X and Liang, Z and Zhang, Y}, title = {Decoding of movement-related cortical potentials at different speeds.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {6}, pages = {3859-3872}, pmid = {39712134}, issn = {1871-4080}, abstract = {The decoding of electroencephalogram (EEG) signals, especially motion-related cortical potentials (MRCP), is vital for the early detection of motor intent before movement execution. To enhance the decoding accuracy of MRCP and promote the application of early motion intention in active rehabilitation training, we propose a method for decoding MRCP signals. Specifically, an experimental paradigm is designed for the efficient capture of MRCP signals. Moreover, a feature extraction method based on differentiation is proposed to effectively characterize action variability. Six subjects were recruited to validate the effectiveness of the decoding method. Experiments such as fixed-window classification, sliding-window detection, and asynchronous analysis demonstrate that the method can detect motion intention 316 milliseconds before action execution and is capable of continuously detecting both rapid and slow movements.}, }
@article {pmid39712133, year = {2024}, author = {Wu, C and Wang, Y and Qiu, S and He, H}, title = {A bimodal deep learning network based on CNN for fine motor imagery.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {6}, pages = {3791-3804}, pmid = {39712133}, issn = {1871-4080}, abstract = {Motor imagery (MI) is an important brain-computer interface (BCI) paradigm. The traditional MI paradigm (imagining different limbs) limits the intuitive control of the outer devices, while fine MI paradigm (imagining different joint movements from the same limb) can control the mechanical arm without cognitive disconnection. However, the decoding performance of fine MI limits its application. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are widely used in BCI systems because of their portability and easy operation. In this study, a fine MI paradigm including four classes (hand, wrist, shoulder and rest) was designed, and the data of EEG-fNIRS bimodal brain activity was collected from 12 subjects. Event-related desynchronization (ERD) from EEG signals shows a contralateral dominant phenomenon, and there is difference between the ERD of the four classes. For fNIRS signal in the time dimension, the time periods with significant difference can be observed in the activation patterns of four MI tasks. Spatially, the signal peak based brain topographic map also shows difference of these four MI tasks. The EEG signal and fNIRS signal of these four classes are distinguishable. In this study, a bimodal fusion network is proposed to improve the fine MI tasks decoding performance. The features of these two modalities are extracted separately by two feature extractors based on convolutional neural networks (CNN). The recognition performance was significantly improved by the bimodal method proposed in this study, compared with the performance of the single-modal network. The proposed method outperformed all comparison methods, and achieved a four-class accuracy of 58.96%. This paper demonstrates the feasibility of EEG and fNIRS bimodal BCI systems for fine MI, and shows the effectiveness of the proposed bimodal fusion method. This research is supposed to support fine MI-based BCI systems with theories and techniques.}, }
@article {pmid39712131, year = {2024}, author = {Meng, M and Xu, B and Ma, Y and Gao, Y and Luo, Z}, title = {STGAT-CS: spatio-temporal-graph attention network based channel selection for MI-based BCI.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {6}, pages = {3663-3678}, pmid = {39712131}, issn = {1871-4080}, abstract = {Brain-computer interface (BCI) based on the motor imagery paradigm typically utilizes multi-channel electroencephalogram (EEG) to ensure accurate capture of physiological phenomena. However, excessive channels often contain redundant information and noise, which can significantly degrade BCI performance. Although there have been numerous studies on EEG channel selection, most of them require manual feature extraction, and the extracted features are difficult to fully represent the effective information of EEG signals. In this paper, we propose a spatio-temporal-graph attention network for channel selection (STGAT-CS) of EEG signals. We consider the EEG channels and their inter-channel connectivity as a graph and treat the channel selection problem as a node classification problem on the graph. We leverage the multi-head attention mechanism of graph attention network to dynamically capture topological relationships between nodes and update node features accordingly. Additionally, we introduce one-dimensional convolution to automatically extract temporal features from each channel in the original EEG signal, thereby obtaining more comprehensive spatiotemporal characteristics. In the classification tasks of the BCI Competition III Dataset IVa and BCI Competition IV Dataset I, STGAT-CS achieved average accuracies of 91.5% and 85.4% respectively, demonstrating the effectiveness of the proposed method.}, }
@article {pmid39712122, year = {2024}, author = {Li, M and Li, J and Zheng, X and Ge, J and Xu, G}, title = {MSHANet: a multi-scale residual network with hybrid attention for motor imagery EEG decoding.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {6}, pages = {3463-3476}, pmid = {39712122}, issn = {1871-4080}, abstract = {EEG decoding plays a crucial role in the development of motor imagery brain-computer interface. Deep learning has great potential to automatically extract EEG features for end-to-end decoding. Currently, the deep learning is faced with the chanllenge of decoding from a large amount of time-variant EEG to retain a stable peroformance with different sessions. This study proposes a multi-scale residual network with hybrid attention (MSHANet) to decode four motor imagery classes. The MSHANet combines a multi-head attention and squeeze-and-excitation attention to hybridly focus on important information of the EEG features; and applies a multi-scale residual block to extracts rich EEG features, sharing part of the block parameters to extract common features. Compared with seven state-of-the-art methods, the MSHANet exhits the best accuracy on BCI Competition IV 2a with an accuracy of 83.18% for session- specific task and 80.09% for cross-session task. Thus, the proposed MSHANet decodes the time-varying EEG robustly and can save the time cost of MI-BCI, which is beneficial for long-term use.}, }
@article {pmid39712121, year = {2024}, author = {Rahman, N and Khan, DM and Masroor, K and Arshad, M and Rafiq, A and Fahim, SM}, title = {Advances in brain-computer interface for decoding speech imagery from EEG signals: a systematic review.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {6}, pages = {3565-3583}, pmid = {39712121}, issn = {1871-4080}, abstract = {Numerous individuals encounter challenges in verbal communication due to various factors, including physical disabilities, neurological disorders, and strokes. In response to this pressing need, technology has actively pursued solutions to bridge the communication gap, recognizing the inherent difficulties faced in verbal communication, particularly in contexts where traditional methods may be inadequate. Electroencephalogram (EEG) has emerged as a primary non-invasive method for measuring brain activity, offering valuable insights from a cognitive neurodevelopmental perspective. It forms the basis for Brain-Computer Interfaces (BCIs) that provide a communication channel for individuals with neurological impairments, thereby empowering them to express themselves effectively. EEG-based BCIs, especially those adapted to decode imagined speech from EEG signals, represent a significant advancement in enabling individuals with speech disabilities to communicate through text or synthesized speech. By utilizing cognitive neurodevelopmental insights, researchers have been able to develop innovative approaches for interpreting EEG signals and translating them into meaningful communication outputs. To aid researchers in effectively addressing this complex challenge, this review article synthesizes key findings from state-of-the-art significant studies. It investigates into the methodologies employed by various researchers, including preprocessing techniques, feature extraction methods, and classification algorithms utilizing Deep Learning and Machine Learning approaches and their integration. Furthermore, the review outlines the potential avenues for future research, with the goal of advancing the practical implementation of EEG-based BCI systems for decoding imagined speech from a cognitive neurodevelopmental perspective.}, }
@article {pmid39712116, year = {2024}, author = {Tang, C and Gao, T and Wang, G and Chen, B}, title = {Coherence-based channel selection and Riemannian geometry features for magnetoencephalography decoding.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {6}, pages = {3535-3548}, pmid = {39712116}, issn = {1871-4080}, abstract = {Magnetoencephalography (MEG) records the extremely weak magnetic fields on the surface of the scalp through highly sensitive sensors. Multi-channel MEG data provide higher spatial and temporal resolution when measuring brain activities, and can be applied for brain-computer interfaces as well. However, a large number of channels leads to high computational complexity and can potentially impact decoding accuracy. To improve the accuracy of MEG decoding, this paper proposes a new coherence-based channel selection method that effectively identifies task-relevant channels, reducing the presence of noisy and redundant information. Riemannian geometry is then used to extract effective features from selected channels of MEG data. Finally, MEG decoding is achieved by training a support vector machine classifier with the Radial Basis Function kernel. Experiments were conducted on two public MEG datasets to validate the effectiveness of the proposed method. The results from Dataset 1 show that Riemannian geometry achieves higher classification accuracy (compared to common spatial patterns and power spectral density) in the single-subject visual decoding task. Moreover, coherence-based channel selection significantly (P = 0.0002) outperforms the use of all channels. Moving on to Dataset 2, the results reveal that coherence-based channel selection is also significantly (P <0.0001) superior to all channels and channels around C3 and C4 in cross-session mental imagery decoding tasks. Additionally, the proposed method outperforms state-of-the-art methods in motor imagery tasks.}, }
@article {pmid39712104, year = {2024}, author = {Yu, H and Hu, Z and Zhao, Q and Liu, J}, title = {Deep source transfer learning for the estimation of internal brain dynamics using scalp EEG.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {6}, pages = {3507-3520}, pmid = {39712104}, issn = {1871-4080}, abstract = {Electroencephalography (EEG) provides high temporal resolution neural data for brain-computer interfacing via noninvasive electrophysiological recording. Estimating the internal brain activity by means of source imaging techniques can further improve the spatial resolution of EEG and enhance the reliability of neural decoding and brain-computer interaction. In this work, we propose a novel EEG data-driven source imaging scheme for precise and efficient estimation of macroscale spatiotemporal brain dynamics across thalamus and cortical regions with deep learning methods. A deep source imaging framework with a convolutional-recurrent neural network is designed to estimate the internal brain dynamics from high-density EEG recordings. Moreover, a brain model including 210 cortical regions and 16 thalamic nuclei is established based on human brain connectome to provide synthetic training data, which manifests intrinsic characteristics of underlying brain dynamics in spontaneous, stimulation-evoked, and pathological states. Transfer learning algorithm is further applied to the trained network to reduce the dynamical differences between synthetic and realistic EEG. Extensive experiments exhibit that the proposed deep-learning method can accurately estimate the spatial and temporal activity of brain sources and achieves superior performance compared to the state-of-the-art approaches. Moreover, the EEG data-driven source imaging framework is effective in the location of seizure onset zone in epilepsy and reconstruction of dynamical thalamocortical interactions during sensory processing of acupuncture stimulation, implying its applicability in brain-computer interfacing for neuroscience research and clinical applications.}, }
@article {pmid39712096, year = {2024}, author = {Zhang, Z and Chen, Y and Zhao, X and Fan, W and Peng, D and Li, T and Zhao, L and Fu, Y}, title = {A review of ethical considerations for the medical applications of brain-computer interfaces.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {6}, pages = {3603-3614}, pmid = {39712096}, issn = {1871-4080}, abstract = {The development and potential applications of brain-computer interfaces (BCIs) are directly related to the human brain and may have adverse effects on the users' physical and mental health. Ethical issues, particularly those associated with BCIs, including both non-medical and medical applications, have captured societal attention. This article initially reviews the application of three ethical frameworks in BCI technology: consequentialism, deontology, and virtue ethics. Subsequently, it introduces the ethical standards under consideration within the medical objective framework for BCI medical applications. Finally, the paper discusses and forecasts the ethical standards for BCI medical applications. The paper emphasizes the necessity to differentiate between the ethical issues of implantable and non-implantable BCIs, to approach the research on BCI-based "controlling the brain" with caution, and to establish standardized operational procedures and efficacy evaluation methods for BCI medical applications. This paper aims to provide ideas for the establishment of ethical standards in BCI medical applications.}, }
@article {pmid39712090, year = {2024}, author = {Huang, Y and Huan, Y and Zou, Z and Wang, Y and Gao, X and Zheng, L}, title = {Data-driven natural computational psychophysiology in class.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {6}, pages = {3477-3489}, pmid = {39712090}, issn = {1871-4080}, abstract = {Objective. The assessment of mental fatigue (MF) and attention span in educational and healthcare settings frequently relies on subjective scales or methods such as induced-task interruption tools. However, these approaches are deficient in real-time evaluation and dynamic definitions. To address this gap, this paper proposes a Continuous Quantitative Scale (CQS) that allows for the natural and real-time measurement of MF based on group-synchronized electroencephalogram (EEG) data. Approach. In this study, computational psychophysiology was used to measure MF scores during a realistic class. Our methodology continuously monitored participants' psychological states without interrupting their regular routines, providing an objective evaluation. By analyzing multi-subject brain-computer interface (mBCI) data with a collaborative computing approach, the group-synchronized data were obtained from 10 healthy participants to assess MF levels. Each participant wore an EEG headset for only 10 min of preparation before performing a sustained task for 80 min. Main results. Our findings indicate that a lecture duration of 18.9 min is most effective, while a duration of 43.1 min leads to heightened MF levels. By focusing on the group-level simultaneous data analysis, the effects of individual variability were mitigated and the efficiency of cognitive computing was improved. From the perspective of a neurocomputational measure, these results confirm previous research. Significance. The proposed CQS provides a reliable, objective, memory- and emotion-free approach to the assessment of MF and attention span. These findings have significant implications not only for education, but also for the study of group cognitive mechanisms and for improving the quality of mental healthcare.}, }
@article {pmid39711742, year = {2024}, author = {Li, J and Nan, Z and Qi, G and Cai, J and Zhao, X and Li, X and Liu, S and Wang, Y and Wu, Y and Miao, X and Yu, G}, title = {Assessing severity of pediatric pneumonia using multimodal transformers with multi-task learning.}, journal = {Digital health}, volume = {10}, number = {}, pages = {20552076241305168}, pmid = {39711742}, issn = {2055-2076}, abstract = {OBJECTIVE: While current multimodal approaches in the diagnosis and severity assessment of pneumonia demonstrate remarkable performance, they frequently overlook the issue of modality absence-a common challenge in clinical practice. Thus, we present the robust multimodal transformer (RMT) model, crafted to bridge this gap. The RMT model aims to enhance diagnosis and severity assessment accuracy in situations with incomplete data, thereby ensuring it meets the complex needs of real-world clinical settings.<br><br>METHOD: The RMT model leverages multimodal data, integrating X-ray images and clinical text data through a sophisticated AI-driven framework. It employs a Transformer-based architecture, enhanced by multi-task learning and mask attention mechanism. This approach aims to optimize the model's performance across different modalities, particularly under conditions of modality absence.<br><br>RESULTS: The RMT model demonstrates superior performance over traditional diagnostic methods and baseline models in accuracy, precision, sensitivity, and specificity. In tests involving various scenarios, including single-modal and multimodal tasks, the model shows remarkable robustness in handling incomplete data. Its effectiveness is further validated through extensive comparative analysis and ablation studies.<br><br>CONCLUSION: The RMT model represents a substantial advancement in pediatric pneumonia severity assessment. It successfully harnesses multimodal data and advanced AI techniques to improve assessment precision. While the RMT model sets a new precedent in AI applications in medical diagnostics, the development of a comprehensive pediatric pneumonia dataset marks a pivotal contribution, providing a robust foundation for future research.}, }
@article {pmid39711704, year = {2024}, author = {Deng, G and Niu, M and Luo, Y and Rao, S and Sun, J and Xie, J and Yu, Z and Liu, W and Zhao, S and Pan, G and Li, X and Deng, W and Guo, W and Li, T and Jiang, H}, title = {LPSGM: A Unified Flexible Large PSG Model for Sleep Staging and Mental Disorder Diagnosis.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, pmid = {39711704}, abstract = {We present the Large PSG Model (LPSGM), a unified and flexible framework for sleep staging and disease diagnosis using polysomnography (PSG) data. LPSGM is designed to address the challenges of cross-center generalization in sleep staging and to enable fine-tuning for downstream disease diagnosis tasks. LPSGM introduces a unified training framework for heterogeneous datasets and allows flexible channel input adjustments during inference. The model is first trained on 220,500 hours whole-night PSG from 16 public datasets, achieving robust sleep staging performance. It is then fine-tuned on target center data for various disease classification tasks, including narcolepsy diagnosis, anxiety and depression detection, and the classification of healthy versus depressed individuals. LPSGM outperforms baseline models on both sleep staging and disease diagnosis tasks. Our results demonstrate that LPSGM not only enhances sleep staging accuracy but also improves the diagnosis of sleep-related and psychiatric disorders, showing promise for clinical applications in sleep medicine and psychiatry.}, }
@article {pmid39705724, year = {2025}, author = {de Melo, GC and Castellano, G and Forner-Cordero, A}, title = {Identification and analysis of reference-independent movement event-related desynchronization.}, journal = {Biomedical physics &amp; engineering express}, volume = {11}, number = {2}, pages = {}, doi = {10.1088/2057-1976/ada1dc}, pmid = {39705724}, issn = {2057-1976}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Movement ; Male ; Female ; Adult ; Electrodes ; Signal Processing, Computer-Assisted ; Algorithms ; Cortical Synchronization/physiology ; Young Adult ; Brain/physiology ; Motor Cortex/physiology ; }, abstract = {Characterization of the electroencephalography (EEG) signals related to motor activity, such as alpha- and beta-band motor event-related desynchronizations (ERDs), is essential for Brain Computer Interface (BCI) development. Determining the best electrode combination to detect the ERD is crucial for the success of the BCI. Considering that the EEG signals are bipolar, this involves the choice of the main and reference electrodes. So far, no strategy to guarantee signals free of the activity from the reference electrode has achieved consensus among the scientific community. Therefore, mapping the ERD in terms of the spatial distribution of the main and reference electrodes can provide additional perspectives for the BCI field. The goal of this work is to identify subject-specific channels where ERD is temporally coupled to the initiation of an upper-limb motor task. We defined a criterion to determine the presence of the ERD linked to the movement onset and searched, separately for each subject, for the single channel with the most prominent ERD. The search was conducted over all available channels composed by a pair of electrodes, and the selected signals were analyzed according to their temporal and spatial characteristics. We found that alpha- and beta-band ERD temporarily linked to movement onset can be detected in atypical channels (pairs of electrodes) across the scalp. The selected channels were different across subjects. Four ERD temporal patterns were observed in terms of the initiation instant of the ERD. These patterns revealed that the M1 cortex seems to be related to later ERDs. Moreover, they were also associated to different cortical processes related to the motor task. To the best of our knowledge, this is the first time these findings are reported. Aiming at BCI development, further experiments with more subjects and with motor-imagery tasks are desirable for more robustness and applicability of these findings.}, }
@article {pmid39704693, year = {2024}, author = {Svejgaard, BJ and Modrau, B and Hernández-Gloria, JJ and Wested, CL and Dosen, S and Stevenson, AJT and Mrachacz-Kersting, N}, title = {Associative brain computer interface training increases wrist extensor corticospinal excitability in subacute stroke patients.}, journal = {Journal of neurophysiology}, volume = {}, number = {}, pages = {}, doi = {10.1152/jn.00452.2024}, pmid = {39704693}, issn = {1522-1598}, support = {2021-0008//Sundhedsinnovationspuljen, Region Nordjylland/ ; 229643//Melsen Fonden/ ; 21.584//Grosserer L. F. Foghts Fond (Grosserer LF Foghts Fund)/ ; 7683//Jascha Fonden (Jascha Foundation)/ ; }, abstract = {In a recently developed associative rehabilitative brain computer interface system, electroencephalography is used to identify the most active phase of the motor cortex during attempted movement and deliver precisely timed peripheral stimulation during training. This approach has been demonstrated to facilitate corticospinal excitability and functional recovery in patients with lower limb weakness following stroke. The current study expands those findings by investigating changes in corticospinal excitability following the associative BCI intervention in post-stroke patients with upper limb weakness. In a randomized controlled trial, 24 subacute stroke patients, subdivided into an intervention group and a "sham" control group, performed 30 wrist extensions. The intervention comprised 30 pairings of single peripheral nerve stimulation at the motor threshold, timed so that the generated afferent volley arrived at the motor cortex during the peak negativity of the MRCP, which was identified using EEG. The sham group underwent the same intervention, though the intensity of the nerve stimulation was below the perception threshold. Immediately after training, patients in the associative group exhibited significantly larger amplitudes of muscular evoked potentials, compared to pre-training measurements in response to transcranial magnetic stimulation. These changes persisted for at least 30 minutes, and were not observed in the sham group. We demonstrate that motor evoked potential amplitudes increased significantly following paired associative BCI training targeting upper limb muscles in subacute stroke patients, which is in line with results from lower limb muscles.}, }
@article {pmid39703669, year = {2024}, author = {Cao, Y and Gao, S and Yu, H and Zhao, Z and Zang, D and Wang, C}, title = {A motor imagery classification model based on hybrid brain-computer interface and multitask learning of electroencephalographic and electromyographic deep features.}, journal = {Frontiers in physiology}, volume = {15}, number = {}, pages = {1487809}, pmid = {39703669}, issn = {1664-042X}, abstract = {OBJECTIVE: Extracting deep features from participants' bioelectric signals and constructing models are key research directions in motor imagery (MI) classification tasks. In this study, we constructed a multimodal multitask hybrid brain-computer interface net (2M-hBCINet) based on deep features of electroencephalogram (EEG) and electromyography (EMG) to effectively accomplish motor imagery classification tasks.<br><br>METHODS: The model first used a variational autoencoder (VAE) network for unsupervised learning of EEG and EMG signals to extract their deep features, and subsequently applied the channel attention mechanism (CAM) to select these deep features and highlight the advantageous features and minimize the disadvantageous ones. Moreover, in this study, multitask learning (MTL) was applied to train the 2M-hBCINet model, incorporating the primary task that is the MI classification task, and auxiliary tasks including EEG reconstruction task, EMG reconstruction task, and a feature metric learning task, each with distinct loss functions to enhance the performance of each task. Finally, we designed module ablation experiments, multitask learning comparison experiments, multi-frequency band comparison experiments, and muscle fatigue experiments. Using leave-one-out cross-validation(LOOCV), the accuracy and effectiveness of each module of the 2M-hBCINet model were validated using the self-made MI-EEMG dataset and the public datasets WAY-EEG-GAL and ESEMIT.<br><br>RESULTS: The results indicated that compared to comparative models, the 2M-hBCINet model demonstrated good performance and achieved the best results across different frequency bands and under muscle fatigue conditions.<br><br>CONCLUSION: The 2M-hBCINet model constructed based on EMG and EEG data innovatively in this study demonstrated excellent performance and strong generalization in the MI classification task. As an excellent end-to-end model, 2M-hBCINet can be generalized to be used in EEG-related fields such as anomaly detection and emotion analysis.}, }
@article {pmid39703101, year = {2024}, author = {Zhou, H and Wang, M and Xu, T and Zhang, X and Zhao, X and Tang, L and Zhao, P and Wang, D and Lai, J and Wang, F and Zhang, S and Hu, S}, title = {Cognitive Remediation in Patients With Bipolar Disorder: A Randomized Trial by Sequential tDCS and Navigated rTMS Targeting the Primary Visual Cortex.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {30}, number = {12}, pages = {e70179}, pmid = {39703101}, issn = {1755-5949}, support = {82201675//National Natural Science Foundation of China/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 226-2022-00002//Fundamental Research Funds for the Central Universities/ ; 226-2022-00193//Fundamental Research Funds for the Central Universities/ ; 2021C03107//Key Research and Development Program of Zhejiang Province/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; }, mesh = {Humans ; Male ; Female ; *Transcranial Direct Current Stimulation/methods ; *Bipolar Disorder/therapy/psychology ; *Transcranial Magnetic Stimulation/methods ; Adult ; Middle Aged ; *Cognitive Remediation/methods ; *Visual Cortex ; Young Adult ; Treatment Outcome ; }, abstract = {BACKGROUND: Non-invasive brain stimulation (NIBS), such as transcranial direct current stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS), has emerged as a promising alternative in the precise treatment of clinical symptoms, such as the cognitive impairment of bipolar disorder (BD). Optimizing the neurocognitive effects by combining tDCS and rTMS to strengthen the clinical outcome is a challenging research issue.<br><br>OBJECTIVE: In this randomized, controlled trial, we first combined tDCS and neuronavigated rTMS targeting the V1 region to explore the efficacy on neurocognitive function in BD patients with depressive episodes.<br><br>METHODS: Eligible individuals (n&#x2009;=&#x2009;105) were assigned into three groups, Group A (active tDCS-active rTMS), Group B (sham tDCS-active rTMS), and Group C (active tDCS-sham rTMS). All participants received 3-week treatment in which every participant received 15 sessions of stimulation through the study, 5 sessions every week, with tDCS treatment followed by neuronavigated rTMS every session. We evaluated the cognitive, emotional, and safety outcomes at week-0 (w0, baseline), week-3 (w3, immediately post-treatment), and week-8 (w8, follow-up period). The THINC-integrated tool (THINC-it), 17-item Hamilton Depression Rating Scale, and Young Mania Rating Scale were applied for evaluating the cognitive function and emotional state, respectively. Data were analyzed by repeated measure ANOVA and paired t-test.<br><br>RESULTS: Eventually, 32 patients in Group A, 27 in Group B, and 23 in Group C completed the entire treatment. Compared to Groups B and C, Group A showed greater improvement in Symbol Check items (Time and Accuracy) at W3 and Symbol Check Accuracy at W8 (p&#x2009;<&#x2009;0.01). The W0-W3 analysis indicated a significant improvement in depressive symptoms in both Group A and Group B (p&#x2009;<&#x2009;0.01). Additionally, neuroimaging data revealed increased activity in the calcarine sulcus in Group A, suggesting potential neuroplastic changes in the visual cortex following the electromagnetic stimulation.<br><br>CONCLUSIONS: These findings provide preliminary evidence that the combination of navigated rTMS with tDCS targeting V1 region may serve as a potential treatment strategy for improving cognitive impairment and depressive symptoms in BD patients.<br><br>TRIAL REGISTRATION: Clinical Trial Registry number: NCT05596461.}, }
@article {pmid39702315, year = {2024}, author = {Capecci, M and Gandolfi, M and Straudi, S and Calabrò, RS and Baldini, N and Pepa, L and Andrenelli, E and Smania, N and Ceravolo, MG and Morone, G and Bonaiuti, D and , }, title = {Advancing public health through technological rehabilitation: insights from a national clinician survey.}, journal = {BMC health services research}, volume = {24}, number = {1}, pages = {1626}, pmid = {39702315}, issn = {1472-6963}, mesh = {Humans ; Cross-Sectional Studies ; Italy ; Surveys and Questionnaires ; Male ; Female ; *Public Health ; Physical and Rehabilitation Medicine ; Middle Aged ; Adult ; Rehabilitation ; }, abstract = {INTRODUCTION: In the evolving healthcare landscape, technology has emerged as a key component in enhancing system efficiency and offering new avenues for patient rehabilitation. Despite its growing importance, detailed information on technology's specific use, types, and applications in clinical rehabilitation settings, particularly within the Italian framework, remains unclear. This study aimed to explore the use of technology and its needs by Physical Medicine and Rehabilitation medical doctors in Italy.<br><br>METHODS: We conducted a cross-sectional online survey aimed at 186 Italian clinicians affiliated with the Italian Society of Physical and Rehabilitation Medicine (SIMFER). The online questionnaire consists of 71 structured questions designed to collect demographic and geographical data of the respondents, as well as detailed insights into the prevalence and range of technologies they use, together with their specific applications in clinical settings."<br><br>RESULTS: A broad range of technologies, predominantly commercial medical devices, has been documented. These technologies are employed for various conditions, including common neurological diseases, musculoskeletal disorders, dementia, and rheumatologic issues. The application of these technologies indicates a broadening scope beyond enhancing sensorimotor functions, addressing both physical and social aspects of patient care.<br><br>DISCUSSION: In recent years, there's been a notable surge in using technology for rehabilitation across various disorders. The upcoming challenge is to update health policies to integrate these technologies better, aiming to extend their benefits to a wider range of disabling conditions, marking a progressive shift in public health and rehabilitation practices.}, }
@article {pmid39700898, year = {2024}, author = {Zhang, Y and Gao, S and Liang, C and Bustillo, J and Kochunov, P and Turner, JA and Calhoun, VD and Wu, L and Fu, Z and Jiang, R and Zhang, D and Jiang, J and Wu, F and Peng, T and Xu, X and Qi, S}, title = {Consistent frontal-limbic-occipital connections in distinguishing treatment-resistant and non-treatment-resistant schizophrenia.}, journal = {NeuroImage. Clinical}, volume = {45}, number = {}, pages = {103726}, doi = {10.1016/j.nicl.2024.103726}, pmid = {39700898}, issn = {2213-1582}, abstract = {BACKGROUND AND HYPOTHESIS: Treatment-resistant schizophrenia (TR-SZ) and non-treatment-resistant schizophrenia (NTR-SZ) lack specific biomarkers to distinguish from each other. This investigation aims to identify consistent dysfunctional brain connections with different atlases, multiple feature selection strategies, and several classifiers in distinguishing TR-SZ and NTR-SZ.<br><br>STUDY DESIGN: 55 TR-SZs, 239 NTR-SZs, and 87 healthy controls (HCs) were recruited from the Affiliated Brain Hospital of Nanjing Medical University. Resting-state functional connection (FC) matrices were constructed from automated anatomical labeling (AAL), Yeo-Networks (YEO) and Brainnetome (BNA) atlases. Two feature selection methods (Select From Model and Recursive Feature Elimination) and four classifiers (Adaptive Boost, Bernoulli Na&#xef;ve Bayes, Gradient Boosting and Random Forest) were combined to identify the consistent FCs in distinguishing TR-SZ and HC, NTR-SZ and HC, TR-SZ and NTR-SZ.<br><br>STUDY RESULTS: The whole brain FCs, except the temporal-occipital FC, were consistent in distinguishing SZ and HC. Abnormal frontal-limbic, frontal-parietal and occipital-temporal FCs were consistent in distinguishing TR-SZ and NTR-SZ, that were further correlated with disease progression, symptoms and medication dosage. Moreover, the frontal-limbic and frontal-parietal FCs were highly consistent for the diagnosis of SZ (TR-SZ vs. HC, NTR-SZ vs. HC and TR-SZ vs. NTR-SZ). The BNA atlas achieved the highest classification accuracy (>90&#xa0;%) comparing with AAL and YEO in the most diagnostic tasks.<br><br>CONCLUSIONS: These results indicate that the frontal-limbic and the frontal-parietal FCs are the robust neural pathways in the diagnosis of SZ, whereas the frontal-limbic, frontal-parietal and occipital-temporal FCs may be informative in recognizing those TR-SZ in the clinical practice.}, }
@article {pmid39700269, year = {2024}, author = {Li, W and Li, J and Li, J and Wei, C and Laviv, T and Dong, M and Lin, J and Calubag, M and Colgan, LA and Jin, K and Zhou, B and Shen, Y and Li, H and Cui, Y and Gao, Z and Li, T and Hu, H and Yasuda, R and Ma, H}, title = {Boosting neuronal activity-driven mitochondrial DNA transcription improves cognition in aged mice.}, journal = {Science (New York, N.Y.)}, volume = {386}, number = {6728}, pages = {eadp6547}, doi = {10.1126/science.adp6547}, pmid = {39700269}, issn = {1095-9203}, support = {R01 MH080047/MH/NIMH NIH HHS/United States ; R35 NS116804/NS/NINDS NIH HHS/United States ; }, mesh = {Animals ; Male ; Mice ; *Aging/genetics ; *Brain/metabolism/physiology ; Cell Nucleus/metabolism ; *Cognition ; Cognitive Dysfunction/genetics ; *DNA, Mitochondrial/genetics/metabolism ; Mice, Inbred C57BL ; Mitochondria/metabolism ; *Neurons/metabolism/physiology ; *Transcription, Genetic ; }, abstract = {Deciphering the complex interplay between neuronal activity and mitochondrial function is pivotal in understanding brain aging, a multifaceted process marked by declines in synaptic function and mitochondrial performance. Here, we identified an age-dependent coupling between neuronal and synaptic excitation and mitochondrial DNA transcription (E-TCmito), which operates differently compared to classic excitation-transcription coupling in the nucleus (E-TCnuc). We demonstrated that E-TCmito repurposes molecules traditionally associated with E-TCnuc to regulate mitochondrial DNA expression in areas closely linked to synaptic activation. The effectiveness of E-TCmito weakens with age, contributing to age-related neurological deficits in mice. Boosting brain E-TCmito in aged animals ameliorated these impairments, offering a potential target to counteract age-related cognitive decline.}, }
@article {pmid39698084, year = {2024}, author = {Morozova, M and Yakovlev, L and Syrov, N and Lebedev, M and Kaplan, A}, title = {Tactile imagery affects cortical responses to vibrotactile stimulation of the fingertip.}, journal = {Heliyon}, volume = {10}, number = {23}, pages = {e40807}, pmid = {39698084}, issn = {2405-8440}, abstract = {Mental imagery is a crucial cognitive process, yet its underlying neural mechanisms remain less understood compared to perception. Furthermore, within the realm of mental imagery, the somatosensory domain is particularly underexplored compared to other sensory modalities. This study aims to investigate the influence of tactile imagery (TI) on cortical somatosensory processing. We explored the cortical manifestations of TI by recording EEG activity in healthy human subjects. We investigated event-related somatosensory oscillatory dynamics during TI compared to actual tactile stimulation, as well as somatosensory evoked potentials (SEPs) in response to short vibrational stimuli, examining their amplitude-temporal characteristics and spatial distribution across the scalp. EEG activity exhibited significant changes during TI compared to the no-imagery baseline. TI caused event-related desynchronization (ERD) of the contralateral μ-rhythm, with a notable correlation between ERD during imagery and real stimulation across subjects. TI also modulated several SEP components in sensorimotor and frontal areas, showing increases in the contralateral P100 and P300, contra- and ipsilateral P300, frontal P200, and parietal P600 components. The results clearly indicate that TI affects cortical processing of somatosensory stimuli, impacting EEG responses in various cortical areas. The assessment of SEPs in EEG could serve as a versatile marker of tactile imagery in practical applications. We propose incorporating TI in imagery-based brain-computer interfaces (BCIs) to enhance sensorimotor restoration and sensory substitution. This approach underscores the importance of somatosensory mental imagery in cognitive neuroscience and its potential applications in neurorehabilitation and assistive technologies.}, }
@article {pmid39697780, year = {2024}, author = {Wan, X and Xing, S and Zhang, Y and Duan, D and Liu, T and Li, D and Yu, H and Wen, D}, title = {Combining motion performance with EEG for diagnosis of mild cognitive impairment: a new perspective.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1476730}, pmid = {39697780}, issn = {1662-4548}, }
@article {pmid39697779, year = {2024}, author = {Cui, S and Lee, D and Wen, D}, title = {Toward brain-inspired foundation model for EEG signal processing: our opinion.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1507654}, pmid = {39697779}, issn = {1662-4548}, }
@article {pmid39696695, year = {2024}, author = {Xue, YY and Zhang, ZS and Lin, RR and Huang, HF and Zhu, KQ and Chen, DF and Wu, ZY and Tao, QQ}, title = {CD2AP deficiency aggravates Alzheimer's disease phenotypes and pathology through p38 MAPK activation.}, journal = {Translational neurodegeneration}, volume = {13}, number = {1}, pages = {64}, pmid = {39696695}, issn = {2047-9158}, support = {81970998//National Natural Science Foundation of China/ ; 2024SSYS0018//Key Research and Development Program of Zhejiang Province/ ; 2021ZD0201103//Science Innovation 2030-Brain Science and Brain-Inspired Intelligence Technology Major Projects/ ; 2021ZD0201803//Science Innovation 2030-Brain Science and Brain-Inspired Intelligence Technology Major Projects/ ; }, mesh = {Animals ; *Alzheimer Disease/genetics/pathology/metabolism ; *p38 Mitogen-Activated Protein Kinases/genetics/metabolism ; Mice ; *Adaptor Proteins, Signal Transducing/genetics/deficiency ; Mice, Transgenic ; Phenotype ; Disease Models, Animal ; Humans ; Mice, Knockout ; Neurons/pathology/metabolism ; Cytoskeletal Proteins ; }, abstract = {BACKGROUND: Alzheimer's disease (AD) is the most common form of neurodegenerative disorder, which is characterized by a decline in cognitive abilities. Genome-wide association and clinicopathological studies have demonstrated that the CD2-associated protein (CD2AP) gene is one of the most important genetic risk factors for AD. However, the precise mechanisms by which CD2AP is linked to AD pathogenesis remain unclear.<br><br>METHODS: The spatiotemporal expression pattern of CD2AP was determined. Then, we generated and characterized an APP/PS1 mouse model with neuron-specific Cd2ap deletion, using immunoblotting, immunofluorescence, enzyme-linked immunosorbent assay, electrophysiology and behavioral tests. Additionally, we established a stable CD2AP-knockdown SH-SY5Y cell line to further elucidate the specific molecular mechanisms by which CD2AP contributes to AD pathogenesis. Finally, the APP/PS1 mice with neuron-specific Cd2ap deletion were treated with an inhibitor targeting the pathway identified above to further validate our findings.<br><br>RESULTS: CD2AP is widely expressed in various regions of the mouse brain, with predominant expression in neurons and vascular endothelial cells. In APP/PS1 mice, neuronal knockout of Cd2ap significantly aggravated tau pathology, synaptic impairments and cognitive deficits. Mechanistically, the knockout of Cd2ap activated p38 mitogen-activated protein kinase (MAPK) signaling, which contributed to increased tau phosphorylation, synaptic injury, neuronal apoptosis and cognitive impairment. Furthermore, the phenotypes of neuronal Cd2ap knockout were ameliorated by a p38 MAPK inhibitor.<br><br>CONCLUSION: Our study presents the first in vivo evidence that CD2AP deficiency exacerbates the phenotypes and pathology of AD through the p38 MAPK pathway, identifying CD2AP/p38 MAPK as promising therapeutic targets for AD.}, }
@article {pmid39695740, year = {2024}, author = {Lowers, V and Kirby, R and Young, B and Harris, RV}, title = {Scoping review of fidelity strategies used in behaviour change trials delivered in primary dental care settings.}, journal = {Trials}, volume = {25}, number = {1}, pages = {824}, pmid = {39695740}, issn = {1745-6215}, mesh = {Humans ; *Randomized Controlled Trials as Topic/standards/methods ; *Primary Health Care/standards ; *Dental Care/standards ; Health Behavior ; Research Design/standards ; Behavior Therapy/methods/standards ; Oral Health/standards ; Health Knowledge, Attitudes, Practice ; }, abstract = {BACKGROUND: Primary dental care settings are strategically important locations where randomised controlled trials (RCTs) of behaviour change interventions (BCIs) can be tested to tackle oral diseases. Findings have so far produced equivocal results. Improving treatment fidelity is posed as a mechanism to improve scientific rigour, consistency and implementation of BCIs. The National Institutes of Health Behaviour Change Consortium (NIH BCC) developed a tool to assess and evaluate treatment fidelity in health behaviour change interventions, which has yet to be applied to the primary dental care BCI literature.<br><br>METHOD: We conducted a scoping review of RCTs delivered in primary dental care by dental team members (in real-world settings) between 1980 and 2023. Eligible studies were coded using the NIH BCC checklist to determine the presence of reported fidelity strategies across domains: design, training, delivery, receipt and enactment.<br><br>RESULTS: We included 34 eligible articles, reporting 21 RCTs. Fidelity reporting variations were found both between and within NIH BCC domains: strategy reporting ranged from 9.5 to 85.7% in design, 9.5 to 57.1% in training, 0 to 66.7% in delivery, 14.3 to 36.8% in receipt and 13.3 to 33.3% in enactment. The most reported domain was design (M&#x2009;=&#x2009;0.45), and the least reported domain was delivery (M&#x2009;=&#x2009;0.21). Only one study reported over 50% of the recommended strategies in every domain.<br><br>CONCLUSIONS: This review revealed inconsistencies in fidelity reporting with no evidence that fidelity guidelines or frameworks were being used within primary dental care trials. This has highlighted issues with interpretability, reliability and reproducibility of research findings. Recommendations are proposed to assist primary dental care trialists with embedding fidelity strategies into future research.}, }
@article {pmid39694240, year = {2024}, author = {Fonseca, M and Kurban, D and Roy, JP and Santschi, DE and Molgat, E and Yang, DA and Dufour, S}, title = {Usefulness of differential somatic cell count for udder health monitoring: Identifying referential values for differential somatic cell count in healthy quarters and quarters with subclinical mastitis.}, journal = {Journal of dairy science}, volume = {}, number = {}, pages = {}, doi = {10.3168/jds.2024-25403}, pmid = {39694240}, issn = {1525-3198}, abstract = {Mastitis, an inflammation of the udder primarily caused by an intramammary infection, is one of the most common diseases in dairy cattle. Somatic cell count (SCC) has been widely used as an indicator of udder inflammation, assisting in the detection of subclinical mastitis. More recently, differential somatic cell count (DSCC), which represents the combined proportion of lymphocytes and polymorphonuclear leukocytes, has become available for routine dairy milk screening, though it was not yet widely studied. Therefore, the objective of this study was to assess and compare the usefulness of quarter-level somatic cell score (SCS) or DSCC to predict the probability of subclinical mastitis. Additionally, our goals included estimating the sensitivity (Se) and specificity (Sp) of SCS and DSCC across all potential cut-off values. The current study was an observational study conducted on commercial dairy farms. Five dairy herds were selected using a convenience sampling. A Gaussian finite mixture model (GFMM) was applied to investigate the latent quarter subclinical mastitis status with either measurement, i.e., SCS or DSCC. Posterior values for SCS and DSCC obtained from the GFMM were used for predictive estimation of the parameters. The estimated SCS distribution for healthy quarters had a mean (standard deviation) of 1.4 (1.3), while, for quarters with subclinical mastitis, it was 4.5 (2.4). For DSCC, the estimated mean was 55.6% (15.2) for healthy quarters, whereas it was 80.4% (6.4) for quarters with subclinical mastitis. The most discriminant cut-off for SCS, as indicated by the Youden index, was 3.0, corresponding to exactly 100,000 cells/mL. At this threshold, the Se and Sp of SCS were 0.73 (95% Bayesian Credible Interval [BCI]: 0.70-0.77) and 0.90 (95% BCI: 0.89-0.91), respectively. The most discriminant cut-off point for DSCC was 70.0%, with corresponding the Se and Sp values of 0.95 (0.93, 0.96) and 0.83 (0.81, 0.85), respectively. For the SCS analysis, we obtained predictive probabilities of subclinical mastitis approaching 0 and 100%, with only a narrow range of SCS results yielding intermediate probabilities. On the other hand, predictive probabilities ranging from 0 to 90% were obtained for DSCC analysis, with a large range of DSCC results presenting intermediate probabilities. Thus, SCS seemed to surpass DSCC for predicting subclinical mastitis. These findings provided a foundation for future studies to further explore and validate the efficacy of GFMM for diagnostic tests yielding quantitative results.}, }
@article {pmid39693762, year = {2024}, author = {Oxley, T and Deo, DR and Cernera, S and Sawyer, A and Putrino, D and Ramsey, NF and Fry, A}, title = {The 'Brussels 4': essential requirements for implantable brain-computer interface user autonomy.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ada0e6}, pmid = {39693762}, issn = {1741-2552}, abstract = {Implantable brain-computer interfaces (iBCIs) hold great promise for individuals with severe paralysis and are advancing toward commercialization. The features required to achieve autonomous use of an iBCI may be under recognized in traditional academic measures of iBCI function and deserve further consideration to achieve successful clinical translation and patient adoption. Here, we present four key considerations to achieve autonomous use, reflecting the authors' perspectives based on discussions during various sessions and workshops across the 10th International BCI Society Meeting, Brussels, 2023: (1) immediate use, (2) easy to use, (3) continuous use, and (4) stable system use.}, }
@article {pmid39693735, year = {2024}, author = {Kim, J and Cho, YS and Kim, SP}, title = {Task-relevant stimulus design improves P300-based brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ada0e3}, pmid = {39693735}, issn = {1741-2552}, abstract = {OBJECTIVE: In the pursuit of refining P300-based brain-computer interfaces (BCIs), our research aims to propose a novel stimulus design focused on selective attention and task relevance to address the challenges of P300-based BCIs, including the necessity of repetitive stimulus presentations, accuracy improvement, user variability, and calibration demands.<br><br>APPROACH: In the oddball task for P300-based BCIs, we develop a stimulus design involving task-relevant dynamic stimuli implemented as finger-tapping to enhance the elicitation and consistency of event-related potentials (ERPs). We further improve the performance of P300-based BCIs by optimizing ERP feature extraction and classification in offline analyses.<br><br>MAIN RESULTS: With the proposed stimulus design, online P300-based BCIs in 37 healthy participants achieve an accuracy of 91.2% and an information transfer rate (ITR) of 28.37 bits/min with two stimulus repetitions. With optimized computational modeling in BCIs, our offline analyses reveal the possibility of single-trial execution, showcasing an accuracy of 91.7% and an ITR of 59.92 bits/min. Furthermore, our exploration into the feasibility of across-subject zero-calibration BCIs through offline analyses, where a BCI built on a dataset of 36 participants is directly applied to a left-out participant with no calibration, yields an accuracy of 94.23% and the ITR of 31.56 bits/min with two stimulus repetitions and the accuracy of 87.75% and the ITR of 52.61 bits/min with single-trial execution. When using the finger-tapping stimulus, the variability in performance among participants is the lowest, and a greater increase in performance is observed especially for those showing lower performance using the conventional color-changing stimulus. Signficance. Using a novel task-relevant dynamic stimulus design, this study achieves one of the highest levels of P300-based BCI performance to date. This underscores the importance of coupling stimulus paradigms with computational methods for improving P300-based BCIs.}, }
@article {pmid39693734, year = {2024}, author = {Srisrisawang, N and Müller-Putz, GR}, title = {Simultaneous encoding of speed, distance, and direction in discrete reaching: an EEG study.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ada0ea}, pmid = {39693734}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; *Hand/physiology ; *Movement/physiology ; Young Adult ; *Psychomotor Performance/physiology ; Adult ; Biomechanical Phenomena/physiology ; }, abstract = {Objective.The complicated processes of carrying out a hand reach are still far from fully understood. In order to further the understanding of the kinematics of hand movement, the simultaneous representation of speed, distance, and direction in the brain is explored.Approach.We utilized electroencephalography (EEG) signals and hand position recorded during a four-direction center-out reaching task with either quick or slow speed, near and far distance. Linear models were employed in two modes: decoding and encoding. First, to test the discriminability of speed, distance, and direction. Second, to find the contribution of the cortical sources via the source localization. Additionally, we compared the decoding accuracy when using features obtained from EEG signals and source-localized EEG signals based on the results from the encoding model.Main results.Speed, distance, and direction can be classified better than chance. The accuracy of the speed was also higher than the distance, indicating a stronger representation of the speed than the distance. The speed and distance showed similar significant sources in the central regions related to the movement initiation, while the direction indicated significant sources in the parieto-occipital regions related to the movement preparation. The combination of the features from EEG and source localized signals improved the classification.Significance.Directional and non-directional information are represented in two separate networks. The quick movement resulted in improvement in the direction classification. Our results enhance our understanding of hand movement in the brain and help us make informed decisions when designing an improved paradigm in the future.}, }
@article {pmid39693678, year = {2024}, author = {Shen, J and Wang, K and Gao, W and Liu, JK and Xu, Q and Pan, G and Chen, X and Tang, H}, title = {Temporal spiking generative adversarial networks for heading direction decoding.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {184}, number = {}, pages = {106975}, doi = {10.1016/j.neunet.2024.106975}, pmid = {39693678}, issn = {1879-2782}, abstract = {The spike-based neuronal responses within the ventral intraparietal area (VIP) exhibit intricate spatial and temporal dynamics in the posterior parietal cortex, presenting decoding challenges such as limited data availability at the biological population level. The practical difficulty in collecting VIP neuronal response data hinders the application of sophisticated decoding models. To address this challenge, we propose a unified spike-based decoding framework leveraging spiking neural networks (SNNs) for both generative and decoding purposes, for their energy efficiency and suitability for neural decoding tasks. We propose the Temporal Spiking Generative Adversarial Networks (T-SGAN), a model based on a spiking transformer, to generate synthetic time-series data reflecting the neuronal response of VIP neurons. T-SGAN incorporates temporal segmentation to reduce the temporal dimension length, while spatial self-attention facilitates the extraction of associated information among VIP neurons. This is followed by recurrent SNNs decoder equipped with an attention mechanism, designed to capture the intricate spatial and temporal dynamics for heading direction decoding. Experimental evaluations conducted on biological datasets from monkeys showcase the effectiveness of the proposed framework. Results indicate that T-SGAN successfully generates realistic synthetic data, leading to a significant improvement of up to 1.75% in decoding accuracy for recurrent SNNs. Furthermore, the SNN-based decoding framework capitalizes on the low power consumption advantages, offering substantial benefits for neuronal response decoding applications.}, }
@article {pmid39692757, year = {2024}, author = {van Oosterhout, K and Chilundo, A and Branco, MP and Aarnoutse, EJ and Timmermans, M and Fattori, M and Ramsey, NF and Cantatore, E}, title = {Brain-Computer Interfaces Using Flexible Electronics: An a-IGZO Front-End for Active ECoG Electrodes.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e2408576}, doi = {10.1002/advs.202408576}, pmid = {39692757}, issn = {2198-3844}, support = {//Dutch Research Council (NWO)/ ; 17608//Stichting voor de Technische Wetenschappen/ ; 19072//Stichting voor de Technische Wetenschappen/ ; UEBIT//EWUU Alliance/ ; }, abstract = {Brain-computer interfaces (BCIs) are evolving toward higher electrode count and fully implantable solutions, which require extremely low power densities (<15mW cm[-2]). To achieve this target, and allow for a large and scalable number of channels, flexible electronics can be used as a multiplexing interface. This work introduces an active analog front-end fabricated with amorphous Indium-Gallium-Zinx-Oxide (a-IGZO) Thin-Film Transistors (TFTs) on foil capable of active matrix multiplexing. The circuit achieves only 70nV per sqrt(Hz) input referred noise, consuming 46µW, or 3.5mW cm[-2]. It demonstrates for the first time in literature a flexible front-end with a noise efficiency factor comparable with Silicon solutions (NEF = 9.8), which is more than 10X lower compared to previously reported flexible front-ends. These results have been achieved using a modified bootstrap-load amplifier. The front end is tested by playing through it recordings obtained from a conventional BCI system. A gesture classification based on the flexible front-end outputs achieves 94% accuracy. Using a flexible active front end can improve the state-of-the-art in high channel count BCI systems by lowering the multiplexer noise and enabling larger areas of the brain to be monitored while reducing power density. Therefore, this work enables a new generation of high channel-count active BCI electrode grids.}, }
@article {pmid39692430, year = {2024}, author = {Nie, J and Huang, T and Sun, Y and Peng, Z and Dong, W and Chen, J and Zheng, D and Guo, F and Shi, W and Ling, Y and Zhao, W and Yang, H and Shui, T and Yan, X}, title = {Influence of the Enterovirus 71 Vaccine and the COVID-19 Pandemic on Hand, Foot, and Mouth Disease in China Based on Counterfactual Models: Observational Study.}, journal = {JMIR public health and surveillance}, volume = {10}, number = {}, pages = {e63146}, pmid = {39692430}, issn = {2369-2960}, mesh = {*Hand, Foot and Mouth Disease/epidemiology/prevention &amp; control ; Humans ; China/epidemiology ; *COVID-19/epidemiology/prevention &amp; control ; Infant ; Child, Preschool ; Incidence ; *Enterovirus A, Human ; *Viral Vaccines/administration &amp; dosage ; Male ; Child ; Female ; Adolescent ; Pandemics/prevention &amp; control ; }, abstract = {BACKGROUND: Hand, foot, and mouth disease (HFMD) is a highly contagious viral illness. Understanding the long-term trends of HFMD incidence and its epidemic characteristics under the circumstances of the enterovirus 71 (EV71) vaccination program and the outbreak of COVID-19 is crucial for effective disease surveillance and control.<br><br>OBJECTIVE: We aim to give an overview of the trends of HFMD over the past decades and evaluate the impact of the EV71 vaccination program and the COVID-19 pandemic on the epidemic trends of HFMD.<br><br>METHODS: Using official surveillance data from the Yunnan Province, China, we described long-term incidence trends and severity rates of HFMD as well as the variation of enterovirus proportions among cases. We conducted the autoregressive integrated moving average (ARIMA) of time series analyses to predict monthly incidences based on given subsets. The difference between the actual incidences and their counterfactual predictions was compared using absolute percentage errors (APEs) for periods after the EV71 vaccination program and the COVID-19 pandemic, respectively.<br><br>RESULTS: The annual incidence of HFMD fluctuated between 25.62 cases per 100,000 people in 2008 and 221.52 cases per 100,000 people in 2018. The incidence for men ranged from 30 to 250 cases per 100,000 people from 2008 to 2021, which was constantly higher than that for women. The annual incidence for children aged 1 to 2 years old ranged from 54.54 to 630.06 cases per 100,000 people, which was persistently higher than that for other age groups. For monthly incidences, semiannual peaks were observed for each year. All actual monthly incidences of 2014 to 2015 fell within the predicted 95% CI by the ARIMA(1,0,1)(1,1,0)[12] model. The average APE was 19% for a 2-year prediction. After the EV71 vaccination program, the actual monthly incidence of HFMD was consistently lower than the counterfactual predictions by ARIMA(1,0,1)(1,1,0)[12], with negative APEs ranging from -11% to -229% from January 2017 to April 2018. In the meantime, the proportion of EV71 among the enteroviruses causing HFMD decreased significantly, and the proportion was highly correlated (r=0.73, P=.004) with the severity rate. After the onset of the COVID-19 pandemic in 2020, the actual monthly incidence of HFMD consistently maintained a lower magnitude compared to the counterfactual predictions-ARIMA(1,0,1)(0,1,0)[12]-from February to September 2020, with considerable negative APEs (ranging from -31% to -2248%).<br><br>CONCLUSIONS: EV71 vaccination alleviated severe HFMD cases and altered epidemiological trends. The HFMD may also benefit from nonpharmaceutical interventions during outbreaks such as the COVID-19 pandemic. Further development of a multivalent virus vaccine is crucial for effectively controlling HFMD outbreaks. Policymakers should implement nonpharmaceutical interventions and emphasize personal hygiene for routine prevention when appropriate.}, }
@article {pmid39691819, year = {2024}, author = {Vieira, R and Moreno, P and Vourvopoulos, A}, title = {EEG-based action anticipation in human-robot interaction: a comparative pilot study.}, journal = {Frontiers in neurorobotics}, volume = {18}, number = {}, pages = {1491721}, pmid = {39691819}, issn = {1662-5218}, abstract = {As robots become integral to various sectors, improving human-robot collaboration is crucial, particularly in anticipating human actions to enhance safety and efficiency. Electroencephalographic (EEG) signals offer a promising solution, as they can detect brain activity preceding movement by over a second, enabling predictive capabilities in robots. This study explores how EEG can be used for action anticipation in human-robot interaction (HRI), leveraging its high temporal resolution and modern deep learning techniques. We evaluated multiple Deep Learning classification models on a motor imagery (MI) dataset, achieving up to 80.90% accuracy. These results were further validated in a pilot experiment, where actions were accurately predicted several hundred milliseconds before execution. This research demonstrates the potential of combining EEG with deep learning to enhance real-time collaborative tasks, paving the way for safer and more efficient human-robot interactions.}, }
@article {pmid39691581, year = {2024}, author = {Eken, A and Yüce, M and Yükselen, G and Erdoğan, SB}, title = {Explainable fNIRS-based pain decoding under pharmacological conditions via deep transfer learning approach.}, journal = {Neurophotonics}, volume = {11}, number = {4}, pages = {045015}, pmid = {39691581}, issn = {2329-423X}, abstract = {SIGNIFICANCE: Assessment of pain and its clinical diagnosis rely on subjective methods which become even more complicated under analgesic drug administrations.<br><br>AIM: We aim to propose a deep learning (DL)-based transfer learning (TL) methodology for objective classification of functional near-infrared spectroscopy (fNIRS)-derived cortical oxygenated hemoglobin responses to painful and non-painful stimuli presented under different timings post-analgesic and placebo drug administration.<br><br>APPROACH: A publicly available fNIRS dataset obtained during painful/non-painful stimuli was used. Separate fNIRS scans were taken under the same protocol before drug (morphine and placebo) administration and at three different timings (30, 60, and 90&#xa0;min) post-administration. Data from pre-drug fNIRS scans were utilized for constructing a base DL model. Knowledge generated from the pre-drug model was transferred to six distinct post-drug conditions by following a TL approach. The DeepSHAP method was utilized to unveil the contribution weights of nine regions of interest for each of the pre-drug and post-drug decoding models.<br><br>RESULTS: Accuracy, sensitivity, specificity, and area under curve (AUC) metrics of the pre-drug model were above 90%, whereas each of the post-drug models demonstrated a performance above 90% for the same metrics. Post-placebo models had higher decoding accuracy than post-morphine models. Knowledge obtained from a pre-drug base model could be successfully utilized to build pain decoding models for six distinct brain states that were scanned at three different timings after either analgesic or placebo drug administration. The contribution of different cortical regions to classification performance varied across the post-drug models.<br><br>CONCLUSIONS: The proposed DL-based TL methodology may remove the necessity to build DL models for data collected at clinical or daily life conditions for which obtaining training data is not practical or building a new decoding model will have a computational cost. Unveiling the explanation power of different cortical regions may aid the design of more computationally efficient fNIRS-based brain-computer interface (BCI) system designs that target other application areas.}, }
@article {pmid39688131, year = {2024}, author = {Wang, S and Song, X and Xu, J and Wang, J and Yu, L}, title = {Flexible silicon for high-performance photovoltaics, photodetectors and bio-interfaced electronics.}, journal = {Materials horizons}, volume = {}, number = {}, pages = {}, doi = {10.1039/d4mh01466a}, pmid = {39688131}, issn = {2051-6355}, abstract = {Silicon (Si) is currently the most mature and reliable semiconductor material in the industry, playing a pivotal role in the development of modern microelectronics, renewable energy, and bio-electronic technologies. In recent years, widespread research attention has been devoted to the development of advanced flexible electronics, photovoltaics, and bio-interfaced sensors/detectors, boosting their emerging applications in distributed energy sources, healthcare, environmental monitoring, and brain-computer interfaces (BCIs). Despite the rigid and brittle nature of Si, a series of new fabrication technologies and integration strategies have been developed to enable a wide range of c-Si-based high-performance flexible photovoltaics and electronics, which were previously only achievable with intrinsically soft organic and polymer semiconductors. More interestingly, programmable geometric engineering of crystalline silicon (c-Si) units and logic circuits has been explored to enable the fabrication of various highly flexible nanoprobes for intracellular sensing and the deployment of soft BCI matrices to record and understand brain neural activities for the development of advanced neuroprosthetics. This review will systematically examine the latest progress in the fabrication of Si-based flexible solar cells, photodetectors, and biological probing interfaces over the past decade, identifying key design principles, mechanisms, and technological milestones achieved through novel geometry, morphology, and composition control. These advancements, when combined, will not only promote the practical applications of sustainable energy and wearable electronics but also spur new breakthroughs in emerging human-machine interfaces (HMIs) and artificial intelligence applications, which hold significant implications for understanding neural activities, implementing more efficient artificial Intelligence (AI) algorithms, and developing new therapies or treatments. Finally, we will summarize and provide an outlook on the current challenges and future opportunities of Si-based electronics, flexible optoelectronics, and bio-sensing.}, }
@article {pmid39687714, year = {2024}, author = {Hofmann, MJ and Chang, YN and Brouwer, H and Zock, M}, title = {Editorial: Neurocomputational models of language processing.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1524366}, doi = {10.3389/fnhum.2024.1524366}, pmid = {39687714}, issn = {1662-5161}, }
@article {pmid39687306, year = {2024}, author = {Kancaoğlu, M and Kuntalp, M}, title = {Low-cost, mobile EEG hardware for SSVEP applications.}, journal = {HardwareX}, volume = {19}, number = {}, pages = {e00567}, pmid = {39687306}, issn = {2468-0672}, abstract = {The global shortage of integrated circuits due to the COVID-19 pandemic has made it challenging to build biopotential acquisition devices like electroencephalography (EEG) hardware. To address this issue, a new hardware system using common ICs has been designed, which is cost-effective, precise, and easily accessible from global distributors. The hardware system comprises 8-channel inputs EEG hardware with a mobile headset capable of acquiring 5-30Hz EEG signals. First two channels of the design is enabled for steady-state visual evoked potential (SSVEP) operations, and the remaining channels can be powered up as needed. A small 3D-printable enclosure is also designed for the hardware board, which is attached to protective glasses to be used as a head-mounted device. The board includes an additional green LED, 4 pulse width modulation (PWM) outputs for general-purpose input/output (GPIO), 2 buttons for input, and exposed programming pins and digital-to-analog converter (DAC) output from the microcontroller unit (MCU). The proposed hardware system is expected to enable students and young researchers to experiment with EEG signals, especially SSVEP, before investing in professional equipment with the availability of programming codes.}, }
@article {pmid39687114, year = {2024}, author = {Ma, J and Rui, Z and Zou, Y and Qin, Z and Zhao, Z and Zhang, Y and Mao, Z and Bai, H and Zhang, J}, title = {Neurosurgical and BCI approaches to visual rehabilitation in occipital lobe tumor patients.}, journal = {Heliyon}, volume = {10}, number = {23}, pages = {e39072}, pmid = {39687114}, issn = {2405-8440}, abstract = {This study investigates the effects of occipital lobe tumors on visual processing and the role of brain-computer interface (BCI) technologies in post-surgical visual rehabilitation. Through a combination of pre-surgical functional magnetic resonance imaging (fMRI) and Diffusion Tensor Imaging (DTI), intra-operative direct cortical stimulation (DCS) and Electrocorticography (ECoG), and post-surgical BCI interventions, we provide insight into the complex dynamics between occipital lobe tumors and visual function. Our results highlight a discrepancy between clinical assessments of visual field damage and the patient's reported visual experiences, suggesting a residual functional capacity within the damaged occipital regions. Additionally, the absence of expected visual phenomena during surgery and the promising outcomes from BCI-driven rehabilitation underscore the complexity of visual processing and the potential of technology-enhanced rehabilitation strategies. This work emphasizes the need for an interdisciplinary approach in developing effective treatments for visual impairments related to brain tumors, illustrating the significant implications for neurosurgical practices and the advancement of rehabilitation sciences.}, }
@article {pmid39686818, year = {2024}, author = {Lv, Q and Li, Q and Cao, P and Wei, C and Li, Y and Wang, Z and Wang, L}, title = {Designing Silk Biomaterials toward Better Future Healthcare: The Development and Application of Silk-Based Implantable Electronic Devices in Clinical Diagnosis and Therapy.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {}, number = {}, pages = {e2411946}, doi = {10.1002/adma.202411946}, pmid = {39686818}, issn = {1521-4095}, support = {2022YFC2408100//National Key Research and Development Program of China/ ; 82372511//National Natural Science Foundation of China/ ; 82302585//National Natural Science Foundation of China/ ; 82072167//National Natural Science Foundation of China/ ; 82272277//National Natural Science Foundation of China/ ; 82173315//National Natural Science Foundation of China/ ; 81974382//National Natural Science Foundation of China/ ; [2022] No.11//Hubei Province Science and Technology Innovation Team Project/ ; 2022BCA013//the Major Scientific and Technological Innovation Projects of Hubei Province/ ; 2021CFB416//Natural Science Foundation of Hubei Province/ ; 2024AFB652//Natural Science Foundation of Hubei Province/ ; 2022CFB736//Natural Science Foundation of Hubei Province/ ; }, abstract = {Implantable medical electronic devices (IMEDs) have attracted great attention and shown versatility for solving clinical problems ranging from real-time monitoring of physiological/ pathological states to electrical stimulation therapy and from monitoring brain cell activity to deep brain stimulation. The ongoing challenge is to select appropriate materials in target device configuration for biomedical applications. Currently, silk-based biomaterials have been developed for the design of diagnostic and therapeutic electronic devices due to their excellent properties and abundant active sites in the structure. Herein, the aim is to summarize the structural characteristics, physicochemical properties, and bioactivities of natural silk biomaterials as well as their derived materials, with a particular focus on the silk-based implantable biomedical electronic devices, such as implantable devices for invasive brain-computer interfaces, neural recording, and in vivo electrostimulation. In addition, future opportunities and challenges are also envisioned, hoping to spark the interests of researchers in interdisciplinary fields such as biomaterials, clinical medicine, and electronics.}, }
@article {pmid39686393, year = {2024}, author = {Zhuang, W and Zhang, Y and Wang, Y and He, K}, title = {3D-BCLAM: A Lightweight Neurodynamic Model for Assessing Student Learning Effectiveness.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {23}, pages = {}, pmid = {39686393}, issn = {1424-8220}, mesh = {Humans ; *Neural Networks, Computer ; Learning/physiology ; Students ; Emotions/physiology ; Deep Learning ; Machine Learning ; Memory, Short-Term/physiology ; }, abstract = {Evaluating students' learning effectiveness is of great importance for gaining a deeper understanding of the learning process, accurately diagnosing learning barriers, and developing effective teaching strategies. Emotion, as a key factor influencing learning outcomes, provides a novel perspective for identifying cognitive states and emotional experiences. However, traditional evaluation methods suffer from one sidedness in feature extraction and high complexity in model construction, often making it difficult to fully explore the deep value of emotional data. To address this challenge, we have innovatively proposed a lightweight neurodynamic model: 3D-BCLAM. This model cleverly integrates Bidirectional Convolutional Long Short-Term Memory (BCL) and dynamic attention mechanism, in order to efficiently capture emotional dynamic changes in time series with extremely low computational cost. 3D-BCLAM can achieve a comprehensive evaluation of students' learning outcomes, covering not only the cognitive level but also delving into the emotional dimension for detailed analysis. Under testing on public datasets, 3D-BCLAM has demonstrated outstanding performance, significantly outperforming traditional machine learning and deep learning models based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). This achievement not only validates the effectiveness of the 3D-BCLAM model, but also provides strong support for promoting the innovation of student learning effectiveness assessment.}, }
@article {pmid39686227, year = {2024}, author = {Megalingam, RK and Sankardas, KS and Manoharan, SK}, title = {An Empirical Model-Based Algorithm for Removing Motion-Caused Artifacts in Motor Imagery EEG Data for Classification Using an Optimized CNN Model.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {23}, pages = {}, pmid = {39686227}, issn = {1424-8220}, mesh = {*Electroencephalography/methods ; Humans ; *Artifacts ; *Algorithms ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Motion ; Signal Processing, Computer-Assisted ; Wheelchairs ; Imagination/physiology ; }, abstract = {Electroencephalography (EEG) is a non-invasive technique with high temporal resolution and cost-effective, portable, and easy-to-use features. Motor imagery EEG (MI-EEG) data classification is one of the key applications within brain-computer interface (BCI) systems, utilizing EEG signals from motor imagery tasks. BCI is very useful for people with severe mobility issues like quadriplegics, spinal cord injury patients, stroke patients, etc., giving them the freedom to a certain extent to perform activities without the need for a caretaker, like driving a wheelchair. However, motion artifacts can significantly affect the quality of EEG recordings. The conventional EEG enhancement algorithms are effective in removing ocular and muscle artifacts for a stationary subject but not as effective when the subject is in motion, e.g., a wheelchair user. In this research study, we propose an empirical error model-based artifact removal approach for the cross-subject classification of motor imagery (MI) EEG data using a modified CNN-based deep learning algorithm, designed to assist wheelchair users with severe mobility issues. The classification method applies to real tasks with measured EEG data, focusing on accurately interpreting motor imagery signals for practical application. The empirical error model evolved from the inertial sensor-based acceleration data of the subject in motion, the weight of the wheelchair, the weight of the subject, and the surface friction of the terrain under the wheelchair. Three different wheelchairs and five different terrains, including road, brick, concrete, carpet, and marble, are used for artifact data recording. After evaluating and benchmarking the proposed CNN and empirical model, the classification accuracy achieved is 94.04% for distinguishing between four specific classes: left, right, front, and back. This accuracy demonstrates the model's effectiveness compared to other state-of-the-art techniques. The comparative results show that the proposed approach is a potentially effective way to raise the decoding efficiency of motor imagery BCI.}, }
@article {pmid39686148, year = {2024}, author = {Li, LL and Cao, GZ and Zhang, YP and Li, WC and Cui, F}, title = {MACNet: A Multidimensional Attention-Based Convolutional Neural Network for Lower-Limb Motor Imagery Classification.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {23}, pages = {}, pmid = {39686148}, issn = {1424-8220}, support = {U1813212//the State Key Program of National Natural Science Foundation of China/ ; 52277061//National Natural Science Foundation of China/ ; JCYJ20220818095804009 and JSGG20200701095406010//Shenzhen Science and Technology Program/ ; 20220809200041001//Shenzhen Science and Technology Program/ ; }, mesh = {Humans ; *Neural Networks, Computer ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Lower Extremity/physiology ; Imagination/physiology ; Attention/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Brain/physiology ; }, abstract = {Decoding lower-limb motor imagery (MI) is highly important in brain-computer interfaces (BCIs) and rehabilitation engineering. However, it is challenging to classify lower-limb MI from electroencephalogram (EEG) signals, because lower-limb motions (LLMs) including MI are excessively close to physiological representations in the human brain and generate low-quality EEG signals. To address this challenge, this paper proposes a multidimensional attention-based convolutional neural network (CNN), termed MACNet, which is specifically designed for lower-limb MI classification. MACNet integrates a temporal refining module and an attention-enhanced convolutional module by leveraging the local and global feature representation abilities of CNNs and attention mechanisms. The temporal refining module adaptively investigates critical information from each electrode channel to refine EEG signals along the temporal dimension. The attention-enhanced convolutional module extracts temporal and spatial features while refining the feature maps across the channel and spatial dimensions. Owing to the scarcity of public datasets available for lower-limb MI, a specified lower-limb MI dataset involving four routine LLMs is built, consisting of 10 subjects over 20 sessions. Comparison experiments and ablation studies are conducted on this dataset and a public BCI Competition IV 2a EEG dataset. The experimental results show that MACNet achieves state-of-the-art performance and outperforms alternative models for the subject-specific mode. Visualization analysis reveals the excellent feature learning capabilities of MACNet and the potential relationship between lower-limb MI and brain activity. The effectiveness and generalizability of MACNet are verified.}, }
@article {pmid39685825, year = {2024}, author = {Villiger, AS and Hoehn, D and Ruggeri, G and Vaineau, C and Nirgianakis, K and Imboden, S and Kuhn, A and Mueller, MD}, title = {Lower Urinary Tract Dysfunction Among Patients Undergoing Surgery for Deep Infiltrating Endometriosis: A Prospective Cohort Study.}, journal = {Journal of clinical medicine}, volume = {13}, number = {23}, pages = {}, pmid = {39685825}, issn = {2077-0383}, abstract = {Background/Objectives: Postsurgical lower urinary tract dysfunction (LUTD) is a common problem following deep infiltrating endometriosis (DIE) resection. The condition may be caused either by surgically induced damage to the bladder innervation or by pre-existing endometriosis-associated nerve damage. The aim of this study is to evaluate the efficacy of preoperative and postoperative multichannel urodynamic testing (UD) in identifying pre-existing or surgically induced LUTD among patients with DIE. Methods: Women with suspected DIE and planned surgical resection of DIE at the Department of Obstetrics and Gynecology at the University Hospital of Bern from September 2015 to October 2022 were invited to participate in this prospective cohort study. UD was performed before and 6 weeks after surgery. The primary outcome was the maximum flow rate (uroflow), an indicator of LUTD. Secondary outcomes were further urodynamic observations of cystometry and pressure flow studies, lower urinary tract symptoms (LUTS) as assessed by the International Prostate Symptom Score (IPSS), and pain as assessed by the visual analog scale (VAS). Results: A total of 51 patients requiring surgery for DIE were enrolled in this study. All patients underwent surgical excision of the DIE. The cohort demonstrated a uroflow of 22.1 mL/s prior to surgery, which decreased postoperatively to 21.5 mL/s (p = 0.56, 95%CI -1.5-2.71). The mean bladder contractility index (BCI) exhibited a notable decline from 130.4 preoperatively to 116.6 postoperatively (p = 0.046, 95%CI 0.23-27.27). Significant improvements were observed in the prevalence of dysmenorrhea, abdominal pain, dyspareunia, and dyschezia following surgical intervention (p = <0.001). The IPSS score was within the lower moderate range both pre- and postoperatively (mean 8.37 vs. 8.51, p = 0.893, 95%CI -2.35-2.05). Subgroup analysis identified previous endometriosis surgery as a significant preoperative risk factor for elevated post-void residual (43.6 mL, p = 0.026, 95%CI 13.89-73.37). The postoperative post-void residual increased among participants with DIE on the rectum to 54.39 mL (p = 0.078, 95%CI 24.06-84.71). Participants who underwent hysterectomy exhibited a significantly decreased uroflow (16.4 mL/s, p = 0.014, 95%CI 12-20) and BCI (75.1, p = 0.036, 95%CI 34.9-115.38). Conclusions: Nerve-respecting laparoscopy for DIE may alter bladder function. UD is not advisable before surgery, but the measurement may detect patients with LUTD.}, }
@article {pmid39681925, year = {2024}, author = {Garrott, K and Ogilvie, D and Panter, J and Petticrew, M and Sowden, A and Jones, CP and Foubister, C and Lawlor, ER and Ikeda, E and Patterson, R and van Tulleken, D and Armstrong-Moore, R and Vethanayakam, G and Bo, L and White, M and Adams, J}, title = {Development and application of the Demands for Population Health Interventions (Depth) framework for categorising the agentic demands of population health interventions.}, journal = {BMC global and public health}, volume = {2}, number = {1}, pages = {13}, pmid = {39681925}, issn = {2731-913X}, support = {Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_12015/6/MRC_/Medical Research Council/United Kingdom ; MC_UU_12015/6/MRC_/Medical Research Council/United Kingdom ; MC_UU_12015/6/MRC_/Medical Research Council/United Kingdom ; MC_UU_12015/6/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; }, abstract = {BACKGROUND: The 'agentic demand' of population health interventions (PHIs) refers to the capacity, resources and freedom to act that interventions demand of their recipients to benefit, which have a socio-economical pattern. Highly agentic interventions, e.g. information campaigns, rely on recipients noticing and responding to the intervention and thus might affect intervention effectiveness and equity. The absence of an adequate framework to classify agentic demands limits the fields' ability to systematically explore these associations.<br><br>METHODS: We systematically developed the Demands for Population Health Interventions (Depth) framework using an iterative approach: (1) developing the Depth framework by systematically identifying examples of PHIs aiming to promote healthier diets and physical activity, coding of intervention actors and actions and synthesising the data to develop the framework; (2) testing the Depth framework in online workshops with academic and policy experts and a quantitative reliability assessment. We applied the final framework in a proof-of-concept review, extracting studies from three existing equity-focused systematic reviews on framework category, overall effectiveness and differential socioeconomic effects and visualised the findings in harvest plots.<br><br>RESULTS: The Depth framework identifies three constructs influencing agentic demand: exposure - initial contact with intervention (two levels), mechanism of action - how the intervention enables or discourages behaviour (five levels) and engagement - recipient response (two levels). When combined, these constructs form a matrix of 20 possible classifications. In the proof-of-concept review, we classified all components of 31 interventions according to the Depth framework. Intervention components were concentrated in a small number of Depth classifications; Depth classification appeared to be related to intervention equity but not effectiveness.<br><br>CONCLUSIONS: This framework holds potential for future research, policy and practice, facilitating the design, selection and evaluation of interventions and evidence synthesis.}, }
@article {pmid39679398, year = {2024}, author = {Wang, S and Zhang, W and Fu, P and Zhong, Y and Piatkevich, KD and Zhang, D and Lee, HJ}, title = {Structural diversity of Alzheimer-related protein aggregations revealed using photothermal ratio-metric micro-spectroscopy.}, journal = {Biomedical optics express}, volume = {15}, number = {12}, pages = {6768-6782}, pmid = {39679398}, issn = {2156-7085}, abstract = {The crucial link between pathological protein aggregations and lipids in Alzheimer's disease pathogenesis is increasingly recognized, yet its spatial dynamics remain challenging for labeling-based microscopy. Here, we demonstrate photothermal ratio-metric infrared spectro-microscopy (PRISM) to investigate the in situ structural and molecular compositions of pathological features in brain tissues at submicron resolution. By identifying the vibrational spectroscopic signatures of protein secondary structures and lipids, PRISM tracks the structural dynamics of pathological proteins, including amyloid and hyperphosphorylated Tau (pTau). Amyloid-associated lipid features in major brain regions were observed, notably the enrichment of lipid-dissociated plaques in the hippocampus. Spectroscopic profiling of pTau revealed significant heterogeneity in phosphorylation levels and a distinct lipid-pTau relationship that contrasts with the anticipated lipid-plaque correlation. Beyond in vitro studies, our findings provide direct visualization evidence of aggregate-lipid interactions across the brain, offering new insights into mechanistic and therapeutic research of neurodegenerative diseases.}, }
@article {pmid39678727, year = {2024}, author = {Pan, H and Song, W and Li, L and Qin, X}, title = {The design and implementation of multi-character classification scheme based on EEG signals of visual imagery.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {2299-2309}, pmid = {39678727}, issn = {1871-4080}, abstract = {In visual-imagery-based brain-computer interface (VI-BCI), there are problems of singleness of imagination task and insufficient description of feature information, which seriously hinder the development and application of VI-BCI technology in the field of restoring communication. In this paper, we design and optimize a multi-character classification scheme based on electroencephalogram (EEG) signals of visual imagery (VI), which is used to classify 29 characters including 26 lowercase English letters and three punctuation marks. Firstly, a new paradigm of randomly presenting characters and including preparation stage is designed to acquire EEG signals and construct a multi-character dataset, which can eliminate the influence between VI tasks. Secondly, tensor data is obtained by the Morlet wavelet transform, and a feature extraction algorithm based on tensor-uncorrelated multilinear principal component analysis is used to extract high-quality features. Finally, three classifiers, namely support vector machine, K-nearest neighbor, and extreme learning machine, are employed for classifying multi-character, and the results are compared. The experimental results demonstrate that, the proposed scheme effectively extracts character features with minimal redundancy, weak correlation, and strong representation capability, and successfully achieves an average classification accuracy 97.59% for 29 characters, surpassing existing research in terms of both accuracy and quantity of classification. The present study designs a new paradigm for acquiring EEG signals of VI, and combines the Morlet wavelet transform and UMPCA algorithm to extract the character features, enabling multi-character classification in various classifiers. This research paves a novel pathway for establishing direct brain-to-world communication.}, }
@article {pmid39678535, year = {2024}, author = {Wang, Y and Gong, L and Zhao, Y and Yu, Y and Liu, H and Yang, X}, title = {Dynamic graph attention network based on multi-scale frequency domain features for motion imagery decoding in hemiplegic patients.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1493264}, pmid = {39678535}, issn = {1662-4548}, abstract = {Brain-computer interfaces (BCIs) establish a direct communication pathway between the brain and external devices and have been widely applied in upper limb rehabilitation for hemiplegic patients. However, significant individual variability in motor imagery electroencephalogram (MI-EEG) signals leads to poor generalization performance of MI-based BCI decoding methods to new patients. This paper proposes a Multi-scale Frequency domain Feature-based Dynamic graph Attention Network (MFF-DANet) for upper limb MI decoding in hemiplegic patients. MFF-DANet employs convolutional kernels of various scales to extract feature information across multiple frequency bands, followed by a channel attention-based average pooling operation to retain the most critical frequency domain features. Additionally, MFF-DANet integrates a graph attention convolutional network to capture spatial topological features across different electrode channels, utilizing electrode positions as prior knowledge to construct and update the graph adjacency matrix. We validated the performance of MFF-DANet on the public PhysioNet dataset, achieving optimal decoding accuracies of 61.6% for within-subject case and 52.7% for cross-subject case. t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization of the features demonstrates the effectiveness of each designed module, and visualization of the adjacency matrix indicates that the extracted spatial topological features have physiological interpretability.}, }
@article {pmid39678531, year = {2024}, author = {Wang, N and Tu, WJ}, title = {Editorial: Brain-computer interfaces in neurological disorders: expanding horizons for diagnosis, treatment, and rehabilitation.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1526723}, pmid = {39678531}, issn = {1662-4548}, }
@article {pmid39677402, year = {2024}, author = {Ninenko, I and Medvedeva, A and Efimova, VL and Kleeva, DF and Morozova, M and Lebedev, MA}, title = {Olfactory neurofeedback: current state and possibilities for further development.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1419552}, pmid = {39677402}, issn = {1662-5161}, abstract = {This perspective considers the novel concept of olfactory neurofeedback (O-NFB) within the framework of brain-computer interfaces (BCIs), where olfactory stimuli are integrated in various BCI control loops. In particular, electroencephalography (EEG)-based O-NFB systems are capable of incorporating different components of complex olfactory processing - from simple discrimination tasks to using olfactory stimuli for rehabilitation of neurological disorders. In our own work, EEG theta and alpha rhythms were probed as control variables for O-NFB. Additionaly, we developed an olfactory-based instructed-delay task. We suggest that the unique functions of olfaction offer numerous medical and consumer applications where O-NFB is combined with sensory inputs of other modalities within a BCI framework to engage brain plasticity. We discuss the ways O-NFB could be implemented, including the integration of different types of olfactory displays in the experiment set-up and EEG features to be utilized. We emphasize the importance of synchronizing O-NFB with respiratory rhythms, which are known to influence EEG patterns and cognitive processing. Overall, we expect that O-NFB systems will contribute to both practical applications in the clinical world and the basic neuroscience of olfaction.}, }
@article {pmid39674592, year = {2025}, author = {Barker, N and Parker, H}, title = {Hybrid performances in sport: Cybathlon spectatorship for critically imagining technologies for disability futures.}, journal = {Medical humanities}, volume = {50}, number = {4}, pages = {657-669}, doi = {10.1136/medhum-2024-013031}, pmid = {39674592}, issn = {1473-4265}, mesh = {Humans ; *Disabled Persons ; *Sports ; *Self-Help Devices ; Brain-Computer Interfaces ; Robotics ; Anthropology, Cultural ; Politics ; }, abstract = {Disabled bodies have been historically marginalised in sporting arenas and spectacles. Assistive technologies have been increasingly featuring in, and changing, sporting landscapes. In some ways recent shifts have made disability more present and visible across many (para) sporting cultures, and yet sport continues to operate on a tiered system that assumes a normative able body. This paper responds to this moment by offering imaginaries of future hybrid performances that critically engage with the politics and possibilities of novel technologies in sporting arenas and their wider impact on disability futures. These were generated from a collaborative ethnography that centred on becoming spectators of the Cybathlon Games. The Cybathlon Games began in 2016 as a global event where people with disabilities compete with technologies such as Brain-Computer Interfaces or robotic Prosthesis. Our imaginings are presented as three speculative fragments in the form of pages ripped from a comic book series, The In/Visibles These fragments and critical reflections are grounded on themes generated through watching the Games together. The purpose of this paper is not to offer predictions or even visions of desirable futures. Rather we present future technologised sporting bodies and spectacles with a view to extend critical posthuman discussions to these arenas. Through this we highlight: (1) The arbitrariness of where to draw the between un/natural performances; (2) The absurdities of unrestricted and open use of performance technologies when hybrid forms and functions are judged through current sporting-humanist values; and (3) The need to stay alert to socioeconomic and political drivers of sporting and disability futures. We offer these three zones of friction to guide further research when navigating the complex and shifting relations between sport, technology and the (dis)abled body now and into the future.}, }
@article {pmid39656892, year = {2024}, author = {Guo, D and Yao, B and Shao, WW and Zuo, JC and Chang, ZH and Shi, JX and Hu, N and Bao, SQ and Chen, MM and Fan, X and Li, XH}, title = {The Critical Role of YAP/BMP/ID1 Axis on Simulated Microgravity-Induced Neural Tube Defects in Human Brain Organoids.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e2410188}, doi = {10.1002/advs.202410188}, pmid = {39656892}, issn = {2198-3844}, support = {2021YFF1200800//National Key Research and Development Program of China/ ; 82171861//National Natural Science Foundation of China/ ; 81971782//National Natural Science Foundation of China/ ; 82101448//National Natural Science Foundation of China/ ; }, abstract = {Integrated biochemical and biophysical signals regulate embryonic development. Correct neural tube formation is critical for the development of central nervous system. However, the role of microgravity in neurodevelopment and its underlying molecular mechanisms remain unclear. In this study, the effects of stimulated microgravity (SMG) on the development of human brain organoids are investigated. SMG impairs N-cadherin-based adherens junction formation, leading to neural tube defects associated with dysregulated self-renewal capacity and neuroepithelial disorganization in human brain organoids. Bulk gene expression analyses reveal that SMG alters Hippo and BMP signaling in brain organoids. The neuropathological deficits in SMG-treated organoids can be rescued by regulating YAP/BMP/ID1 axis. Furthermore, sing-cell RNA sequencing data show that SMG results in perturbations in the number and function of neural stem and progenitor cell subpopulations. One of these subpopulations senses SMG cues and transmits BMP signals to the subpopulation responsible for tube morphogenesis, ultimately affecting the proliferating cell population. Finally, SMG intervention leads to persistent neurologic damage even after returning to normal gravity conditions. Collectively, this study reveals molecular and cellular abnormalities associated with SMG during human brain development, providing opportunities for countermeasures to maintain normal neurodevelopment in space.}, }
@article {pmid39672531, year = {2024}, author = {Li, K and Qian, L and Zhang, C and Zhang, J and Xue, C and Zhang, Y and Deng, W}, title = {The entorhinal cortex and cognitive impairment in schizophrenia: A comprehensive review.}, journal = {Progress in neuro-psychopharmacology &amp; biological psychiatry}, volume = {136}, number = {}, pages = {111218}, doi = {10.1016/j.pnpbp.2024.111218}, pmid = {39672531}, issn = {1878-4216}, abstract = {Schizophrenia, a severe mental illness characterized by cognitive impairment and olfactory dysfunction, remains an enigma with its pathological mechanism yet to be fully elucidated. The entorhinal cortex, a pivotal structure involved in numerous neural loop circuits related to olfaction, cognition, and emotion, has garnered significant attention due to its structural and functional abnormalities, which have been implicated in the pathogenesis of schizophrenia. This review focuses on the abnormal structural and functional changes in the entorhinal cortex in schizophrenia patients, as evidenced by neuroimaging, cellular biology, and genetic studies. These changes are posited to play a crucial role in the pathogenesis of cognitive impairment in schizophrenia. Furthermore, this review explores the various intervention strategies targeting the entorhinal cortex in current treatment modalities and proposes potential directions for future research endeavors, thereby providing a novel perspective on unraveling the complexity of neural mechanisms underlying schizophrenia and developing innovative therapeutic approaches for schizophrenia.}, }
@article {pmid39672015, year = {2024}, author = {Hameed, I and Khan, DM and Ahmed, SM and Aftab, SS and Fazal, H}, title = {Enhancing motor imagery EEG signal decoding through machine learning: A systematic review of recent progress.}, journal = {Computers in biology and medicine}, volume = {185}, number = {}, pages = {109534}, doi = {10.1016/j.compbiomed.2024.109534}, pmid = {39672015}, issn = {1879-0534}, abstract = {This systematic literature review explores the intersection of neuroscience and deep learning in the context of decoding motor imagery Electroencephalogram (EEG) signals to enhance the quality of life for individuals with motor disabilities. Currently, the most used non-invasive method for measuring brain activity is the EEG, due to its high temporal resolution, user-friendliness, and safety. A Brain Computer Interface (BCI) framework can be made using these signals which can provide a new communication channel to people that are suffering from motor disabilities or other neurological disorders. However, implementing EEG-based BCI systems in real-world scenarios for motor imagery recognition presents challenges, primarily due to the inherent variability among individuals and low signal-to-noise ratio (SNR) of EEG signals. To assist researchers in navigating this complex problem, a comprehensive review article is presented, summarizing the key findings from relevant studies since 2017. This review primarily focuses on the datasets, preprocessing methods, feature extraction techniques, and deep learning models employed by various researchers. This review aims to contribute valuable insights and serve as a resource for researchers, practitioners, and enthusiasts interested in the combination of neuroscience and deep learning, ultimately hoping to contribute to advancements that bridge the gap between the human mind and machine interfaces.}, }
@article {pmid39671976, year = {2024}, author = {Brooks, KA and Kolousek, A and Holman, EK and Evans, SS and Govil, N and Alfonso, KP}, title = {MED-EL Bonebridge implantation in pediatric patients age 11 Years and younger: Is it safe and effective?.}, journal = {International journal of pediatric otorhinolaryngology}, volume = {188}, number = {}, pages = {112198}, doi = {10.1016/j.ijporl.2024.112198}, pmid = {39671976}, issn = {1872-8464}, abstract = {OBJECTIVE: To present our experience with off-label MED-EL Bonebridge implantation in pediatric patients younger than 12 years of age and compare outcomes to pediatric patients 12 years and older.<br><br>METHODS: Pediatric patients who underwent Bonebridge implantation were included in a retrospective cohort study and were categorized by off-label use (<12 years) and &#x2265;12 years at time of bone conduction implantation (BCI). Hearing outcomes were collected after implant activation, which was typically 4-8 weeks post-implantation. Mann-Whitney U tests were performed to assess for differences between audiometric outcomes. Significance was set at p&#xa0;<&#xa0;0.05.<br><br>RESULTS: Twenty patients (25 implants)&#xa0;<&#xa0;12 years of age and 17 patients (23 implants) &#x2265;12 years of age underwent BCI. Pre-BCI speech recognition threshold (SRT) was better for the older patient group (median 50&#xa0;dB) than the younger patient group (median 60&#xa0;dB). Post-BCI SRT, however, was significantly lower in the younger patient group (median 22.5&#xa0;dB) as compared to the older patient group (median 35&#xa0;dB), (p&#xa0;<&#xa0;0.001, Z&#xa0;=&#xa0;3.1). The two groups performed similarly on age-appropriate wordlists presented at 50&#xa0;dB HL in aided conditions (p&#xa0;>&#xa0;0.05, -1<Z&#xa0;<&#xa0;1). Postoperative complication rate was low for both age groups (9&#xa0;% vs 8&#xa0;%) and operative time was similar for both groups of patients (p=0.96, Z=0.52).<br><br>CONCLUSION: Pediatric patients younger than 12 years showed similar or better audiometric benefit from off-label Bonebridge implantation when compared to older patients. Pediatric patients younger than 12 years can be considered Bonebridge implant candidates if clinically indicated; Bonebridge implantation in this age group appears safe and technically feasible.}, }
@article {pmid39671798, year = {2024}, author = {Daly, I and Williams, N and Nasuto, SJ}, title = {TMS-evoked potential propagation reflects effective brain connectivity.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad9ee0}, pmid = {39671798}, issn = {1741-2552}, mesh = {Humans ; *Transcranial Magnetic Stimulation/methods ; Male ; Female ; Adult ; *Electroencephalography/methods ; *Brain/physiology ; Young Adult ; Nerve Net/physiology ; Evoked Potentials/physiology ; Feedback, Sensory/physiology ; Connectome/methods ; Evoked Potentials, Motor/physiology ; Psychomotor Performance/physiology ; }, abstract = {Objective.Cognition is achieved through communication between brain regions. Consequently, there is considerable interest in measuring effective connectivity. A promising effective connectivity metric is transcranial magnetic stimulation (TMS) evoked potentials (TEPs), an inflection in amplitude of the electroencephalogram recorded from one brain region as a result of TMS applied to another region. However, the TEP is confounded by multiple factors and there is a need for further investigation of the TEP as a measure of effective connectivity and to compare it to existing statistical measures of effective connectivity.Approach.To this end, we used a pre-existing experimental dataset to compare TEPs between a motor control task with and without visual feedback. We then used the results to compare our TEP-based measures of effective connectivity to established statistical measures of effective connectivity provided by multivariate auto-regressive modelling.Main results.Our results reveal significantly more negative TEPs when feedback is not presented from 40 ms to 100 ms post-TMS over frontal and central channels. We also see significantly more positive later TEPs from 280-400 ms on the contra-lateral hemisphere motor and parietal channels when no feedback is presented. These results suggest differences in effective connectivity are induced by visual feedback of movement. We further find that the variation in one of these early TEPs (the N40) is reliably related to directed coherence.Significance.Taken together, these results indicate components of the TEPs serve as a measure of effective connectivity. Furthermore, our results also support the idea that effective connectivity is a dynamic process and, importantly, support the further use of TEPs in delineating region-to-region maps of changes in effective connectivity as a result of motor control feedback.}, }
@article {pmid39671787, year = {2025}, author = {H Liu, D and Hsieh, JC and Alawieh, H and Kumar, S and Iwane, F and Pyatnitskiy, I and Ahmad, ZJ and Wang, H and Millán, JDR}, title = {Novel AIRTrode-based wearable electrode supports long-term, online brain-computer interface operations.}, journal = {Journal of neural engineering}, volume = {22}, number = {1}, pages = {}, doi = {10.1088/1741-2552/ad9edf}, pmid = {39671787}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods/instrumentation ; Male ; *Electrodes ; *Wearable Electronic Devices ; Adult ; Female ; Young Adult ; Online Systems ; }, abstract = {Objective.Non-invasive electroencephalograms (EEG)-based brain-computer interfaces (BCIs) play a crucial role in a diverse range of applications, including motor rehabilitation, assistive and communication technologies, holding potential promise to benefit users across various clinical spectrums. Effective integration of these applications into daily life requires systems that provide stable and reliable BCI control for extended periods. Our prior research introduced the AIRTrode, a self-adhesive (A), injectable (I), and room-temperature (RT) spontaneously-crosslinked hydrogel electrode (AIRTrode). The AIRTrode has shown lower skin-contact impedance and greater stability than dry electrodes and, unlike wet gel electrodes, does not dry out after just a few hours, enhancing its suitability for long-term application. This study aims to demonstrate the efficacy of AIRTrodes in facilitating reliable, stable and long-term online EEG-based BCI operations.Approach.In this study, four healthy participants utilized AIRTrodes in two BCI control tasks-continuous and discrete-across two sessions separated by six hours. Throughout this duration, the AIRTrodes remained attached to the participants' heads. In the continuous task, participants controlled the BCI through decoding of upper-limb motor imagery (MI). In the discrete task, the control was based on decoding of error-related potentials (ErrPs).Main Results.Using AIRTrodes, participants demonstrated consistently reliable online BCI performance across both sessions and tasks. The physiological signals captured during MI and ErrPs tasks were valid and remained stable over sessions. Lastly, both the BCI performances and physiological signals captured were comparable with those from freshly applied, research-grade wet gel electrodes, the latter requiring inconvenient re-application at the start of the second session.Significance.AIRTrodes show great potential promise for integrating non-invasive BCIs into everyday settings due to their ability to support consistent BCI performances over extended periods. This technology could significantly enhance the usability of BCIs in real-world applications, facilitating continuous, all-day functionality that was previously challenging with existing electrode technologies.}, }
@article {pmid39671281, year = {2024}, author = {Ghodrati, MT and Aghababaei, S and Mirfathollahi, A and Shalchyan, V and Zarrindast, MR and Daliri, MR}, title = {Protocol for state-based decoding of hand movement parameters using neural signals.}, journal = {STAR protocols}, volume = {5}, number = {4}, pages = {103503}, pmid = {39671281}, issn = {2666-1667}, mesh = {Humans ; *Hand/physiology ; *Movement/physiology ; Biomechanical Phenomena/physiology ; Brain-Computer Interfaces ; Somatosensory Cortex/physiology ; }, abstract = {We present a protocol for decoding kinematic and kinetic parameters from the primary somatosensory cortex during active and passive hand movements in a center-out reaching task using state-based and conventional decoders. We describe steps for preparing data and using the state-based model to classify movement directions into states via feature extraction and predict parameters with regression models (partial least squares and multilinear regression) trained per state. This state-based approach outperforms conventional methods, enhancing accuracy for brain-computer interface applications. For complete details on the use and execution of this protocol, please refer to Mirfathollahi et al.[1].}, }
@article {pmid39670475, year = {2024}, author = {Xie, S and He, C}, title = {An empirical study on native Mandarin-speaking children's metonymy comprehension development.}, journal = {Journal of child language}, volume = {}, number = {}, pages = {1-28}, doi = {10.1017/S0305000924000539}, pmid = {39670475}, issn = {1469-7602}, abstract = {This study investigates Mandarin-speaking children's (age 3-7) comprehension development of novel and conventional metonymy, combining online and offline methods. Both online and offline data show significantly better performances from the oldest group (6-to-7-year-old) and a delayed acquisition of conventional metonymy compared with novel metonymy. However, part of offline data shows no significant difference between adjacent age groups, while the eye-tracking data show a chronological development from age 3-7. Furthermore, in offline tasks, the three-year-old group features a high choice randomness and the four-to-five-year-olds show the longest reaction time. Therefore, we argue that, not only age but also metonymy type can influence metonymy acquisition, and that a lack of socio-cultural experience can be a source of acquisition difficulty for children under six. Methodologically speaking, we believe that online methods should not be considered superior to offline ones as they investigate different aspects of implicit and explicit language comprehension.}, }
@article {pmid39669979, year = {2024}, author = {Nakamura, D and Kaji, S and Kanai, R and Hayashi, R}, title = {Unsupervised method for representation transfer from one brain to another.}, journal = {Frontiers in neuroinformatics}, volume = {18}, number = {}, pages = {1470845}, pmid = {39669979}, issn = {1662-5196}, abstract = {Although the anatomical arrangement of brain regions and the functional structures within them are similar across individuals, the representation of neural information, such as recorded brain activity, varies among individuals owing to various factors. Therefore, appropriate conversion and translation of brain information is essential when decoding neural information using a model trained using another person's data or to achieving brain-to-brain communication. We propose a brain representation transfer method that involves transforming a data representation obtained from one person's brain into that obtained from another person's brain, without relying on corresponding label information between the transferred datasets. We defined the requirements to enable such brain representation transfer and developed an algorithm that distills the assumption of common similarity structure across the brain datasets into a rotational and reflectional transformation across low-dimensional hyperspheres using encoders for non-linear dimensional reduction. We first validated our proposed method using data from artificial neural networks as substitute neural activity and examining various experimental factors. We then evaluated the applicability of our method to real brain activity using functional magnetic resonance imaging response data acquired from human participants. The results of these validation experiments showed that our method successfully performed representation transfer and achieved transformations in some cases that were similar to those obtained when using corresponding label information. Additionally, we reconstructed images from individuals' data without training personalized decoders by performing brain representation transfer. The results suggest that our unsupervised transfer method is useful for the reapplication of existing models personalized to specific participants and datasets to decode brain information from other individuals. Our findings also serve as a proof of concept for the methodology, enabling the exchange of the latent properties of neural information representing individuals' sensations.}, }
@article {pmid39669288, year = {2024}, author = {Nejatbakhsh, A and Fumarola, F and Esteki, S and Toyoizumi, T and Kiani, R and Mazzucato, L}, title = {Predicting the effect of micro-stimulation on macaque prefrontal activity based on spontaneous circuit dynamics.}, journal = {Physical review research}, volume = {5}, number = {4}, pages = {}, pmid = {39669288}, issn = {2643-1564}, support = {R01 DA055439/DA/NIDA NIH HHS/United States ; R01 MH109180/MH/NIMH NIH HHS/United States ; R01 MH127375/MH/NIMH NIH HHS/United States ; R01 NS118461/NS/NINDS NIH HHS/United States ; }, abstract = {A crucial challenge in targeted manipulation of neural activity is to identify perturbation sites whose stimulation exerts significant effects downstream with high efficacy, a procedure currently achieved by labor-intensive and potentially harmful trial and error. Can one predict the effects of electrical stimulation on neural activity based on the circuit dynamics during spontaneous periods? Here we show that the effects of single-site micro-stimulation on ensemble activity in an alert monkey's prefrontal cortex can be predicted solely based on the ensemble's spontaneous activity. We first inferred the ensemble's causal flow based on the directed functional interactions inferred during spontaneous periods using convergent cross-mapping and showed that it uncovers a causal hierarchy between the recording electrodes. We find that causal flow inferred at rest successfully predicts the spatiotemporal effects of micro-stimulation. We validate the computational features underlying causal flow using ground truth data from recurrent neural network models, showing that it is robust to noise and common inputs. A detailed comparison between convergent-cross mapping and alternative methods based on information theory reveals the advantages of the former method in predicting perturbation effects. Our results elucidate the causal interactions within neural ensembles and will facilitate the design of intervention protocols and targeted circuit manipulations suitable for brain-machine interfaces.}, }
@article {pmid39667504, year = {2024}, author = {Cao, HL and Wei, W and Meng, YJ and Tao, YJ and Yang, X and Li, T and Guo, WJ}, title = {Association of altered cortical gyrification and working memory in male early abstinent alcohol-dependent individuals.}, journal = {Brain research bulletin}, volume = {220}, number = {}, pages = {111166}, doi = {10.1016/j.brainresbull.2024.111166}, pmid = {39667504}, issn = {1873-2747}, abstract = {BACKGROUND: Alcohol dependence (AD) is an addictive disorder with multifaceted neurobiological features. Recent research on the pathophysiological mechanisms of AD has emphasized the important role of dysconnectivity. Cortical gyrification is known to be a reliable marker of neural connectivity. This study aimed to explore cortical gyrification using the local gyrification index (LGI) between alcohol-dependent patients and controls.<br><br>METHODS: Magnetic resonance images were collected from 60 early abstinent patients with AD (5-12 days after stopping alcohol consumption) and 59 controls and preprocessed using FreeSurfer, followed by surface-based morphometry (SBM) analysis to compare the LGI between the two groups. Cognitive performance was assessed using the Spatial Working Memory (SWM) test in the Cambridge Neuropsychological Test Automated Battery (CANTAB). The relationship between LGI, cognitive performance, and clinical variables was also explored in the patient group.<br><br>RESULTS: Compared with controls, patients with AD exhibited significantly decreased LGI in several regions, including the postcentral gyrus, precentral gyrus, middle frontal, superior temporal, middle temporal, insula, superior parietal, and inferior parietal cortex. AD patients did worse than controls in several SWM measures. Furthermore, decreased LGI in the left postcentral was negatively correlated with working memory performance after multiple comparison corrections in the patient group.<br><br>CONCLUSION: Alcohol-dependent individuals exhibit abnormal patterns of cortical gyrification, which may be underlying neurobiological markers of AD. Our findings further indicate that working memory deficits may be related to abnormalities in cortical gyrification in alcohol addiction.}, }
@article {pmid39667216, year = {2024}, author = {Sun, P and De Winne, J and Zhang, M and Devos, P and Botteldooren, D}, title = {Delayed knowledge transfer: Cross-modal knowledge transfer from delayed stimulus to EEG for continuous attention detection based on spike-represented EEG signals.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {183}, number = {}, pages = {107003}, doi = {10.1016/j.neunet.2024.107003}, pmid = {39667216}, issn = {1879-2782}, abstract = {Decoding visual and auditory stimuli from brain activities, such as electroencephalography (EEG), offers promising advancements for enhancing machine-to-human interaction. However, effectively representing EEG signals remains a significant challenge. In this paper, we introduce a novel Delayed Knowledge Transfer (DKT) framework that employs spiking neurons for attention detection, using our experimental EEG dataset. This framework extracts patterns from audiovisual stimuli to model brain responses in EEG signals, while accounting for inherent response delays. By aligning audiovisual features with EEG signals through a shared embedding space, our approach improves the performance of brain-computer interface (BCI) systems. We also present WithMeAttention, a multimodal dataset designed to facilitate research in continuously distinguishing between target and distractor responses. Our methodology demonstrates a 3% improvement in accuracy on the WithMeAttention dataset compared to a baseline model that decodes EEG signals from scratch. This significant performance increase highlights the effectiveness of our approach Comprehensive analysis across four distinct conditions shows that rhythmic enhancement of visual information can optimize multi-sensory information processing. Notably, the two conditions featuring rhythmic target presentation - with and without accompanying beeps - achieved significantly superior performance compared to other scenarios. Furthermore, the delay distribution observed under different conditions indicates that our delay layer effectively emulates the neural processing delays in response to stimuli.}, }
@article {pmid39667215, year = {2024}, author = {Lan, Y and Wang, Y and Zhang, Y and Zhu, H}, title = {Low-power and lightweight spiking transformer for EEG-based auditory attention detection.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {183}, number = {}, pages = {106977}, doi = {10.1016/j.neunet.2024.106977}, pmid = {39667215}, issn = {1879-2782}, abstract = {EEG signal analysis can be used to study brain activity and the function and structure of neural networks, helping to understand neural mechanisms such as cognition, emotion, and behavior. EEG-based auditory attention detection is using EEG signals to determine an individual's level of attention to specific auditory stimuli. In this technique, researchers record and analyze a subject's electrical activity to infer whether an individual is paying attention to a specific auditory stimulus. The model deployed in edge devices will be greatly convenient for subjects to use. However, most of the existing EEG-based auditory attention detection models use traditional neural network models, and their high computing load makes deployment on edge devices challenging. We present a pioneering approach in the form of a binarized spiking Transformer for EEG-based auditory attention detection, which is characterized by high accuracy, low power consumption, and lightweight design, making it highly suitable for deployment on edge devices. In terms of low power consumption, the network is constructed using spiking neurons, which emit sparse and binary spike sequences, which can effectively reduce computing power consumption. In terms of lightweight, we use a post-training quantization strategy to quantize the full-precision network weights into binary weights, which greatly reduces the model size. In addition, the structure of the Transformer ensures that the model can learn effective information and ensure its high performance. We verify the model through mainstream datasets, and experimental results show that our model performance can exceed the existing state-of-the-art models, and the model size can be reduced by more than 21 times compared with the original full-precision network counterpart.}, }
@article {pmid39665789, year = {2024}, author = {Tang, C and Wang, P and Li, Z and Zhong, S and Yang, L and Li, G}, title = {Neural functional rehabilitation: exploring neuromuscular reconstruction technology advancements and challenges.}, journal = {Neural regeneration research}, volume = {}, number = {}, pages = {}, doi = {10.4103/NRR.NRR-D-24-00613}, pmid = {39665789}, issn = {1673-5374}, abstract = {Neural machine interface technology is a pioneering approach that aims to address the complex challenges of neurological dysfunctions and disabilities resulting from conditions such as congenital disorders, traumatic injuries, and neurological diseases. Neural machine interface technology establishes direct connections with the brain or peripheral nervous system to restore impaired motor, sensory, and cognitive functions, significantly improving patients' quality of life. This review analyzes the chronological development and integration of various neural machine interface technologies, including regenerative peripheral nerve interfaces, targeted muscle and sensory reinnervation, agonist-antagonist myoneural interfaces, and brain-machine interfaces. Recent advancements in flexible electronics and bioengineering have led to the development of more biocompatible and high-resolution electrodes, which enhance the performance and longevity of neural machine interface technology. However, significant challenges remain, such as signal interference, fibrous tissue encapsulation, and the need for precise anatomical localization and reconstruction. The integration of advanced signal processing algorithms, particularly those utilizing artificial intelligence and machine learning, has the potential to improve the accuracy and reliability of neural signal interpretation, which will make neural machine interface technologies more intuitive and effective. These technologies have broad, impactful clinical applications, ranging from motor restoration and sensory feedback in prosthetics to neurological disorder treatment and neurorehabilitation. This review suggests that multidisciplinary collaboration will play a critical role in advancing neural machine interface technologies by combining insights from biomedical engineering, clinical surgery, and neuroengineering to develop more sophisticated and reliable interfaces. By addressing existing limitations and exploring new technological frontiers, neural machine interface technologies have the potential to revolutionize neuroprosthetics and neurorehabilitation, promising enhanced mobility, independence, and quality of life for individuals with neurological impairments. By leveraging detailed anatomical knowledge and integrating cutting-edge neuroengineering principles, researchers and clinicians can push the boundaries of what is possible and create increasingly sophisticated and long-lasting prosthetic devices that provide sustained benefits for users.}, }
@article {pmid39664295, year = {2024}, author = {Chen, LX and Zhang, MD and Xu, HF and Ye, HQ and Chen, DF and Wang, PS and Bao, ZW and Zou, SM and Lv, YT and Wu, ZY and Li, HF}, title = {Single-Nucleus RNA Sequencing Reveals the Spatiotemporal Dynamics of Disease-Associated Microglia in Amyotrophic Lateral Sclerosis.}, journal = {Research (Washington, D.C.)}, volume = {7}, number = {}, pages = {0548}, pmid = {39664295}, issn = {2639-5274}, abstract = {Disease-associated microglia (DAM) are observed in neurodegenerative diseases, demyelinating disorders, and aging. However, the spatiotemporal dynamics and evolutionary trajectory of DAM during the progression of amyotrophic lateral sclerosis (ALS) remain unclear. Using a mouse model of ALS that expresses a human SOD1 gene mutation, we found that the microglia subtype DAM begins to appear following motor neuron degeneration, primarily in the brain stem and spinal cord. Using reverse transcription quantitative polymerase chain reaction, RNAscope in situ hybridization, and flow cytometry, we found that DAM increased in number as the disease progressed, reaching their peak in the late disease stage. DAM responded to disease progression in both SOD1[G93A] mice and sporadic ALS and C9orf72-mutated patients. Motor neuron loss in SOD1[G93A] mice exhibited 2 accelerated phases: P90 to P110 (early stage) and P130 to P150 (late stage). Some markers were synchronized with the accelerated phase of motor neuron loss, suggesting that these proteins may be particularly responsive to disease progression. Through pseudotime trajectory analysis, we tracked the dynamic transition of homeostatic microglia into DAM and cluster 6 microglia. Interestingly, we used the colony-stimulating factor 1 receptor (CSF1R) inhibitor PLX5622 to deplete microglia in SOD1[G93A] mice and observed that DAM survival is independent of CSF1R. An in vitro phagocytosis assay directly confirmed that DAM could phagocytose more beads than other microglia subtypes. These findings reveal that the induction of the DAM phenotype is a shared cross-species and cross-subtype characteristic in ALS. Inducing the DAM phenotype and enhancing its function during the early phase of disease progression, or the time window between P130 and P150 where motor neuron loss slows, could serve as a neuroprotective strategy for ALS.}, }
@article {pmid39663729, year = {2024}, author = {Qin, Y and Zhao, H and Chang, Q and Liu, Y and Jing, Z and Yu, D and Mugo, SM and Wang, H and Zhang, Q}, title = {Amylopectin-based Hydrogel Probes for Brain-machine Interfaces.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {}, number = {}, pages = {e2416926}, doi = {10.1002/adma.202416926}, pmid = {39663729}, issn = {1521-4095}, support = {22377122//National Natural Science Foundation of China/ ; U22A20183//National Natural Science Foundation of China/ ; SKL202402018//Jilin Province Science and Technology Development Plan/ ; 029GJHZ2024038FN//International Partnership Program of the Chinese Academy of Sciences/ ; }, abstract = {Implantable neural probes hold promise for acquiring brain data, modulating neural circuits, and treating various brain disorders. However, traditional implantable probes face significant challenges in practical applications, such as balancing sensitivity with biocompatibility and the difficulties of in situ neural information monitoring and neuromodulation. To address these challenges, this study developed an implantable hydrogel probe capable of recording neural signals, modulating neural circuits, and treating stroke. Amylopectin is integrated into the hydrogels, which can induce reorientation of the poly(3,4-ethylenedioxythiophene) (PEDOT) chain and create compliant interfaces with brain tissues, enhancing both sensitivity and biocompatibility. The hydrogel probe shows the capability of continuously recording deep brain signals for 8 weeks. The hydrogel probe is effectively utilized to study deep brain signals associated with various physiological activities. Neuromodulation and neural signal monitoring are performed directly in the primary motor cortex of rats, enabling control over their limb behaviors through evoked signals. When applied to the primary motor cortex of stroke-affected rats, neuromodulation significantly reduced the brain infarct area, promoted synaptic reorganization, and restored motor functions and balance. This research represents a significant scientific breakthrough in the design of neural probes for brain monitoring, neural circuit modulation, and the development of brain disease therapies.}, }
@article {pmid39662472, year = {2024}, author = {Xin, Q and Zheng, D and Zhou, T and Xu, J and Ni, Z and Hu, H}, title = {Deconstructing the neural circuit underlying social hierarchy in mice.}, journal = {Neuron}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neuron.2024.11.007}, pmid = {39662472}, issn = {1097-4199}, abstract = {Social competition determines hierarchical social status, which profoundly influences animals' behavior and health. The dorsomedial prefrontal cortex (dmPFC) plays a fundamental role in regulating social competitions, but it was unclear how the dmPFC orchestrates win- and lose-related behaviors through its downstream neural circuits. Here, through whole-brain c-Fos mapping, fiber photometry, and optogenetics- or chemogenetics-based manipulations, we identified anatomically segregated win- and lose-related neural pathways downstream of the dmPFC in mice. Specifically, layer 5 neurons projecting to the dorsal raphe nucleus (DRN) and periaqueductal gray (PAG) promote social competition, whereas layer 2/3 neurons projecting to the anterior basolateral amygdala (aBLA) suppress competition. These two neuronal populations show opposite changes in activity during effortful pushes in competition. In vivo and in vitro electrophysiology recordings revealed inhibition from the lose-related pathway to the win-related pathway. Such antagonistic interplay may represent a central principle in how the mPFC orchestrates complex behaviors through top-down control.}, }
@article {pmid39661668, year = {2024}, author = {Fu, P and Zhang, Y and Wang, S and Ye, X and Wu, Y and Yu, M and Zhu, S and Lee, HJ and Zhang, D}, title = {INSPIRE: Single-beam probed complementary vibrational bioimaging.}, journal = {Science advances}, volume = {10}, number = {50}, pages = {eadm7687}, pmid = {39661668}, issn = {2375-2548}, mesh = {*Spectrum Analysis, Raman/methods ; *Vibration ; Humans ; *Molecular Imaging/methods ; Animals ; Mice ; Spectrophotometry, Infrared/methods ; }, abstract = {Molecular spectroscopy provides intrinsic contrast for in situ chemical imaging, linking the physiochemical properties of biomolecules to the functions of living systems. While stimulated Raman imaging has found successes in deciphering biological machinery, many vibrational modes are Raman inactive or weak, limiting the broader impact of the technique. It can potentially be mitigated by the spectral complementarity from infrared (IR) spectroscopy. However, the vastly different optical windows make it challenging to develop such a platform. Here, we introduce in situ pump-probe IR and Raman excitation (INSPIRE) microscopy, a nascent cross-modality spectroscopic imaging approach by encoding the ultrafast Raman and the IR photothermal relaxation into a single probe beam for simultaneous detection. INSPIRE inherits the merits of complementary modalities and demonstrates high-content molecular imaging of chemicals, cells, tissues, and organisms. Furthermore, INSPIRE applies to label-free and molecular tag imaging, offering possibilities for optical sensing and imaging in biomedicine and materials science.}, }
@article {pmid39661515, year = {2024}, author = {Chen, Z and Tang, S and Xiao, X and Hong, Y and Fu, B and Li, X and Shao, Y and Chen, L and Yuan, D and Long, Y and Wang, H and Hong, H}, title = {Adiponectin receptor 1-mediated basolateral amygdala-prelimbic cortex circuit regulates methamphetamine-associated memory.}, journal = {Cell reports}, volume = {43}, number = {12}, pages = {115074}, doi = {10.1016/j.celrep.2024.115074}, pmid = {39661515}, issn = {2211-1247}, mesh = {Animals ; *Methamphetamine/pharmacology ; *Memory/drug effects/physiology ; *Receptors, Adiponectin/metabolism ; *Basolateral Nuclear Complex/metabolism/drug effects ; Male ; Mice ; *Mice, Inbred C57BL ; Neurons/metabolism/drug effects ; Calcium-Calmodulin-Dependent Protein Kinase Type 2/metabolism ; Signal Transduction/drug effects ; Reward ; AMP-Activated Protein Kinases/metabolism ; Piperidines ; }, abstract = {The association between drug-induced rewards and environmental cues represents a promising strategy to address addiction. However, the neural networks and molecular mechanisms orchestrating methamphetamine (MA)-associated memories remain incompletely characterized. In this study, we demonstrated that AdipoRon (AR), a specific adiponectin receptor (AdipoR) agonist, inhibits the formation of MA-induced conditioned place preference (CPP) in MA-conditioned mice, accompanied by suppression of basolateral amygdala (BLA) CaMKIIα neuron activity. Furthermore, we identified an association between the excitatory circuit from the BLA to the prelimbic cortex (PrL) and the integration of MA-induced rewards with environmental cues. We also determined that the phosphorylated AMPK (p-AMPK)/Cav1.3 signaling pathway mediates the modulatory effects of AdipoR1 in PrL-projecting BLA CaMKIIα neurons on the formation of MA reward memories, a process influenced by physical exercise. These findings highlight the critical function of AdipoR1 in the BLA[CaMKIIα]→PrL[CaMKIIα] circuit in regulating MA-related memory formation, suggesting a potential target for managing MA use disorders.}, }
@article {pmid39660042, year = {2024}, author = {Kojima, S and Kanoh, S}, title = {Four-class ASME BCI: investigation of the feasibility and comparison of two strategies for multiclassing.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1461960}, pmid = {39660042}, issn = {1662-5161}, abstract = {INTRODUCTION: The ASME (stands for Auditory Stream segregation Multiclass ERP) paradigm is proposed and used for an auditory brain-computer interface (BCI). In this paradigm, a sequence of sounds that are perceived as multiple auditory streams are presented simultaneously, and each stream is an oddball sequence. The users are requested to focus selectively on deviant stimuli in one of the streams, and the target of the user attention is detected by decoding event-related potentials (ERPs). To achieve multiclass ASME BCI, the number of streams must be increased. However, increasing the number of streams is not easy because of a person's limited audible frequency range. One method to achieve multiclass ASME with a limited number of streams is to increase the target stimuli in a single stream.<br><br>METHODS: Two approaches for the ASME paradigm, ASME-4stream (four streams with a single target stimulus in each stream) and ASME-2stream (two streams with two target stimuli in each stream) were investigated. Fifteen healthy subjects with no neurological disorders participated in this study. An electroencephalogram was acquired, and ERPs were analyzed. The binary classification and BCI simulation (detecting the target class of the trial out of four) were conducted with the help of linear discriminant analysis, and its performance was evaluated offline. Its usability and workload were also evaluated using a questionnaire.<br><br>RESULTS: Discriminative ERPs were elicited in both paradigms. The average accuracies of the BCI simulations were 0.83 (ASME-4stream) and 0.86 (ASME-2stream). In the ASME-2stream paradigm, the latency and the amplitude of P300 were shorter and larger, the average binary classification accuracy was higher, and the average weighted workload was smaller.<br><br>DISCUSSION: Both four-class ASME paradigms achieved a sufficiently high accuracy (over 80%). The shorter latency and larger amplitude of P300 and the smaller workload indicated that subjects could perform the task confidently and had high usability in ASME-2stream compared to ASME-4stream paradigm. A paradigm with multiple target stimuli in a single stream could create a multiclass ASME BCI with limited streams while maintaining task difficulty. These findings expand the potential for an ASME BCI multiclass extension, offering practical auditory BCI choices for users.}, }
@article {pmid39658529, year = {2024}, author = {Wang, WW and Ji, SY and Xu, P and Zhang, Y and Zhang, Y}, title = {The future of G protein-coupled receptor therapeutics: Apelin receptor acts as a prototype for the advancement of precision drug design.}, journal = {Clinical and translational medicine}, volume = {14}, number = {12}, pages = {e70120}, pmid = {39658529}, issn = {2001-1326}, support = {//National Natural Science Foundation of China/ ; 2024C03147//Yan Zhang [ZJU]); the ''Pioneer'' and ''Leading Goose'' R&amp;D Program of Zhejiang/ ; //Ministry of Science and Technology/ ; 2021C03039//the Key R&amp;D Projects of Zhejiang Province/ ; 2020R01006//the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang/ ; 82325004//the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang/ ; 92168114//the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang/ ; 32400575//the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang/ ; 2024T170783//the China Postdoctoral Science Foundation/ ; GZC20232326//Postdoctoral Fellowship Program of CPSF/ ; }, }
@article {pmid39657314, year = {2024}, author = {Li, S and Tian, M and Xu, R and Cichocki, A and Jin, J}, title = {Decoding continuous motion trajectories of upper limb from EEG signals based on feature selection and nonlinear methods.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad9cc1}, pmid = {39657314}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Upper Extremity/physiology ; *Nonlinear Dynamics ; *Movement/physiology ; Male ; Female ; Adult ; Algorithms ; Motion ; }, abstract = {Objective.Brain-computer interface (BCI) system has emerged as a promising technology that provides direct communication and control between the human brain and external devices. Among the various applications of BCI, limb motion decoding has gained significant attention due to its potential for patients with motor impairment to regain independence and improve their quality of life. However, the reconstruction of continuous motion trajectories in BCI systems based on electroencephalography (EEG) signals remains a challenge in practical life.Approach.This study investigates the feasibility of applying feature selection and nonlinear regression for decoding motion trajectory from EEG. We propose to fix the time window, select the optimal feature set, and reconstruct the motion trajectory of motor execution tasks using polynomial regression. The proposed approach is validated on a public dataset consisting of EEG and hand position data recorded from 15 subjects. Several methods including ridge regression and multiple linear regression are employed as comparisons.Main results.The cross-validation results show that the proposed reconstructed method has the highest correlation with actual motion trajectories, with an average value of 0.511 ± 0.019 (p< 0.05).Significance.This finding demonstrates the great potential of our approach for real-world motor kinematics BCI applications.}, }
@article {pmid39657294, year = {2024}, author = {Du, L and Zeng, J and Yu, H and Chen, B and Deng, W and Li, T}, title = {Efficacy of bright light therapy improves outcomes of perinatal depression: A systematic review and meta-analysis of randomized controlled trials.}, journal = {Psychiatry research}, volume = {344}, number = {}, pages = {116303}, doi = {10.1016/j.psychres.2024.116303}, pmid = {39657294}, issn = {1872-7123}, abstract = {The efficacy of bright light therapy (BLT) in the context of perinatal depression remains underexplored. This meta-analysis aimed to systematically assess the effectiveness of BLT among perinatal depression. A comprehensive literature search was performed across several databases, including the Cochrane Central Register of Controlled Trials, PubMed, Embase, CNKI and the clinical trials registry platform, covering the period from the inception of each database up to January 2024. The Cochrane Collaboration's bias assessment tool was employed to evaluate the quality of the included studies. Review Manager 5.3 Software was utilized to conduct the meta-analysis. Six trials, encompassed a total of 167 participants diagnosed with perinatal depression were incorporated quantitative analysis, all of those have been published in English, with no restriction on publication year, and used BLT and dim light therapy (DLT) as intervention. The relative risk (RR) of BLT compared to DLT for perinatal depression is 1.46 (fixed effects model, p = 0.04, 95 % CI = [1.02, 2.10]), indicating a significant improvement in depression outcomes compared to DLT groups. The heterogeneity test yielded an I[2] value of 41 % (p = 0.13), indicated a low degree of heterogeneity. Considering the small sample size, we conducted a sensitivity analysis, found RR increased to 2.33 (fixed effects model, p = 0.001, CI = 1.39-3.92). Cochrane Risk of Bias Tool showed only a single study was deemed high quality. This study indicates a beneficial impact of BLT on perinatal depression, subgroup analysis finds no significant mediation effects of different parameters after sensitivity analyses. It is recommended that future studies with larger samples be conducted to explore the effects of BLT on perinatal depression.}, }
@article {pmid39652971, year = {2024}, author = {Hinss, MF and Jahanpour, ES and Brock, AM and Roy, RN}, title = {A passive brain-computer interface for operator mental fatigue estimation in monotonous surveillance operations: time-on-task and performance labeling issues.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad9bed}, pmid = {39652971}, issn = {1741-2552}, mesh = {Humans ; *Mental Fatigue/psychology/physiopathology ; Male ; *Brain-Computer Interfaces ; Adult ; Female ; Young Adult ; *Electroencephalography/methods ; Machine Learning ; Psychomotor Performance/physiology ; Eye Movements/physiology ; Algorithms ; }, abstract = {Objective: A central component of search and rescue missions is the visual search of survivors. In large parts, this depends on human operators and is, therefore, subject to the constraints of human cognition, such as mental fatigue (MF). This makes detecting MF a critical step to be implemented in future systems. However, to the best of our knowledge, it has seldom been evaluated using a realistic visual search task. In addition, an accuracy discrepancy exists between studies that use time-on-task (TOT)-the popular method-and performance metrics for labels. Yet, to our knowledge, they have never been directly compared.Approach: This study was designed to address both issues: the use of a realistic task to elicit MF during a monotonous visual search task and the labeling type used for intra-participant fatigue estimation. Over four blocks of 15 min, participants had to identify targets on a computer while their cardiac, cerebral (EEG), and eye-movement activities were recorded. The recorded data were then fed into several physiological computing pipelines.Main results: The results show that the capability of a machine learning algorithm to detect MF depends less on the input data but rather on how MF is defined. Using TOT, very high classification accuracies are obtained (e.g. 99.3%). On the other hand, if MF is estimated based on behavioral performance, a metric with a much greater operational value, classification accuracies return to chance level (i.e. 52.2%).Significance: TOT-based MF estimation is popular, and strong classification accuracies can be achieved with a multitude of sensors. These factors contribute to the popularity of this method, but both usability and the relation to the concept of MF are neglected.}, }
@article {pmid39652893, year = {2024}, author = {Noorbasha, SK and Kumar, A}, title = {VME-EFD : A novel framework to eliminate the Electrooculogram artifact from single-channel EEGs.}, journal = {Biomedical physics &amp; engineering express}, volume = {11}, number = {1}, pages = {}, doi = {10.1088/2057-1976/ad9bb6}, pmid = {39652893}, issn = {2057-1976}, mesh = {Humans ; *Artifacts ; *Electrooculography/methods ; *Electroencephalography/methods ; *Algorithms ; *Signal Processing, Computer-Assisted ; *Fourier Analysis ; Blinking ; Eye Movements/physiology ; Computer Simulation ; Brain/physiology/diagnostic imaging ; }, abstract = {The diagnosis of neurological disorders often involves analyzing EEG data, which can be contaminated by artifacts from eye movements or blinking (EOG). To improve the accuracy of EEG-based analysis, we propose a novel framework, VME-EFD, which combines Variational Mode Extraction (VME) and Empirical Fourier Decomposition (EFD) for effective EOG artifact removal. In this approach, the EEG signal is first decomposed by VME into two segments: the desired EEG signal and the EOG artifact. The EOG component is further processed by EFD, where decomposition levels are analyzed based on energy and skewness. The level with the highest energy and skewness, corresponding to the artifact, is discarded, while the remaining levels are reintegrated with the desired EEG. Simulations on both synthetic and real EEG datasets demonstrate that VME-EFD outperforms existing methods, with lower RRMSE (0.1358 versus 0.1557, 0.1823, 0.2079, 0.2748), lower ΔPSD in theαband (0.10 ± 0.01 and 0.17 ± 0.04 versus 0.89 ± 0.91 and 0.22 ± 0.19, 1.32 ± 0.23 and 1.10 ± 0.07, 2.86 ± 1.30 and 1.19 ± 0.07, 3.96 ± 0.56 and 2.42 ± 2.48), and higher correlation coefficient (CC: 0.9732 versus 0.9695, 0.9514, 0.8994, 0.8730). The framework effectively removes EOG artifacts and preserves critical EEG features, particularly in theαband, making it highly suitable for brain-computer interface (BCI) applications.}, }
@article {pmid39651250, year = {2024}, author = {Liang, KF and Kao, JC}, title = {A reinforcement learning based software simulator for motor brain-computer interfaces.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2024.11.25.625180}, pmid = {39651250}, issn = {2692-8205}, abstract = {Intracortical motor brain-computer interfaces (BCIs) are expensive and time-consuming to design because accurate evaluation traditionally requires real-time experiments. In a BCI system, a user interacts with an imperfect decoder and continuously changes motor commands in response to unexpected decoded movements. This "closed-loop" nature of BCI leads to emergent interactions between the user and decoder that are challenging to model. The gold standard for BCI evaluation is therefore real-time experiments, which significantly limits the speed and community of BCI research. We present a new BCI simulator that enables researchers to accurately and quickly design BCIs for cursor control entirely in software. Our simulator replaces the BCI user with a deep reinforcement learning (RL) agent that interacts with a simulated BCI system and learns to optimally control it. We demonstrate that our simulator is accurate and versatile, reproducing the published results of three distinct types of BCI decoders: (1) a state-of-the-art linear decoder (FIT-KF), (2) a "two-stage" BCI decoder requiring closed-loop decoder adaptation (ReFIT-KF), and (3) a nonlinear recurrent neural network decoder (FORCE).}, }
@article {pmid39651231, year = {2024}, author = {Sorrell, E and Wilson, DE and Rule, ME and Yang, H and Forni, F and Harvey, CD and O'Leary, T}, title = {An optical brain-machine interface reveals a causal role of posterior parietal cortex in goal-directed navigation.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2024.11.29.626034}, pmid = {39651231}, issn = {2692-8205}, abstract = {Cortical circuits contain diverse sensory, motor, and cognitive signals, and form densely recurrent networks. This creates challenges for identifying causal relationships between neural populations and behavior. We developed a calcium imaging-based brain-machine interface (BMI) to study the role of posterior parietal cortex (PPC) in controlling navigation in virtual reality. By training a decoder to estimate navigational heading and velocity from PPC activity during virtual navigation, we discovered that mice could immediately navigate toward goal locations when control was switched to BMI. No learning or adaptation was observed during BMI, indicating that naturally occurring PPC activity patterns are sufficient to drive navigational trajectories in real time. During successful BMI trials, decoded trajectories decoupled from the mouse's physical movements, suggesting that PPC activity relates to intended trajectories. Our work demonstrates a role for PPC in navigation and offers a BMI approach for investigating causal links between neural activity and behavior.}, }
@article {pmid39648165, year = {2024}, author = {Zhang, J and Dong, E and Zhang, Y and Zhang, Y}, title = {Harnessing the power of structure-based design: A new lease on life for cardiovascular drug development with apelin receptor modulators.}, journal = {Clinical and translational medicine}, volume = {14}, number = {12}, pages = {e70116}, pmid = {39648165}, issn = {2001-1326}, support = {2021YFF0501401//from the National Key R&amp;D Program of China/ ; 2018YFA0800501//from the National Key R&amp;D Program of China/ ; 82325004//National Natural Science Foundation of China/ ; 92168114//National Natural Science Foundation of China/ ; 92353303//National Natural Science Foundation of China/ ; 32141004//National Natural Science Foundation of China/ ; 82300286//National Natural Science Foundation of China/ ; 92168113//National Natural Science Foundation of China/ ; 32430051//National Natural Science Foundation of China/ ; 22HHXBSS00007//Haihe Laboratory of Cell Ecosystem Innovation Fund/ ; BX20220023//China Postdoctoral Science Foundation/ ; 2022M720288//China Postdoctoral Science Foundation/ ; }, }
@article {pmid39645364, year = {2024}, author = {Abuduaini, Y and Pu, Y and Chen, W and Kong, XZ}, title = {Imaging the Unseen: Charting Amygdalar Tau's Link to Affective Symptoms in Preclinical Alzheimer's Disease.}, journal = {Biological psychiatry. Cognitive neuroscience and neuroimaging}, volume = {9}, number = {12}, pages = {1236-1238}, doi = {10.1016/j.bpsc.2024.10.003}, pmid = {39645364}, issn = {2451-9030}, }
@article {pmid39645086, year = {2024}, author = {Li, S and Zhou, Y and Kong, D and Miao, Y and Guan, N and Gao, G and Jin, J and Ye, H}, title = {A visually-induced optogenetically-engineered system enables autonomous glucose homeostasis in mice.}, journal = {Journal of controlled release : official journal of the Controlled Release Society}, volume = {378}, number = {}, pages = {27-37}, doi = {10.1016/j.jconrel.2024.12.006}, pmid = {39645086}, issn = {1873-4995}, abstract = {With the global population increasing and the demographic shifting toward an aging society, the number of patients diagnosed with conditions such as peripheral neuropathies resulting from diabetes is expected to rise significantly. This growing health burden has emphasized the need for innovative solutions, such as brain-computer interfaces. brain-computer interfaces, a multidisciplinary field that integrates neuroscience, engineering, and computer science, enable direct communication between the human brain and external devices. In this study, we developed an autonomous diabetes therapeutic system that employs visually-induced electroencephalography devices to capture and decode event-related potentials using machine learning techniques. We present the visually-induced optogenetically-engineered system for therapeutic expression regulation (VISITER), which generates diverse output commands to control illumination durations. This system regulates insulin expression through optogenetically-engineered cells, achieving blood glucose homeostasis in mice. Our results demonstrate that VISITER effectively and precisely modulates therapeutic protein expression in mammalian cells, facilitating the rapid restoration of blood glucose homeostasis in diabetic mice. These findings underscore the potential for diabetic patients to manage insulin levels autonomously by focusing on target images, paving the way for a more self-directed approach to blood glucose control.}, }
@article {pmid39644999, year = {2025}, author = {Wang, Y and Wang, X and Wang, L and Zheng, L and An, X and Zheng, C}, title = {Attenuated task-responsive representations of hippocampal place cells induced by amyloid-beta accumulation.}, journal = {Behavioural brain research}, volume = {480}, number = {}, pages = {115384}, doi = {10.1016/j.bbr.2024.115384}, pmid = {39644999}, issn = {1872-7549}, mesh = {Animals ; *Amyloid beta-Peptides/metabolism ; Male ; *Place Cells/physiology ; *Spatial Memory/physiology ; *Hippocampus/metabolism ; Rats ; Action Potentials/physiology ; Association Learning/physiology ; }, abstract = {Alzheimer's disease (AD) is a typical neurodegenerative disease featuring deficits in spatial memory, which relies on spatial representations by hippocampal place cells. Place cells exhibit task-responsive representation to support memory encoding and retrieval processes. Yet, it remains unclear how this task-responsive spatial representation was interrupted under AD pathologies. Here, we employed a delayed match-to-place spatial memory task with associative and predictive memory processes, during which we electrophysiologically recorded hippocampal place cells with multi-tetrode hyperdrives in rats with i.c.v. amyloid/saline injection. We found that the directional selectivity of place cells coding was maintained in the Amyloid group. The firing stability was higher during predictive memory than during associative memory in both groups. However, the spatial specificity was decreased in the Amyloid group during both associative and predictive memory. Importantly, the place cells in the Amyloid group exhibited attenuated task-responsive representations, i.e. lack of spatial over-representations towards the goal zone and a higher representation of the rest zone, especially during the predictive memory stage. These results raise a hypothesis that the disrupted task-responsive representations of place cells could be an underlying mechanism of spatial memory deficits induced by amyloid proteins.}, }
@article {pmid39643773, year = {2024}, author = {Liu, X and Zhu, J and Zheng, J and Xu, H}, title = {Role of the Thalamic Reticular Nucleus in Social Memory.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {39643773}, issn = {1995-8218}, }
@article {pmid39643728, year = {2024}, author = {Haghi, B and Aflalo, T and Kellis, S and Guan, C and Gamez de Leon, JA and Huang, AY and Pouratian, N and Andersen, RA and Emami, A}, title = {Enhanced control of a brain-computer interface by tetraplegic participants via neural-network-mediated feature extraction.}, journal = {Nature biomedical engineering}, volume = {}, number = {}, pages = {}, pmid = {39643728}, issn = {2157-846X}, abstract = {To infer intent, brain-computer interfaces must extract features that accurately estimate neural activity. However, the degradation of signal quality over time hinders the use of feature-engineering techniques to recover functional information. By using neural data recorded from electrode arrays implanted in the cortices of three human participants, here we show that a convolutional neural network can be used to map electrical signals to neural features by jointly optimizing feature extraction and decoding under the constraint that all the electrodes must use the same neural-network parameters. In all three participants, the neural network led to offline and online performance improvements in a cursor-control task across all metrics, outperforming the rate of threshold crossings and wavelet decomposition of the broadband neural data (among other feature-extraction techniques). We also show that the trained neural network can be used without modification for new datasets, brain areas and participants.}, }
@article {pmid39642366, year = {2024}, author = {Ikegaya, N and Mallela, AN and Warnke, PC and Kunigk, NG and Liu, F and Schone, HR and Verbaarschot, C and Hatsopoulos, NG and Downey, JE and Boninger, ML and Gaunt, R and Collinger, JL and Gonzalez-Martinez, JA}, title = {A novel robot-assisted method for implanting intracortical sensorimotor devices for brain-computer interface studies: principles, surgical techniques, and challenges.}, journal = {Journal of neurosurgery}, volume = {}, number = {}, pages = {1-9}, doi = {10.3171/2024.7.JNS241296}, pmid = {39642366}, issn = {1933-0693}, abstract = {Precise anatomical implantation of a microelectrode array is fundamental for successful brain-computer interface (BCI) surgery, ensuring high-quality, robust signal communication between the brain and the computer interface. Robotic neurosurgery can contribute to this goal, but its application in BCI surgery has been underexplored. Here, the authors present a novel robot-assisted surgical technique to implant rigid intracortical microelectrode arrays for the BCI. Using this technique, the authors performed surgery in a 31-year-old male with tetraplegia due to a traumatic C4 spinal cord injury that occurred a decade earlier. Each of the arrays was embedded into the parenchyma with a single insertion without complication. Postoperative imaging verified that the devices were placed as intended. With the motor cortex arrays, the participant successfully accomplished 2D control of a virtual arm and hand, with a success rate of 20 of 20 attempts, and recording quality was maintained at 100 and 200 days postimplantation. Intracortical microstimulation of the somatosensory cortex arrays elicited sensations in the fingers and palm. A robotic neurosurgery technique was successfully translated into BCI device implantation as part of an early feasibility trial with the long-term goal of restoring upper-limb function. The technique was demonstrated to be accurate and subsequently contributed to high-quality signal communication.}, }
@article {pmid39642216, year = {2024}, author = {Li, C and Zhang, S and Jiang, J and Wang, S and He, S and Song, J}, title = {Laser-induced adhesives with excellent adhesion enhancement and reduction capabilities for transfer printing of microchips.}, journal = {Science advances}, volume = {10}, number = {49}, pages = {eads9226}, pmid = {39642216}, issn = {2375-2548}, abstract = {Transfer printing based on tunable and reversible adhesive that enables the heterogeneous integration of materials is essential for developing envisioned electronic systems. An adhesive with both adhesion enhancement and reduction capabilities in a rapid and selective manner is challenging. Here, we report a laser-induced adhesive, featuring a geometrically simple shape memory polymer layer on a glass backing, with excellent adhesion modulation capability for programmable pickup and noncontact printing of microchips. Selective and rapid laser heating substantially enhances the adhesive's adhesion strength from kilopascal to megapascal within 10 ms due to the shape fixing effect, allowing for precise and programmable pickup. Conversely, the enhanced adhesion can be quickly reduced and eliminated within 3 ms through the shape recovery effect, enabling noncontact printing. Demonstrations of transfer printing microlight-emitting diodes (LEDs) and mini-LEDs onto various low-adhesive flat, rough, and curved surfaces highlight the unusual capabilities of this adhesive for deterministic assembly.}, }
@article {pmid39640342, year = {2024}, author = {Deruelle, F}, title = {Microwave radiofrequencies, 5G, 6G, graphene nanomaterials: Technologies used in neurological warfare.}, journal = {Surgical neurology international}, volume = {15}, number = {}, pages = {439}, pmid = {39640342}, issn = {2229-5097}, abstract = {BACKGROUND: Scientific literature, with no conflicts of interest, shows that even below the limits defined by the International Commission on Non-Ionizing Radiation Protection, microwaves from telecommunication technologies cause numerous health effects: neurological, oxidative stress, carcinogenicity, deoxyribonucleic acid and immune system damage, electro-hypersensitivity. The majority of these biological effects of non-thermal microwave radiation have been known since the 1970s.<br><br>METHODS: Detailed scientific, political, and military documents were analyzed. Most of the scientific literature comes from PubMed. The other articles (except for a few) come from impacted journals . The rare scientific documents that were not peer reviewed were produced by recognized scientists in their fields. The rest of the documentation comes from official sources: political (e.g., European Union and World Health Organization), military (e.g., US Air Force and NATO), patents, and national newspapers.<br><br>RESULTS: (1) Since their emergence, the authorities have deployed and encouraged the use of wireless technologies (2G, 3G, 4G, WiFi, WiMAX, DECT, Bluetooth, cell phone towers/masts/base stations, small cells, etc.) in full awareness of their harmful effects on health. (2) Consequences of microwave radiation from communication networks are comparable to the effects of low-power directed-energy microwave weapons, whose objectives include behavioral modification through neurological (brain) targeting. Above 20 gigahertz, 5G behaves like an unconventional chemical weapon. (3) Biomedical engineering (via graphene-based nanomaterials) will enable brain-computer connections, linked wirelessly to the Internet of Everything through 5G and 6G networks (2030) and artificial intelligence, gradually leading to human-machine fusion (cyborg) before the 2050s.<br><br>CONCLUSION: Despite reports and statements from the authorities presenting the constant deployment of new wireless communication technologies, as well as medical research into nanomaterials, as society's ideal future, in-depth research into these scientific fields shows, above all, an objective linked to the current cognitive war. It could be hypothesized that, in the future, this aim will correspond to the control of humanity by machines.}, }
@article {pmid39639181, year = {2025}, author = {Qian, M and Wang, J and Gao, Y and Chen, M and Liu, Y and Zhou, D and Lu, HD and Zhang, X and Hu, JM and Roe, AW}, title = {Multiple loci for foveolar vision in macaque monkey visual cortex.}, journal = {Nature neuroscience}, volume = {28}, number = {1}, pages = {137-149}, pmid = {39639181}, issn = {1546-1726}, support = {31627802//National Natural Science Foundation of China (National Science Foundation of China)/ ; 52277232//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81701774//National Natural Science Foundation of China (National Science Foundation of China)/ ; 61771423//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Animals ; *Visual Cortex/physiology ; *Magnetic Resonance Imaging ; Visual Pathways/physiology ; Macaca mulatta ; Fovea Centralis/physiology ; Photic Stimulation/methods ; Male ; Brain Mapping ; }, abstract = {In humans and nonhuman primates, the central 1° of vision is processed by the foveola, a retinal structure that comprises a high density of photoreceptors and is crucial for primate-specific high-acuity vision, color vision and gaze-directed visual attention. Here, we developed high-spatial-resolution ultrahigh-field 7T functional magnetic resonance imaging methods for functional mapping of the foveolar visual cortex in awake monkeys. In the ventral pathway (visual areas V1-V4 and the posterior inferior temporal cortex), viewing of a small foveolar spot elicits a ring of multiple (eight) foveolar representations per hemisphere. This ring surrounds an area called the 'foveolar core', which is populated by millimeter-scale functional domains sensitive to fine stimuli and high spatial frequencies, consistent with foveolar visual acuity, color and achromatic information and motion. Thus, this elaborate rerepresentation of central vision coupled with a previously unknown foveolar core area signifies a cortical specialization for primate foveation behaviors.}, }
@article {pmid39638842, year = {2024}, author = {Zhang, X and Wei, W and Qian, L and Yao, L and Jin, X and Xing, L and Qian, Z}, title = {Real-time monitoring of bioelectrical impedance for minimizing tissue carbonization in microwave ablation of porcine liver.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {30404}, pmid = {39638842}, issn = {2045-2322}, support = {xcxjh20230333//Nanjing University of Aeronautics and Astronautics Research and Practice Innovation Program/ ; 81727804//National Major Scientifc Instruments and Equipment Development Project Funded by National Natural Science Foundation of China/ ; 82151311//National Natural Science Foundation of China/ ; 12372306//National Natural Science Foundation of China/ ; NP2024102//Fundamental Research Funds for the Central Universities/ ; NJ2024016//Fundamental Research Funds for the Central Universities/ ; NJ2024029//Fundamental Research Funds for the Central Universities/ ; }, mesh = {Animals ; *Microwaves ; *Electric Impedance ; *Liver/surgery/metabolism ; Swine ; Ablation Techniques/methods ; Finite Element Analysis ; }, abstract = {The charring tissue generated by the high temperature during microwave ablation can affect the therapeutic effect, such as limiting the volume of the coagulation zone and causing rejection. This paper aimed to prevent tissue carbonization while delivering an appropriate thermal dose for effective ablations by employing a treatment protocol with real-time bioelectrical impedance monitoring. Firstly, the current field response under different microwave ablation statuses is analyzed based on finite element simulation. Next, the change of impedance measured by the electrodes is correlated with the physical state of the ablated tissue, and a microwave ablation carbonization control protocol based on real-time electrical impedance monitoring was established. The finite element simulation results show that the dielectric properties of biological tissues changed dynamically during the ablation process. Finally, the relative change rule of the electrical impedance magnitude of the ex vivo porcine liver throughout the entire MWA process and the reduction of the central zone carbonization were obtained by the MWA experiment. Charring tissue was eliminated without water cooling at 40 W and significantly reduced at 50 W and 60 W. The carbonization during MWA can be reduced according to the changes in tissue electrical impedance to optimize microwave thermal ablation efficacy.}, }
@article {pmid39638804, year = {2024}, author = {Ma, X and Chen, LN and Liao, M and Zhang, L and Xi, K and Guo, J and Shen, C and Shen, DD and Cai, P and Shen, Q and Qi, J and Zhang, H and Zang, SK and Dong, YJ and Miao, L and Qin, J and Ji, SY and Li, Y and Liu, J and Mao, C and Zhang, Y and Chai, R}, title = {Molecular insights into the activation mechanism of GPR156 in maintaining auditory function.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {10601}, pmid = {39638804}, issn = {2041-1723}, mesh = {Humans ; *Receptors, G-Protein-Coupled/metabolism/genetics/chemistry ; *Cryoelectron Microscopy ; Animals ; HEK293 Cells ; Protein Binding ; Protein Multimerization ; Mice ; GTP-Binding Protein alpha Subunits, Gi-Go/metabolism/chemistry/genetics ; Models, Molecular ; }, abstract = {The class C orphan G-protein-coupled receptor (GPCR) GPR156, which lacks the large extracellular region, plays a pivotal role in auditory function through Gi2/3. Here, we firstly demonstrate that GPR156 with high constitutive activity is essential for maintaining auditory function, and further reveal the structural basis of the sustained role of GPR156. We present the cryo-EM structures of human apo GPR156 and the GPR156-Gi3 complex, unveiling a small extracellular region formed by extracellular loop 2 (ECL2) and the N-terminus. The GPR156 dimer in both apo state and Gi3 protein-coupled state adopt a transmembrane (TM)5/6-TM5/6 interface, indicating the high constitutive activity of GPR156 in the apo state. Furthermore, C-terminus in G-bound subunit of GPR156 plays a dual role in promoting G protein binding within G-bound subunit while preventing the G-free subunit from binding to additional G protein. Together, these results explain how GPR156 constitutive activity is maintained through dimerization and provide a mechanistic insight into the sustained role of GPR156 in maintaining auditory function.}, }
@article {pmid39637463, year = {2024}, author = {Wimmer, M and Pepicelli, A and Volmer, B and ElSayed, N and Cunningham, A and Thomas, BH and Müller-Putz, GR and Veas, EE}, title = {Counting on AR: EEG responses to incongruent information with real-world context.}, journal = {Computers in biology and medicine}, volume = {185}, number = {}, pages = {109483}, doi = {10.1016/j.compbiomed.2024.109483}, pmid = {39637463}, issn = {1879-0534}, abstract = {Augmented Reality (AR) technologies enhance the real world by integrating contextual digital information about physical entities. However, inconsistencies between physical reality and digital augmentations, which may arise from errors in the visualized information or the user's mental context, can considerably impact user experience. This work characterizes the brain dynamics associated with processing incongruent information within an AR environment. To study these effects, we designed an interactive paradigm featuring the manipulation of a Rubik's cube serving as a physical referent. Congruent and incongruent information regarding the cube's current status was presented via symbolic (digits) and non-symbolic (graphs) stimuli, thus examining the impact of different means of data representation. The analysis of electroencephalographic signals from 19 participants revealed the presence of centro-parietal N400 and P600 components following the processing of incongruent information, with significantly increased latencies for non-symbolic stimuli. Additionally, we explored the feasibility of exploiting incongruency effects for brain-computer interfaces. Hence, we implemented decoders using linear discriminant analysis, support vector machines, and EEGNet, achieving comparable performances with all methods. Therefore, this work contributes to the design of adaptive AR systems by demonstrating that above-chance detection of incongruent information based on physiological signals is feasible. The successful decoding of incongruency-induced modulations can inform systems about the current mental state of users without making it explicit, aiming for more coherent and contextually appropriate AR interactions.}, }
@article {pmid39634507, year = {2024}, author = {Wei, M and Lin, X and Xu, K and Wu, Y and Wang, C and Wang, Z and Lei, K and Bao, K and Li, J and Li, L and Li, E and Lin, H}, title = {Inverse design of compact nonvolatile reconfigurable silicon photonic devices with phase-change materials.}, journal = {Nanophotonics (Berlin, Germany)}, volume = {13}, number = {12}, pages = {2183-2192}, pmid = {39634507}, issn = {2192-8614}, abstract = {In the development of silicon photonics, the continued downsizing of photonic integrated circuits will further increase the integration density, which augments the functionality of photonic chips. Compared with the traditional design method, inverse design presents a novel approach for achieving compact photonic devices. However, achieving compact, reconfigurable photonic devices with the inverse design that employs the traditional modulation method exemplified by the thermo-optic effect poses a significant challenge due to the weak modulation capability. Low-loss phase change materials (PCMs) exemplified by Sb2Se3 are a promising candidate for solving this problem benefiting from their high refractive index contrast. In this work, we first developed a robust inverse design method to realize reconfigurable silicon and phase-change materials hybrid photonic devices including mode converter and optical switch. The mode converter exhibits a broadband operation of >100 nm. The optical switch shows an extinction ratio of >25 dB and a multilevel switching of 41 (>5 bits) by simply changing the crystallinity of Sb2Se3. Here, we experimentally demonstrated a Sb2Se3/Si hybrid integrated optical switch for the first time, wherein routing can be switched by the phase transition of the whole Sb2Se3. Our work provides an effective solution for the design of photonic devices that is insensitive to fabrication errors, thereby paving the way for high integration density in future photonic chips.}, }
@article {pmid39632923, year = {2024}, author = {Karthiga, M and Suganya, E and Sountharrajan, S and Balusamy, B and Selvarajan, S}, title = {Eeg based smart emotion recognition using meta heuristic optimization and hybrid deep learning techniques.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {30251}, pmid = {39632923}, issn = {2045-2322}, mesh = {*Electroencephalography/methods ; *Deep Learning ; Humans ; *Emotions/physiology ; *Brain-Computer Interfaces ; Heuristics ; Algorithms ; Signal Processing, Computer-Assisted ; Male ; Neural Networks, Computer ; }, abstract = {In the domain of passive brain-computer interface applications, the identification of emotions is both essential and formidable. Significant research has recently been undertaken on emotion identification with electroencephalogram (EEG) data. The aim of this project is to develop a system that can analyse an individual's EEG and differentiate among positive, neutral, and negative emotional states. The suggested methodology use Independent Component Analysis (ICA) to remove artefacts from Electromyogram (EMG) and Electrooculogram (EOG) in EEG channel recordings. Filtering techniques are employed to improve the quality of EEG data by segmenting it into alpha, beta, gamma, and theta frequency bands. Feature extraction is performed with a hybrid meta-heuristic optimisation technique, such as ABC-GWO. The Hybrid Artificial Bee Colony and Grey Wolf Optimiser are employed to extract optimised features from the selected dataset. Finally, comprehensive evaluations are conducted utilising DEAP and SEED, two publically accessible datasets. The CNN model attains an accuracy of approximately 97% on the SEED dataset and 98% on the DEAP dataset. The hybrid CNN-ABC-GWO model achieves an accuracy of approximately 99% on both datasets, with ABC-GWO employed for hyperparameter tuning and classification. The proposed model demonstrates an accuracy of around 99% on the SEED dataset and 100% on the DEAP dataset. The experimental findings are contrasted utilising a singular technique, a widely employed hybrid learning method, or the cutting-edge method; the proposed method enhances recognition performance.}, }
@article {pmid39632897, year = {2024}, author = {Chen, Z and Tang, Y and Liu, X and Li, W and Hu, Y and Hu, B and Xu, T and Zhang, R and Xia, L and Zhang, JX and Xiao, Z and Chen, J and Feng, Z and Zhou, Y and He, Q and Qiu, J and Lei, X and Chen, H and Qin, S and Feng, T}, title = {Edge-centric connectome-genetic markers of bridging factor to comorbidity between depression and anxiety.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {10560}, pmid = {39632897}, issn = {2041-1723}, support = {32300907//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Connectome ; Male ; Female ; *Comorbidity ; Adult ; *Depression/genetics/epidemiology ; *Anxiety/genetics/epidemiology ; Young Adult ; Genetic Markers ; Adolescent ; Child ; Anxiety Disorders/genetics/epidemiology ; Brain/diagnostic imaging/metabolism ; Magnetic Resonance Imaging ; }, abstract = {Depression-anxiety comorbidity is commonly attributed to the occurrence of specific symptoms bridging the two disorders. However, the significant heterogeneity of most bridging symptoms presents challenges for psychopathological interpretation and clinical applicability. Here, we conceptually established a common bridging factor (cb factor) to characterize a general structure of these bridging symptoms, analogous to the general psychopathological p factor. We identified a cb factor from 12 bridging symptoms in depression-anxiety comorbidity network. Moreover, this cb factor could be predicted using edge-centric connectomes with robust generalizability, and was characterized by connectome patterns in attention and frontoparietal networks. In an independent twin cohort, we found that these patterns were moderately heritable, and identified their genetic connectome-transcriptional markers that were associated with the neurobiological enrichment of vasculature and cerebellar development, particularly during late-childhood-to-young-adulthood periods. Our findings revealed a general factor of bridging symptoms and its neurobiological architectures, which enriched neurogenetic understanding of depression-anxiety comorbidity.}, }
@article {pmid39630811, year = {2024}, author = {Grogan, M and Blum, KP and Wu, Y and Harston, JA and Miller, LE and Faisal, AA}, title = {Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders.}, journal = {PLoS computational biology}, volume = {20}, number = {12}, pages = {e1012614}, pmid = {39630811}, issn = {1553-7358}, support = {/WT_/Wellcome Trust/United Kingdom ; F32 MH120893/MH/NIMH NIH HHS/United States ; R01 NS095251/NS/NINDS NIH HHS/United States ; }, mesh = {*Proprioception/physiology ; Animals ; *Models, Neurological ; *Somatosensory Cortex/physiology/anatomy &amp; histology ; Neurons/physiology ; Movement/physiology ; Computational Biology ; Macaca mulatta ; Brain-Computer Interfaces ; Humans ; }, abstract = {Proprioception is one of the least understood senses, yet fundamental for the control of movement. Even basic questions of how limb pose is represented in the somatosensory cortex are unclear. We developed a topographic variational autoencoder with lateral connectivity (topo-VAE) to compute a putative cortical map from a large set of natural movement data. Although not fitted to neural data, our model reproduces two sets of observations from monkey centre-out reaching: 1. The shape and velocity dependence of proprioceptive receptive fields in hand-centered coordinates despite the model having no knowledge of arm kinematics or hand coordinate systems. 2. The distribution of neuronal preferred directions (PDs) recorded from multi-electrode arrays. The model makes several testable predictions: 1. Encoding across the cortex has a blob-and-pinwheel-type geometry of PDs. 2. Few neurons will encode just a single joint. Our model provides a principled basis for understanding of sensorimotor representations, and the theoretical basis of neural manifolds, with applications to the restoration of sensory feedback in brain-computer interfaces and the control of humanoid robots.}, }
@article {pmid39630448, year = {2024}, author = {Kelly, BC and Cova, TJ and Debbink, MP and Onega, T and Brewer, SC}, title = {Racial and Ethnic Disparities in Regulatory Air Quality Monitor Locations in the US.}, journal = {JAMA network open}, volume = {7}, number = {12}, pages = {e2449005}, pmid = {39630448}, issn = {2574-3805}, mesh = {Humans ; United States ; *Air Pollution/analysis/statistics &amp; numerical data ; Cross-Sectional Studies ; *Environmental Monitoring/methods ; *Air Pollutants/analysis ; Particulate Matter/analysis ; Ozone/analysis ; Ethnicity/statistics &amp; numerical data ; United States Environmental Protection Agency ; Racial Groups/statistics &amp; numerical data ; Nitrogen Dioxide/analysis ; Sulfur Dioxide/analysis ; Environmental Exposure/analysis/statistics &amp; numerical data ; }, abstract = {IMPORTANCE: Understanding exposure to air pollution is important to public health, and disparities in the spatial distribution of regulatory air quality monitors could lead to exposure misclassification bias.<br><br>OBJECTIVE: To determine whether racial and ethnic disparities exist in Environmental Protection Agency (EPA) regulatory air quality monitor locations in the US.<br><br>This national cross-sectional study included air quality monitors in the EPA Air Quality System regulatory monitoring repository, as well as 2022 American Community Survey Census block group estimates for racial and ethnic composition and population size. Bayesian mixed-effects models of the count of criteria pollutant monitors measuring an area were used, adjusting for population size and accounting for spatial autocorrelation. Data were analyzed from March to June 2024.<br><br>EXPOSURE: Census block group-level racial and ethnic composition.<br><br>MAIN OUTCOME AND MEASURES: Number of regulatory monitors measuring a census block group by criteria pollutant (particulate matter [PM], ozone [O3], nitrogen dioxide [NO2], sulfur dioxide [SO2], lead [Pb], and carbon monoxide [CO]).<br><br>RESULTS: This analysis included 329&#x202f;725&#x202f;481 individuals living in 237&#x202f;631 block groups in the US (1&#x202f;936&#x202f;842 [0.6%] American Indian and Alaska Native, 18&#x202f;554&#x202f;697 [5.6%] Asian, 40&#x202f;196&#x202f;302 [12.2%] Black, 60&#x202f;806&#x202f;969 [18.4%] Hispanic, 555&#x202f;712 [0.2%] Native Hawaiian and Other Pacific Islander, 196&#x202f;010&#x202f;370 [59.4%] White, 1&#x202f;208&#x202f;267 [0.3%] some other race, and 10&#x202f;456&#x202f;322 [0.4%] 2 or more races). Adjusting for population size, monitoring disparities were identified for each criteria pollutant. Relative to the White non-Latino population, all groups were associated with fewer NO2, O3, Pb, and PM monitors. Disparities were consistently largest for Native Hawaiian and Other Pacific Islander populations, followed by American Indian and Alaska Native populations and those of 2 or more races. An increase in percentage of Native Hawaiian and Other Pacific Islander race was associated with fewer monitors for SO2 (adjusted odds ratio [aOR], 0.91; 95% BCI, 0.90-0.91), CO (aOR, 0.95; 95% BCI, 0.94-0.95), O3 (aOR, 0.95; 95% BCI, 0.94-0.95), NO2 (aOR, 0.97; 95% BCI, 0.91-0.94), and PM (aOR, 0.96; 95% BCI, 0.95-0.96). An increase in the percentage of those of Asian race was associated with slightly more SO2 (aOR, 1.04; 95% BCI, 1.03-1.04) monitors.<br><br>CONCLUSIONS AND RELEVANCE: This cross-sectional study of racial and ethnic disparities in the location of EPA regulatory monitors determined that data may not be equitably representative of air quality, particularly for areas with predominantly Native Hawaiian and Other Pacific Islander or American Indian or Alaska Native populations. Integration of multiple data sources may aid in filling monitoring gaps across race and ethnicity. Where possible, researchers should quantify uncertainty in exposure estimates.}, }
@article {pmid39628656, year = {2024}, author = {Pryss, R and Vom Brocke, J and Reichert, M and Rukzio, E and Schlee, W and Weber, B}, title = {Editorial: Application of neuroscience in information systems and software engineering.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1402603}, pmid = {39628656}, issn = {1662-4548}, }
@article {pmid39628388, year = {2024}, author = {Wang, X and Xu, M and Yang, H and Jiang, W and Jiang, J and Zou, D and Zhu, Z and Tao, C and Ni, S and Zhou, Z and Sun, L and Li, M and Nie, Y and Zhao, Y and He, F and Tao, TH and Wei, X}, title = {Ultraflexible Neural Electrodes Enabled Synchronized Long-Term Dopamine Detection and Wideband Chronic Recording Deep in Brain.}, journal = {ACS nano}, volume = {18}, number = {50}, pages = {34272-34287}, doi = {10.1021/acsnano.4c12429}, pmid = {39628388}, issn = {1936-086X}, mesh = {*Dopamine/analysis ; Animals ; *Graphite/chemistry ; *Brain/metabolism/physiology ; Rats ; Rats, Sprague-Dawley ; Electrodes ; Nanocomposites/chemistry ; Male ; Polystyrenes/chemistry ; Electrodes, Implanted ; Thiophenes ; }, abstract = {Ultraflexible neural electrodes have shown superior stability compared with rigid electrodes in long-term in vivo recordings, owing to their low mechanical mismatch with brain tissue. It is desirable to detect neurotransmitters as well as electrophysiological signals for months in brain science. This work proposes a stable electronic interface that can simultaneously detect neural electrical activity and dopamine concentration deep in the brain. This ultraflexible electrode is modified by a nanocomposite of reduced graphene oxide (rGO) and poly(3,4-ethylenedioxythiophene):poly(sodium 4-styrenesulfonate) (rGO/PEDOT:PSS), enhancing the electrical stability of the coating and increasing its specific surface area, thereby improving the sensitivity to dopamine response with 15 pA/μM. This electrode can detect dopamine fluctuations and can conduct long-term, stable recordings of local field potentials (LFPs), spiking activities, and amplitudes with high spatial and temporal resolution across multiple regions, especially in deep brain areas. The electrodes were implanted into the brains of rodent models to monitor the changes in neural and electrochemical signals across different brain regions during the administration of nomifensine. Ten minutes after drug injection, enhanced neuronal firing activity and increased LFP power were detected in the motor cortex and deeper cortical layers, accompanied by a gradual rise in dopamine levels with 192 ± 29 nM. The in vivo recording consistently demonstrates chronic high-quality neural signal monitoring with electrochemical signal stability for up to 6 weeks. These findings highlight the high quality and stability of our electrophysiological/electrochemical codetection neural electrodes, underscoring their tremendous potential for applications in neuroscience research and brain-machine interfaces.}, }
@article {pmid39627937, year = {2024}, author = {Gielas, AM}, title = {Wounds and Vulnerabilities. The Participation of Special Operations Forces in Experimental Brain-Computer Interface Research.}, journal = {Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees}, volume = {}, number = {}, pages = {1-22}, doi = {10.1017/S096318012400063X}, pmid = {39627937}, issn = {1469-2147}, abstract = {Brain-computer interfaces (BCIs) exemplify a dual-use neurotechnology with significant potential in both civilian and military contexts. While BCIs hold promise for treating neurological conditions such as spinal cord injuries and amyotrophic lateral sclerosis in the future, military decisionmakers in countries such as the United States and China also see their potential to enhance combat capabilities. Some predict that U.S. Special Operations Forces (SOF) will be early adopters of BCI enhancements. This article argues for a shift in focus: the U.S. Special Operations Command (SOCOM) should pursue translational research of medical BCIs for treating severely injured or ill SOF personnel. After two decades of continuous military engagement and on-going high-risk operations, SOF personnel face unique injury patterns, both physical and psychological, which BCI technology could help address. The article identifies six key medical applications of BCIs that could benefit wounded SOF members and discusses the ethical implications of involving SOF personnel in translational research related to these applications. Ultimately, the article challenges the traditional civilian-military divide in neurotechnology, arguing that by collaborating more closely with military stakeholders, scientists can not only help individuals with medical needs, including servicemembers, but also play a role in shaping the future military applications of BCI technology.}, }
@article {pmid39627867, year = {2024}, author = {Patarini, F and Tamburella, F and Pichiorri, F and Mohebban, S and Bigioni, A and Ranieri, A and Di Tommaso, F and Tagliamonte, NL and Serratore, G and Lorusso, M and Ciaramidaro, A and Cincotti, F and Scivoletto, G and Mattia, D and Toppi, J}, title = {On the role of visual feedback and physiotherapist-patient interaction in robot-assisted gait training: an eye-tracking and HD-EEG study.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {21}, number = {1}, pages = {211}, pmid = {39627867}, issn = {1743-0003}, support = {GR2019-12369207//Ministero della Salute/ ; RM123188F229EC72//Sapienza Universit&#xe0; di Roma/ ; }, mesh = {Humans ; *Robotics/instrumentation/methods ; Male ; Female ; *Feedback, Sensory/physiology ; Adult ; *Electroencephalography/methods ; Middle Aged ; *Eye-Tracking Technology ; Spinal Cord Injuries/rehabilitation ; Physical Therapists ; Exercise Therapy/methods/instrumentation ; Gait/physiology ; Gait Disorders, Neurologic/rehabilitation ; }, abstract = {BACKGROUND: Treadmill based Robotic-Assisted Gait Training (t-RAGT) provides for automated locomotor training to help the patient achieve a physiological gait pattern, reducing the physical effort required by therapist. By introducing the robot as a third agent to the traditional one-to-one physiotherapist-patient (Pht-Pt) relationship, the therapist might not be fully aware of the patient's motor performance. This gap has been bridged by the integration in rehabilitation robots of a visual FeedBack (FB) that informs about patient's performance. Despite the recognized importance of FB in t-RAGT, the optimal role of the therapist in the complex patient-robot interaction is still unclear. This study aimed to describe whether the type of FB combined with different modalities of Pht's interaction toward Pt would affect the patients' visual attention and emotional engagement during t-RAGT.<br><br>METHODS: Ten individuals with incomplete Spinal Cord Injury (C or D ASIA Impairment Scale level) were assessed using eye-tracking (ET) and high-density EEG during seven t-RAGT sessions with Lokomat where (i) three types of visual FB (chart, emoticon and game) and (ii) three levels of Pht-Pt interaction (low, medium and high) were randomly combined. ET metrics (fixations and saccades) were extracted for each of the three defined areas of interest (AoI) (monitor, Pht and surrounding) and compared among the different experimental conditions (FB, Pht-Pt interaction level). The EEG spectral activations in theta and alpha bands were reconstructed for each FB type applying Welch periodogram to data localised in the whole grey matter volume using sLORETA.<br><br>RESULTS: We found an effect of FB type factor on all the ET metrics computed in the three AoIs while the factor Pht-Pt interaction level also combined with FB type showed an effect only on the ET metrics calculated in Pht and surrounding AoIs. Neural activation in brain regions crucial for social cognition resulted for high Pht-Pt interaction level, while activation of the insula was found during low interaction, independently on the FB used.<br><br>CONCLUSIONS: The type of FB and the way in which Pht supports the patients both have a strong impact on patients' engagement and should be considered in the design of a t-RAGT-based rehabilitation session.}, }
@article {pmid39622415, year = {2025}, author = {Zhu, M and Yang, Y and Niu, X and Peng, Y and Liu, R and Zhang, M and Han, Y and Wang, Z}, title = {Different responses of MVL neurons when pigeons attend to local versus global information during object classification.}, journal = {Behavioural brain research}, volume = {480}, number = {}, pages = {115363}, doi = {10.1016/j.bbr.2024.115363}, pmid = {39622415}, issn = {1872-7549}, mesh = {Animals ; *Columbidae/physiology ; *Neurons/physiology ; Pattern Recognition, Visual/physiology ; Attention/physiology ; Male ; Visual Perception/physiology ; Behavior, Animal/physiology ; Photic Stimulation/methods ; }, abstract = {Most prior studies have indicated that pigeons have a tendency to rely on local information for target categorization, yet there is a lack of electrophysiological evidence to support this claim. The mesopallium ventrolaterale (MVL) is believed to play a role in processing both local and global information during visual cognition. The difference between responses of MVL neurons when pigeons are focusing on local versus global information during visual object categorization remain unknown. In this study, pigeons were trained to categorize hierarchical stimuli that maintained consistency in local and global information. Subsequently, stimuli with different local and global components were presented to examine the pigeons' behavioral preferences. Not surprisingly, the behavioral findings revealed that pigeons predominantly attended to the local elements when performing categorization tasks. Moreover, MVL neurons exhibited significantly distinct responses when pigeons prioritized local versus global information. Specifically, most recording sites showed heightened gamma band power and increased nonlinear entropy values, indicating strong neural responses and rich information when pigeons concentrated on the local components of an object. Furthermore, neural population functional connectivity was weaker when the pigeons focused on local elements, suggesting that individual neurons operated more independently and effectively when focusing on local features. These findings offer electrophysiological evidence supporting the notion of pigeons displaying a behavioral preference for local information. The study provides valuable insight into the understanding of cognitive processes of pigeons when presented with complex objects, and further sheds light on the neural mechanisms underlying pigeons' behavioral preference for attending to local information.}, }
@article {pmid39622169, year = {2024}, author = {Wei, X and Narayan, J and Faisal, A}, title = {The 'Sandwich' meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad9957}, pmid = {39622169}, issn = {1741-2552}, abstract = {Machine learning has enhanced the performance of decoding signals indicating human behaviour. EEG decoding, as an exemplar indicating neural activity and human thoughts non-invasively, has aided patients via brain-computer interfaces in neural activity analysis. However, training machine learning algorithms on EEG encounters two primary challenges: variability across data sets and privacy concerns using data from individuals and data centres. Our objective is to address these challenges by integrating transfer learning for data variability and federated learning for data privacy into a unified approach. We introduce the Sandwich as a novel deep privacy-preserving meta-framework combining transfer learning and federated learning. The Sandwich framework comprises three components: federated networks (first layers) that handle data set differences at the input level, a shared network (middle layer) learning common rules and applying transfer learning, and individual classifiers (final layers) for specific tasks of each data set. It enables the central network (central server) to benefit from multiple data sets, while local branches (local servers) maintain data and label privacy. We evaluated the `Sandwich' meta-architecture in various configurations using the BEETL motor imagery challenge, a benchmark for heterogeneous EEG data sets. Compared with baseline models, our `Sandwich' implementations showed superior performance. The best-performing model, the Inception Sandwich with deep set alignment (Inception-SD-Deepset), exceeded baseline methods by 9%. The `Sandwich' framework demonstrates significant advancements in federated deep transfer learning for diverse tasks and data sets. It outperforms conventional deep learning methods, showcasing the potential for effective use of larger, heterogeneous data sets with enhanced privacy as a model-agnostic meta-framework.}, }
@article {pmid39622162, year = {2024}, author = {Chen, C and Fang, H and Yang, Y and Zhou, Y}, title = {Model-agnostic meta-learning for EEG-based inter-subject emotion recognition.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad9956}, pmid = {39622162}, issn = {1741-2552}, abstract = {Developing an efficient and generalizable method for inter-subject emotion recognition from neural signals is an emerging and challenging problem in affective computing. In particular, human subjects usually have heterogeneous neural signal characteristics and variable emotional activities that challenge the existing recognition algorithms from achieving high inter-subject emotion recognition accuracy. Approach. In this work, we propose a model-agnostic meta-learning algorithm to learn an adaptable and generalizable Electroencephalogram (EEG)-based emotion decoder at the subject's population level. Different from many prior end-to-end emotion recognition algorithms, our learning algorithms include a pre-training step and an adaptation step. Specifically, our meta-decoder first learns on diverse known subjects and then further adapts it to unknown subjects with one-shot adaptation. More importantly, our algorithm is compatible with a variety of mainstream machine learning decoders for emotion recognition. Main results. We evaluate the adapted decoders obtained by our proposed algorithm on three Emotion-EEG datasets: SEED, DEAP, and DREAMER. Our comprehensive experimental results show that the adapted meta-emotion decoder achieves state-of-the-art inter-subject emotion recognition accuracy and outperforms the classical supervised learning baseline across different decoder architectures. Significance. Our results hold promise to incorporate the proposed meta-learning emotion recognition algorithm to effectively improve the inter-subject generalizability in designing future affective brain-computer interfaces (BCIs).}, }
@article {pmid39621862, year = {2024}, author = {Xia, Y and He, M and Basang, S and Sha, L and Huang, Z and Jin, L and Duan, Y and Tang, Y and Li, H and Lai, W and Chen, L}, title = {Semiology Extraction and Machine Learning-Based Classification of Electronic Health Records for Patients With Epilepsy: Retrospective Analysis.}, journal = {JMIR medical informatics}, volume = {12}, number = {}, pages = {e57727}, pmid = {39621862}, issn = {2291-9694}, mesh = {Humans ; *Electronic Health Records ; *Epilepsy/diagnosis/classification ; *Machine Learning ; Retrospective Studies ; *Natural Language Processing ; Female ; Male ; Adult ; Middle Aged ; China ; Adolescent ; Child ; Young Adult ; }, abstract = {BACKGROUND: Obtaining and describing semiology efficiently and classifying seizure types correctly are crucial for the diagnosis and treatment of epilepsy. Nevertheless, there exists an inadequacy in related informatics resources and decision support tools.<br><br>OBJECTIVE: We developed a symptom entity extraction tool and an epilepsy semiology ontology (ESO) and used machine learning to achieve an automated binary classification of epilepsy in this study.<br><br>METHODS: Using present history data of electronic health records from the Southwest Epilepsy Center in China, we constructed an ESO and a symptom-entity extraction tool to extract seizure duration, seizure symptoms, and seizure frequency from the unstructured text by combining manual annotation with natural language processing techniques. In addition, we achieved automatic classification of patients in the study cohort with high accuracy based on the extracted seizure feature data using multiple machine learning methods.<br><br>RESULTS: Data included present history from 10,925 cases between 2010 and 2020. Six annotators labeled a total of 2500 texts to obtain 5844 words of semiology and construct an ESO with 702 terms. Based on the ontology, the extraction tool achieved an accuracy rate of 85% in symptom extraction. Furthermore, we trained a stacking ensemble learning model combining XGBoost and random forest with an F1-score of 75.03%. The random forest model had the highest area under the curve (0.985).<br><br>CONCLUSIONS: This work demonstrated the feasibility of natural language processing-assisted structural extraction of epilepsy medical record texts and downstream tasks, providing open ontology resources for subsequent related work.}, }
@article {pmid39621615, year = {2024}, author = {Yuan, X and Li, H and Guo, F}, title = {Temperature cues are integrated in a flexible circadian neuropeptidergic feedback circuit to remodel sleep-wake patterns in flies.}, journal = {PLoS biology}, volume = {22}, number = {12}, pages = {e3002918}, pmid = {39621615}, issn = {1545-7885}, mesh = {Animals ; *Sleep/physiology ; *Circadian Rhythm/physiology ; *Drosophila Proteins/metabolism/genetics ; *Connectome ; *Drosophila melanogaster/physiology ; *Neuropeptides/metabolism/genetics ; Neurons/physiology/metabolism ; Temperature ; Wakefulness/physiology ; Feedback, Physiological ; Brain/physiology/metabolism ; Drosophila/physiology ; Cues ; Signal Transduction ; }, abstract = {Organisms detect temperature signals through peripheral neurons, which relay them to central circadian networks to drive adaptive behaviors. Despite recent advances in Drosophila research, how circadian circuits integrate temperature cues with circadian signals to regulate sleep/wake patterns remains unclear. In this study, we used the FlyWire brain electron microscopy connectome to map neuronal connections, identifying lateral posterior neurons LPNs as key nodes for integrating temperature information into the circadian network. LPNs receive input from both circadian and temperature-sensing neurons, promoting sleep behavior. Through connectome analysis, genetic manipulation, and behavioral assays, we demonstrated that LPNs, downstream of thermo-sensitive anterior cells (ACs), suppress activity-promoting lateral dorsal neurons LNds via the AstC pathway, inducing sleep Disrupting LPN-LNd communication through either AstCR1 RNAi in LNds or in an AstCR1 mutant significantly impairs the heat-induced reduction in the evening activity peak. Conversely, optogenetic calcium imaging and behavioral assays revealed that cold-activated LNds subsequently stimulate LPNs through NPF-NPFR signaling, establishing a negative feedback loop. This feedback mechanism limits LNd activation to appropriate levels, thereby fine-tuning the evening peak increase at lower temperatures. In conclusion, our study constructed a comprehensive connectome centered on LPNs and identified a novel peptidergic circadian feedback circuit that coordinates temperature and circadian signals, offering new insights into the regulation of sleep patterns in Drosophila.}, }
@article {pmid39619679, year = {2024}, author = {Zhang, W and Tang, X and Wang, M}, title = {Attention model of EEG signals based on reinforcement learning.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1442398}, pmid = {39619679}, issn = {1662-5161}, abstract = {BACKGROUND: Applying convolutional neural networks to a large number of EEG signal samples is computationally expensive because the computational complexity is linearly proportional to the number of dimensions of the EEG signal. We propose a new Gated Recurrent Unit (GRU) network model based on reinforcement learning, which considers the implementation of attention mechanisms in Electroencephalogram (EEG) signal processing scenarios as a reinforcement learning problem.<br><br>METHODS: The model can adaptively select target regions or position sequences from inputs and effectively extract information from EEG signals of different resolutions at multiple scales. Just as convolutional neural networks benefit from translation invariance, our proposed network also has a certain degree of translation invariance, making its computational complexity independent of the EEG signal dimension, thus maintaining a lower learning cost. Although the introduction of reinforcement learning makes the model non differentiable, we use policy gradient methods to achieve end-to-end learning of the model.<br><br>RESULTS: We evaluated our proposed model on publicly available EEG dataset (BCI Competition IV-2a). The proposed model outperforms the current state-of-the-art techniques in the BCI Competition IV- 2a dataset with an accuracy of 86.78 and 71.54% for the subject-dependent and subject-independent modes, respectively.<br><br>CONCLUSION: In the field of EEG signal processing, attention models that combine reinforcement learning principles can focus on key features, automatically filter out noise and redundant data, and improve the accuracy of signal decoding.}, }
@article {pmid39619531, year = {2024}, author = {Patel, M and Gosai, J and Patel, P and Roy, M and Solanki, A}, title = {Insights of BDAPbI4-Based Flexible Memristor for Artificial Synapses and In-Memory Computing.}, journal = {ACS omega}, volume = {9}, number = {47}, pages = {46841-46850}, pmid = {39619531}, issn = {2470-1343}, abstract = {Inspired by brain-like spiking computational frameworks, neuromorphic computing-brain-inspired computing for machine intelligence promises to realize artificial intelligence (AI) while reducing the energy requirements of computing platforms. In this work, we show the potential of advanced learnings of butane-1,4-diammonium based low-dimensional Dion-Jacobson hybrid perovskite (BDAPbI4) memristor devices in the realm of artificial synapses and neuromorphic computing. Memristors validate Hebbian learning rules with various spike-dependent plasticity within a 10 ± 2 ms time frame, reminiscent of the human brains under flat and bending conditions (∼5 mm radium). A high recognition accuracy of ∼94% of handwritten images from the MNIST database via an artificial neural network (ANN) is achieved with only 50 epochs. An efficient demonstration of second-order memristors and the Pavlovian dog experiment exhibit significant promise in expediting learning and memory consolidation. To showcase the in-memory computing potential, a flexible 4 × 4 crossbar array is designed with measured data retention up to ∼10[3] s along with 26 multilevel resistance states. The crossbar array is successfully programmed for the facile configurability of image "Z". In conclusion, the integration of supervised, unsupervised, and associative learning holds great promise across a spectrum of future technologies, ranging from the realm of spiking neural networks to neuromorphic computing, brain-machine interfaces, and adaptive control systems.}, }
@article {pmid39615858, year = {2024}, author = {Xue, Z and Zhong, W and Cao, Y and Liu, S and An, X}, title = {Impact of different auditory environments on task performance and EEG activity.}, journal = {Brain research bulletin}, volume = {220}, number = {}, pages = {111142}, doi = {10.1016/j.brainresbull.2024.111142}, pmid = {39615858}, issn = {1873-2747}, abstract = {Mental workload could affect human performance. An inappropriate workload level, whether too high or too low, leads to discomfort and decreased task performance. Auditory stimuli have been shown to act as an emotional medium to influence the workload. For example, the 'Mozart effect' has been shown to enhance performance in spatial reasoning tasks. However, the impact of auditory stimuli on task performance and brain activity remains unclear. This study examined the effects of three different environments-quiet, music, and white noise-on task performance and EEG activities. The N-back task was employed to induce mental workload, and the Psychomotor Vigilance Task assessed participants' alertness. We proposed a novel, statistically-based method to construct the brain functional network, avoiding issues associated with subjective threshold selection. This method systematically analyzed the connectivity patterns under different environments. Our analysis revealed that white noise negatively affected participants, primarily impacting brain activity in high-frequency ranges. This study provided deeper insights into the relationship between auditory stimuli and mental workload, offering a robust framework for future research on mental workload regulation.}, }
@article {pmid39615554, year = {2024}, author = {Cai, Z and Gao, Y and Fang, F and Zhang, Y and Du, S}, title = {Multi-layer transfer learning algorithm based on improved common spatial pattern for brain-computer interfaces.}, journal = {Journal of neuroscience methods}, volume = {415}, number = {}, pages = {110332}, doi = {10.1016/j.jneumeth.2024.110332}, pmid = {39615554}, issn = {1872-678X}, abstract = {In the application of brain-computer interface, the differences in imaging methods and brain structure between subjects hinder the effectiveness of decoding algorithms when applied on different subjects. Transfer learning has been designed to solve this problem. There have been many applications of transfer learning in motor imagery (MI), however the effectiveness is still limited due to the inconsistent domain alignment, lack of prominent data features and allocation of weights in trails. In this paper, a Multi-layer transfer learning algorithm based on improved Common Spatial Patterns (MTICSP) was proposed to solve these problems. Firstly, the source domain data and target domain data were aligned by Target Alignment (TA)method to reduce distribution differences between subjects. Secondly, the mean covariance matrix of the two classes was re-weighted by calculating the distance between the covariance matrix of each trial in the source domain and the target domain. Thirdly, the improved Common Spatial Patterns (CSP) by introducing regularization coefficient was proposed to further reduce the difference between source domain and target domain to extract features. Finally, the feature blocks of the source domain and target domain were aligned again by Joint Distribution Adaptation (JDA) method. Experiments on two public datasets in two transfer paradigms multi-source to single-target (MTS) and single-source to single-target (STS) verified the effectiveness of our proposed method. The MTS and STS in the 5-person dataset were 80.21% and 77.58%, respectively, and 80.10% and 73.91%, respectively, in the 9-person dataset. Experimental results also showed that the proposed algorithm was superior to other state-of-the-art algorithms. In addition, the generalization ability of our algorithm MTICSP was validated on the fatigue EEG dataset collected by ourselves, and obtained 94.83% and 87.41% accuracy in MTS and STS experiments respectively. The proposed method combines improved CSP with transfer learning to extract the features of source and target domains effectively, providing a new method for combining transfer learning with motor imagination.}, }
@article {pmid39612132, year = {2024}, author = {Yang, Y and Li, M and Wang, L}, title = {An adaptive session-incremental broad learning system for continuous motor imagery EEG classification.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {39612132}, issn = {1741-0444}, abstract = {Motor imagery electroencephalography (MI-EEG) is usually used as a driving signal in neuro-rehabilitation systems, and its feature space varies with the recovery progress. It is required to endow the recognition model with continuous learning and self-updating capability. Broad learning system (BLS) can be remodeled in an efficient incremental learning way. However, its architecture is intractable to change automatically to adapt to new incoming MI-EEG with time-varying and complex temporal-spatial characteristics. In this paper, an adaptive session-incremental BLS (ASiBLS) is proposed based on mutual information theory and BLS. For the initial session data, a compact temporal-spatial feature extractor (CTS) is designed to acquire the temporal-spatial features, which are input to a baseline BLS (bBLS). Furthermore, for new session data, a mutual information maximization constraint (MIMC) is introduced into the loss function of CTS to make the features' probability distribution sufficiently similar to that of the previous session, a new incremental BLS sequence (iBLS) is obtained by adding a small number of nodes to the previous model, and so on. Experiments are conducted based on the BCI Competition IV-2a dataset with two sessions and IV-2b dataset with five sessions, ASiBLS achieves average decoding accuracies of 79.89% and 87.04%, respectively. The kappa coefficient and forgetting rate are also used to evaluate the model performance. The results show that ASiBLS can adaptively generate an optimized and reduced model for each session successively, which has better plasticity in learning new knowledge and stability in retaining old knowledge as well.}, }
@article {pmid39609786, year = {2024}, author = {Sun, J and Chen, S and Wang, S and Guo, H and Wang, X}, title = {The relationship between work-family conflict, stress and depression among Chinese correctional officers: a mediation and network analysis study.}, journal = {BMC public health}, volume = {24}, number = {1}, pages = {3317}, pmid = {39609786}, issn = {1471-2458}, support = {2021ZD0200700//the 2030 Plan Technology and Innovation of China/ ; }, mesh = {Adult ; Female ; Humans ; Male ; Middle Aged ; China/epidemiology ; Correctional Facilities Personnel/psychology ; *Cross-Sectional Studies ; *Depression/epidemiology/psychology ; East Asian People/psychology ; Family/psychology ; Mediation Analysis ; Occupational Stress/psychology/epidemiology ; Police/psychology ; Prisons ; Social Network Analysis ; Stress, Psychological/epidemiology/psychology ; }, abstract = {BACKGROUND: Numerous studies have found that depression is prevalent among correctional officers (COs), which may be related to the work-family conflict (WFC) faced by this cohort. Role conflict theory posits that WFC emerges from the incompatibility between the demands of work and family roles, which induces stress and, in turn, results in emotional problems. Thus, this study seeks to investigate the association between WFC and depression, along with examining the mediating role of stress. Further network analysis is applied to identify the core and bridge symptoms within the network of WFC, stress, and depression, providing a basis for targeted interventions.<br><br>OBJECTIVE: This study aims to investigate the relationship between work-family conflict (WFC) and depressive symptoms among a larger sample of Chinese correctional officers (COs), exploring the potential mechanisms of stress in this population through network analysis.<br><br>METHODS: A cross-sectional study of 472 Chinese COs was conducted from October 2021 to January 2022. WFC, stress, and depressive symptoms were evaluated using the Work-Family Conflict Scale (WFCS) and the Depression Anxiety Stress Scale (DASS). Subsequently, correlation and regression analyses were conducted using SPSS 26.0, while mediation analysis was performed using Model 4 in PROCESS. By using the EBICglasso model, network analyses were utilized to estimate the network structure of WFC, stress and depression. Visualization and centrality measures were performed using the R package.<br><br>RESULTS: The results showed that (1) there was a significant positive correlation between WFC and stress and depression, as well as between stress and depression, (2) WFC and stress had a significant positive predictive effect on depression, (3) stress mediated the relationship between WFC and depression, with a total mediating effect of 0.262 (BootSE&#x2009;=&#x2009;0.031, BCI 95% = 0.278, 0.325), which accounted for 81.62% of the total effect, and (4) in the WFC, stress, and depression network model, strain-based work interference with family (SWF, (betweenness&#x2009;=&#x2009;2.24, closeness = -0.19, strength&#x2009;=&#x2009;1.40), difficult to relax (DR, betweenness&#x2009;=&#x2009;1.20, closeness&#x2009;=&#x2009;1.85, strength&#x2009;=&#x2009;1.06), and had nothing (HN, betweenness = -0.43, closeness&#x2009;=&#x2009;0.62, strength&#x2009;=&#x2009;0.73) were the core symptoms, and SWF, IT, and DH were the bridge symptoms, and (5) first-line COs had significantly higher levels of WFC, stress, and depression than non-first-line correctional officers.<br><br>CONCLUSION: Our findings elucidate the interrelationships between WFC, stress, and depression among COs. The study also enhances the understanding of the factors influencing WFC in this population and provides valuable guidance for the development of future interventions, offering practical clinical significance.}, }
@article {pmid39609406, year = {2024}, author = {Huang, W and Jin, N and Guo, J and Shen, C and Xu, C and Xi, K and Bonhomme, L and Quast, RB and Shen, DD and Qin, J and Liu, YR and Song, Y and Gao, Y and Margeat, E and Rondard, P and Pin, JP and Zhang, Y and Liu, J}, title = {Structural basis of orientated asymmetry in a mGlu heterodimer.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {10345}, pmid = {39609406}, issn = {2041-1723}, support = {ANR 18-CE11-0004-01//Agence Nationale de la Recherche (French National Research Agency)/ ; ANR 18-CE11-0004-01//Agence Nationale de la Recherche (French National Research Agency)/ ; ANR-10-INBS-04//Agence Nationale de la Recherche (French National Research Agency)/ ; }, mesh = {*Receptors, Metabotropic Glutamate/metabolism/chemistry/ultrastructure ; *Cryoelectron Microscopy ; Humans ; *Protein Multimerization ; HEK293 Cells ; Allosteric Regulation ; Glutamic Acid/metabolism/chemistry ; Models, Molecular ; Fluorescence Resonance Energy Transfer ; Animals ; Protein Binding ; Binding Sites ; }, abstract = {The structural basis for the allosteric interactions within G protein-coupled receptors (GPCRs) heterodimers remains largely unknown. The metabotropic glutamate (mGlu) receptors are complex dimeric GPCRs important for the fine tuning of many synapses. Heterodimeric mGlu receptors with specific allosteric properties have been identified in the brain. Here we report four cryo-electron microscopy structures of mGlu2-4 heterodimer in different states: an inactive state bound to antagonists, two intermediate states bound to either mGlu2 or mGlu4 agonist only and an active state bound to both glutamate and a mGlu4 positive allosteric modulator (PAM) in complex with Gi protein. In addition to revealing a unique PAM binding pocket among mGlu receptors, our data bring important information for the asymmetric activation of mGlu heterodimers. First, we show that agonist binding to a single subunit in the extracellular domain is not sufficient to stabilize an active dimer conformation. Single-molecule FRET data show that the monoliganded mGlu2-4 can be found in both intermediate states and an active one. Second, we provide a detailed view of the asymmetric interface in seven-transmembrane (7TM) domains and identified key residues within the mGlu2 7TM that limits its activation leaving mGlu4 as the only subunit activating G proteins.}, }
@article {pmid39606007, year = {2024}, author = {Chang, C and Piao, Y and Zhang, M and Liu, Y and Du, M and Yang, M and Mei, T and Wu, C and Wang, Y and Chen, X and Zeng, GQ and Zhang, X}, title = {Evaluation of tolerability and safety of transcranial electrical stimulation with gel particle electrodes in healthy subjects.}, journal = {Frontiers in psychiatry}, volume = {15}, number = {}, pages = {1441533}, pmid = {39606007}, issn = {1664-0640}, abstract = {BACKGROUND: With the advancement of transcranial electrical stimulation (tES) technology, an increasing number of stimulation devices and treatment protocols have emerged. However, safety and tolerability remain critical concerns before new strategies can be implemented. Particularly, the use of gel particle electrodes brings new challenges to the safety and tolerability of tES, which hinders its widespread adoption and further research.<br><br>OBJECTIVE: Our study utilized a specially designed and validated transcranial electrical stimulation stimulator along with preconfigured gel particle electrodes placed at F3 and F4 in the prefrontal lobes. We aimed to assess the tolerance and safety of these electrodes in healthy subjects by administering different durations and types of tES.<br><br>METHODS: Each participant underwent ten sessions of either transcranial direct current stimulation (tDCS) or transcranial alternating current stimulation (tACS), with session durations varying. In the experiment, we collected various measurement data from participants, including self-report questionnaire data and behavioral keystroke data. Tolerability was evaluated through adverse events (AEs), the relationship of adverse events with tES (AEs-rela), the Self-Rating Anxiety Scale (SAS), and the Visual Analog Mood Scale-Revised (VAMS-R). Safety was assessed using the Visual Analog Scale (VAS), the Skin Sensation Rating (SSR), Montreal Cognitive Assessment (MoCA), and Stroop task. These data were analyzed to determine the impact of different parameters on the tolerability and safety of tES.<br><br>RESULTS: There were no significant changes in the results of the MoCA and SAS scales before and after the experiment. However, significant differences were observed in VAS, SSR, AEs, and AEs-rela between tDCS and tACS. Additionally, fatigue increased, and energy levels decreased on VAMS-R with longer durations. No significant differences were found in other neuropsychological tests.<br><br>CONCLUSION: Our study revealed significant differences in tolerability and safety between tDCS and tACS, underscoring the importance of considering the stimulation type when evaluating these factors. Although tolerance and safety did not vary significantly across different stimulation durations in this study, future research may benefit from exploring shorter durations to further assess tolerability and safety efficiently.}, }
@article {pmid39605556, year = {2024}, author = {Singer-Clark, T and Hou, X and Card, NS and Wairagkar, M and Iacobacci, C and Peracha, H and Hochberg, LR and Stavisky, SD and Brandman, DM}, title = {Speech motor cortex enables BCI cursor control and click.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {39605556}, issn = {2692-8205}, support = {DP2 DC021055/DC/NIDCD NIH HHS/United States ; }, abstract = {Decoding neural activity from ventral (speech) motor cortex is known to enable high-performance speech brain-computer interface (BCI) control. It was previously unknown whether this brain area could also enable computer control via neural cursor and click, as is typically associated with dorsal (arm and hand) motor cortex. We recruited a clinical trial participant with ALS and implanted intracortical microelectrode arrays in ventral precentral gyrus (vPCG), which the participant used to operate a speech BCI in a prior study. We developed a cursor BCI driven by the participant's vPCG neural activity, and evaluated performance on a series of target selection tasks. The reported vPCG cursor BCI enabled rapidly-calibrating (40 seconds), accurate (2.90 bits per second) cursor control and click. The participant also used the BCI to control his own personal computer independently. These results suggest that placing electrodes in vPCG to optimize for speech decoding may also be a viable strategy for building a multi-modal BCI which enables both speech-based communication and computer control via cursor and click.}, }
@article {pmid39605372, year = {2024}, author = {Kumaresan, V and Hung, CY and Hermann, BP and Seshu, J}, title = {Role of Dual Specificity Phosphatase 1 (DUSP1) in influencing inflammatory pathways in macrophages modulated by Borrelia burgdorferi lipoproteins.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {39605372}, issn = {2692-8205}, support = {G12 MD007591/MD/NIMHD NIH HHS/United States ; R21 AI149263/AI/NIAID NIH HHS/United States ; }, abstract = {Borrelia burgdorferi (Bb), the spirochetal agent of Lyme disease, has a large array of lipoproteins that play a significant role in mediating host-pathogen interactions within ticks and vertebrates. Although there is substantial information on the effects of B. burgdorferi lipoproteins (BbLP) on immune modulatory pathways, the application of multi-omics methodologies to decode the transcriptional and proteomic patterns associated with host cell responses induced by lipoproteins in murine bone marrow-derived macrophages (BMDMs) has identified additional effectors and pathways. Single-cell RNA-Seq (scRNA-Seq) performed on BMDMs treated with various concentrations of borrelial lipoproteins revealed macrophage subsets within the BMDMs. Differential expression analysis showed that genes encoding various receptors, type I IFN-stimulated genes, signaling chemokines, and mitochondrial genes are altered in BMDMs in response to lipoproteins. Unbiased proteomics analysis of lysates of BMDMs treated with lipoproteins corroborated several of these findings. Notably, dual specificity phosphatase 1 (Dusp1) gene was upregulated during the early stages of BMDM exposure to BbLP. Pre-treatment with benzylidene-3-cyclohexylamino-1-indanone hydrochloride (BCI), an inhibitor of both DUSP1 and 6 prior to exposure to BbLP, demonstrated that DUSP1 negatively regulates NLRP3-mediated pro-inflammatory signaling and positively regulates the expression of interferon-stimulated genes and those encoding Ccl5, Il1b, and Cd274. Moreover, DUSP1, IkB kinase complex and MyD88 also modulate mitochondrial changes in BMDMs treated with borrelial lipoproteins. These findings advance the potential for exploiting DUSP1 as a therapeutic target to regulate host responses in reservoir hosts to limit survival of B. burgdorferi during its infectious cycle between ticks and mammalian hosts.}, }
@article {pmid39603445, year = {2025}, author = {Kerezoudis, P and Jensen, MA and Huang, H and Ojemann, JG and Klassen, BT and Ince, NF and Hermes, D and Miller, KJ}, title = {Spatial and spectral changes in cortical surface potentials during pinching versusthumb and index finger flexion.}, journal = {Neuroscience letters}, volume = {845}, number = {}, pages = {138062}, doi = {10.1016/j.neulet.2024.138062}, pmid = {39603445}, issn = {1872-7972}, mesh = {Humans ; *Fingers/physiology ; Adult ; Male ; *Electrocorticography/methods ; Female ; *Motor Cortex/physiology ; *Movement/physiology ; Brain Mapping/methods ; Young Adult ; Epilepsy/physiopathology ; }, abstract = {Electrocorticographic (ECoG) signals provide high-fidelity representations of sensorimotor cortex activation during contralateral hand movements. Understanding the relationship between independent and coordinated finger movements along with their corresponding ECoG signals is crucial for precise brain mapping and neural prosthetic development. We analyzed subdural ECoG signals from three adult epilepsy patients with subdural electrode arrays implanted for seizure foci identification. Patients performed a cue-based task consisting of thumb flexion, index finger flexion or a pinching movement of both fingers together. Broadband power changes were estimated using principal component analysis of the power spectrum. All patients showed significant increases in broadband power during each movement compared to rest. We created topological maps for each movement type on brain renderings and quantified spatial overlap between movement types using a resampling metric. Pinching exhibited the highest spatial overlap with index flexion, followed by superimposed index and thumb flexion, with the least overlap observed for thumb flexion alone. This analysis provides practical insights into the complex overlap of finger representations in the motor cortex during various movement types and may help guide more nuanced approaches to brain-computer interfaces and neural prosthetics.}, }
@article {pmid39600948, year = {2024}, author = {Adams, M and Cottrell, J}, title = {Development and characterization of an in vitro fluorescently tagged 3D bone-cartilage interface model.}, journal = {Frontiers in endocrinology}, volume = {15}, number = {}, pages = {1484912}, pmid = {39600948}, issn = {1664-2392}, mesh = {Animals ; Mice ; *Cell Differentiation ; *Osteoblasts/metabolism/cytology ; *Chondrocytes/metabolism/cytology ; *Osteoclasts/metabolism/cytology ; Bone and Bones/metabolism/cytology ; Cartilage/metabolism/cytology ; RAW 264.7 Cells ; Osteocytes/metabolism/cytology ; Cell Culture Techniques, Three Dimensional/methods ; Osteogenesis/physiology ; Cell Line ; }, abstract = {Three-dimensional cultures are widely used to study bone and cartilage. These models often focus on the interaction between osteoblasts and osteoclasts or osteoblasts and chondrocytes. A culture of osteoblasts, osteoclasts and chondrocytes would represent the cells that interact in the joint and a model with these cells could be used to study many diseases that affect the joints. The goal of this study was to develop 3D bone-cartilage interface (3D-BCI) that included osteoblasts, osteocytes, osteoclasts, and cartilage. Fluorescently tagged cell lines were developed to assess the interactions as cells differentiate to form bone and cartilage. Mouse cell line, MC3T3, was labeled with a nuclear GFP tag and differentiated into osteoblasts and osteocytes in Matrigel. Raw264.7 cells transfected with a red cytoplasmic tag were added to the system and differentiated with the MC3T3 cells to form osteoclasts. A new method was developed to differentiate chondrocyte cell line ATDC5 in a cartilage spheroid, and the ATDC5 spheroid was added to the MC3T3 and Raw264.7 cell model. We used an Incucyte and functional analysis to assess the cells throughout the differentiation process. The 3D-BCI model was found to be positive for TRAP, ALP, Alizarin red and Alcian blue staining to confirm osteoblastogenesis, osteoclastogenesis, and cartilage formation. Gene expression confirmed differentiation of cells based on increased expression of osteoblast markers: Alpl, Bglap, Col1A2, and Runx2, cartilage markers: Acan, Col2A1, Plod2, and osteoclast markers: Acp5, Rank and Ctsk. Based on staining, protein expression and gene expression results, we conclude that we successfully developed a mouse model with a 3D bone-cartilage interface.}, }
@article {pmid39600168, year = {2024}, author = {Amandusson, &#xc5; and Nilsson, J and Pequito, S}, title = {[The role of EEG in tomorrow's medicine].}, journal = {Lakartidningen}, volume = {121}, number = {}, pages = {}, pmid = {39600168}, issn = {1652-7518}, mesh = {Humans ; Artificial Intelligence ; Brain/physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Wearable Electronic Devices ; }, abstract = {There is a breathtakingly rapid development in various areas that take advantage of the ever-improving possibilities to record and analyze the electrical activity generated in the brain. In this article, we attempt to briefly describe some of these areas, including AI-assisted EEG interpretation, the use of BCI (brain-computer interface) in a medical setting, and the possible new applications connected to the development of very small wearable EEG devices. Furthermore, we discuss the concerns and challenges presented by these advancements in neurotechnology.}, }
@article {pmid39598903, year = {2024}, author = {Angulo Medina, AS and Aguilar Bonilla, MI and Rodríguez Giraldo, ID and Montenegro Palacios, JF and Cáceres Gutiérrez, DA and Liscano, Y}, title = {Electroencephalography-Based Brain-Computer Interfaces in Rehabilitation: A Bibliometric Analysis (2013-2023).}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {22}, pages = {}, pmid = {39598903}, issn = {1424-8220}, support = {Convocatoria Interna No. 01-2024//Universidad Santiago de Cali/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Bibliometrics ; Artificial Intelligence ; Rehabilitation/methods ; Brain/physiology ; }, abstract = {EEG-based Brain-Computer Interfaces (BCIs) have gained significant attention in rehabilitation due to their non-invasive, accessible ability to capture brain activity and restore neurological functions in patients with conditions such as stroke and spinal cord injuries. This study offers a comprehensive bibliometric analysis of global EEG-based BCI research in rehabilitation from 2013 to 2023. It focuses on primary research and review articles addressing technological innovations, effectiveness, and system advancements in clinical rehabilitation. Data were sourced from databases like Web of Science, and bibliometric tools (bibliometrix R) were used to analyze publication trends, geographic distribution, keyword co-occurrences, and collaboration networks. The results reveal a rapid increase in EEG-BCI research, peaking in 2022, with a primary focus on motor and sensory rehabilitation. EEG remains the most commonly used method, with significant contributions from Asia, Europe, and North America. Additionally, there is growing interest in applying BCIs to mental health, as well as integrating artificial intelligence (AI), particularly machine learning, to enhance system accuracy and adaptability. However, challenges remain, such as system inefficiencies and slow learning curves. These could be addressed by incorporating multi-modal approaches and advanced neuroimaging technologies. Further research is needed to validate the applicability of EEG-BCI advancements in both cognitive and motor rehabilitation, especially considering the high global prevalence of cerebrovascular diseases. To advance the field, expanding global participation, particularly in underrepresented regions like Latin America, is essential. Improving system efficiency through multi-modal approaches and AI integration is also critical. Ethical considerations, including data privacy, transparency, and equitable access to BCI technologies, must be prioritized to ensure the inclusive development and use of these technologies across diverse socioeconomic groups.}, }
@article {pmid39597167, year = {2024}, author = {Li, W and Zhou, J and Sheng, W and Jia, Y and Xu, W and Zhang, T}, title = {Highly Flexible and Compressible 3D Interconnected Graphene Foam for Sensitive Pressure Detection.}, journal = {Micromachines}, volume = {15}, number = {11}, pages = {}, pmid = {39597167}, issn = {2072-666X}, support = {12072151 and 12472153//National Natural Science Foundation of China/ ; 2019YFA0705400//National Key Research and Development Program of China/ ; MCAS-E-0124Y03//Research Fund of State Key Laboratory of Mechanics and Control for Aerospace Structures/ ; }, abstract = {A flexible pressure sensor, capable of effectively detecting forces exerted on soft or deformable surfaces, has demonstrated broad application in diverse fields, including human motion tracking, health monitoring, electronic skin, and artificial intelligence systems. However, the design of convenient sensors with high sensitivity and excellent stability is still a great challenge. Herein, we present a multi-scale 3D graphene pressure sensor composed of two types of 3D graphene foam. The sensor exhibits a high sensitivity of 0.42 kPa[-1] within the low-pressure range of 0-390 Pa and 0.012 kPa[-1] within the higher-pressure range of 0.4 to 42 kPa, a rapid response time of 62 ms, and exceptional repeatability and stability exceeding 10,000 cycles. These characteristics empower the sensor to realize the sensation of a drop of water, the speed of airflow, and human movements.}, }
@article {pmid39597097, year = {2024}, author = {Ji, W and Su, H and Jin, S and Tian, Y and Li, G and Yang, Y and Li, J and Zhou, Z and Wei, X and Tao, TH and Qin, L and Ye, Y and Sun, L}, title = {A Wireless Bi-Directional Brain-Computer Interface Supporting Both Bluetooth and Wi-Fi Transmission.}, journal = {Micromachines}, volume = {15}, number = {11}, pages = {}, pmid = {39597097}, issn = {2072-666X}, support = {2022YFF0706500//National Key R &amp; D Program of China/ ; 2022ZD0209300//National Key R &amp; D Program of China/ ; 2021ZD0201600//National Key R &amp; D Program of China/ ; 2019YFA0905200//National Key R &amp; D Program of China/ ; 2021YFC2501500//National Key R &amp; D Program of China/ ; 2021YFF1200700//National Key R &amp; D Program of China/ ; 2022ZD0212300//National Key R &amp; D Program of China/ ; 61974154//National Natural Science Foundation of China/ ; ZDBS-LY-JSC024//Key Research Program of Frontier Sciences, CAS/ ; JCYJ-SHFY-2022-01//Shanghai Pilot Program for Basic Research-Chinese Academy of Science, Shanghai Branch/ ; 2021SHZDZX//Shanghai Municipal Science and Technology Major Project/ ; 21PJ1415100//Shanghai Pujiang Program/ ; 19PJ1410900//Shanghai Pujiang Program/ ; 21JM0010200//Science and Technology Commission Foundation of Shanghai/ ; 21142200300//Science and Technology Commission Foundation of Shanghai/ ; 22QA1410900//Shanghai Rising-Star Program/ ; 22YF1454700//Shanghai Sailing Program/ ; 20212ABC03W07//Jiangxi Province 03 Special Project and 5G Project/ ; 20201ZDE04013//Fund for Central Government in Guidance of Local Science and Technology Development/ ; 2021B0909060002//Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province/ ; 2021B0909050004//Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province/ ; }, abstract = {Wireless neural signal transmission is essential for both neuroscience research and neural disorder therapies. However, conventional wireless systems are often constrained by low sampling rates, limited channel counts, and their support of only a single transmission mode. Here, we developed a wireless bi-directional brain-computer interface system featuring dual transmission modes. This system supports both low-power Bluetooth transmission and high-sampling-rate Wi-Fi transmission, providing flexibility for various application scenarios. The Bluetooth mode, with a maximum sampling rate of 14.4 kS/s, is well suited for detecting low-frequency signals, as demonstrated by both in vitro recordings of signals from 10 to 50 Hz and in vivo recordings of 16-channel local field potentials in mice. More importantly, the Wi-Fi mode, offering a maximum sampling rate of 56.8 kS/s, is optimized for recording high-frequency signals. This capability was validated through in vitro recordings of signals from 500 to 2000 Hz and in vivo recordings of single-neuron spike firings with amplitudes reaching hundreds of microvolts and high signal-to-noise ratios. Additionally, the system incorporates a wireless stimulation function capable of delivering current pulses up to 2.55 mA, with adjustable pulse width and polarity. Overall, this dual-mode system provides an efficient and flexible solution for both neural recording and stimulation applications.}, }
@article {pmid39595855, year = {2024}, author = {Tubbs, A and Vazquez, EA}, title = {Engineering and Technological Advancements in Repetitive Transcranial Magnetic Stimulation (rTMS): A Five-Year Review.}, journal = {Brain sciences}, volume = {14}, number = {11}, pages = {}, pmid = {39595855}, issn = {2076-3425}, abstract = {In the past five years, repetitive transcranial magnetic stimulation (rTMS) has evolved significantly, driven by advancements in device design, treatment protocols, software integration, and brain-computer interfaces (BCIs). This review evaluates how these innovations enhance the safety, efficacy, and accessibility of rTMS while identifying key challenges such as protocol standardization and ethical considerations. A structured review of peer-reviewed studies from 2019 to 2024 focused on technological and clinical advancements in rTMS, including AI-driven personalized treatments, portable devices, and integrated BCIs. AI algorithms have optimized patient-specific protocols, while portable devices have expanded access. Enhanced coil designs and BCI integration offer more precise and adaptive neuromodulation. However, challenges remain in standardizing protocols, addressing device complexity, and ensuring equitable access. While recent innovations improve rTMS's clinical utility, gaps in long-term efficacy and ethical concerns persist. Future research must prioritize standardization, accessibility, and robust ethical frameworks to ensure rTMS's sustainable impact.}, }
@article {pmid39593731, year = {2024}, author = {Nuñez Ponasso, G and Wartman, WA and McSweeney, RC and Lai, P and Haueisen, J and Maess, B and Knösche, TR and Weise, K and Noetscher, GM and Raij, T and Makaroff, SN}, title = {Improving EEG Forward Modeling Using High-Resolution Five-Layer BEM-FMM Head Models: Effect on Source Reconstruction Accuracy.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {11}, number = {11}, pages = {}, pmid = {39593731}, issn = {2306-5354}, support = {R01 EB035484/EB/NIBIB NIH HHS/United States ; R01EB035484/EB/NIBIB NIH HHS/United States ; 2018 IZN 004//Free State of Thuringia/ ; R01MH130490/MH/NIMH NIH HHS/United States ; R01 MH130490/MH/NIMH NIH HHS/United States ; 01GQ2201//Bundesministerium f&#xfc;r Bildung und Forschung/ ; 1R01NS126337/NS/NINDS NIH HHS/United States ; R01 NS126337/NS/NINDS NIH HHS/United States ; 01GQ2304A//Bundesministerium f&#xfc;r Bildung und Forschung/ ; 2018 IZN 004//European Regional Development Fund/ ; }, abstract = {Electroencephalographic (EEG) source localization is a fundamental tool for clinical diagnoses and brain-computer interfaces. We investigate the impact of model complexity on reconstruction accuracy by comparing the widely used three-layer boundary element method (BEM) as an inverse method against a five-layer BEM accelerated by the fast multipole method (BEM-FMM) and coupled with adaptive mesh refinement (AMR) as forward solver. Modern BEM-FMM with AMR can solve high-resolution multi-tissue models efficiently and accurately. We generated noiseless 256-channel EEG data from 15 subjects in the Connectome Young Adult dataset, using four anatomically relevant dipole positions, three conductivity sets, and two head segmentations; we mapped localization errors across the entire grey matter from 4000 dipole positions. The average location error among our four selected dipoles is ∼5mm (±2mm) with an orientation error of ∼12∘ (±7∘). The average source localization error across the entire grey matter is ∼9mm (±4mm), with a tendency for smaller errors on the occipital lobe. Our findings indicate that while three-layer models are robust under noiseless conditions, substantial localization errors (10-20mm) are common. Therefore, models of five or more layers may be needed for accurate source reconstruction in critical applications involving noisy EEG data.}, }
@article {pmid39592732, year = {2024}, author = {Zheng, MM and Li, JY and Guo, HJ and Zhang, J and Wang, LS and Jiang, KF and Wu, HH and He, QJ and Ding, L and Yang, B}, title = {IMPDH inhibitors upregulate PD-L1 in cancer cells without impairing immune checkpoint inhibitor efficacy.}, journal = {Acta pharmacologica Sinica}, volume = {}, number = {}, pages = {}, pmid = {39592732}, issn = {1745-7254}, abstract = {Tumor cells are characterized by rapid proliferation. In order to provide purines for DNA and RNA synthesis, inosine 5'-monophosphate dehydrogenase (IMPDH), a key enzyme in the de novo guanosine biosynthesis, is highly expressed in tumor cells. In this study we investigated whether IMPDH was involved in cancer immunoregulation. We revealed that the IMPDH inhibitors AVN944, MPA or ribavirin concentration-dependently upregulated PD-L1 expression in non-small cell lung cancer cell line NCI-H292. This effect was reproduced in other non-small cell lung cancer cell lines H460, H1299 and HCC827, colon cancer cell lines HT29, RKO and HCT116, as well as kidney cancer cell line Huh7. In NCI-H292 cells, we clarified that IMPDH inhibitors increased CD274 mRNA levels by enhancing CD274 mRNA stability. IMPDH inhibitors improved the affinity of the ARE-binding protein HuR for CD274 mRNA, thereby stabilizing CD274 mRNA. Guanosine supplementation abolished the IMPDH inhibitor-induced increase in PD-L1 expression. In CT26 and EMT6 tumor models used for ICIs based studies, we showed that despite its immunosuppressive properties, the IMPDH inhibitor mycophenolate mofetil did not reduce the clinical response of checkpoint inhibitors, representing an important clinical observation given that this class of drugs is approved for use in multiple diseases. We conclude that PD-L1 induction contributes to the immunosuppressive effect of IMPDH inhibitors. Furthermore, the IMPDH inhibitor mycophenolate mofetil does not antagonize immune checkpoint blockade.}, }
@article {pmid39592434, year = {2024}, author = {Paban, V and Feraud, L and Weills, A and Duplan, F}, title = {Exploring neurofeedback as a therapeutic intervention for subjective cognitive decline.}, journal = {The European journal of neuroscience}, volume = {60}, number = {12}, pages = {7164-7182}, pmid = {39592434}, issn = {1460-9568}, support = {214535, UMR7077//Janssen Horizon/ ; }, mesh = {Humans ; *Neurofeedback/methods ; *Cognitive Dysfunction/therapy/physiopathology/rehabilitation ; Female ; Male ; Aged ; Middle Aged ; *Electroencephalography/methods ; }, abstract = {IMPACT STATEMENT: This study addresses the pressing issue of subjective cognitive decline in aging populations by investigating neurofeedback (NFB) as a potential early therapeutic intervention. By evaluating the efficacy of individualised NFB training compared to standard protocols, tailored to each participant's EEG profile, it provides novel insights into personalised treatment approaches. The incorporation of innovative elements and rigorous analytical techniques contributes to advancing our understanding of NFB's modulatory effects on EEG frequencies and cognitive function in aging individuals.<br><br>ABSTRACT: In the context of an aging population, concerns surrounding memory function become increasingly prevalent, particularly as individuals transition into middle age and beyond. This study investigated neurofeedback (NFB) as a potential early therapeutic intervention to address subjective cognitive decline (SCD) in aging populations. NFB, a biofeedback technique utilising a brain-computer interface, has demonstrated promise in the treatment of various neurological and psychological conditions. Here, we evaluated the efficacy of individualised NFB training, tailored to each participant's EEG profile, compared to a standard NFB training protocol aimed at increasing peak alpha frequency power, in enhancing cognitive function among individuals experiencing SCD. Our NFB protocol incorporated innovative elements, including the implementation of a criterion for learning success to ensure consistent achievement levels by the conclusion of the training sessions. Additionally, we introduced a non-learner group to account for individuals who do not demonstrate the expected proficiency in NFB regulation. Analysis of electroencephalographic (EEG) signals during NFB sessions, as well as before and after training, provides insights into the modulatory effects of NFB on EEG frequencies. Contrary to expectations, our rigorous analysis revealed that the ability of individuals with SCD to modulate EEG signal power and duration at specific frequencies was not exclusive to the intended frequency target. Furthermore, examination of EEG signals recorded using a high-density EEG showed no discernible alteration in signal power between pre- and post-NFB training sessions. Similarly, no significant effects were observed on questionnaire scores when comparing pre- and post-NFB training assessments.}, }
@article {pmid39591752, year = {2024}, author = {Sun, Y and Zhang, F and Li, Z and Liu, X and Zheng, D and Zhang, S and Fan, S and Wu, X}, title = {Multi-layer ear-scalp distillation framework for ear-EEG classification enhancement.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad9778}, pmid = {39591752}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Scalp/physiology ; *Evoked Potentials, Visual/physiology ; Male ; Ear/physiology ; Adult ; Female ; Young Adult ; Photic Stimulation/methods ; }, abstract = {BACKGROUND: Ear-electroencephalography (ear-EEG) holds significant promise as a practical tool in brain-computer interfaces (BCIs) due to its enhanced unobtrusiveness, comfort, and mobility compared to traditional steady-state visual evoked potential (SSVEP)-based BCI systems. However, achieving accurate SSVEP classification with ear-EEG remains a major challenge due to the significant attenuation and distortion of the signal amplitude.<br><br>OBJECTIVE: Our aim is to enhance the classification performance of SSVEP using ear-EEG and to increase its practical application value.<br><br>APPROACH: To address this challenge, we focus on enhancing ear-EEG feature representations by training the model to learn features similar to those of scalp-EEG. We introduce a novel framework, termed multi-layer ear-scalp distillation (MESD), designed to optimize SSVEP target classification in ear-EEG data. This framework combines signals from the scalp to obtain multi-layer distilled knowledge through the cooperation of mid-layer feature distillation and output layer response distillation.Mainresults.We improve the classification of the initial 1 s data and achieved a maximum classification accuracy of 81.1%. We evaluate the proposed MESD framework through single-session, cross-session, and cross-subject transfer decoding, comparing it with baseline methods. The results demonstrate that the proposed framework achieves the best classification results in all experiments.<br><br>SIGNIFICANCE: Our study enhances the classification accuracy of SSVEP based on ear-EEG within a short time window. These results offer insights for the application of ear-EEG brain-computer interfaces in tasks such as auxiliary control and rehabilitation training in future endeavors.}, }
@article {pmid39591745, year = {2024}, author = {Smedemark-Margulies, N and Wang, Y and Koike-Akino, T and Liu, J and Parsons, K and Bicer, Y and Erdoğmuş, D}, title = {Improving subject transfer in EEG classification with divergence estimation.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad9777}, pmid = {39591745}, issn = {1741-2552}, mesh = {*Electroencephalography/methods/classification ; Humans ; Algorithms ; Neural Networks, Computer ; Brain-Computer Interfaces ; }, abstract = {Objective. Classification models for electroencephalogram (EEG) data show a large decrease in performance when evaluated on unseen test subjects. We improve performance using new regularization techniques during model training.Approach. We propose several graphical models to describe an EEG classification task. From each model, we identify statistical relationships that should hold true in an idealized training scenario (with infinite data and a globally-optimal model) but that may not hold in practice. We design regularization penalties to enforce these relationships in two stages. First, we identify suitable proxy quantities (divergences such as Mutual Information and Wasserstein-1) that can be used to measure statistical independence and dependence relationships. Second, we provide algorithms to efficiently estimate these quantities during training using secondary neural network models.Main results. We conduct extensive computational experiments using a large benchmark EEG dataset, comparing our proposed techniques with a baseline method that uses an adversarial classifier. We first show the performance of each method across a wide range of hyperparameters, demonstrating that each method can be easily tuned to yield significant benefits over an unregularized model. We show that, using ideal hyperparameters for all methods, our first technique gives significantly better performance than the baseline regularization technique. We also show that, across hyperparameters, our second technique gives significantly more stable performance than the baseline. The proposed methods require only a small computational cost at training time that is equivalent to the cost of the baseline.Significance. The high variability in signal distribution between subjects means that typical approaches to EEG signal modeling often require time-intensive calibration for each user, and even re-calibration before every use. By improving the performance of population models in the most stringent case of zero-shot subject transfer, we may help reduce or eliminate the need for model calibration.}, }
@article {pmid39590012, year = {2024}, author = {AlQahtani, NJ and Al-Naib, I and Ateeq, IS and Althobaiti, M}, title = {Hybrid Functional Near-Infrared Spectroscopy System and Electromyography for Prosthetic Knee Control.}, journal = {Biosensors}, volume = {14}, number = {11}, pages = {}, pmid = {39590012}, issn = {2079-6374}, support = {KSRG-2023-195//King Salman Center for Disability Research/ ; }, mesh = {Humans ; *Spectroscopy, Near-Infrared ; *Electromyography ; Male ; Adult ; Knee Prosthesis ; Knee/physiology ; Brain-Computer Interfaces ; Artificial Limbs ; }, abstract = {The increasing number of individuals with limb loss worldwide highlights the need for advancements in prosthetic knee technology. To improve control and quality of life, integrating brain-computer communication with motor imagery offers a promising solution. This study introduces a hybrid system that combines electromyography (EMG) and functional near-infrared spectroscopy (fNIRS) to address these limitations and enhance the control of knee movements for individuals with above-knee amputations. The study involved an experiment with nine healthy male participants, consisting of two sessions: real execution and imagined execution using motor imagery. The OpenBCI Cyton board collected EMG signals corresponding to the desired movements, while fNIRS monitored brain activity in the prefrontal and motor cortices. The analysis of the simultaneous measurement of the muscular and hemodynamic responses demonstrated that combining these data sources significantly improved the classification accuracy compared to using each dataset alone. The results showed that integrating both the EMG and fNIRS data consistently achieved a higher classification accuracy. More specifically, the Support Vector Machine performed the best during the motor imagery tasks, with an average accuracy of 49.61%, while the Linear Discriminant Analysis excelled in the real execution tasks, achieving an average accuracy of 89.67%. This research validates the feasibility of using a hybrid approach with EMG and fNIRS to enable prosthetic knee control through motor imagery, representing a significant advancement potential in prosthetic technology.}, }
@article {pmid39589888, year = {2024}, author = {Liu, J and Li, Z and Sun, M and Zhou, L and Wu, X and Lu, Y and Shao, Y and Liu, C and Huang, N and Hu, B and Wu, Z and You, C and Li, L and Wang, M and Tao, L and Di, Z and Sheng, X and Mei, Y and Song, E}, title = {Flexible bioelectronic systems with large-scale temperature sensor arrays for monitoring and treatments of localized wound inflammation.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {121}, number = {49}, pages = {e2412423121}, pmid = {39589888}, issn = {1091-6490}, support = {2022ZD0209900//STI 2030-Major Project/ ; 62204057 62304044 12022209//the National Natural Science Foundation of China/ ; 22ZR1406400//Science and Technology Commission of Shanghai Municipality (STCSM)/ ; LG-QS-202202-02//Lingang Laboratory/ ; }, mesh = {Animals ; Humans ; *Inflammation/therapy ; *Wound Healing ; Hydrogels/chemistry ; Wearable Electronic Devices ; Temperature ; Monitoring, Physiologic/instrumentation/methods ; Rats ; Biosensing Techniques/instrumentation/methods ; }, abstract = {Continuous monitoring and closed-loop therapy of soft wound tissues is of particular interest in biomedical research and clinical practices. An important focus is on the development of implantable bioelectronics that can measure time-dependent temperature distribution related to localized inflammation over large areas of wound and offer in situ treatment. Existing approaches such as thermometers/thermocouples provide limited spatial resolution, inapplicable to a wearable/implantable format. Here, we report a conformal, scalable device package that integrates a flexible amorphous silicon-based temperature sensor array and drug-loaded hydrogel for the healing process. This system can enable the spatial temperature mapping at submillimeter resolution and high sensitivity of 0.1 °C, for dynamically localizing the inflammation regions associated with temperature change, automatically followed with heat-triggered drug delivery from hydrogel triggered by wearable infrared light-emitting-diodes. We establish the operational principles experimentally and computationally and evaluate system functionalities with a wide range of targets including live animal models and human subjects. As an example of medical utility, this system can yield closed-loop monitoring/treatments by tracking of temperature distribution over wound areas of live rats, in designs that can be integrated with automated wireless control. These findings create broad utilities of these platforms for clinical diagnosis and advanced therapy for wound healthcare.}, }
@article {pmid39589717, year = {2024}, author = {Chan, RW and Edelman, BJ and Tsang, SY and Gao, K and Yu, AC}, title = {Opportunities for System Neuroscience.}, journal = {Advances in neurobiology}, volume = {41}, number = {}, pages = {247-253}, pmid = {39589717}, issn = {2190-5215}, mesh = {Humans ; *Neurosciences ; *Brain/diagnostic imaging/physiology ; *Brain-Computer Interfaces ; Magnetic Resonance Imaging ; Neuroimaging ; Precision Medicine ; Nerve Net/diagnostic imaging/physiology ; }, abstract = {Systems neuroscience explores the intricate organization and dynamic function of neural circuits and networks within the brain. By elucidating how these complex networks integrate to execute mental operations, this field aims to deepen our understanding of the biological basis of cognition, behavior, and consciousness. In this chapter, we outline the promising future of systems neuroscience, highlighting the emerging opportunities afforded by powerful technological innovations and their applications. Cutting-edge tools such as awake functional MRI, ultrahigh field strength neuroimaging, functional ultrasound imaging, and optoacoustic techniques have revolutionized the field, enabling unprecedented observation and analysis of brain activity. The insights gleaned from these advanced methodologies have empowered the development of a suite of exciting applications across diverse domains. These include brain-machine interfaces (BMIs) for neural prosthetics, cognitive enhancement therapies, personalized mental health interventions, and precision medicine approaches. As our comprehension of neural systems continues to grow, it is envisioned that these and related applications will become increasingly refined and impactful in improving human health and well-being.}, }
@article {pmid39588722, year = {2025}, author = {Xiang, Z and Yang, L and Yu, B and Zeng, Q and Huang, T and Shi, S and Yu, H and Zhang, Y and Wu, J and Zhu, M}, title = {Recent advances in polymer-based thin-film electrodes for ECoG applications.}, journal = {Journal of materials chemistry. B}, volume = {13}, number = {2}, pages = {454-471}, doi = {10.1039/d4tb02090a}, pmid = {39588722}, issn = {2050-7518}, mesh = {*Polymers/chemistry ; *Electrodes ; Humans ; *Electrocorticography/instrumentation ; Brain-Computer Interfaces ; Animals ; }, abstract = {Electrocorticography (ECoG) has garnered widespread attention owing to its superior signal resolution compared to conventional electroencephalogram (EEG). While ECoG signal acquisition entails invasiveness, the invasive rigid electrode used inevitably inflicts damage on brain tissue. Polymer electrodes that combine conductivity and transparency have garnered great interest because they not only facilitate high-quality signal acquisition but also provide additional insights while preserving the health of the brain, positioning them as the future frontier in the brain-computer interface (BCI). This review summarizes the multifaceted functions of polymers in ECoG thin-film electrodes for the BCI. We present the abilities of sensitive and structural polymers focusing on impedance reduction, signal quality improvement, good flexibility, and transparency. Typically, two sensitive polymers and four structural polymers are analyzed in detail in terms of ECoG electrode properties. Moreover, the underlying mechanism of polymer-based electrodes in signal quality enhancement is revealed. Finally, the remaining challenges and perspectives are discussed.}, }
@article {pmid39588687, year = {2024}, author = {Wang, J and Jiang, Y and Xiong, T and Lu, J and He, X and Yu, P and Mao, L}, title = {Optically Modulated Nanofluidic Ionic Transistor for Neuromorphic Functions.}, journal = {Angewandte Chemie (International ed. in English)}, volume = {}, number = {}, pages = {e202418949}, doi = {10.1002/anie.202418949}, pmid = {39588687}, issn = {1521-3773}, support = {Z230022//Natural Science Foundation of Beijing/ ; 22134002//the National Natural Science Foundation of China/ ; 22174146, 22474011//the National Natural Science Foundation of China/ ; }, abstract = {Neuromorphic systems that can emulate the behavior of neurons have garnered increasing interest across interdisciplinary fields due to their potential applications in neuromorphic computing, artificial intelligence and brain-machine interfaces. However, the optical modulation of nanofluidic ion transport for neuromorphic functions has been scarcely reported. Herein, inspired by biological systems that rely on ions as signal carriers for information perception and processing, we present a nanofluidic transistor based on a metal-organic framework membrane (MOFM) with optically modulated ion transport properties, which can mimic the functions of biological synapses. Through the dynamic modulation of synaptic weight, we successfully replicate intricate learning-experience behaviors and Pavlovian associate learning processes by employing sequential optical stimuli. Additionally, we demonstrate the application of the International Morse Code with the nanofluidic device using patterned optical pulse signals, showing its encoding and decoding capabilities in information processing process. This study would largely advance the development of nanofluidic neuromorphic devices for biomimetic iontronics integrated with sensing, memory and computing functions.}, }
@article {pmid39586499, year = {2025}, author = {Thenmozhi, T and Helen, R and Mythili, S}, title = {Classification of motor imagery EEG with ensemble RNCA model.}, journal = {Behavioural brain research}, volume = {479}, number = {}, pages = {115345}, doi = {10.1016/j.bbr.2024.115345}, pmid = {39586499}, issn = {1872-7549}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; Bayes Theorem ; Brain/physiology ; Motor Activity/physiology ; Movement/physiology ; Adult ; }, abstract = {Motor Imagery (MI) based brain-computer interface (BCI) systems are used for regaining the motor functions of neurophysiologically affected persons. But the performance of MI tasks is degraded due to the presence of redundant EEG channels. Hence, a novel ensemble regulated neighborhood component analysis (ERNCA) method provides a perfect identification of neural region that stimulate motor movements. Domains of statistical, frequency, spatial and transform-based features narrowed down the misclassification rate. The gradient boosting method selects the relevant features thereby reduces the computational complexity. Finally, Bayesian optimized ensemble classifier finetuned the classification accuracies of 97.22 % and 91.62 % for Datasets IIIa and IVa respectively. This approach is further strengthened by analyzing real-time data with the accuracy of 93.75 %. This method qualifies out of four benchmark methods with significant percent of improvement in accuracy for these three datasets. As per the spatial distribution of refined EEG channels, majority of the brain's motor functions concentrates on frontal and central cortex regions of brain.}, }
@article {pmid39586422, year = {2025}, author = {Mao, W and Shen, X and Bai, X and Wang, A}, title = {Neural correlates of empathy in donation decisions: Insights from EEG and machine learning.}, journal = {Neuroscience}, volume = {564}, number = {}, pages = {214-225}, doi = {10.1016/j.neuroscience.2024.11.044}, pmid = {39586422}, issn = {1873-7544}, mesh = {Humans ; *Empathy/physiology ; Male ; *Electroencephalography/methods ; Female ; Young Adult ; *Machine Learning ; *Decision Making/physiology ; Adult ; Brain/physiology ; Evoked Potentials/physiology ; Adolescent ; Emotions/physiology ; }, abstract = {Empathy is central to individual and societal well-being. Numerous studies have examined how trait of empathy affects prosocial behavior. However, little studies explored the psychological and neural mechanisms by which different dimensions of trait empathy influence prosocial behavior. Addressing this knowledge gap is important to understand empathy-driven prosocial behavior. We employed an EEG experiment combined with interpretable machine learning methods to probe these questions. We found that empathic concern (EC) played the most pivotal role in donation decision. Behaviorally, EC negatively moderates the effect of perceived closeness and deservedness of charity projects on the willingness to donate. The machine learning results indicate that EC significantly predicts late positive potential (LPP) and beta-band activity during donation information processing. Further regression analysis results indicate that EC, rather than other dimensions of trait empathy, can positively predict LPP amplitude and negatively predict beta-band activity. These results indicated that participants with higher EC scores may experience heightened emotional arousal and the vicarious experience of others' emotions while processing donation information. Our work adds weight to understanding the relationship between trait empathy and prosocial behavior and provides electrophysiological evidence.}, }
@article {pmid39586421, year = {2025}, author = {Hong, T and Zhou, H and Xi, W and Li, X and Du, Y and Liu, J and Geng, F and Hu, Y}, title = {Acting with awareness is positively correlated with dorsal anterior cingulate cortex glutamate concentration but both are impaired in Internet gaming disorder.}, journal = {Neuroscience}, volume = {564}, number = {}, pages = {226-235}, doi = {10.1016/j.neuroscience.2024.11.054}, pmid = {39586421}, issn = {1873-7544}, mesh = {Humans ; *Gyrus Cinguli/metabolism/diagnostic imaging ; *Glutamic Acid/metabolism ; Male ; *Internet Addiction Disorder/metabolism/physiopathology ; Young Adult ; Adult ; Female ; *Awareness/physiology ; Video Games ; Magnetic Resonance Spectroscopy ; Proton Magnetic Resonance Spectroscopy ; Adolescent ; }, abstract = {Internet gaming disorder (IGD) is increasingly recognized as a public concern for its adverse impacts on cognition and mental health. In IGD, the transition from goal-directed actions to habitual and eventually compulsive behaviors is accompanied by altered neural response within the dorsal anterior cingulate cortex (dACC), a critical region involved in conscious actions. However, the neurochemical profile of the dACC in IGD and its relationship with behavioral awareness remain poorly understood. In this study, [1]H-magnetic resonance spectroscopy was employed to quantify dACC glutamate concentration and examine its association with the capacity for 'acting with awareness' among 21 participants with IGD and 19 recreational game users. Results indicated that dACC glutamate levels and behavioral awareness were significantly lower in the IGD group compared to recreational game users. Moreover, a significant positive correlation between awareness and dACC glutamate concentration emerged in the recreational game users' group, a relationship attenuated in those with IGD. In an independent cohort of 107 participants, the positive association between awareness and dACC glutamate concentration was replicated. These findings suggest that reduced dACC glutamate in IGD may underlie diminished awareness of maladaptive habitual behaviors. Enhancing dACC neural excitability through neuromodulation or mindfulness training could represent a potential intervention to restore behavioral awareness.}, }
@article {pmid39586380, year = {2025}, author = {Higashi, H}, title = {Single-channel electroencephalography decomposition by detector-atom network and its pre-trained model.}, journal = {Journal of neuroscience methods}, volume = {414}, number = {}, pages = {110323}, doi = {10.1016/j.jneumeth.2024.110323}, pmid = {39586380}, issn = {1872-678X}, mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; Brain/physiology ; Brain-Computer Interfaces ; Algorithms ; }, abstract = {Signal decomposition techniques utilizing multi-channel spatial features are critical for analyzing, denoising, and classifying electroencephalography (EEG) signals. To facilitate the decomposition of signals recorded with limited channels, this paper presents a novel single-channel decomposition approach that does not rely on multi-channel features. Our model posits that an EEG signal comprises short, shift-invariant waves, referred to as atoms. We design a decomposer as an artificial neural network aimed at estimating these atoms and detecting their time shifts and amplitude modulations within the input signal. The efficacy of our method was validated across various scenarios in brain-computer interfaces and neuroscience, demonstrating enhanced performance. Additionally, cross-dataset validation indicates the feasibility of a pre-trained model, enabling a plug-and-play signal decomposition module.}, }
@article {pmid39583262, year = {2024}, author = {Abo Alzahab, N and Iorio, AD and Apollonio, L and Alshalak, M and Gravina, A and Antognoli, L and Baldi, M and Scalise, L and Alchalabi, B}, title = {Auditory evoked potential electroencephalography-biometric dataset.}, journal = {Data in brief}, volume = {57}, number = {}, pages = {111065}, pmid = {39583262}, issn = {2352-3409}, abstract = {This work aims to assess the use of electroencephalographic (EEG) signals as a means of biometric authentication. More than 240 recordings, each lasting 2 min, were gathered from 20 subjects involved in the data collection. Data include the results of experiments performed both in a resting state and in the presence of auditory stimuli. The resting-state EEG signals were acquired with both open and closed eyes. The auditory stimuli EEG signals consist of six experiments divided into two scenarios. The first scenario considers in-ear stimuli, while the second scenario considers bone-conducting stimuli. For each of the two scenarios, experiments include a native language song, a non-native language song and some neutral music. This data could be used to develop biometric systems for authentication or identification. Additionally, they could be used to study the effect of auditory stimuli such as music on EEG activity and to compare it with the resting state condition.}, }
@article {pmid39582417, year = {2024}, author = {Wang, X and Zhu, K and Wu, W and Zhou, D and Lu, H and Du, J and Cai, L and Yan, X and Li, W and Qian, X and Wang, X and Ma, C and Hu, Y and Tian, C and Sun, B and Fang, Z and Wu, J and Jiang, P and Liu, J and Liu, C and Fan, J and Cui, H and Shen, Y and Duan, S and Bao, A and Yang, Y and Qiu, W and Zhang, J}, title = {Prevalence of mixed neuropathologies in age-related neurodegenerative diseases: A community-based autopsy study in China.}, journal = {Alzheimer's &amp; dementia : the journal of the Alzheimer's Association}, volume = {}, number = {}, pages = {}, doi = {10.1002/alz.14369}, pmid = {39582417}, issn = {1552-5279}, support = {2021ZD0201100//STI2030-Major Project/ ; 2024C03098//Key R&amp;D Program of Zhejiang province/ ; 2024SSYS0018//Key R&amp;D Program of Zhejiang province/ ; //Innovative Institute of Basic Medical Science of Zhejiang University/ ; 2021-I2M-1-025//CAMS Innovation Fund for Medical Sciences/ ; 2024SSYS0018//Key Research and Development Program of Zhejiang Province/ ; 81971184//National Natural Science Foundation of China/ ; }, abstract = {INTRODUCTION: Despite extensive studies on mixed neuropathologies, data from China are limited. This study aims to fill this gap by analyzing brain samples from Chinese brain banks.<br><br>METHODS: A total of 1142 brains from six Chinese brain banks were examined using standardized methods. Independent pathologists conducted evaluations with stringent quality control. Prevalence and correlations of neurological disorders were analyzed.<br><br>RESULTS: Significant proportions of brains displayed primary age-related tauopathy (PART, 35%), limbic-predominant age-related TDP-43 encephalopathy (LATE, 46%), and aging-related tau astrogliopathy (ARTAG, 12%). Alzheimer's disease neuropathological change (ADNC, 48%), Lewy body disease (LBD, 13%), and cerebrovascular disease (CVD, 63%) were also prevalent, often co-occurring with regional variations. CVD emerged as the potential most early contributor to neuropathological changes.<br><br>DISCUSSION: This analysis highlights the prevalence of PART, LATE, ARTAG, ADNC, LBD, and CVD, with regional differences. The findings suggest CVD may be the earliest contributing factor, potentially preceding other neuropathologies. Highlights The prevalence of primary age-related tauopathy (PART), limbic-predominant age-related TDP-43 encephalopathy (LATE), aging-related tau astrogliopathy (ARTAG), Alzheimer's disease neuropathologic change, Lewy body disease, and cerebrovascular disease (CVD) in China, increasing with age, is comparable to other countries. Significant regional differences in the prevalences of diseases are noted. CVD develops prior to any other disorders, including PART, LATE, and ARTAG.}, }
@article {pmid39581346, year = {2025}, author = {Brands, R and Bartsch, J and Thommes, M}, title = {Complemental hard modeling in Raman spectroscopy: A case study on titanium dioxide-free coating in-line monitoring.}, journal = {Journal of pharmaceutical sciences}, volume = {114}, number = {1}, pages = {577-585}, doi = {10.1016/j.xphs.2024.10.044}, pmid = {39581346}, issn = {1520-6017}, mesh = {*Titanium/chemistry ; *Spectrum Analysis, Raman/methods ; Least-Squares Analysis ; Calcium Carbonate/chemistry ; Tablets/chemistry ; Tablets, Enteric-Coated/chemistry ; }, abstract = {Tablets are coated for taste or odor modification, for modified release profiles or as a protective layer to increase the stability. Here, titanium dioxide is frequently added as a coating component due to its opaque properties. Furthermore, its Raman activity makes it an integral part of in-line monitoring models. However, due to the carcinogenic potential of titanium dioxide, calcium carbonate is utilized as a substitute, exhibiting similar opaque properties. Calcium carbonate tends to exhibit overlapped peaks with carbon hydrates in the Raman spectrum. Consequently, new models based on e.g. hard modeling are required instead of peak integration. In this study, tablets were coated with a coating including calcium carbonate. Partial Least Squares Regression (PLS) and Complemental Hard Modeling (CHM) were examined as feasible in-line monitoring approaches. Furthermore, two different measurement positions in the coater were compared, orthogonal and tangential with respect to the moving tablet bed. Cross-validation exhibited improved CHM performance with reduced RMSECV values of about 5 %. The prediction of the coating mass growth occurred comparable with RMSEP values in a similar range of 2-5 %. Despite this, the CHM´s achieved improved performance with reduced training data quantity and quality. The different measurement positions indicated no process-relevant differences.}, }
@article {pmid39581146, year = {2024}, author = {Anwar, F and Zhang, K and Sun, C and Pang, M and Zhou, W and Li, H and He, R and Liu, X and Ming, D}, title = {Hydrocephalus: An update on latest progress in pathophysiological and therapeutic research.}, journal = {Biomedicine &amp; pharmacotherapy = Biomedecine &amp; pharmacotherapie}, volume = {181}, number = {}, pages = {117702}, doi = {10.1016/j.biopha.2024.117702}, pmid = {39581146}, issn = {1950-6007}, mesh = {Animals ; Humans ; *Genetic Therapy/methods ; *Hydrocephalus/genetics/physiopathology/therapy ; }, abstract = {Hydrocephalus is a severe and life-threatening disease associated with the imbalance of CSF dynamics and affects millions globally at any age, including infants. One cause of pathology that is wide-ranging is genetic mutations to post-traumatic injury. The most effective current pharmacological treatments provide only symptomatic relief and do not address the underlying pathology. At the same time, surgical procedures such as VP shunts performed in lower-income countries are often poorly tolerated due to insufficient diagnostic resources and suboptimal outcomes partially attributable to inferior materials. These problems are compounded by an overall lack of funding that keeps high-quality medical devices out of reach for all but the most developed countries and even among those states. There is a massive variance in treatment effectiveness. This review indicates the necessity for innovative and low-cost, accessible treatment strategies to close these gaps, focusing on current advances in novel therapies, including Pharmacological, gene therapy, and nano-based technologies, which are currently at different stages of clinical trial phases. This review provides an overview of pathophysiology, current treatments, and promising new therapeutic strategies for hydrocephalus.}, }
@article {pmid39577701, year = {2025}, author = {Xia, G and Wang, L and Xiong, S and Deng, J}, title = {Enhancing detection of SSVEP-based BCIs via a novel temporally local canonical correlation analysis.}, journal = {Journal of neuroscience methods}, volume = {414}, number = {}, pages = {110325}, doi = {10.1016/j.jneumeth.2024.110325}, pmid = {39577701}, issn = {1872-678X}, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Brain/physiology ; Adult ; Male ; Algorithms ; Female ; Young Adult ; }, abstract = {BACKGROUND: In recent years, spatial filter-based frequency recognition methods have become popular in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. However, these methods are ineffective in suppressing local noise, and they rely on the length of the data. In practical applications, enhancing recognition performance with short data windows is a significant challenge for the BCI systems.<br><br>NEW METHOD: With extracting temporal information and eliminating local noise, a temporally local canonical correlation analysis based on training data-driven (TI-tdCCA) method is proposed to enhance the recognition performance of SSVEPs. Based on a novel framework, the filters are derived by incorporating the Laplacian matrix through the use of TI-CCA between the concatenated training data and individual templates. The target frequency is subsequently determined by applying the appropriate spatial filters and Laplacian matrix.<br><br>RESULTS: The experimental results on two datasets, consisting of 40 classes and recording from 35 and 70 subjects respectively, demonstrate that the proposed method consistently outperforms the eight competing methods in the majority of cases. The proposed method is simultaneously evaluated by an extended version that incorporates artificial reference signals. The extended method demonstrates a significant improvement over the proposed method. Specifically, with a time window of 0.7&#x202f;s, the average recognition accuracy of the subjects increases by 10.71&#x202f;% on the Benchmark dataset and by 6.98&#x202f;% on the BETA dataset, respectively.<br><br>Our extended method outperforms the state-of-the-art methods by at least 3&#x202f;%, and it effectively suppresses local noise and maintains excellent scalability.<br><br>The proposed method can effectively combine spatial and temporal filters to improve the recognition performance of SSVEPs.}, }
@article {pmid39577098, year = {2024}, author = {Oxley, T}, title = {A 10-year journey towards clinical translation of an implantable endovascular BCI A keynote lecture given at the BCI society meeting in Brussels.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad9633}, pmid = {39577098}, issn = {1741-2552}, abstract = {In the rapidly evolving field of brain-computer interfaces (BCIs), a novel modality for recording electrical brain signals has quietly emerged over the past decade. The technology is endovascular electrocorticography, an innovation that stands alongside well-established methods such as electroencephalography (EEG), traditional electrocorticography (ECoG), and single/multi-unit activity recording. This system was inspired by advancements in interventional cardiology, particularly the integration of electronics into various medical interventions. This breakthrough led to the development of the Stentrode system, which employs stent-mounted electrodes to record electrical brain activity for applications in a motor neuroprosthesis. This Perspective explores four key areas in our quest to bring the Stentrode BCI to market: the critical patient need for autonomy driving our efforts, the hurdles and achievements in assessing BCI performance, the compelling advantages of our unique endovascular approach, and the essential steps for clinical translation and product commercialization.}, }
@article {pmid39576681, year = {2024}, author = {Liu, D and Wei, Y}, title = {CVR-BBI: an open-source VR platform for multi-user collaborative brain to brain interfaces.}, journal = {Bioinformatics (Oxford, England)}, volume = {40}, number = {12}, pages = {}, pmid = {39576681}, issn = {1367-4811}, support = {12101570//National Natural Science Foundation of China/ ; 2024C01142//Key Research and Development Program of Zhejiang Province/ ; }, mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; *Brain/physiology ; Virtual Reality ; Software ; Male ; Adult ; User-Computer Interface ; Female ; }, abstract = {SUMMARY: As brain imaging and neurofeedback technologies advance, the brain-to-brain interface (BBI) has emerged as an innovative field, enabling in-depth exploration of cross-brain information exchange and enhancing our understanding of collaborative intelligence. However, no open-source virtual reality (VR) platform currently supports the rapid and efficient configuration of multi-user, collaborative BBIs. To address this gap, we introduce the Collaborative Virtual Reality Brain-to-Brain Interface (CVR-BBI), an open-source platform consisting of a client and server. The CVR-BBI client enables users to participate in collaborative experiments, collect electroencephalogram (EEG) data, and manage interactive multisensory stimuli within the VR environment. Meanwhile, the CVR-BBI server manages multi-user collaboration paradigms, and performs real-time analysis of the EEG data. We evaluated the CVR-BBI platform using the SSVEP paradigm and observed that collaborative decoding outperformed individual decoding, validating the platform's effectiveness in collaborative settings. The CVR-BBI offers a pioneering platform that facilitates the development of innovative BBI applications within collaborative VR environments, thereby enhancing the understanding of brain collaboration and cognition.<br><br>CVR-BBI is released as an open-source platform, with its source code being available at https://github.com/DILIU1/CVR-BBI.}, }
@article {pmid39576281, year = {2025}, author = {Farina, D and Merletti, R and Enoka, RM}, title = {The extraction of neural strategies from the surface EMG: 2004-2024.}, journal = {Journal of applied physiology (Bethesda, Md. : 1985)}, volume = {138}, number = {1}, pages = {121-135}, doi = {10.1152/japplphysiol.00453.2024}, pmid = {39576281}, issn = {1522-1601}, support = {810346//EC | ERC | HORIZON EUROPE European Research Council (ERC)/ ; RG-2206-39688//National Multiple Sclerosis Society/ ; EP/T020970/1//UKRI | Engineering and Physical Sciences Research Council (EPSRC)/ ; }, mesh = {*Electromyography/methods ; Humans ; *Muscle, Skeletal/physiology ; Muscle Contraction/physiology ; Motor Neurons/physiology ; Algorithms ; Animals ; Movement/physiology ; Signal Processing, Computer-Assisted ; }, abstract = {This review follows two previous papers [Farina et al. Appl Physiol (1985) 96: 1486-1495, 2004; Farina et al. J Appl Physiol (1985) 117: 1215-1230, 2014] in which we reflected on the use of surface electromyography (EMG) in the study of the neural control of movement. This series of papers began with an analysis of the indirect approaches of EMG processing to infer the neural control strategies and then closely followed the progress in EMG technology. In this third paper, we focus on three main areas: surface EMG modeling; surface EMG processing, with an emphasis on decomposition; and interfacing applications of surface EMG recordings. We highlight the latest advances in EMG models that allow fast generation of simulated signals from realistic volume conductors, with applications ranging from validation of algorithms to identification of nonmeasurable parameters by inverse modeling. Surface EMG decomposition is currently an established state-of-the-art tool for physiological investigations of motor units. It is now possible to identify large samples of motor units, to track motor units over multiple sessions, to partially compensate for the nonstationarities in dynamic contractions, and to decompose signals in real time. The latter achievement has facilitated advances in myocontrol, by using the online decoded neural drive as a control signal, such as in the interfacing of prostheses. Looking back over the 20 yr since our first review, we conclude that the recording and analysis of surface EMG signals have seen breakthrough advances in this period. Although challenges in its application and interpretation remain, surface EMG is now a solid and unique tool for the study of the neural control of movement.}, }
@article {pmid39572612, year = {2024}, author = {Lee, AH and Lee, J and Leung, V and Larson, L and Nurmikko, A}, title = {Patterned electrical brain stimulation by a wireless network of implantable microdevices.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {10093}, pmid = {39572612}, issn = {2041-1723}, support = {S10 OD025181/OD/NIH HHS/United States ; 232600//National Science Foundation (NSF)/ ; }, mesh = {Animals ; *Wireless Technology/instrumentation ; *Electric Stimulation/instrumentation/methods ; Rats ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Brain/physiology ; Male ; Motor Cortex/physiology ; Behavior, Animal/physiology ; }, abstract = {Transmitting meaningful information into brain circuits by electronic means is a challenge facing brain-computer interfaces. A key goal is to find an approach to inject spatially structured local current stimuli across swaths of sensory areas of the cortex. Here, we introduce a wireless approach to multipoint patterned electrical microstimulation by a spatially distributed epicortically implanted network of silicon microchips to target specific areas of the cortex. Each sub-millimeter-sized microchip harvests energy from an external radio-frequency source and converts this into biphasic current injected focally into tissue by a pair of integrated microwires. The amplitude, period, and repetition rate of injected current from each chip are controlled across the implant network by implementing a pre-scheduled, collision-free bitmap wireless communication protocol featuring sub-millisecond latency. As a proof-of-concept technology demonstration, a network of 30 wireless stimulators was chronically implanted into motor and sensory areas of the cortex in a freely moving rat for three months. We explored the effects of patterned intracortical electrical stimulation on trained animal behavior at average RF powers well below regulatory safety limits.}, }
@article {pmid39572577, year = {2024}, author = {Zhang, Z and Ding, X and Bao, Y and Zhao, Y and Liang, X and Qin, B and Liu, T}, title = {Chisco: An EEG-based BCI dataset for decoding of imagined speech.}, journal = {Scientific data}, volume = {11}, number = {1}, pages = {1265}, pmid = {39572577}, issn = {2052-4463}, support = {U22B2059//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62176079//National Natural Science Foundation of China (National Science Foundation of China)/ ; U22B2059//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62176079//National Natural Science Foundation of China (National Science Foundation of China)/ ; Y02022F005//Natural Science Foundation of Heilongjiang Province/ ; Y02022F005//Natural Science Foundation of Heilongjiang Province/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography ; *Speech ; *Imagination ; Adult ; Language ; Brain/physiology ; Young Adult ; }, abstract = {The rapid advancement of deep learning has enabled Brain-Computer Interfaces (BCIs) technology, particularly neural decoding techniques, to achieve higher accuracy and deeper levels of interpretation. Interest in decoding imagined speech has significantly increased because its concept akin to "mind reading". However, previous studies on decoding neural language have predominantly focused on brain activity patterns during human reading. The absence of imagined speech electroencephalography (EEG) datasets has constrained further research in this field. We present the Chinese Imagined Speech Corpus (Chisco), including over 20,000 sentences of high-density EEG recordings of imagined speech from healthy adults. Each subject's EEG data exceeds 900 minutes, representing the largest dataset per individual currently available for decoding neural language to date. Furthermore, the experimental stimuli include over 6,000 everyday phrases across 39 semantic categories, covering nearly all aspects of daily language. We believe that Chisco represents a valuable resource for the fields of BCIs, facilitating the development of more user-friendly BCIs.}, }
@article {pmid39571645, year = {2024}, author = {Ma, J and Li, Z and Zheng, Q and Li, S and Zong, R and Qin, Z and Wan, L and Zhao, Z and Mao, Z and Zhang, Y and Yu, X and Bai, H and Zhang, J}, title = {Investigating unilateral and bilateral motor imagery control using electrocorticography and fMRI in awake craniotomy.}, journal = {NeuroImage}, volume = {303}, number = {}, pages = {120949}, doi = {10.1016/j.neuroimage.2024.120949}, pmid = {39571645}, issn = {1095-9572}, mesh = {Humans ; *Electrocorticography/methods ; *Magnetic Resonance Imaging/methods ; *Imagination/physiology ; Male ; Female ; Adult ; *Craniotomy/methods ; Middle Aged ; Wakefulness/physiology ; Motor Cortex/physiology/diagnostic imaging ; Young Adult ; Brain-Computer Interfaces ; Brain Mapping/methods ; Movement/physiology ; }, abstract = {BACKGROUND: The rapid development of neurosurgical techniques, such as awake craniotomy, has increased opportunities to explore the mysteries of the brain. This is crucial for deepening our understanding of motor control and imagination processes, especially in developing brain-computer interface (BCI) technologies and improving neurorehabilitation strategies for neurological disorders.<br><br>OBJECTIVE: This study aimed to analyze brain activity patterns in patients undergoing awake craniotomy during actual movements and motor imagery, mainly focusing on the motor control processes of the bilateral limbs.<br><br>METHODS: We conducted detailed observations of patients undergoing awake craniotomies. The experimenter requested participants to perform and imagine a series of motor tasks involving their hands and tongues. Brain activity during these tasks was recorded using functional magnetic resonance imaging (fMRI) and intraoperative electrocorticography (ECoG). The study included left and right finger tapping, tongue protrusion, hand clenching, and imagined movements corresponding to these actions.<br><br>RESULTS: fMRI revealed significant activation in the brain's motor areas during task performance, mainly involving bilateral brain regions during imagined movement. ECoG data demonstrated a marked desynchronization pattern in the ipsilateral motor cortex during bilateral motor imagination, especially in bilateral coordination tasks. This finding suggests a potential controlling role of the unilateral cerebral cortex in bilateral motor imagination.<br><br>CONCLUSION: Our study highlights the unilateral cerebral cortex's significance in controlling bilateral limb motor imagination, offering new insights into future brain network remodeling in patients with hemiplegia. Additionally, these findings provide important insights into understanding motor imagination and its impact on BCI and neurorehabilitation.}, }
@article {pmid39571386, year = {2025}, author = {Li, Y and Chen, B and Yoshimura, N and Koike, Y and Yamashita, O}, title = {Sparse Bayesian correntropy learning for robust muscle activity reconstruction from noisy brain recordings.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {182}, number = {}, pages = {106899}, doi = {10.1016/j.neunet.2024.106899}, pmid = {39571386}, issn = {1879-2782}, mesh = {*Bayes Theorem ; Humans ; *Brain/physiology ; *Brain-Computer Interfaces ; *Algorithms ; Machine Learning ; Muscle, Skeletal/physiology ; }, abstract = {Sparse Bayesian learning has promoted many effective frameworks of brain activity decoding for the brain-computer interface, including the direct reconstruction of muscle activity using brain recordings. However, existing sparse Bayesian learning algorithms mainly use Gaussian distribution as error assumption in the reconstruction task, which is not necessarily the truth in the real-world application. On the other hand, brain recording is known to be highly noisy and contains many non-Gaussian noises, which could lead to large performance degradation for sparse Bayesian learning algorithms. The goal of this paper is to propose a novel robust implementation of sparse Bayesian learning so that robustness and sparseness can be realized simultaneously. Motivated by the exceptional robustness of maximum correntropy criterion (MCC), we proposed integrating MCC to the sparse Bayesian learning regime. To be specific, we derived the explicit error assumption inherent in the MCC, and then leveraged it for the likelihood function. Meanwhile, we utilized the automatic relevance determination technique as the sparse prior distribution. To fully evaluate the proposed method, a synthetic example and a real-world muscle activity reconstruction task with two different brain modalities were leveraged. Experimental results showed, our proposed sparse Bayesian correntropy learning framework significantly improves the robustness for the noisy regression tasks. Our proposed algorithm could realize higher correlation coefficients and lower root mean squared errors for the real-world muscle activity reconstruction scenario. Sparse Bayesian correntropy learning provides a powerful approach for brain activity decoding which will promote the development of brain-computer interface technology.}, }
@article {pmid39570849, year = {2024}, author = {Liang, HJ and Li, LL and Cao, GZ}, title = {FDCN-C: A deep learning model based on frequency enhancement, deformable convolution network, and crop module for electroencephalography motor imagery classification.}, journal = {PloS one}, volume = {19}, number = {11}, pages = {e0309706}, pmid = {39570849}, issn = {1932-6203}, mesh = {*Electroencephalography/methods ; *Deep Learning ; Humans ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Imagination/physiology ; }, abstract = {Motor imagery (MI)-electroencephalography (EEG) decoding plays an important role in brain-computer interface (BCI), which enables motor-disabled patients to communicate with external world via manipulating smart equipment. Currently, deep learning (DL)-based methods are popular for EEG decoding. Whereas the utilization efficiency of EEG features in frequency and temporal domain is not sufficient, which results in poor MI classification performance. To address this issue, an EEG-based MI classification model based on a frequency enhancement module, a deformable convolutional network, and a crop module (FDCN-C) is proposed. Firstly, the frequency enhancement module is innovatively designed to address the issue of extracting frequency information. It utilizes convolution kernels at continuous time scales to extract features across different frequency bands. These features are screened by calculating attention and integrated into the original EEG data. Secondly, for temporal feature extraction, a deformable convolution network is employed to enhance feature extraction capabilities, utilizing offset parameters to modulate the convolution kernel size. In spatial domain, a one-dimensional convolution layer is designed to integrate all channel information. Finally, a dilated convolution is used to form a crop classification module, wherein the diverse receptive fields of the EEG data are computed multiple times. Two public datasets are employed to verify the proposed FDCN-C model, the classification accuracy obtained from the proposed model is greater than that of state-of-the-art methods. The model's accuracy has improved by 14.01% compared to the baseline model, and the ablation study has confirmed the effectiveness of each module in the model.}, }
@article {pmid39570847, year = {2024}, author = {Assiri, FY and Ragab, M}, title = {Boosted Harris Hawks Shuffled Shepherd Optimization Augmented Deep Learning based motor imagery classification for brain computer interface.}, journal = {PloS one}, volume = {19}, number = {11}, pages = {e0313261}, pmid = {39570847}, issn = {1932-6203}, mesh = {*Brain-Computer Interfaces ; *Deep Learning ; Humans ; *Electroencephalography/methods ; Imagination/physiology ; Brain/physiology ; Support Vector Machine ; Algorithms ; }, abstract = {Motor imagery (MI) classification has been commonly employed in making brain-computer interfaces (BCI) to manage the outside tools as a substitute neural muscular path. Effectual MI classification in BCI improves communication and mobility for people with a breakdown or motor damage, delivering a bridge between the brain's intentions and exterior actions. Employing electroencephalography (EEG) or aggressive neural recordings, machine learning (ML) methods are used to interpret patterns of brain action linked with motor image tasks. These models frequently depend upon models like support vector machine (SVM) or deep learning (DL) to distinguish among dissimilar MI classes, such as visualizing left or right limb actions. This procedure allows individuals, particularly those with motor disabilities, to utilize their opinions to command exterior devices like robotic limbs or computer borders. This article presents a Boosted Harris Hawks Shuffled Shepherd Optimization Augmented Deep Learning (BHHSHO-DL) technique based on Motor Imagery Classification for BCI. The BHHSHO-DL technique mainly exploits the hyperparameter-tuned DL approach for MI identification for BCI. Initially, the BHHSHO-DL technique performs data preprocessing utilizing the wavelet packet decomposition (WPD) model. Besides, the enhanced densely connected networks (DenseNet) model extracts the preprocessed data's complex and hierarchical feature patterns. Meanwhile, the BHHSHO technique-based hyperparameter tuning process is accomplished to elect optimal parameter values of the enhanced DenseNet model. Finally, the classification procedure is implemented by utilizing the convolutional autoencoder (CAE) model. The simulation value of the BHHSHO-DL methodology is performed on a benchmark dataset. The performance validation of the BHHSHO-DL methodology portrayed a superior accuracy value of 98.15% and 92.23% over other techniques under BCIC-III and BCIC-IV datasets.}, }
@article {pmid39569894, year = {2024}, author = {Brannigan, JFM and Liyanage, K and Horsfall, HL and Bashford, L and Muirhead, W and Fry, A}, title = {Brain-computer interfaces patient preferences: a systematic review.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad94a6}, pmid = {39569894}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; Humans ; *Patient Preference ; Adult ; Middle Aged ; Spinal Cord Injuries/rehabilitation/psychology/physiopathology ; Amyotrophic Lateral Sclerosis/psychology/rehabilitation/physiopathology ; }, abstract = {Objective. Brain-computer interfaces (BCIs) have the potential to restore motor capabilities and functional independence in individuals with motor impairments. Despite accelerating advances in the performance of implanted devices, few studies have identified patient preferences underlying device design, and each study typically captures a single aetiology of motor impairment. We aimed to characterise BCI patient preferences in a large cohort across multiple aetiologies.Approach. We performed a systematic review of all published studies reporting patient preferences for BCI devices, including both qualitative and quantitative data. We searched MEDLINE, Embase, and CINAHL from inception to 18 April 2023. Two reviewers independently screened articles and extracted data on demographic information, device use, invasiveness preference, device design, and functional preferences.Main results. From 1316 articles identified, 28 studies met inclusion criteria, capturing preferences from 1701 patients (mean age 42.1-64.3 years). The most represented conditions were amyotrophic lateral sclerosis (n= 15 studies, 53.6%) and spinal cord injury (n= 13 studies 46.4%). Individuals with motor impairments prioritised device accuracy over other design characteristics. In four studies where patients ranked performance characteristics, accuracy was ranked first each time. We found that the speed and accuracy of BCI systems in recent publications exceeds reported patient preferences, however this performance has been achieved with a level of training and setup burden that would not be tolerated by most patients. Preferences varied by disease aetiology and severity; amyotrophic lateral sclerosis patients typically prioritised communication functions, whereas spinal cord injury patients emphasised limb control and sphincteric functions.Significance.Our findings highlight that despite advances in BCI performance exceeding patient expectations, there remains a need to reduce training and setup burdens to enhance usability. Moreover, patient preferences differ across conditions and impairment severities, underscoring the importance of personalised BCI configurations and tailored training regimens to meet individual needs.}, }
@article {pmid39569892, year = {2024}, author = {Estiveira, J and Soares, E and Pires, G and Nunes, UJ and Sousa, T and Ribeiro, S and Castelo-Branco, M}, title = {SSVEP modulation via non-volitional neurofeedback: anin silicoproof of concept.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad94a5}, pmid = {39569892}, issn = {1741-2552}, mesh = {Humans ; *Neurofeedback/methods ; *Evoked Potentials, Visual/physiology ; *Visual Cortex/physiology ; *Electroencephalography/methods ; Male ; Adult ; Female ; *Photic Stimulation/methods ; Computer Simulation ; Brain-Computer Interfaces ; Young Adult ; Volition/physiology ; }, abstract = {Objective.Neuronal oscillatory patterns are believed to underpin multiple cognitive mechanisms. Accordingly, compromised oscillatory dynamics were shown to be associated with neuropsychiatric conditions. Therefore, the possibility of modulating, or controlling, oscillatory components of brain activity as a therapeutic approach has emerged. Typical non-invasive brain-computer interfaces based on EEG have been used to decode volitional motor brain signals for interaction with external devices. Here we aimed at feedback through visual stimulation which returns directly back to the visual cortex.Approach.Our architecture permits the implementation of feedback control-loops capable of controlling, or at least modulating, visual cortical activity. As this type of neurofeedback depends on early visual cortical activity, mainly driven by external stimulation it is called non-volitional or implicit neurofeedback. Because retino-cortical 40-100 ms delays in the feedback loop severely degrade controller performance, we implemented a predictive control system, called a Smith-Predictor (SP) controller, which compensates for fixed delays in the control loop by building an internal model of the system to be controlled, in this case the EEG response to stimuli in the visual cortex.Main results. Response models were obtained by analyzing, EEG data (n= 8) of experiments using periodically inverting stimuli causing prominent parieto-occipital oscillations, the steady-state visual evoked potentials (SSVEPs). Averaged subject-specific SSVEPs, and associated retina-cortical delays, were subsequently used to obtain the SP controller's linear, time-invariant models of individual responses. The SSVEP models were first successfully validated against the experimental data. When placed in closed loop with the designed SP controller configuration, the SSVEP amplitude level oscillated around several reference values, accounting for inter-individual variability.Significance. In silicoandin vivodata matched, suggesting model's robustness, paving the way for the experimental validation of this non-volitional neurofeedback system to control the amplitude of abnormal brain oscillations in autism and attention and hyperactivity deficits.}, }
@article {pmid39569866, year = {2024}, author = {Thomson, CJ and Tully, TN and Stone, ES and Morrell, CB and Scheme, EJ and Warren, DJ and Hutchinson, DT and Clark, GA and George, JA}, title = {Enhancing neuroprosthesis calibration: the advantage of integrating prior training over exclusive use of new data.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, pmid = {39569866}, issn = {1741-2552}, support = {DP5 OD029571/OD/NIH HHS/United States ; }, mesh = {Humans ; Calibration ; *Electromyography/methods ; Male ; Neural Networks, Computer ; Neural Prostheses ; Amputees/rehabilitation ; Algorithms ; Artificial Limbs ; Female ; Middle Aged ; Adult ; Machine Learning ; }, abstract = {Objective.Neuroprostheses typically operate under supervised learning, in which a machine-learning algorithm is trained to correlate neural or myoelectric activity with an individual's motor intent. Due to the stochastic nature of neuromyoelectric signals, algorithm performance decays over time. This decay is accelerated when attempting to regress proportional control of multiple joints in parallel, compared with the more typical classification-based pattern recognition control. To overcome this degradation, neuroprostheses and commercial myoelectric prostheses are often recalibrated and retrained frequently so that only the most recent, up-to-date data influences the algorithm performance. Here, we introduce and validate an alternative training paradigm in which training data from past calibrations is aggregated and reused in future calibrations for regression control.Approach.Using a cohort of four transradial amputees implanted with intramuscular electromyographic recording leads, we demonstrate that aggregating prior datasets improves prosthetic regression-based control in offline analyses and an online human-in-the-loop task. In offline analyses, we compared the performance of a convolutional neural network (CNN) and a modified Kalman filter (MKF) to simultaneously regress the kinematics of an eight-degree-of-freedom prosthesis. Both algorithms were trained under the traditional paradigm using a single dataset, as well as under the new paradigm using aggregated datasets from the past five or ten trainings.Main results.Dataset aggregation reduced the root-mean-squared error (RMSE) of algorithm estimates for both the CNN and MKF, although the CNN saw a greater reduction in error. Further offline analyses revealed that dataset aggregation improved CNN robustness when reusing the same algorithm on subsequent test days, as indicated by a smaller increase in RMSE per day. Finally, data from an online virtual-target-touching task with one amputee showed significantly better real-time prosthetic control when using aggregated training data from just two prior datasets.Significance.Altogether, these results demonstrate that training data from past calibrations should not be discarded but, rather, should be reused in an aggregated training dataset such that the increased amount and diversity of data improve algorithm performance. More broadly, this work supports a paradigm shift for the field of neuroprostheses away from daily data recalibration for linear classification models and towards daily data aggregation for non-linear regression models.}, }
@article {pmid39567538, year = {2024}, author = {Forenzo, D and Zhu, H and He, B}, title = {A continuous pursuit dataset for online deep learning-based EEG brain-computer interface.}, journal = {Scientific data}, volume = {11}, number = {1}, pages = {1256}, pmid = {39567538}, issn = {2052-4463}, support = {U18 EB029354/EB/NIBIB NIH HHS/United States ; R01 AT009263/AT/NCCIH NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; RF1 NS131069/NS/NINDS NIH HHS/United States ; R01 NS127849/NS/NINDS NIH HHS/United States ; NS127849, NS096761, NS131069, NS124564//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01 NS124564/NS/NINDS NIH HHS/United States ; AT009263//U.S. Department of Health &amp; Human Services | NIH | National Center for Complementary and Integrative Health (NCCIH)/ ; EB029354//U.S. Department of Health &amp; Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB)/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography ; *Deep Learning ; Algorithms ; }, abstract = {This dataset is from an EEG brain-computer interface (BCI) study investigating the use of deep learning (DL) for online continuous pursuit (CP) BCI. In this task, subjects use Motor Imagery (MI) to control a cursor to follow a randomly moving target, instead of a single stationary target used in other traditional BCI tasks. DL methods have recently achieved promising performance in traditional BCI tasks, but most studies investigate offline data analysis using DL algorithms. This dataset consists of ~168 hours of EEG recordings from complex CP BCI experiments, collected from 28 unique human subjects over multiple sessions each, with an online DL-based decoder. The large amount of subject specific data from multiple sessions may be useful for developing new BCI decoders, especially DL methods that require large amounts of training data. By providing this dataset to the public, we hope to help facilitate the development of new or improved BCI decoding algorithms for the complex CP paradigm for continuous object control, bringing EEG-based BCIs closer to real-world applications.}, }
@article {pmid39567330, year = {2024}, author = {Pu, Y and Francks, C and Kong, XZ}, title = {Global brain asymmetry.}, journal = {Trends in cognitive sciences}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.tics.2024.10.008}, pmid = {39567330}, issn = {1879-307X}, abstract = {Lateralization is a defining characteristic of the human brain, often studied through localized approaches that focus on interhemispheric differences between homologous pairs of regions. It is also important to emphasize an integrative perspective of global brain asymmetry, in which hemispheric differences are understood through global patterns across the entire brain.}, }
@article {pmid39567230, year = {2024}, author = {Walters, GI and Foley, H and Huntley, CC and Naveed, A and Nettleton, K and Reilly, C and Thomas, M and Walker, C and Wheeler, K}, title = {Could a behaviour change intervention be used to address under-recognition of work-related asthma in primary care? A systematic review.}, journal = {BJGP open}, volume = {}, number = {}, pages = {}, doi = {10.3399/BJGPO.2024.0094}, pmid = {39567230}, issn = {2398-3795}, abstract = {BACKGROUND: Work-related asthma (WRA) is prevalent yet under-recognized in UK primary care.<br><br>AIM: We aimed to identify behaviour change interventions (BCI) intended for use in primary care to identify WRA, or any other chronic disease (that could be adapted for use in WRA).<br><br>DESIGN &amp; SETTING: Systematic review METHOD: We searched CCRCT, Embase, PsychINFO and Ovid-MEDLINE databases (1946-2023) for studies describing development and/or evaluation of BCIs for case finding any chronic disease in primary care settings, aimed at either healthcare professionals and/or patients. Two blinded, independent reviewers screened abstracts and assessed full text articles. We undertook narrative synthesis for outcomes of usability and effectiveness, and for BCI development processes.<br><br>RESULTS: We included 14 studies from n=768 retrieved citations, comprising 3 randomised control trials, 1 uncontrolled experimental study, and 10 studies employing recognized multi-step BC methodologies. None of the studies were concerned with identification of asthma. BCIs had been developed for facilitating screening programmes (5), implementing guidelines (3) and individual case finding (6). Five studies measured effectiveness, in terms of screening adherence rates, pre-/post-intervention competency, satisfaction and usability, for clinicians, though none measured diagnostic rates.<br><br>CONCLUSION: No single or multi-component BCIs has been developed specifically to aid identification of asthma or WRA, though other chronic diseases have been targeted. Development has used BC methodologies that involved gathering data from a range of sources, and developing content specific to defined at-risk populations, so are not immediately transferable. Such methodologies could be used similarly to develop a primary acre-based BCI for WRA.}, }
@article {pmid39565521, year = {2024}, author = {Guan, Z and Zhang, X and Huang, W and Li, K and Chen, D and Li, W and Sun, J and Chen, L and Mao, Y and Sun, H and Tang, X and Cao, L and Li, Y}, title = {A Method for Detecting Depression in Adolescence Based on an Affective Brain-Computer Interface and Resting-State Electroencephalogram Signals.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {39565521}, issn = {1995-8218}, abstract = {Depression is increasingly prevalent among adolescents and can profoundly impact their lives. However, the early detection of depression is often hindered by the time-consuming diagnostic process and the absence of objective biomarkers. In this study, we propose a novel approach for depression detection based on an affective brain-computer interface (aBCI) and the resting-state electroencephalogram (EEG). By fusing EEG features associated with both emotional and resting states, our method captures comprehensive depression-related information. The final depression detection model, derived through decision fusion with multiple independent models, further enhances detection efficacy. Our experiments involved 40 adolescents with depression and 40 matched controls. The proposed model achieved an accuracy of 86.54% on cross-validation and 88.20% on the independent test set, demonstrating the efficiency of multimodal fusion. In addition, further analysis revealed distinct brain activity patterns between the two groups across different modalities. These findings hold promise for new directions in depression detection and intervention.}, }
@article {pmid39565505, year = {2024}, author = {Wang, XN and Zhang, T and Han, BC and Luo, WW and Liu, WH and Yang, ZY and Disi, A and Sun, Y and Yang, JC}, title = {Wearable EEG Neurofeedback Based-on Machine Learning Algorithms for Children with Autism: A Randomized, Placebo-controlled Study.}, journal = {Current medical science}, volume = {44}, number = {6}, pages = {1141-1147}, pmid = {39565505}, issn = {2523-899X}, mesh = {Humans ; *Neurofeedback/methods/instrumentation ; Child ; Male ; Female ; *Electroencephalography/methods ; Child, Preschool ; *Machine Learning ; *Wearable Electronic Devices ; Autism Spectrum Disorder/therapy/physiopathology ; Autistic Disorder/therapy/physiopathology ; Algorithms ; }, abstract = {OBJECTIVE: Behavioral interventions have been shown to ameliorate the electroencephalogram (EEG) dynamics underlying the behavioral symptoms of autism spectrum disorder (ASD), while studies have also demonstrated that mirror neuron mu rhythm-based EEG neurofeedback training improves the behavioral functioning of individuals with ASD. This study aimed to test the effects of a wearable mu rhythm neurofeedback training system based on machine learning algorithms for children with autism.<br><br>METHODS: A randomized, placebo-controlled study was carried out on 60 participants aged 3 to 6 years who were diagnosed with autism, at two center-based intervention sites. The neurofeedback group received active mu rhythm neurofeedback training, while the control group received a sham neurofeedback training. Other behavioral intervention programs were similar between the two groups.<br><br>RESULTS: After 60 sessions of treatment, both groups showed significant improvements in several domains including language, social and problem behavior. The neurofeedback group showed significantly greater improvements in expressive language (P=0.013) and cognitive awareness (including joint attention, P=0.003) than did the placebo-controlled group.<br><br>CONCLUSION: Artificial intelligence-powered wearable EEG neurofeedback, as a type of brain-computer interface application, is a promising assistive technology that can provide targeted intervention for the core brain mechanisms underlying ASD symptoms.}, }
@article {pmid39561980, year = {2024}, author = {Vacca, F and Galluzzi, F and Blanco-Formoso, M and Gianiorio, T and De Fazio, AF and Tantussi, F and Stürmer, S and Haq, W and Zrenner, E and Chaffiol, A and Joffrois, C and Picaud, S and Benfenati, F and De Angelis, F and Colombo, E}, title = {Solid-State Nanopores for Spatially Resolved Chemical Neuromodulation.}, journal = {Nano letters}, volume = {24}, number = {48}, pages = {15215-15225}, pmid = {39561980}, issn = {1530-6992}, mesh = {Animals ; *Nanopores ; *Neurons/drug effects/metabolism ; Mice ; *Neurotransmitter Agents/chemistry ; Synaptic Transmission/drug effects ; Glutamic Acid/chemistry ; Drug Delivery Systems ; Neural Prostheses ; Ceramics/chemistry ; }, abstract = {Most neural prosthetic devices are based on electrical stimulation, although the modulation of neuronal activity by a localized chemical delivery would better mimic physiological synaptic machinery. In the past decade, various drug delivery approaches attempted to emulate synaptic transmission, although they were hampered by poor retention of their cargo while reaching the target destination, low spatial resolution, and poor biocompatibility and stability of the materials involved. Here, we propose a planar solid-state device for multisite neurotransmitter translocation at the nanoscale consisting of a nanopatterned ceramic membrane connected to a reservoir designed to store neurotransmitters. We achieved diffusion-mediated glutamate stimulation of primary neurons, while we showed the feasibility to translocate other molecules through the pores by either pressure or diffusion, proving the versatility of the proposed technology. Finally, the system proved to be a promising neuronal stimulation interface in mice and nonhuman primates ex vivo, paving the way toward a biomimetic chemical stimulation in neural prosthetics and brain machine interfaces.}, }
@article {pmid39560446, year = {2025}, author = {Xu, F and Shi, W and Lv, C and Sun, Y and Guo, S and Feng, C and Zhang, Y and Jung, TP and Leng, J}, title = {Enhancing Motor Imagery Classification with Residual Graph Convolutional Networks and Multi-Feature Fusion.}, journal = {International journal of neural systems}, volume = {35}, number = {1}, pages = {2450069}, doi = {10.1142/S0129065724500692}, pmid = {39560446}, issn = {1793-6462}, mesh = {Humans ; *Electroencephalography/methods ; *Imagination/physiology ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; Male ; Stroke/physiopathology ; Middle Aged ; Stroke Rehabilitation/methods ; Female ; Adult ; Brain/physiopathology ; Aged ; Signal Processing, Computer-Assisted ; }, abstract = {Stroke, an abrupt cerebrovascular ailment resulting in brain tissue damage, has prompted the adoption of motor imagery (MI)-based brain-computer interface (BCI) systems in stroke rehabilitation. However, analyzing electroencephalogram (EEG) signals from stroke patients poses challenges. To address the issues of low accuracy and efficiency in EEG classification, particularly involving MI, the study proposes a residual graph convolutional network (M-ResGCN) framework based on the modified S-transform (MST), and introduces the self-attention mechanism into residual graph convolutional network (ResGCN). This study uses MST to extract EEG time-frequency domain features, derives spatial EEG features by calculating the absolute Pearson correlation coefficient (aPcc) between channels, and devises a method to construct the adjacency matrix of the brain network using aPcc to measure the strength of the connection between channels. Experimental results involving 16 stroke patients and 16 healthy subjects demonstrate significant improvements in classification quality and robustness across tests and subjects. The highest classification accuracy reached 94.91% and a Kappa coefficient of 0.8918. The average accuracy and F1 scores from 10 times 10-fold cross-validation are 94.38% and 94.36%, respectively. By validating the feasibility and applicability of brain networks constructed using the aPcc in EEG signal analysis and feature encoding, it was established that the aPcc effectively reflects overall brain activity. The proposed method presents a novel approach to exploring channel relationships in MI-EEG and improving classification performance. It holds promise for real-time applications in MI-based BCI systems.}, }
@article {pmid39560167, year = {2025}, author = {Shah, AM}, title = {Hopeful progress in artificial vision.}, journal = {Artificial organs}, volume = {49}, number = {1}, pages = {5-6}, doi = {10.1111/aor.14912}, pmid = {39560167}, issn = {1525-1594}, mesh = {Humans ; *Brain-Computer Interfaces ; *Visual Prosthesis ; Eye, Artificial ; Prosthesis Design ; Vision, Ocular ; Vision Disorders/therapy ; }, abstract = {Visual impairment has been augmented by glasses for centuries. With the advent of newer technologies, correction of more severe visual impairment may be possible with brain-computer interface and eye implants.}, }
@article {pmid39556950, year = {2024}, author = {Kılınç Bülbül, D and Walston, ST and Duvan, FT and Garrido, JA and Güçlü, B}, title = {Decoding sensorimotor information from somatosensory cortex by flexible epicorticalμECoG arrays in unrestrained behaving rats.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad9405}, pmid = {39556950}, issn = {1741-2552}, mesh = {Animals ; Rats ; *Somatosensory Cortex/physiology ; *Brain-Computer Interfaces ; Male ; *Electrodes, Implanted ; Electrocorticography/instrumentation/methods ; Graphite ; Rats, Sprague-Dawley ; Microelectrodes ; }, abstract = {Objective.Brain-computer interfaces (BCI) are promising for severe neurological conditions and there are ongoing efforts to develop state-of-the-art neural interfaces, hardware, and software tools. We tested the potential of novel reduced graphene oxide (rGO) electrodes implanted epidurally over the hind limb representation of the primary somatosensory (S1) cortex of rats, and compared them to commercial platinum-iridium (Pt-Ir) 16-channel electrodes (active site diameter: 25μm).Approach.Motor and somatosensory information was decoded offline from microelectrocorticography (μECoG) signals recorded while unrestrained rats performed a simple behavioral task: pressing a lever and the subsequent vibrotactile stimulation of the glabrous skin at three displacement amplitude levels and at two sinusoidal frequencies.μECoG data were initially analyzed by standard time-frequency methods. Next, signal powers of oscillatory bands recorded from multiple electrode channels were used as features for sensorimotor classification by a machine learning algorithm.Main results.Both electrode types performed quite well and similar to each other for predicting the motor interval and the presence of the vibrotactile stimulus. Average accuracies were relatively lower for predicting 3-class vibrotactile frequency and 4-class amplitude level by both electrode types.Significance.Given some confounding factors during the free movement of rats, the results show that both sensory and motor information can be recorded reliably from the hind limb area of S1 cortex by usingμECoG arrays. The chronic use of novel rGO electrodes was demonstrated successfully. The hind limb area may be convenient for the future evaluation of new tools in neurotechnology, especially those for bidirectional BCIs.}, }
@article {pmid39556943, year = {2024}, author = {Leng, J and Gao, L and Jiang, X and Lou, Y and Sun, Y and Wang, C and Li, J and Zhao, H and Feng, C and Xu, F and Zhang, Y and Jung, TP}, title = {A multi-feature fusion graph attention network for decoding motor imagery intention in spinal cord injury patients.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad9403}, pmid = {39556943}, issn = {1741-2552}, mesh = {Humans ; *Spinal Cord Injuries/physiopathology ; *Electroencephalography/methods ; *Imagination/physiology ; Male ; *Intention ; Adult ; Female ; Attention/physiology ; Brain-Computer Interfaces ; Neural Networks, Computer ; Young Adult ; Middle Aged ; }, abstract = {Objective.Electroencephalogram (EEG) signals exhibit temporal-frequency-spatial multi-domain feature, and due to the nonplanar nature of the brain surface, the electrode distributions follow non-Euclidean topology. To fully resolve the EEG signals, this study proposes a temporal-frequency-spatial multi-domain feature fusion graph attention network (GAT) for motor imagery (MI) intention recognition in spinal cord injury (SCI) patients.Approach.The proposed model uses phase-locked value (PLV) to extract spatial phase connectivity information between EEG channels and continuous wavelet transform to extract valid EEG information in the time-frequency domain. It then models as a graph data structure containing multi-domain information. The gated recurrent unit and GAT learn EEG's dynamic temporal-spatial information. Finally, the fully connected layer outputs the MI intention recognition results.Main results.After 10 times 10-fold cross-validation, the proposed model can achieve an average accuracy of 95.82%. Furthermore, this study analyses the event-related desynchronization/event-related synchronization and PLV brain network to explore the brain activity of SCI patients during MI.Significance.This study confirms the potential of the proposed model in terms of EEG decoding performance and provides a reference for the mechanism of neural activity in SCI patients.}, }
@article {pmid39556340, year = {2024}, author = {Sang, Y and Li, B and Su, T and Zhan, H and Xiong, Y and Huang, Z and Wang, C and Cong, X and Du, M and Wu, Y and Yu, H and Yang, X and Ding, K and Wang, X and Miao, X and Gong, W and Wang, L and Zhao, J and Zhou, Y and Liu, W and Hu, X and Sun, Q}, title = {Visualizing ER-phagy and ER architecture in vivo.}, journal = {The Journal of cell biology}, volume = {223}, number = {12}, pages = {}, pmid = {39556340}, issn = {1540-8140}, support = {32025012//National Natural Science Foundation/ ; 2021YFC2700901//Ministry of Science and Technology of the People's Republic of China/ ; }, mesh = {Animals ; *Mice, Transgenic ; *Endoplasmic Reticulum/metabolism/genetics ; Mice ; Green Fluorescent Proteins/metabolism/genetics ; Luminescent Proteins/genetics/metabolism ; Red Fluorescent Protein ; Genes, Reporter ; Mice, Inbred C57BL ; }, abstract = {ER-phagy is an evolutionarily conserved mechanism crucial for maintaining cellular homeostasis. However, significant gaps persist in our understanding of how ER-phagy and the ER network vary across cell subtypes, tissues, and organs. Furthermore, the pathophysiological relevance of ER-phagy remains poorly elucidated. Addressing these questions requires developing quantifiable methods to visualize ER-phagy and ER architecture in vivo. We generated two transgenic mouse lines expressing an ER lumen-targeting tandem RFP-GFP (ER-TRG) tag, either constitutively or conditionally. This approach enables precise spatiotemporal measurements of ER-phagy and ER structure at single-cell resolution in vivo. Systemic analysis across diverse organs, tissues, and primary cultures derived from these ER-phagy reporter mice unveiled significant variations in basal ER-phagy, both in vivo and ex vivo. Furthermore, our investigation uncovered substantial remodeling of ER-phagy and the ER network in different tissues under stressed conditions such as starvation, oncogenic transformation, and tissue injury. In summary, both reporter models represent valuable resources with broad applications in fundamental research and translational studies.}, }
@article {pmid39555298, year = {2024}, author = {Li, J and Shi, W and Li, Y}, title = {An effective classification approach for EEG-based motor imagery tasks combined with attention mechanisms.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {2689-2707}, pmid = {39555298}, issn = {1871-4080}, abstract = {Currently, electroencephalogram (EEG)-based motor imagery (MI) signals have been received extensive attention, which can assist disabled subjects to control wheelchair, automatic driving and other activities. However, EEG signals are easily affected by some factors, such as muscle movements, wireless devices, power line, etc., resulting in the low signal-to-noise ratios and the worse recognition results on EEG decoding. Therefore, it is crucial to develop a stable model for decoding MI-EEG signals. To address this issue and further improve the decoding performance for MI tasks, a hybrid structure combining convolutional neural networks and bidirectional long short-term memory (BLSTM) model, namely CBLSTM, is developed in this study to handle the various EEG-based MI tasks. Besides, the attention mechanism (AM) model is further adopted to adaptively assign the weight of EEG vital features and enhance the expression which beneficial to classification for MI tasks. First of all, the spatial features and the time series features are extracted by CBLSTM from preprocessed MI-EEG data, respectively. Meanwhile, more effective features information can be mined by the AM model, and the softmax function is utilized to recognize intention categories. Ultimately, the numerical results illustrate that the model presented achieves an average accuracy of 98.40% on the public physioNet dataset and faster training process for decoding MI tasks, which is superior to some other advanced models. Ablation experiment performed also verifies the effectiveness and feasibility of the developed model. Moreover, the established network model provides a good basis for the application of brain-computer interface in rehabilitation medicine.}, }
@article {pmid39555297, year = {2024}, author = {Yin, Y and Kong, W and Tang, J and Li, J and Babiloni, F}, title = {PSPN: Pseudo-Siamese Pyramid Network for multimodal emotion analysis.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {2883-2896}, pmid = {39555297}, issn = {1871-4080}, abstract = {Emotion recognition plays an important role in human life and healthcare. The EEG has been extensively researched as an objective indicator of intense emotions. However, current existing methods lack sufficient analysis of shallow and deep EEG features. In addition, human emotions are complex and variable, making it difficult to comprehensively represent emotions using a single-modal signal. As a signal associated with gaze tracking and eye movement detection, Eye-related signals provide various forms of supplementary information for multimodal emotion analysis. Therefore, we propose a Pseudo-Siamese Pyramid Network (PSPN) for multimodal emotion analysis. The PSPN model employs a Depthwise Separable Convolutional Pyramid (DSCP) to extract and integrate intrinsic emotional features at various levels and scales from EEG signals. Simultaneously, we utilize a fully connected subnetwork to extract the external emotional features from eye-related signals. Finally, we introduce a Pseudo-Siamese network that integrates a flexible cross-modal dual-branch subnetwork to collaboratively utilize EEG emotional features and eye-related behavioral features, achieving consistency and complementarity in multimodal emotion recognition. For evaluation, we conducted experiments on the DEAP and SEED-IV public datasets. The experimental results demonstrate that multimodal fusion significantly improves the accuracy of emotion recognition compared to single-modal approaches. Our PSPN model achieved the best accuracy of 96.02% and 96.45% on the valence and arousal dimensions of the DEAP dataset, and 77.81% on the SEED-IV dataset, respectively. Our code link is: https://github.com/Yinyanyan003/PSPN.git.}, }
@article {pmid39555294, year = {2024}, author = {Leng, J and Yu, X and Wang, C and Zhao, J and Zhu, J and Chen, X and Zhu, Z and Jiang, X and Zhao, J and Feng, C and Yang, Q and Li, J and Jiang, L and Xu, F and Zhang, Y}, title = {Functional connectivity of EEG motor rhythms after spinal cord injury.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {3015-3029}, pmid = {39555294}, issn = {1871-4080}, abstract = {Spinal cord injury (SCI), which is the injury of the spinal cord site resulting in motor dysfunction, has prompted the use of motor imagery (MI)-based brain computer interface (BCI) systems for motor function reconstruction. However, analyzing electroencephalogram signals and brain function mechanisms for SCI patients is challenging. This is due to their low signal-to-noise ratio and high variability. We propose using the phase locking value (PLV) to construct the brain network in α and β rhythms for both SCI patients and healthy individuals. This approach aims to analyze the changes in brain network connectivity and brain function mechanisms following SCI. The results show that the connection strength of the α rhythm in the healthy control (HC) group is stronger than that in the SCI group, and the connection strength in the β rhythm of the SCI group is stronger than that in the HC group. Moreover, we extract the PLV with common spatial pattern (PLV-CSP) feature from the MI data of the SCI group. The experimental results for 12 SCI patients include that the peak classification accuracy is 100%, and the average accuracy of the ten-fold cross-verification is 95.6%. Our proposed approach can be used as a potential valuable method for SCI pathological studies and MI-based BCI rehabilitation systems.}, }
@article {pmid39555292, year = {2024}, author = {Liu, H and Jin, X and Liu, D and Kong, W and Tang, J and Peng, Y}, title = {Affective EEG-based cross-session person identification using hierarchical graph embedding.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {2897-2908}, pmid = {39555292}, issn = {1871-4080}, abstract = {The electroencephalogram (EEG) signal is being investigated as a more confidential biometric for person identification. Despite recent advancements, a persistent challenge lies in the influence of variations in affective states. Affective states consistently exist during data collection, regardless of the protocol used. Additionally, the inherently non-stationary nature of EEG makes it susceptible to fluctuations in affective states over time. Therefore, it would be highly crucial to perform precise EEG-based person identification under varying affective states. This paper employed an integrated Multi-scale Convolution and Graph Pooling network (MCGP) to mitigate the impact of affective state variations. MCGP utilized multiple 1D convolutions at different scales to dynamically extract and fuse features. Additionally, a graph pooling layer with an attention mechanism was incorporated to generate hierarchical graph embeddings. These embeddings were concatenated as inputs for a fully connected classification layer. Experiments were conducted on the SEED and SEED-V dataset, revealing that MCGP achieved an average accuracy of 85.51% for SEED and 88.69% for SEED-V in cross-session conditions involving mixed affective states. Under single affective state cross-session scenario, MCGP achieved an average accuracy of 85.75% for SEED and 88.06% for SEED-V for the same affective states, while obtaining 79.57% for SEED and 84.52% for SEED-V for different affective states. Results indicated that, compared to the baseline methods, MCGP effectively mitigated the impact of variations in affective states across different sessions. In single affective state cross-session scenario, identification performance for the same affective states was slightly higher than that for different affective states.}, }
@article {pmid39555282, year = {2024}, author = {Chen, L and Gao, H and Wang, Z and Gu, B and Zhou, W and Pang, M and Zhang, K and Liu, X and Ming, D}, title = {Vagus nerve electrical stimulation in the recovery of upper limb motor functional impairment after ischemic stroke.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {3107-3124}, pmid = {39555282}, issn = {1871-4080}, abstract = {Ischemic stroke (IS) is characterized by high mortality, disability rates, and a high risk of recurrence. Motor dysfunction, such as limb hemiparesis, dysphagia, auditory disorders, and speech disorders, usually persists after stroke, which imposes a heavy burden on society and the health care system. Traditional rehabilitation therapies may be ineffective in promoting functional recovery after stroke, and alternative strategies are urgently needed. The Food and Drug Administration (FDA) has approved invasive vagus nerve stimulation (iVNS) for the improvement of refractory epilepsy, treatment-resistant depression, obesity, and moderate to severe upper limb motor impairment following chronic ischemic stroke. Additionally, the FDA has approved transcutaneous vagus nerve stimulation (tVNS) for the improvement of cluster headaches and acute migraines. Recent studies have demonstrated that vagus nerve stimulation (VNS) has neuroprotective effects in both transient and permanent cerebral ischemia animal models, significantly improving upper limb motor impairments, auditory deficits, and swallowing difficulties. Firstly, this article reviews two potential neuronal death pathways following IS, including autophagy and inflammatory responses. Then delves into the current status of preclinical and clinical research on the functional recovery following IS with VNS, as well as the potential mechanisms mediating its neuroprotective effects. Finally, the optimal parameters and timing of VNS application are summarized, and the future challenges and directions of VNS in the treatment of IS are discussed. The application of VNS in stroke rehabilitation research has reached a critical stage, and determining how to safely and effectively translate this technology into clinical practice is of utmost importance. Further preclinical and clinical studies are needed to elucidate the therapeutic mechanisms of VNS.}, }
@article {pmid39555271, year = {2024}, author = {Liu, Y and Yu, S and Li, J and Ma, J and Wang, F and Sun, S and Yao, D and Xu, P and Zhang, T}, title = {Brain state and dynamic transition patterns of motor imagery revealed by the bayes hidden markov model.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {2455-2470}, pmid = {39555271}, issn = {1871-4080}, abstract = {UNLABELLED: Motor imagery (MI) is a high-level cognitive process that has been widely applied to brain-computer inference (BCI) and motor recovery. In practical applications, however, huge individual differences and unclear neural mechanisms have seriously hindered the application of MI and BCI systems. Thus, it is urgently needed to explore MI from a new perspective. Here, we applied a hidden Markov model (HMM) to explore the dynamic organization patterns of left- and right-hand MI tasks. Eleven distinct HMM states were identified based on MI-related EEG data. We found that these states can be divided into three metastates by clustering analysis, showing a highly organized structure. We also assessed the probability activation of each HMM state across time. The results showed that the state probability activation of task-evoked have similar trends to that of event-related desynchronization/synchronization (ERD/ERS). By comparing the differences in temporal features of HMM states between left- and right-hand MI, we found notable variations in fractional occupancy, mean life time, mean interval time, and transition probability matrix across stages and states. Interestingly, we found that HMM states activated in the left occipital lobe had higher occupancy during the left-hand MI task, and conversely, during the right-hand MI task, HMM states activated in the right occipital lobe had higher occupancy. Moreover, significant correlations were observed between BCI performance and features of HMM states. Taken together, our findings explored dynamic networks underlying the MI-related process and provided a complementary understanding of different MI tasks, which may contribute to improving the MI-BCI systems.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-024-10099-9.}, }
@article {pmid39555270, year = {2024}, author = {Wang, Y and Zhang, M and Li, M and Cui, H and Chen, X}, title = {Development of a humanoid robot control system based on AR-BCI and SLAM navigation.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {2857-2870}, pmid = {39555270}, issn = {1871-4080}, abstract = {Brain-computer interface (BCI)-based robot combines BCI and robotics technology to realize the brain's intention to control the robot, which not only opens up a new way for the daily care of the disabled individuals, but also provides a new way of communication for normal people. However, the existing systems still have shortcomings in many aspects such as friendliness of human-computer interaction, and interaction efficient. This study developed a humanoid robot control system by integrating an augmented reality (AR)-based BCI with a simultaneous localization and mapping (SLAM)-based scheme for autonomous indoor navigation. An 8-target steady-state visual evoked potential (SSVEP)-based BCI was implemented to enable direct control of the humanoid robot by the user. A Microsoft HoloLens was utilized to display visual stimuli for eliciting SSVEPs. Filter bank canonical correlation analysis (FBCCA), a training-free method, was used to detect SSVEPs in this study. By leveraging SLAM technology, the proposed system alleviates the need for frequent control commands transmission from the user, thereby effectively reducing their workload. Online results from 12 healthy subjects showed this developed BCI system was able to select a command out of eight potential targets with an average accuracy of 94.79%. The autonomous navigation subsystem enabled the humanoid robot to autonomously navigate to a destination chosen utilizing the proposed BCI. Furthermore, all participants successfully completed the experimental task using the developed system without any prior training. These findings illustrate the feasibility of the developed system and its potential to contribute novel insights into humanoid robots control strategies.}, }
@article {pmid39555269, year = {2024}, author = {Shi, X and Li, B and Wang, W and Qin, Y and Wang, H and Wang, X}, title = {Classification algorithm for motor imagery fusing CNN and attentional mechanisms based on functional near-infrared spectroscopy brain image.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {2871-2881}, pmid = {39555269}, issn = {1871-4080}, abstract = {With the continuing development of brain-computer interface technology, the analysis and interpretation of brain signals are becoming increasingly important. In the field of brain-computer interfaces, motor imagery (MI) is an important paradigm for generating specific brain signals through thought alone, rather than actual movement, for computer decoding. Functional near-infrared spectroscopy (fNIRS) imaging technology has been increasingly used in brain-computer interfaces due to its advantages of non-invasiveness, low resource requirements, low cost, and high spatial resolution. Scientists have done a lot of work in channel selection, feature selection, and then applying traditional machine learning methods for classification, but the results achieved so far are still insufficient to meet the conditions for realizing fNIRS brain-computer interfaces. To achieve a higher level of classification of fNIRS signals, we propose a method that fuses CNN and attention mechanisms to analyze the near-infrared signals of motor imagery and mental arithmetic data, which is fed into a neural network by deriving signals of changes in oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) concentrations through the modified Beer-Lambert law, and then applied to the fNIRS dataset of 29 healthy subjects to validate the proposed method. In the fNIRS-based BCI, the average classification accuracy of the MI signal from HbR and HbO reaches 85.92% and 86.21%, respectively, and the average classification accuracy of the MA signal reaches 89.66% and 88.79%, respectively. The advantage of our approach is that it is lightweight and improves the classification accuracy of current BCI fNIRS signals.}, }
@article {pmid39555266, year = {2024}, author = {Wang, Z and Song, X and Chen, L and Nan, J and Sun, Y and Pang, M and Zhang, K and Liu, X and Ming, D}, title = {Research progress of epileptic seizure prediction methods based on EEG.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {2731-2750}, pmid = {39555266}, issn = {1871-4080}, abstract = {At present, at least 30% of refractory epilepsy patients in the world cannot be effectively controlled and treated. The suddenness and unpredictability of seizures greatly affect the physical and mental health and even the life safety of patients, and the realization of early prediction of seizures and the adoption of interventions are of great significance to the improvement of patients' quality of life. In this paper, we firstly introduce the design process of EEG-based seizure prediction methods, introduce several databases commonly used in the research, and summarize the commonly used methods in pre-processing, feature extraction, classification and identification, and post-processing. Then, based on scalp EEG and intracranial EEG respectively, we reviewed the current status of epileptic seizure prediction research from five commonly used feature analysis methods, and make a comprehensive evaluation of both. Finally, this paper describes the reasons why the current algorithms cannot be applied to the clinic, summarizes their limitations, and gives corresponding suggestions, aiming to provide improvement directions for subsequent research. In addition, deep learning algorithms have emerged in recent years, and this paper also compares the advantages and disadvantages of deep learning algorithms with traditional machine learning methods, in the hope of providing researchers with new technologies and new ideas and making significant breakthroughs in the field of epileptic seizure prediction.}, }
@article {pmid39555257, year = {2024}, author = {Ma, J and Yang, B and Rong, F and Gao, S and Wang, W}, title = {Motor imagery decoding using source optimized transfer learning based on multi-loss fusion CNN.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {2521-2534}, pmid = {39555257}, issn = {1871-4080}, abstract = {Transfer learning is increasingly used to decode multi-class motor imagery tasks. Previous transfer learning ignored the optimizability of the source model, weakened the adaptability to the target domain and limited the performance. This paper first proposes the multi-loss fusion convolutional neural network (MF-CNN) to make an optimizable source model. Then we propose a novel source optimized transfer learning (SOTL), which optimizes the source model to make it more in line with the target domain's features to improve the target model's performance. We transfer the model trained from 16 healthy subjects to 16 stroke patients. The average classification accuracy achieves 51.2 ± 0.17% in the four types of unilateral upper limb motor imagery tasks, which is significantly higher than the classification accuracy of deep learning (p < 0.001) and transfer learning (p < 0.05). In this paper, an MI model from the data of healthy subjects can be used for the classification of stroke patients and can demonstrate good classification results, which provides experiential support for the study of transfer learning and the modeling of stroke rehabilitation training.}, }
@article {pmid39555252, year = {2024}, author = {Pan, Y and Li, N and Zhang, Y and Xu, P and Yao, D}, title = {Short-length SSVEP data extension by a novel generative adversarial networks based framework.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {5}, pages = {2925-2945}, pmid = {39555252}, issn = {1871-4080}, abstract = {Steady-state visual evoked potentials (SSVEPs) based brain-computer interface (BCI) has received considerable attention due to its high information transfer rate (ITR) and available quantity of targets. However, the performance of frequency identification methods heavily hinges on the amount of user calibration data and data length, which hinders the deployment in real-world applications. Recently, generative adversarial networks (GANs)-based data generation methods have been widely adopted to create synthetic electroencephalography data, holds promise to address these issues. In this paper, we proposed a GAN-based end-to-end signal transformation network for Time-window length Extension, termed as TEGAN. TEGAN transforms short-length SSVEP signals into long-length artificial SSVEP signals. Additionally, we introduced a two-stage training strategy and the LeCam-divergence regularization term to regularize the training process of GAN during the network implementation. The proposed TEGAN was evaluated on two public SSVEP datasets (a 4-class and 12-class dataset). With the assistance of TEGAN, the performance of traditional frequency recognition methods and deep learning-based methods have been significantly improved under limited calibration data. And the classification performance gap of various frequency recognition methods has been narrowed. This study substantiates the feasibility of the proposed method to extend the data length for short-time SSVEP signals for developing a high-performance BCI system. The proposed GAN-based methods have the great potential of shortening the calibration time and cutting down the budget for various real-world BCI-based applications.}, }
@article {pmid39554850, year = {2024}, author = {Dan, Y and Zhou, D and Wang, Z}, title = {Discriminative possibilistic clustering promoting cross-domain emotion recognition.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1458815}, pmid = {39554850}, issn = {1662-4548}, abstract = {The affective Brain-Computer Interface (aBCI) systems strive to enhance prediction accuracy for individual subjects by leveraging data from multiple subjects. However, significant differences in EEG (Electroencephalogram) feature patterns among subjects often hinder these systems from achieving the desired outcomes. Although studies have attempted to address this challenge using subject-specific classifier strategies, the scarcity of labeled data remains a major hurdle. In light of this, Domain Adaptation (DA) technology has gradually emerged as a prominent approach in the field of EEG-based emotion recognition, attracting widespread research interest. The crux of DA learning lies in resolving the issue of distribution mismatch between training and testing datasets, which has become a focal point of academic attention. Currently, mainstream DA methods primarily focus on mitigating domain distribution discrepancies by minimizing the Maximum Mean Discrepancy (MMD) or its variants. Nevertheless, the presence of noisy samples in datasets can lead to pronounced shifts in domain means, thereby impairing the adaptive performance of DA methods based on MMD and its variants in practical applications to some extent. Research has revealed that the traditional MMD metric can be transformed into a 1-center clustering problem, and the possibility clustering model is adept at mitigating noise interference during the data clustering process. Consequently, the conventional MMD metric can be further relaxed into a possibilistic clustering model. Therefore, we construct a distributed distance measure with Discriminative Possibilistic Clustering criterion (DPC), which aims to achieve two objectives: (1) ensuring the discriminative effectiveness of domain distribution alignment by finding a shared subspace that minimizes the overall distribution distance between domains while maximizing the semantic distribution distance according to the principle of "sames attract and opposites repel"; and (2) enhancing the robustness of distribution distance measure by introducing a fuzzy entropy regularization term. Theoretical analysis confirms that the proposed DPC is an upper bound of the existing MMD metric under certain conditions. Therefore, the MMD objective can be effectively optimized by minimizing the DPC. Finally, we propose a domain adaptation in Emotion recognition based on DPC (EDPC) that introduces a graph Laplacian matrix to preserve the geometric structural consistency between data within the source and target domains, thereby enhancing label propagation performance. Simultaneously, by maximizing the use of source domain discriminative information to minimize domain discrimination errors, the generalization performance of the DA model is further improved. Comparative experiments on several representative domain adaptation learning methods using multiple EEG datasets (i.e., SEED and SEED-IV) show that, in most cases, the proposed method exhibits better or comparable consistent generalization performance.}, }
@article {pmid39554511, year = {2024}, author = {Herbozo Contreras, LF and Truong, ND and Eshraghian, JK and Xu, Z and Huang, Z and Bersani-Veroni, TV and Aguilar, I and Leung, WH and Nikpour, A and Kavehei, O}, title = {Neuromorphic neuromodulation: Towards the next generation of closed-loop neurostimulation.}, journal = {PNAS nexus}, volume = {3}, number = {11}, pages = {pgae488}, pmid = {39554511}, issn = {2752-6542}, abstract = {Neuromodulation techniques have emerged as promising approaches for treating a wide range of neurological disorders, precisely delivering electrical stimulation to modulate abnormal neuronal activity. While leveraging the unique capabilities of AI holds immense potential for responsive neurostimulation, it appears as an extremely challenging proposition where real-time (low-latency) processing, low-power consumption, and heat constraints are limiting factors. The use of sophisticated AI-driven models for personalized neurostimulation depends on the back-telemetry of data to external systems (e.g. cloud-based medical mesosystems and ecosystems). While this can be a solution, integrating continuous learning within implantable neuromodulation devices for several applications, such as seizure prediction in epilepsy, is an open question. We believe neuromorphic architectures hold an outstanding potential to open new avenues for sophisticated on-chip analysis of neural signals and AI-driven personalized treatments. With more than three orders of magnitude reduction in the total data required for data processing and feature extraction, the high power- and memory-efficiency of neuromorphic computing to hardware-firmware co-design can be considered as the solution-in-the-making to resource-constraint implantable neuromodulation systems. This perspective introduces the concept of Neuromorphic Neuromodulation, a new breed of closed-loop responsive feedback system. It highlights its potential to revolutionize implantable brain-machine microsystems for patient-specific treatment.}, }
@article {pmid39553842, year = {2024}, author = {Zhou, L and Wu, B and Qin, B and Gao, F and Li, W and Hu, H and Zhu, Q and Qian, Z}, title = {Cortico-muscular coherence of time-frequency and spatial characteristics under movement observation, movement execution, and movement imagery.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {3}, pages = {1079-1096}, pmid = {39553842}, issn = {1871-4080}, abstract = {Studies show that movement observation (MO), movement imagery (MI), or movement execution (ME) based brain-computer interface systems are promising in promoting the rehabilitation and reorganization of damaged motor function. This study was aimed to explore and compare the motor function rehabilitation mechanism among MO, MI, and ME. 64-channel electroencephalogram and 4-channel electromyogram data were collected from 39 healthy participants (25 males, 14 females; 18-23 years old) during MO, ME, and MI. We analyzed and compared the inter-cortical, inter-muscular, cortico-muscular, and spatial coherence under MO, ME, and MI. Under MO, ME, and MI, cortico-muscular coherence was strongest at the beta-lh band, which means the beta frequency band for cortical signals and the lh frequency band for muscular signals. 56.25-96.88% of the coherence coefficients were significantly larger than 0.5 (ps < 0.05) at the beta-lh band. MO and ME had a contralateral advantage in the spatial coherence between cortex and muscle, while MI had an ipsilateral advantage in the spatial coherence between cortex and muscle. Our results show that the cortico-muscular beta-lh band plays a critical role in the synchronous coupling between cortex and muscle. Also, our findings suggest that the primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), supplementary motor area (SMA), and premotor cortex (PMC) are the specific regions of MO, ME, and MI. However, their pathways of regulating muscles are different under MO, ME, and MI. This study is important for better understanding the motor function rehabilitation mechanism in MO, MI, and ME.}, }
@article {pmid39551888, year = {2024}, author = {Lin, RR and Zhang, K}, title = {Survey of real-time brainmedia in artistic exploration.}, journal = {Visual computing for industry, biomedicine, and art}, volume = {7}, number = {1}, pages = {27}, pmid = {39551888}, issn = {2524-4442}, support = {2021JC02G114//Grant #2021JC02G114./ ; }, abstract = {This survey examines the evolution and impact of real-time brainmedia on artistic exploration, contextualizing developments within a historical framework. To enhance knowledge on the entanglement between the brain, mind, and body in an increasingly mediated world, this work defines a clear scope at the intersection of bio art and interactive art, concentrating on real-time brainmedia artworks developed in the 21st century. It proposes a set of criteria and a taxonomy based on historical notions, interaction dynamics, and media art representations. The goal is to provide a comprehensive overview of real-time brainmedia, setting the stage for future explorations of new paradigms in communication between humans, machines, and the environment.}, }
@article {pmid39551818, year = {2024}, author = {Zhao, H and Zhang, C and Tao, R and Wang, M and Yin, Y and Xu, S}, title = {Dyadic Similarity in Social Value Orientation Modulates Hyper-Brain Network Dynamics During Interpersonal Coordination: An fNIRS-Based Hyperscanning Study.}, journal = {Brain topography}, volume = {38}, number = {1}, pages = {15}, pmid = {39551818}, issn = {1573-6792}, support = {23092179-Y//Science Foundation of Zhejiang Sci-Tech University/ ; 72171151//National Natural Science Foundation of China/ ; 2023DSYL028//Academic Mentoring Program of Shanghai International Studies University/ ; }, mesh = {Humans ; Male ; Young Adult ; Female ; *Spectroscopy, Near-Infrared/methods ; *Brain/physiology ; *Interpersonal Relations ; Cooperative Behavior ; Adult ; Brain Mapping/methods ; Social Behavior ; Neural Pathways/physiology ; }, abstract = {As the fundamental dispositional determinant of social motivation, social value orientation (SVO) may modulate individuals' response patterns in interpersonal coordination contexts. Adopting fNIRS-based hyperscanning approach, the present investigation uncovered the hyper-brain network topological dynamics underlying the effect of the dyadic similarity in the social value orientation on interpersonal coordination. Our findings indicated that the dyads in proself group exhibited the higher degree of competitive intensity during the competitive coordination block, and the dyads in the prosocial group exhibited a higher degree of cooperative coordination during the cooperative coordination block. Distinct hyper-brain functional connectivity patterns and network topological characteristics were identified during the competitive and cooperative coordination blocks in the proself and prosocial groups. The nodal-network global efficiency at the right frontopolar area further mediated the effect of the dyadic deviation in social value orientation similarity on effective adjustments after the negative feedback during the cooperative coordination block in the prosocial group. Our findings manifested distinct behavioral performances and hyper-brain functional connectivity patterns underlying the effect of the dyadic similarity in social value orientation on interpersonal coordination in the real-time mode.}, }
@article {pmid39550056, year = {2024}, author = {Chen, Y and Bai, J and Shi, N and Jiang, Y and Chen, X and Ku, Y and Gao, X}, title = {Intermodulation frequency components in steady-state visual evoked potentials: Generation, characteristics and applications.}, journal = {NeuroImage}, volume = {303}, number = {}, pages = {120937}, doi = {10.1016/j.neuroimage.2024.120937}, pmid = {39550056}, issn = {1095-9572}, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Photic Stimulation/methods ; Brain/physiology ; Signal Processing, Computer-Assisted ; }, abstract = {The steady-state visual evoked potentials (SSVEPs), evoked by dual-frequency or multi-frequency stimulation, likely contains intermodulation frequency components (IMs). Visual IMs are products of nonlinear integration of neural signals and can be evoked by various paradigms that induce neural interaction. IMs have demonstrated many interesting and important characteristics in cognitive psychology, clinical neuroscience, brain-computer interface and other fields, and possess substantial research potential. In this paper, we first review the definition of IMs and summarize the stimulation paradigms capable of inducing them, along with the possible neural origins of IMs. Subsequently, we describe the characteristics and derived applications of IMs in previous studies, and then introduced three signal processing methods favored by researchers to enhance the signal-to-noise ratio of IMs. Finally, we summarize the characteristics of IMs, and propose several potential future research directions related to IMs.}, }
@article {pmid39549531, year = {2025}, author = {Imtiaz, MN and Khan, N}, title = {Enhanced cross-dataset electroencephalogram-based emotion recognition using unsupervised domain adaptation.}, journal = {Computers in biology and medicine}, volume = {184}, number = {}, pages = {109394}, doi = {10.1016/j.compbiomed.2024.109394}, pmid = {39549531}, issn = {1879-0534}, mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Female ; Male ; Adult ; Algorithms ; }, abstract = {Emotion recognition holds great promise in healthcare and in the development of affect-sensitive systems such as brain-computer interfaces (BCIs). However, the high cost of labeled data and significant differences in electroencephalogram (EEG) signals among individuals limit the cross-domain application of EEG-based emotion recognition models. Addressing cross-dataset scenarios poses greater challenges due to changes in subject demographics, recording devices, and stimuli presented. To tackle these challenges, we propose an improved method for classifying EEG-based emotions across domains with different distributions. We propose a Gradual Proximity-guided Target Data Selection (GPTDS) technique, which gradually selects reliable target domain samples for training based on their proximity to the source clusters and the model's confidence in predicting them. This approach avoids negative transfer caused by diverse and unreliable samples. Additionally, we introduce a cost-effective test-time augmentation (TTA) technique named Prediction Confidence-aware Test-Time Augmentation (PC-TTA). Traditional TTA methods often face substantial computational burden, limiting their practical utility. By applying TTA only when necessary, based on the model's predictive confidence, our approach improves the model's performance during inference while minimizing computational costs compared to traditional TTA approaches. Experiments on the DEAP and SEED datasets demonstrate that our method outperforms state-of-the-art approaches, achieving accuracies of 67.44% when trained on DEAP and tested on SEED, and 59.68% vice versa, with improvements of 7.09% and 6.07% over the baseline. It excels in detecting both positive and negative emotions, highlighting its effectiveness for practical emotion recognition in healthcare applications. Moreover, our proposed PC-TTA technique reduces computational time by a factor of 15 compared to traditional full TTA approaches.}, }
@article {pmid39549493, year = {2025}, author = {Hu, P and Zhang, X and Li, M and Zhu, Y and Shi, L}, title = {TSOM: Small object motion detection neural network inspired by avian visual circuit.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {182}, number = {}, pages = {106881}, doi = {10.1016/j.neunet.2024.106881}, pmid = {39549493}, issn = {1879-2782}, mesh = {Animals ; *Neural Networks, Computer ; *Motion Perception/physiology ; *Visual Pathways/physiology ; *Columbidae/physiology ; Retina/physiology ; Algorithms ; Neurons/physiology ; Superior Colliculi/physiology ; Photic Stimulation/methods ; Birds/physiology ; }, abstract = {Detecting small moving objects in complex backgrounds from an overhead perspective is a highly challenging task for machine vision systems. As an inspiration from nature, the avian visual system is capable of processing motion information in various complex aerial scenes, and the Retina-OT-Rt visual circuit of birds is highly sensitive to capturing the motion information of small objects from high altitudes. However, more needs to be done on small object motion detection algorithms based on the avian visual system. In this paper, we conducted mathematical description based on extensive studies of the biological mechanisms of the Retina-OT-Rt visual circuit. Based on this, we proposed a novel tectum small object motion detection neural network (TSOM). The TSOM neural network includes the retina, SGC dendritic, SGC Soma, and Rt layers, each corresponding to neurons in the visual pathway for precise topographic projection, spatial-temporal encoding, motion feature selection, and multi-directional motion integration. Extensive experiments on pigeon neurophysiological experiments and image sequence data showed that the TSOM is biologically interpretable and effective in extracting reliable small object motion features from complex high-altitude backgrounds.}, }
@article {pmid39549492, year = {2025}, author = {Borra, D and Magosso, E and Ravanelli, M}, title = {A protocol for trustworthy EEG decoding with neural networks.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {182}, number = {}, pages = {106847}, doi = {10.1016/j.neunet.2024.106847}, pmid = {39549492}, issn = {1879-2782}, mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Deep Learning ; Algorithms ; Event-Related Potentials, P300/physiology ; Imagination/physiology ; Brain/physiology ; Brain-Computer Interfaces ; Adult ; Male ; }, abstract = {Deep learning solutions have rapidly emerged for EEG decoding, achieving state-of-the-art performance on a variety of decoding tasks. Despite their high performance, existing solutions do not fully address the challenge posed by the introduction of many hyperparameters, defining data pre-processing, network architecture, network training, and data augmentation. Automatic hyperparameter search is rarely performed and limited to network-related hyperparameters. Moreover, pipelines are highly sensitive to performance fluctuations due to random initialization, hindering their reliability. Here, we design a comprehensive protocol for EEG decoding that explores the hyperparameters characterizing the entire pipeline and that includes multi-seed initialization for providing robust performance estimates. Our protocol is validated on 9 datasets about motor imagery, P300, SSVEP, including 204 participants and 26 recording sessions, and on different deep learning models. We accompany our protocol with extensive experiments on the main aspects influencing it, such as the number of participants used for hyperparameter search, the split into sequential simpler searches (multi-step search), the use of informed vs. non-informed search algorithms, and the number of random seeds for obtaining stable performance. The best protocol included 2-step hyperparameter search via an informed search algorithm, with the final training and evaluation performed using 10 random initializations. The optimal trade-off between performance and computational time was achieved by using a subset of 3-5 participants for hyperparameter search. Our protocol consistently outperformed baseline state-of-the-art pipelines, widely across datasets and models, and could represent a standard approach for neuroscientists for decoding EEG in a trustworthy and reliable way.}, }
@article {pmid39548722, year = {2024}, author = {Farahmand, M and Mousavi, M and Azizi, F and Ramezani Tehrani, F}, title = {Exploring the Influence of Age at Menarche on Metabolic Syndrome and Its Components Across Different Women's Birth Cohorts.}, journal = {Endocrinology, diabetes &amp; metabolism}, volume = {7}, number = {6}, pages = {e70015}, pmid = {39548722}, issn = {2398-9238}, support = {43002333//Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences/ ; }, mesh = {Humans ; *Menarche/physiology ; Female ; *Metabolic Syndrome/epidemiology/etiology ; Cross-Sectional Studies ; Adult ; Adolescent ; Iran/epidemiology ; Age Factors ; Middle Aged ; Child ; Birth Cohort ; Young Adult ; Risk Factors ; Prevalence ; Cohort Studies ; }, abstract = {PURPOSE: Metabolic syndrome (MetS) is the primary cardiovascular risk factor, making it a global issue. Our objective was to assess the association between the age at menarche (AAM) and MetS and its components in different generations of women.<br><br>METHODS: In this cross-sectional study, 5500 eligible women aged &#x2265;&#x2009;20 who participated in the Tehran lipid and glucose study in 2015-2017 were selected. Participants were divided into groups by birth cohorts (BC) (born &#x2264;&#x2009;1959, 1960-1979, and &#x2265;&#x2009;1980) and AAM (&#x2264;&#x2009;11, 12-15, and &#x2265;&#x2009;16&#x2009;years, early, normal, and late, respectively). The status of MetS and its components were compared amongst participants using logistic regression.<br><br>RESULTS: Normal AAM (12-15&#x2009;years) was considered the reference group. The adjusted model revealed that AAM &#x2264;&#x2009;11 is associated with a higher risk of 34% (95% confidence interval (CI): 1.04, 1.71) in MetS, and the prevalence of MetS in the early menarche group was higher in BCI, and BCII (odds ratio (OR): 1.87; 95% CI: 1.04, 3.36 and OR: 1.33; 95% CI: 1.00, 1.89, respectively). Those with late menarche demonstrated a lower risk (OR:0.72; 95% CI: 0.57, 0.91) of abdominal obesity, and early menarche showed a higher risk (OR: 1.45; CI: 1.14, 1.86). This higher risk in early menarche was observed in BCI and BCII (OR: 1.76; 95% CI: 1.16, 2.66 and OR: 1.80; 95% CI: 1.23, 2.64, respectively). However, the protective effect of late menarche was observed in BC II and BC III (OR: 0.74; 95% CI: 0.54, 1.00 and OR: 0.64; 95% CI: 0.44, 0.96, respectively).<br><br>CONCLUSIONS: The influential effect of AAM on metabolic disturbances varies amongst different generations.}, }
@article {pmid39548177, year = {2024}, author = {Ma, J and Ma, W and Zhang, J and Li, Y and Yang, B and Shan, C}, title = {Partial prior transfer learning based on self-attention CNN for EEG decoding in stroke patients.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {28170}, pmid = {39548177}, issn = {2045-2322}, support = {ZRJY2021-QM02//National High Level Hospital Clinical Research Funding and Elite Medical Professionals Project of China-Japan Friendship Hospital/ ; BYESS2023173//Young Elite Scientist Sponsorship Program By Bast/ ; 82272612//National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Stroke/physiopathology ; *Neural Networks, Computer ; Algorithms ; Stroke Rehabilitation/methods ; Male ; Female ; Middle Aged ; Adult ; Machine Learning ; }, abstract = {The utilization of motor imagery-based brain-computer interfaces (MI-BCI) has been shown to assist stroke patients activate motor regions in the brain. In particular, the brain regions activated by unilateral upper limb multi-task are more extensive, which is more beneficial for rehabilitation, but it also increases the difficulty of decoding. In this paper, self-attention convolutional neural network based partial prior transfer learning (SACNN-PPTL) is proposed to improve the classification performance of patients' MI multi-task. The backbone network of the algorithm is SACNN, which accords with the inherent features of electroencephalogram (EEG) and contains the temporal feature module, the spatial feature module and the feature generalization module. In addition, PPTL is introduced to transfer part of the target domain while preserving the generalization of the base model while improving the specificity of the target domain. In the experiment, five backbone networks and three training modes are selected as comparison algorithms. The experimental results show that SACNN-PPTL had a classification accuracy of 55.4%±0.17 in four types of MI tasks for 22 patients, which is significantly higher than comparison algorithms (P < 0.05). SACNN-PPTL effectively improves the decoding performance of MI tasks and promotes the development of BCI-based rehabilitation for unilateral upper limb.}, }
@article {pmid39547137, year = {2025}, author = {Wellman, SM and Forrest, AM and Douglas, MM and Subbaraman, A and Zhang, G and Kozai, TDY}, title = {Dynamic changes in the structure and function of brain mural cells around chronically implanted microelectrodes.}, journal = {Biomaterials}, volume = {315}, number = {}, pages = {122963}, doi = {10.1016/j.biomaterials.2024.122963}, pmid = {39547137}, issn = {1878-5905}, mesh = {*Microelectrodes ; *Pericytes/cytology ; Animals ; *Electrodes, Implanted/adverse effects ; *Brain/blood supply ; Male ; Mice ; Calcium/metabolism ; }, abstract = {Integration of neural interfaces with minimal tissue disruption in the brain is ideal to develop robust tools that can address essential neuroscience questions and combat neurological disorders. However, implantation of intracortical devices provokes severe tissue inflammation within the brain, which requires a high metabolic demand to support a complex series of cellular events mediating tissue degeneration and wound healing. Pericytes, peri-vascular cells involved in blood-brain barrier maintenance, vascular permeability, waste clearance, and angiogenesis, have recently been implicated as potential perpetuators of neurodegeneration in brain injury and disease. While the intimate relationship between pericytes and the cortical microvasculature have been explored in other disease states, their behavior following microelectrode implantation, which is responsible for direct blood vessel disruption and dysfunction, is currently unknown. Using two-photon microscopy we observed dynamic changes in the structure and function of pericytes during implantation of a microelectrode array over a 4-week implantation period. Pericytes respond to electrode insertion through transient increases in intracellular calcium and underlying constriction of capillary vessels. Within days following the initial insertion, we observed an influx of new, proliferating pericytes which contribute to new blood vessel formation. Additionally, we discovered a potentially novel population of reactive immune cells in close proximity to the electrode-tissue interface actively engaging in encapsulation of the microelectrode array. Finally, we determined that intracellular pericyte calcium can be modulated by intracortical microstimulation in an amplitude- and frequency-dependent manner. This study provides a new perspective on the complex biological sequelae occurring at the electrode-tissue interface and will foster new avenues of potential research consideration and lead to development of more advanced therapeutic interventions towards improving the biocompatibility of neural electrode technology.}, }
@article {pmid39545725, year = {2025}, author = {Noneman, KK and Patrick Mayo, J}, title = {Decoding Continuous Tracking Eye Movements from Cortical Spiking Activity.}, journal = {International journal of neural systems}, volume = {35}, number = {1}, pages = {2450070}, doi = {10.1142/S0129065724500709}, pmid = {39545725}, issn = {1793-6462}, mesh = {Animals ; *Eye Movements/physiology ; *Neurons/physiology ; *Machine Learning ; *Macaca mulatta ; *Neural Networks, Computer ; Action Potentials/physiology ; Eye-Tracking Technology ; Models, Neurological ; Male ; }, abstract = {Eye movements are the primary way primates interact with the world. Understanding how the brain controls the eyes is therefore crucial for improving human health and designing visual rehabilitation devices. However, brain activity is challenging to decipher. Here, we leveraged machine learning algorithms to reconstruct tracking eye movements from high-resolution neuronal recordings. We found that continuous eye position could be decoded with high accuracy using spiking data from only a few dozen cortical neurons. We tested eight decoders and found that neural network models yielded the highest decoding accuracy. Simpler models performed well above chance with a substantial reduction in training time. We measured the impact of data quantity (e.g. number of neurons) and data format (e.g. bin width) on training time, inference time, and generalizability. Training models with more input data improved performance, as expected, but the format of the behavioral output was critical for emphasizing or omitting specific oculomotor events. Our results provide the first demonstration, to our knowledge, of continuously decoded eye movements across a large field of view. Our comprehensive investigation of predictive power and computational efficiency for common decoder architectures provides a much-needed foundation for future work on real-time gaze-tracking devices.}, }
@article {pmid39545148, year = {2024}, author = {Wohns, N and Dorfman, N and Klein, E}, title = {Caregivers in implantable brain-computer interface research: a scoping review.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1490066}, pmid = {39545148}, issn = {1662-5161}, abstract = {INTRODUCTION: While the ethical significance of caregivers in neurological research has increasingly been recognized, the role of caregivers in brain-computer interface (BCI) research has received relatively less attention.<br><br>OBJECTIVES: This report investigates the extent to which caregivers are mentioned in publications describing implantable BCI (iBCI) research for individuals with motor dysfunction, communication impairment, and blindness.<br><br>METHODS: The scoping review was conducted in June 2024 using the PubMed and Web of Science bibliographic databases. The articles were systematically searched using query terms for caregivers, family members, and guardians, and the results were quantitatively and qualitatively analyzed.<br><br>RESULTS: Our search yielded 315 unique studies, 78 of which were included in this scoping review. Thirty-four (43.6%) of the 78 articles mentioned the study participant's caregivers. We sorted these into 5 categories: Twenty-two (64.7%) of the 34 articles thanked caregivers in the acknowledgement section, 6 (17.6%) articles described the caregiver's role with regard to the consent process, 12 (35.3%) described the caregiver's role in the technical maintenance and upkeep of the BCI system or in other procedural aspects of the study, 9 (26.5%) discussed how the BCI enhanced participant communication and goal-directed behavior with the help of a caregiver, and 3 (8.8%) articles included general comments that did not fit into the other categories but still related to the importance of caregivers in the lives of the research participants.<br><br>DISCUSSION: Caregivers were mentioned in less than half of BCI studies in this review. The studies that offered more robust discussions of caregivers provide valuable insight into the integral role that caregivers play in supporting the study participants and the research process. Attention to the role of caregivers in successful BCI research studies can help guide the responsible development of future BCI study protocols.}, }
@article {pmid39545146, year = {2024}, author = {Wu, X and Ju, X and Dai, S and Li, X and Li, M}, title = {Multi-source domain adaptation for EEG emotion recognition based on inter-domain sample hybridization.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1464431}, pmid = {39545146}, issn = {1662-5161}, abstract = {BACKGROUND: Electroencephalogram (EEG) is widely used in emotion recognition due to its precision and reliability. However, the nonstationarity of EEG signals causes significant differences between individuals or sessions, making it challenging to construct a robust model. Recently, domain adaptation (DA) methods have shown excellent results in cross-subject EEG emotion recognition by aligning marginal distributions. Nevertheless, these methods do not consider emotion category labels, which can lead to label confusion during alignment. Our study aims to alleviate this problem by promoting conditional distribution alignment during domain adaptation to improve cross-subject and cross-session emotion recognition performance.<br><br>METHOD: This study introduces a multi-source domain adaptation common-branch network for EEG emotion recognition and proposes a novel sample hybridization method. This method enables the introduction of target domain data information by directionally hybridizing source and target domain samples without increasing the overall sample size, thereby enhancing the effectiveness of conditional distribution alignment in domain adaptation. Cross-subject and cross-session experiments were conducted on two publicly available datasets, SEED and SEED-IV, to validate the proposed model.<br><br>RESULT: In cross-subject emotion recognition, our method achieved an average accuracy of 90.27% on the SEED dataset, with eight out of 15 subjects attaining a recognition accuracy higher than 90%. For the SEED-IV dataset, the recognition accuracy also reached 73.21%. Additionally, in the cross-session experiment, we sequentially used two out of the three session data as source domains and the remaining session as the target domain for emotion recognition. The proposed model yielded average accuracies of 94.16 and 75.05% on the two datasets, respectively.<br><br>CONCLUSION: Our proposed method aims to alleviate the difficulties of emotion recognition from the limited generalization ability of EEG features across subjects and sessions. Though adapting the multi-source domain adaptation and the sample hybridization method, the proposed method can effectively transfer the emotion-related knowledge of known subjects and achieve accurate emotion recognition on unlabeled subjects.}, }
@article {pmid39544330, year = {2024}, author = {Wang, HZ and Saeed, S and Zhang, JY and Hu, SH}, title = {Bridging Three Years of Insights: Examining the Association Between Depression and Gallstone Disease.}, journal = {Journal of clinical medicine research}, volume = {16}, number = {10}, pages = {472-482}, pmid = {39544330}, issn = {1918-3003}, abstract = {BACKGROUND: Despite sharing common pathophysiological risk factors, the relationship between gallstones and depression requires further evidence for a clearer understanding. This study combines the National Health and Nutrition Examination Survey 2017 - 2020 observational data and Mendelian randomization (MR) analysis to shed light on the potential correlation between these conditions.<br><br>METHODS: By analyzing the National Health and Nutrition Examination Survey 2017 - 2020 data through weighted multivariable-adjusted logistic regression, we examined the association between depression and gallstone risk. MR was subsequently applied, utilizing genetic instruments from a large genome-wide association study on depression (excluding 23andMe, 500,199 participants) and gallstone data (28,627 cases, 348,373 controls), employing the main inverse variance-weighted method alongside other MR methods to explore the causal relationship. Sensitivity analyses validated the study's conclusions.<br><br>RESULTS: Among the 5,303 National Health and Nutrition Examination Survey participants, a significant association was found between depressive symptoms and increased gallstone risk (initial odds ratio (OR) = 2.001; 95% confidence interval (CI) = 1.523 - 2.598; P < 0.001), with the association persisting after comprehensive adjustments (final OR = 1.687; 95% CI = 1.261 - 2.234; P < 0.001). MR findings also indicated a causal link between genetically predicted depression and higher gallstone risk (OR = 1.164; 95% CI = 1.053 - 1.286; P = 0.003).<br><br>CONCLUSIONS: Depression is significantly associated with a higher risk of gallstones, supported by genetic evidence suggesting a causal link. These findings highlight the importance of considering depression in gallstone risk assessments and management strategies.}, }
@article {pmid39543314, year = {2024}, author = {Rybář, M and Poli, R and Daly, I}, title = {Using data from cue presentations results in grossly overestimating semantic BCI performance.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {28003}, pmid = {39543314}, issn = {2045-2322}, mesh = {Humans ; *Brain-Computer Interfaces ; *Semantics ; *Cues ; *Electroencephalography/methods ; Male ; Female ; Adult ; Young Adult ; Brain/physiology ; }, abstract = {Neuroimaging studies have reported the possibility of semantic neural decoding to identify specific semantic concepts from neural activity. This offers promise for brain-computer interfaces (BCIs) for communication. However, translating these findings into a BCI paradigm has proven challenging. Existing EEG-based semantic decoding studies often rely on neural activity recorded when a cue is present, raising concerns about decoding reliability. To address this, we investigate the effects of cue presentation on EEG-based semantic decoding. In an experiment with a clear separation between cue presentation and mental task periods, we attempt to differentiate between semantic categories of animals and tools in four mental tasks. By using state-of-the-art decoding analyses, we demonstrate significant mean classification accuracies up to 71.3% during cue presentation but not during mental tasks, even with adapted analyses from previous studies. These findings highlight a potential issue when using neural activity recorded during cue presentation periods for semantic decoding. Additionally, our results show that semantic decoding without external cues may be more challenging than current state-of-the-art research suggests. By bringing attention to these issues, we aim to stimulate discussion and drive advancements in the field toward more effective semantic BCI applications.}, }
@article {pmid39542998, year = {2024}, author = {Kober, SE and Wood, G and Berger, LM}, title = {Controlling Virtual Reality With Brain Signals: State of the Art of Using VR-Based Feedback in Neurofeedback Applications.}, journal = {Applied psychophysiology and biofeedback}, volume = {}, number = {}, pages = {}, pmid = {39542998}, issn = {1573-3270}, abstract = {The rapid progress of commercial virtual reality (VR) technology, open access to VR development software as well as open-source instructions for creating brain-VR interfaces have increased the number of VR-based neurofeedback (NF) training studies. Controlling a VR environment with brain signals has potential advantages for NF applications. More entertaining, multimodal and adaptive virtual feedback modalities might positively affect subjective user experience and could consequently enhance NF training performance and outcome. Nevertheless, there are certain pitfalls and contraindications that make VR-based NF not suitable for everyone. In the present review, we summarize applications of VR-based NF and discuss positive effects of VR-based NF training as well as contraindications such as cybersickness in VR or age- and sex-related differences. The existing literature implies that VR-based feedback is a promising tool for the improvement of NF training performance. Users generally rate VR-based feedback more positively than traditional 2D feedback, albeit to draw meaningful conclusions and to rule out adverse effects of VR, more research on this topic is necessary. The pace in the development of brain-VR synchronization furthermore necessitates ethical considerations on these technologies.}, }
@article {pmid39539351, year = {2024}, author = {Liu, M and Fang, M and Liu, M and Jin, S and Liu, B and Wu, L and Li, Z}, title = {Knowledge mapping and research trends of brain-computer interface technology in rehabilitation: a bibliometric analysis.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1486167}, pmid = {39539351}, issn = {1662-5161}, abstract = {BACKGROUND: Although the application of brain-computer interface (BCI) technology in rehabilitation has been extensively studied, a systematic and comprehensive bibliometric analysis of this area remains lacking. Thus, this study aims to analyze the research progress of BCI technology in rehabilitation through bibliometric methods.<br><br>METHODS: The study retrieved relevant publications on BCI technology in rehabilitation from the Web of Science Core Collection (WoSCC) between January 1, 2004, and June 30, 2024. The search was conducted using thematic queries, and the document types included "original articles" and "review articles." Bibliometric analysis and knowledge mapping were performed using the Bibliometrix package in R software and CiteSpace software.<br><br>RESULTS: During the study period, a total of 1,431 publications on BCI technology in rehabilitation were published by 4,932 authors from 1,281 institutions across 79 countries in 386 academic journals. The volume of research literature in this field has shown a steady upward trend. The United States of America (USA) and China are the primary contributors, with Eberhard Karls University of T&#xfc;bingen being the most active research institution. The journal Frontiers in Neuroscience published the most articles, while the Journal of Neural Engineering was the most cited. Niels Birbaumer not only authored the most articles but also received the highest number of citations. The main research areas include neurology, sports medicine, and ophthalmology. The diverse applications of BCI technology in stroke and spinal cord injury rehabilitation, as well as the evaluation of BCI performance, are current research hotspots. Moreover, deep learning has demonstrated significant potential in BCI technology rehabilitation applications.<br><br>CONCLUSION: This bibliometric study provides an overview of the research landscape and developmental trends of BCI technology in rehabilitation, offering valuable reference points for researchers in formulating future research strategies.}, }
@article {pmid39539350, year = {2024}, author = {Xia, R and Yang, S}, title = {Factors influencing the social acceptance of brain-computer interface technology among Chinese general public: an exploratory study.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1423382}, pmid = {39539350}, issn = {1662-5161}, abstract = {This study investigates the impact of social factors on public acceptance of brain-computer interface (BCI) technology within China's general population. As BCI emerges as a pivotal advancement in artificial intelligence and a cornerstone of Industry 5.0, understanding its societal reception is crucial. Utilizing data from the Psychological and Behavioral Study of Chinese Residents (N = 1,923), this research examines the roles of learning ability, age, health, social support, and socioeconomic status in BCI acceptance, alongside considerations of gender and the level of monthly household income. Multiple regression analysis via STATA-MP18 reveals that while health, socioeconomic status, social support, and learning ability significantly positively correlate with acceptance, and age presents an inverse relationship, gender and household income do not demonstrate a significant effect. Notably, the prominence of learning ability and social support as principal factors suggests targeted avenues for increasing BCI technology adoption. These findings refine the current understanding of technology acceptance and offer actionable insights for BCI policy and practical applications.}, }
@article {pmid39537730, year = {2024}, author = {Tian, P and Xu, G and Han, C and Du, C and Li, H and Chen, R and Xie, J and Wang, J and Jiang, H and Guo, X and Zhang, S and Wu, Q}, title = {A subjective and objective fusion visual fatigue assessment system for different hardware and software parameters in SSVEP-based BCI applications.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {27872}, pmid = {39537730}, issn = {2045-2322}, support = {No.20231055//The First Affiliated Hospital of Xi'an Jiaotong University/ ; No.20231056//The First Affiliated Hospital of Xi'an Jiaotong University/ ; Program No. 2023-JC-QN-0501//Natural Science Basic Research Program of Shaanxi Province/ ; No.2021ZD0204300//National Key Research and Development projects/ ; }, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; *Software ; Adult ; Algorithms ; Female ; Young Adult ; Fatigue/physiopathology ; Asthenopia/physiopathology ; Photic Stimulation ; }, abstract = {With the development of brain-computer interface industry, large amounts of related applications have entered people's vision. BCI applications based on steady-state visual evoked potentials (SSVEP) are widely used because they do not require pre-training and have high information transmission rates. However, in the actual use of SSVEP stimulus paradigm, the subjects will produce visual fatigue with the use, and fatigue will affect the transmission efficiency. In this experiment, an experimental environment consisting of two paradigm stimulus frequencies (7.5 Hz, 15 Hz), three resolutions (800 × 600, 1280 × 720, 1920 × 1080) and three refresh rates (120 Hz, 240 Hz, 360 Hz) is set up. The Likert scale is used to collect subjective fatigue and preference scores, and the EEG acquisition system and eye tracker are used to collect objective data. Using the proposed information entropy-CRITIC algorithm to combine subjective and objective indicators, a fatigue assessment system (display screen fitness-DSF) is innovated to score different experimental environments. The higher the DSF score, the better the visual experience. The results show that when using the 7.5 Hz SSVEP paradigm, the combination of 360 Hz and 1920 × 1080 can bring the best visual experience. When using the 15 Hz SSVEP paradigm, the combination of 240 Hz and 1280 × 720 is the best. DSF provides powerful help for hardware and software selection guidance and vision protection when using SSVEP-based BCI applications.}, }
@article {pmid39537459, year = {2024}, author = {Lai, W and Sha, L and Li, R and Yu, S and Jin, L and Yang, R and Yang, C and Chen, L}, title = {Urine multi-omics markers to predict seizure one day in advance.}, journal = {Science bulletin}, volume = {69}, number = {24}, pages = {3844-3848}, doi = {10.1016/j.scib.2024.10.035}, pmid = {39537459}, issn = {2095-9281}, }
@article {pmid39536406, year = {2025}, author = {Chen, M and Guo, K and Lu, K and Meng, K and Lu, J and Pang, Y and Zhang, L and Hu, Y and Yu, R and Zhang, R}, title = {Localizing the seizure onset zone and predicting the surgery outcomes in patients with drug-resistant epilepsy: A new approach based on the causal network.}, journal = {Computer methods and programs in biomedicine}, volume = {258}, number = {}, pages = {108483}, doi = {10.1016/j.cmpb.2024.108483}, pmid = {39536406}, issn = {1872-7565}, mesh = {Humans ; *Drug Resistant Epilepsy/surgery/diagnostic imaging/physiopathology ; Retrospective Studies ; Female ; Male ; *Seizures/surgery/physiopathology/diagnostic imaging ; Adult ; Treatment Outcome ; Electroencephalography/methods ; Young Adult ; Adolescent ; Brain/diagnostic imaging/surgery/physiopathology ; Child ; Electrocorticography/methods ; Algorithms ; }, abstract = {BACKGROUND AND OBJECTIVE: Accurate localization of the seizure onset zone (SOZ) is crucial for surgical treatment in patients with drug-resistant epilepsy (DRE). However, clinical identification of SOZ often relies on physician experience and has a certain subjectivity. Therefore, it is emergent to develop quantitative computational tools to assist clinicians in identifying SOZ.<br><br>METHODS: We conduct a retrospective study on intracranial electroencephalography (iEEG) data from 46 patients with DRE. The interactions between different brain regions are quantified by using the phase transfer entropy (PTE), based on which the causal influence index (CII) is proposed to quantify the degree of influence of nodes on the network. Subsequently, the features extracted by the CII are used to construct a random forest classification model, which the performance in identifying SOZ and the generalizability are validated in patients with successful surgeries. Then, based on the CII features of the clinically labeled SOZ, a logistic regression prediction model is constructed to predict the probability of surgical success. The statistical analysis between patients with successful and failed surgery is conducted with the Mann-Whitney U test. Finally, the consistency between the predicted SOZ and the clinically labeled SOZ is verified across different Engel classes.<br><br>RESULTS: The classification model combining the low-frequency and high-frequency features can achieve an accuracy of 82.18% (sensitivity: 85.01%, specificity: 79.69%) and an area under curve (AUC) of 0.90 in identifying SOZ. Furthermore, the model exhibits strong generalizability in identifying SOZ in patients with MRI lesional and non-lesional, as well as those implanted with electrocorticography (ECOG) and stereotactic EEG (SEEG) electrodes. Moreover, the prediction model could achieve an average accuracy of 79.8% and an AUC of 0.84. Of note, the prediction of surgical success probability is significant between patients with successful and failed surgeries (P<0.001). Correspondingly, the highest consistency between model-predicted SOZ and clinically labeled SOZ can be observed in patients with successful surgeries, but this consistency gradually decreases with increasing Engel classes.<br><br>CONCLUSIONS: These results demonstrate that the CII may be a potential biomarker for identifying the SOZ in patients with DRE, which may provide a new perspective for the treatment of epilepsy.}, }
@article {pmid39535986, year = {2024}, author = {Officer, K and Arango-Sabogal, JC and Dufour, S and Lyashchenko, KP and Cracknell, J and Thomson, S and Cheng, S and Warren, K and Jackson, B}, title = {Bayesian accuracy estimates for diagnostic tests to detect tuberculosis in captive sun bears (Helarctos malayanus) and Asiatic black bears (Ursus thibetanus) in Cambodia and Vietnam.}, journal = {PloS one}, volume = {19}, number = {11}, pages = {e0313007}, pmid = {39535986}, issn = {1932-6203}, mesh = {Animals ; *Ursidae/microbiology ; *Bayes Theorem ; Cambodia/epidemiology ; Vietnam/epidemiology ; Diagnostic Tests, Routine/methods ; Sensitivity and Specificity ; Tuberculosis, Pulmonary/diagnosis/epidemiology/microbiology ; Tuberculosis/diagnosis/epidemiology ; Retrospective Studies ; Mycobacterium tuberculosis/isolation &amp; purification ; Bronchoalveolar Lavage Fluid/microbiology ; Male ; Female ; }, abstract = {Effective control of tuberculosis (TB) depends on early diagnosis of disease, yet available tests are unable to perfectly detect infected individuals. In novel hosts diagnostic testing methods for TB are extrapolated from other species, with unknown accuracy. The primary challenge to evaluating the accuracy of TB tests is the lack of a perfect reference test. Here we use a Bayesian latent class analysis approach to evaluate five tests available for ante-mortem detection of pulmonary TB in captive sun bears and Asiatic black bears in Southeast Asia. Using retrospective results from screening of 344 bears at three rescue centres, we estimate accuracy parameters for thoracic radiography, a serological assay (DPP VetTB), and three microbiological tests (microscopy, PCR (Xpert MTB/RIF, Xpert MTB/RIF Ultra), mycobacterial culture) performed on bronchoalveolar lavage samples. While confirming the high specificities (≥ 0.99) of the three microbiological tests, our model demonstrated their sub-optimal sensitivities (<0.7). Thoracic radiography was the only diagnostic method with sensitivity (0.95, 95% BCI: 0.76, 0.998) and specificity (0.95, 95% BCI: 0.91, 0.98) estimated above 0.9. We recommend caution when interpreting DPP VetTB results, with the increased sensitivity resulting from treatment of weakly visible reactions as positive accompanied by a drop in specificity, and we illustrate how the diagnostic value of weak DPP VetTB reactions is particularly reduced if disease prevalence and/or clinical suspicion is low. Conversely, the reduced utility of negative microbiological tests on bronchoalveolar lavage fluid samples when prevalence and/or clinical suspicion is high is demonstrated. Taken together our results suggest multiple tests should be applied and accompanied by consideration of the testing context, to minimise the consequences of misclassification of disease status of bears at risk of TB in sanctuary settings.}, }
@article {pmid39528522, year = {2024}, author = {Hou, Z and Li, X and Yang, J and Xu, SY}, title = {Enhancing mathematical learning outcomes through a low-cost single-channel BCI system.}, journal = {NPJ science of learning}, volume = {9}, number = {1}, pages = {65}, pmid = {39528522}, issn = {2056-7936}, abstract = {This study investigates the effectiveness of a Low-Cost Single-Channel BCI system in improving mathematical learning outcomes, self-efficacy, and alpha power in university students. Eighty participants were randomly assigned to either a BCI group receiving real-time neurofeedback based on alpha rhythms or a sham feedback group. Results showed that the BCI group had significantly higher mathematical performance, self-efficacy, and alpha power compared to the sham feedback group. Mathematics performance, alpha wave intensity, and self-efficacy showed significant positive correlations after training, indicating that neurofeedback training may have promoted their interaction and integration. These findings demonstrate the potential of BCI technology in enhancing mathematical learning outcomes and highlight the importance of considering pre-test performance and self-efficacy in predicting learning outcomes, with implications for personalized learning interventions and the integration of BCI technology in educational settings.}, }
@article {pmid39534365, year = {2024}, author = {Huang, Z and Wei, Q}, title = {Tensor decomposition-based channel selection for motor imagery-based brain-computer interfaces.}, journal = {Cognitive neurodynamics}, volume = {18}, number = {3}, pages = {877-892}, pmid = {39534365}, issn = {1871-4080}, abstract = {The number of electrode channels in a brain-computer interface (BCI) affects not only its classification performance, but also its convenience in practical applications. Despite many studies on channel selection in motor imagery (MI)-based BCI systems, they consist in matrix analysis of EEG signals, which inevitably loses the interactive information among multiple domains such as space, time and frequency. In this paper, a tensor decomposition-based channel selection (TCS) method is employed for MI BCIs. A three-way tensor is yielded by wavelet transform of a single-trial EEG signal and decomposed into three factor matrices by a regularized canonical polyadic decomposition (CPD). The channel factor matrix is used for channel selection and the important channels are selected by calculating the correlation between channels. Regularized common spatial pattern (RCSP) is employed for feature extraction and support vector machine (SVM) for classification. The proposed TCS-RCSP algorithm was evaluated on three BCI data sets and compared with the RCSP with all channels (AC-RCSP) and the RCSP with selected channels by correlation-based channel selection method (CCS-RCSP). The results indicate that TCS-RCSP achieved significantly better overall accuracy than AC-RCSP (94.4% vs. 86.3%) with ρ < 0.01 and CCS-RCSP (94.4% vs. 90.2%) with ρ < 0.05, proving the efficacy of the proposed algorithm for classifying MI tasks.}, }
@article {pmid39531567, year = {2024}, author = {Wang, J and Cui, Y and Zhang, H and Wu, H and Yang, C}, title = {An Asynchronous Training-free SSVEP-BCI Detection Algorithm for Non-Equal Prior Probability Scenarios.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2024.3496727}, pmid = {39531567}, issn = {1558-0210}, abstract = {SSVEP-based brain-computer interface (BCI) systems have received a lot of attention due to their relatively high Signal to Noise Ratio (SNR) and less training requirements. Most of the existing steady-state visual evoked potential (SSVEP) detection algorithms treat the prior probability of each alternative target being selected as equal. In this study, the prior probability distribution of alternative targets was introduced into the SSVEP recognition algorithm, and an asynchronous training-free SSVEP-BCI detection algorithm for non-equal prior probability scenarios was proposed. This algorithm is based on the Spatio-temporal equalization multi-window technique (STE-MW) and introduces the Maximum A Posteriori criterion (MAP), which makes full use of prior information to improve the performance of the asynchronous training-free BCI system. In addition, we proposed a mutual information-based performance evaluation metric called Mutual information rate (MIR) specifically for non-equal prior probability scenarios. This evaluation framework is designed to provide a more accurate estimation of the information transmission performance of BCI systems in such scenarios. A 10-target simulated vehicle control offline experiment involving 17 subjects showed that the proposed method improved the average MIR by 6.48%. Online free control experiments involving 12 subjects showed that the proposed method improved the average MIR by 14.93%, and significantly reduced the average instruction time. The proposed algorithm is more suitable for practical engineering application scenarios that are asynchronous and training-free; the extremely high accuracy is guaranteed while maintaining a low false alarm rate, which can be applied to asynchronous BCI systems that require high stability.}, }
@article {pmid39530641, year = {2024}, author = {Garcia Cerqueira, EM and de Medeiros, RE and da Silva Fiorin, F and de Arújo E Silva, M and Hypolito Lima, R and Azevedo Dantas, AFO and Rodrigues, AC and Delisle-Rodriguez, D}, title = {Local field potential-based brain-machine interface to inhibit epileptic seizures by spinal cord electrical stimulation.}, journal = {Biomedical physics &amp; engineering express}, volume = {11}, number = {1}, pages = {}, doi = {10.1088/2057-1976/ad9155}, pmid = {39530641}, issn = {2057-1976}, mesh = {Animals ; *Brain-Computer Interfaces ; Rats ; *Rats, Wistar ; *Seizures/therapy ; *Epilepsy/therapy ; Spinal Cord ; Motor Cortex/physiopathology ; Hippocampus ; Male ; Spinal Cord Stimulation/methods ; Algorithms ; Machine Learning ; Pentylenetetrazole ; Electric Stimulation/methods ; Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Wavelet Analysis ; }, abstract = {Objective.This study proposes a closed-loop brain-machine interface (BMI) based on spinal cord stimulation to inhibit epileptic seizures, applying a semi-supervised machine learning approach that learns from Local Field Potential (LFP) patterns acquired on the pre-ictal (preceding the seizure) condition.Approach.LFP epochs from the hippocampus and motor cortex are band-pass filtered from 1 to 13 Hz, to obtain the time-frequency representation using the continuous Wavelet transform, and successively calculate the phase lock values (PLV). As a novelty, theZ-score-based PLV normalization using both modifiedk-means and Davies-Bouldin's measure for clustering is proposed here. Consequently, a generic seizure's detector is calibrated for detecting seizures on the normalized PLV, and enables the spinal cord stimulation for periods of 30 s in a closed-loop, while the BMI system detects seizure events. To calibrate the proposed BMI, a dataset with LFP signals recorded on five Wistar rats during basal state and epileptic crisis was used. The epileptic crisis was induced by injecting pentylenetetrazol (PTZ). Afterwards, two experiments without/with our BMI were carried out, inducing epileptic crisis by PTZ in Wistar rats.Main results.Stronger seizure events of high LFP amplitudes and long time periods were observed in the rat, when the BMI system was not used. In contrast, short-time seizure events of relative low intensity were observed in the rat, using the proposed BMI. The proposed system detected on unseen data the synchronized seizure activity in the hippocampus and motor cortex, provided stimulation appropriately, and consequently decreased seizure symptoms.Significance.Low-frequency LFP signals from the hippocampus and motor cortex, and cord spinal stimulation can be used to develop accurate closed-loop BMIs for early epileptic seizures inhibition, as an alternative treatment.}, }
@article {pmid39529486, year = {2024}, author = {Blanken, TF and Kok, R and Obbels, J and Lambrichts, S and Sienaert, P and Verwijk, E}, title = {Prediction of electroconvulsive therapy outcome: A network analysis approach.}, journal = {Acta psychiatrica Scandinavica}, volume = {}, number = {}, pages = {}, doi = {10.1111/acps.13770}, pmid = {39529486}, issn = {1600-0447}, support = {//Flemish Fund for Scientific Research/ ; //Amsterdam Brain and Cognition/ ; }, abstract = {OBJECTIVE: While electroconvulsive therapy (ECT) for the treatment of major depressive disorder is effective, individual response is variable and difficult to predict. These difficulties may in part result from heterogeneity at the symptom level. We aim to predict remission using baseline depression symptoms, taking the associations among symptoms into account, by using a network analysis approach.<br><br>METHOD: We combined individual patient data from two randomized controlled trials (total N&#x2009;=&#x2009;161) and estimated a Mixed Graphical Model to estimate which baseline depression symptoms (corresponding to HRSD-17 items) uniquely predicted remission (defined as either HRSD&#x2264;7 or MADRS<10). We included study as moderator to evaluate study heterogeneity. For symptoms directly predictive of remission we computed odds ratios.<br><br>RESULTS: Three baseline symptoms were uniquely predictive of remission: suicidality negatively predicted remission (OR&#x2009;=&#x2009;0.75; bootstrapped confidence interval (bCI)&#x2009;=&#x2009;0.44-1.00) whereas retardation (OR&#x2009;=&#x2009;1.21; bCI&#x2009;=&#x2009;1.00-2.02) and hypochondriasis (OR&#x2009;=&#x2009;1.31; bCI&#x2009;=&#x2009;1.00-2.25) positively predicted remission. The estimated effects did not differ across trials as no moderation effects were found.<br><br>CONCLUSION: By using a network analysis approach this study identified that the presence of suicidal ideation predicts an overall worse treatment outcome. Psychomotor retardation and hypochondriasis, on the other hand, seem to be associated with a better outcome.}, }
@article {pmid39529200, year = {2024}, author = {Liu, DY and Li, M and Yu, J and Gao, Y and Zhang, X and Hu, D and Northoff, G and Song, XM and Zhu, J}, title = {Sex differences in the human brain related to visual motion perception.}, journal = {Biology of sex differences}, volume = {15}, number = {1}, pages = {92}, pmid = {39529200}, issn = {2042-6410}, support = {2024SSYS0019//the key R&amp;D program of zhejiang/ ; 2022C03096//the key R&amp;D program of zhejiang/ ; 82272112//the national natural science foundation of china grants/ ; 62076248//the national natural science foundation of china grants/ ; 52293424//the national natural science foundation of china grants/ ; LR23E070001//zhejiang proveincial natural science foundation of china/ ; }, mesh = {Humans ; Female ; Male ; *Motion Perception/physiology ; *Magnetic Resonance Imaging ; *Sex Characteristics ; Adult ; *Brain/physiology/diagnostic imaging ; Young Adult ; Photic Stimulation ; }, abstract = {BACKGROUND: Previous studies have found that the temporal duration required for males to perceive visual motion direction is significantly shorter than that for females. However, the neural correlates of such shortened duration perception remain yet unclear. Given that motion perception is primarily associated with the neural activity of the middle temporal visual complex (MT+), we here test the novel hypothesis that the neural mechanism of these behavioral sex differences is mainly related to the MT+ region.<br><br>METHODS: We utilized ultra-high field (UHF) MRI to investigate sex differences in the MT+ brain region. A total of 95 subjects (48 females) participated in two separate studies. Cohort 1, consisting of 33 subjects (16 females), completed task-fMRI (drafting grating stimuli) experiment. Cohort 2, comprising 62 subjects (32 females), engaged in a psychophysical experiment measuring motion perception along different temporal thresholds as well as conducting structural and functional MRI scanning of MT+.<br><br>RESULTS: Our findings show pronounced sex differences in major brain parameters within the left MT+ (but not the right MT+, i.e., laterality). In particular, males demonstrate (i) larger gray matter volume (GMV) and higher brain's spontaneous activity at the fastest infra-slow frequency band in the left MT+; and (ii) stronger functional connectivity between the left MT+ and the left centromedial amygdala (CM). Meanwhile, both female and male participants exhibited comparable correlations between motion perception ability and the multimodal imaging indexes of the MT+ region, i.e., larger GMV, higher brain's spontaneous activity, and faster motion discrimination.<br><br>CONCLUSIONS: Our findings reveal sex differences of imaging indicators of structure and function in the MT+ region, which also relate to the temporal threshold of motion discrimination. Overall, these results show how behavioral sex differences in visual motion perception are generated, and advocate considering sex as a crucial biological variable in both human brain and behavioral research.}, }
@article {pmid39528479, year = {2024}, author = {Tian, F and Zhang, Y and Schriver, KE and Hu, JM and Roe, AW}, title = {Author Correction: A novel interface for cortical columnar neuromodulation with multipoint infrared neural stimulation.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {9742}, doi = {10.1038/s41467-024-54090-8}, pmid = {39528479}, issn = {2041-1723}, }
@article {pmid39527418, year = {2024}, author = {Shi, Y and Jiang, A and Zhong, J and Li, M and Zhu, Y}, title = {Multiclass Classification Framework of Motor Imagery EEG by Riemannian Geometry Networks.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2024.3496757}, pmid = {39527418}, issn = {2168-2208}, abstract = {In motor imagery (MI) tasks for brain computer interfaces (BCIs), the spatial covariance matrix (SCM) of electroencephalogram (EEG) signals plays a critical role in accurate classification. Given that SCMs are symmetric positive definite (SPD), Riemannian geometry is widely utilized to extract classification features. However, calculating distances between SCMs is computationally intensive due to operations like eigenvalue decomposition, and classical optimization techniques, such as gradient descent, cannot be directly applied to Riemannian manifolds, making the computation of the Riemannian mean more complex and reliant on iterative methods or approximations. In this paper, we propose a novel multiclass classification framework that integrates Riemannian geometry and neural networks to mitigate these challenges. The framework comprises two modules: a Riemannian module with multiple branches and a classification module. During training, a fusion loss function is introduced to update the branch corresponding to the true label, while other branches are updated using different loss functions along with the classification module. Comprehensive experiments on four sets of MI EEG data demonstrate the efficiency and effectiveness of the proposed model.}, }
@article {pmid39523453, year = {2024}, author = {Shen, B and Yao, Q and Zhang, Y and Jiang, Y and Wang, Y and Jiang, X and Zhao, Y and Zhang, H and Dong, S and Li, D and Chen, Y and Pan, Y and Yan, J and Han, F and Li, S and Zhu, Q and Zhang, D and Zhang, L and Wu, YC}, title = {Static and Dynamic Functional Network Connectivity in Parkinson's Disease Patients With Postural Instability and Gait Disorder.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {30}, number = {11}, pages = {e70115}, pmid = {39523453}, issn = {1755-5949}, support = {NO.QRX17026//Medical Science and Technology Development Foundation, Nanjing Municipality Health Bureau/ ; //Nanjing Rehabilitation Medicine Center Project/ ; NO.BE2022842//Special Funds of the Jiangsu Provincial Key Research and Development Program/ ; NO.LD2021013//Jiangsu Province Elderly Health Project/ ; NO.81971185//National Natural Science Foundation of China/ ; NO.82171243//National Natural Science Foundation of China/ ; NO.82171249//National Natural Science Foundation of China/ ; NO.82204352//National Natural Science Foundation of China/ ; NO.BJ20005//Jiangsu Provincial Cadre Health Projects/ ; 23-25-2R11//Nanjing Brain Hospital Youth Talent Project/ ; 23-25-2R6//Nanjing Brain Hospital Youth Talent Project/ ; NJ2024029//Fundamental Research Funds for the Central Universities/ ; }, mesh = {Humans ; *Parkinson Disease/physiopathology/diagnostic imaging ; *Postural Balance/physiology ; Female ; Male ; Middle Aged ; *Gait Disorders, Neurologic/physiopathology/etiology ; Aged ; *Magnetic Resonance Imaging ; Nerve Net/physiopathology/diagnostic imaging ; Brain/physiopathology/diagnostic imaging ; Connectome ; Neural Pathways/physiopathology ; }, abstract = {AIMS: The exact cause of the parkinsonism gait remains uncertain. We first focus on understanding the underlying neurological reasons for these symptoms through the examination of both static functional network connectivity (SFNC) and dynamic functional network connectivity (DFNC).<br><br>METHODS: We recruited 64 postural instability and gait disorder-dominated Parkinson's disease (PIGD-PD) patients, 31 non-PIGD-PD (nPIGD-PD) patients, and 54 healthy controls (HC) from Nanjing Brain Hospital. The GIFT software identified five distinct independent components: the basal ganglia (BG), cerebellum (CB), sensory networks (SMN), default mode network (DMN), and central executive network (CEN). We conducted a comparison between the SFNC and DFNC of the five networks and analyzed their correlations with postural instability and gait disorder (PIGD) symptoms.<br><br>RESULTS: Compared with nPIGD-PD patients, the PIGD-PD patients demonstrated reduced connectivity between CEN and DMN while spending less mean dwell time (MDT) in state 4. This is characterized by strong connections. Compared with HC, PIGD-PD patients exhibited enhanced connectivity in the SFNC between CB and CEN, as well as the network between CB and DMN. Patients with PIGD-PD spent more MDT in state 1, which is characterized by few connections, and less MDT in state 4. In state 3, there was an increase in the functional connectivity between the CB and DMN in patients with PIGD-PD. The nPIGD patients showed increased SFNC connectivity between CB and DMN compared to HC. These patients spent more MDT in state 1 and less in state 4. The MDT and fractional windows of state 2 showed a positive link with PIGD scores.<br><br>CONCLUSION: Patients with PIGD-PD exhibit a higher likelihood of experiencing reduced brain connectivity and impaired information processing. The enhanced connection between the cerebellum and DMN networks is considered a type of dynamic compensation.}, }
@article {pmid39523287, year = {2024}, author = {Rosenfeld, JV}, title = {Neurosurgery and the Brain-Computer Interface.}, journal = {Advances in experimental medicine and biology}, volume = {1462}, number = {}, pages = {513-527}, pmid = {39523287}, issn = {0065-2598}, mesh = {*Brain-Computer Interfaces/ethics ; Humans ; Brain/physiology ; Neurosurgical Procedures/methods ; Electrodes, Implanted ; Neurosurgery/methods/instrumentation ; }, abstract = {Brain-computer interfaces (BCIs) are devices that connect the human brain to an effector via a computer and electrode interface. BCIs may also transmit sensory data to the brain. We describe progress with the many types of surgically implanted BCIs, in which electrodes contact or penetrate the cerebral cortex. BCIs developed for restoration of movement in paralyzed limbs or control a robotic arm; restoration of somatic sensation, speech, vision, memory, hearing, and olfaction are also presented. Most devices remain experimental. Commercialization is costly, incurs financial risk, and is time consuming. There are many ethical principles that should be considered by neurosurgeons and by all those responsible for the care of people with serious neurological disability. These considerations are also paramount when the technology is used in for the purpose of enhancement of normal function and where commercial gain is a factor. A new regulatory and legislative framework is urgently required. The evolution of BCIs is occurring rapidly with advances in computer science, artificial intelligence, electronic engineering including wireless transmission, and materials science. The era of the brain-"cloud" interface is approaching.}, }
@article {pmid39521778, year = {2024}, author = {Pacheco-Ramírez, MA and Ramírez-Moreno, MA and Kukkar, K and Rao, N and Huber, D and Brandt, AK and Noble, A and Noble, D and Ealey, B and Contreras-Vidal, JL}, title = {Neural Dynamics of Creative Movements During the Rehearsal and Performance of "LiveWire".}, journal = {Scientific data}, volume = {11}, number = {1}, pages = {1208}, pmid = {39521778}, issn = {2052-4463}, support = {IUCRC BRAIN #2137255//National Science Foundation (NSF)/ ; }, mesh = {Humans ; *Dancing/physiology ; *Electroencephalography ; *Brain/physiology ; Electrooculography ; Movement ; Music ; Creativity ; }, abstract = {This report contains a description of physiological and motion data, recorded simultaneously and in synchrony using the hyperscanning method from two professional dancers using wireless mobile brain-body imaging (MoBI) technology during rehearsals and public performances of "LiveWire" - a new composition comprised of five choreographed music and dance sections inspired by neuroscience principles. Brain and ocular activity were measured using 28-channel scalp electroencephalography (EEG), and 4-channel electrooculography (EOG), respectively; and head motion was recorded using an inertial measurement unit (IMU) placed on the forehead of each dancer. Video recordings were obtained for each session to allow for tagging of physiological and motion signals and for behavioral analysis. Data recordings were collected from 10 sessions over a 4-month period, in which the dancers rehearsed or performed (in front of an audience) choreographed expressive movements. A detailed explanation of the experimental set-up, the steps carried out for data collection, and an explanation on the usage are provided in this report.}, }
@article {pmid39520382, year = {2024}, author = {Kim, D and Lee, JW and Kim, YT and Choe, J and Kim, G and Ha, CM and Kim, JG and Song, KH and Yang, S}, title = {Minimally Invasive Syringe-Injectable Hydrogel with Angiogenic Factors for Ischemic Stroke Treatment.}, journal = {Advanced healthcare materials}, volume = {}, number = {}, pages = {e2403119}, doi = {10.1002/adhm.202403119}, pmid = {39520382}, issn = {2192-2659}, support = {//National Research Foundation of Korea/ ; NRF-2022R1A4A5034121//Korean government/ ; NRF-2022R1A2C1007876//Korean government/ ; NRF-2021R1C1C1010633//Korean government/ ; NRF-RS-2023-00229062//Korean government/ ; 24-BR-03-02//Ministry of Science, ICT and Future Planning/ ; }, abstract = {Ischemic stroke (IS) accounts for most stroke incidents and causes intractable damage to brain tissue. This condition manifests as diverse aftereffects, such as motor impairment, emotional disturbances, and dementia. However, a fundamental approach to curing IS remains unclear. This study proposes a novel approach for treating IS by employing minimally invasive and injectable jammed gelatin-norbornene nanofibrous hydrogels (GNF) infused with growth factors (GFs). The developed GNF/GF hydrogels are administered to the motor cortex of a rat IS model to evaluate their therapeutic effects on IS-induced motor dysfunction. GNFs mimic a natural fibrous extracellular matrix architecture and can be precisely injected into a targeted brain area. The syringe-injectable jammed nanofibrous hydrogel system increased angiogenesis, inflammation, and sensorimotor function in the IS-affected brain. For clinical applications, the biocompatible GNF hydrogel has the potential to efficiently load disease-specific drugs, enabling targeted therapy for treating a wide range of neurological diseases.}, }
@article {pmid39517980, year = {2024}, author = {Chen, Y and Shi, X and De Silva, V and Dogan, S}, title = {Steady-State Visual Evoked Potential-Based Brain-Computer Interface System for Enhanced Human Activity Monitoring and Assessment.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {21}, pages = {}, pmid = {39517980}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Male ; Adult ; Female ; Young Adult ; Brain/physiology ; }, abstract = {Advances in brain-computer interfaces (BCIs) have enabled direct and functional connections between human brains and computing systems. Recent developments in artificial intelligence have also significantly improved the ability to detect brain activity patterns. In particular, using steady-state visual evoked potentials (SSVEPs) in BCIs has enabled noticeable advances in human activity monitoring and identification. However, the lack of publicly available electroencephalogram (EEG) datasets has limited the development of SSVEP-based BCI systems (SSVEP-BCIs) for human activity monitoring and assisted living. This study aims to provide an open-access multicategory EEG dataset created under the SSVEP-BCI paradigm, with participants performing forward, backward, left, and right movements to simulate directional control commands in a virtual environment developed in Unity. The purpose of these actions is to explore how the brain responds to visual stimuli of control commands. An SSVEP-BCI system is proposed to enable hands-free control of a virtual target in the virtual environment allowing participants to maneuver the virtual target using only their brain activity. This work demonstrates the feasibility of using SSVEP-BCIs in human activity monitoring and assessment. The preliminary experiment results indicate the effectiveness of the developed system with high accuracy, successfully classifying 89.88% of brainwave activity.}, }
@article {pmid39517978, year = {2024}, author = {Ma, S and Zhang, D}, title = {A Cross-Attention-Based Class Alignment Network for Cross-Subject EEG Classification in a Heterogeneous Space.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {21}, pages = {}, pmid = {39517978}, issn = {1424-8220}, support = {12271211//the National Natural Science Foundation of China/ ; 2022J011275&#xff0c;2021J01861//the Natural Science Foundation of Fujian Province/ ; ZQ2023022//the Doctoral Research Initiation Fund of Jimei University/ ; FJKX-2023XKB007//the Research Project of Fujian Association for Science and Technology Innovation Think Tank/ ; 2023SXLMMS06//Fujian Alliance Of Mathematics/ ; }, mesh = {*Electroencephalography/methods ; Humans ; *Algorithms ; Signal Processing, Computer-Assisted ; Brain/physiology ; Neural Networks, Computer ; }, abstract = {BACKGROUND: Domain adaptation (DA) techniques have emerged as a pivotal strategy in addressing the challenges of cross-subject classification. However, traditional DA methods are inherently limited by the assumption of a homogeneous space, requiring that the source and target domains share identical feature dimensions and label sets, which is often impractical in real-world applications. Therefore, effectively addressing the challenge of EEG classification under heterogeneous spaces has emerged as a crucial research topic.<br><br>METHODS: We present a comprehensive framework that addresses the challenges of heterogeneous spaces by implementing a cross-domain class alignment strategy. We innovatively construct a cross-encoder to effectively capture the intricate dependencies between data across domains. We also introduce a tailored class discriminator accompanied by a corresponding loss function. By optimizing the loss function, we facilitate the aggregation of features with corresponding classes between the source and target domains, while ensuring that features from non-corresponding classes are dispersed.<br><br>RESULTS: Extensive experiments were conducted on two publicly available EEG datasets. Compared to advanced methods that combine label alignment with transfer learning, our method demonstrated superior performance across five heterogeneous space scenarios. Notably, in four heterogeneous label space scenarios, our method outperformed the advanced methods by an average of 7.8%. Moreover, in complex scenarios involving both heterogeneous label spaces and heterogeneous feature spaces, our method outperformed the state-of-the-art methods by an average of 4.1%.<br><br>CONCLUSIONS: This paper presents an efficient model for cross-subject EEG classification under heterogeneous spaces, which significantly addresses the challenges of EEG classification within heterogeneous spaces, thereby opening up new perspectives and avenues for research in related fields.}, }
@article {pmid39517915, year = {2024}, author = {Wei, P and Chen, T and Zhang, J and Li, J and Hong, J and Zhang, L}, title = {Study of the Brain Functional Connectivity Processes During Multi-Movement States of the Lower Limbs.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {21}, pages = {}, pmid = {39517915}, issn = {1424-8220}, support = {52305294//National Natural Science Foundation of China/ ; xhj032021010-03//the Fundamental Research Funds for the Central Universities/ ; }, mesh = {Humans ; *Lower Extremity/physiology ; *Electroencephalography/methods ; *Walking/physiology ; Male ; *Brain/physiology/diagnostic imaging ; *Brain-Computer Interfaces ; *Movement/physiology ; Female ; Adult ; Gait/physiology ; }, abstract = {Studies using source localization results have shown that cortical involvement increased in treadmill walking with brain-computer interface (BCI) control. However, the reorganization of cortical functional connectivity in treadmill walking with BCI control is largely unknown. To investigate this, a public dataset, a mobile brain-body imaging dataset recorded during treadmill walking with a brain-computer interface, was used. The electroencephalography (EEG)-coupling strength of the between-region and within-region during the continuous self-determinant movements of lower limbs were analyzed. The time-frequency cross-mutual information (TFCMI) method was used to calculate the coupling strength. The results showed the frontal-occipital connection increased in the gamma and delta bands (the threshold of the edge was >0.05) during walking with BCI, which may be related to the effective communication when subjects adjust their gaits to control the avatar. In walking with BCI control, the results showed theta oscillation within the left-frontal, which may be related to error processing and decision making. We also found that between-region connectivity was suppressed in walking with and without BCI control compared with in standing states. These findings suggest that walking with BCI may accelerate the rehabilitation process for lower limb stroke.}, }
@article {pmid39517862, year = {2024}, author = {Rehman, M and Anwer, H and Garay, H and Alemany-Iturriaga, J and Díez, IT and Siddiqui, HUR and Ullah, S}, title = {Decoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {21}, pages = {}, pmid = {39517862}, issn = {1424-8220}, mesh = {*Electroencephalography/methods ; Humans ; *Deep Learning ; *Brain/physiology ; *Signal Processing, Computer-Assisted ; Visual Perception/physiology ; Algorithms ; Photic Stimulation ; }, abstract = {The perception and recognition of objects around us empower environmental interaction. Harnessing the brain's signals to achieve this objective has consistently posed difficulties. Researchers are exploring whether the poor accuracy in this field is a result of the design of the temporal stimulation (block versus rapid event) or the inherent complexity of electroencephalogram (EEG) signals. Decoding perceptive signal responses in subjects has become increasingly complex due to high noise levels and the complex nature of brain activities. EEG signals have high temporal resolution and are non-stationary signals, i.e., their mean and variance vary overtime. This study aims to develop a deep learning model for the decoding of subjects' responses to rapid-event visual stimuli and highlights the major factors that contribute to low accuracy in the EEG visual classification task.The proposed multi-class, multi-channel model integrates feature fusion to handle complex, non-stationary signals. This model is applied to the largest publicly available EEG dataset for visual classification consisting of 40 object classes, with 1000 images in each class. Contemporary state-of-the-art studies in this area investigating a large number of object classes have achieved a maximum accuracy of 17.6%. In contrast, our approach, which integrates Multi-Class, Multi-Channel Feature Fusion (MCCFF), achieves a classification accuracy of 33.17% for 40 classes. These results demonstrate the potential of EEG signals in advancing EEG visual classification and offering potential for future applications in visual machine models.}, }
@article {pmid39517744, year = {2024}, author = {Rettore Andreis, F and Meijs, S and Nielsen, TGNDS and Janjua, TAM and Jensen, W}, title = {Comparison of Subdural and Intracortical Recordings of Somatosensory Evoked Responses.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {21}, pages = {}, pmid = {39517744}, issn = {1424-8220}, support = {DNRF121//Danish National Research Foundation/ ; }, mesh = {*Evoked Potentials, Somatosensory/physiology ; Animals ; Swine ; *Somatosensory Cortex/physiology ; *Signal-To-Noise Ratio ; Brain-Computer Interfaces ; Electrocorticography/methods ; Microelectrodes ; Electric Stimulation/methods ; Electrodes, Implanted ; }, abstract = {Micro-electrocorticography (µECoG) electrodes have emerged to balance the trade-off between invasiveness and signal quality in brain recordings. However, its large-scale applicability is still hindered by a lack of comparative studies assessing the relationship between ECoG and traditional recording methods such as penetrating electrodes. This study aimed to compare somatosensory evoked potentials (SEPs) through the lenses of a µECoG and an intracortical microelectrode array (MEA). The electrodes were implanted in the pig's primary somatosensory cortex, while SEPs were generated by applying electrical stimulation to the ulnar nerve. The SEP amplitude, signal-to-noise ratio (SNR), power spectral density (PSD), and correlation structure were analysed. Overall, SEPs resulting from MEA recordings had higher amplitudes and contained significantly more spectral power, especially at higher frequencies. However, the SNRs were similar between the interfaces. These results demonstrate the feasibility of using µECoG to decode SEPs with wide-range applications in physiology monitoring and brain-computer interfaces.}, }
@article {pmid39517124, year = {2024}, author = {Zhang, A and Jiang, J and Zhang, C and Xu, H and Yu, W and Zhang, ZN and Yuan, L and Lu, Z and Deng, Y and Fan, H and Fang, C and Wang, X and Shao, A and Chen, S and Li, H and Ni, J and Wang, W and Zhang, X and Zhang, J and Luan, B}, title = {Thermogenic Adipocytes Promote M2 Macrophage Polarization through CNNM4-Mediated Mg Secretion.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {11}, number = {47}, pages = {e2401140}, pmid = {39517124}, issn = {2198-3844}, support = {2023YFC2705700//National Key Research and Development Program of China/ ; 32471220//National Natural Science Foundation of China/ ; 82401598//National Natural Science Foundation of China/ ; 82350710799//National Natural Science Foundation of China/ ; 32400982//National Natural Science Foundation of China/ ; 52201300//National Natural Science Foundation of China/ ; 2023ZD0512800//Science and Technology Innovation 2030: Major national science and technology projects/ ; 2021M702090//China Postdoctoral Science Foundation/ ; 2023M742639//China Postdoctoral Science Foundation/ ; BX20240257//China Postdoctoral Science Foundation/ ; 21SG21//Shuguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission/ ; 2023573//Shanghai Post-doctoral Excellence Program/ ; }, mesh = {Animals ; Mice ; *Macrophages/metabolism ; *Thermogenesis ; *Adipocytes/metabolism ; *Magnesium/metabolism ; Mice, Inbred C57BL ; Cation Transport Proteins/metabolism/genetics ; Signal Transduction ; Obesity/metabolism ; Disease Models, Animal ; Male ; }, abstract = {M2 macrophages promote adipose tissue thermogenesis which dissipates energy in the form of heat to combat obesity. However, the regulation of M2 macrophages by thermogenic adipocytes is unclear. Here, it is identified magnesium (Mg) as a thermogenic adipocyte-secreted factor to promote M2 macrophage polarization. Mg transporter Cyclin and CBS domain divalent metal cation transport mediator 4 (CNNM4) induced by ADRB3-PKA-CREB signaling in thermogenic adipocytes during cold exposure mediates Mg efflux and Mg in turn binds to the DFG motif in mTOR to facilitate mTORC2 activation and M2 polarization in macrophages. In obesity, downregulation of CNNM4 expression inhibits Mg secretion from thermogenic adipocytes, which leads to decreased M2 macrophage polarization and thermogenesis. As a result, CNNM4 overexpression in adipocytes or Mg supplementation in adipose tissue ameliorates obesity by promoting thermogenesis. Importantly, an Mg wire implantation (AMI) approach is introduced to achieve adipose tissue-specific long-term Mg supplement. AMI promotes M2 macrophage polarization and thermogenesis and ameliorates obesity in mice. Taken together, a reciprocal regulation of thermogenic adipocytes and M2 macrophages important for thermogenesis is identified, and AMI is offered as a promising strategy against obesity.}, }
@article {pmid39514976, year = {2024}, author = {Ji, L and Yi, L and Huang, C and Li, H and Han, W and Zhang, N}, title = {Classification of hand movements from EEG using a FusionNet based LSTM network.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad905d}, pmid = {39514976}, issn = {1741-2552}, mesh = {*Electroencephalography/methods/classification ; Humans ; *Hand/physiology ; *Movement/physiology ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; }, abstract = {Objective. Accurate classification of electroencephalogram (EEG) signals is crucial for advancing brain-computer interface (BCI) technology. However, current methods face significant challenges in classifying hand movement EEG signals, including effective spatial feature extraction, capturing temporal dependencies, and representing underlying signal dynamics.Approach. This paper introduces a novel multi-model fusion approach, FusionNet-Long Short-Term Memory (LSTM), designed to address these issues. Specifically, it integrates Convolutional Neural Networks for spatial feature extraction, Gated Recurrent Units and LSTM networks for capturing temporal dependencies, and Autoregressive (AR) models for representing signal dynamics.Main results. Compared to single models and state-of-the-art methods, this fusion approach demonstrates substantial improvements in classification accuracy. Experimental results show that the proposed model achieves an accuracy of 87.1% in cross-subject data classification and 99.1% in within-subject data classification. Additionally, Gradient Boosting Trees were employed to evaluate the significance of various EEG features to the model.Significance. This study highlights the advantages of integrating multiple models and introduces a superior classification model, which is pivotal for the advancement of BCI systems.}, }
@article {pmid39513467, year = {2024}, author = {Nie, A and Li, M and Wang, Q and Zhang, C}, title = {The isolation between part-set cues and social collaboration in episodic memory is dependent: Insight from ongoing and post-collaboration.}, journal = {Scandinavian journal of psychology}, volume = {65}, number = {6}, pages = {981-999}, doi = {10.1111/sjop.13042}, pmid = {39513467}, issn = {1467-9450}, support = {202303021221150//Natural Science Foundation of Shanxi Province of China/ ; 2024-090//Research Project Supported by Shanxi Scholarship Council of China/ ; 21YJA190005//Humanities and Social Sciences, Ministry of Education of China/ ; LY21C090002//Zhejiang Provincial Natural Science Foundation of China/ ; 31300831//National Natural Science Foundation of China/ ; //Initial Scientific Research Fund of Shanxi Normal University/ ; }, mesh = {Humans ; *Cues ; Female ; Male ; *Memory, Episodic ; *Mental Recall/physiology ; Young Adult ; Adult ; *Social Interaction ; Cooperative Behavior ; Stereotyping ; Emotions/physiology ; }, abstract = {It has been demonstrated that both part-set cues and social interaction can produce detrimental effects on memory. Specifically, part-set cues lead to part-set cueing impairment, while social interaction can result in collaborative inhibition. However, there is less evidence on whether these factors have isolated or comparable impacts on memory. Additionally, it is still unknown whether the effects behave similarly on item memory and source memory, whether the effects are comparable between ongoing and post-social collaboration, and whether stimulus features influence their respective roles. To address these issues, we conducted the current experiment where participants were exposed to gender stereotype-consistent or -inconsistent words, categorized as positive, neutral, or negative. The words were read out by either a male or a female. Two recall sessions were conducted: Recall 1 was carried out either individually or collaboratively, whereas Recall 2 was always collaborative. Some participants performed Recall 1 under the part-set cued condition while others were under the no-cued condition. Both item memory and source memory were assessed in both recall sessions. The results have three implications. First, during the ongoing collaborative session, two effects were observed on item memory: part-set cueing impairment and collaborative inhibition. Further, the contributions elicited by part-set cues and social collaboration are isolated. The part-set cueing impairment was influenced by both emotional valence and stereotypical consistency. Second, post-collaboration analysis indicated that both the utilization of part-set cues and collaboration itself enhanced item memory, resulting in the part-set cueing enhancement and post-collaborative memory benefit. Additionally, there was evidence indicating that the mechanisms prompted by these two factors intertwined when emotional valence and stereotypical consistency were considered. Third, in both ongoing and post-collaboration scenarios, the detrimental and beneficial effects on item memory and source memory exhibited different patterns, thereby supporting the dual-process models. These findings enhance our comprehension of the insolation and the interplay between part-set cues and collaboration in memory.}, }
@article {pmid39511344, year = {2024}, author = {Fan, Y and Wang, M and Fang, F and Ding, N and Luo, H}, title = {Two-dimensional neural geometry underpins hierarchical organization of sequence in human working memory.}, journal = {Nature human behaviour}, volume = {}, number = {}, pages = {}, pmid = {39511344}, issn = {2397-3374}, support = {2023M740124//China Postdoctoral Science Foundation/ ; T2421004//Science Fund for Creative Research Groups (Fund for Creative Research Groups)/ ; 31930053//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32222035//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31930052//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Working memory (WM) is constructive in nature. Instead of passively retaining information, WM reorganizes complex sequences into hierarchically embedded chunks to overcome capacity limits and facilitate flexible behaviour. Here, to investigate the neural mechanisms underlying hierarchical reorganization in WM, we performed two electroencephalography and one magnetoencephalography experiments, wherein humans retain in WM a temporal sequence of items, that is, syllables, which are organized into chunks, that is, multisyllabic words. We demonstrate that the one-dimensional sequence is represented by two-dimensional neural representational geometry in WM arising from left prefrontal and temporoparietal regions, with separate dimensions encoding item position within a chunk and chunk position in the sequence. Critically, this two-dimensional geometry is observed consistently in different experimental settings, even during tasks not encouraging hierarchical reorganization in WM and correlates with WM behaviour. Overall, these findings strongly support that complex sequences are reorganized into factorized multidimensional neural representational geometry in WM, which also speaks to general structure-based organizational principles given WM's involvement in many cognitive functions.}, }
@article {pmid39511257, year = {2024}, author = {Xue, S and Jin, B and Jiang, J and Guo, L and Liu, J}, title = {A hybrid local-global neural network for visual classification using raw EEG signals.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {27170}, pmid = {39511257}, issn = {2045-2322}, support = {2023ZD0121201//National Science and Technology Major Project/ ; 2023ZD0121201//National Science and Technology Major Project/ ; 2023ZD0121201//National Science and Technology Major Project/ ; 2023ZD0121201//National Science and Technology Major Project/ ; 2023ZD0121201//National Science and Technology Major Project/ ; U21B2043//National Natural Science Foundation of China/ ; U21B2043//National Natural Science Foundation of China/ ; U21B2043//National Natural Science Foundation of China/ ; U21B2043//National Natural Science Foundation of China/ ; U21B2043//National Natural Science Foundation of China/ ; }, mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Algorithms ; Photic Stimulation ; }, abstract = {EEG-based brain-computer interfaces (BCIs) have the potential to decode visual information. Recently, artificial neural networks (ANNs) have been used to classify EEG signals evoked by visual stimuli. However, methods using ANNs to extract features from raw signals still perform lower than traditional frequency-domain features, and the methods are typically evaluated on small-scale datasets at a low sample rate, which can hinder the capabilities of deep-learning models. To overcome these limitations, we propose a hybrid local-global neural network, which can be trained end-to-end from raw signals without handcrafted features. Specifically, we first propose a reweight module to learn channel weights adaptively. Then, a local feature extraction module is designed to capture basic EEG features. Next, a spatial integration module fuses information from each electrode, and a global feature extraction module integrates overall time-domain characteristics. Additionally, a feature fusion module is proposed to extract efficient features in high sampling rate settings. The proposed model achieves state-of-the-art results on two commonly used small-scale datasets and outperforms baseline methods on three under-studied large-scale datasets. Ablation experimental results demonstrate that the proposed modules have a stable performance improvement ability on multiple datasets across different sample rates, providing a robust end-to-end learning framework.}, }
@article {pmid39509814, year = {2025}, author = {Li, X and Wei, W and Qiu, S and He, H}, title = {A temporal-spectral fusion transformer with subject-specific adapter for enhancing RSVP-BCI decoding.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {181}, number = {}, pages = {106844}, doi = {10.1016/j.neunet.2024.106844}, pmid = {39509814}, issn = {1879-2782}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Algorithms ; Male ; Adult ; Visual Perception/physiology ; Photic Stimulation/methods ; Brain/physiology ; }, abstract = {The Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an efficient technology for target retrieval using electroencephalography (EEG) signals. The performance improvement of traditional decoding methods relies on a substantial amount of training data from new test subjects, which increases preparation time for BCI systems. Several studies introduce data from existing subjects to reduce the dependence of performance improvement on data from new subjects, but their optimization strategy based on adversarial learning with extensive data increases training time during the preparation procedure. Moreover, most previous methods only focus on the single-view information of EEG signals, but ignore the information from other views which may further improve performance. To enhance decoding performance while reducing preparation time, we propose a Temporal-Spectral fusion transformer with Subject-specific Adapter (TSformer-SA). Specifically, a cross-view interaction module is proposed to facilitate information transfer and extract common representations across two-view features extracted from EEG temporal signals and spectrogram images. Then, an attention-based fusion module fuses the features of two views to obtain comprehensive discriminative features for classification. Furthermore, a multi-view consistency loss is proposed to maximize the feature similarity between two views of the same EEG signal. Finally, we propose a subject-specific adapter to rapidly transfer the knowledge of the model trained on data from existing subjects to decode data from new subjects. Experimental results show that TSformer-SA significantly outperforms comparison methods and achieves outstanding performance with limited training data from new subjects. This facilitates efficient decoding and rapid deployment of BCI systems in practical use.}, }
@article {pmid39509308, year = {2024}, author = {Liu, D and Ding, Q and Tong, C and Ai, J and Wang, F}, title = {Incremental Classification for High-Dimensional EEG Manifold Representation Using Bidirectional Dimensionality Reduction and Prototype Learning.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2024.3491096}, pmid = {39509308}, issn = {2168-2208}, abstract = {In brain-computer interface (BCI) systems, symmetric positive definite (SPD) manifold within Riemannian space has been frequently utilized to extract spatial features from electroencephalogram (EEG) signals. However, the intrinsic high dimensionality of SPD matrices introduces too much computational burden to hinder the real-time applications of such BCI, especially in handling dynamic tasks, like incremental learning. Directly reducing the dimensionality of SPD matrices with conventional dimensionality reduction (DR) methods will alter the fundamental properties of SPD matrices. Moreover, current DR methods for incremental learning always necessitate retaining old data to update their representations under new mapping. To this end, a bidirectional two-dimensional principal component analysis for SPD manifold (B2DPCA-SPD) is proposed to reduce the dimensionality of SPD matrices, in such way that the reduced matrices remain on SPD manifold. Afterwards, the B2DPCA-SPD is extended to adapt to incremental learning tasks without saving old data. The incremental B2DPCA-SPD can be seamlessly integrated with the matrix-formed growing neural gas network (MF-GNG) to achieve an incremental EEG classification, where the new low-dimensional representations of the prototypes in old classifiers can be easily recalculated with the updated projection matrix. Extensive experiments are conducted on two public datasets to perform the EEG classification. The results demonstrate that our method significantly reduces computation time by 38.53% and 35.96%, and outperforms conventional methods in classification accuracy by 4.21% to 19.59%.}, }
@article {pmid39508555, year = {2024}, author = {Shah, NP and Phillips, AJ and Madugula, S and Lotlikar, A and Gogliettino, AR and Hays, MR and Grosberg, L and Brown, J and Dusi, A and Tandon, P and Hottowy, P and Dabrowski, W and Sher, A and Litke, AM and Mitra, S and Chichilnisky, EJ}, title = {Precise control of neural activity using dynamically optimized electrical stimulation.}, journal = {eLife}, volume = {13}, number = {}, pages = {}, pmid = {39508555}, issn = {2050-084X}, support = {2146755//National Science Foundation Graduate Research Fellowship/ ; 1828993//National Science Foundation/ ; F30-EY-030776-03/EY/NEI NIH HHS/United States ; T32MH-020016/MH/NIMH NIH HHS/United States ; F31-EY-033636/EY/NEI NIH HHS/United States ; DEC-2013/10/M/NZ4/00268//Polish Academy of Sciences/ ; R01-EY021271/EY/NEI NIH HHS/United States ; R01-EY029247/EY/NEI NIH HHS/United States ; P30-EY019005/EY/NEI NIH HHS/United States ; NSF/CRCNS//National Science Foundation/ ; }, mesh = {Animals ; Rats ; *Electric Stimulation/methods ; *Retinal Ganglion Cells/physiology ; Photic Stimulation ; Macaca ; Electrodes, Implanted ; Visual Prosthesis ; Macaca mulatta ; }, abstract = {Neural implants have the potential to restore lost sensory function by electrically evoking the complex naturalistic activity patterns of neural populations. However, it can be difficult to predict and control evoked neural responses to simultaneous multi-electrode stimulation due to nonlinearity of the responses. We present a solution to this problem and demonstrate its utility in the context of a bidirectional retinal implant for restoring vision. A dynamically optimized stimulation approach encodes incoming visual stimuli into a rapid, greedily chosen, temporally dithered and spatially multiplexed sequence of simple stimulation patterns. Stimuli are selected to optimize the reconstruction of the visual stimulus from the evoked responses. Temporal dithering exploits the slow time scales of downstream neural processing, and spatial multiplexing exploits the independence of responses generated by distant electrodes. The approach was evaluated using an experimental laboratory prototype of a retinal implant: large-scale, high-resolution multi-electrode stimulation and recording of macaque and rat retinal ganglion cells ex vivo. The dynamically optimized stimulation approach substantially enhanced performance compared to existing approaches based on static mapping between visual stimulus intensity and current amplitude. The modular framework enabled parallel extensions to naturalistic viewing conditions, incorporation of perceptual similarity measures, and efficient implementation for an implantable device. A direct closed-loop test of the approach supported its potential use in vision restoration.}, }
@article {pmid39508456, year = {2024}, author = {Park, S and Lipton, M and Dadarlat, MC}, title = {Decoding multi-limb movements from two-photon calcium imaging of neuronal activity using deep learning.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad83c0}, pmid = {39508456}, issn = {1741-2552}, mesh = {Animals ; *Deep Learning ; Mice ; *Movement/physiology ; *Calcium/metabolism ; Neurons/physiology ; Brain-Computer Interfaces ; Mice, Inbred C57BL ; Male ; Extremities/innervation/physiology ; }, abstract = {Objective.Brain-machine interfaces (BMIs) aim to restore sensorimotor function to individuals suffering from neural injury and disease. A critical step in implementing a BMI is to decode movement intention from recorded neural activity patterns in sensorimotor areas. Optical imaging, including two-photon (2p) calcium imaging, is an attractive approach for recording large-scale neural activity with high spatial resolution using a minimally-invasive technique. However, relating slow two-photon calcium imaging data to fast behaviors is challenging due to the relatively low optical imaging sampling rates. Nevertheless, neural activity recorded with 2p calcium imaging has been used to decode information about stereotyped single-limb movements and to control BMIs. Here, we expand upon prior work by applying deep learning to decode multi-limb movements of running mice from 2p calcium imaging data.Approach.We developed a recurrent encoder-decoder network (LSTM-encdec) in which the output is longer than the input.Main results.LSTM-encdec could accurately decode information about all four limbs (contralateral and ipsilateral front and hind limbs) from calcium imaging data recorded in a single cortical hemisphere.Significance.Our approach provides interpretability measures to validate decoding accuracy and expands the utility of BMIs by establishing the groundwork for control of multiple limbs. Our work contributes to the advancement of neural decoding techniques and the development of next-generation optical BMIs.}, }
@article {pmid39507693, year = {2024}, author = {Zhang, L and Xia, J and Li, B and Cao, Z and Dong, S}, title = {Multimodal integrated flexible neural probe for in situ monitoring of EEG and lactic acid.}, journal = {RSC advances}, volume = {14}, number = {48}, pages = {35520-35528}, pmid = {39507693}, issn = {2046-2069}, abstract = {In physiological activities, the brain's electroencephalogram (EEG) signal and chemical concentration change are crucial for diagnosing and treating neurological disorders. Despite the advantages of flexible neural probes, such as their flexibility and biocompatibility, it remains a challenge to achieve in situ monitoring of electrophysiological and chemical signals on a small scale simultaneously. This study developed a new method to construct an efficient dual-sided multimodal integrated flexible neural probe, which combines a density electrode array for EEG recordings and an electrochemical sensor for detecting lactic acid. The EEG electrode array includes a 6-channel recording electrode array with each electrode 30 × 50 μm in size, and the lactic acid sensor with overall contact is approximately 100 μm wide. The EEG electrodes have an average impedance of 2.57 kΩ at 1 kHz and remained stable after immersing in NS (normal saline) for 3 months. The lactic acid sensor showed a sensitivity of 52.8 nA mM[-1]. The in vivo experiments demonstrated that the probe can reliably monitor electrophysiological signals. The probe is able to be implanted into the desired site with the help of a guide port. This flexible neural probe can provide more comprehensive insights into brain activity in the field of neuroscience and clinical practices.}, }
@article {pmid39506628, year = {2024}, author = {Theofanopoulou, C and Paez, S and Huber, D and Todd, E and Ramírez-Moreno, MA and Khaleghian, B and Sánchez, AM and Barceló, L and Gand, V and Contreras-Vidal, JL}, title = {Mobile brain imaging in butoh dancers: from rehearsals to public performance.}, journal = {BMC neuroscience}, volume = {25}, number = {1}, pages = {62}, pmid = {39506628}, issn = {1471-2202}, mesh = {Humans ; *Dancing/physiology ; *Electroencephalography/methods ; *Brain/physiology ; Female ; Male ; Adult ; Brain-Computer Interfaces ; Young Adult ; }, abstract = {BACKGROUND: Dissecting the neurobiology of dance would shed light on a complex, yet ubiquitous, form of human communication. In this experiment, we sought to study, via mobile electroencephalography (EEG), the brain activity of five experienced dancers while dancing butoh, a postmodern dance that originated in Japan.<br><br>RESULTS: We report the experimental design, methods, and practical execution of a highly interdisciplinary project that required the collaboration of dancers, engineers, neuroscientists, musicians, and multimedia artists, among others. We explain in detail how we technically validated all our EEG procedures (e.g., via impedance value monitoring) and minimized potential artifacts in our recordings (e.g., via electrooculography and inertial measurement units). We also describe the engineering details and hardware that enabled us to achieve synchronization between signals recorded at different sampling frequencies, along with a signal preprocessing and denoising pipeline that we used for data re-sampling and power line noise removal. As our experiment culminated in a live performance, where we generated a real-time visualization of the dancers' interbrain synchrony on a screen via an artistic brain-computer interface, we outline all the methodology (e.g., filtering, time-windows, equation) we used for online bispectrum estimations. Additionally, we provide access to all the raw EEG data and codes we used in our recordings. We, lastly, discuss how we envision that the data could be used to address several hypotheses, such as that of interbrain synchrony or the motor theory of vocal learning.<br><br>CONCLUSIONS: Being, to our knowledge, the first study to report synchronous and simultaneous recording from five dancers, we expect that our findings will inform future art-science collaborations, as well as dance-movement therapies.}, }
@article {pmid39505863, year = {2024}, author = {Hu, N and Shi, JX and Chen, C and Xu, HH and Chang, ZH and Hu, PF and Guo, D and Zhang, XW and Shao, WW and Fan, X and Zuo, JC and Ming, D and Li, XH}, title = {Constructing organoid-brain-computer interfaces for neurofunctional repair after brain injury.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {9580}, pmid = {39505863}, issn = {2041-1723}, mesh = {*Brain-Computer Interfaces ; *Organoids ; Animals ; *Brain Injuries/therapy/physiopathology ; *Brain/physiology ; Humans ; Male ; Neurons/physiology ; Mice ; }, abstract = {The reconstruction of damaged neural circuits is critical for neurological repair after brain injury. Classical brain-computer interfaces (BCIs) allow direct communication between the brain and external controllers to compensate for lost functions. Importantly, there is increasing potential for generalized BCIs to input information into the brains to restore damage, but their effectiveness is limited when a large injured cavity is caused. Notably, it might be overcome by transplantation of brain organoids into the damaged region. Here, we construct innovative BCIs mediated by implantable organoids, coined as organoid-brain-computer interfaces (OBCIs). We assess the prolonged safety and feasibility of the OBCIs, and explore neuroregulatory strategies. OBCI stimulation promotes progressive differentiation of grafts and enhances structural-functional connections within organoids and the host brain, promising to repair the damaged brain via regenerating and regulating, potentially directing neurons to preselected targets and recovering functional neural networks in the future.}, }
@article {pmid39504276, year = {2024}, author = {Yang, X and Xiong, X and Li, X and Lian, Q and Zhu, J and Zhang, J and Qi, Y and Wang, Y}, title = {Reconstructing Multi-Stroke Characters from Brain Signals toward Generalizable Handwriting Brain-Computer Interfaces.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2024.3492191}, pmid = {39504276}, issn = {1558-0210}, abstract = {Handwriting Brain-Computer Interfaces (BCIs) provides a promising communication avenue for individuals with paralysis. While English-based handwriting BCIs have achieved rapid typewriting with 26 lowercase letters (mostly containing one stroke each), it is difficult to extend to complex characters, especially those with multiple strokes and large character sets. The Chinese characters, including over 3500 commonly used characters with 10.3 strokes per character on average, represent a highly complex writing system. This paper proposes a Chinese handwriting BCI system, which reconstructs multi-stroke handwriting trajectories from brain signals. Through the recording of cortical neural signals from the motor cortex, we reveal distinct neural representations for stroke-writing and pen-lift phases. Leveraging this finding, we propose a stroke-aware approach to decode stroke-writing trajectories and pen-lift movements individually, which can reconstruct recognizable characters (accuracy of 86% with 400 characters). Our approach demonstrates high stability over 5 months, shedding light on generalized and adaptable handwriting BCIs.}, }
@article {pmid39504275, year = {2024}, author = {Jia, T and Mo, L and McGeady, C and Sun, J and Liu, A and Ji, L and Xi, J and Li, C}, title = {Cortical Activation Patterns Determine Effectiveness of rTMS-induced Motor Imagery Decoding Enhancement in Stroke Patients.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2024.3492977}, pmid = {39504275}, issn = {1558-2531}, abstract = {Combination therapy with motor imagery (MI)-based brain-computer interface (BCI) and repetitive transcranial magnetic stimulation (rTMS) is a promising therapy for poststroke neurorehabilitation. However, with patients' individual differences, the clinical effects vary greatly. This study aims to explore the hypothesis that stroke patients show individualized cortical response to rTMS treatments, which determine the effectiveness of rTMS-induced MI decoding enhancement. We applied four kinds of rTMS treatments respectively to four groups of subacute stroke patients, twenty-six patients in total, and observed their EEG dynamics, MI decoding performance, and Fugl-Meyer assessment changes following 2-week neuromodulation. Four treatments consisted of ipsilesional 10 Hz rTMS, contralesional 1 Hz rTMS, ipsilesional 1 Hz rTMS, and sham stimulation. Results showed stroke patients with different neural reorganization patterns responded individually to rTMS therapy. Patients with cortical lesions mostly showed contralesional recruitment and patients without cortical lesions mostly presented ipsilesional focusing. Significant activation increases in the ipsilesional hemisphere (pre: -15.7% ∓ 8.2%, post: -17.3% ∓ 8.1%, p = 0.037) and MI decoding accuracy enhancement (pre: 76.3 ± 13.8%, post: 86.6 ± 8.2%, p = 0.037) were concurrently found in no-cortical-lesion patients with ipsilesional activation treatment. In the group of patients without cortical lesions, recovery rate in those receiving ipsilesional activation therapy (23.5 ± 10.4%) was higher than those receiving ipsilesional suppression therapy (9.9 ± 9.3%) (p = 0.041). This study reveals that tailoring neuromodulation therapy by recognizing cortical activation patterns is promising for improving effectiveness of the combination therapy with BCI and rTMS.}, }
@article {pmid39504274, year = {2024}, author = {Jin, J and Zhao, X and Daly, I and Li, S and Wang, X and Cichocki, A and Jung, TP}, title = {A Growing Bubble Speller Paradigm for Brain-Computer Interface Based on Event-related Potentials.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2024.3492506}, pmid = {39504274}, issn = {1558-2531}, abstract = {OBJECTIVE: Event-related potentials (ERPs) reflect electropotential changes within specific cortical regions in response to specific events or stimuli during cognitive processes. The P300 speller is an important application of ERP-based brain-computer interfaces (BCIs), offering potential assistance to individuals with severe motor disabilities by decoding their electroencephalography (EEG) to communicate.<br><br>METHODS: This study introduced a novel speller paradigm using a dynamically growing bubble (GB) visualization as the stimulus, departing from the conventional flash stimulus (TF). Additionally, we proposed a "Lock a Target by Two Flashes" (LT2F) method to offer more versatile stimulus flash rules, complementing the row and column (RC) and single character (SC) modes. We applied the "Sub and Global" multi-window mode to EEGNet (mwEEGNet) to enhance classification and explored the performance of eight other representative algorithms.<br><br>RESULTS: Twenty healthy volunteers participated in the experiments. Our analysis revealed that our proposed pattern elicited more pronounced negative peaks in the parietal and occipital brain regions between 200 ms and 230 ms post-stimulus onset compared with the TF pattern. Compared to the TF pattern, the GB pattern yielded a 2.00% increase in online character accuracy (ACC) and a 5.39 bits/min improvement in information transfer rate (ITR) when using mwEEGNet. Furthermore, results demonstrated that mwEEGNet outperformed other methods in classification performance.<br><br>CONCLUSION AND SIGNIFICANCE: These results underscore the significance of our work in advancing ERP-based BCIs.}, }
@article {pmid39502788, year = {2024}, author = {Ahn, M and Edelman, BJ and He, B and Müller-Putz, GR and Röhrbein, F}, title = {Editorial: Advances in hybrid and application-driven BCI systems.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1498196}, doi = {10.3389/fnhum.2024.1498196}, pmid = {39502788}, issn = {1662-5161}, }
@article {pmid39501690, year = {2024}, author = {Ji, M and Lee, D and Lee, S}, title = {Effects of wearing a KF94 face mask on performance, perceptual parameters, and physiological responses during resistance exercise.}, journal = {Physical activity and nutrition}, volume = {28}, number = {3}, pages = {17-26}, pmid = {39501690}, issn = {2733-7545}, abstract = {PURPOSE: Wearing face masks in indoor public places, including fitness centers, is an effective strategy for preventing the airborne transmission of viruses. Despite this, limited research has addressed the effects of wearing a mask during resistance exercise, which is primarily performed in indoor fitness centers. This study investigated the effects of wearing a KF94 face mask on exercise volume, perceptual parameters, and cardiorespiratory and cardiovascular responses during resistance exercise.<br><br>METHODS: Twenty young men (23.8 &#xb1; 0.5 years old) participated in this randomized crossover trial. The participants performed moderate-intensity resistance exercise (60% of 1RM) sessions under two different conditions (KF94 mask vs. no mask). Cardiorespiratory parameters, exercise volume, rating of perceived exertion (RPE), and dyspnea were measured. Blood lactate concentration, blood pressure, arterial stiffness, and perceptual parameters were measured pre- and post-exercise.<br><br>RESULTS: Wearing the KF94 mask significantly reduced exercise volume, ventilation volume, and ventilation efficiency compared to exercising without a mask (p < 0.05). Although blood lactate concentration remained unchanged between the two conditions, RPE and dyspnea were significantly higher with the KF94 mask (p < 0.01). Central arterial stiffness post-exercise was significantly higher with the KF94 mask than without it (p < 0.01).<br><br>CONCLUSION: Wearing a KF94 face mask during resistance exercise affected exercise volume, perceptual parameters, and cardiorespiratory and cardiovascular responses. These findings suggest that coaches and trainers should consider the individual characteristics when designing exercise prescriptions and modifying resistance exercise variables while wearing KF94 masks.}, }
@article {pmid39500903, year = {2024}, author = {Li, J and Zhang, F and Xia, X and Zhang, K and Wu, J and Liu, Y and Zhang, C and Cai, X and Lu, J and Xu, L and Wan, R and Hazarika, D and Xuan, W and Chen, J and Cao, Z and Li, Y and Jin, H and Dong, S and Zhang, S and Ye, Z and Yang, M and Chen, PY and Luo, J}, title = {An ultrasensitive multimodal intracranial pressure biotelemetric system enabled by exceptional point and iontronics.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {9557}, pmid = {39500903}, issn = {2041-1723}, mesh = {*Intracranial Pressure/physiology ; Monitoring, Physiologic/instrumentation/methods ; *Heart Rate/physiology ; Animals ; Humans ; Respiratory Rate ; Male ; Transducers, Pressure ; }, abstract = {The accurate monitoring of vital physiological parameters, exemplified by heart rate, respiratory rate, and intracranial pressure (ICP), is of paramount importance, particularly for managing severe cranial injuries. Despite the rapid development of implantable ICP sensing systems over the past decades, they still suffer from, for example, wire connection, low sensitivity, poor resolution, and the inability to monitor multiple variables simultaneously. Here, we propose an ultrasensitive multimodal biotelemetric system that amalgamates an iontronic pressure transducer with exceptional point (EP) operation for the monitoring of ICP signals. The proposed system can exhibit extraordinary performance regarding the detection of minuscule ICP fluctuation, demonstrated by the sensitivity of 115.95 kHz/mmHg and the sensing resolution down to 0.003 mmHg. Our system excels not only in the accurate quantification of ICP levels but also in distinguishing respiration and cardiac activities from ICP signals, thereby achieving the multimodal monitoring of ICP, respiratory, and heart rates within a single system. Our work may provide a pragmatic avenue for the real-time wireless monitoring of ICP and thus hold great potential to be extended to the monitoring of other vital physiological indicators.}, }
@article {pmid39500053, year = {2024}, author = {Kocanaogullari, D and Gall, R and Mak, J and Huang, X and Mullen, K and Ostadabbas, S and Wittenberg, GF and Grattan, ES and Akcakaya, M}, title = {Patient-specific visual neglect severity estimation for stroke patients with neglect using EEG.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad8efc}, pmid = {39500053}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; *Perceptual Disorders/diagnosis/physiopathology/etiology ; *Stroke/physiopathology/complications/diagnosis ; Male ; Female ; Brain-Computer Interfaces ; Middle Aged ; Aged ; Severity of Illness Index ; }, abstract = {Objective.We aim to assess the severity of spatial neglect (SN) through detailing patients' field of view (FOV) using EEG. Spatial neglect, a prevalent neurological syndrome in stroke patients, typically results from unilateral brain injuries, leading to inattention to the contralesional space. Commonly used Neglect detection methods like the Behavioral Inattention Test-conventional lack the capability to assess the full extent and severity of neglect. Although the Catherine Bergego Scale provides valuable clinical information, it does not detail the specific FOV affected in neglect patients.Approach.Building on our previously developed EEG-based brain-computer interface system, AR-guided EEG-based neglect detection, assessment, and rehabilitation system (AREEN), we aim to map neglect severity across a patient's FOV. We have demonstrated that AREEN can assess neglect severity in a patient-agnostic manner. However, its effectiveness in patient-specific scenarios, which is crucial for creating a generalizable plug-and-play system, remains unexplored. This paper introduces a novel EEG-based combined spatio-temporal network (ESTNet) that processes both time and frequency domain data to capture essential frequency band information associated with SN. We also propose a FOV correction system using Bayesian fusion, leveraging AREEN's recorded response times for enhanced accuracy by addressing noisy labels within the dataset.Main results.Extensive testing of ESTNet on our proprietary dataset has demonstrated its superiority over benchmark methods, achieving 79.62% accuracy, 76.71% sensitivity, and 86.36% specificity. Additionally, we provide saliency maps to enhance model explainability and establish clinical correlations.Significance.These findings underscore ESTNet's potential combined with Bayesian fusion-based FOV correction as an effective tool for generalized neglect assessment in clinical settings.}, }
@article {pmid39500051, year = {2024}, author = {Dehais, F and Cabrera Castillos, K and Ladouce, S and Clisson, P}, title = {Leveraging textured flickers: a leap toward practical, visually comfortable, and high-performance dry EEG code-VEP BCI.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad8ef7}, pmid = {39500051}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; Female ; Male ; Adult ; Young Adult ; *Photic Stimulation/methods ; }, abstract = {Objective.Reactive brain-computer interfaces typically rely on repetitive visual stimuli, which can strain the eyes and cause attentional distraction. To address these challenges, we propose a novel approach rooted in visual neuroscience to design visual Stimuli for Augmented Response (StAR). The StAR stimuli consist of small randomly-orientedGabororRickerpatches that optimize foveal neural response while reducing peripheral distraction.Approach.In a factorial design study, 24 participants equipped with an 8-dry electrode EEG system focused on series of target flickers presented under three formats: traditionalPlainflickers,Gabor-based, orRicker-based flickers. These flickers were part of a five-class code visually evoked potentials paradigm featuring low frequency, short, and aperiodic visual flashes.Main results.Subjective ratings revealed thatGaborandRickerstimuli were visually comfortable and nearly invisible in peripheral vision compared to plain flickers. Moreover,GaborandRicker-based textures achieved higher accuracy (93.6% and 96.3%, respectively) with only 88 s of calibration data, compared to plain flickers (65.6%). A follow-up online implementation of this experiment was conducted to validate our findings within the frame of naturalistic operations. During this trial, remarkable accuracies of 97.5% in a cued task and 94.3% in an asynchronous digicode task were achieved, with a mean decoding time as low as 1.68 s.Significance.This work demonstrates the potential to expand BCI applications beyond the lab by integrating visually unobtrusive systems with gel-free, low density EEG technology, thereby making BCIs more accessible and efficient. The datasets, algorithms, and BCI implementations are shared through open-access repositories.}, }
@article {pmid39500044, year = {2024}, author = {Tremmel, C and Krusienski, DJ and Schraefel, M}, title = {Estimating cognitive workload using a commercial in-ear EEG headset.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad8ef8}, pmid = {39500044}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; Male ; *Workload ; Female ; *Cognition/physiology ; Adult ; Young Adult ; }, abstract = {Objective.This study investigated the potential of estimating various mental workload levels during two different tasks using a commercial in-ear electroencephalography (EEG) system, the IDUN 'Guardian'.Approach.Participants performed versions of two classical workload tasks: an n-back task and a mental arithmetic task. Both in-ear and conventional EEG data were simultaneously collected during these tasks. In an effort to facilitate a more comprehensive comparison, the complexity of the tasks was intentionally increased beyond typical levels. Special emphasis was also placed on understanding the significance ofγband activity in workload estimations. Therefore, each signal was analyzed across low frequency (1-35 Hz) and high frequency (1-100 Hz) ranges. Additionally, surrogate in-ear EEG measures, derived from the conventional EEG recordings, were extracted and examined.Main results.Workload estimation using in-ear EEG yielded statistically significant performance levels, surpassing chance levels with 44.1% for four classes and 68.4% for two classes in the n-back task and was better than a naive predictor for the mental arithmetic task. Conventional EEG exhibited significantly higher performance compared to in-ear EEG, achieving 80.3% and 92.9% accuracy for the respective tasks, along with lower error rates than the naive predictor. The developed surrogate measures achieved improved results, reaching accuracies of 57.5% and 85.5%, thus providing insights for enhancing future in-ear systems. Notably, most high frequency range signals outperformed their low frequency counterparts in terms of accuracy validating that high frequencyγband features can improve workload estimation.Significance.The application of EEG-based Brain-Computer Interfaces beyond laboratory settings is often hindered by practical limitations. In-ear EEG systems offer a promising solution to this problem, potentially enabling everyday use. This study evaluates the performance of a commercial in-ear headset and provides guidelines for increased effectiveness.}, }
@article {pmid39496663, year = {2024}, author = {Agrawal, R and Dhule, C and Shukla, G and Singh, S and Agrawal, U and Alsubaie, N and Alqahtani, MS and Abbas, M and Soufiene, BO}, title = {Design of EEG based thought identification system using EMD &amp; deep neural network.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {26621}, pmid = {39496663}, issn = {2045-2322}, mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Algorithms ; Deep Learning ; Brain/physiology/physiopathology ; }, abstract = {Biological communication system for neurological disorder patients is similar to the Brain Computer Interface in a way that it facilitates the connection to the outside world in real time. The interdisciplinary field of Electroencephalogram based message depiction is gaining importance as it assists the paralysed person to communicate. In the proposed method a novel approach of feature extraction is done by Empirical Mode Decomposition on non- stationary &amp; non-linear kind of EEG signal. EMD helps in the effective time frequency analysis by disintegrating the EEG signal in the form of six Intrinsic Mode Functions with help of the frequency components. In all nine features are extracted from the decomposed IMFs so as to predict the states or messages of the patient. The above computed features are then served to the Deep Neural Network to perform the classification. The performance of suggested method is studied through applying it to the acquired database generated by the designed hardware as well as also in real time message depiction. The maximum classification accuracy 97% for the acquired database &amp; 85% in real time are obtained respectively by comparative analysis. The command messages generated from the proposed system helps the person suffering from neurological disorder to establish the communication link with the outside world in an efficient way. Thus, the proposed novel method shows better performance in real time message depiction purpose as related to other existing methods.}, }
@article {pmid39496200, year = {2024}, author = {Bunterngchit, C and Wang, J and Su, J and Wang, Y and Liu, S and Hou, ZG}, title = {Temporal attention fusion network with custom loss function for EEG-fNIRS classification.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad8e86}, pmid = {39496200}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; *Spectroscopy, Near-Infrared/methods ; *Attention/physiology ; Male ; Adult ; Female ; Young Adult ; Neural Networks, Computer ; Epilepsy/physiopathology/diagnosis ; }, abstract = {Objective.Methods that can detect brain activities accurately are crucial owing to the increasing prevalence of neurological disorders. In this context, a combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offers a powerful approach to understanding normal and pathological brain functions, thereby overcoming the limitations of each modality, such as susceptibility to artifacts of EEG and limited temporal resolution of fNIRS. However, challenges such as class imbalance and inter-class variability within multisubject data hinder their full potential.Approach.To address this issue, we propose a novel temporal attention fusion network (TAFN) with a custom loss function. The TAFN model incorporates attention mechanisms to its long short-term memory and temporal convolutional layers to accurately capture spatial and temporal dependencies in the EEG-fNIRS data. The custom loss function combines class weights and asymmetric loss terms to ensure the precise classification of cognitive and motor intentions, along with addressing class imbalance issues.Main results.Rigorous testing demonstrated the exceptional cross-subject accuracy of the TAFN, exceeding 99% for cognitive tasks and 97% for motor imagery (MI) tasks. Additionally, the ability of the model to detect subtle differences in epilepsy was analyzed using scalp topography in MI tasks.Significance.This study presents a technique that outperforms traditional methods for detecting high-precision brain activity with subtle differences in the associated patterns. This makes it a promising tool for applications such as epilepsy and seizure detection, in which discerning subtle pattern differences is of paramount importance.}, }
@article {pmid39494592, year = {2024}, author = {Zhu, Y and Ma, J and Li, Y and Gu, M and Feng, X and Shao, Y and Tan, L and Lou, HF and Sun, L and Liu, Y and Zeng, LH and Qiu, Z and Li, XM and Duan, S and Yu, YQ}, title = {Adenosine-Dependent Arousal Induced by Astrocytes in a Brainstem Circuit.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {11}, number = {48}, pages = {e2407706}, pmid = {39494592}, issn = {2198-3844}, support = {2021ZD0203400//STI2030-Major Projects/ ; T2293733//National Natural Science Foundation of China Major Project/ ; T2293730//National Natural Science Foundation of China Major Project/ ; 31970939//National Natural Science Foundation of China Major Project/ ; 82288101//National Natural Science Foundation of China Major Project/ ; 82090033//National Natural Science Foundation of China Major Project/ ; U20A6005//National Natural Science Foundation of China Major Project/ ; 32171007//National Natural Science Foundation of China Major Project/ ; 32100814//National Natural Science Foundation of China Major Project/ ; LZ22H090001//Natural Science Foundation of Zhejiang Province/ ; 2024SSYS0019//Key R&amp;D Program of Zhejiang Province/ ; 2022C03034 to Y.Q.Y//Key R&amp;D Program of Zhejiang Province/ ; 2024SSYS0017//Key R&amp;D Program of Zhejiang Province/ ; 2020C03009//Key R&amp;D Program of Zhejiang Province/ ; 2019-I2M-5-057//CAMS Innovation Fund for Medical Sciences/ ; 2019B030335001//Key R&amp;D Program of Guangdong Province/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2023-PT310-01//Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; }, mesh = {*Astrocytes/metabolism ; Animals ; *Adenosine/metabolism ; *Arousal/physiology ; Mice ; *Brain Stem/metabolism/physiology ; Male ; Sleep/physiology ; Wakefulness/physiology ; Models, Animal ; }, abstract = {Astrocytes play a crucial role in regulating sleep-wake behavior. However, how astrocytes govern a specific sleep-arousal circuit remains unknown. Here, the authors show that parafacial zone (PZ) astrocytes responded to sleep-wake cycles with state-differential Ca[2+] activity, peaking during transitions from sleep to wakefulness. Using chemogenetic and optogenetic approaches, they find that activating PZ astrocytes elicited and sustained wakefulness by prolonging arousal episodes while impeding transitions from wakefulness to non-rapid eye movement (NREM) sleep. Activation of PZ astrocytes specially induced the elevation of extracellular adenosine through the ATP hydrolysis pathway but not equilibrative nucleoside transporter (ENT) mediated transportation. Strikingly, the rise in adenosine levels induced arousal by activating A1 receptors, suggesting a distinct role for adenosine in the PZ beyond its conventional sleep homeostasis modulation observed in the basal forebrain (BF) and cortex. Moreover, at the circuit level, PZ astrocyte activation induced arousal by suppressing the GABA release from the PZ[GABA] neurons, which promote NREM sleep and project to the parabrachial nucleus (PB). Thus, their study unveils a distinctive arousal-promoting effect of astrocytes within the PZ through extracellular adenosine and elucidates the underlying mechanism at the neural circuit level.}, }
@article {pmid39493873, year = {2024}, author = {Han, J and Wang, R and Wang, M and Yu, Z and Zhu, L and Zhang, J and Zhu, J and Zhang, S and Xi, W and Wu, H}, title = {Dynamic lateralization in contralateral-projecting corticospinal neurons during motor learning.}, journal = {iScience}, volume = {27}, number = {11}, pages = {111078}, pmid = {39493873}, issn = {2589-0042}, abstract = {Understanding the adaptability of the motor cortex in response to bilateral motor tasks is crucial for advancing our knowledge of neural plasticity and motor learning. Here we aim to investigate the dynamic lateralization of contralateral-projecting corticospinal neurons (cpCSNs) during such tasks. Utilizing in vivo two-photon calcium imaging, we observe cpCSNs in mice performing a "left-right" lever-press task. Our findings reveal heterogeneous populational dynamics in cpCSNs: a marked decrease in activity during ipsilateral motor learning, in contrast to maintained activity during contralateral motor learning. Notably, individual cpCSNs show dynamic shifts in engagement with ipsilateral and contralateral movements, displaying an evolving pattern of activation over successive days. It suggests that cpCSNs exhibit adaptive changes in activation patterns in response to ipsilateral and contralateral movements, highlighting a flexible reorganization during motor learning This reconfiguration underscores the dynamic nature of cortical lateralization in motor learning and offers insights for neuromotor rehabilitation.}, }
@article {pmid39490945, year = {2024}, author = {Gwon, D and Ahn, M}, title = {Motor task-to-task transfer learning for motor imagery brain-computer interfaces.}, journal = {NeuroImage}, volume = {302}, number = {}, pages = {120906}, doi = {10.1016/j.neuroimage.2024.120906}, pmid = {39490945}, issn = {1095-9572}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Imagination/physiology ; Female ; Adult ; Young Adult ; *Electroencephalography/methods ; Transfer, Psychology/physiology ; Psychomotor Performance/physiology ; Motor Activity/physiology ; Movement/physiology ; }, abstract = {Motor imagery (MI) is one of the popular control paradigms in the non-invasive brain-computer interface (BCI) field. MI-BCI generally requires users to conduct the imagination of movement (e.g., left or right hand) to collect training data for generating a classification model during the calibration phase. However, this calibration phase is generally time-consuming and tedious, as users conduct the imagination of hand movement several times without being given feedback for an extended period. This obstacle makes MI-BCI non user-friendly and hinders its use. On the other hand, motor execution (ME) and motor observation (MO) are relatively easier tasks, yield lower fatigue than MI, and share similar neural mechanisms to MI. However, few studies have integrated these three tasks into BCIs. In this study, we propose a new task-to-task transfer learning approach of 3-motor tasks (ME, MO, and MI) for building a better user-friendly MI-BCI. For this study, 28 subjects participated in 3-motor tasks experiment, and electroencephalography (EEG) was acquired. User opinions regarding the 3-motor tasks were also collected through questionnaire survey. The 3-motor tasks showed a power decrease in the alpha rhythm, known as event-related desynchronization, but with slight differences in the temporal patterns. In the classification analysis, the cross-validated accuracy (within-task) was 67.05 % for ME, 65.93 % for MI, and 73.16 % for MO on average. Consistently with the results, the subjects scored MI (3.16) as the most difficult task compared with MO (1.42) and ME (1.41), with p < 0.05. In the analysis of task-to-task transfer learning, where training and testing are performed using different task datasets, the ME-trained model yielded an accuracy of 65.93 % (MI test), which is statistically similar to the within-task accuracy (p > 0.05). The MO-trained model achieved an accuracy of 60.82 % (MI test). On the other hand, combining two datasets yielded interesting results. ME and 50 % of the MI-trained model (50-shot) classified MI with a 69.21 % accuracy, which outperformed the within-task accuracy (p < 0.05), and MO and 50 % of the MI-trained model showed an accuracy of 66.75 %. Of the low performers with a within-task accuracy of 70 % or less, 90 % (n = 21) of the subjects improved in training with ME, and 76.2 % (n = 16) improved in training with MO on the MI test at 50-shot. These results demonstrate that task-to-task transfer learning is possible and could be a promising approach to building a user-friendly training protocol in MI-BCI.}, }
@article {pmid39490524, year = {2024}, author = {Pirelli, L and Grubb, KJ and George, I and Goldsweig, AM and Nazif, TM and Dahle, G and Myers, PO and Ouzounian, M and Szeto, WY and Maisano, F and Geirsson, A and Vahl, TP and Kodali, SK and Kaneko, T and Tang, GHL}, title = {The role of cardiac surgeons in transcatheter structural heart disease interventions: The evolution of cardiac surgery.}, journal = {The Journal of thoracic and cardiovascular surgery}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.jtcvs.2024.10.037}, pmid = {39490524}, issn = {1097-685X}, }
@article {pmid39490518, year = {2024}, author = {Ahmed Taha, B and Addie, AJ and Saeed, AQ and Haider, AJ and Chaudhary, V and Arsad, N}, title = {Nanostructured Photonics Probes: A Transformative Approach in Neurotherapeutics and Brain Circuitry.}, journal = {Neuroscience}, volume = {562}, number = {}, pages = {106-124}, doi = {10.1016/j.neuroscience.2024.10.046}, pmid = {39490518}, issn = {1873-7544}, mesh = {Humans ; *Nanostructures ; *Brain/physiology ; Animals ; Optics and Photonics/methods/instrumentation ; Nanotechnology/methods ; Optogenetics/methods/instrumentation ; }, abstract = {Neuroprobes that use nanostructured photonic interfaces are capable of multimodal sensing, stimulation, and imaging with unprecedented spatio-temporal resolution. In addition to electrical recording, optogenetic modulation, high-resolution optical imaging, and molecular sensing, these advanced probes combine nanophotonic waveguides, optical transducers, nanostructured electrodes, and biochemical sensors. The potential of this technology lies in unraveling the mysteries of neural coding principles, mapping functional connectivity in complex brain circuits, and developing new therapeutic interventions for neurological disorders. Nevertheless, achieving the full potential of nanostructured photonic neural probes requires overcoming challenges such as ensuring long-term biocompatibility, integrating nanoscale components at high density, and developing robust data-analysis pipelines. In this review, we summarize and discuss the role of photonics in neural probes, trends in electrode diameter for neural interface technologies, nanophotonic technologies using nanostructured materials, advances in nanofabrication photonics interface engineering, and challenges and opportunities. Finally, interdisciplinary efforts are required to unlock the transformative potential of next-generation neuroscience therapies.}, }
@article {pmid39487147, year = {2024}, author = {Wang, X and Wu, S and Yang, H and Bao, Y and Li, Z and Gan, C and Deng, Y and Cao, J and Li, X and Wang, Y and Ren, C and Yang, Z and Zhao, Z}, title = {Intravascular delivery of an ultraflexible neural electrode array for recordings of cortical spiking activity.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {9442}, pmid = {39487147}, issn = {2041-1723}, mesh = {Animals ; *Electrodes, Implanted ; Sheep ; Action Potentials/physiology ; Neurons/physiology ; Brain-Computer Interfaces ; Occipital Lobe/physiology ; }, abstract = {Although intracranial neural electrodes have significantly contributed to both fundamental research and clinical treatment of neurological diseases, their implantation requires invasive surgery to open craniotomies, which can introduce brain damage and disrupt normal brain functions. Recent emergence of endovascular neural devices offers minimally invasive approaches for neural recording and stimulation. However, existing endovascular neural devices are unable to resolve single-unit activity in large animal models or human patients, impeding a broader application as neural interfaces in clinical practice. Here, we present the ultraflexible implantable neural electrode as an intravascular device (uFINE-I) for recording brain activity at single-unit resolution. We successfully implanted uFINE-Is into the sheep occipital lobe by penetrating through the confluence of sinuses and recorded both local field potentials (LFPs) and multi-channel single-unit spiking activity under spontaneous and visually evoked conditions. Imaging and histological analysis revealed minimal tissue damage and immune response. The uFINE-I provides a practical solution for achieving high-resolution neural recording with minimal invasiveness and can be readily transferred to clinical settings for future neural interface applications such as brain-machine interfaces (BMIs) and the treatment of neurological diseases.}, }
@article {pmid39484299, year = {2024}, author = {Premchand, B and Liang, L and Phua, KS and Zhang, Z and Wang, C and Guo, L and Ang, J and Koh, J and Yong, X and Ang, KK}, title = {Wearable EEG-Based Brain-Computer Interface for Stress Monitoring.}, journal = {NeuroSci}, volume = {5}, number = {4}, pages = {407-428}, pmid = {39484299}, issn = {2673-4087}, abstract = {Detecting stress is important for improving human health and potential, because moderate levels of stress may motivate people towards better performance at cognitive tasks, while chronic stress exposure causes impaired performance and health risks. We propose a Brain-Computer Interface (BCI) system to detect stress in the context of high-pressure work environments. The BCI system includes an electroencephalogram (EEG) headband with dry electrodes and an electrocardiogram (ECG) chest belt. We collected EEG and ECG data from 40 participants during two stressful cognitive tasks: the Cognitive Vigilance Task (CVT), and the Multi-Modal Integration Task (MMIT) we designed. We also recorded self-reported stress levels using the Dundee Stress State Questionnaire (DSSQ). The DSSQ results indicated that performing the MMIT led to significant increases in stress, while performing the CVT did not. Subsequently, we trained two different models to classify stress from non-stress states, one using EEG features, and the other using heart rate variability (HRV) features extracted from the ECG. Our EEG-based model achieved an overall accuracy of 81.0% for MMIT and 77.2% for CVT. However, our HRV-based model only achieved 62.1% accuracy for CVT and 56.0% for MMIT. We conclude that EEG is an effective predictor of stress in the context of stressful cognitive tasks. Our proposed BCI system shows promise in evaluating mental stress in high-pressure work environments, particularly when utilizing an EEG-based BCI.}, }
@article {pmid39484239, year = {2024}, author = {Leinders, S and Aarnoutse, EJ and Branco, MP and Freudenburg, ZV and Geukes, SH and Schippers, A and Verberne, MSW and van den Boom, M and van der Vijgh, B and Crone, NE and Denison, T and Ramsey, NF and Vansteensel, MJ}, title = {DO NOT LOSE SLEEP OVER IT: IMPLANTED BRAIN-COMPUTER INTERFACE FUNCTIONALITY DURING NIGHTTIME IN LATE-STAGE AMYOTROPHIC LATERAL SCLEROSIS.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, pmid = {39484239}, support = {U01 DC016686/DC/NIDCD NIH HHS/United States ; UH3 NS114439/NS/NINDS NIH HHS/United States ; }, abstract = {BACKGROUND AND OBJECTIVES: Brain-computer interfaces (BCIs) hold promise as augmentative and alternative communication technology for people with severe motor and speech impairment (locked-in syndrome) due to neural disease or injury. Although such BCIs should be available 24/7, to enable communication at all times, feasibility of nocturnal BCI use has not been investigated. Here, we addressed this question using data from an individual with amyotrophic lateral sclerosis (ALS) who was implanted with an electrocorticography-based BCI that enabled the generation of click-commands for spelling words and call-caregiver signals.<br><br>METHODS: We investigated nocturnal dynamics of neural signal features used for BCI control, namely low (LFB: 10-30Hz) and high frequency band power (HFB: 65-95Hz). Additionally, we assessed the nocturnal performance of a BCI decoder that was trained on daytime data by quantifying the number of unintentional BCI activations at night. Finally, we developed and implemented a nightmode decoder that allowed the participant to call a caregiver at night, and assessed its performance.<br><br>RESULTS: Power and variance in HFB and LFB were significantly higher at night than during the day in the majority of the nights, with HFB variance being higher in 88% of nights. Daytime decoders caused 245 unintended selection-clicks and 13 unintended caregiver-calls per hour when applied to night data. The developed nightmode decoder functioned error-free in 79% of nights over a period of &#xb1;1.5 years, allowing the user to reliably call the caregiver, with unintended activations occurring only once every 12 nights.<br><br>DISCUSSION: Reliable nighttime use of a BCI requires decoders that are adjusted to sleep-related signal changes. This demonstration of a reliable BCI nightmode and its long-term use by an individual with advanced ALS underscores the importance of 24/7 BCI reliability.<br><br>TRIAL REGISTRATION: This trial is registered in clinicaltrials.gov under number NCT02224469 (https://clinicaltrials.gov/study/NCT02224469?term=NCT02224469&amp;rank=1). Date of submission to registry: August 21, 2014. Enrollment of first participant: September 7, 2015.}, }
@article {pmid39483493, year = {2024}, author = {Guerrero-Mendez, CD and Blanco-Diaz, CF and Rivera-Flor, H and Fabriz-Ulhoa, PH and Fragoso-Dias, EA and de Andrade, RM and Delisle-Rodriguez, D and Bastos-Filho, TF}, title = {Influence of Temporal and Frequency Selective Patterns Combined with CSP Layers on Performance in Exoskeleton-Assisted Motor Imagery Tasks.}, journal = {NeuroSci}, volume = {5}, number = {2}, pages = {169-183}, pmid = {39483493}, issn = {2673-4087}, abstract = {Common Spatial Pattern (CSP) has been recognized as a standard and powerful method for the identification of Electroencephalography (EEG)-based Motor Imagery (MI) tasks when implementing brain-computer interface (BCI) systems towards the motor rehabilitation of lost movements. The combination of BCI systems with robotic systems, such as upper limb exoskeletons, has proven to be a reliable tool for neuromotor rehabilitation. Therefore, in this study, the effects of temporal and frequency segmentation combined with layer increase for spatial filtering were evaluated, using three variations of the CSP method for the identification of passive movement vs. MI+passive movement. The passive movements were generated using a left upper-limb exoskeleton to assist flexion/extension tasks at two speeds (high-85 rpm and low-30 rpm). Ten healthy subjects were evaluated in two recording sessions using Linear Discriminant Analysis (LDA) as a classifier, and accuracy (ACC) and False Positive Rate (FPR) as metrics. The results allow concluding that the use of temporal, frequency or spatial selective information does not significantly (p < 0.05) improve task identification performance. Furthermore, dynamic temporal segmentation strategies may perform better than static segmentation tasks. The findings of this study are a starting point for the exploration of complex MI tasks and their application to neurorehabilitation, as well as the study of brain effects during exoskeleton-assisted MI tasks.}, }
@article {pmid39483271, year = {2024}, author = {Yang, Y and Li, Y and Tang, L and Li, J}, title = {Single-Molecule Bioelectronic Sensors with AI-Aided Data Analysis: Convergence and Challenges.}, journal = {Precision chemistry}, volume = {2}, number = {10}, pages = {518-538}, pmid = {39483271}, issn = {2771-9316}, abstract = {Single-molecule bioelectronic sensing, a groundbreaking domain in biological research, has revolutionized our understanding of molecules by revealing deep insights into fundamental biological processes. The advent of emergent technologies, such as nanogapped electrodes and nanopores, has greatly enhanced this field, providing exceptional sensitivity, resolution, and integration capabilities. However, challenges persist, such as complex data sets with high noise levels and stochastic molecular dynamics. Artificial intelligence (AI) has stepped in to address these issues with its powerful data processing capabilities. AI algorithms effectively extract meaningful features, detect subtle changes, improve signal-to-noise ratios, and uncover hidden patterns in massive data. This review explores the synergy between AI and single-molecule bioelectronic sensing, focusing on how AI enhances signal processing and data analysis to boost accuracy and reliability. We also discuss current limitations and future directions for integrating AI, highlighting its potential to advance biological research and technological innovation.}, }
@article {pmid39483192, year = {2024}, author = {Buthut, M and Starke, G and Akmazoglu, TB and Colucci, A and Vermehren, M and van Beinum, A and Bublitz, C and Chandler, J and Ienca, M and Soekadar, SR}, title = {HYBRIDMINDS-summary and outlook of the 2023 international conference on the ethics and regulation of intelligent neuroprostheses.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1489307}, pmid = {39483192}, issn = {1662-5161}, abstract = {Neurotechnology and Artificial Intelligence (AI) have developed rapidly in recent years with an increasing number of applications and AI-enabled devices that are about to enter the market. While promising to substantially improve quality of life across various severe medical conditions, there are also concerns that the convergence of these technologies, e.g., in the form of intelligent neuroprostheses, may have undesirable consequences and compromise cognitive liberty, mental integrity, or mental privacy. Therefore, various international organizations, such as the Organization for Economic Cooperation and Development (OECD) or United Nations Educational, Scientific and Cultural Organization (UNESCO), have formed initiatives to tackle such questions and develop recommendations that mitigate risks while fostering innovation. In this context, a first international conference on the ethics and regulation of intelligent neuroprostheses was held in Berlin, Germany, in autumn 2023. The conference gathered leading experts in neuroscience, engineering, ethics, law, philosophy as well as representatives of industry, policy making and the media. Here, we summarize the highlights of the conference, underline the areas in which a broad consensus was found among participants, and provide an outlook on future challenges in development, deployment, and regulation of intelligent neuroprostheses.}, }
@article {pmid39488002, year = {2024}, author = {Xu, Z and Khazaee, M and Duy Truong, N and Havenga, D and Nikpour, A and Ahnood, A and Kavehei, O}, title = {A leadless power transfer and wireless telemetry solutions for an endovascular electrocorticography.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad8dfe}, pmid = {39488002}, issn = {1741-2552}, mesh = {*Electrocorticography/methods/instrumentation ; *Telemetry/instrumentation/methods ; *Wireless Technology/instrumentation ; *Brain-Computer Interfaces ; Animals ; Endovascular Procedures/methods/instrumentation ; Equipment Design/methods ; Electric Power Supplies ; Electrodes, Implanted ; Humans ; }, abstract = {Objective. Endovascular brain-computer interfaces (eBCIs) offer a minimally invasive way to connect the brain to external devices, merging neuroscience, engineering, and medical technology. Currently, solutions for endovascular electrocorticography (ECoG) include a stent in the brain with sensing electrodes, a chest implant to accommodate electronic components to provide power and data telemetry, and a long (tens of centimeters) cable travel through vessels with a set of wires in between. Removing this long cable is the key to the clinical viability of eBCIS as it carries risks and limitations, especially for patients with fragile vasculature.Approach. This work introduces a wireless and leadless telemetry and power transfer solution for ECoG. The proposed solution includes an optical telemetry module and a focused ultrasound (FUS) power transfer system. The proposed system can be miniaturised to fit in an endovascular stent, removing the need for long, intrusive cables.Main results. The optical telemetry achieves data transmission speeds of over 2 Mbit/s, capable of supporting 41 ECoG channels at a 2 kHz sampling rate with 24-bit resolution. The FUS power transfer system delivers up to 10 mW of power to the implant through the scalp(6 mm), skull(10 mm), and subdural space(5 mm), adhering to safety limits. Testing on bovine tissue (10 mm thick bone, 7 mm thick skin) confirmed the system's efficacy.Significance. This leadless and wireless solution eliminates the need for long cables and auxiliary implants, potentially reducing complications and enhancing the clinical applicability of eBCIs. The proposed system represents a step forward in enabling safer and more effective ECoG for a broader range of patients.}, }
@article {pmid39486261, year = {2025}, author = {Sun, Y and Gao, Y and Shen, A and Sun, J and Chen, X and Gao, X}, title = {Creating ionic current pathways: A non-implantation approach to achieving cortical electrical signals for brain-computer interface.}, journal = {Biosensors &amp; bioelectronics}, volume = {268}, number = {}, pages = {116882}, doi = {10.1016/j.bios.2024.116882}, pmid = {39486261}, issn = {1873-4235}, mesh = {*Brain-Computer Interfaces ; Animals ; Swine ; Electrodes, Implanted ; Electrocorticography/instrumentation/methods ; Biosensing Techniques/instrumentation ; Signal-To-Noise Ratio ; Cerebral Cortex/physiology ; Humans ; Brain/physiology ; Electroencephalography/instrumentation ; }, abstract = {This study introduces a novel method for acquiring brain electrical signals comparable to intracranial recordings without the health risks associated with implanted electrodes. We developed a technique using ultrasonic tools to create micro-holes in the skull and insert hollow implants, preventing natural healing. This approach establishes an artificial ionic current path (AICP) using tissue fluid, facilitating signal transmission from the cortex to the scalp surface. Experiments were conducted on pigs to validate the method's effectiveness. We synchronized our recordings with perforated electrocorticography (ECoG) for comparison. The AICP method yielded signal quality comparable to implanted ECoG in the low-frequency range, with a significant improvement in signal-to-noise ratio for evoked potentials. Our results demonstrate that this non-invasive technique can acquire high-quality brain signals, offering potential applications in neurophysiology, clinical research, and brain-computer interfaces. This innovative approach of utilizing tissue fluid as a natural conduction path opens new avenues for brain signal acquisition and analysis.}, }
@article {pmid39485790, year = {2024}, author = {Zhang, D and Wang, Z and Qian, Y and Zhao, Z and Liu, Y and Hao, X and Li, W and Lu, S and Zhu, H and Chen, L and Xu, K and Li, Y and Lu, J}, title = {A brain-to-text framework for decoding natural tonal sentences.}, journal = {Cell reports}, volume = {43}, number = {11}, pages = {114924}, doi = {10.1016/j.celrep.2024.114924}, pmid = {39485790}, issn = {2211-1247}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Speech/physiology ; *Brain/physiology ; *Language ; Female ; Adult ; Young Adult ; Bayes Theorem ; }, abstract = {Speech brain-computer interfaces (BCIs) directly translate brain activity into speech sound and text. Despite successful applications in non-tonal languages, the distinct syllabic structures and pivotal lexical information conveyed through tonal nuances present challenges in BCI decoding for tonal languages like Mandarin Chinese. Here, we designed a brain-to-text framework to decode Mandarin sentences from invasive neural recordings. Our framework dissects speech onset, base syllables, and lexical tones, integrating them with contextual information through Bayesian likelihood and a Viterbi decoder. The results demonstrate accurate tone and syllable decoding during naturalistic speech production. The overall word error rate (WER) for 10 offline-decoded tonal sentences with a vocabulary of 40 high-frequency Chinese characters is 21% (chance: 95.3%) averaged across five participants, and tone decoding accuracy reaches 93% (chance: 25%), surpassing previous intracranial Mandarin tonal syllable decoders. This study provides a robust and generalizable approach for brain-to-text decoding of continuous tonal speech sentences.}, }
@article {pmid39481863, year = {2025}, author = {Sellwood, D and McLeod, L and Williams, K and Brown, K and Pullin, G}, title = {Imagining alternative futures with augmentative and alternative communication: a manifesto.}, journal = {Medical humanities}, volume = {50}, number = {4}, pages = {620-623}, doi = {10.1136/medhum-2024-013022}, pmid = {39481863}, issn = {1473-4265}, mesh = {Humans ; *Disabled Persons ; *Communication Aids for Disabled ; *Artificial Intelligence ; *Brain-Computer Interfaces ; Communication ; Imagination ; Forecasting ; }, abstract = {This manifesto seeks to challenge dominant narratives about the future of augmentative and alternative communication (AAC). Current predictions are mainly driven by technological developments-technologies usually being developed for different markets-and are often based on ableist assumptions. In online conversations and a discussion panel at the 2023 International Society for Augmentative and Alternative Communication conference, we explored alternative futures by adopting different starting positions. Our case is presented under five headings: questioning the dominance of predictions that artificial intelligence and brain-computer interfaces will define the future of AAC; resisting disability being framed medically, as a problem to be solved, yet acknowledging both the pleasures and pains of being disabled; declaring that people who use AAC-as cyborgs of necessity rather than choice-should have choice and ownership of our technologies; challenging notions of independence as the necessary end goal for disabled bodies and considering interdependence as a human right; imagining alternative futures in which all people who use AAC are accepted and embraced for our communication and self-expression. This manifesto is an invitation for further discussion, and we welcome responses. While our focus is AAC, and three of the authors use AAC, we believe that our stance could be relevant to other disability communities in turn. This paper is about who gets to imagine disability futures and whose voices are left out. It is about how uncritical these futures can be, often presuming values that disabled people, in all their diversity, may not share.}, }
@article {pmid39480049, year = {2024}, author = {Kaleem, MI and Javeed, S and Plog, BA and Gupta, VP and Ray, WZ}, title = {Restorative Treatments for Cervical Spinal Cord Injury, a Narrative Review.}, journal = {Clinical spine surgery}, volume = {37}, number = {9}, pages = {451-458}, doi = {10.1097/BSD.0000000000001699}, pmid = {39480049}, issn = {2380-0194}, mesh = {Humans ; *Spinal Cord Injuries/therapy ; Cervical Cord/injuries ; Recovery of Function ; Cervical Vertebrae ; Nerve Transfer/methods ; }, abstract = {STUDY DESIGN: A narrative review.<br><br>OBJECTIVE: To summarize relevant data from representative studies investigating upper limb restorative therapies for cervical spinal cord injury.<br><br>SUMMARY OF BACKGROUND DATA: Cervical spinal cord injury (SCI) is a debilitating condition resulting in tetraplegia, lifelong disability, and reduced quality of life. Given the dependence of all activities on hand function, patients with tetraplegia rank regaining hand function as one of their highest priorities. Recovery from cervical SCI is heterogeneous and often incomplete; currently, various novel therapies are under investigation to improve neurological function and eventually better quality of life in patients with tetraplegia.<br><br>METHODS: In this article, a narrative literature review was performed to identify treatment options targeting the restoration of function in patients with cervical SCI. Studies were included from available literature based on the availability of clinical data and whether they are applicable to restoration of arm and hand function in patients with cervical SCI.<br><br>RESULTS: We describe relevant studies including indications and outcomes with a focus on arm and hand function. Different treatment modalities described include nerve transfers, tendon transfers, spinal cord stimulation, functional electrical stimulation, non-invasive brain stimulation, brain-machine interfaces and neuroprosthetics, stem cell therapy, and immunotherapy. As the authors' institution leads one of the largest clinical trials on nerve transfers for cervical SCI, we also describe how patients undergoing nerve transfers are managed and followed at our center.<br><br>CONCLUSIONS: While complete recovery from cervical spinal cord injury may not be possible, novel therapies aimed at the restoration of upper limb motor function have made significant progress toward the realization of complete recovery.}, }
@article {pmid39479901, year = {2024}, author = {Druschel, LN and Kasthuri, NM and Song, SS and Wang, JJ and Hess-Dunning, A and Chan, ER and Capadona, JR}, title = {Cell-specific spatial profiling of targeted protein expression to characterize the impact of intracortical microelectrode implantation on neuronal health.}, journal = {Journal of materials chemistry. B}, volume = {12}, number = {47}, pages = {12307-12319}, pmid = {39479901}, issn = {2050-7518}, support = {I01 RX002611/RX/RRD VA/United States ; R01 NS131502/NS/NINDS NIH HHS/United States ; R01 NS110823/NS/NINDS NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; IK6 RX003077/RX/RRD VA/United States ; }, mesh = {Animals ; *Microelectrodes ; *Neurons/metabolism ; Rats ; Rats, Sprague-Dawley ; Male ; Nerve Tissue Proteins/metabolism ; Microtubule-Associated Proteins/metabolism ; }, abstract = {Intracortical microelectrode arrays (MEAs) can record neuronal activity and advance brain-computer interface (BCI) devices. Implantation of the invasive MEA kills local neurons, which has been documented using immunohistochemistry (IHC). Neuronal nuclear protein (NeuN), a protein that lines the nuclei of exclusively neuronal cells, has been used as a marker for neuronal health and survival for decades in neuroscience and neural engineering. NeuN staining is often used to describe the neuronal response to intracortical microelectrode array (MEA) implantation. However, IHC is semiquantitative, relying on intensity readings rather than directly counting expressed proteins. To supplement previous IHC studies, we evaluated the expression of proteins representing different aspects of neuronal structure or function: microtubule-associated protein 2 (MAP2), neurofilament light (NfL), synaptophysin (SYP), myelin basic protein (MBP), and oligodendrocyte transcription factor 2 (OLIG2) following a neural injury caused by intracortical MEA implantation. Together, these five proteins evaluate the cytoskeletal structure, neurotransmitter release, and myelination of neurons. To fully evaluate neuronal health in NeuN-positive (NeuN+) regions, we only quantified protein expression in NeuN+ regions, making this the first-ever cell-specific spatial profiling evaluation of targeted proteins by multiplex immunochemistry following MEA implantation. We performed our protein quantification along with NeuN IHC to compare the results of the two techniques directly. We found that NeuN immunohistochemical analysis does not show the same trends as MAP2, NfL, SYP, MBP, and OLIG2 expression. Further, we found that all five quantified proteins show a decreased expression pattern that aligns more with historic intracortical MEA recording performance.}, }
@article {pmid39476487, year = {2024}, author = {Craik, A and Dial, HR and Contreras-Vidal, JL}, title = {Continuous and discrete decoding of overt speech with scalp electroencephalography (EEG).}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad8d0a}, pmid = {39476487}, issn = {1741-2552}, abstract = {Neurological disorders affecting speech production adversely impact quality of life for over 7 million individuals in the US. Traditional speech interfaces like eyetracking devices and P300 spellers are slow and unnatural for these patients. An alternative solution, speech Brain-Computer Interfaces (BCIs), directly decodes speech characteristics, offering a more natural communication mechanism. This research explores the feasibility of decoding speech features using non-invasive EEG. Nine neurologically intact participants were equipped with a 63-channel EEG system with additional sensors to eliminate eye artifacts. Participants read aloud sentences displayed on a screen selected for phonetic similarity to the English language. Deep learning models, including Convolutional Neural Networks and Recurrent Neural Networks with and without attention modules, were optimized with a focus on minimizing trainable parameters and utilizing small input window sizes for real-time application. These models were employed for discrete and continuous speech decoding tasks, achieving statistically significant participant-independent decoding performance for discrete classes and continuous characteristics of the produced audio signal. A frequency sub-band analysis highlighted the significance of certain frequency bands (delta, theta, and gamma) for decoding performance, and a perturbation analysis was used to identify crucial channels. Assessed channel selection methods did not significantly improve performance, suggesting a distributed representation of speech information encoded in the EEG signals. Leave-One-Out training demonstrated the feasibility of utilizing common speech neural correlates, reducing data collection requirements from individual participants.}, }
@article {pmid39476381, year = {2024}, author = {Sun, YH and Hu, BW and Tan, LH and Lin, L and Cao, SX and Wu, TX and Wang, H and Yu, B and Wang, Q and Lian, H and Chen, J and Li, XM}, title = {Posterior Basolateral Amygdala is a Critical Amygdaloid Area for Temporal Lobe Epilepsy.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {11}, number = {48}, pages = {e2407525}, pmid = {39476381}, issn = {2198-3844}, support = {82090030//National Natural Science Foundation of China/ ; 32071022//National Natural Science Foundation of China/ ; 81870898//National Natural Science Foundation of China/ ; 82090031//National Natural Science Foundation of China/ ; 82288101//National Natural Science Foundation of China/ ; 2019B030335001//Key-Area Research and Development Program of Guangdong Province/ ; 2019-I2M-5-057//CAMS Innovation Fund for Medical Sciences/ ; 2023-PT310-01//Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2019YFA0110103//Ministry of Science and Technology/ ; 2021YFA1101700//National Key Research and Development Program of China/ ; LR18H090002//Zhejiang Provincial Natural Science Foundation/ ; 2021ZD0202700//STI2030-Major Projects/ ; 010904005//Grants from Nanhu Brain-computer Interface Institute/ ; }, mesh = {*Epilepsy, Temporal Lobe/physiopathology/pathology ; Animals ; Mice ; *Basolateral Nuclear Complex/metabolism ; *Disease Models, Animal ; Male ; Amygdala ; Neurons/metabolism ; }, abstract = {The amygdaloid complex consists of multiple nuclei and is a key node in controlling temporal lobe epilepsy (TLE) in both human and animal model studies. However, the specific nucleus in the amygdaloid complex and the neural circuitry governing seizures remain unknown. Here, it is discovered that activation of glutamatergic neurons in the posterior basolateral amygdala (pBLA) induces severe seizures and even mortality. The pBLA glutamatergic neurons project collateral connections to multiple brain regions, including the insular cortex (IC), bed nucleus of the stria terminalis (BNST), and central amygdala (CeA). Stimulation of pBLA-targeted IC neurons triggers seizures, whereas ablation of IC neurons suppresses seizures induced by activating pBLA glutamatergic neurons. GABAergic neurons in the BNST and CeA establish feedback inhibition on pBLA glutamatergic neurons. Deleting GABAergic neurons in the BNST or CeA leads to sporadic seizures, highlighting their role in balancing pBLA activity. Furthermore, pBLA neurons receive glutamatergic inputs from the ventral hippocampal CA1 (vCA1). Ablation of pBLA glutamatergic neurons mitigates both acute and chronic seizures in the intrahippocampal kainic acid-induced mouse model of TLE. Together, these findings identify the pBLA as a pivotal nucleus in the amygdaloid complex for regulating epileptic seizures in TLE.}, }
@article {pmid39475413, year = {2024}, author = {Pitt, KM}, title = {Development and preliminary evaluation of a grid design application for adults and children using scanning and bci-based augmentative and alternative communication.}, journal = {Assistive technology : the official journal of RESNA}, volume = {}, number = {}, pages = {1-8}, doi = {10.1080/10400435.2024.2415368}, pmid = {39475413}, issn = {1949-3614}, abstract = {Augmentative and alternative communication (AAC) supports offer communication aids for individuals with severe speech and physical impairments. This study presents the development and proof of concept for an iPad application designed to evaluate the design preferences of both adults and children for AAC scanning and emerging P300-brain-computer interface access to AAC (BCI-AAC), both of which utilize item highlighting. Developed through a multidisciplinary and iterative process, the application incorporates customizable highlighting methods and display options for spelling-based and pictorial symbol interfaces. Initial testing involved five participants, including two adults with physical impairments and three children without physical impairments. Participants created unique interface displays using picture overlays, motion, and other highlighting methods. Feedback indicated strong usability and enjoyment during application use. Recommendations included expanded sound options and pre-made templates. This study demonstrates preliminary proof of concept for the application and supports the need for further research to explore user preferences and optimize communication outcomes across various AAC modalities. While BCI-AAC technology remains in its early stages, its integration into this application helps promote user-centered BCI-AAC development.}, }
@article {pmid39473791, year = {2024}, author = {Zhu, Y and Bayin, C and Li, H and Shu, X and Deng, J and Yuan, H and Shen, H and Liang, Z and Li, Y}, title = {A flexible, stable, semi-dry electrode with low impedance for electroencephalography recording.}, journal = {RSC advances}, volume = {14}, number = {46}, pages = {34415-34427}, pmid = {39473791}, issn = {2046-2069}, abstract = {Brain-computer interfaces (BCIs) provide promising prospects for the field of healthcare and rehabilitation, presenting significant advantages for humanity. The development of electrodes that exhibit satisfactory performance characteristics, including high electrical conductivity, optimal comfort, and exceptional stability, is crucial for the effective implementation of electroencephalography (EEG) recording in BCI systems. The present study introduces a novel EEG electrode design that utilizes a composite material consisting of reduced graphene oxide (RGO) and polyurethane (PU) sponge. This electrode is characterized by its low impedance, stability, and flexibility. This work offers a high level of comfort while in touch with the skin and is designed to be user-friendly. Due to its notable moisturizing capacity, adaptable structure, and the presence of conductive RGO networks, the RGOPU semi-dry electrode exhibits a skin-contact impedance of less than 5.6 kΩ. This value is equivalent to that of a wet electrode and lower than that of a commercially available semi-dry electrode. The stability tests have demonstrated the outstanding electrical and mechanical performance of the material, hence confirming its suitability for long-term EEG recording. Additionally, the RGOPU semi-dry electrode demonstrates stable recording of EEG data and accurate detection of action potentials. Furthermore, the correlation coefficient between the RGOPU semi-dry electrode and wet electrodes exceeds 0.9. Additionally, it acquires electroencephalogram signals characterized by high signal-to-noise ratios (SNRs) in the context of alpha-wave and steady-state visual evoked potential (SSVEP) tests. The accuracy of the BCI is similar to that of wet electrodes, indicating a potential capability for sensing EEG in BCI applications.}, }
@article {pmid39469033, year = {2024}, author = {Hu, F and Wang, F and Bi, J and An, Z and Chen, C and Qu, G and Han, S}, title = {HASTF: a hybrid attention spatio-temporal feature fusion network for EEG emotion recognition.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1479570}, pmid = {39469033}, issn = {1662-4548}, abstract = {INTRODUCTION: EEG-based emotion recognition has gradually become a new research direction, known as affective Brain-Computer Interface (aBCI), which has huge application potential in human-computer interaction and neuroscience. However, how to extract spatio-temporal fusion features from complex EEG signals and build learning method with high recognition accuracy and strong interpretability is still challenging.<br><br>METHODS: In this paper, we propose a hybrid attention spatio-temporal feature fusion network for EEG-based emotion recognition. First, we designed a spatial attention feature extractor capable of merging shallow and deep features to extract spatial information and adaptively select crucial features under different emotional states. Then, the temporal feature extractor based on the multi-head attention mechanism is integrated to perform spatio-temporal feature fusion to achieve emotion recognition. Finally, we visualize the extracted spatial attention features using feature maps, further analyzing key channels corresponding to different emotions and subjects.<br><br>RESULTS: Our method outperforms the current state-of-the-art methods on two public datasets, SEED and DEAP. The recognition accuracy are 99.12% &#xb1; 1.25% (SEED), 98.93% &#xb1; 1.45% (DEAP-arousal), and 98.57% &#xb1; 2.60% (DEAP-valence). We also conduct ablation experiments, using statistical methods to analyze the impact of each module on the final result. The spatial attention features reveal that emotion-related neural patterns indeed exist, which is consistent with conclusions in the field of neurology.<br><br>DISCUSSION: The experimental results show that our method can effectively extract and fuse spatial and temporal information. It has excellent recognition performance, and also possesses strong robustness, performing stably across different datasets and experimental environments for emotion recognition.}, }
@article {pmid39468119, year = {2024}, author = {Keutayeva, A and Fakhrutdinov, N and Abibullaev, B}, title = {Compact convolutional transformer for subject-independent motor imagery EEG-based BCIs.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {25775}, pmid = {39468119}, issn = {2045-2322}, support = {OPISSAI2020001//Institute of Smart Systems and Artificial Intelligence (ISSAI)/ ; OPISSAI2020001//Institute of Smart Systems and Artificial Intelligence (ISSAI)/ ; OPISSAI2020001//Institute of Smart Systems and Artificial Intelligence (ISSAI)/ ; }, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Imagination/physiology ; Algorithms ; }, abstract = {Motor imagery electroencephalography (EEG) analysis is crucial for the development of effective brain-computer interfaces (BCIs), yet it presents considerable challenges due to the complexity of the data and inter-subject variability. This paper introduces EEGCCT, an application of compact convolutional transformers designed specifically to improve the analysis of motor imagery tasks in EEG. Unlike traditional approaches, EEGCCT model significantly enhances generalization from limited data, effectively addressing a common limitation in EEG datasets. We validate and test our models using the open-source BCI Competition IV datasets 2a and 2b, employing a Leave-One-Subject-Out (LOSO) strategy to ensure subject-independent performance. Our findings demonstrate that EEGCCT not only outperforms conventional models like EEGNet in standard evaluations but also achieves better performance compared to other advanced models such as Conformer, Hybrid s-CViT, and Hybrid t-CViT, while utilizing fewer parameters and achieving an accuracy of 70.12%. Additionally, the paper presents a comprehensive ablation study that includes targeted data augmentation, hyperparameter optimization, and architectural improvements.}, }
@article {pmid39466862, year = {2024}, author = {Wei, Y and Meng, J and Luo, R and Mai, X and Li, S and Xia, Y and Zhu, X}, title = {Action Observation with Rhythm Imagery (AORI): A Novel Paradigm to Activate Motor-Related Pattern for High-Performance Motor Decoding.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2024.3487133}, pmid = {39466862}, issn = {1558-2531}, abstract = {OBJECTIVE: The Motor Imagery (MI) paradigm has been widely used in brain-computer interface (BCI) for device control and motor rehabilitation. However, the MI paradigm faces challenges such as comprehension difficulty and limited decoding accuracy. Therefore, we propose the Action Observation with Rhythm Imagery (AORI) as a natural paradigm to provide distinct features for high-performance decoding.<br><br>METHODS: Twenty subjects were recruited in the current study to perform the AORI task. Spectral-spatial, temporal and time-frequency analyses were conducted to investigate the AORI-activated brain pattern. Task-discriminant component analysis (TDCA) was utilized to perform multiclass motor decoding.<br><br>RESULTS: The results demonstrated distinct lateralized ERD in the alpha and beta bands, and clear lateralized steady-state movement-related rhythm (SSMRR) at the movement frequencies and their first harmonics. The activated brain areas included frontal, sensorimotor, posterior parietal, and occipital regions. Notably, the decoding accuracy reached 92.16% &#xb1; 7.61% in the four-class scenario.<br><br>CONCLUSION AND SIGNIFICANCE: We proposed the AORI paradigm, revealed the activated motor-related pattern and proved its efficacy for high-performance motor decoding. These findings provide new possibilities for designing a natural and robust BCI for motor control and motor rehabilitation.}, }
@article {pmid39466848, year = {2024}, author = {Gordon, EC and Seth, AK}, title = {Ethical considerations for the use of brain-computer interfaces for cognitive enhancement.}, journal = {PLoS biology}, volume = {22}, number = {10}, pages = {e3002899}, pmid = {39466848}, issn = {1545-7885}, mesh = {Humans ; *Brain-Computer Interfaces/ethics ; *Cognition/physiology ; Brain/physiology ; Privacy ; }, abstract = {Brain-computer interfaces (BCIs) enable direct communication between the brain and external computers, allowing processing of brain activity and the ability to control external devices. While often used for medical purposes, BCIs may also hold great promise for nonmedical purposes to unlock human neurocognitive potential. In this Essay, we discuss the prospects and challenges of using BCIs for cognitive enhancement, focusing specifically on invasive enhancement BCIs (eBCIs). We discuss the ethical, legal, and scientific implications of eBCIs, including issues related to privacy, autonomy, inequality, and the broader societal impact of cognitive enhancement technologies. We conclude that the development of eBCIs raises challenges far beyond practical pros and cons, prompting fundamental questions regarding the nature of conscious selfhood and about who-and what-we are, and ought, to be.}, }
@article {pmid39465107, year = {2024}, author = {Lee, S and Kim, M and Ahn, M}, title = {Evaluation of consumer-grade wireless EEG systems for brain-computer interface applications.}, journal = {Biomedical engineering letters}, volume = {14}, number = {6}, pages = {1433-1443}, pmid = {39465107}, issn = {2093-985X}, abstract = {With the growing popularity of consumer-grade electroencephalogram (EEG) devices for health, entertainment, and cognitive research, assessing their signal quality is essential. In this study, we evaluated four consumer-grade wireless and dry-electrode EEG systems widely used for brain-computer interface (BCI) research and applications, comparing them with a research-grade system. We designed an EEG phantom method that reproduced µV-level amplitude EEG signals and evaluated the five devices based on their spectral responses, temporal patterns of event-related potential (ERP), and spectral patterns of resting-state EEG. We discovered that the consumer-grade devices had limited bandwidth compared with the research-grade device. A late component (e.g., P300) was detectable in the consumer-grade devices, but the overall ERP temporal pattern was distorted. Only one device showed an ERP temporal pattern comparable to that of the research-grade device. On the other hand, we confirmed that the activation of the alpha rhythm was observable in all devices. The results provide valuable insights for researchers and developers when it comes to selecting suitable EEG devices for BCI research and applications.}, }
@article {pmid39464623, year = {2024}, author = {Tang, Z and Cui, Z and Wang, H and Liu, P and Xu, X and Yang, K}, title = {A 4-DOF Exosuit Using a Hybrid EEG-Based Control Approach for Upper-Limb Rehabilitation.}, journal = {IEEE journal of translational engineering in health and medicine}, volume = {12}, number = {}, pages = {622-634}, pmid = {39464623}, issn = {2168-2372}, mesh = {Humans ; *Electroencephalography/methods ; *Upper Extremity/physiopathology ; *Stroke Rehabilitation/methods/instrumentation ; Male ; Adult ; Exoskeleton Device ; Evoked Potentials, Visual/physiology ; Signal Processing, Computer-Assisted ; Female ; Neural Networks, Computer ; Young Adult ; }, abstract = {Rehabilitation devices, such as traditional rigid exoskeletons or exosuits, have been widely used to rehabilitate upper limb function post-stroke. In this paper, we have developed an exosuit with four degrees of freedom to enable users to involve more joints in the rehabilitation process. Additionally, a hybrid electroencephalogram-based (EEG-based) control approach has been developed to promote active user engagement and provide more control commands.The hybrid EEG-based control approach includes steady-state visual evoked potential (SSVEP) paradigm and motor imagery (MI) paradigm. Firstly, the rehabilitation movement was selected by SSVEP paradigm, and the multivariate variational mode decomposition (MVMD) and canonical correlation analysis (CCA) method was used for SSVEP EEG recognition; then, the motion intention was obtained by MI paradigm, and the convolutional neural network (CNN) and long short-term memory network (LSTM) were used to build a CNN-LSTM model for MI EEG recognition; finally, the recognition results were translated into control commands of Bowden cables to achieve multi-degree-of-freedom rehabilitation.Experimental results show that the average classification accuracy of the CNN-LSTM model reaches to 90.07% ± 2.23%, and the overall accuracy of the hybrid EEG-based control approach reaches to 85.26% ± 1.95%. The twelve subjects involved in the usability assessment demonstrated an average system usability scale (SUS) score of 81.25 ± 5.82. Additionally, four participants who underwent a 35-day rehabilitation training demonstrated an average 10.33% increase in range of motion (ROM) across 4 joints, along with a 11.35% increase in the average electromyography (EMG) amplitude of the primary muscle involved.The exosuit demonstrates good accuracy in control, exhibits favorable usability, and shows certain efficacy in multi-joint rehabilitation. Our study has taken into account the neuroplastic principles, aiming to achieve active user engagement while introducing additional degrees of freedom, offering novel ideas and methods for potential brain-computer interface (BCI)-based rehabilitation strategies and hardware development.Clinical impact: Our study presents an exosuit with four degrees of freedom for stroke rehabilitation, enabling multi-joint movement and improved motor recovery. The hybrid EEG-based control approach enhances active user engagement, offering a promising strategy for more effective and user-driven rehabilitation, potentially improving clinical outcomes.Clinical and Translational Impact Statement: By developing an exosuit and a hybrid EEG-based control approach, this study enhances stroke rehabilitation through better user engagement and multi-joint capabilities. These innovations consider neuroplasticity principles, integrating rehabilitation theory with rehabilitation device.}, }
@article {pmid39463078, year = {2024}, author = {Jin, K and Liu, X and Hu, S and Li, Y and Wu, Y and Li, J and Mo, C}, title = {[Discussion on Magnetic Resonance Compatibility of Implantable Brain-Computer Interface Devices].}, journal = {Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation}, volume = {48}, number = {5}, pages = {486-492}, doi = {10.12455/j.issn.1671-7104.240232}, pmid = {39463078}, issn = {1671-7104}, mesh = {*Brain-Computer Interfaces ; *Magnetic Resonance Imaging ; Humans ; Brain/diagnostic imaging ; Electrodes, Implanted ; }, abstract = {Brain-computer interface (BCI) devices are crucial tools for neural stimulation and recording, offering broad prospects in the diagnosis and treatment of neurological disorders. Furthermore, magnetic resonance imaging (MRI) is an effective and non-invasive technique for capturing whole-brain signals, providing detailed information on brain structures and activation patterns. Integrating the neural stimulation/recording capabilities of BCI devices with the non-invasive detection function of MRI is considered highly significant for brain function analysis. However, this combination imposes specific requirements on the magnetic and electronic performance of neural interface devices. The interaction between BCI devices and MRI is initially explored. Subsequently, potential safety risks arising from their combination are summarized and organized. Starting from the source of these hazards, such as the metallic electrodes and wires of BCI devices, the issues are analyzed, and current research countermeasures are summarized. In conclusion, the regulatory oversight of BCI's magnetic resonance safety is briefly discussed, and suggestions for enhancing the magnetic resonance compatibility of related BCI devices are proposed.}, }
@article {pmid39460240, year = {2024}, author = {Belwafi, K and Ghaffari, F}, title = {Thought-Controlled Computer Applications: A Brain-Computer Interface System for Severe Disability Support.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {20}, pages = {}, pmid = {39460240}, issn = {1424-8220}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Disabled Persons/rehabilitation ; Signal Processing, Computer-Assisted ; Machine Learning ; User-Computer Interface ; Male ; Adult ; Female ; Event-Related Potentials, P300/physiology ; Self-Help Devices ; }, abstract = {This study introduces an integrated computational environment that leverages Brain-Computer Interface (BCI) technology to enhance information access for individuals with severe disabilities. Traditional assistive technologies often rely on physical interactions, which can be challenging for this demographic. Our innovation focuses on creating new assistive technologies that use novel Human-Computer interfaces to provide a more intuitive and accessible experience. The proposed system offers four key applications to users controlled by four thoughts: an email client, a web browser, an e-learning tool, and both command-line and graphical user interfaces for managing computer resources. The BCI framework translates ElectroEncephaloGraphy (EEG) signals into commands or events using advanced signal processing and machine learning techniques. These identified commands are then processed by an integrative strategy that triggers the appropriate actions and provides real-time feedback on the screen. Our study shows that our framework achieved an 82% average classification accuracy using four distinct thoughts of 62 subjects and a 95% recognition rate for P300 signals from two users, highlighting its effectiveness in translating brain signals into actionable commands. Unlike most existing prototypes that rely on visual stimulation, our system is controlled by thought, inducing brain activity to manage the system's Application Programming Interfaces (APIs). It switches to P300 mode for a virtual keyboard and text input. The proposed BCI system significantly improves the ability of individuals with severe disabilities to interact with various applications and manage computer resources. Our approach demonstrates superior performance in terms of classification accuracy and signal recognition compared to existing methods.}, }
@article {pmid39460125, year = {2024}, author = {Magruder, RD and Kukkar, KK and Contreras-Vidal, JL and Parikh, PJ}, title = {Cross-Task Differences in Frontocentral Cortical Activations for Dynamic Balance in Neurotypical Adults.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {20}, pages = {}, pmid = {39460125}, issn = {1424-8220}, support = {1P2CHD086844-01A1/NH/NIH HHS/United States ; 1R25HD106896-05A1/NH/NIH HHS/United States ; }, mesh = {Humans ; *Postural Balance/physiology ; Male ; Female ; *Electroencephalography/methods ; *Transcranial Magnetic Stimulation/methods ; Adult ; Young Adult ; Motor Cortex/physiology ; }, abstract = {Although significant progress has been made in understanding the cortical correlates underlying balance control, these studies focused on a single task, limiting the ability to generalize the findings. Different balance tasks may elicit cortical activations in the same regions but show different levels of activation because of distinct underlying mechanisms. In this study, twenty young, neurotypical adults were instructed to maintain standing balance while the standing support surface was either translated or rotated. The differences in cortical activations in the frontocentral region between these two widely used tasks were examined using electroencephalography (EEG). Additionally, the study investigated whether transcranial magnetic stimulation could modulate these cortical activations during the platform translation task. Higher delta and lower alpha relative power were found over the frontocentral region during the platform translation task when compared to the platform rotation task, suggesting greater engagement of attentional and sensory integration resources for the former. Continuous theta burst stimulation over the supplementary motor area significantly reduced delta activity in the frontocentral region but did not alter alpha activity during the platform translation task. The results provide a direct comparison of neural activations between two commonly used balance tasks and are expected to lay a strong foundation for designing neurointerventions for balance improvements with effects generalizable across multiple balance scenarios.}, }
@article {pmid39460066, year = {2024}, author = {Senadheera, I and Hettiarachchi, P and Haslam, B and Nawaratne, R and Sheehan, J and Lockwood, KJ and Alahakoon, D and Carey, LM}, title = {AI Applications in Adult Stroke Recovery and Rehabilitation: A Scoping Review Using AI.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {20}, pages = {}, pmid = {39460066}, issn = {1424-8220}, support = {2004443//National Health and Medical Research Council/ ; }, mesh = {Humans ; *Stroke Rehabilitation/methods ; *Artificial Intelligence ; *Stroke/physiopathology ; Brain-Computer Interfaces ; Neural Networks, Computer ; Recovery of Function/physiology ; Adult ; Robotics/methods ; Machine Learning ; }, abstract = {Stroke is a leading cause of long-term disability worldwide. With the advancements in sensor technologies and data availability, artificial intelligence (AI) holds the promise of improving the amount, quality and efficiency of care and enhancing the precision of stroke rehabilitation. We aimed to identify and characterize the existing research on AI applications in stroke recovery and rehabilitation of adults, including categories of application and progression of technologies over time. Data were collected from peer-reviewed articles across various electronic databases up to January 2024. Insights were extracted using AI-enhanced multi-method, data-driven techniques, including clustering of themes and topics. This scoping review summarizes outcomes from 704 studies. Four common themes (impairment, assisted intervention, prediction and imaging, and neuroscience) were identified, in which time-linked patterns emerged. The impairment theme revealed a focus on motor function, gait and mobility, while the assisted intervention theme included applications of robotic and brain-computer interface (BCI) techniques. AI applications progressed over time, starting from conceptualization and then expanding to a broader range of techniques in supervised learning, artificial neural networks (ANN), natural language processing (NLP) and more. Applications focused on upper limb rehabilitation were reviewed in more detail, with machine learning (ML), deep learning techniques and sensors such as inertial measurement units (IMU) used for upper limb and functional movement analysis. AI applications have potential to facilitate tailored therapeutic delivery, thereby contributing to the optimization of rehabilitation outcomes and promoting sustained recovery from rehabilitation to real-world settings.}, }
@article {pmid39457727, year = {2024}, author = {Calderone, A and Latella, D and Bonanno, M and Quartarone, A and Mojdehdehbaher, S and Celesti, A and Calabrò, RS}, title = {Towards Transforming Neurorehabilitation: The Impact of Artificial Intelligence on Diagnosis and Treatment of Neurological Disorders.}, journal = {Biomedicines}, volume = {12}, number = {10}, pages = {}, pmid = {39457727}, issn = {2227-9059}, abstract = {Background and Objectives: Neurological disorders like stroke, spinal cord injury (SCI), and Parkinson's disease (PD) significantly affect global health, requiring accurate diagnosis and long-term neurorehabilitation. Artificial intelligence (AI), such as machine learning (ML), may enhance early diagnosis, personalize treatment, and optimize rehabilitation through predictive analytics, robotic systems, and brain-computer interfaces, improving outcomes for patients. This systematic review examines how AI and ML systems influence diagnosis and treatment in neurorehabilitation among neurological disorders. Materials and Methods: Studies were identified from an online search of PubMed, Web of Science, and Scopus databases with a search time range from 2014 to 2024. This review has been registered on Open OSF (n) EH9PT. Results: Recent advancements in AI and ML are revolutionizing motor rehabilitation and diagnosis for conditions like stroke, SCI, and PD, offering new opportunities for personalized care and improved outcomes. These technologies enhance clinical assessments, therapy personalization, and remote monitoring, providing more precise interventions and better long-term management. Conclusions: AI is revolutionizing neurorehabilitation, offering personalized, data-driven treatments that enhance recovery in neurological disorders. Future efforts should focus on large-scale validation, ethical considerations, and expanding access to advanced, home-based care.}, }
@article {pmid39455586, year = {2024}, author = {Isaev, MR and Mokienko, OA and Lyukmanov, RK and Ikonnikova, ES and Cherkasova, AN and Suponeva, NA and Piradov, MA and Bobrov, PD}, title = {A multiple session dataset of NIRS recordings from stroke patients controlling brain-computer interface.}, journal = {Scientific data}, volume = {11}, number = {1}, pages = {1168}, pmid = {39455586}, issn = {2052-4463}, mesh = {Humans ; *Brain-Computer Interfaces ; *Spectroscopy, Near-Infrared ; *Stroke/physiopathology ; Male ; Female ; Middle Aged ; Aged ; Imagination ; }, abstract = {This paper presents an open dataset of over 50 hours of near infrared spectroscopy (NIRS) recordings. Fifteen stroke patients completed a total of 237 motor imagery brain-computer interface (BCI) sessions. The BCI was controlled by imagined hand movements; visual feedback was presented based on the real-time data classification results. We provide the experimental records, patient demographic profiles, clinical scores (including ARAT and Fugl-Meyer), online BCI performance, and a simple analysis of hemodynamic response. We assume that this dataset can be useful for evaluating the effectiveness of various near-infrared spectroscopy signal processing and analysis techniques in patients with cerebrovascular accidents.}, }
@article {pmid39454612, year = {2024}, author = {Liu, R and Song, Q and Ma, T and Pan, H and Li, H and Zhao, X}, title = {SoftBoMI: a non-invasive wearable body-machine interface for mapping movement of shoulder to commands.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad8b6e}, pmid = {39454612}, issn = {1741-2552}, mesh = {Humans ; *Wearable Electronic Devices ; *Shoulder/physiology ; Male ; *Movement/physiology ; Adult ; Wheelchairs ; User-Computer Interface ; Amputees/rehabilitation ; Female ; Brain-Computer Interfaces ; Spinal Cord Injuries/rehabilitation/physiopathology ; Middle Aged ; }, abstract = {Objective.Customized human-machine interfaces for controlling assistive devices are vital in improving the self-help ability of upper limb amputees and tetraplegic patients. Given that most of them possess residual shoulder mobility, using it to generate commands to operate assistive devices can serve as a complementary approach to brain-computer interfaces.Approach.We propose a hybrid body-machine interface prototype that integrates soft sensors and an inertial measurement unit. This study introduces both a rule-based data decoding method and a user intent inference-based decoding method to map human shoulder movements into continuous commands. Additionally, by incorporating prior knowledge of the user's operational performance into a shared autonomy framework, we implement an adaptive switching command mapping approach. This approach enables seamless transitions between the two decoding methods, enhancing their adaptability across different tasks.Main results.The proposed method has been validated on individuals with cervical spinal cord injury, bilateral arm amputation, and healthy subjects through a series of center-out target reaching tasks and a virtual powered wheelchair driving task. The experimental results show that using both the soft sensors and the gyroscope exhibits the most well-rounded performance in intent inference. Additionally, the rule-based method demonstrates better dynamic performance for wheelchair operation, while the intent inference method is more accurate but has higher latency. Adaptive switching decoding methods offer the best adaptability by seamlessly transitioning between decoding methods for different tasks. Furthermore, we discussed the differences and characteristics among the various types of participants in the experiment.Significance.The proposed method has the potential to be integrated into clothing, enabling non-invasive interaction with assistive devices in daily life, and could serve as a tool for rehabilitation assessment in the future.}, }
@article {pmid39453797, year = {2024}, author = {Wang, Z and Liu, Y and Huang, S and Huang, H and Wu, W and Wang, Y and An, X and Ming, D}, title = {Enhancing ERD Activation and Functional Connectivity via the Sixth-Finger Motor Imagery in Stroke Patients.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {3902-3912}, doi = {10.1109/TNSRE.2024.3486551}, pmid = {39453797}, issn = {1558-0210}, mesh = {Humans ; Male ; *Imagination/physiology ; Female ; *Stroke Rehabilitation/methods ; Middle Aged ; *Fingers ; *Stroke/physiopathology/complications ; *Brain-Computer Interfaces ; *Sensorimotor Cortex/physiopathology ; Aged ; Electroencephalography ; Adult ; Cortical Synchronization ; Movement/physiology ; Algorithms ; }, abstract = {Motor imagery (MI) is widely employed in stroke rehabilitation due to the event-related desynchronization (ERD) phenomenon in sensorimotor cortex induced by MI is similar to actual movement. However, the traditional BCI paradigm, in which the patient imagines the movement of affected hand (AH-MI) with a weak ERD caused by the damaged brain regions, retards motor relearning process. In this work, we applied a novel MI paradigm based on the "sixth-finger" (SF-MI) in stroke patients and systematically uncovered the ERD pattern enhancement of novel MI paradigm compared to traditional MI paradigm. Twenty stroke patients were recruited for this experiment. Event-related spectral perturbation was adopted to supply details about ERD. Brain activation region, intensity and functional connectivity were compared between SF-MI and AH-MI to reveal the ERD enhancement performance of novel MI paradigm. A "wider range, stronger intensity, greater connection" ERD activation pattern was induced in stroke patients by novel SF-MI paradigm compared to traditional AH-MI paradigm. The bilateral sensorimotor and prefrontal modulation was found in SF-MI, which was different in AH-MI only weak sensorimotor modulation was exhibited. The ERD enhancement is mainly concentrated in mu rhythm. More synchronized and intimate neural activity between different brain regions was found during SF-MI tasks compared to AH-MI tasks. Classification results (>80% in SF-MI vs. REST) also indicated the feasibility of applying novel MI paradigm to clinical stroke rehabilitation. This work provides a novel MI paradigm and demonstrates its neural activation-enhancing performance, helping to develop more effective MI-based BCI system for stroke rehabilitation.}, }
@article {pmid39451800, year = {2024}, author = {Drăgoi, MV and Nisipeanu, I and Frimu, A and Tălîngă, AM and Hadăr, A and Dobrescu, TG and Suciu, CP and Manea, AR}, title = {Real-Time Home Automation System Using BCI Technology.}, journal = {Biomimetics (Basel, Switzerland)}, volume = {9}, number = {10}, pages = {}, pmid = {39451800}, issn = {2313-7673}, abstract = {A Brain-Computer Interface (BCI) processes and converts brain signals to provide commands to output devices to carry out certain tasks. The main purpose of BCIs is to replace or restore the missing or damaged functions of disabled people, including in neuromuscular disorders like Amyotrophic Lateral Sclerosis (ALS), cerebral palsy, stroke, or spinal cord injury. Hence, a BCI does not use neuromuscular output pathways; it bypasses traditional neuromuscular pathways by directly interpreting brain signals to command devices. Scientists have used several techniques like electroencephalography (EEG) and intracortical and electrocorticographic (ECoG) techniques to collect brain signals that are used to control robotic arms, prosthetics, wheelchairs, and several other devices. A non-invasive method of EEG is used for collecting and monitoring the signals of the brain. Implementing EEG-based BCI technology in home automation systems may facilitate a wide range of tasks for people with disabilities. It is important to assist and empower individuals with paralysis to engage with existing home automation systems and gadgets in this particular situation. This paper proposes a home security system to control a door and a light using an EEG-based BCI. The system prototype consists of the EMOTIV Insight™ headset, Raspberry Pi 4, a servo motor to open/close the door, and an LED. The system can be very helpful for disabled people, including arm amputees who cannot close or open doors or use a remote control to turn on or turn off lights. The system includes an application made in Flutter to receive notifications on a smartphone related to the status of the door and the LEDs. The disabled person can control the door as well as the LED using his/her brain signals detected by the EMOTIV Insight™ headset.}, }
@article {pmid39451366, year = {2024}, author = {Caffi, L and Romito, LM and Palmisano, C and Aloia, V and Arlotti, M and Rossi, L and Marceglia, S and Priori, A and Eleopra, R and Levi, V and Mazzoni, A and Isaias, IU}, title = {Adaptive vs. Conventional Deep Brain Stimulation: One-Year Subthalamic Recordings and Clinical Monitoring in a Patient with Parkinson's Disease.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {11}, number = {10}, pages = {}, pmid = {39451366}, issn = {2306-5354}, support = {//Grigioni Foundation for Parkinson's disease/ ; Project-ID 424778381 - TRR 295//Deutsche Forschungsgemeinschaft/ ; //New York University School of Medicine/ ; //The Marlene and Paolo Fresco Institute for Parkinson's and Movement Disorders/ ; NRRP "Fit4MedRob - Fit for Medical Robotics" Grant (# PNC0000007).//Italian Ministry of Research/ ; Next Generation EU - NRRP M6C2 - Investment 2.1 Enhancement and strengthening of bio-medical research in the NHS//European Union/ ; }, abstract = {Conventional DBS (cDBS) for Parkinson's disease uses constant, predefined stimulation parameters, while the currently available adaptive DBS (aDBS) provides the possibility of adjusting current amplitude with respect to subthalamic activity in the beta band (13-30 Hz). This preliminary study on one patient aims to describe how these two stimulation modes affect basal ganglia dynamics and, thus, behavior in the long term. We collected clinical data (UPDRS-III and -IV) and subthalamic recordings of one patient with Parkinson's disease treated for one year with aDBS, alternated with short intervals of cDBS. Moreover, after nine months, the patient discontinued all dopaminergic drugs while keeping aDBS. Clinical benefits of aDBS were superior to those of cDBS, both with and without medications. This improvement was paralleled by larger daily fluctuations of subthalamic beta activity. Moreover, with aDBS, subthalamic beta activity decreased during asleep with respect to awake hours, while it remained stable in cDBS. These preliminary data suggest that aDBS might be more effective than cDBS in preserving the functional role of daily beta fluctuations, thus leading to superior clinical benefit. Our results open new perspectives for a restorative brain network effect of aDBS as a more physiological, bidirectional, brain-computer interface.}, }
@article {pmid39451342, year = {2024}, author = {Kaviri, SM and Vinjamuri, R}, title = {Integrating Electroencephalography Source Localization and Residual Convolutional Neural Network for Advanced Stroke Rehabilitation.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {11}, number = {10}, pages = {}, pmid = {39451342}, issn = {2306-5354}, support = {HCC-2053498//National Science Foundation/ ; CNS-2333292//National Science Foundation/ ; }, abstract = {Motor impairments caused by stroke significantly affect daily activities and reduce quality of life, highlighting the need for effective rehabilitation strategies. This study presents a novel approach to classifying motor tasks using EEG data from acute stroke patients, focusing on left-hand motor imagery, right-hand motor imagery, and rest states. By using advanced source localization techniques, such as Minimum Norm Estimation (MNE), dipole fitting, and beamforming, integrated with a customized Residual Convolutional Neural Network (ResNetCNN) architecture, we achieved superior spatial pattern recognition in EEG data. Our approach yielded classification accuracies of 91.03% with dipole fitting, 89.07% with MNE, and 87.17% with beamforming, markedly surpassing the 55.57% to 72.21% range of traditional sensor domain methods. These results highlight the efficacy of transitioning from sensor to source domain in capturing precise brain activity. The enhanced accuracy and reliability of our method hold significant potential for advancing brain-computer interfaces (BCIs) in neurorehabilitation. This study emphasizes the importance of using advanced EEG classification techniques to provide clinicians with precise tools for developing individualized therapy plans, potentially leading to substantial improvements in motor function recovery and overall patient outcomes. Future work will focus on integrating these techniques into practical BCI systems and assessing their long-term impact on stroke rehabilitation.}, }
@article {pmid39449239, year = {2024}, author = {Hong, J and Cai, M and Qin, X}, title = {Multimodal human computer interaction of wheelchairs supporting lower limb active rehabilitation.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-12}, doi = {10.1080/10255842.2024.2417204}, pmid = {39449239}, issn = {1476-8259}, abstract = {Currently, an important challenge in stroke rehabilitation is how to effectively restore motor functions of lower limbs. This paper presents multimodal human computer interaction (HCI) of wheelchairs supporting lower limb active rehabilitation. First, multimodal HCI incorporating motor imagery electroencephalography (EEG), electromyography (EMG) and speech is designed. Second, prototype supporting wheelchair active rehabilitation method is illustrated in details. Third, the preliminary brain-computer interfaces (BCI) and speech recognition task experiments are carried out respectively, and the results are obtained. Finally, discussion is conducted and conclusion is drawn. This study has important practical significance in auxiliary movements and neurorehabilitation for stroke patients.}, }
@article {pmid39448109, year = {2024}, author = {Chang, CT and Pai, KJ and Huang, CH and Chou, CY and Liu, KW and Lin, HB}, title = {Relationship of SSVEP response between flash frequency conditions.}, journal = {Progress in brain research}, volume = {290}, number = {}, pages = {123-139}, doi = {10.1016/bs.pbr.2024.07.002}, pmid = {39448109}, issn = {1875-7855}, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; Male ; *Electroencephalography ; Adult ; *Photic Stimulation/methods ; Female ; *Brain-Computer Interfaces ; Young Adult ; Visual Cortex/physiology ; }, abstract = {This study delves into the application of Brain-Computer Interfaces (BCIs), focusing on exploiting Steady-State Visual Evoked Potentials (SSVEPs) as communication tools for individuals facing mobility impairments. SSVEP-BCI systems can swiftly transmit substantial volumes of information, rendering them suitable for diverse applications. However, the efficacy of SSVEP responses can be influenced by variables such as the frequency and color of visual stimuli. Through experiments involving participants equipped with electrodes on the brain's visual cortex, visual stimuli were administered at 4, 17, 25, and 40Hz, using white, red, yellow, green, and blue light sources. The results reveal that white and green stimuli evoke higher SSVEP responses at lower frequencies, with color's impact diminishing at higher frequencies. At low light intensity (1W), white and green stimuli elicit significantly higher SSVEP responses, while at high intensity (3W), responses across colors tend to equalize. Notably, due to seizure risks, red and blue lights should be used cautiously, with white and green lights preferred for SSVEP-BCI systems. This underscores the critical consideration of color and frequency in the design of effective and safe SSVEP-BCI systems, necessitating further research to optimize designs for clinical applications.}, }
@article {pmid39448108, year = {2024}, author = {Chang, CT and Pai, KJ and Huang, CH and Chou, CY and Liu, KW and Lin, HB}, title = {Optimizing user experience in SSVEP-BCI systems.}, journal = {Progress in brain research}, volume = {290}, number = {}, pages = {105-121}, doi = {10.1016/bs.pbr.2024.05.010}, pmid = {39448108}, issn = {1875-7855}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography ; *Photic Stimulation/methods ; Male ; *Evoked Potentials, Visual/physiology ; Adult ; Female ; Young Adult ; Visual Perception/physiology ; Signal-To-Noise Ratio ; }, abstract = {The emergence of brain-computer interface (BCI) technology provides enormous potential for human medical and daily applications. Therefore, allowing users to tolerate the visual response of SSVEP for a long time has always been an important issue in the SSVEP-BCI system. We recruited three subjects and conducted visual experiments in groups using different frequencies (17 and 25Hz) and 60Hz light. After recording the physiological signal, use FFT to perform a time-frequency analysis on the physiological signal to check whether there is any difference in the signal-to-noise ratio and amplitude of the 60Hz light source compared with a single low-frequency signal source. The results show that combining a 60Hz light source with low-frequency LEDs can reduce participants' eye discomfort while achieving effective light stimulation control. At the same time, there was no significant difference in signal-to-noise ratio and amplitude between the groups. This also means that 60Hz can make vision more continuous and improve the subject's experience and comfort. At the same time, it does not affect the performance of the original SSVEP-induced response. This study highlights the importance of considering technical aspects and user comfort when designing SSVEP-BCI systems to increase the usability of SSVEP systems for long-term flash viewing.}, }
@article {pmid39446156, year = {2024}, author = {Muirhead, WR and Layard Horsfall, H and Aicardi, C and Carolan, J and Akram, H and Vanhoestenberghe, A and Schaefer, AT and Marcus, HJ}, title = {Implanted cortical neuroprosthetics for speech and movement restoration.}, journal = {Journal of neurology}, volume = {271}, number = {11}, pages = {7156-7168}, pmid = {39446156}, issn = {1432-1459}, support = {FC001153/WT_/Wellcome Trust/United Kingdom ; FC001153/WT_/Wellcome Trust/United Kingdom ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Cerebral Cortex/physiopathology/physiology ; Neural Prostheses ; Movement Disorders/therapy/physiopathology ; Movement/physiology ; Speech/physiology ; Speech Disorders/etiology/therapy/physiopathology ; }, abstract = {Implanted cortical neuroprosthetics (ICNs) are medical devices developed to replace dysfunctional neural pathways by creating information exchange between the brain and a digital system which can facilitate interaction with the external world. Over the last decade, researchers have explored the application of ICNs for diverse conditions including blindness, aphasia, and paralysis. Both transcranial and endovascular approaches have been used to record neural activity in humans, and in a laboratory setting, high-performance decoding of the signals associated with speech intention has been demonstrated. Particular progress towards a device which can move into clinical practice has been made with ICNs focussed on the restoration of speech and movement. This article provides an overview of contemporary ICNs for speech and movement restoration, their mechanisms of action and the unique ethical challenges raised by the field.}, }
@article {pmid39443437, year = {2024}, author = {Heinonen, GA and Carmona, JC and Grobois, L and Kruger, LS and Velazquez, A and Vrosgou, A and Kansara, VB and Shen, Q and Egawa, S and Cespedes, L and Yazdi, M and Bass, D and Saavedra, AB and Samano, D and Ghoshal, S and Roh, D and Agarwal, S and Park, S and Alkhachroum, A and Dugdale, L and Claassen, J}, title = {A Survey of Surrogates and Health Care Professionals Indicates Support of Cognitive Motor Dissociation-Assisted Prognostication.}, journal = {Neurocritical care}, volume = {}, number = {}, pages = {}, pmid = {39443437}, issn = {1556-0961}, support = {R01 LM011826/LM/NLM NIH HHS/United States ; UL1TR001873 from NCATS/NIH//Clinical and Translational Science Awards/ ; R01 NS106014/NS/NINDS NIH HHS/United States ; LM011826//U.S. National Library of Medicine/ ; NS106014/NS/NINDS NIH HHS/United States ; }, abstract = {BACKGROUND: Prognostication of patients with acute disorders of consciousness is imprecise but more accurate technology-supported predictions, such as cognitive motor dissociation (CMD), are emerging. CMD refers to the detection of willful brain activation following motor commands using functional magnetic resonance imaging or machine learning-supported analysis of the electroencephalogram in clinically unresponsive patients. CMD is associated with long-term recovery, but acceptance by surrogates and health care professionals is uncertain. The objective of this study was to determine receptiveness for CMD to inform goals of care (GoC) decisions and research participation among health care professionals and surrogates of behaviorally unresponsive patients.<br><br>METHODS: This was a two-center study of surrogates of and health care professionals caring for unconscious patients with severe neurological injury who were enrolled in two prospective US-based studies. Participants completed a 13-item survey to assess demographics, religiosity, minimal acceptable level of recovery, enthusiasm for research participation, and receptiveness for CMD to support GoC decisions.<br><br>RESULTS: Completed surveys were obtained from 196 participants (133 health care professionals and 63 surrogates). Across all respondents, 93% indicated that they would want their loved one or the patient they cared for to participate in a research study that supports recovery of consciousness if CMD were detected, compared to 58% if CMD were not detected. Health care professionals were more likely than surrogates to change GoC with a positive (78% vs. 59%, p&#x2009;=&#x2009;0.005) or negative (83% vs. 59%, p&#x2009;=&#x2009;0.0002) CMD result. Participants who reported religion was the most important part of their life were least likely to change GoC with or without CMD. Participants who identified as Black (odds ratio [OR] 0.12, 95% confidence interval [CI] 0.04-0.36) or Hispanic/Latino (OR 0.39, 95% CI 0.2-0.75) and those for whom religion was the most important part of their life (OR 0.18, 95% CI 0.05-0.64) were more likely to accept a lower minimum level of recovery.<br><br>CONCLUSIONS: Technology-supported prognostication and enthusiasm for clinical trial participation was supported across a diverse spectrum of health care professionals and surrogate decision-makers. Education for surrogates and health care professionals should accompany integration of technology-supported prognostication.}, }
@article {pmid39441155, year = {2024}, author = {Demnati, B and Chabihi, Z and Boumediane, EM and Dkhissi, S and Idarrha, F and Fath Elkhir, Y and Benhima, MA and Abkari, I and Rafai, M and Ibn Moussa, S and Rahmi, M}, title = {Psychological impact of peri-implant fractures: A cross-sectional study.}, journal = {La Tunisie medicale}, volume = {102}, number = {10}, pages = {708-714}, pmid = {39441155}, issn = {2724-7031}, mesh = {Humans ; Cross-Sectional Studies ; Male ; Female ; Middle Aged ; *Quality of Life/psychology ; Adult ; *Adaptation, Psychological ; Aged ; *Depression/psychology/epidemiology/etiology ; Anxiety/psychology/etiology/epidemiology ; Periprosthetic Fractures/psychology/epidemiology/surgery ; Surveys and Questionnaires ; Stress Disorders, Post-Traumatic/psychology/epidemiology/etiology/diagnosis ; Stress, Psychological/psychology/epidemiology/etiology ; Orthopedic Procedures/psychology/adverse effects ; }, abstract = {INTRODUCTION: Peri-implant fractures (PIFs) are uncommon yet critical complications following orthopedic surgery. These complications can significantly impact a patient's psychological well-being and overall quality of life.<br><br>AIM: This study aimed to investigate the psychological effects of PIFs.<br><br>METHODS: This was a cross-sectional study that involved 136 patients who underwent surgery for PIFs between 2018 and 2022. We utilized various validated scales and questionnaires such as Hospital Anxiety and Depression Scale (HADS), Perceived Stress Scale (PSS), Impact of Event Scale Revised (IES-R), 36-Item Short Form Survey (SF-36), and Brief COPE Inventory (BCI) to assess their psychological state.<br><br>RESULT: The results revealed that patients with PIFs experienced higher levels of anxiety, depression, stress, and post-traumatic stress compared to the general population. Additionally, they reported lower physical and mental health. Factors such as the number of surgeries, treatment delay, post-operative pain levels, and complications significantly influenced their psychological outcomes. Notably, acceptance, positive reframing, and seeking emotional support were the most common coping mechanisms employed by these patients. Conversely, denial, substance use, and self blame were the least employed strategies.<br><br>CONCLUSION: This study suggests that psychological interventions could significantly benefit patients with PIFs, potentially reducing their distress and improving their quality of life.}, }
@article {pmid39440972, year = {2024}, author = {Tong, C and Xiao, D and Li, Q and Gou, J and Wang, S and Zeng, Z and Xiong, W}, title = {First insights into the prevalence, genetic characteristics, and pathogenicity of Bacillus cereus from generations worldwide.}, journal = {mSphere}, volume = {9}, number = {11}, pages = {e0070224}, pmid = {39440972}, issn = {2379-5042}, support = {2020B0301030005//Guangdong Major Project of Basic and Applied Basic Research/ ; 2023A1515030137//Guangdong Basic and Applied Basic Research Foundation/ ; }, mesh = {*Bacillus cereus/genetics/pathogenicity ; Virulence/genetics ; *Whole Genome Sequencing ; Prevalence ; *Biofilms/growth &amp; development ; Humans ; Genome, Bacterial ; Virulence Factors/genetics ; Global Health ; Gram-Positive Bacterial Infections/microbiology/epidemiology ; Foodborne Diseases/microbiology/epidemiology ; Phylogeny ; Asia/epidemiology ; Europe/epidemiology ; Oceania/epidemiology ; }, abstract = {Bacillus cereus, a global threat, is one of the major causes of toxin-induced foodborne diseases. However, a comprehensive assessment of the prevalence and characteristics of B. cereus worldwide is still lacking. Here, we applied whole-genome sequence analysis to 191 B. cereus collected in Africa, America, Asia, Europe, and Oceania from the 1900s to 2022, finding that CC142 dominated the global B. cereus clonal complex. The results provided direct evidence that B. cereus could spread through the food chain and intercontinentally. B. cereus from different generations worldwide showed coherence in the antibiotic-resistant gene and virulence and biofilm gene profiles, although with high genomic heterogeneity. The BCI-BCII-vanZF-fosB profiles and virulence and biofilm genes were detected at high rates, and we emphasized that B. cereus would pose a serious challenge to global public health and clinical treatment.IMPORTANCEThis study first emphasized the prevalence, genetic characteristics, and pathogenicity of Bacillus cereus worldwide from the 1900s to 2022 using whole-genome sequence analysis. The CC142 dominated the global Bacillus cereus clonal complex. Moreover, we revealed a close evolutionary relationship between the isolates from different sources. B. cereus isolates from different generations worldwide showed coherence in potential pathogenicity, although with high genomic heterogeneity. The BCI-BCII-vanZF-fosB profiles and virulence and biofilm genes were detected at high rates, and we emphasized that B. cereus would pose a serious challenge to global public health and clinical treatment.}, }
@article {pmid39439491, year = {2024}, author = {Xu, S and Liu, Y and Lee, H and Li, W}, title = {Neural interfaces: Bridging the brain to the world beyond healthcare.}, journal = {Exploration (Beijing, China)}, volume = {4}, number = {5}, pages = {20230146}, pmid = {39439491}, issn = {2766-2098}, abstract = {Neural interfaces, emerging at the intersection of neurotechnology and urban planning, promise to transform how we interact with our surroundings and communicate. By recording and decoding neural signals, these interfaces facilitate direct connections between the brain and external devices, enabling seamless information exchange and shared experiences. Nevertheless, their development is challenged by complexities in materials science, electrochemistry, and algorithmic design. Electrophysiological crosstalk and the mismatch between electrode rigidity and tissue flexibility further complicate signal fidelity and biocompatibility. Recent closed-loop brain-computer interfaces, while promising for mood regulation and cognitive enhancement, are limited by decoding accuracy and the adaptability of user interfaces. This perspective outlines these challenges and discusses the progress in neural interfaces, contrasting non-invasive and invasive approaches, and explores the dynamics between stimulation and direct interfacing. Emphasis is placed on applications beyond healthcare, highlighting the need for implantable interfaces with high-resolution recording and stimulation capabilities.}, }
@article {pmid39438593, year = {2024}, author = {Miklós, G and Halász, L and Hasslberger, M and Toth, E and Manola, L and Hagh Gooie, S and van Elswijk, G and Várkuti, B and Erőss, L}, title = {Sensory-substitution based sound perception using a spinal computer-brain interface.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {24879}, pmid = {39438593}, issn = {2045-2322}, mesh = {Humans ; Male ; Female ; Middle Aged ; *Auditory Perception/physiology ; *Brain-Computer Interfaces ; Adult ; Spinal Cord Stimulation/methods ; Aged ; Acoustic Stimulation ; Spinal Cord/physiology ; Hearing Aids ; }, abstract = {Sensory substitution offers a promising approach to restore lost sensory functions. Here we show that spinal cord stimulation (SCS), typically used for chronic pain management, can potentially serve as a novel auditory sensory substitution device. We recruited 13 patients undergoing SCS implantation and translated everyday sound samples into personalized SCS patterns during their trial phase. In a sound identification task-where chance-level performance was 33.3%-participants (n = 8) achieved a mean accuracy of 72.8% using only SCS input. We observed a weak positive correlation between stimulation bitrate and identification accuracy. A follow-up discrimination task (n = 5) confirmed that reduced bitrates significantly impaired participants' ability to distinguish between consecutive SCS patterns, indicating effective processing of additional information at higher bitrates. These findings demonstrate the feasibility of using existing SCS technology to create a novel neural interface for a sound prosthesis. Our results pave the way for future research to enhance stimulation fidelity, assess long-term training effects, and explore integration with other auditory aids for comprehensive hearing rehabilitation.}, }
@article {pmid39437806, year = {2024}, author = {Zuo, M and Yu, B and Sui, L}, title = {Classification of EEG evoked in 2D and 3D virtual reality: traditional machine learning versus deep learning.}, journal = {Biomedical physics &amp; engineering express}, volume = {11}, number = {1}, pages = {}, doi = {10.1088/2057-1976/ad89c5}, pmid = {39437806}, issn = {2057-1976}, mesh = {Humans ; *Electroencephalography/methods ; *Deep Learning ; *Virtual Reality ; Male ; Adult ; Female ; Support Vector Machine ; Machine Learning ; Young Adult ; Brain/physiology ; Neural Networks, Computer ; Brain-Computer Interfaces ; Algorithms ; }, abstract = {Backgrounds. Virtual reality (VR) simulates real-life events and scenarios and is widely utilized in education, entertainment, and medicine. VR can be presented in two dimensions (2D) or three dimensions (3D), with 3D VR offering a more realistic and immersive experience. Previous research has shown that electroencephalogram (EEG) profiles induced by 3D VR differ from those of 2D VR in various aspects, including brain rhythm power, activation, and functional connectivity. However, studies focused on classifying EEG in 2D and 3D VR contexts remain limited.Methods. A 56-channel EEG was recorded while visual stimuli were presented in 2D and 3D VR. The recorded EEG signals were classified using two machine learning approaches: traditional machine learning and deep learning. In the traditional approach, features such as power spectral density (PSD) and common spatial patterns (CSP) were extracted, and three classifiers-support vector machines (SVM), K-nearest neighbors (KNN), and random forests (RF)-were used. For the deep learning approach, a specialized convolutional neural network, EEGNet, was employed. The classification performance of these methods was then compared.Results. In terms of accuracy, precision, recall, and F1-score, the deep learning method outperformed traditional machine learning approaches. Specifically, the classification accuracy using the EEGNet deep learning model reached up to 97.86%.Conclusions. EEGNet-based deep learning significantly outperforms conventional machine learning methods in classifying EEG signals induced by 2D and 3D VR. Given EEGNet's design for EEG-based brain-computer interfaces (BCI), this superior classification performance suggests that it can enhance the application of 3D VR in BCI systems.}, }
@article {pmid39435615, year = {2025}, author = {Song, X and Li, R and Chu, X and Li, Q and Li, R and Li, Q and Tong, KY and Gu, X and Ming, D}, title = {Multilevel analysis of the central-peripheral-target organ pathway: contributing to recovery after peripheral nerve injury.}, journal = {Neural regeneration research}, volume = {20}, number = {10}, pages = {2807-2822}, doi = {10.4103/NRR.NRR-D-24-00641}, pmid = {39435615}, issn = {1673-5374}, abstract = {Peripheral nerve injury is a common neurological condition that often leads to severe functional limitations and disabilities. Research on the pathogenesis of peripheral nerve injury has focused on pathological changes at individual injury sites, neglecting multilevel pathological analysis of the overall nervous system and target organs. This has led to restrictions on current therapeutic approaches. In this paper, we first summarize the potential mechanisms of peripheral nerve injury from a holistic perspective, covering the central nervous system, peripheral nervous system, and target organs. After peripheral nerve injury, the cortical plasticity of the brain is altered due to damage to and regeneration of peripheral nerves; changes such as neuronal apoptosis and axonal demyelination occur in the spinal cord. The nerve will undergo axonal regeneration, activation of Schwann cells, inflammatory response, and vascular system regeneration at the injury site. Corresponding damage to target organs can occur, including skeletal muscle atrophy and sensory receptor disruption. We then provide a brief review of the research advances in therapeutic approaches to peripheral nerve injury. The main current treatments are conducted passively and include physical factor rehabilitation, pharmacological treatments, cell-based therapies, and physical exercise. However, most treatments only partially address the problem and cannot complete the systematic recovery of the entire central nervous system-peripheral nervous system-target organ pathway. Therefore, we should further explore multilevel treatment options that produce effective, long-lasting results, perhaps requiring a combination of passive (traditional) and active (novel) treatment methods to stimulate rehabilitation at the central-peripheral-target organ levels to achieve better functional recovery.}, }
@article {pmid39435350, year = {2024}, author = {Beauchemin, N and Charland, P and Karran, A and Boasen, J and Tadson, B and Sénécal, S and Léger, PM}, title = {Enhancing learning experiences: EEG-based passive BCI system adapts learning speed to cognitive load in real-time, with motivation as catalyst.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1416683}, pmid = {39435350}, issn = {1662-5161}, abstract = {Computer-based learning has gained popularity in recent years, providing learners greater flexibility and freedom. However, these learning environments do not consider the learner's mental state in real-time, resulting in less optimized learning experiences. This research aimed to explore the effect on the learning experience of a novel EEG-based Brain-Computer Interface (BCI) that adjusts the speed of information presentation in real-time during a learning task according to the learner's cognitive load. We also explored how motivation moderated these effects. In accordance with three experimental groups (non-adaptive, adaptive, and adaptive with motivation), participants performed a calibration task (n-back), followed by a memory-based learning task concerning astrological constellations. Learning gains were assessed based on performance on the learning task. Self-perceived mental workload, cognitive absorption and satisfaction were assessed using a post-test questionnaire. Between-group analyses using Mann-Whitney tests suggested that combining BCI and motivational factors led to more significant learning gains and an improved learning experience. No significant difference existed between the BCI without motivational factor and regular non-adaptive interface for overall learning gains, self-perceived mental workload, and cognitive absorption. However, participants who undertook the experiment with an imposed learning pace reported higher overall satisfaction with their learning experience and a higher level of temporal stress. Our findings suggest BCI's potential applicability and feasibility in improving memorization-based learning experiences. Further work should seek to optimize the BCI adaptive index and explore generalizability to other learning contexts.}, }
@article {pmid39434212, year = {2024}, author = {Ahmadi-Dastgerdi, N and Hosseini-Nejad, H and Alinejad-Rokny, H}, title = {A Hardware-Efficient Novelty-Aware Spike Sorting Approach for Brain-Implantable Microsystems.}, journal = {International journal of neural systems}, volume = {34}, number = {12}, pages = {2450067}, doi = {10.1142/S0129065724500679}, pmid = {39434212}, issn = {1793-6462}, mesh = {*Brain-Computer Interfaces ; *Action Potentials/physiology ; *Brain/physiology ; Neurons/physiology ; Signal Processing, Computer-Assisted ; Humans ; Algorithms ; }, abstract = {Unsupervised spike sorting, a vital processing step in real-time brain-implantable microsystems, is faced with the prominent challenge of managing nonstationarity in neural signals. In long-term recordings, spike waveforms gradually change and new source neurons are likely to become activated. Adaptive spike sorters combined with on-implant training units effectively process the nonstationary signals at the cost of high hardware resource utilization. On the other hand, static approaches, while being hardware-friendly, are subjected to decreased processing performance in such recordings where the neural signal characteristics gradually change. To strike a balance between the hardware cost and processing performance, this study proposes a hardware-efficient novelty-aware spike sorting approach that is capable of dealing with both variated spike waveforms and spike waveforms generated from new source neurons. Its improved hardware efficiency compared to adaptive ones and capability of dealing with nonstationary signals make it attractive for implantable applications. The proposed novelty-aware spike sorting especially would be a good fit for brain-computer interfaces where long-term, real-time interaction with the brain is required, and the available on-implant hardware resources are limited. Our unsupervised spike sorting benefits from a novelty detection process to deal with neural signal variations. It tracks the spike features so that in case of detecting an unexpected change (novelty detection) both on and off-implant parameters are updated to preserve the performance in new state. To make the proposed approach agile enough to be suitable for brain implants, the on-implant computations are reduced while the computational burden is realized off-implant. The performance of our proposed approach is evaluated using both synthetic and real datasets. The results demonstrate that, in the mean, it is capable of detecting 94.31% of novel spikes (wave-drifted or emerged spikes) with a classification accuracy (CA) of 96.31%. Moreover, an FPGA prototype of the on-implant circuit is implemented and tested. It is shown that in comparison to the OSORT algorithm, a pivotal spike sorting method, our spike sorting provides a higher CA at significantly lower hardware resources. The proposed circuit is also implemented in a 180-nm standard CMOS process, achieving a power consumption of 1.78[Formula: see text][Formula: see text] per channel and a chip area of 0.07[Formula: see text]mm[2] per channel.}, }
@article {pmid39433844, year = {2024}, author = {Pun, TK and Khoshnevis, M and Hosman, T and Wilson, GH and Kapitonava, A and Kamdar, F and Henderson, JM and Simeral, JD and Vargas-Irwin, CE and Harrison, MT and Hochberg, LR}, title = {Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces.}, journal = {Communications biology}, volume = {7}, number = {1}, pages = {1363}, pmid = {39433844}, issn = {2399-3642}, support = {T32MH115895//U.S. Department of Health &amp; Human Services | NIH | National Institute of Mental Health (NIMH)/ ; U01DC017844, R01DC014034//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; T32 MH115895/MH/NIMH NIH HHS/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; N2864C, A2295R, A2827R, A3803R//U.S. Department of Veterans Affairs (Department of Veterans Affairs)/ ; U01 NS098968/NS/NINDS NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; UH2NS095548, U01NS098968//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Quadriplegia/physiopathology ; Adult ; Female ; Middle Aged ; }, abstract = {Intracortical brain-computer interfaces (iBCIs) enable people with tetraplegia to gain intuitive cursor control from movement intentions. To translate to practical use, iBCIs should provide reliable performance for extended periods of time. However, performance begins to degrade as the relationship between kinematic intention and recorded neural activity shifts compared to when the decoder was initially trained. In addition to developing decoders to better handle long-term instability, identifying when to recalibrate will also optimize performance. We propose a method, "MINDFUL", to measure instabilities in neural data for useful long-term iBCI, without needing labels of user intentions. Longitudinal data were analyzed from two BrainGate2 participants with tetraplegia as they used fixed decoders to control a computer cursor spanning 142 days and 28 days, respectively. We demonstrate a measure of instability that correlates with changes in closed-loop cursor performance solely based on the recorded neural activity (Pearson r = 0.93 and 0.72, respectively). This result suggests a strategy to infer online iBCI performance from neural data alone and to determine when recalibration should take place for practical long-term use.}, }
@article {pmid39433597, year = {2024}, author = {Candrea, DN and Shah, S and Luo, S and Angrick, M and Rabbani, Q and Coogan, C and Milsap, GW and Nathan, KC and Wester, BA and Anderson, WS and Rosenblatt, KR and Uchil, A and Clawson, L and Maragakis, NJ and Vansteensel, MJ and Tenore, FV and Ramsey, NF and Fifer, MS and Crone, NE}, title = {A click-based electrocorticographic brain-computer interface enables long-term high-performance switch scan spelling.}, journal = {Communications medicine}, volume = {4}, number = {1}, pages = {207}, pmid = {39433597}, issn = {2730-664X}, support = {UH3 NS114439/NS/NINDS NIH HHS/United States ; }, abstract = {BACKGROUND: Brain-computer interfaces (BCIs) can restore communication for movement- and/or speech-impaired individuals by enabling neural control of computer typing applications. Single command click detectors provide a basic yet highly functional capability.<br><br>METHODS: We sought to test the performance and long-term stability of click decoding using a chronically implanted high density electrocorticographic (ECoG) BCI with coverage of the sensorimotor cortex in a human clinical trial participant (ClinicalTrials.gov, NCT03567213) with amyotrophic lateral sclerosis. We trained the participant's click detector using a small amount of training data (<44&#x2009;min across 4&#x2009;days) collected up to 21&#x2009;days prior to BCI use, and then tested it over a period of 90&#x2009;days without any retraining or updating.<br><br>RESULTS: Using a click detector to navigate a switch scanning speller interface, the study participant can maintain a median spelling rate of 10.2 characters per min. Though a transient reduction in signal power modulation can interrupt usage of a fixed model, a new click detector can achieve comparable performance despite being trained with even less data (<15&#x2009;min, within 1&#x2009;day).<br><br>CONCLUSIONS: These results demonstrate that a click detector can be trained with a small ECoG dataset while retaining robust performance for extended periods, providing functional text-based communication to BCI users.}, }
@article {pmid39433073, year = {2024}, author = {Zeng, P and Fan, L and Luo, Y and Shen, H and Hu, D}, title = {Task-oriented EEG denoising generative adversarial network for enhancing SSVEP-BCI performance.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad8963}, pmid = {39433073}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; *Signal-To-Noise Ratio ; Adult ; Male ; *Neural Networks, Computer ; Female ; Young Adult ; Photic Stimulation/methods ; }, abstract = {Objective.The quality of electroencephalogram (EEG) signals directly impacts the performance of brain-computer interface (BCI) tasks. Many methods have been proposed to eliminate noise from EEG signals, but most of these methods focus solely on signal denoising itself, disregarding the impact on subsequent tasks, which deviates from the original intention of EEG denoising. The main objective of this study is to optimize EEG denoising models with a purpose of improving the performance of BCI tasks.Approach.To this end, we proposed an innovative task-oriented EEG denoising generative adversarial network (TOED-GAN) method. This network utilizes the generator of GAN to decompose and reconstruct clean signals from the raw EEG signals, and the discriminator to learn to distinguish the generated signals from the true clean signals, resulting in a remarkable increase of the signal-to-noise ratio by simultaneously enhancing task-related components and removing task-irrelevant noise from the original contaminated signals.Main results.We evaluated the performance of the model on a public dataset and a self-collected dataset respectively, with canonical correlation analysis classification tasks of the steady-state visual evoked potential (SSVEP) based BCI. Experimental results demonstrate that TOED-GAN exhibits excellent performance in removing EEG noise and improving performance for SSVEP-BCI, with accuracy improvement rates reaching 18.47% and 21.33% in contrast to the baseline methods of convolutional neural networks, respectively.Significance.This work proves that the proposed TOED-GAN, as an EEG denoising method tailored for SSVEP tasks, contributes to enhancing the performance of BCIs in practical application scenarios.}, }
@article {pmid39433072, year = {2024}, author = {Guetschel, P and Ahmadi, S and Tangermann, M}, title = {Review of deep representation learning techniques for brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad8962}, pmid = {39433072}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; Humans ; *Deep Learning ; *Electroencephalography/methods ; }, abstract = {In the field of brain-computer interfaces (BCIs), the potential for leveraging deep learning techniques for representing electroencephalogram (EEG) signals has gained substantial interest.Objective: This review synthesizes empirical findings from a collection of articles using deep representation learning techniques for BCI decoding, to provide a comprehensive analysis of the current state-of-the-art.Approach: Each article was scrutinized based on three criteria: (1) the deep representation learning technique employed, (2) the underlying motivation for its utilization, and (3) the approaches adopted for characterizing the learned representations.Main results: Among the 81 articles finally reviewed in depth, our analysis reveals a predominance of 31 articles using autoencoders. We identified 13 studies employing self-supervised learning (SSL) techniques, among which ten were published in 2022 or later, attesting to the relative youth of the field. However, at the time being, none of these have led to standard foundation models that are picked up by the BCI community. Likewise, only a few studies have introspected their learned representations. We observed that the motivation in most studies for using representation learning techniques is for solving transfer learning tasks, but we also found more specific motivations such as to learn robustness or invariances, as an algorithmic bridge, or finally to uncover the structure of the data.Significance: Given the potential of foundation models to effectively tackle these challenges, we advocate for a continued dedication to the advancement of foundation models specifically designed for EEG signal decoding by using SSL techniques. We also underline the imperative of establishing specialized benchmarks and datasets to facilitate the development and continuous improvement of such foundation models.}, }
@article {pmid39433071, year = {2024}, author = {Chen, X and Meng, L and Xu, Y and Wu, D}, title = {Adversarial artifact detection in EEG-based brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {21}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ad8964}, pmid = {39433071}, issn = {1741-2552}, mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Artifacts ; Neural Networks, Computer ; Algorithms ; Machine Learning ; }, abstract = {Objective. machine learning has achieved significant success in electroencephalogram (EEG) based brain-computer interfaces (BCIs), with most existing research focusing on improving the decoding accuracy. However, recent studies have shown that EEG-based BCIs are vulnerable to adversarial attacks, where small perturbations added to the input can cause misclassification. Detecting adversarial examples is crucial for both understanding this phenomenon and developing effective defense strategies.Approach. this paper, for the first time, explores adversarial detection in EEG-based BCIs. We extend several popular adversarial detection approaches from computer vision to BCIs. Two new Mahalanobis distance based adversarial detection approaches, and three cosine distance based adversarial detection approaches, are also proposed, which showed promising performance in detecting three kinds of white-box attacks.Main results. we evaluated the performance of eight adversarial detection approaches on three EEG datasets, three neural networks, and four types of adversarial attacks. Our approach achieved an area under the curve score of up to 99.99% in detecting white-box attacks. Additionally, we assessed the transferability of different adversarial detectors to unknown attacks.Significance. through extensive experiments, we found that white-box attacks may be easily detected, and differences exist in the distributions of different types of adversarial examples. Our work should facilitate understanding the vulnerability of existing BCI models and developing more secure BCIs in the future.}, }
@article {pmid39432989, year = {2025}, author = {Yang, H and Zhu, Z and Ni, S and Wang, X and Nie, Y and Tao, C and Zou, D and Jiang, W and Zhao, Y and Zhou, Z and Sun, L and Li, M and Tao, TH and Liu, K and Wei, X}, title = {Silk fibroin-based bioelectronic devices for high-sensitivity, stable, and prolonged in vivo recording.}, journal = {Biosensors &amp; bioelectronics}, volume = {267}, number = {}, pages = {116853}, doi = {10.1016/j.bios.2024.116853}, pmid = {39432989}, issn = {1873-4235}, mesh = {*Fibroins/chemistry ; Animals ; *Biosensing Techniques/instrumentation ; Mice ; Bombyx/chemistry ; Biocompatible Materials/chemistry ; Equipment Design ; Signal-To-Noise Ratio ; }, abstract = {Silk fibroin, recognized for its biocompatibility and modifiable properties, has significant potential in bioelectronics. Traditional silk bioelectronic devices, however, face rapid functional losses in aqueous or in vivo environments due to high water absorption of silk fibroin, which leads to expansion, structural damage, and conductive failure. In this study, we developed a novel approach by creating oriented crystallization (OC) silk fibroin through physical modification of the silk protein. This advancement enabled the fabrication of electronic interfaces for chronic biopotential recording. A pre-stretching treatment of the silk membrane allowed for tunable molecular orientation and crystallization, markedly enhancing its aqueous stability, biocompatibility, and electronic shielding capabilities. The OC devices demonstrated robust performance in sensitive detection and motion tracking of cutaneous electrical signals, long-term (over seven days) electromyographic signal acquisition in live mice with high signal-to-noise ratio (SNR >20), and accurate detection of high-frequency oscillations (HFO) in epileptic models (200-500 Hz). This work not only improves the structural and functional integrity of silk fibroin but also extends its application in durable bioelectronics and interfaces suited for long-term physiological environments.}, }
@article {pmid39432777, year = {2024}, author = {Jiang, L and Qi, X and Lai, M and Zhou, J and Yuan, M and You, J and Liu, Q and Pan, J and Zhao, L and Ying, M and Ji, J and Li, K and Zhang, Y and Pan, W and He, Q and Yang, B and Cao, J}, title = {WDR20 prevents hepatocellular carcinoma senescence by orchestrating the simultaneous USP12/46-mediated deubiquitination of c-Myc.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {121}, number = {44}, pages = {e2407904121}, pmid = {39432777}, issn = {1091-6490}, support = {2021YFA1300604//National Key R&amp;D Program of China/ ; 82304514//Youth Fund of the National Natural Science Foundation of China/ ; LQ23H310004//Zhejiang Provincial Natural Science Foundation of China/ ; 226-2023-00059//the Fundamental Research Funds for the Central Universities/ ; }, mesh = {Humans ; *Carcinoma, Hepatocellular/metabolism/genetics/pathology ; *Liver Neoplasms/metabolism/genetics/pathology ; Animals ; *Proto-Oncogene Proteins c-myc/metabolism/genetics ; *Cellular Senescence ; Mice ; *Ubiquitination ; *Ubiquitin Thiolesterase/metabolism/genetics ; Cell Line, Tumor ; Cell Proliferation ; Gene Expression Regulation, Neoplastic ; Mice, Transgenic ; Carrier Proteins ; }, abstract = {The dysfunction of the ubiquitin-proteasome system (UPS) facilitates the malignant progression of hepatocellular carcinoma (HCC). While targeting the UPS for HCC therapy has been proposed, identifying effective targets has been challenging. In this study, we conducted a focused screen of siRNA libraries targeting UPS-related WD40 repeat (WDR) proteins and found that silencing WDR20, a deubiquitinating enzyme activating factor, selectively inhibited the proliferation of HCC cells without affecting normal hepatocytes. Moreover, the downregulation of WDR20 expression induced HCC cellular senescence and suppressed tumor progression in xenograft, sleeping beauty transposon/transposase, and hydrodynamic tail vein injection-induced HCC models, and Alb-Cre[+]/MYC[+] HCC transgenic mouse models. Mechanistically, we found that WDR20 silencing disturbed the protein stability of c-Myc, orchestrating the simultaneous USP12/46-mediated deubiquitination of c-Myc, thereby promoting the transcriptional activation of CDKN1A. Further investigation revealed a positive coexpression of WDR20 and c-Myc in a tissue microarray with 88 HCC clinical samples. By employing three patient-derived organoids from individuals with HCC, we have validated the decrease in c-Myc expression and the significant induction of senescence and growth inhibition following silencing of WDR20. This study not only uncovers the biological function of WDR20 and elucidates the molecular mechanism underlying its negative regulation of HCC cellular senescence but also highlight the potential of WDR20 as a promising target for HCC therapy.}, }
@article {pmid39430565, year = {2024}, author = {Ratasukharom, N and Niwitpong, SA and Niwitpong, S}, title = {Estimation methods for the variance of Birnbaum-Saunders distribution containing zero values with application to wind speed data in Thailand.}, journal = {PeerJ}, volume = {12}, number = {}, pages = {e18272}, pmid = {39430565}, issn = {2167-8359}, mesh = {Thailand ; *Wind ; Models, Statistical ; Monte Carlo Method ; Air Pollution/analysis ; Humans ; Particulate Matter/analysis ; Confidence Intervals ; Computer Simulation ; }, abstract = {Thailand is currently grappling with a severe problem of air pollution, especially from small particulate matter (PM), which poses considerable threats to public health. The speed of the wind is pivotal in spreading these harmful particles across the atmosphere. Given the inherently unpredictable wind speed behavior, our focus lies in establishing the confidence interval (CI) for the variance of wind speed data. To achieve this, we will employ the delta-Birnbaum-Saunders (delta-BirSau) distribution. This statistical model allows for analyzing wind speed data and offers valuable insights into its variability and potential implications for air quality. The intervals are derived from ten different methods: generalized confidence interval (GCI), bootstrap confidence interval (BCI), generalized fiducial confidence interval (GFCI), and normal approximation (NA). Specifically, we apply GCI, BCI, and GFCI while considering the estimation of the proportion of zeros using the variance stabilized transformation (VST), Wilson, and Hannig methods. To evaluate the performance of these methods, we conduct a simulation study using Monte Carlo simulations in the R statistical software. The study assesses the coverage probabilities and average widths of the proposed confidence intervals. The simulation results reveal that GFCI based on the Wilson method is optimal for small sample sizes, GFCI based on the Hannig method excels for medium sample sizes, and GFCI based on the VST method stands out for large sample sizes. To further validate the practical application of these methods, we employ daily wind speed data from an industrial area in Prachin Buri and Rayong provinces, Thailand.}, }
@article {pmid39429223, year = {2024}, author = {Zhu, L and Xu, M and Huang, A and Zhang, J and Tan, X}, title = {Multi-source domain transfer network based on subdomain adaptation and minimum class confusion for EEG emotion recognition.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-13}, doi = {10.1080/10255842.2024.2417212}, pmid = {39429223}, issn = {1476-8259}, abstract = {Electroencephalogram (EEG) signals, which objectively reflect the state of the brain, are widely favored in emotion recognition research. However, the presence of cross-session and cross-subject variation in EEG signals has hindered the practical implementation of EEG-based emotion recognition technologies. In this article, we propose a multi-source domain transfer method based on subdomain adaptation and minimum class confusion (MS-SAMCC) in response to the addressed issue. First, we introduce the mix-up data augmentation technique to generate augmented samples. Next, we propose a minimum class confusion subdomain adaptation method (MCCSA) as a sub-module of the multi-source domain adaptation module. This approach enables global alignment between each source domain and the target domain, while also achieving alignment among individual subdomains within them. Additionally, we employ minimum class confusion (MCC) as a regularizer for this sub-module. We performed experiments on SEED, SEED IV, and FACED datasets. In the cross-subject experiments, our method achieved mean classification accuracies of 87.14% on SEED, 63.24% on SEED IV, and 42.07% on FACED. In the cross-session experiments, our approach obtained average classification accuracies of 94.20% on SEED and 71.66% on SEED IV. These results demonstrate that the MS-SAMCC approach proposed in this study can effectively address EEG-based emotion recognition tasks.}, }
@article {pmid39429090, year = {2024}, author = {Wang, D and Zhou, H and Zhou, XL and Hu, YZ and Xu, HZ and Fu, JF}, title = {[Research advances of food addiction and obesity in children].}, journal = {Zhonghua er ke za zhi = Chinese journal of pediatrics}, volume = {62}, number = {11}, pages = {1121-1124}, doi = {10.3760/cma.j.cn112140-20240415-00268}, pmid = {39429090}, issn = {0578-1310}, mesh = {Humans ; Child ; *Food Addiction/psychology ; *Pediatric Obesity ; Weight Gain ; Feeding Behavior ; Behavior, Addictive ; }, }
@article {pmid39428886, year = {2024}, author = {Ruiz, S and Valera, L and Ramos, P and Sitaram, R}, title = {Neurorights in the Constitution: from neurotechnology to ethics and politics.}, journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences}, volume = {379}, number = {1915}, pages = {20230098}, pmid = {39428886}, issn = {1471-2970}, support = {//National Agency for Research and Development Millennium Science Initiative/ ; //Fondo Nacional de Desarrollo Cient&#xed;fico y Tecnol&#xf3;gico/ ; }, mesh = {Chile ; Humans ; *Politics ; *Neurosciences/ethics ; Neuroimaging/ethics ; Brain-Computer Interfaces/ethics ; Neurofeedback ; }, abstract = {Neuroimaging technologies such as brain-computer interfaces and neurofeedback have evolved rapidly as new tools for cognitive neuroscience and as potential clinical interventions. However, along with these developments, concern has grown based on the fear of the potential misuse of neurotechnology. In October 2021, Chile became the first country to include neurorights in its Constitution. The present article is divided into two parts. In the first section, we describe the path followed by neurorights that led to its inclusion in the Chilean Constitution, and the neurotechnologies usually involved in neurorights discussions. In the second part, we discuss two potential problems of neurorights. We begin by pointing out some epistemological concerns regarding neurorights, mainly referring to the ambiguity of the concepts used in neurolegislations, the difficult relationship between neuroscience and politics and the weak reasons for urgency in legislating. We then describe the dangers of overprotective laws in medical research, based on the detrimental effect of recent legislation in Chile and the potential risk posed by neurorights to the benefits of neuroscience development. This article aims to engage with the scientific community interested in neurotechnology and neurorights in an interdisciplinary reflection of the potential consequences of neurorights.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.}, }
@article {pmid39428881, year = {2024}, author = {Sulzer, J and Papageorgiou, TD and Goebel, R and Hendler, T}, title = {Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation.}, journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences}, volume = {379}, number = {1915}, pages = {20230081}, pmid = {39428881}, issn = {1471-2970}, mesh = {*Neurofeedback/methods ; Humans ; *Brain/physiology ; Neurotransmitter Agents/metabolism ; Cognition ; }, abstract = {Neurofeedback (NF) is endogenous neuromodulation of circumscribed brain circuitry. While its use of real-time brain activity in a closed-loop system is similar to brain-computer interfaces, instead of controlling an external device like the latter, the goal of NF is to change a targeted brain function. In this special issue on NF, we present current and future methods for extracting and manipulating neural function, how these methods may reveal new insights about brain function, applications, and rarely discussed ethical considerations of guiding and interpreting the brain activity of others. Together, the articles in this issue outline the possibilities of NF use and impact in the real world, poising to influence the development of more effective and personalized NF protocols, improving the understanding of underlying psychological and neurological mechanisms and enhancing treatment precision for various neurological and psychiatric conditions.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.}, }
@article {pmid39428875, year = {2024}, author = {Sitaram, R and Sanchez-Corzo, A and Vargas, G and Cortese, A and El-Deredy, W and Jackson, A and Fetz, E}, title = {Mechanisms of brain self-regulation: psychological factors, mechanistic models and neural substrates.}, journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences}, volume = {379}, number = {1915}, pages = {20230093}, pmid = {39428875}, issn = {1471-2970}, support = {//National Institute of Health/ ; //ATR Computational Neuroscience Laboratories/ ; }, mesh = {Humans ; *Neurofeedback ; *Brain/physiology ; *Self-Control ; Cognition/physiology ; Animals ; Brain-Computer Interfaces ; Models, Neurological ; }, abstract = {While neurofeedback represents a promising tool for neuroscience and a brain self-regulation approach to psychological rehabilitation, the field faces several problems and challenges. Current research has shown great variability and even failure among human participants in learning to self-regulate target features of brain activity with neurofeedback. A better understanding of cognitive mechanisms, psychological factors and neural substrates underlying self-regulation might help improve neurofeedback's scientific and clinical practices. This article reviews the current understanding of the neural mechanisms of brain self-regulation by drawing on findings from human and animal studies in neurofeedback, brain-computer/machine interfaces and neuroprosthetics. In this article, we look closer at the following topics: cognitive processes and psychophysiological factors affecting self-regulation, theoretical models and neural substrates underlying self-regulation, and finally, we provide an outlook on the outstanding gaps in knowledge and technical challenges. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.}, }
@article {pmid39428037, year = {2025}, author = {V, HM and Begum, BS}, title = {Towards imagined speech: Identification of brain states from EEG signals for BCI-based communication systems.}, journal = {Behavioural brain research}, volume = {477}, number = {}, pages = {115295}, doi = {10.1016/j.bbr.2024.115295}, pmid = {39428037}, issn = {1872-7549}, mesh = {Humans ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Electroencephalography/methods ; *Speech/physiology ; Adult ; Male ; Female ; Young Adult ; Machine Learning ; Brain/physiology ; Neural Networks, Computer ; }, abstract = {BACKGROUND: The electroencephalogram (EEG) based brain-computer interface (BCI) system employing imagined speech serves as a mechanism for decoding EEG signals to facilitate control over external devices or communication with the external world at the moment the user desires. To effectively deploy such BCIs, it is imperative to accurately discern various brain states from continuous EEG signals when users initiate word imagination.<br><br>NEW METHOD: This study involved the acquisition of EEG signals from 15 subjects engaged in four states: resting, listening, imagined speech, and actual speech, each involving a predefined set of 10 words. The EEG signals underwent preprocessing, segmentation, spatio-temporal and spectral analysis of each state, and functional connectivity analysis using the phase locking value (PLV) method. Subsequently, five features were extracted from the frequency and time-frequency domains. Classification tasks were performed using four machine learning algorithms in both pair-wise and multiclass scenarios, considering subject-dependent and subject-independent data.<br><br>RESULTS: In the subject-dependent scenario, the random forest (RF) classifier achieved a maximum accuracy of 94.60&#x202f;% for pairwise classification, while the artificial neural network (ANN) classifier achieved a maximum accuracy of 66.92&#x202f;% for multiclass classification. In the subject-independent scenario, the random forest (RF) classifier achieved maximum accuracies of 81.02&#x202f;% for pairwise classification and 55.58&#x202f;% for multiclass classification. Moreover, EEG signals were classified based on frequency bands and brain lobes, revealing that the theta (&#x3b8;) and delta (&#x3b4;) bands, as well as the frontal and temporal lobes, are sufficient for distinguishing between brain states.<br><br>CONCLUSION: The findings promise to develop a system capable of automatically segmenting imagined speech segments from continuous EEG signals.}, }
@article {pmid39426564, year = {2025}, author = {Sleziona, D and Ely, DR and Thommes, M}, title = {Mechanisms of drug release from a melt-milled, poorly soluble drug substance.}, journal = {Journal of pharmaceutical sciences}, volume = {114}, number = {1}, pages = {394-401}, doi = {10.1016/j.xphs.2024.10.016}, pmid = {39426564}, issn = {1520-6017}, mesh = {*Solubility ; *Griseofulvin/chemistry ; *Drug Liberation ; *Particle Size ; Excipients/chemistry ; Xylitol/chemistry ; Chemistry, Pharmaceutical/methods ; Kinetics ; Crystallization/methods ; Drug Compounding/methods ; }, abstract = {Increasing the dissolution kinetics of low aqueous soluble drugs is one of the main priorities in drug formulation. New strategies must be developed, which should consider the two main dissolution mechanisms: surface reaction and diffusion. One promising tool is the so-called solid crystal suspension, a solid dispersion consisting of purely crystalline substances. In this concept, reducing the drug particle size and embedding the particles in a hydrophilic excipient increases the dissolution kinetics. Therefore, a solid crystal suspension containing submicron drug particles was produced via a modified stirred media milling process. A geometrical phase-field approach was used to model the dissolution behavior of the drug particles. A carrier material, xylitol, and the model drug substance, griseofulvin, were ground in a pearl mill. The in-vitro dissolution profile of the product was modeled to gain a deep physical understanding of the dissolution process. The used numerical tool has the potential to be a valuable approach for predicting the dissolution behavior of newly developed formulation strategies.}, }
@article {pmid39426071, year = {2024}, author = {Chowdhury, RS and Bose, S and Ghosh, S and Konar, A}, title = {Attention Induced Dual Convolutional-Capsule Network (AIDC-CN): A deep learning framework for motor imagery classification.}, journal = {Computers in biology and medicine}, volume = {183}, number = {}, pages = {109260}, doi = {10.1016/j.compbiomed.2024.109260}, pmid = {39426071}, issn = {1879-0534}, mesh = {Humans ; *Deep Learning ; *Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Imagination/physiology ; Brain/physiology ; Neural Networks, Computer ; Brain-Computer Interfaces ; Attention/physiology ; }, abstract = {In recent times, Electroencephalography (EEG)-based motor imagery (MI) decoding has garnered significant attention due to its extensive applicability in healthcare, including areas such as assistive robotics and rehabilitation engineering. Nevertheless, the decoding of EEG signals presents considerable challenges owing to their inherent complexity, non-stationary characteristics, and low signal-to-noise ratio. Notably, deep learning-based classifiers have emerged as a prominent focus for addressing the EEG signal decoding process. This study introduces a novel deep learning classifier named the Attention Induced Dual Convolutional-Capsule Network (AIDC-CN) with the specific aim of accurately categorizing various motor imagination class labels. To enhance the classifier's performance, a dual feature extraction approach leveraging spectrogram and brain connectivity networks has been employed, diversifying the feature set in the classification task. The main highlights of the proposed AIDC-CN classifier includes the introduction of a dual convolution layer to handle the brain connectivity and spectrogram features, addition of a novel self-attention module (SAM) to accentuate the relevant parts of the convolved spectrogram features, introduction of a new cross-attention module (CAM) to refine the outputs obtained from the dual convolution layers and incorporation of a Gaussian Error Linear Unit (GELU) based dynamic routing algorithm to strengthen the coupling among the primary and secondary capsule layers. Performance analysis undertaken on four public data sets depict the superior performance of the proposed model with respect to the state-of-the-art techniques. The code for this model is available at https://github.com/RiteshSurChowdhury/AIDC-CN.}, }
@article {pmid39425602, year = {2024}, author = {Mahalungkar, SP and Shrivastava, R and Angadi, S}, title = {A brief survey on human activity recognition using motor imagery of EEG signals.}, journal = {Electromagnetic biology and medicine}, volume = {43}, number = {4}, pages = {312-327}, doi = {10.1080/15368378.2024.2415089}, pmid = {39425602}, issn = {1536-8386}, mesh = {Humans ; *Electroencephalography ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Motor Activity/physiology ; Human Activities ; Brain/physiology ; }, abstract = {Human being's biological processes and psychological activities are jointly connected to the brain. So, the examination of human activity is more significant for the well-being of humans. There are various models for brain activity detection considering neuroimaging for attaining decreased time requirement, increased control commands, and enhanced accuracy. Motor Imagery (MI)-based Brain-Computer Interface (BCI) systems create a way in which the brain can interact with the environment by processing Electroencephalogram (EEG) signals. Human Activity Recognition (HAR) deals with identifying the physiological activities of human beings based on sensory signals. This survey reviews the different methods available for HAR based on MI-EEG signals. A total of 50 research articles based on HAR from EEG signals are considered in this survey. This survey discusses the challenges faced by various techniques for HAR. Moreover, the papers are assessed considering various parameters, techniques, publication year, performance metrics, utilized tools, employed databases, etc. There were many techniques developed to solve the problem of HAR and they are classified as Machine Learning (ML) and Deep Learning (DL)models. At last, the research gaps and limitations of the techniques were discussed that contribute to developing an effective HAR.}, }
@article {pmid39424369, year = {2024}, author = {Berling, D and Baroni, L and Chaffiol, A and Gauvain, G and Picaud, S and Antolík, J}, title = {Optogenetic Stimulation Recruits Cortical Neurons in a Morphology-Dependent Manner.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {44}, number = {49}, pages = {}, pmid = {39424369}, issn = {1529-2401}, mesh = {Animals ; *Optogenetics/methods ; Cats ; Male ; Female ; Neurons/physiology ; Photic Stimulation/methods ; Pyramidal Cells/physiology ; Cerebral Cortex/physiology/cytology ; Models, Neurological ; }, abstract = {Single-photon optogenetics enables precise, cell-type-specific modulation of neuronal circuits, making it a crucial tool in neuroscience. Its miniaturization in the form of fully implantable wide-field stimulator arrays enables long-term interrogation of cortical circuits and bears promise for brain-machine interfaces for sensory and motor function restoration. However, achieving selective activation of functional cortical representations poses a challenge, as studies show that targeted optogenetic stimulation results in activity spread beyond one functional domain. While recurrent network mechanisms contribute to activity spread, here we demonstrate with detailed simulations of isolated pyramidal neurons from cats of unknown sex that already neuron morphology causes a complex spread of optogenetic activity at the scale of one cortical column. Since the shape of a neuron impacts its optogenetic response, we find that a single stimulator at the cortical surface recruits a complex spatial distribution of neurons that can be inhomogeneous and vary with stimulation intensity and neuronal morphology across layers. We explore strategies to enhance stimulation precision, finding that optimizing stimulator optics may offer more significant improvements than the preferentially somatic expression of the opsin through genetic targeting. Our results indicate that, with the right optical setup, single-photon optogenetics can precisely activate isolated neurons at the scale of functional cortical domains spanning several hundred micrometers.}, }
@article {pmid39423832, year = {2024}, author = {Karpowicz, BM and Bhaduri, B and Nason-Tomaszewski, SR and Jacques, BG and Ali, YH and Flint, RD and Bechefsky, PH and Hochberg, LR and AuYong, N and Slutzky, MW and Pandarinath, C}, title = {Reducing power requirements for high-accuracy decoding in iBCIs.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, pmid = {39423832}, issn = {1741-2552}, support = {R01 NS112942/NS/NINDS NIH HHS/United States ; RF1 NS125026/NS/NINDS NIH HHS/United States ; K08 NS060223/NS/NINDS NIH HHS/United States ; DP2 NS127291/NS/NINDS NIH HHS/United States ; F32 HD112173/HD/NICHD NIH HHS/United States ; RF1 DA055667/DA/NIDA NIH HHS/United States ; }, mesh = {*Brain-Computer Interfaces ; Humans ; Animals ; *Action Potentials/physiology ; Macaca mulatta ; Male ; Models, Neurological ; Electric Power Supplies ; }, abstract = {Objective.Current intracortical brain-computer interfaces (iBCIs) rely predominantly on threshold crossings ('spikes') for decoding neural activity into a control signal for an external device. Spiking data can yield high accuracy online control during complex behaviors; however, its dependence on high-sampling-rate data collection can pose challenges. An alternative signal for iBCI decoding is the local field potential (LFP), a continuous-valued signal that can be acquired simultaneously with spiking activity. However, LFPs are seldom used alone for online iBCI control as their decoding performance has yet to achieve parity with spikes.Approach.Here, we present a strategy to improve the performance of LFP-based decoders by first training a neural dynamics model to use LFPs to reconstruct the firing rates underlying spiking data, and then decoding from the estimated rates. We test these models on previously-collected macaque data during center-out and random-target reaching tasks as well as data collected from a human iBCI participant during attempted speech.Main results.In all cases, training models from LFPs enables firing rate reconstruction with accuracy comparable to spiking-based dynamics models. In addition, LFP-based dynamics models enable decoding performance exceeding that of LFPs alone and approaching that of spiking-based models. In all applications except speech, LFP-based dynamics models also facilitate decoding accuracy exceeding that of direct decoding from spikes.Significance.Because LFP-based dynamics models operate on lower bandwidth and with lower sampling rate than spiking models, our findings indicate that iBCI devices can be designed to operate with lower power requirements than devices dependent on recorded spiking activity, without sacrificing high-accuracy decoding.}, }
@article {pmid39423831, year = {2024}, author = {Carrara, I and Aristimunha, B and Corsi, MC and de Camargo, RY and Chevallier, S and Papadopoulo, T}, title = {Geometric neural network based on phase space for BCI-EEG decoding.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad88a2}, pmid = {39423831}, issn = {1741-2552}, abstract = {\textbf{Objective:} The integration of Deep Learning (DL) algorithms on brain signal analysis is still in its nascent stages compared to their success in fields like Computer Vision. This is particularly true for BCI, where the brain activity is decoded to control external devices without requiring muscle control. Electroencephalography (EEG) is a widely adopted choice for designing BCI systems due to its non-invasive and cost-effective nature and excellent temporal resolution. Still, it comes at the expense of limited training data, poor signal-to-noise, and a large variability across and within-subject recordings. Finally, setting up a BCI system with many electrodes takes a long time, hindering the widespread adoption of reliable DL architectures in BCIs outside research laboratories. To improve adoption, we need to improve user comfort using, for instance, reliable algorithms that operate with few electrodes. \textbf{Approach:} Our research aims to develop a DL algorithm that delivers effective results with a limited number of electrodes. Taking advantage of the Augmented Covariance Method and the framework of SPDNet, we propose the \method{} architecture and analyze its performance and the interpretability of the results. The evaluation is conducted on 5-fold cross-validation, using only three electrodes positioned above the Motor Cortex. The methodology was tested on nearly 100 subjects from several open-source datasets using the Mother Of All BCI Benchmark (MOABB) framework. \textbf{Main results:} The results of our \method{} demonstrate that the augmented approach combined with the SPDNet significantly outperforms all the current state-of-the-art DL architecture in MI decoding. \textbf{Significance:} This new architecture is explainable and with a low number of trainable parameters.}, }
@article {pmid39423829, year = {2024}, author = {Valle, C and Mendez-Orellana, C and Herff, C and Rodriguez-Fernandez, M}, title = {Identification of perceived sentences using deep neural networks in EEG.}, journal = {Journal of neural engineering}, volume = {21}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ad88a3}, pmid = {39423829}, issn = {1741-2552}, mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; Adult ; *Speech Perception/physiology ; *Neural Networks, Computer ; Young Adult ; Deep Learning ; Brain-Computer Interfaces ; }, abstract = {Objetive. Decoding speech from brain activity can enable communication for individuals with speech disorders. Deep neural networks (DNNs) have shown great potential for speech decoding applications. However, the limited availability of large datasets containing neural recordings from speech-impaired subjects poses a challenge. Leveraging data from healthy participants can mitigate this limitation and expedite the development of speech neuroprostheses while minimizing the need for patient-specific training data.Approach. In this study, we collected a substantial dataset consisting of recordings from 56 healthy participants using 64 EEG channels. Multiple neural networks were trained to classify perceived sentences in the Spanish language using subject-independent, mixed-subjects, and fine-tuning approaches. The dataset has been made publicly available to foster further research in this area.Main results. Our results demonstrate a remarkable level of accuracy in distinguishing sentence identity across 30 classes, showcasing the feasibility of training DNNs to decode sentence identity from perceived speech using EEG. Notably, the subject-independent approach rendered accuracy comparable to the mixed-subjects approach, although with higher variability among subjects. Additionally, our fine-tuning approach yielded even higher accuracy, indicating an improved capability to adapt to individual subject characteristics, which enhances performance. This suggests that DNNs have effectively learned to decode universal features of brain activity across individuals while also being adaptable to specific participant data. Furthermore, our analyses indicate that EEGNet and DeepConvNet exhibit comparable performance, outperforming ShallowConvNet for sentence identity decoding. Finally, our Grad-CAM visualization analysis identifies key areas influencing the network's predictions, offering valuable insights into the neural processes underlying language perception and comprehension.Significance. These findings advance our understanding of EEG-based speech perception decoding and hold promise for the development of speech neuroprostheses, particularly in scenarios where subjects cannot provide their own training data.}, }
@article {pmid39423826, year = {2024}, author = {Chen, X and Li, S and Tu, Y and Wang, Z and Wu, D}, title = {User-wise perturbations for user identity protection in EEG-based BCIs.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad88a5}, pmid = {39423826}, issn = {1741-2552}, abstract = {OBJECTIVE: An electroencephalogram (EEG)-based brain-computer interface (BCI) is a direct communication pathway between the human brain and a computer. Most research so far studied more accurate BCIs, but much less attention has been paid to the ethics of BCIs. Aside from task-specific information, EEG signals also contain rich private information, e.g., user identity, emotion, disorders, etc., which should be protected.<br><br>APPROACH: We show for the first time that adding user-wise perturbations can make identity information in EEG unlearnable. We propose four types of user-wise privacy-preserving perturbations, i.e., random noise, synthetic noise, error minimization noise, and error maximization noise. After adding the proposed perturbations to EEG training data, the user identity information in the data becomes unlearnable, while the BCI task information remains unaffected.<br><br>MAIN RESULTS: Experiments on six EEG datasets using three neural network classifiers and various traditional machine learning models demonstrated the robustness and practicability of the proposed perturbations.<br><br>SIGNIFICANCE: Our research shows the feasibility of hiding user identity information in EEG data without impacting the primary BCI task information.}, }
@article {pmid39423083, year = {2024}, author = {Kostoglou, K and Muller-Putz, GR}, title = {Motor-Related EEG Analysis Using a Pole Tracking Approach.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {3837-3847}, doi = {10.1109/TNSRE.2024.3483294}, pmid = {39423083}, issn = {1558-0210}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; *Spinal Cord Injuries/physiopathology ; Female ; *Movement/physiology ; Adult ; *Algorithms ; *Imagination/physiology ; Young Adult ; Reproducibility of Results ; Motor Cortex/physiology ; Middle Aged ; Healthy Volunteers ; Evoked Potentials, Motor/physiology ; Sensitivity and Specificity ; }, abstract = {This study introduces an alternative approach to electroencephalography (EEG) time-frequency analysis based on time-varying autoregressive (TV-AR) models in a cascade configuration to independently monitor key EEG spectral components. The method is evaluated for its neurophysiological interpretation and effectiveness in motor-related brain-computer interface (BCI) applications. Specifically, we assess the ability of the tracked EEG poles to discriminate between rest, movement execution (ME) and movement imagination (MI) in healthy subjects, as well as movement attempts (MA) in individuals with spinal cord injury (SCI). Our results show that pole tracking effectively captures broad changes in EEG dynamics, such as transitions between rest and movement-related states. It outperformed traditional EEG-based features, increasing detection accuracy for ME by an average of 4.1% (with individual improvements reaching as high as 15%) and MI by an average of 4.5% (up to 13.8%) compared to time-domain low-frequency EEG features. Similarly, compared to alpha/beta band power, the method improved ME detection by an average of 5.9% (up to 10.4%) and MI by an average of 4.3% (up to 10.2%), with results averaged across 15 healthy participants. In one participant with SCI, pole tracking improved MA detection by 12.9% over low-frequency EEG features and 4.8% over alpha/beta band power. However, its ability to distinguish finer movement details within specific movement types was limited. Additionally, the temporal evolution of the extracted pole tracking features revealed event-related desynchronization phenomena, typically observed during ME, MA and MI, as well as increases in frequency, which are of neurophysiological interest.}, }
@article {pmid39421856, year = {2024}, author = {Klee, D and Memmott, T and Oken, B}, title = {The Effect of Jittered Stimulus Onset Interval on Electrophysiological Markers of Attention in a Brain-Computer Interface Rapid Serial Visual Presentation Paradigm.}, journal = {Signals}, volume = {5}, number = {1}, pages = {18-39}, pmid = {39421856}, issn = {2624-6120}, support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; }, abstract = {Brain responses to discrete stimuli are modulated when multiple stimuli are presented in sequence. These alterations are especially pronounced when the time course of an evoked response overlaps with responses to subsequent stimuli, such as in a rapid serial visual presentation (RSVP) paradigm used to control a brain-computer interface (BCI). The present study explored whether the measurement or classification of select brain responses during RSVP would improve through application of an established technique for dealing with overlapping stimulus presentations, known as irregular or "jittered" stimulus onset interval (SOI). EEG data were collected from 24 healthy adult participants across multiple rounds of RSVP calibration and copy phrase tasks with varying degrees of SOI jitter. Analyses measured three separate brain signals sensitive to attention: N200, P300, and occipitoparietal alpha attenuation. Presentation jitter visibly reduced intrusion of the SSVEP, but in general, it did not positively or negatively affect attention effects, classification, or system performance. Though it remains unclear whether stimulus overlap is detrimental to BCI performance overall, the present study demonstrates that single-trial classification approaches may be resilient to rhythmic intrusions like SSVEP that appear in the averaged EEG.}, }
@article {pmid39421849, year = {2024}, author = {Deng, L and Tang, H and Roy, K}, title = {Editorial: Understanding and bridging the gap between neuromorphic computing and machine learning, volume II.}, journal = {Frontiers in computational neuroscience}, volume = {18}, number = {}, pages = {1455530}, pmid = {39421849}, issn = {1662-5188}, }
@article {pmid39421767, year = {2024}, author = {Wang, Z and Xiao, X and Zhou, Z and Chen, Y and Xia, T and Sheng, X and Han, Y and Gong, W and Si, K}, title = {FLUID: a fluorescence-friendly lipid-compatible ultrafast clearing method.}, journal = {Biomedical optics express}, volume = {15}, number = {10}, pages = {5609-5624}, pmid = {39421767}, issn = {2156-7085}, abstract = {Many clearing methods achieve high transparency by removing lipid components from tissues, which damages microstructure and limits their application in lipid research. As for methods which preserve lipid, it is difficult to balance transparency, fluorescence preservation and clearing speed. In this study, we propose a rapid water-based clearing method that is fluorescence-friendly and preserves lipid components. FLUID allows for preservation of endogenous fluorescence over 60 days. It shows negligible tissue distortion and is compatible with various types of fluorescent labeling and tissue staining methods. High quality imaging of human brain tissue and compatibility with pathological staining demonstrated the potential of our method for three-dimensional (3D) biopsy and clinical pathological diagnosis.}, }
@article {pmid39421626, year = {2024}, author = {Mokienko, OA}, title = {Brain-Computer Interfaces with Intracortical Implants for Motor and Communication Functions Compensation: Review of Recent Developments.}, journal = {Sovremennye tekhnologii v meditsine}, volume = {16}, number = {1}, pages = {78-89}, pmid = {39421626}, issn = {2309-995X}, mesh = {*Brain-Computer Interfaces ; Humans ; Electrodes, Implanted ; Prostheses and Implants ; }, abstract = {Brain-computer interfaces allow the exchange of data between the brain and an external device, bypassing the muscular system. Clinical studies of invasive brain-computer interface technologies have been conducted for over 20 years. During this time, there has been a continuous improvement of approaches to neuronal signal processing in order to improve the quality of control of external devices. Currently, brain-computer interfaces with intracortical implants allow completely paralyzed patients to control robotic limbs for self-service, use a computer or a tablet, type text, and reproduce speech at an optimal speed. Studies of invasive brain-computer interfaces regularly provide new fundamental data on functioning of the central nervous system. In recent years, breakthrough discoveries and achievements have been annually made in this sphere. This review analyzes the results of clinical experiments of brain-computer interfaces with intracortical implants, provides information on the stages of this technology development, its main discoveries and achievements.}, }
@article {pmid39421603, year = {2024}, author = {Ye, M and Yang, C and Cheng, JX and Lee, HJ and Jiang, Y and Shi, L}, title = {Editorial: Neuromodulation technology: advances in optics and acoustics.}, journal = {Frontiers in cellular neuroscience}, volume = {18}, number = {}, pages = {1494457}, pmid = {39421603}, issn = {1662-5102}, }
@article {pmid39420525, year = {2024}, author = {Shang, S and Shi, Y and Zhang, Y and Liu, M and Zhang, H and Wang, P and Zhuang, L}, title = {Artificial intelligence for brain disease diagnosis using electroencephalogram signals.}, journal = {Journal of Zhejiang University. Science. B}, volume = {25}, number = {10}, pages = {914-940}, pmid = {39420525}, issn = {1862-1783}, support = {2021ZD0200405//the National Key Research and Development Project of China/ ; 62271443, 32250008 and 82330064//the National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Artificial Intelligence ; *Brain Diseases/diagnosis/physiopathology ; *Brain-Computer Interfaces ; Algorithms ; Machine Learning ; Brain/physiology ; Deep Learning ; Signal Processing, Computer-Assisted ; }, abstract = {Brain signals refer to electrical signals or metabolic changes that occur as a consequence of brain cell activity. Among the various non-invasive measurement methods, electroencephalogram (EEG) stands out as a widely employed technique, providing valuable insights into brain patterns. The deviations observed in EEG reading serve as indicators of abnormal brain activity, which is associated with neurological diseases. Brain‒computer interface (BCI) systems enable the direct extraction and transmission of information from the human brain, facilitating interaction with external devices. Notably, the emergence of artificial intelligence (AI) has had a profound impact on the enhancement of precision and accuracy in BCI technology, thereby broadening the scope of research in this field. AI techniques, encompassing machine learning (ML) and deep learning (DL) models, have demonstrated remarkable success in classifying and predicting various brain diseases. This comprehensive review investigates the application of AI in EEG-based brain disease diagnosis, highlighting advancements in AI algorithms.}, }
@article {pmid39419976, year = {2024}, author = {Chen, D and Zhao, Z and Shi, J and Li, S and Xu, X and Wu, Z and Tang, Y and Liu, N and Zhou, W and Ni, C and Ma, B and Wang, J and Zhang, J and Huang, L and You, Z and Zhang, P and Tang, Z}, title = {Harnessing the sensing and stimulation function of deep brain-machine interfaces: a new dawn for overcoming substance use disorders.}, journal = {Translational psychiatry}, volume = {14}, number = {1}, pages = {440}, pmid = {39419976}, issn = {2158-3188}, support = {92148206//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82071330//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Substance-Related Disorders/therapy ; *Brain-Computer Interfaces ; *Deep Brain Stimulation/methods ; Brain/physiopathology ; Behavior, Addictive/therapy/physiopathology ; }, abstract = {Substance use disorders (SUDs) imposes profound physical, psychological, and socioeconomic burdens on individuals, families, communities, and society as a whole, but the available treatment options remain limited. Deep brain-machine interfaces (DBMIs) provide an innovative approach by facilitating efficient interactions between external devices and deep brain structures, thereby enabling the meticulous monitoring and precise modulation of neural activity in these regions. This pioneering paradigm holds significant promise for revolutionizing the treatment landscape of addictive disorders. In this review, we carefully examine the potential of closed-loop DBMIs for addressing SUDs, with a specific emphasis on three fundamental aspects: addictive behaviors-related biomarkers, neuromodulation techniques, and control policies. Although direct empirical evidence is still somewhat limited, rapid advancements in cutting-edge technologies such as electrophysiological and neurochemical recordings, deep brain stimulation, optogenetics, microfluidics, and control theory offer fertile ground for exploring the transformative potential of closed-loop DBMIs for ameliorating symptoms and enhancing the overall well-being of individuals struggling with SUDs.}, }
@article {pmid39419932, year = {2024}, author = {Deak, F}, title = {Alzheimer's disease and other memory disorders in the age of AI: reflection and perspectives on the 120th anniversary of the birth of Dr. John von Neumann.}, journal = {GeroScience}, volume = {}, number = {}, pages = {}, pmid = {39419932}, issn = {2509-2723}, support = {NIH NIA R01 AG062655/AG/NIA NIH HHS/United States ; SAGA23-1142437/ALZ/Alzheimer's Association/United States ; }, abstract = {Two themes are coming to the forefront in this decade: Cognitive impairment of an aging population and the quantum leap in developing artificial intelligence (AI). Both can be described as growing exponentially and presenting serious challenges. Although many questions have been addressed about the dangers of AI, we want to go beyond the fearful aspects of this topic and focus on the possible contribution of AI to solve the problem of chronic disorders of the elderly leading to cognitive impairment, like Alzheimer's disease, Parkinson's disease, and Lewy body dementia. Our second goal is to look at the ways in which modern neuroscience can influence the future design of computers and the development of AI. We wish to honor the memory of Dr. John von Neumann, who came up with many breakthrough details of the first electronic computer. Remarkably, Dr. von Neumann dedicated his last book to the comparison of the human brain and the computer as it stood in those years of the mid-1950s. We will point out how his ideas are more relevant than ever in the age of supercomputers, AI and brain implants.}, }
@article {pmid39419108, year = {2024}, author = {de Seta, V and Colamarino, E and Pichiorri, F and Savina, G and Patarini, F and Riccio, A and Cincotti, F and Mattia, D and Toppi, J}, title = {Brain and muscle derived features to discriminate simple hand motor tasks for a rehabilitative BCI: comparative study on healthy and post-stroke individuals.}, journal = {Journal of neural engineering}, volume = {21}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad8838}, pmid = {39419108}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Stroke Rehabilitation/methods ; *Hand/physiopathology/physiology ; Middle Aged ; *Stroke/physiopathology ; Aged ; Adult ; *Muscle, Skeletal/physiopathology/physiology ; *Electroencephalography/methods ; Brain/physiopathology ; Movement/physiology ; Electromyography/methods ; }, abstract = {Objective.Brain-Computer Interfaces targeting post-stroke recovery of the upper limb employ mainly electroencephalography to decode movement-related brain activation. Recently hybrid systems including muscular activity were introduced. We compared the motor task discrimination abilities of three different features, namely event-related desynchronization/synchronization (ERD/ERS) and movement-related cortical potential (MRCP) as brain-derived features and cortico-muscular coherence (CMC) as a hybrid brain-muscle derived feature, elicited in 13 healthy subjects and 13 stroke patients during the execution/attempt of two simple hand motor tasks (finger extension and grasping) commonly employed in upper limb rehabilitation protocols.Approach. We employed a three-way statistical design to investigate whether their ability to discriminate the two movements follows a specific temporal evolution along the movement execution and is eventually different among the three features and between the two groups. We also investigated the differences in performance at the single-subject level.Main results. The ERD/ERS and the CMC-based classification showed similar temporal evolutions of the performance with a significant increase in accuracy during the execution phase while MRCP-based accuracy peaked at movement onset. Such temporal dynamics were similar but slower in stroke patients when the movements were attempted with the affected hand (AH). Moreover, CMC outperformed the two brain features in healthy subjects and stroke patients when performing the task with their unaffected hand, whereas a higher variability across subjects was observed in patients performing the tasks with their AH. Interestingly, brain features performed better in this latter condition with respect to healthy subjects.Significance.Our results provide hints to improve the design of Brain-Computer Interfaces for post-stroke rehabilitation, emphasizing the need for personalized approaches tailored to patients' characteristics and to the intended rehabilitative target.}, }
@article {pmid39419104, year = {2024}, author = {Afonso, M and Sánchez-Cuesta, F and González-Zamorano, Y and Pablo Romero, J and Vourvopoulos, A}, title = {Investigating the synergistic neuromodulation effect of bilateral rTMS and VR brain-computer interfaces training in chronic stroke patients.}, journal = {Journal of neural engineering}, volume = {21}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ad8836}, pmid = {39419104}, issn = {1741-2552}, mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Middle Aged ; *Transcranial Magnetic Stimulation/methods ; *Stroke Rehabilitation/methods ; Aged ; *Stroke/physiopathology/therapy ; *Electroencephalography/methods ; Virtual Reality ; Adult ; Chronic Disease ; Neurofeedback/methods ; Treatment Outcome ; }, abstract = {Objective.Stroke is a major cause of adult disability worldwide, resulting in motor impairments. To regain motor function, patients undergo rehabilitation, typically involving repetitive movement training. For those who lack volitional movement, novel technology-based approaches have emerged that directly involve the central nervous system, through neuromodulation techniques such as transcranial magnetic stimulation (TMS), and closed-loop neurofeedback like brain-computer interfaces (BCIs). This, can be augmented through proprioceptive feedback delivered many times by embodied virtual reality (VR). Nonetheless, despite a growing body of research demonstrating the individual efficacy of each technique, there is limited information on their combined effects.Approach.In this study, we analyzed the Electroencephalographic (EEG) signals acquired from 10 patients with more than 4 months since stroke during a longitudinal intervention with repetitive TMS followed by VR-BCI training. From the EEG, the event related desynchronization (ERD) and individual alpha frequency (IAF) were extracted, evaluated over time and correlated with clinical outcome.Main results.Every patient's clinical outcome improved after treatment, and ERD magnitude increased during simultaneous rTMS and VR-BCI. Additionally, IAF values showed a significant correlation with clinical outcome, nonetheless, no relationship was found between differences in ERD pre- post- intervention with the clinical improvement.Significance.This study furnishes empirical evidence supporting the efficacy of the joint action of rTMS and VR-BCI in enhancing patient recovery. It also suggests a relationship between IAF and rehabilitation outcomes, that could potentially serve as a retrievable biomarker for stroke recovery.}, }
@article {pmid39419091, year = {2024}, author = {Liu, J and Younk, R and M Drahos, L and S Nagrale, S and Yadav, S and S Widge, A and Shoaran, M}, title = {Neural decoding and feature selection methods for closed-loop control of avoidance behavior.}, journal = {Journal of neural engineering}, volume = {21}, number = {5}, pages = {}, pmid = {39419091}, issn = {1741-2552}, support = {R01 MH123634/MH/NIMH NIH HHS/United States ; }, mesh = {Animals ; Rats ; *Avoidance Learning/physiology ; Male ; Rats, Sprague-Dawley ; Algorithms ; Amygdala/physiology ; }, abstract = {Objective.Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as the foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors.Approach.We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance.Main results.Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low training/inference time and memory usage, requiring<310 ms for training,<0.051 ms for inference, and 16.6 kB of memory, using a single core of AMD Ryzen Threadripper PRO 5995WX CPU.Significance.Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.}, }
@article {pmid39419024, year = {2024}, author = {Agudelo-Toro, A and Michaels, JA and Sheng, WA and Scherberger, H}, title = {Accurate neural control of a hand prosthesis by posture-related activity in the primate grasping circuit.}, journal = {Neuron}, volume = {112}, number = {24}, pages = {4115-4129.e8}, doi = {10.1016/j.neuron.2024.09.018}, pmid = {39419024}, issn = {1097-4199}, mesh = {Animals ; *Brain-Computer Interfaces ; *Posture/physiology ; *Hand Strength/physiology ; *Hand/physiology ; *Macaca mulatta ; Male ; Psychomotor Performance/physiology ; Motor Cortex/physiology ; Prostheses and Implants ; }, abstract = {Brain-computer interfaces (BCIs) have the potential to restore hand movement for people with paralysis, but current devices still lack the fine control required to interact with objects of daily living. Following our understanding of cortical activity during arm reaches, hand BCI studies have focused primarily on velocity control. However, mounting evidence suggests that posture, and not velocity, dominates in hand-related areas. To explore whether this signal can causally control a prosthesis, we developed a BCI training paradigm centered on the reproduction of posture transitions. Monkeys trained with this protocol were able to control a multidimensional hand prosthesis with high accuracy, including execution of the very intricate precision grip. Analysis revealed that the posture signal in the target grasping areas was the main contributor to control. We present, for the first time, neural posture control of a multidimensional hand prosthesis, opening the door for future interfaces to leverage this additional information channel.}, }
@article {pmid39416663, year = {2024}, author = {Tang, Q and Yang, X and Sun, M and He, M and Sa, R and Zhang, K and Zhu, B and Li, T}, title = {Research trends and hotspots of post-stroke upper limb dysfunction: a bibliometric and visualization analysis.}, journal = {Frontiers in neurology}, volume = {15}, number = {}, pages = {1449729}, pmid = {39416663}, issn = {1664-2295}, abstract = {BACKGROUND: The global prevalence of stroke has been increasing. Motor dysfunction is observed in approximately 55 to 75% of stroke patients, with upper limb impairment affecting around 85% of them. Following upper limb dysfunction, the body's recovery time is not only slower compared to the lower limbs, but the restoration of its fine motor skills is significantly more challenging, greatly impacting the daily lives of patients. Consequently, there is an increasing urgency for study on the upper limb function in stroke.<br><br>METHODS: A search was conducted in the Web of Science Core Collection: Science Citation Index Expanded (SCI-Expanded) database for material published from January 1, 2004 to December 31, 2023. We included all relevant literature reports and conducted an analysis of annual publications, countries/regions, institutions, journals, co-cited references, and keywords using the software packages CiteSpace, VOSviewer, and Bibliometrix R. Next, we succinctly outlined the research trends and hotspots in post-stroke upper limb dysfunction.<br><br>RESULTS: This analysis comprised 1,938 articles from 1,897 institutions, 354 journals, and 53 countries or regions. A yearly rise in the production of publications was noted. The United States is the foremost nation on the issue. Northwestern University has the most amounts of papers compared to all other institutions. The journal Neurorehabilitation and Neural Repair is a highly significant publication in this field, with Catherine E. Lang serving as the principal author. The majority of the most-cited references focus on subjects such as the reliability and validity of assessment instruments, RCT of therapies, systematic reviews, and meta-analyses. The intervention measures primarily comprise three types of high-frequency phrases that are related, as determined by keyword analysis: intelligent rehabilitation, physical factor therapy, and occupational therapy. Current areas of focus in research include randomized clinical trials, neurorehabilitation, and robot-assisted therapy.<br><br>CONCLUSION: Current research has shown a growing interest in studying upper limb function assessment, occupational therapy, physical therapy, robot-assisted therapy, virtual reality, brain-computer interface, telerehabilitation, cortical reorganisation, and neural plasticity. These topics have become popular and are expected to be the focus of future research.}, }
@article {pmid39416032, year = {2024}, author = {Lee, JY and Lee, S and Mishra, A and Yan, X and McMahan, B and Gaisford, B and Kobashigawa, C and Qu, M and Xie, C and Kao, JC}, title = {Non-invasive brain-machine interface control with artificial intelligence copilots.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {39416032}, issn = {2692-8205}, support = {DP2 NS122037/NS/NINDS NIH HHS/United States ; }, abstract = {Motor brain-machine interfaces (BMIs) decode neural signals to help people with paralysis move and communicate. Even with important advances in the last two decades, BMIs face key obstacles to clinical viability. Invasive BMIs achieve proficient cursor and robotic arm control but require neurosurgery, posing significant risk to patients. Non-invasive BMIs do not have neurosurgical risk, but achieve lower performance, sometimes being prohibitively frustrating to use and preventing widespread adoption. We take a step toward breaking this performance-risk tradeoff by building performant non-invasive BMIs. The critical limitation that bounds decoder performance in non-invasive BMIs is their poor neural signal-to-noise ratio. To overcome this, we contribute (1) a novel EEG decoding approach and (2) artificial intelligence (AI) copilots that infer task goals and aid action completion. We demonstrate that with this "AI-BMI," in tandem with a new adaptive decoding approach using a convolutional neural network (CNN) and ReFIT-like Kalman filter (KF), healthy users and a paralyzed participant can autonomously and proficiently control computer cursors and robotic arms. Using an AI copilot improves goal acquisition speed by up to 4.3× in the standard center-out 8 cursor control task and enables users to control a robotic arm to perform the sequential pick-and-place task, moving 4 randomly placed blocks to 4 randomly chosen locations. As AI copilots improve, this approach may result in clinically viable non-invasive AI-BMIs.}, }
@article {pmid39415112, year = {2024}, author = {Guo, X and Kong, L and Wen, Y and Chen, L and Hu, S}, title = {Impact of second-generation antipsychotics monotherapy or combined therapy in cytokine, lymphocyte subtype, and thyroid antibodies for schizophrenia: a retrospective study.}, journal = {BMC psychiatry}, volume = {24}, number = {1}, pages = {695}, pmid = {39415112}, issn = {1471-244X}, support = {2021C03107//the Zhejiang Provincial Key Research and Development Program/ ; 2021R52016//the Leading Talent of Scientific and Technological Innovation - "Ten Thousand Talents Program" of Zhejiang Province/ ; 2020R01001//the Innovation team for precision diagnosis and treatment of major brain diseases/ ; 226-2022-00193, 226-2022-00002//the Fundamental Research Funds for the Central Universities/ ; }, mesh = {Humans ; *Schizophrenia/drug therapy/immunology/blood ; Male ; Female ; Retrospective Studies ; Adult ; *Antipsychotic Agents/therapeutic use ; *Cytokines/blood/immunology ; Middle Aged ; *Autoantibodies/blood/immunology ; *Drug Therapy, Combination ; Lymphocyte Subsets/immunology/drug effects ; Iodide Peroxidase/immunology ; }, abstract = {BACKGROUND: Schizophrenia (SCZ) shares high clinical relevance with the immune system, and the potential interactions of psychopharmacological drugs with the immune system are still an overlooked area. Here, we aimed to identify whether the second-generation antipsychotics (SGA) monotherapy or combined therapy of SGA with other psychiatric medications influence the routine blood immunity biomarkers of patients with SCZ.<br><br>METHODS: Medical records of inpatients with SCZ from January 2019 to June 2023 were retrospectively screened from June 2023 to August 2023. The demographic data and peripheral levels of cytokines (IL-2, IL-4, IL-6, TNF-&#x3b1;, INF-&#x3b3;, and IL-17&#xa0;A), lymphocyte subtype proportions (CD3+, CD4+, CD8&#x2009;+&#x2009;T-cell, and natural killer (NK) cells), and thyroid autoimmune antibodies (thyroid peroxidase antibody (TPOAb), and antithyroglobulin antibody (TGAb)) were collected and analyzed.<br><br>RESULTS: 30 drug-na&#xef;ve patients, 64 SGA monotherapy (20 for first-episode SCZ, 44 for recurrent SCZ) for at least one week, 39 combined therapies for recurrent SCZ (18 with antidepressant, 10 with benzodiazepine, and 11 with mood stabilizer) for at least two weeks, and 23 used to receive SGA monotherapy (had withdrawn for at least two weeks) were included despite specific medication. No difference in cytokines was found between the SGA monotherapy sub-groups (p&#x2009;>&#x2009;0.05). Of note, SGA monotherapy appeared to induce a down-regulation of IFN-&#x3b3; in both first (mean [95% confidence interval]: 1.08 [0.14-2.01] vs. 4.60 [2.11-7.08], p&#x2009;=&#x2009;0.020) and recurrent (1.88 [0.71-3.05] vs. 4.60 [2.11-7.08], p&#x2009;=&#x2009;0.027) episodes compared to drug-na&#xef;ve patients. However, the lymphocyte proportions and thyroid autoimmune antibodies remained unchanged after at least two weeks of SGA monotherapy (p&#x2009;>&#x2009;0.05). In combined therapy groups, results mainly resembled the SGA monotherapy for recurrent SCZ (p&#x2009;>&#x2009;0.05).<br><br>CONCLUSION: The study demonstrated that SGA monotherapy possibly achieved its comfort role via modulating IFN-&#x3b3;, and SGA combined therapy showed an overall resemblance to monotherapy.}, }
@article {pmid39413730, year = {2024}, author = {Wang, J and Chen, ZS}, title = {Closed-loop neural interfaces for pain: Where do we stand?.}, journal = {Cell reports. Medicine}, volume = {5}, number = {10}, pages = {101662}, pmid = {39413730}, issn = {2666-3791}, support = {R01 NS121776/NS/NINDS NIH HHS/United States ; UG3 NS135170/NS/NINDS NIH HHS/United States ; RF1 NS121776/NS/NINDS NIH HHS/United States ; R01 MH118928/MH/NIMH NIH HHS/United States ; P50 MH132642/MH/NIMH NIH HHS/United States ; RF1 DA056394/DA/NIDA NIH HHS/United States ; }, mesh = {Animals ; Humans ; Brain-Computer Interfaces ; *Chronic Pain/therapy/physiopathology ; Pain Management/methods ; }, abstract = {Advances in closed-loop neural interfaces and neuromodulation have offered a potentially effective and non-addictive treatment for chronic pain. These interfaces link neural sensors with device outputs to provide temporally precise stimulation. We discuss challenges and trends of state-of-the-art neural interfaces for treating pain in animal models and human pilot trials.}, }
@article {pmid39413689, year = {2024}, author = {Rebouillat, B and Barascud, N and Kouider, S}, title = {Partial awareness during voluntary endogenous decision.}, journal = {Consciousness and cognition}, volume = {125}, number = {}, pages = {103769}, doi = {10.1016/j.concog.2024.103769}, pmid = {39413689}, issn = {1090-2376}, mesh = {Humans ; *Awareness/physiology ; Male ; Adult ; Female ; *Decision Making/physiology ; Young Adult ; *Metacognition/physiology ; Electroencephalography ; }, abstract = {Despite our feeling of control over decisions, our ability to consciously access choices before execution remains debated. Recent research reveals prospective access to intention to act, allowing potential vetoes of impending decisions. However, whether the content of impending decision can be accessed remain debated. Here we track neural signals during participants' early deliberation in free decisions. Participants chose freely between two options but sometimes had to reject their current decision just before execution. The initially preferred option, tracked in real time, significantly predicts the upcoming choice, but remain mostly outside of conscious awareness. Participants often display overconfidence in their access to this content. Instead, confidence is associated with a neural marker of self-initiated decision, indicating a qualitative confusion in the confidence evaluation process. Our results challenge the notion of complete agency over choices, suggesting inflated awareness of forthcoming decisions and providing insights into metacognitive processes in free decision-making.}, }
@article {pmid39413360, year = {2024}, author = {Trott, J and Slaymaker, C and Niznik, G and Althoff, T and Netherton, B}, title = {Brain Computer Interfaces: An Introduction for Clinical Neurodiagnostic Technologists.}, journal = {The Neurodiagnostic journal}, volume = {}, number = {}, pages = {1-14}, doi = {10.1080/21646821.2024.2408501}, pmid = {39413360}, issn = {2164-6821}, abstract = {Brain-computer interface (BCI) is a term used to describe systems that translate biological information into commands that can control external devices such as computers, prosthetics, and other machinery. While BCI is used in military applications, home control systems, and a wide array of entertainment, much of its modern interest and funding can be attributed to its utility in the medical community, where it has rapidly propelled advancements in the restoration or replacement of critical functions robbed from victims of disease, stroke, and traumatic injury. BCI devices can allow patients to move prosthetic limbs, operate devices such as wheelchairs or computers, and communicate through writing and speech-generating devices. In this article, we aim to provide an introductory summary of the historical context and modern growing utility of BCI, with specific interest in igniting the conversation of where and how the neurodiagnostics community and its associated parties can embrace and contribute to the world of BCI.}, }
@article {pmid39412979, year = {2024}, author = {Zhang, Y and Yu, Y and Li, H and Wu, A and Zeng, LL and Hu, D}, title = {MASER: Enhancing EEG Spatial Resolution With State Space Modeling.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {32}, number = {}, pages = {3858-3868}, doi = {10.1109/TNSRE.2024.3481886}, pmid = {39412979}, issn = {1558-0210}, mesh = {*Electroencephalography/methods ; Humans ; *Algorithms ; Brain-Computer Interfaces ; Reproducibility of Results ; Electrodes ; }, abstract = {Consumer-grade Electroencephalography (EEG) devices equipped with few electrodes often suffer from low spatial resolution, hindering the accurate capture of intricate brain activity patterns. To address this issue, we propose MASER, a novel super-resolution approach for EEG recording. In MASER, we design the eMamba block for extracting EEG features based on the principles of state space models (SSMs). We further stack eMamba blocks to form a low-resolution feature extractor and a high-resolution signal predictor, which enhances the feature representation. During the training of MASER, we fully consider the characteristics of multidimensional biological series signals, incorporating a smoothness constraint loss to achieve more consistent high-resolution reconstructions. MASER pioneers EEG-oriented state space modeling, effectively capturing the temporal dynamics and latent states, thereby revealing complex neural interactions over time. Extensive experiments show that the proposed MASER outperforms the state-of-the-art methods in super-resolution quality on two public EEG datasets, with normalized mean square error reduced by 16.25% and Pearson correlation improved by 1.13%. Moreover, a case study of motor imagery recognition highlights the advantages conferred by high-resolution EEG signals. With a 4x increase in spatial resolution by MASER, the recognition accuracy improves by 5.74%, implying a significant performance elevation in brain-computer interface (BCI) command mapping. By enhancing the spatial resolution of EEG signals, MASER makes EEG-based applications more accessible, reducing cost and setup time while maintaining high performance across various domains such as gaming, education, and healthcare.}, }
@article {pmid39409506, year = {2024}, author = {Xu, H and Haider, W and Aziz, MZ and Sun, Y and Yu, X}, title = {Transforming Motor Imagery Analysis: A Novel EEG Classification Framework Using AtSiftNet Method.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {19}, pages = {}, pmid = {39409506}, issn = {1424-8220}, support = {52172387//National Natural Science Foundation of China/ ; U2033202, U1333119//Joint Fund of National Natural Science Foundation of China and Civil Aviation Administration of China/ ; ILA22032-1A//Fundamental Research Funds for the Central Universities/ ; 2022Z071052001//Aeronautical Science Foundation of China/ ; 2022JGZ14//Northwestern Polytechnical University/ ; }, mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Principal Component Analysis ; *Machine Learning ; *Imagination/physiology ; Neural Networks, Computer ; Algorithms ; Signal Processing, Computer-Assisted ; Support Vector Machine ; }, abstract = {This paper presents an innovative approach for the Feature Extraction method using Self-Attention, incorporating various Feature Selection techniques known as the AtSiftNet method to enhance the classification performance of motor imaginary activities using electrophotography (EEG) signals. Initially, the EEG signals were sorted and then denoised using multiscale principal component analysis to obtain clean EEG signals. However, we also conducted a non-denoised experiment. Subsequently, the clean EEG signals underwent the Self-Attention feature extraction method to compute the features of each trial (i.e., 350×18). The best 1 or 15 features were then extracted through eight different feature selection techniques. Finally, five different machine learning and neural network classification models were employed to calculate the accuracy, sensitivity, and specificity of this approach. The BCI competition III dataset IV-a was utilized for all experiments, encompassing the datasets of five volunteers who participated in the competition. The experiment findings reveal that the average accuracy of classification is highest among ReliefF (i.e., 99.946%), Mutual Information (i.e., 98.902%), Independent Component Analysis (i.e., 99.62%), and Principal Component Analysis (i.e., 98.884%) for both 1 and 15 best-selected features from each trial. These accuracies were obtained for motor imagery using a Support Vector Machine (SVM) as a classifier. In addition, five-fold validation was performed in this paper to assess the fair performance estimation and robustness of the model. The average accuracy obtained through five-fold validation is 99.89%. The experiments' findings indicate that the suggested framework provides a resilient biomarker with minimal computational complexity, making it a suitable choice for advancing Motor Imagery Brain-Computer Interfaces (BCI).}, }
@article {pmid39409418, year = {2024}, author = {Borirakarawin, M and Siribunyaphat, N and Aung, ST and Punsawad, Y}, title = {The Development of a Multicommand Tactile Event-Related Potential-Based Brain-Computer Interface Utilizing a Low-Cost Wearable Vibrotactile Stimulator.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {19}, pages = {}, pmid = {39409418}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods/instrumentation ; Male ; *Wearable Electronic Devices ; *Touch/physiology ; Adult ; Female ; *Vibration ; Evoked Potentials/physiology ; Young Adult ; Event-Related Potentials, P300/physiology ; }, abstract = {A tactile event-related potential (ERP)-based brain-computer interface (BCI) system is an alternative for enhancing the control and communication abilities of quadriplegic patients with visual or auditory impairments. Hence, in this study, we proposed a tactile stimulus pattern using a vibrotactile stimulator for a multicommand BCI system. Additionally, we observed a tactile ERP response to the target from random vibrotactile stimuli placed in the left and right wrist and elbow positions to create commands. An experiment was conducted to explore the location of the proposed vibrotactile stimulus and to verify the multicommand tactile ERP-based BCI system. Using the proposed features and conventional classification methods, we examined the classification efficiency of the four commands created from the selected EEG channels. The results show that the proposed vibrotactile stimulation with 15 stimulus trials produced a prominent ERP response in the Pz channels. The average classification accuracy ranged from 61.9% to 79.8% over 15 stimulus trials, requiring 36 s per command in offline processing. The P300 response in the parietal area yielded the highest average classification accuracy. The proposed method can guide the development of a brain-computer interface system for physically disabled people with visual or auditory impairments to control assistive and rehabilitative devices.}, }
@article {pmid39409405, year = {2024}, author = {Silvoni, S and Occhigrossi, C and Di Giorgi, M and Lulé, D and Birbaumer, N}, title = {Brain Function, Learning, and Role of Feedback in Complete Paralysis.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {19}, pages = {}, pmid = {39409405}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; *Learning/physiology ; *Brain/physiology/physiopathology ; Paralysis/physiopathology/psychology ; Communication ; Feedback ; }, abstract = {The determinants and driving forces of communication abilities in the locked-in state are poorly understood so far. Results from an experimental-clinical study on a completely paralyzed person involved in communication sessions after the implantation of a microelectrode array were retrospectively analyzed. The aim was to focus on the prerequisites and determinants for learning to control a brain-computer interface for communication in paralysis. A comparative examination of the communication results with the current literature was carried out in light of an ideomotor theory of thinking. We speculate that novel skill learning took place and that several aspects of the wording of sentences during the communication sessions reflect preserved cognitive and conscious processing. We also present some speculations on the operant learning procedure used for communication, which argues for the reformulation of the previously postulated hypothesis of the extinction of response planning and goal-directed ideas in the completely locked-in state. We highlight the importance of feedback and reinforcement in the thought-action-consequence associative chain necessary to maintain purposeful communication. Finally, we underline the necessity to consider the psychosocial context of patients and the duration of complete immobilization as determinants of the 'extinction of thinking' theory and to identify the actual barriers preventing communication in these patients.}, }
@article {pmid39409342, year = {2024}, author = {Liu, M and Liu, Y and Feleke, AG and Fei, W and Bi, L}, title = {Neural Signature and Decoding of Unmanned Aerial Vehicle Operators in Emergency Scenarios Using Electroencephalography.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {19}, pages = {}, pmid = {39409342}, issn = {1424-8220}, support = {JCKY2022602C024//Basic Research Plan/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; Adult ; Evoked Potentials/physiology ; Brain/physiology ; Aircraft ; Young Adult ; Female ; Emergencies ; }, abstract = {Brain-computer interface (BCI) offers a novel means of communication and control for individuals with disabilities and can also enhance the interactions between humans and machines for the broader population. This paper explores the brain neural signatures of unmanned aerial vehicle (UAV) operators in emergencies and develops an operator's electroencephalography (EEG) signals-based detection method for UAV emergencies. We found regularity characteristics similar to classic event-related potential (ERP) components like visual mismatch negativity (vMMN) and contingent negative variation (CNV). Source analysis revealed a sequential activation of the occipital, temporal, and frontal lobes following the onset of emergencies, corresponding to the processing of attention, emotion, and motor intention triggered by visual stimuli. Furthermore, an online detection system was implemented and tested. Experimental results showed that the system achieved an average accuracy of over 88% in detecting emergencies with a detection latency of 431.95 ms from the emergency onset. This work lays a foundation for understanding the brain activities of operators in emergencies and developing an EEG-based detection method for emergencies to assist UAV operations.}, }
@article {pmid39409201, year = {2024}, author = {Shokri, R and Koolivand, Y and Shoaei, O and Caviglia, DD and Aiello, O}, title = {A Reconfigurable, Nonlinear, Low-Power, VCO-Based ADC for Neural Recording Applications.}, journal = {Sensors (Basel, Switzerland)}, volume = {24}, number = {19}, pages = {}, pmid = {39409201}, issn = {1424-8220}, mesh = {*Analog-Digital Conversion ; Humans ; Signal-To-Noise Ratio ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Brain/physiology ; Neurons/physiology ; Equipment Design ; Nonlinear Dynamics ; }, abstract = {Neural recording systems play a crucial role in comprehending the intricacies of the brain and advancing treatments for neurological disorders. Within these systems, the analog-to-digital converter (ADC) serves as a fundamental component, converting the electrical signals from the brain into digital data that can be further processed and analyzed by computing units. This research introduces a novel nonlinear ADC designed specifically for spike sorting in biomedical applications. Employing MOSFET varactors and voltage-controlled oscillators (VCOs), this ADC exploits the nonlinear capacitance properties of MOSFET varactors, achieving a parabolic quantization function that digitizes the noise with low resolution and the spikes with high resolution, effectively suppressing the background noise present in biomedical signals. This research aims to develop a reconfigurable, nonlinear voltage-controlled oscillator (VCO)-based ADC, specifically designed for implantable neural recording systems used in neuroprosthetics and brain-machine interfaces. The proposed design enhances the signal-to-noise ratio and reduces power consumption, making it more efficient for real-time neural data processing. By improving the performance and energy efficiency of these devices, the research contributes to the development of more reliable medical technologies for monitoring and treating neurological disorders. The quantization step of the ADC spans from 44.8 mV in the low-amplitude range to 1.4 mV in the high-amplitude range. The circuit was designed and simulated utilizing a 180 nm CMOS process; however, no physical prototype has been fabricated at this stage. Post-layout simulations confirm the expected performance. Occupying a silicon area is 0.09 mm[2]. Operating at a sampling frequency of 16 kS/s and a supply voltage of 1 volt, this ADC consumes 62.4 µW.}, }
@article {pmid39405625, year = {2025}, author = {Kasprzyk-Hordern, B and Jagadeesan, K and Sims, N and Farkas, K and Proctor, K and Bagnall, J and Robertson, M and Jones, DL and Wade, MJ}, title = {Multi-biomarker approach for estimating population size in a national-scale wastewater-based epidemiology study.}, journal = {Water research}, volume = {268}, number = {Pt A}, pages = {122527}, doi = {10.1016/j.watres.2024.122527}, pmid = {39405625}, issn = {1879-2448}, mesh = {*Biomarkers ; Humans ; *Wastewater ; Population Density ; England ; Wastewater-Based Epidemiological Monitoring ; }, abstract = {This study identifies biochemical markers (BCIs) that can be used as population markers in wastewater-based epidemiology (WBE) and compares their estimates with other established population size estimation (PE) methods, including census data (PECEN). Several groups of BCIs (64 targets: genetic and chemical markers) were investigated in an intercity study, including 10 cities/towns within England equating to a population of ∼7 million people. Several selection criteria were applied to identify the best BCIs to provide robust estimation of population size at a catchment level: (1) excellent performance with analytical methods; (2) excellent fit of the linear regression model which indicates PE-driven BCI daily loads; (3) low temporal variability in usage; (4) human-linked origin. Only a few tested BCIs showed a strong positive linear correlation between daily BCI loads and PE indicating their low spatiotemporal variability. These are: cimetidine, clarithromycin, metformin, cotinine, bezafibrate, metronidazole and hydroxymetronidazole, diclofenac, and benzophenone 1. However, only high/long term usage pharmaceuticals: cimetidine and metformin as well as cotinine (metabolite of nicotine) performed well when tested in two independent datasets and catchments accounting for both spatial and temporal scales. Strong seasonal usage trends were observed for antihistamines, NSAIDs (anti-inflammatories), antibiotics and UV filters, invalidating them as PE markers. Key conclusions from the study are: (1) Cimetidine is the best performing BCI; (2) Chemical markers outperform genetic markers as PE BCIs; (3) Water utility PE estimates (PEWW) align well with PECEN and PEBCI values; (4) Ammonium/orthophosphate as well as viral PE markers suffer from high temporal variability, hence, they are not recommended as PEBCI markers, and, most importantly, (5) PEBCI calibration/validation at the country/region level is advised in order to establish the best PE markers suited for local/national needs and accounting for site/region specific uncertainties.}, }
@article {pmid39402900, year = {2024}, author = {Zhang, X and Pei, X and Shi, Y and Yang, Y and Bai, X and Chen, T and Zhao, Y and Yang, Q and Ye, J and Leng, X and Yang, Q and Bai, R and Wang, Y and Sui, B}, title = {Unveiling connections between venous disruption and cerebral small vessel disease using diffusion tensor image analysis along perivascular space (DTI-ALPS): A 7-T MRI study.}, journal = {International journal of stroke : official journal of the International Stroke Society}, volume = {}, number = {}, pages = {17474930241293966}, doi = {10.1177/17474930241293966}, pmid = {39402900}, issn = {1747-4949}, abstract = {BACKGROUND: Cerebral venous disruption is one of the characteristic findings in cerebral small vessel disease (CSVD), and its disruption may impede perivascular glymphatic drainage. And lower diffusivity along perivascular space (DTI-ALPS) index has been suggested to be with the presence and severity of CSVD. However, the relationships between venous disruption, DTI-ALPS index, and CSVD neuroimaging features remain unclear.<br><br>AIMS: To investigate the association between venous integrity and perivascular diffusion activity, and explore the mediating role of DTI-ALPS index between venous disruption and CSVD imaging features.<br><br>METHODS: In this cross-sectional study, 31 patients (mean age, 59.0&#x2009;&#xb1;&#x2009;9.9&#x2009;years) were prospectively enrolled and underwent 7-T magnetic resonance (MR) imaging. DTI-ALPS index was measured to quantify the perivascular diffusivity. The visibility and continuity of deep medullary veins (DMVs) were evaluated based on a brain region-based visual score on high-resolution susceptibility-weighted imaging. White matter hyperintensity (WMH) and perivascular space (PVS) were assessed using qualitative and quantitative methods. Linear regression and mediation analysis were performed to analyze the relationships among DMV scores, DTI-ALPS index, and CSVD features.<br><br>RESULTS: The DTI-ALPS index was significantly associated with the parietal DMV score (&#x3b2;&#x2009;=&#x2009;-0.573, p corrected&#x2009;=&#x2009;0.004). Parietal DMV score was associated with WMH volume (&#x3b2;&#x2009;=&#x2009;0.463, p corrected&#x2009;=&#x2009;0.013) and PVS volume in basal ganglia (&#x3b2;&#x2009;=&#x2009;0.415, p corrected&#x2009;=&#x2009;0.028). Mediation analyses showed that DTI-ALPS index manifested a full mediating effect on the association between parietal DMV score and WMH (indirect effect&#x2009;=&#x2009;0.115, Pm&#x2009;=&#x2009;43.1%), as well as between parietal DMV score and PVS volume in basal ganglia (indirect effect&#x2009;=&#x2009;0.161, Pm&#x2009;=&#x2009;42.8%).<br><br>CONCLUSION: Cerebral venous disruption is associated with glymphatic activity, and with WMH and PVS volumes. Our results suggest cerebral venous integrity may play a critical role in preserving perivascular glymphatic activity; while disruption of small veins may impair the perivascular diffusivity, thereby contributing to the development of WMH and PVS enlargement.}, }
@article {pmid39401512, year = {2024}, author = {Eisma, YB and van Vliet, ST and Nederveen, AJ and de Winter, JCF}, title = {Assessing the influence of visual stimulus properties on steady-state visually evoked potentials and pupil diameter.}, journal = {Biomedical physics &amp; engineering express}, volume = {10}, number = {6}, pages = {}, doi = {10.1088/2057-1976/ad865d}, pmid = {39401512}, issn = {2057-1976}, mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Pupil/physiology ; *Photic Stimulation ; Male ; Female ; *Electroencephalography/methods ; Adult ; *Brain-Computer Interfaces ; Young Adult ; Signal-To-Noise Ratio ; }, abstract = {Steady-State Visual Evoked Potentials (SSVEPs) are brain responses measurable via electroencephalography (EEG) in response to continuous visual stimulation at a constant frequency. SSVEPs have been instrumental in advancing our understanding of human vision and attention, as well as in the development of brain-computer interfaces (BCIs). Ongoing questions remain about which type of visual stimulus causes the most potent SSVEP response. The current study investigated the effects of color, size, and flicker frequency on the signal-to-noise ratio of SSVEPs, complemented by pupillary light reflex measurements obtained through an eye-tracker. Six participants were presented with visual stimuli that differed in terms of color (white, red, green), shape (circles, squares, triangles), size (10,000 to 30,000 pixels), flicker frequency (8 to 25 Hz), and grouping (one stimulus at a time versus four stimuli presented in a 2 × 2 matrix to simulate a BCI). The results indicated that larger stimuli elicited stronger SSVEP responses and more pronounced pupil constriction. Additionally, the results revealed an interaction between stimulus color and flicker frequency, with red being more effective at lower frequencies and white at higher frequencies. Future SSVEP research could focus on the recommended waveform, interactions between SSVEP and power grid frequency, a wider range of flicker frequencies, a larger sample of participants, and a systematic comparison of the information transfer obtained through SSVEPs, pupil diameter, and eye movements.}, }
@article {pmid39400422, year = {2025}, author = {Vírseda-Chamorro, M and Salinas-Casado, J and Adot-Zurbano, JM and Méndez-Rubio, S and Moreno-Sierra, J}, title = {Can We Differentiate Between Organic and Functional Bladder Outlet Obstruction in Males With Parkinson's Disease?.}, journal = {Neurourology and urodynamics}, volume = {44}, number = {1}, pages = {171-177}, doi = {10.1002/nau.25599}, pmid = {39400422}, issn = {1520-6777}, support = {//The authors received no specific funding for this work./ ; }, mesh = {Humans ; *Urinary Bladder Neck Obstruction/physiopathology/diagnosis ; Male ; *Parkinson Disease/physiopathology/complications/diagnosis ; Aged ; Case-Control Studies ; *Urodynamics ; *Prostatic Hyperplasia/physiopathology/complications/diagnosis ; Middle Aged ; Urinary Bladder/physiopathology ; Pressure ; Aged, 80 and over ; Diagnosis, Differential ; }, abstract = {OBJECTIVES: To determine the type of bladder outlet obstruction (BOO) in patients with Parkinson's disease (PD).<br><br>MATERIAL AND METHOD: A case-control study was carried out in 46 patients divided into two groups. Group 1 formed by 23 PD patients with BOO (a URA parameter &#x2265;&#x2009;29&#x2009;cm H2O). Group 2 formed by 23 patients with benign prostatic hyperplasia (BPH) and compressive obstruction (an opening pressure >&#x2009;35&#x2009;cm H2O) and URA parameter &#x2265;&#x2009;29&#x2009;cm H2O). Both groups underwent a pressure-flow study to calculate Dynamic Urethral Resistance Relationship (DURR) patterns. Based on previous research, we describe two types of DURR pattern. Pattern A typical of dynamic or functional obstruction and pattern B typical of static or organic obstruction.<br><br>RESULTS: We found that PD patients had a significantly higher frequency of pattern A (70%) than BPH patients (4%). Other significant differences between groups were age (greater in PD group), bladder compliance (greater in PD group), maximum flow rate [Qmax (greater in BPH group)], maximum detrusor pressure [Pmax (greater in BPH group)], detrusor pressure at maximum flow rate [PQmax (greater in BPH group)], opening detrusor pressure (greater in BPH group), and the bladder contractility parameters BCI and Wmax (greater in BPH group). There were no significant differences in perineal voiding electromyography (EMG) activity between groups nor relationship between voiding EMG activity and the type of DURR pattern.<br><br>CONCLUSIONS: Our results are consistent with the usefulness of the DURR pattern to differentiate between functional and organic BOO in PD patients. Most PD patients have functional obstruction although a minority has organic obstruction consistent with BPH.}, }
@article {pmid39399473, year = {2024}, author = {Zhang, K and Zhou, W and Yu, H and Pang, M and Gao, H and Anwar, F and Yu, K and Zhou, Z and Guo, F and Liu, X and Ming, D}, title = {Insights on pathophysiology of hydrocephalus rats induced by kaolin injection.}, journal = {FASEB bioAdvances}, volume = {6}, number = {9}, pages = {351-364}, pmid = {39399473}, issn = {2573-9832}, abstract = {Hydrocephalus can affect brain function and motor ability. Current treatments mostly involve invasive surgeries, with a high risk of postoperative infections and failure. A successful animal model plays a significant role in developing new treatments for hydrocephalus. Hydrocephalus was induced in Sprague-Dawley rats by injecting 25% kaolin into the subarachnoid space at the cerebral convexities with different volumes of 30, 60 and 90 μL. Magnetic resonance imaging (MRI) was performed 1 month and 4 months after kaolin injection. The behavioral performance was assessed weekly, lasting for 7 weeks. The histopathological analyses were conducted to the lateral ventricles by hematoxylin-eosin (HE) staining. Transcriptomic analysis was used between Normal Pressure Hydrocephalus (NPH) patients and hydrocephalus rats. MRI showed a progressive enlargement of ventricles in hydrocephalus group. Kaolin-60 μL and kaolin-90 μL groups showed larger ventricular size, higher anxiety level, bigger decline in body weight, motor ability and cognitive competence. These symptoms may be due to higher-grade inflammatory infiltrate and the damage of the structure of ependymal layer of the ventricles, indicated by HE staining. The overlap upregulated genes and pathways mainly involve immunity and inflammation. Transcriptomic revealed shared pathogenic genes CD40, CD44, CXCL10, and ICAM1 playing a dominance role. 60 μL injection might be recommended for the establishment of hydrocephalus animal model, with a high successful rate and high stability. The hydrocephalus model was able to resemble the inflammatory mechanism and behavioral performance observed in human NPH patients, providing insights for identifying therapeutic targets for hydrocephalus.}, }
@article {pmid39398473, year = {2024}, author = {Hanada, GM and Kalabic, M and Ferris, DP}, title = {Mobile brain-body imaging data set of indoor treadmill walking and outdoor walking with a visual search task.}, journal = {Data in brief}, volume = {57}, number = {}, pages = {110968}, doi = {10.1016/j.dib.2024.110968}, pmid = {39398473}, issn = {2352-3409}, abstract = {To fully understand brain processes in the real world, it is necessary to record and quantitatively analyse brain processes during real world human experiences. Mobile electroencephalography (EEG) and physiological data sensors provide new opportunities for studying humans outside of the laboratory. The purpose of this study was to document data from high-density EEG and mobile physiological sensors while humans performed a visual search task both on a treadmill in a laboratory setting and overground in a natural outdoor setting. The data set includes 49 young, healthy participants on an outdoor arboretum path and on a treadmill in a laboratory with a large virtual reality screen. The data provide a valuable research tool for scientists interested in signal processing, electrocortical brain processes, mobile brain imaging, and brain-computer interfaces based on mobile EEG. Given the comparison data between laboratory and real world conditions, researchers can test the viability of new processing algorithms across conditions or investigate changes in electrocortical activity related to behavioural dynamics coded into the data.}, }
@article {pmid39397592, year = {2025}, author = {Deepika, D and Rekha, G}, title = {A hybrid capsule attention-based convolutional bi-GRU method for multi-class mental task classification based brain-computer Interface.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {28}, number = {1}, pages = {90-106}, doi = {10.1080/10255842.2024.2410221}, pmid = {39397592}, issn = {1476-8259}, mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography ; Signal Processing, Computer-Assisted ; Attention/physiology ; }, abstract = {Electroencephalography analysis is critical for brain computer interface research. The primary goal of brain-computer interface is to establish communication between impaired people and others via brain signals. The classification of multi-level mental activities using the brain-computer interface has recently become more difficult, which affects the accuracy of the classification. However, several deep learning-based techniques have attempted to identify mental tasks using multidimensional data. The hybrid capsule attention-based convolutional bidirectional gated recurrent unit model was introduced in this study as a hybrid deep learning technique for multi-class mental task categorization. Initially, the obtained electroencephalography data is pre-processed with a digital low-pass Butterworth filter and a discrete wavelet transform to remove disturbances. The spectrally adaptive common spatial pattern is used to extract characteristics from pre-processed electroencephalography data. The retrieved features were then loaded into the suggested classification model, which was used to extract the features deeply and classify the mental tasks. To improve classification results, the model's parameters are fine-tuned using a dung beetle optimization approach. Finally, the proposed classifier is assessed for several types of mental task classification using the provided dataset. The simulation results are compared with the existing state-of-the-art techniques in terms of accuracy, precision, recall, etc. The accuracy obtained using the proposed approach is 97.87%, which is higher than that of the other existing methods.}, }
@article {pmid39397321, year = {2024}, author = {Gokhale, SM and Bhatia, M}, title = {Lymphoscintigraphy With SPECT-CT in Detecting the Site of Chyle Leak in Postoperative Patient.}, journal = {Clinical nuclear medicine}, volume = {49}, number = {12}, pages = {e664-e667}, doi = {10.1097/RLU.0000000000005496}, pmid = {39397321}, issn = {1536-0229}, mesh = {Humans ; *Lymphoscintigraphy ; *Single Photon Emission Computed Tomography Computed Tomography ; Male ; Chyle/diagnostic imaging ; Middle Aged ; Aged ; Postoperative Complications/diagnostic imaging ; Esophagectomy/adverse effects ; }, abstract = {Here is a case of chyle leak post McKeown esophagectomy. Lymphoscintigraphy with 99m Tc-filtered sulfur colloid revealed tracer accumulation along the thoracic duct and in the left hemithorax. Precise localization of leak was done by SPECT-CT imaging. This enabled timely surgical intervention and reduced further morbidity. This procedure is not only precise but also cost-effective as compared with the other available investigations.}, }
@article {pmid39397043, year = {2024}, author = {Shi, C and Jiang, J and Li, C and Chen, C and Jian, W and Song, J}, title = {Precision-induced localized molten liquid metal stamps for damage-free transfer printing of ultrathin membranes and 3D objects.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {8839}, pmid = {39397043}, issn = {2041-1723}, support = {U21A20502//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12225209//National Natural Science Foundation of China (National Science Foundation of China)/ ; U20A6001//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12321002//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12302214//National Natural Science Foundation of China (National Science Foundation of China)/ ; LQ23A020006//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; }, abstract = {Transfer printing, a crucial technique for heterogeneous integration, has gained attention for enabling unconventional layouts and high-performance electronic systems. Elastomer stamps are typically used for transfer printing, where localized heating for elastomer stamp can effectively control the transfer process. A key challenge is the potential damage to ultrathin membranes from the contact force of elastic stamps, especially with fragile inorganic nanomembranes. Herein, we present a precision-induced localized molten technique that employs either laser-induced transient heating or hotplate-induced directional heating to precisely melt solid gallium (Ga). By leveraging the fluidity of localized molten Ga, which provides gentle contact force and exceptional conformal adaptability, this technique avoids damage to fragile thin films and improves operational reliability compared to fully liquefied Ga stamps. Furthermore, the phase transition of Ga provides a reversible adhesion with high adhesion switchability. Once solidified, the Ga stamp hardens and securely adheres to the micro/nano-membrane during the pick-up process. The solidified stamp also exhibits the capability to maneuver arbitrarily shaped objects by generating a substantial grip force through the interlocking effects. Such a robust, damage-free, simply operable protocol illustrates its promising capabilities in transfer printing diverse ultrathin membranes and objects on complex surfaces for developing high-performance unconventional electronics.}, }
@article {pmid39396768, year = {2024}, author = {Zhang, J and Wang, L and Guo, H and Kong, S and Li, W and He, Q and Ding, L and Yang, B}, title = {The role of Tim-3 blockade in the tumor immune microenvironment beyond T cells.}, journal = {Pharmacological research}, volume = {209}, number = {}, pages = {107458}, doi = {10.1016/j.phrs.2024.107458}, pmid = {39396768}, issn = {1096-1186}, mesh = {*Hepatitis A Virus Cellular Receptor 2/antagonists &amp; inhibitors/immunology/metabolism ; *Tumor Microenvironment/immunology ; Humans ; Animals ; *Neoplasms/immunology/drug therapy/pathology ; Killer Cells, Natural/immunology ; T-Lymphocytes/immunology/drug effects ; Macrophages/immunology ; Dendritic Cells/immunology ; }, abstract = {Numerous preclinical studies have demonstrated the inhibitory function of T cell immunoglobulin mucin domain-containing protein 3 (Tim-3) on T cells as an inhibitory receptor, leading to the clinical development of anti-Tim-3 blocking antibodies. However, recent studies have shown that Tim-3 is expressed not only on T cells but also on multiple cell types in the tumor microenvironment (TME), including dendritic cells (DCs), natural killer (NK) cells, macrophages, and tumor cells. Therefore, Tim-3 blockade in the immune microenvironment not only affect the function of T cells but also influence the functions of other cells. For example, Tim-3 blockade can enhance the ability of DCs to regulate innate and adaptive immunity. The role of Tim-3 blockade in NK cells function is controversial, as it can enhance the antitumor function of NK cells under certain conditions while having the opposite effect in other situations. Additionally, Tim-3 blockade can promote the reversal of macrophage polarization from the M2 phenotype to the M1 phenotype. Furthermore, Tim-3 blockade can inhibit tumor development by suppressing the proliferation and metastasis of tumor cells. In summary, increasing evidence has shown that Tim-3 in other cell types also plays a critical role in the efficacy of anti-Tim-3 therapy. Understanding the function of anti-Tim-3 therapy in non-T cells can help elucidate the diverse responses observed in clinical patients, leading to better development of relevant therapeutic strategies. This review aims to discuss the role of Tim-3 in the TME and emphasize the impact of Tim-3 blockade in the tumor immune microenvironment beyond T cells.}, }
@article {pmid39396567, year = {2025}, author = {Su, H and Zhan, G and Lin, Y and Wang, L and Jia, J and Zhang, L and Gan, Z and Kang, X}, title = {Analysis of brain network differences in the active, motor imagery, and passive stoke rehabilitation paradigms based on the task-state EEG.}, journal = {Brain research}, volume = {1846}, number = {}, pages = {149261}, doi = {10.1016/j.brainres.2024.149261}, pmid = {39396567}, issn = {1872-6240}, mesh = {Humans ; Male ; Female ; *Electroencephalography/methods ; Middle Aged ; *Brain/physiopathology/physiology ; *Stroke Rehabilitation/methods ; *Imagination/physiology ; *Stroke/physiopathology ; Aged ; Nerve Net/physiopathology/physiology ; Movement/physiology ; Adult ; Motor Cortex/physiology/physiopathology ; }, abstract = {Different movement paradigms have varying effects on stroke rehabilitation, and their mechanisms of action on the brain are not fully understood. This study aims to investigate disparities in brain network and functional connectivity of three movement paradigms (active, motor imagery, passive) on stroke recovery. EEG signals were recorded from 11 S patients (SP) and 13 healthy controls (HC) during fist clenching and opening tasks under the three paradigms. Brain networks were constructed to analyze alterations in brain network connectivity, node strength (NS), clustering coefficients (CC), characteristic path length (CPL), and small-world index(S). Our findings revealed increased activity in the contralateral motor area in SP and higher activity in the ipsilateral motor area in HC. In the beta band, SP exhibited significantly higher CC in motor imagery (MI) than in active and passive tasks. Furthermore, the small world index of SP during MI tasks in the beta band was significantly smaller than in the active and passive tasks. NS in the gamma band for SP during the MI paradigm was significantly higher than in the active and passive paradigms. These findings suggest reorganization within both ipsilateral and contralateral motor areas of stroke patients during MI tasks, providing evidence for neural restructuring. Collectively, these findings contribute to a deeper understanding of task-state brain network changes and the rehabilitative mechanism of MI on motor function.}, }
@article {pmid39396023, year = {2024}, author = {Zhou, H and Hong, T and Chen, X and Su, C and Teng, B and Xi, W and Cadet, JL and Yang, Y and Geng, F and Hu, Y}, title = {Glutamate concentration of medial prefrontal cortex is inversely associated with addictive behaviors: a translational study.}, journal = {Translational psychiatry}, volume = {14}, number = {1}, pages = {433}, pmid = {39396023}, issn = {2158-3188}, support = {81971245//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {*Glutamic Acid/metabolism ; *Prefrontal Cortex/metabolism/physiopathology ; Male ; Animals ; Rats ; Humans ; *Internet Addiction Disorder/physiopathology/metabolism ; *Behavior, Addictive/physiopathology/metabolism ; *Drug-Seeking Behavior/physiology ; Adult ; *Methamphetamine ; Young Adult ; Gyrus Cinguli/metabolism/physiopathology ; Female ; Disease Models, Animal ; Rats, Sprague-Dawley ; Translational Research, Biomedical ; Self Administration ; }, abstract = {In both preclinical and clinical settings, dysregulated frontostriatal circuits have been identified as the underlying neural substrates of compulsive seeking/taking behaviors manifested in substance use disorders and behavioral addictions including internet gaming disorder (IGD). However, the neurochemical substrates for these disorders remain elusive. The lack of comprehensive cognitive assessments in animal models has hampered our understanding of neural plasticity in addiction from these models. In this study, combining data from a rat model of compulsive taking/seeking and human participants with various levels of IGD severity, we investigated the relationship between regional glutamate (Glu) concentration and addictive behaviors. We found that Glu levels were significantly lower in the prelimbic cortex (PrL) of rats after 20-days of methamphetamine self-administration (SA), compared to controls. Glu concentration after a punishment phase negatively correlated with acute drug-seeking behavior. In addition, changes in Glu levels from a drug naïve state to compulsive drug taking patterns negatively correlated with drug-seeking during both acute and prolonged abstinence. The human data revealed a significant negative correlation between Glu concentration in the dorsal anterior cingulate cortex (dACC), the human PrL counterpart, and symptoms of IGD. Interestingly, there was a positive correlation between Glu levels in the dACC and self-control, as well as mindful awareness. Further analysis revealed that the dACC Glu concentration mediated the relationship between self-control/mindful awareness and IGD symptoms. These results provide convergent evidence for a protective role of dACC/PrL in addiction, suggesting interventions to enhance dACC glutamatergic functions as a potential strategy for addiction prevention and treatment.}, }
@article {pmid39395910, year = {2024}, author = {Fan, J and Wang, X and Xu, H}, title = {Sex-Differential Neural Circuits and Behavioral Responses for Empathy.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {39395910}, issn = {1995-8218}, }
@article {pmid39387251, year = {2024}, author = {Zhang, W and Bai, L and Xu, W and Liu, J and Chen, Y and Lin, W and Lu, H and Wang, B and Luo, B and Peng, G and Zhang, K and Shen, C}, title = {Sirt6 Mono-ADP-Ribosylates YY1 to Promote Dystrophin Expression for Neuromuscular Transmission.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {11}, number = {44}, pages = {e2406390}, pmid = {39387251}, issn = {2198-3844}, support = {LZ22C110002//Natural Science Foundation of Zhejiang Province/ ; 2021YFA1101100//National Key R&amp;D Program of China/ ; 2022YFF1000500//National Key R&amp;D Program of China/ ; 32271031//National Natural Science Foundation of China/ ; 82230038//National Natural Science Foundation of China/ ; 31871203//National Natural Science Foundation of China/ ; 32071032//National Natural Science Foundation of China/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; }, mesh = {Animals ; *Sirtuins/metabolism/genetics ; *YY1 Transcription Factor/metabolism/genetics ; Mice ; *Neuromuscular Junction/metabolism/drug effects ; *Dystrophin/genetics/metabolism ; Synaptic Transmission/drug effects ; Muscle, Skeletal/metabolism/drug effects ; Disease Models, Animal ; Male ; ADP-Ribosylation ; }, abstract = {The degeneration of the neuromuscular junction (NMJ) and the decline in motor function are common features of aging, but the underlying mechanisms have remained largely unclear. This study reveals that Sirt6 is reduced in aged mouse muscles. Ablation of Sirt6 in skeletal muscle causes a reduction of Dystrophin levels, resulting in premature NMJ degeneration, compromised neuromuscular transmission, and a deterioration in motor performance. Mechanistic studies show that Sirt6 negatively regulates the stability of the Dystrophin repressor YY1 (Yin Yang 1). Specifically, Sirt6 mono-ADP-ribosylates YY1, causing its disassociation from the Dystrophin promoter and allowing YY1 to bind to the SMURF2 E3 ligase, leading to its degradation. Importantly, supplementation with nicotinamide mononucleotide (NMN) enhances the mono-ADP-ribosylation of YY1 and effectively delays NMJ degeneration and the decline in motor function in elderly mice. These findings provide valuable insights into the intricate mechanisms underlying NMJ degeneration during aging. Targeting Sirt6 could be a potential therapeutic approach to mitigate the detrimental effects on NMJ degeneration and improve motor function in the elderly population.}, }
@article {pmid39394849, year = {2024}, author = {Li, Z and Meng, M}, title = {An SCA-based classifier for motor imagery EEG classification.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-13}, doi = {10.1080/10255842.2024.2414069}, pmid = {39394849}, issn = {1476-8259}, abstract = {Efficient and accurate multi-class classification of electroencephalogram (EEG) signals poses a significant challenge in the development of motor imagery-based brain-computer interface (MI-BCI). Drawing inspiration from the sine cosine algorithm (SCA), a widely employed swarm intelligence algorithm for optimization problems, we proposed a novel population-based classification algorithm for EEG signals in this article. To fully leverage the characteristics contained in EEG signals, multi-scale sub-signals were constructed in terms of temporal windows and spectral bands simultaneously, and the common spatial pattern (CSP) features were extracted from each sub-signal. Subsequently, we integrated the multi-center optimal vectors mechanism into the classical SCA, resulting in the development of a multi-center SCA (MCSCA) classifier. During the classification stage, the label was assigned to the test trials by evaluating the Euclidean distance between their feature vectors and each optimal vector in MCSCA. Additionally, the weights of feature vectors were exploited to select the sub-signal of specific temporal windows and spectral bands for feature reduction, thereby declining computational effort and eliminating data redundancy. To validate the performance of the MCSCA classifier, we conducted four-class classification experiments using the BCI Competition IV dataset 2a, achieving an average classification accuracy of 71.89%. The experimental results show that the proposed algorithm offers a novel and effective approach for EEG classification.}, }
@article {pmid39391754, year = {2024}, author = {Salari, V and O'Connor, R and Rodrigues, S and Oblak, D}, title = {Editorial: New approaches in Brain-Machine Interfaces with implants.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1485472}, pmid = {39391754}, issn = {1662-4548}, }
@article {pmid39391265, year = {2024}, author = {Ren, C and Li, X and Gao, Q and Pan, M and Wang, J and Yang, F and Duan, Z and Guo, P and Zhang, Y}, title = {The effect of brain-computer interface controlled functional electrical stimulation training on rehabilitation of upper limb after stroke: a systematic review and meta-analysis.}, journal = {Frontiers in human neuroscience}, volume = {18}, number = {}, pages = {1438095}, pmid = {39391265}, issn = {1662-5161}, abstract = {INTRODUCTION: Several clinical studies have demonstrated that brain-computer interfaces (BCIs) controlled functional electrical stimulation (FES) facilitate neurological recovery in patients with stroke. This review aims to evaluate the effectiveness of BCI-FES training on upper limb functional recovery in stroke patients.<br><br>METHODS: PubMed, Embase, Cochrane Library, Science Direct and Web of Science were systematically searched from inception to October 2023. Randomized controlled trials (RCTs) employing BCI-FES training were included. The methodological quality of the RCTs was assessed using the PEDro scale. Meta-analysis was conducted using RevMan 5.4.1 and STATA 18.<br><br>RESULTS: The meta-analysis comprised 290 patients from 10 RCTs. Results showed a moderate effect size in upper limb function recovery through BCI-FES training (SMD&#x2009;=&#x2009;0.50, 95% CI: 0.26-0.73, I[2] =&#x2009;0%, p <&#x2009;0.0001). Subgroup analysis revealed that BCI-FES training significantly enhanced upper limb motor function in BCI-FES vs. FES group (SMD&#x2009;=&#x2009;0.37, 95% CI: 0.00-0.74, I[2] =&#x2009;21%, p =&#x2009;0.05), and the BCI-FES&#x2009;+&#x2009;CR vs. CR group (SMD&#x2009;=&#x2009;0.61, 95% CI: 0.28-0.95, I[2] =&#x2009;0%, p =&#x2009;0.0003). Moreover, BCI-FES training demonstrated effectiveness in both subacute (SMD&#x2009;=&#x2009;0.56, 95% CI: 0.25-0.87, I[2] =&#x2009;0%, p =&#x2009;0.0004) and chronic groups (SMD&#x2009;=&#x2009;0.42, 95% CI: 0.05-0.78, I[2] =&#x2009;45%, p =&#x2009;0.02). Subgroup analysis showed that both adjusting (SMD&#x2009;=&#x2009;0.55, 95% CI: 0.24-0.87, I[2] =&#x2009;0%, p =&#x2009;0.0006) and fixing (SMD&#x2009;=&#x2009;0.43, 95% CI: 0.07-0.78, I[2] =&#x2009;46%, p =&#x2009;0.02). BCI thresholds before training significantly improved motor function in stroke patients. Both motor imagery (MI) (SMD&#x2009;=&#x2009;0.41 95% CI: 0.12-0.71, I[2] =&#x2009;13%, p =&#x2009;0.006) and action observation (AO) (SMD&#x2009;=&#x2009;0.73, 95% CI: 0.26-1.20, I[2] =&#x2009;0%, p =&#x2009;0.002) as mental tasks significantly improved upper limb function in stroke patients.<br><br>DISCUSSION: BCI-FES has significant immediate effects on upper limb function in subacute and chronic stroke patients, but evidence for its long-term impact remains limited. Using AO as the mental task may be a more effective BCI-FES training strategy.<br><br>Identifier: CRD42023485744, https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023485744.}, }
@article {pmid39391047, year = {2024}, author = {Jang, M and Hays, M and Yu, WH and Lee, C and Caragiulo, P and Ramkaj, A and Wang, P and Phillips, AJ and Vitale, N and Tandon, P and Yan, P and Mak, PI and Chae, Y and Chichilnisky, EJ and Murmann, B and Muratore, DG}, title = {A 1024-Channel 268 nW/pixel 36×36 μm[2]/channel Data-Compressive Neural Recording IC for High-Bandwidth Brain-Computer Interfaces.}, journal = {IEEE journal of solid-state circuits}, volume = {59}, number = {4}, pages = {1123-1136}, pmid = {39391047}, issn = {0018-9200}, support = {R01 EY021271/EY/NEI NIH HHS/United States ; R01 EY032900/EY/NEI NIH HHS/United States ; }, abstract = {This paper presents a data-compressive neural recording IC for single-cell resolution high-bandwidth brain-computer interfaces. The IC features wired-OR lossy compression during digitization, thus preventing data deluge and massive data movement. By discarding unwanted baseline samples of the neural signals, the output data rate is reduced by 146× on average while allowing the reconstruction of spike samples. The recording array consists of pulse position modulation-based active digital pixels with a global single-slope analog-to-digital conversion scheme, which enables a low-power and compact pixel design with significantly simple routing and low array readout energy. Fabricated in a 28-nm CMOS process, the neural recording IC features 1024 channels (i.e., 32 × 32 array) with a pixel pitch of 36 μm that can be directly matched to a high-density microelectrode array. The pixel achieves 7.4 μVrms input-referred noise with a -3 dB bandwidth of 300-Hz to 5-kHz while consuming only 268 nW from a single 1-V supply. The IC achieves the smallest area per channel (36 × 36 μm[2]) and the highest energy efficiency among the state-of-the-art neural recording ICs published to date.}, }
@article {pmid39389052, year = {2024}, author = {Zheng, Z and Liu, Y and Mu, R and Guo, X and Feng, Y and Guo, C and Yang, L and Qiu, W and Zhang, Q and Yang, W and Dong, Z and Qiu, S and Dong, Y and Cui, Y}, title = {A small population of stress-responsive neurons in the hypothalamus-habenula circuit mediates development of depression-like behavior in mice.}, journal = {Neuron}, volume = {112}, number = {23}, pages = {3924-3939.e5}, doi = {10.1016/j.neuron.2024.09.012}, pmid = {39389052}, issn = {1097-4199}, mesh = {Animals ; *Habenula/physiology ; *Depression ; *Stress, Psychological ; Mice ; *Neurons/physiology ; *Hypothalamus/metabolism ; Male ; Mice, Inbred C57BL ; Neural Pathways/physiology ; Hypothalamic Area, Lateral/physiology ; }, abstract = {Accumulating evidence has shown that various brain functions are associated with experience-activated neuronal ensembles. However, whether such neuronal ensembles are engaged in the pathogenesis of stress-induced depression remains elusive. Utilizing activity-dependent viral strategies in mice, we identified a small population of stress-responsive neurons, primarily located in the middle part of the lateral hypothalamus (mLH) and the medial part of the lateral habenula (LHbM). These neurons serve as "starter cells" to transmit stress-related information and mediate the development of depression-like behaviors during chronic stress. Starter cells in the mLH and LHbM form dominant connections, which are selectively potentiated by chronic stress. Silencing these connections during chronic stress prevents the development of depression-like behaviors, whereas activating these connections directly elicits depression-like behaviors without stress experience. Collectively, our findings dissect a core functional unit within the LH-LHb circuit that mediates the development of depression-like behaviors in mice.}, }
@article {pmid39386879, year = {2024}, author = {Huang, Y and Yang, L and Yang, L and Xu, Z and Li, M and Shang, Z}, title = {Microstimulation-based path tracking control of pigeon robots through parameter adaptive strategy.}, journal = {Heliyon}, volume = {10}, number = {19}, pages = {e38113}, pmid = {39386879}, issn = {2405-8440}, abstract = {Research on animal robots utilizing neural electrical stimulation is a significant focus within the field of neuro-control, though precise behavior control remains challenging. This study proposes a parameter-adaptive strategy to achieve accurate path tracking. First, the mapping relationship between neural electrical stimulation parameters and corresponding behavioral responses is comprehensively quantified. Next, adjustment rules related to the parameter-adaptive control strategy are established to dynamically generate different stimulation patterns. A parameter-adaptive path tracking control strategy (PAPTCS), based on fuzzy control principles, is designed for the precise path tracking tasks of pigeon robots in open environments. The results indicate that altering stimulation parameter levels significantly affects turning angles, with higher UPN and PTN inducing changes in the pigeons' motion state. In experimental scenarios, the average control efficiency of this system was 82.165%. This study provides a reference method for the precise control of pigeon robot behavior, contributing to research on accurate target path tracking.}, }
@article {pmid39385316, year = {2024}, author = {Wang, D and Guo, X and Huang, Q and Wang, Z and Chen, J and Hu, S}, title = {Efficacy and Safety of Transcranial Direct Current Stimulation as an Add-On Trial Treatment for Acute Bipolar Depression Patients With Suicidal Ideation.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {30}, number = {10}, pages = {e70077}, pmid = {39385316}, issn = {1755-5949}, support = {81971271//National Natural Science Foundation of China/ ; 82201675//National Natural Science Foundation of China/ ; 2021C03107//Zhejiang Provincial Key Research and Development Program/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation-"Ten Thousand Talents Program" of Zhejiang Province/ ; 2020R01001//Innovation Team for Precision Diagnosis and Treatment of Major Brain Diseases/ ; LQ20H090012//Natural Science Foundation of Zhejiang Province/ ; 2020KY134//Health and Family Planning Commission of Zhejiang Province/ ; }, mesh = {Humans ; *Bipolar Disorder/therapy/psychology ; Male ; Female ; *Transcranial Direct Current Stimulation/methods ; Adult ; Double-Blind Method ; *Suicidal Ideation ; Middle Aged ; Treatment Outcome ; Psychiatric Status Rating Scales ; Quetiapine Fumarate/therapeutic use ; Young Adult ; Antipsychotic Agents/therapeutic use/adverse effects ; Combined Modality Therapy/methods ; }, abstract = {AIMS: Bipolar depression poses an overwhelming suicide risk. We aimed to examine the efficacy and safety of transcranial direct current stimulation (tDCS) combined with quetiapine in bipolar patients as a suicidal intervention.<br><br>METHODS: In a single-center, double-blind, treatment-naive bipolar depression patients with suicidal ideation were randomly assigned to quetiapine in combination with either active (n&#x2009;=&#x2009;16) or sham (n&#x2009;=&#x2009;15) tDCS over the left dorsolateral prefrontal cortex for three consecutive weeks. The 30-min, 2-mA tDCS was conducted twice a day on the weekday of the first week and then once a day on the weekdays of the two following weeks. Primary efficacy outcome measure was the change in the Beck Scale for Suicidal Ideation (BSSI). Secondary outcomes included changes on the 17-item Hamilton Depression Rating Scale (HDRS-17) and Montgomery-Asberg Depression Rating Scale (MADRS). Outcome was evaluated on Day 3 and weekend. Safety outcome was based on the reported adverse reactions.<br><br>RESULTS: Active tDCS was superior to sham tDCS on the BSSI at Day 3 and tended to sustain every weekend during the treatment process, compared to baseline. However, no difference between active and sham in HDRS-17 and MADRS was found. Response and remission rate also supported the antisuicide effect of tDCS, with higher response and remission rate in BSSI, but no antidepressant effect, compared to sham, over time. Regarding safety, active tDCS was well tolerated and all the adverse reactions reported were mild and limited to transient scalp discomfort.<br><br>CONCLUSION: The tDCS was effective as an antisuicide treatment for acute bipolar depression patients with suicidal ideation, with minimal side effects reported.}, }
@article {pmid39384601, year = {2024}, author = {Li, D and Li, K and Xia, Y and Dong, J and Lu, R}, title = {Joint hybrid recursive feature elimination based channel selection and ResGCN for cross session MI recognition.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {23549}, pmid = {39384601}, issn = {2045-2322}, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Algorithms ; *Neural Networks, Computer ; Imagination ; Brain/physiology ; }, abstract = {In the field of brain-computer interface (BCI) based on motor imagery (MI), multi-channel electroencephalography (EEG) data is commonly utilized for MI task recognition to achieve sensory compensation or precise human-computer interaction. However, individual physiological differences, environmental variations, or redundant information and noise in certain channels can pose challenges and impact the performance of BCI systems. In this study, we introduce a channel selection method utilizing Hybrid-Recursive Feature Elimination (H-RFE) combined with residual graph neural networks for MI recognition. This channel selection method employs a recursive feature elimination strategy and integrates three classification methods, namely random forest, gradient boosting, and logistic regression, as evaluators for adaptive channel selection tailored to specific subjects. To fully exploit the spatiotemporal information of multi-channel EEG, this study employed a graph neural network embedded with residual blocks to achieve precise recognition of motor imagery. We conducted algorithm testing using the SHU dataset and the PhysioNet dataset. Experimental results show that on the SHU dataset, utilizing 73.44% of the total channels, the cross-session MI recognition accuracy is 90.03%. Similarly, in the PhysioNet dataset, using 72.5% of the channel data, the classification result also reaches 93.99%. Compared to traditional strategies such as selecting three specific channels, correlation-based channel selection, mutual information-based channel selection, and adaptive channel selection based on Pearson coefficients and spatial positions, the proposed method improved classification accuracy by 34.64%, 10.8%, 3.25% and 2.88% on the SHU dataset, and by 46.96%, 5.04%, 5.81% and 2.32% on the PhysioNet dataset, respectively.}, }
@article {pmid39383883, year = {2024}, author = {Ottenhoff, MC and Verwoert, M and Goulis, S and Wagner, L and van Dijk, JP and Kubben, PL and Herff, C}, title = {Global motor dynamics - Invariant neural representations of motor behavior in distributed brain-wide recordings.}, journal = {Journal of neural engineering}, volume = {21}, number = {5}, pages = {}, doi = {10.1088/1741-2552/ad851c}, pmid = {39383883}, issn = {1741-2552}, mesh = {Humans ; Male ; Female ; Adult ; *Movement/physiology ; *Brain/physiology ; *Electroencephalography/methods ; Electrodes, Implanted ; Young Adult ; Hand Strength/physiology ; Psychomotor Performance/physiology ; Motor Activity/physiology ; Electrocorticography/methods ; Epilepsy/physiopathology ; Middle Aged ; }, abstract = {Objective.Motor-related neural activity is more widespread than previously thought, as pervasive brain-wide neural correlates of motor behavior have been reported in various animal species. Brain-wide movement-related neural activity have been observed in individual brain areas in humans as well, but it is unknown to what extent global patterns exist.Approach.Here, we use a decoding approach to capture and characterize brain-wide neural correlates of movement. We recorded invasive electrophysiological data from stereotactic electroencephalographic electrodes implanted in eight epilepsy patients who performed both an executed and imagined grasping task. Combined, these electrodes cover the whole brain, including deeper structures such as the hippocampus, insula and basal ganglia. We extract a low-dimensional representation and classify movement from rest trials using a Riemannian decoder.Main results.We reveal global neural dynamics that are predictive across tasks and participants. Using an ablation analysis, we demonstrate that these dynamics remain remarkably stable under loss of information. Similarly, the dynamics remain stable across participants, as we were able to predict movement across participants using transfer learning.Significance.Our results show that decodable global motor-related neural dynamics exist within a low-dimensional space. The dynamics are predictive of movement, nearly brain-wide and present in all our participants. The results broaden the scope to brain-wide investigations, and may allow combining datasets of multiple participants with varying electrode locations or calibrationless neural decoder.}, }
@article {pmid39383715, year = {2024}, author = {Li, K and Qian, L and Zhang, C and Li, R and Zeng, J and Xue, C and Deng, W}, title = {Deep transcranial magnetic stimulation for treatment-resistant obsessive-compulsive disorder: A meta-analysis of randomized-controlled trials.}, journal = {Journal of psychiatric research}, volume = {180}, number = {}, pages = {96-102}, doi = {10.1016/j.jpsychires.2024.09.043}, pmid = {39383715}, issn = {1879-1379}, mesh = {Humans ; *Obsessive-Compulsive Disorder/therapy ; Outcome Assessment, Health Care ; *Randomized Controlled Trials as Topic ; *Transcranial Magnetic Stimulation/methods ; }, abstract = {BACKGROUND: Deep transcranial magnetic stimulation (dTMS), an advancement of transcranial magnetic stimulation, was created to reach wider and possibly more profound regions of the brain. At present, there is insufficient high-quality evidence to support the effectiveness and safety of dTMS in treating obsessive-compulsive disorder (OCD).<br><br>OBJECTIVE: This study used a meta-analysis to evaluate the effectiveness and safety of dTMS for treating OCD.<br><br>METHODS: Four randomized controlled trials were found by searching PubMed, Embase, Web of Science, and Cochrane Library up to February 2024. The fixed effects meta-analysis model was used for the purpose of data merging in Stata17. The risk ratio (RR) value was used as the measure of effect size to compare response rates and dropout rates between active and sham dTMS.<br><br>RESULTS: The meta-analysis included four randomized-controlled trials involving 252 patients with treatment-resistant OCD. Active dTMS showed a notably greater rate of response on the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) in comparison to sham dTMS after treatment (Y-BOCS: RR&#xa0;=&#xa0;3.71, 95% confidence interval [CI] 2.06 to 6.69) and at the one-month follow-up (Y-BOCS: RR&#xa0;=&#xa0;2.60, 95% CI 1.59 to 4.26). Subgroup analysis revealed that active dTMS with H-coils was more effective than sham dTMS (RR&#xa0;=&#xa0;3.57, 95%CI 1.93 to 6.60). No serious adverse events were documented in the studies that were included.<br><br>CONCLUSION: The findings suggest that dTMS demonstrates notable efficacy and safety in treating patients with treatment-resistant OCD compared to sham dTMS, with sustained effectiveness noted throughout the one-month post-treatment period.}, }
@article {pmid39381774, year = {2024}, author = {Pilacinski, A and Christ, L and Boshoff, M and Iossifidis, I and Adler, P and Miro, M and Kuhlenkötter, B and Klaes, C}, title = {Human in the collaborative loop: a strategy for integrating human activity recognition and non-invasive brain-machine interfaces to control collaborative robots.}, journal = {Frontiers in neurorobotics}, volume = {18}, number = {}, pages = {1383089}, pmid = {39381774}, issn = {1662-5218}, abstract = {Human activity recognition (HAR) and brain-machine interface (BMI) are two emerging technologies that can enhance human-robot collaboration (HRC) in domains such as industry or healthcare. HAR uses sensors or cameras to capture and analyze the movements and actions of humans, while BMI uses human brain signals to decode action intentions. Both technologies face challenges impacting accuracy, reliability, and usability. In this article, we review the state-of-the-art techniques and methods for HAR and BMI and highlight their strengths and limitations. We then propose a hybrid framework that fuses HAR and BMI data, which can integrate the complementary information from the brain and body motion signals and improve the performance of human state decoding. We also discuss our hybrid method's potential benefits and implications for HRC.}, }
@article {pmid39379912, year = {2024}, author = {Chen, B and Dong, J and Guo, W and Li, T}, title = {Sex-specific associations between levels of high-sensitivity C-reactive protein and severity of depression: retrospective cross-sectional analysis of inpatients in China.}, journal = {BMC psychiatry}, volume = {24}, number = {1}, pages = {667}, pmid = {39379912}, issn = {1471-244X}, support = {82230046//Key Project of the National Natural Science Foundation of China/ ; }, mesh = {Humans ; Male ; Female ; *C-Reactive Protein/analysis ; Retrospective Studies ; China/epidemiology ; Middle Aged ; Cross-Sectional Studies ; Adult ; *Severity of Illness Index ; Sex Factors ; *Inpatients ; Aged ; Depression/blood ; Depressive Disorder/blood/epidemiology ; }, abstract = {BACKGROUND: We aimed to clarify the controversial relationship between levels of high-sensitivity C-reactive protein (hs-CRP) and severity of depression in men and women.<br><br>METHODS: Medical records were retrospectively analyzed for 1,236 inpatients at our medical center who were diagnosed with depression at discharge between January 2018 and August 2022. Depression severity was assessed during hospitalization using the 24-item Hamilton Depression Rating Scale. Potential associations between severity scores and hs-CRP levels were explored using multivariate linear regression as well as smooth curve fitting to detect non-linear patterns.<br><br>RESULTS: In male patients, hs-CRP levels between 2.00&#xa0;mg/L and 10.00&#xa0;mg/L showed a non-linear association with depression severity overall (fully adjusted &#x3b2;&#x2009;=&#x2009;1.69, 95% CI 0.65 to 2.72), as well as with severity of specific symptoms such as hopelessness, sluggishness, and cognitive disturbance. In female patients, hs-CRP levels showed a linear association with severity of cognitive disturbance (fully adjusted &#x3b2;&#x2009;=&#x2009;0.07, 95% CI 0.01 to 0.12). These results remained significant after adjusting for age, body mass index, diabetes, hypertension, history of drinking, history of smoking, and estradiol levels.<br><br>DISCUSSION: Levels of hs-CRP show sex-specific associations with depression severity, particularly levels between 2.00 and 10.00&#xa0;mg/L in men. These findings may help develop personalized anti-inflammatory treatments for depression, particularly for men with hs-CRP levels of 2.00-10.00&#xa0;mg/L.}, }
@article {pmid39378126, year = {2024}, author = {Li, J and Wu, W and Chen, J and Xu, Z and Yang, B and He, Q and Yang, X and Yan, H and Luo, P}, title = {Development and safety of investigational and approved drugs targeting the RAS function regulation in RAS mutant cancers.}, journal = {Toxicological sciences : an official journal of the Society of Toxicology}, volume = {202}, number = {2}, pages = {167-178}, doi = {10.1093/toxsci/kfae129}, pmid = {39378126}, issn = {1096-0929}, support = {82373968//National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Neoplasms/drug therapy/genetics ; *Antineoplastic Agents/therapeutic use ; *ras Proteins/genetics/metabolism ; *Mutation ; Animals ; Drug Approval ; Signal Transduction/drug effects ; Drugs, Investigational/therapeutic use/adverse effects ; Drug Development ; }, abstract = {The RAS gene family holds a central position in controlling key cellular activities such as migration, survival, metabolism, and other vital biological processes. The activation of RAS signaling cascades is instrumental in the development of various cancers. Although several RAS inhibitors have gained approval from the US Food and Drug Administration for their substantial antitumor effects, their widespread and severe adverse reactions significantly curtail their practical usage in the clinic. Thus, there exists a pressing need for a comprehensive understanding of these adverse events, ensuring the clinical safety of RAS inhibitors through the establishment of precise management guidelines, suitable intermittent dosing schedules, and innovative combination regimens. This review centers on the evolution of RAS inhibitors in cancer therapy, delving into the common adverse effects associated with these inhibitors, their underlying mechanisms, and the potential strategies for mitigation.}, }
@article {pmid39376540, year = {2024}, author = {Giove, F and Zuo, XN and Calhoun, VD}, title = {Editorial: Insights in brain imaging methods: 2023.}, journal = {Frontiers in neuroscience}, volume = {18}, number = {}, pages = {1488845}, doi = {10.3389/fnins.2024.1488845}, pmid = {39376540}, issn = {1662-4548}, }
@article {pmid39375394, year = {2024}, author = {Katoozian, D and Hosseini-Nejad, H and Dehaqani, MA}, title = {A new approach for neural decoding by inspiring of hyperdimensional computing for implantable intra-cortical BMIs.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {23291}, pmid = {39375394}, issn = {2045-2322}, mesh = {*Brain-Computer Interfaces ; Animals ; *Algorithms ; *Macaca mulatta ; Neurons/physiology ; Male ; Humans ; }, abstract = {In the field of Brain Machine Interface (BMI), the process of translating motor intention into a machine command is denoted as decoding. However, despite recent advancements, decoding remains a formidable challenge within BMI. The utilization of current decoding algorithms in the field of BMI often involves computational complexity and requires the use of computers. This is primarily due to the reliance on mathematical models to address the decoding issue and perform subsequent output calculations. Unfortunately, computers are not feasible for implantable BMI systems due to their size and power consumption. To address this predicament, this study proposes a pioneering approach inspired by hyperdimensional computing. This approach first involves identifying the pattern of each stimulus by considering the normal firing rate distribution of each neuron. Subsequently, the newly observed firing pattern for each input is compared with the patterns detected at each moment for each neuron. The algorithm, which shares similarities with hyperdimensional computing, identifies the most similar pattern as the final output. This approach reduces the dependence on mathematical models. The efficacy of this method is assessed through the utilization of an authentic dataset acquired from the Frontal Eye Field (FEF) of two male rhesus monkeys. The output space encompasses eight possible angles. The results demonstrate an accuracy rate of 51.5% while exhibiting significantly low computational complexity, involving a mere 2050 adder operators. Furthermore, the proposed algorithm is implemented on a field-programmable gate array (FPGA) and as an ASIC designe in a standard CMOS 180 nm technology, underscoring its suitability for real-time implantable BMI applications. The implementation required only 2.3 Kbytes of RAM, occupied an area of 2.2 mm[2], and consumed 9.32 µW at a 1.8 V power supply. Consequently, the proposed solution represents an accurate, low computational complexity, hardware-friendly, and real-time approach.}, }
@article {pmid39374625, year = {2024}, author = {Abbasi, MAA and Abbasi, HF and Yu, X and Aziz, MZ and Yih, NTJY and Fan, Z}, title = {E-SAT: An extreme learning machine based self attention approach for decoding motor imagery EEG in subject-specific tasks.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad83f4}, pmid = {39374625}, issn = {1741-2552}, abstract = {The advancements in Brain-Computer Interface (BCI) have substantially evolved people's lives by enabling direct communication between the human brain and external peripheral devices. In recent years, the integration of machine larning (ML) and deep learning (DL) models have considerably imrpoved the performances of BCIs for decoding the motor imagery (MI) tasks. However, there still exist several limitations, e.g., extensive training time and high sensitivity to noises or outliers with those existing models, which largely hinder the rapid developments of BCIs. To address such issues, this paper proposes a novel extreme learning machine (ELM) based self-attention (E-SAT) mechanism to enhance subject-specific classification performances. Specifically, for E-SAT, ELM is employed both to imrpove self-attention module generalization ability for feature extraction and to optimize the model's parameter initialization process. Meanwhile, the extracted features are also classified using ELM, and the end-to-end ELM based setup is used to evaluate E-SAT performances on different MI EEG signals. Extensive experiments with different datasets, such as BCI Competition III Dataset IV-a, IV-b and BCI Competition IV Datasets 1,2a,2b,3, are conducted to verify the effectiveness of proposed E-SAT strategy. Results show that E-SAT outperforms several state-of-the-art (SOTA) existing methods in subject-specific classification on all the datasets, with an average classification accuracy of 99.8%,99.1%,98.9%,75.8%, 90.8%, and 95.4%, being achieved for each datasets, respectively. The experimental results not only show outstanding performance of E-SAT in feature extractions, but also demonstrate that it helps achieves the best results among nine other robust ones. In addition, results in this study also demonstrate that E-SAT achieves exceptional performance in both binary and multi-class classification tasks, as well as for noisy and non-noisy datatsets. .}, }
@article {pmid39374272, year = {2024}, author = {Lv, R and Chang, W and Yan, G and Nie, W and Zheng, L and Guo, B and Sadiq, MT}, title = {A novel recognition and classification approach for motor imagery based on spatio-temporal features.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2024.3464550}, pmid = {39374272}, issn = {2168-2208}, abstract = {Motor imagery, as a paradigm of brainmachine interfaces, holds vast potential in the field of medical rehabilitation. Addressing the challenges posed by the non-stationarity and low signal-to-noise ratio of EEG signals, the effective extraction of features from motor imagery signals for accurate recognition stands as a key focus in motor imagery brain-machine interface technology. This paper proposes a motor imagery EEG signal classification model that combines functional brain networks with graph convolutional networks. First, functional brain networks are constructed using different brain functional connectivity metrics, and graph theory features are calculated to deeply analyze the characteristics of brain networks under different motor tasks. Then, the constructed functional brain networks are combined with graph convolutional networks for the classification and recognition of motor imagery tasks. The analysis based on brain functional connectivity reveals that the functional connectivity strength during the both fists task is significantly higher than that of other motor imagery tasks, and the functional connectivity strength during actual movement is generally superior to that of motor imagery tasks. In experiments conducted on the Physionet public dataset, the proposed model achieved a classification accuracy of 88.39% under multi-subject conditions, significantly outperforming traditional methods. Under single-subject conditions, the model effectively addressed the issue of individual variability, achieving an average classification accuracy of 99.31%. These results indicate that the proposed model not only exhibits excellent performance in the classification of motor imagery tasks but also provides new insights into the functional connectivity characteristics of different motor tasks and their corresponding brain regions.}, }
@article {pmid39373084, year = {2024}, author = {Kong, F and He, F and Chisholm, RA}, title = {High beta diversity of gaps contributes to plot-level tree diversity in a tropical forest.}, journal = {Ecology}, volume = {105}, number = {11}, pages = {e4443}, doi = {10.1002/ecy.4443}, pmid = {39373084}, issn = {1939-9170}, support = {//China Scholarship Council/ ; }, mesh = {*Biodiversity ; *Forests ; *Trees/classification ; *Tropical Climate ; Panama ; }, abstract = {Canopy gaps are widely recognized as being crucial for maintaining the diversity of forest tree communities. But empirical studies have found mixed results because the differences in diversity between individual gaps and non-gaps are often small and statistically undetectable. One overlooked factor, however, is how small individual gap versus non-gap differences may accumulate across sites and potentially have a large effect on forest diversity at the plot scale. Our study investigated sapling richness, density, and composition in 124 treefall gaps, and 200 non-gap sites in the 50-ha tropical forest plot at Barro Colorado Island (BCI), Panama. Additionally, we analyzed species accumulation curves to understand how species richness increases with increasing stem numbers. We observed that sapling richness and density were only slightly higher in gaps 7 years after formation and statistically indistinguishable from non-gaps after 12 years. However, species accumulation curves across multiple gaps were substantially higher than those across non-gaps. Species composition showed small differences between individual gaps and non-gaps but differed significantly between collections of gaps and non-gaps. Specifically, 55 species specialized in 7-year-old gaps compared with 24 in non-gaps; of these, 23 gap-specialized species and zero non-gap species were pioneers. Our results indicate that tree species richness is higher in gaps because of both higher stem density and the presence of gap-specialized species. Our study has finally provided compelling evidence to support the idea that gaps enhance the overall diversity of tropical forest tree communities.}, }
@article {pmid39372246, year = {2024}, author = {Sakel, M and Saunders, K and Ozolins, C and Biswas, R}, title = {Feasibility and Safety of a Home-based Electroencephalogram Neurofeedback Intervention to Reduce Chronic Neuropathic Pain: A Cohort Clinical Trial.}, journal = {Archives of rehabilitation research and clinical translation}, volume = {6}, number = {3}, pages = {100361}, pmid = {39372246}, issn = {2590-1095}, abstract = {OBJECTIVE: To evaluate the feasibility, safety, and potential health benefits of an 8-week home-based neurofeedback intervention.<br><br>DESIGN: Single-group preliminary study.<br><br>SETTING: Community-based.<br><br>PARTICIPANTS: Nine community dwelling adults with chronic neuropathic pain, 6 women and 3 men, with an average age of 51.9 years (range, 19-78 years) and with a 7-day average minimum pain score of 4 of 10 on the visual analog pain scale.<br><br>INTERVENTIONS: A minimum of 5 neurofeedback sessions per week (40min/session) for 8 consecutive weeks was undertaken with a 12-week follow-up baseline electroencephalography recording period.<br><br>MAIN OUTCOME MEASURES: Primary feasibility outcomes: accessibility, tolerability, safety (adverse events and resolution), and human and information technology (IT) resources required. Secondary outcomes: pain, sensitization, catastrophization, anxiety, depression, sleep, health-related quality of life, electroencephalographic activity, and simple participant feedback.<br><br>RESULTS: Of the 23 people screened, 11 were eligible for recruitment. One withdrew and another completed insufficient sessions for analysis, which resulted in 9 datasets analyzed. Three participants withdrew from the follow-up baselines, leaving 6 who completed the entire trial protocol. Thirteen adverse events were recorded and resolved: 1 was treatment-related, 4 were equipment-related, and 8 were administrative-related (eg, courier communication issues). The human and IT resources necessary for trial implementation were identified. There were also significant improvements in pain levels, depression, and anxiety. Six of 9 participants perceived minimal improvement or no change in symptoms after the trial, and 5 of 9 participants were satisfied with the treatment received.<br><br>CONCLUSIONS: It is feasible and safe to conduct a home-based trial of a neurofeedback intervention for people with chronic neuropathic pain, when the human and IT resources are provided and relevant governance processes are followed. Improvements in secondary outcomes merit investigation with a randomized controlled trial.}, }
@article {pmid39371523, year = {2024}, author = {Jin, W and Zhu, X and Qian, L and Wu, C and Yang, F and Zhan, D and Kang, Z and Luo, K and Meng, D and Xu, G}, title = {Electroencephalogram-based adaptive closed-loop brain-computer interface in neurorehabilitation: a review.}, journal = {Frontiers in computational neuroscience}, volume = {18}, number = {}, pages = {1431815}, pmid = {39371523}, issn = {1662-5188}, abstract = {Brain-computer interfaces (BCIs) represent a groundbreaking approach to enabling direct communication for individuals with severe motor impairments, circumventing traditional neural and muscular pathways. Among the diverse array of BCI technologies, electroencephalogram (EEG)-based systems are particularly favored due to their non-invasive nature, user-friendly operation, and cost-effectiveness. Recent advancements have facilitated the development of adaptive bidirectional closed-loop BCIs, which dynamically adjust to users' brain activity, thereby enhancing responsiveness and efficacy in neurorehabilitation. These systems support real-time modulation and continuous feedback, fostering personalized therapeutic interventions that align with users' neural and behavioral responses. By incorporating machine learning algorithms, these BCIs optimize user interaction and promote recovery outcomes through mechanisms of activity-dependent neuroplasticity. This paper reviews the current landscape of EEG-based adaptive bidirectional closed-loop BCIs, examining their applications in the recovery of motor and sensory functions, as well as the challenges encountered in practical implementation. The findings underscore the potential of these technologies to significantly enhance patients' quality of life and social interaction, while also identifying critical areas for future research aimed at improving system adaptability and performance. As advancements in artificial intelligence continue, the evolution of sophisticated BCI systems holds promise for transforming neurorehabilitation and expanding applications across various domains.}, }
@article {pmid39371161, year = {2024}, author = {Angrick, M and Luo, S and Rabbani, Q and Joshi, S and Candrea, DN and Milsap, GW and Gordon, CR and Rosenblatt, K and Clawson, L and Maragakis, N and Tenore, FV and Fifer, MS and Ramsey, NF and Crone, NE}, title = {Real-time detection of spoken speech from unlabeled ECoG signals: A pilot study with an ALS participant.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, pmid = {39371161}, support = {UH3 NS114439/NS/NINDS NIH HHS/United States ; }, abstract = {OBJECTIVE: Brain-Computer Interfaces (BCIs) hold significant promise for restoring communication in individuals with partial or complete loss of the ability to speak due to paralysis from amyotrophic lateral sclerosis (ALS), brainstem stroke, and other neurological disorders. Many of the approaches to speech decoding reported in the BCI literature have required time-aligned target representations to allow successful training - a major challenge when translating such approaches to people who have already lost their voice.<br><br>APPROACH: In this pilot study, we made a first step toward scenarios in which no ground truth is available. We utilized a graph-based clustering approach to identify temporal segments of speech production from electrocorticographic (ECoG) signals alone. We then used the estimated speech segments to train a voice activity detection (VAD) model using only ECoG signals. We evaluated our approach using held-out open-loop recordings of a single dysarthric clinical trial participant living with ALS, and we compared the resulting performance to previous solutions trained with ground truth acoustic voice recordings.<br><br>MAIN RESULTS: Our approach achieves a median error rate of around 0.5 seconds with respect to the actual spoken speech. Embedded into a real-time BCI, our approach is capable of providing VAD results with a latency of only 10 ms.<br><br>SIGNIFICANCE: To the best of our knowledge, our results show for the first time that speech activity can be predicted purely from unlabeled ECoG signals, a crucial step toward individuals who cannot provide this information anymore due to their neurological condition, such as patients with locked-in syndrome.<br><br>CLINICAL TRIAL INFORMATION: ClinicalTrials.gov, registration number NCT03567213.}, }
@article {pmid39369803, year = {2024}, author = {Jin, C and Li, Y and Yin, Y and Ma, T and Hong, W and Liu, Y and Li, N and Zhang, X and Gao, JH and Zhang, X and Zha, R}, title = {The dorsomedial prefrontal cortex promotes self-control by inhibiting the egocentric perspective.}, journal = {NeuroImage}, volume = {301}, number = {}, pages = {120879}, doi = {10.1016/j.neuroimage.2024.120879}, pmid = {39369803}, issn = {1095-9572}, mesh = {Humans ; *Prefrontal Cortex/physiology/diagnostic imaging ; Male ; Female ; *Self-Control ; Adult ; *Transcranial Direct Current Stimulation ; Young Adult ; *Connectome ; Magnetic Resonance Imaging ; Reward ; Delay Discounting/physiology ; }, abstract = {The dorsomedial prefrontal cortex (dmPFC) plays a crucial role in social cognitive functions, including perspective-taking. Although perspective-taking has been linked to self-control, the mechanism by which the dmPFC might facilitate self-control remains unclear. Using the multimodal neuroimaging dataset from the Human Connectome Project (Study 1, N =978 adults), we established a reliable association between the dmPFC and self-control, as measured by discounting rate-the tendency to prefer smaller, immediate rewards over larger, delayed ones. Experiments (Study 2, N = 36 adults) involving high-definition transcranial direct current stimulation showed that anodal stimulation of the dmPFC reduces the discounting of delayed rewards and decreases the congruency effect in egocentric but not allocentric perspective in the visual perspective-taking tasks. These findings suggest that the dmPFC promotes self-control by inhibiting the egocentric perspective, offering new insights into the neural underpinnings of self-control and perspective-taking, and opening new avenues for interventions targeting disorders characterized by impaired self-regulation.}, }
@article {pmid39369514, year = {2025}, author = {Wang, Y and Han, M and Jing, L and Jia, Q and Lv, S and Xu, Z and Liu, J and Cai, X}, title = {Enhanced neural activity detection with microelectrode arrays modified by drug-loaded calcium alginate/chitosan hydrogel.}, journal = {Biosensors &amp; bioelectronics}, volume = {267}, number = {}, pages = {116837}, doi = {10.1016/j.bios.2024.116837}, pmid = {39369514}, issn = {1873-4235}, mesh = {*Alginates/chemistry ; *Chitosan/chemistry ; *Dexamethasone/pharmacology/chemistry/administration &amp; dosage/analogs &amp; derivatives ; *Microelectrodes ; Animals ; *Hydrogels/chemistry ; *Biosensing Techniques/instrumentation ; *Neurons/drug effects/physiology ; Anti-Inflammatory Agents/pharmacology/chemistry ; Dopamine/chemistry/pharmacology/analysis ; Brain/drug effects/physiology ; Metal Nanoparticles/chemistry ; Platinum/chemistry ; Rats ; }, abstract = {Microelectrode arrays (MEAs) are pivotal brain-machine interface devices that facilitate in situ and real-time detection of neurophysiological signals and neurotransmitter data within the brain. These capabilities are essential for understanding neural system functions, treating brain disorders, and developing advanced brain-machine interfaces. To enhance the performance of MEAs, this study developed a crosslinked hydrogel coating of calcium alginate (CA) and chitosan (CS) loaded with the anti-inflammatory drug dexamethasone sodium phosphate (DSP). By modifying the MEAs with this hydrogel and various conductive nanomaterials, including platinum nanoparticles (PtNPs) and poly (3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT: PSS), the electrical properties and biocompatibility of the electrodes were optimized. The hydrogel coating matches the mechanical properties of brain tissue more effectively and, by actively releasing anti-inflammatory drugs, significantly reduces post-implantation tissue inflammation, extends the electrodes' lifespan, and enhances the quality of neural activity detection. Additionally, this modification ensures high sensitivity and specificity in the detection of dopamine (DA), displaying high-quality dual-mode neural activity during in vivo testing and revealing significant functional differences between neuron types under various physiological states (anesthetized and awake). Overall, this study showcases the significant application value of bioactive hydrogels as excellent nanobiointerfaces and drug delivery carriers for long-term neural monitoring. This approach has the potential to enhance the functionality and acceptance of brain-machine interface devices in medical practice and has profound implications for future neuroscience research and the development of strategies for treating neurological diseases.}, }
@article {pmid39369011, year = {2024}, author = {Peng, Z and Tong, L and Shi, W and Xu, L and Huang, X and Li, Z and Yu, X and Meng, X and He, X and Lv, S and Yang, G and Hao, H and Jiang, T and Miao, X and Ye, L}, title = {Multifunctional human visual pathway-replicated hardware based on 2D materials.}, journal = {Nature communications}, volume = {15}, number = {1}, pages = {8650}, pmid = {39369011}, issn = {2041-1723}, support = {62222404, 62304084 and 92248304//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Visual Pathways/physiology ; *Retina/physiology ; *Brain-Computer Interfaces ; Visual Cortex/physiology ; Tungsten/chemistry ; Robotics/instrumentation ; Selenium/chemistry ; Artificial Intelligence ; }, abstract = {Artificial visual system empowered by 2D materials-based hardware simulates the functionalities of the human visual system, leading the forefront of artificial intelligence vision. However, retina-mimicked hardware that has not yet fully emulated the neural circuits of visual pathways is restricted from realizing more complex and special functions. In this work, we proposed a human visual pathway-replicated hardware that consists of crossbar arrays with split floating gate 2D tungsten diselenide (WSe2) unit devices that simulate the retina and visual cortex, and related connective peripheral circuits that replicate connectomics between the retina and visual cortex. This hardware experimentally displays advanced multi-functions of red-green color-blindness processing, low-power shape recognition, and self-driven motion tracking, promoting the development of machine vision, driverless technology, brain-computer interfaces, and intelligent robotics.}, }
@article {pmid39368632, year = {2024}, author = {Hu, J and Chen, C and Wu, M and Zhang, J and Meng, F and Li, T and Luo, B}, title = {Assessing consciousness in acute coma using name-evoked responses.}, journal = {Brain research bulletin}, volume = {218}, number = {}, pages = {111091}, doi = {10.1016/j.brainresbull.2024.111091}, pmid = {39368632}, issn = {1873-2747}, mesh = {Humans ; Male ; Female ; *Coma/physiopathology ; *Electroencephalography/methods ; Adult ; Middle Aged ; *Electromyography/methods ; *Acoustic Stimulation/methods ; *Consciousness/physiology ; Aged ; Glasgow Coma Scale ; Names ; Brain/physiopathology ; Young Adult ; Evoked Potentials/physiology ; Evoked Potentials, Auditory/physiology ; }, abstract = {Detecting consciousness in clinically unresponsive patients remains a significant challenge. Existing studies demonstrate that electroencephalography (EEG) can detect brain responses in behaviorally unresponsive patients, indicating potential for consciousness detection. However, most of this evidence is based on chronic patients, and there is a lack of studies focusing on acute coma cases. This study aims to detect signs of residual consciousness in patients with acute coma by using bedside EEG and electromyography (EMG) during an auditory oddball paradigm. We recruited patients with acute brain injury (either traumatic brain injury or cardiac arrest) who were admitted to the intensive care unit within two weeks after injury, with a Glasgow Coma Scale (GCS) score of 8 or below. Auditory stimuli included the patients' own names and other common names (referred to as standard names), spoken by the patients' relatives, delivered under two conditions: passive listening (where patients were instructed that sounds would be played) and active listening (where patients were asked to move hands when heard their own names). Brain and muscle activity were recorded using EEG and EMG during the auditory paradigm. Event-related potentials (ERP) and EMG spectra were analyzed and compared between responses to the subject's own name and other standard names in both passive and active listening conditions. A total of 22 patients were included in the final analysis. Subjects exhibited enhanced ERP responses when exposed to their own names, particularly during the active listening task. Compared to standard names or passive listening, distinct differences in brain network connectivity and increased EMG responses were detected during active listening to their own names. These findings suggest the presence of residual consciousness, offering the potential for assessing consciousness in behaviorally unresponsive patients.}, }
@article {pmid39368606, year = {2024}, author = {Takemi, M and Tia, B and Kosugi, A and Castagnola, E and Ansaldo, A and Ricci, D and Fadiga, L and Ushiba, J and Iriki, A}, title = {Posture-dependent modulation of marmoset cortical motor maps detected via rapid multichannel epidural stimulation.}, journal = {Neuroscience}, volume = {560}, number = {}, pages = {263-271}, doi = {10.1016/j.neuroscience.2024.09.047}, pmid = {39368606}, issn = {1873-7544}, mesh = {Animals ; *Callithrix ; *Motor Cortex/physiology ; Male ; *Posture/physiology ; *Forelimb/physiology ; *Brain Mapping/methods ; *Electric Stimulation/methods ; Electrodes, Implanted ; Electromyography ; Muscle, Skeletal/physiology ; Epidural Space/physiology ; }, abstract = {Recent neuroimaging and electrophysiological studies have suggested substantial short-term plasticity in the topographic maps of the primary motor cortex (M1). However, previous methods lack the temporal resolution to detect rapid modulation of these maps, particularly in naturalistic conditions. To address this limitation, we previously developed a rapid stimulation mapping procedure with implanted cortical surface electrodes. In this study, employing our previously established procedure, we examined rapid topographical changes in forelimb M1 motor maps in three awake male marmoset monkeys. The results revealed that although the hotspot (the location in M1 that elicited a forelimb muscle twitch with the lowest stimulus intensity) remained constant across postures, the stimulus intensity required to elicit the forelimb muscle twitch in the perihotspot region and the size of motor representations were posture-dependent. Hindlimb posture was particularly effective in inducing these modulations. The angle of the body axis relative to the gravitational vertical line did not alter the motor maps. These results provide a proof of concept that a rapid stimulation mapping system with chronically implanted cortical electrodes can capture the dynamic regulation of forelimb motor maps in natural conditions. Moreover, they suggest that posture is a crucial variable to be controlled in future studies of motor control and cortical plasticity. Further exploration is warranted into the neural mechanisms regulating forelimb muscle representations in M1 by the hindlimb sensorimotor state.}, }
@article {pmid39366386, year = {2025}, author = {Zhang, L and Wang, HL and Zhang, YF and Mao, XT and Wu, TT and Huang, ZH and Jiang, WJ and Fan, KQ and Liu, DD and Yang, B and Zhuang, MH and Huang, GM and Liang, Y and Zhu, SJ and Zhong, JY and Xu, GY and Li, XM and Cao, Q and Li, YY and Jin, J}, title = {Stress triggers irritable bowel syndrome with diarrhea through a spermidine-mediated decline in type I interferon.}, journal = {Cell metabolism}, volume = {37}, number = {1}, pages = {87-103.e10}, doi = {10.1016/j.cmet.2024.09.002}, pmid = {39366386}, issn = {1932-7420}, mesh = {*Irritable Bowel Syndrome/metabolism ; *Diarrhea/metabolism ; *Spermidine/pharmacology/metabolism ; Animals ; Humans ; Male ; Stress, Psychological/complications/metabolism ; Mice, Inbred C57BL ; Interferon Type I/metabolism ; Mice ; Dendritic Cells/metabolism ; }, abstract = {Irritable bowel syndrome with diarrhea (IBS-D) is a common and chronic gastrointestinal disorder that is characterized by abdominal discomfort and occasional diarrhea. The pathogenesis of IBS-D is thought to be related to a combination of factors, including psychological stress, abnormal muscle contractions, and inflammation and disorder of the gut microbiome. However, there is still a lack of comprehensive analysis of the logical regulatory correlation among these factors. In this study, we found that stress induced hyperproduction of xanthine and altered the abundance and metabolic characteristics of Lactobacillus murinus in the gut. Lactobacillus murinus-derived spermidine suppressed the basal expression of type I interferon (IFN)-α in plasmacytoid dendritic cells by inhibiting the K63-linked polyubiquitination of TRAF3. The reduction in IFN-α unrestricted the contractile function of colonic smooth muscle cells, resulting in an increase in bowel movement. Our findings provided a theoretical basis for the pathological mechanism of, and new drug targets for, stress-exposed IBS-D.}, }
@article {pmid39366088, year = {2024}, author = {Pan, Y and Sequestro, M and Golkar, A and Olsson, A}, title = {Handholding reduces the recovery of threat memories and magnifies prefrontal hemodynamic responses.}, journal = {Behaviour research and therapy}, volume = {183}, number = {}, pages = {104641}, doi = {10.1016/j.brat.2024.104641}, pmid = {39366088}, issn = {1873-622X}, mesh = {Humans ; *Prefrontal Cortex/physiology ; Male ; Female ; Young Adult ; *Hemodynamics/physiology ; *Extinction, Psychological/physiology ; Adult ; *Memory/physiology ; Fear/psychology/physiology ; Magnetic Resonance Imaging ; Touch/physiology ; Adolescent ; }, abstract = {Human touch is a powerful means of social and affective regulation, promoting safety behaviors. Yet, despite its importance across human contexts, it remains unknown how touch can promote the learning of new safety memories and what neural processes underlie such effects. The current study used measures of peripheral physiology and brain activity to examine the effects of interpersonal touch during safety learning (extinction) on the recovery of previously learned threat. We observed that handholding during extinction significantly reduced threat recovery, which was reflected in enhanced prefrontal hemodynamic responses. This effect was absent when learners were instructed to hold a rubber ball, independent of the presence of their partners. Our findings indicate that social touch contributes to safety learning, potentially influencing threat memories via prefrontal circuitry.}, }
@article {pmid39367153, year = {2024}, author = {Miroshnikov, A and Yakovlev, L and Syrov, N and Vasilyev, A and Berkmush-Antipova, A and Golovanov, F and Kaplan, A}, title = {Differential Hemodynamic Responses to Motor and Tactile Imagery: Insights from Multichannel fNIRS Mapping.}, journal = {Brain topography}, volume = {38}, number = {1}, pages = {4}, pmid = {39367153}, issn = {1573-6792}, support = {21-75-30024//Russian Science Foundation/ ; 21-75-30024//Russian Science Foundation/ ; 21-75-30024//Russian Science Foundation/ ; }, mesh = {Humans ; *Spectroscopy, Near-Infrared/methods ; *Imagination/physiology ; Male ; Female ; Adult ; *Hemodynamics/physiology ; Young Adult ; *Brain Mapping/methods ; Touch Perception/physiology ; Touch/physiology ; Somatosensory Cortex/physiology/diagnostic imaging ; Brain/physiology/diagnostic imaging ; Motor Cortex/physiology/diagnostic imaging ; }, abstract = {Tactile and motor imagery are crucial components of sensorimotor functioning and cognitive neuroscience research, yet the neural mechanisms of tactile imagery remain underexplored compared to motor imagery. This study employs multichannel functional near-infrared spectroscopy (fNIRS) combined with image reconstruction techniques to investigate the neural hemodynamics associated with tactile (TI) and motor imagery (MI). In a study of 15 healthy participants, we found that MI elicited significantly greater hemodynamic responses (HRs) in the precentral area compared to TI, suggesting the involvement of different cortical areas involved in two different types of sensorimotor mental imagery. Concurrently, the HRs in S1 and parietal areas exhibited comparable patterns in both TI and MI. During MI, both motor and somatosensory areas demonstrated comparable HRs. However, in TI, somatosensory activation was observed to be more pronounced. Our results highlight the distinctive neural profiles of motor versus tactile imagery and indicate fNIRS technique to be sensitive for this. This distinction is significant for fundamental understanding of sensorimotor integration and for developing advanced neurotechnologies, including imagery-based brain-computer interfaces (BCIs) that can differentiate between different types of mental imagery.}, }
@article {pmid39366955, year = {2024}, author = {Ullah, R and Xue, C and Wang, S and Qin, Z and Rauf, N and Zhan, S and Khan, NU and Shen, Y and Zhou, YD and Fu, J}, title = {Alternate-day fasting delays pubertal development in normal-weight mice but prevents high-fat diet-induced obesity and precocious puberty.}, journal = {Nutrition &amp; diabetes}, volume = {14}, number = {1}, pages = {82}, pmid = {39366955}, issn = {2044-4052}, support = {82350410491//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82370863//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Animals ; *Diet, High-Fat/adverse effects ; *Puberty, Precocious/etiology/prevention &amp; control ; Female ; Mice ; *Fasting ; *Obesity/prevention &amp; control/etiology ; Sexual Maturation/physiology ; Pregnancy ; Mice, Inbred C57BL ; Growth Hormone/blood ; }, abstract = {BACKGROUND/OBJECTIVES: Childhood obesity, particularly in girls, is linked to early puberty onset, heightening risks for adult-onset diseases. Addressing childhood obesity and precocious puberty is vital to mitigate societal burdens. Despite existing costly and invasive medical interventions, introducing lifestyle-based alternatives is essential. Our study investigates alternate-day fasting's (ADF) impact on pubertal development in normal-weight and high-fat diet (HFD)-induced obese female mice.<br><br>METHODS: Four groups of female mice were utilized, with dams initially fed control chow during and before pregnancy. Post-parturition, two groups continued on control chow, while two switched to an HFD. Offspring diets mirrored maternal exposure. One control and one HFD group were subjected to ADF. Morphometry and hormone analyses at various time points were performed.<br><br>RESULTS: Our findings demonstrate that ADF in normal-weight mice led to reduced body length, weight, uterine, and ovarian weights, accompanied by delayed puberty and lower levels of sex hormones and growth hormone (GH). Remarkably, GH treatment effectively prevented ADF-induced growth reduction but did not prevent delayed puberty. Conversely, an HFD increased body length, induced obesity and precocious puberty, and altered sex hormones and leptin levels, which were counteracted by ADF regimen. Our data indicate ADF's potential in managing childhood obesity and precocious puberty.<br><br>CONCLUSIONS: ADF reduced GH and sex hormone levels, contributing to reduced growth and delayed puberty, respectively. Therefore, parents of normal-weight children should be cautious about prolonged overnight fasting. ADF prevented HFD-induced obesity and precocious puberty, offering an alternative to medical approaches; nevertheless, further studies are needed for translation into clinical practice.}, }
@article {pmid39365711, year = {2024}, author = {Wang, Y and Wang, J and Wang, W and Su, J and Bunterngchit, C and Hou, ZG}, title = {TFTL: A Task-Free Transfer Learning Strategy for EEG-based Cross-Subject &amp; Cross-Dataset Motor Imagery BCI.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2024.3474049}, pmid = {39365711}, issn = {1558-2531}, abstract = {OBJECTIVE: Motor imagery-based brain-computer interfaces (MI-BCIs) have been playing an increasingly vital role in neural rehabilitation. However, the long-term task-based calibration required for enhanced model performance leads to an unfriendly user experience, while the inadequacy of EEG data hinders the performance of deep learning models. To address these challenges, a task-free transfer learning strategy (TFTL) for EEG-based cross-subject &amp; cross-dataset MI-BCI is proposed for calibration time reduction and multi-center data co-modeling.<br><br>METHODS: TFTL strategy consists of data alignment, shared feature extractor, and specific classifiers, in which the label predictor for MI tasks classification, as well as domain and dataset discriminator for inter-subject variability reduction are concurrently optimized for knowledge transfer from subjects across different datasets to the target subject. Moreover, only resting data of the target subject is used for subject-specific model construction to achieve task-free.<br><br>RESULTS: We employed three deep learning methods (ShallowConvNet, EEGNet, and TCNet-Fusion) as baseline approaches to evaluate the effectiveness of the proposed strategy on five datasets (BCIC IV Dataset 2a, Dataset 1, Physionet MI, Dreyer 2023, and OpenBMI). The results demonstrate a significant improvement with the inclusion of the TFTL strategy compared to the baseline methods, reaching a maximum enhancement of 15.67% with a statistical significance (p=2.4e-5<0.05). Moreover, task-free resulted in MI trials needed for calibration being 0 for all datasets, which significantly alleviated the calibration burden for patients before usage.<br><br>CONCLUSION/SIGNIFICANCE: The proposed TFTL strategy effectively addresses challenges posed by prolonged calibration periods and insufficient EEG data, thus promoting MI-BCI from laboratory to clinical application.}, }
@article {pmid39362975, year = {2024}, author = {Yu, H and Cao, W and Fang, T and Jin, J and Pei, G}, title = {EEG β oscillations in aberrant data perception under cognitive load modulation.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {22995}, pmid = {39362975}, issn = {2045-2322}, support = {LGG21G010002//Zhejiang Provincial Natural Science Foundation of China/ ; LQ22C090007//Zhejiang Provincial Natural Science Foundation of China/ ; 72271166//National Natural Science Foundation of China/ ; 72401263//National Natural Science Foundation of China/ ; 2023KFKT003//Open Research Project of Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior, Shanghai International Studies University/ ; }, mesh = {Humans ; Male ; *Cognition/physiology ; Female ; *Electroencephalography ; Adult ; Young Adult ; Decision Making/physiology ; Beta Rhythm/physiology ; Brain/physiology ; Perception/physiology ; }, abstract = {Data-driven decision making (DDDM) is becoming an indispensable component of work across various fields, and the perception of aberrant data (PAD) has emerged as an essential skill. Nonetheless, the neural processing mechanisms underpinning PAD remain incompletely elucidated. Direct evidence linking neural oscillations to PAD is currently lacking, and the impact of cognitive load remains ambiguous. We address this issue using EEG time-frequency analysis. Data were collected from 21 healthy participants. The experiment employed a 2 (low vs. high cognitive load) × 2 [PAD+ (aberrant data accurately identified as aberrant) vs. PAD- (non-aberrant data correctly recognized as normal)] within-subject laboratory design. Results indicate that upper β band oscillations (26-30 Hz) were significantly enhanced in the PAD + condition compared to PAD-, with consistent activity observed in the frontal (p < 0.001, [Formula: see text] = 0.41) and parietal lobes (p = 0.028, [Formula: see text] = 0.22) within the 300-350 ms time window. Additionally, as cognitive load increased, the time window of β oscillations for distinguishing PAD+ from PAD- shifted earlier. This study enriches our understanding of the PAD neural basis by exploring the distribution of neural oscillation frequencies, decision-making neural circuits, and the windowing effect induced by cognitive load. These findings have significant implications for elucidating the pathological mechanisms of neurodegenerative disorders, as well as in the initial screening, intervention, and treatment of diseases.}, }
@article {pmid39358539, year = {2024}, author = {Drew, L}, title = {United States sets the pace for implantable brain-computer interfaces.}, journal = {Nature}, volume = {634}, number = {8032}, pages = {S8-S10}, doi = {10.1038/d41586-024-03046-5}, pmid = {39358539}, issn = {1476-4687}, mesh = {Humans ; *Brain-Computer Interfaces/statistics &amp; numerical data/trends ; *Electrodes, Implanted/statistics &amp; numerical data/trends ; United States ; }, }
@article {pmid39358411, year = {2024}, author = {Andreu-Sánchez, C and Martín-Pascual, M&#xc1; and Gruart, A and Delgado-García, JM}, title = {Differences in Mu rhythm when seeing grasping/motor actions in a real context versus on screens.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {22921}, pmid = {39358411}, issn = {2045-2322}, support = {PID2021-122446NB-100//Ministerio de Ciencia e Innovaci&#xf3;n/ ; PID2021-122446NB-100//Ministerio de Ciencia e Innovaci&#xf3;n/ ; PID2021-122446NB-100//Ministerio de Ciencia e Innovaci&#xf3;n/ ; PID2021-122446NB-100//Ministerio de Ciencia e Innovaci&#xf3;n/ ; BIO-122//Junta de Andaluc&#xed;a/ ; BIO-122//Junta de Andaluc&#xed;a/ ; }, mesh = {Humans ; Male ; Female ; Adult ; *Electroencephalography/methods ; *Somatosensory Cortex/physiology ; *Hand Strength/physiology ; Young Adult ; Psychomotor Performance/physiology ; Photic Stimulation ; Brain Waves/physiology ; Visual Perception/physiology ; Brain-Computer Interfaces ; Motor Activity/physiology ; }, abstract = {Mu rhythm (∼8-12 Hz) in the somatosensory cortex has traditionally been linked with doing and seeing motor activities. Here, we aimed to learn how the medium (physical or screened) in which motor actions are seen could impact on that specific brain rhythm. To do so, we presented to 40 participants the very same narrative content both in a one-shot movie with no cuts and in a real theatrical performance. We recorded subjects' brain activities with electroencephalographic (EEG) procedures, and analyzed Mu rhythm present in left (C3) and right (C4) somatosensory areas in relation to the 24 motor activities included in each visual stimulus (screen vs. reality) (24 motor and grasping actions x 40 participants x 2 conditions = 1920 trials). We found lower Mu spectral power in the somatosensory area after the onset of the motor actions in real performance than on-screened content, more pronounced in the left hemisphere. In our results, the sensorimotor Mu-ERD (event-related desynchronization) was stronger during the real-world observation compared to screen observation. This could be relevant in research areas where the somatosensory cortex is important, such as online learning, virtual reality, or brain-computer interfaces.}, }
@article {pmid39358021, year = {2024}, author = {Graczyk, E and Hutchison, B and Valle, G and Bjanes, D and Gates, D and Raspopovic, S and Gaunt, R}, title = {Clinical Applications and Future Translation of Somatosensory Neuroprostheses.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, volume = {44}, number = {40}, pages = {}, pmid = {39358021}, issn = {1529-2401}, mesh = {Humans ; *Somatosensory Cortex/physiology ; Feedback, Sensory/physiology ; Translational Research, Biomedical/trends/methods ; Neural Prostheses ; Brain-Computer Interfaces/trends ; Electric Stimulation/methods ; Prostheses and Implants/trends ; }, abstract = {Somatosensory neuroprostheses restore, replace, or enhance tactile and proprioceptive feedback for people with sensory impairments due to neurological disorders or injury. Somatosensory neuroprostheses typically couple sensor inputs from a wearable device, prosthesis, robotic device, or virtual reality system with electrical stimulation applied to the somatosensory nervous system via noninvasive or implanted interfaces. While prior research has mainly focused on technology development and proof-of-concept studies, recent acceleration of clinical studies in this area demonstrates the translational potential of somatosensory neuroprosthetic systems. In this review, we provide an overview of neurostimulation approaches currently undergoing human testing and summarize recent clinical findings on the perceptual, functional, and psychological impact of somatosensory neuroprostheses. We also cover current work toward the development of advanced stimulation paradigms to produce more natural and informative sensory feedback. Finally, we provide our perspective on the remaining challenges that need to be addressed prior to translation of somatosensory neuroprostheses.}, }
@article {pmid39353205, year = {2024}, author = {Zhang, Y and Xing, H and Li, J and Han, F and Fan, S and Zhang, Y}, title = {Bioinspired Artificial Intelligent Nociceptive Alarm System Based on Fibrous Biomemristors.}, journal = {ACS sensors}, volume = {9}, number = {10}, pages = {5312-5321}, doi = {10.1021/acssensors.4c01568}, pmid = {39353205}, issn = {2379-3694}, mesh = {*Fibroins/chemistry ; Humans ; Wearable Electronic Devices ; Nociceptors/physiology ; Biomimetic Materials/chemistry ; Biomimetics/instrumentation/methods ; Robotics/instrumentation ; }, abstract = {With the advancement of modern medical and brain-computer interface devices, flexible artificial nociceptors with tactile perception hold significant scientific importance and exhibit great potential in the fields of wearable electronic devices and biomimetic robots. Here, a bioinspired artificial intelligent nociceptive alarm system integrating sensing monitoring and transmission functions is constructed using a silk fibroin (SF) fibrous memristor. This memristor demonstrates high stability, low operating power, and the capability to simulate synaptic plasticity. As a result, an artificial pressure nociceptor based on the SF fibrous memristor can detect both fast and chronic pain and provide a timely alarm in the event of a fall or prolonged immobility of the carrier. Further, an array of artificial pressure nociceptors not only monitors the pressure distribution across various parts of the carrier but also provides direct feedback on the extent of long-term pressure to the carrier. This work holds significant implications for medical support in biological carriers or targeted maintenance of electronic carriers.}, }
@article {pmid39352826, year = {2024}, author = {Jin, J and Chen, W and Xu, R and Liang, W and Wu, X and He, X and Wang, X and Cichocki, A}, title = {Multiscale Spatial-Temporal Feature Fusion Neural Network for Motor Imagery Brain-Computer Interfaces.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2024.3472097}, pmid = {39352826}, issn = {2168-2208}, abstract = {Motor imagery, one of the main brain-computer interface (BCI) paradigms, has been extensively utilized in numerous BCI applications, such as the interaction between disabled people and external devices. Precise decoding, one of the most significant aspects of realizing efficient and stable interaction, has received a great deal of intensive research. However, the current decoding methods based on deep learning are still dominated by single-scale serial convolution, which leads to insufficient extraction of abundant information from motor imagery signals. To overcome such challenges, we propose a new end-to-end convolutional neural network based on multiscale spatial-temporal feature fusion (MSTFNet) for EEG classification of motor imagery. The architecture of MSTFNet consists of four distinct modules: feature enhancement module, multiscale temporal feature extraction module, spatial feature extraction module and feature fusion module, with the latter being further divided into the depthwise separable convolution block and efficient channel attention block. Moreover, we implement a straightforward yet potent data augmentation strategy to bolster the performance of MSTFNet significantly. To validate the performance of MSTFNet, we conduct cross-session experiments and leave-one-subject-out experiments. The cross-session experiment is conducted across two public datasets and one laboratory dataset. On the public datasets of BCI Competition IV 2a and BCI Competition IV 2b, MSTFNet achieves classification accuracies of 83.62% and 89.26%, respectively. On the laboratory dataset, MSTFNet achieves 86.68% classification accuracy. Besides, the leave-one-subject-out experiment is performed on the BCI Competition IV 2a dataset, and MSTFNet achieves 66.31% classification accuracy. These experimental results outperform several state-of-the-art methodologies, indicate the proposed MSTFNet's robust capability in decoding EEG signals associated with motor imagery.}, }
@article {pmid39356668, year = {2024}, author = {Simony, E and Grossman, S and Malach, R}, title = {Brain-machine convergent evolution: Why finding parallels between brain and artificial systems is informative.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {121}, number = {41}, pages = {e2319709121}, pmid = {39356668}, issn = {1091-6490}, mesh = {*Brain/physiology ; *Biological Evolution ; *Models, Neurological ; Humans ; Entorhinal Cortex/physiology ; Animals ; Neurons/physiology ; Neural Networks, Computer ; Nerve Net/physiology ; }, abstract = {Central nervous system neurons manifest a rich diversity of selectivity profiles-whose precise role is still poorly understood. Following the striking success of artificial networks, a major debate has emerged concerning their usefulness in explaining neuronal properties. Here we propose that finding parallels between artificial and neuronal networks is informative precisely because these systems are so different from each other. Our argument is based on an extension of the concept of convergent evolution-well established in biology-to the domain of artificial systems. Applying this concept to different areas and levels of the cortical hierarchy can be a powerful tool for elucidating the functional role of well-known cortical selectivities. Importantly, we further demonstrate that such parallels can uncover novel functionalities by showing that grid cells in the entorhinal cortex can be modeled to function as a set of basis functions in a lossy representation such as the well-known JPEG compression. Thus, contrary to common intuition, here we illustrate that finding parallels with artificial systems provides novel and informative insights, particularly in those cases that are far removed from realistic brain biology.}, }
@article {pmid39355672, year = {2024}, author = {Smith, K and Pilger, A and Amorim, MLM and Mircic, S and Reining, Z and Ristow, N and Miller, D and Leonhardt, A and Donovan, JC and Meier, M and Marzullo, TC and Serbe-Kamp, E and Steiner, AP and Gage, GJ}, title = {Low-Cost Classroom and Laboratory Exercises for Investigating Both Wave and Event-Related Electroencephalogram Potentials.}, journal = {Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience}, volume = {22}, number = {3}, pages = {A197-A206}, pmid = {39355672}, issn = {1544-2896}, abstract = {Electroencephalography (EEG) has given rise to a myriad of new discoveries over the last 90 years. EEG is a noninvasive technique that has revealed insights into the spatial and temporal processing of brain activity over many neuroscience disciplines, including sensory, motor, sleep, and memory formation. Most undergraduate students, however, lack laboratory access to EEG recording equipment or the skills to perform an experiment independently. Here, we provide easy-to-follow instructions to measure both wave and event-related EEG potentials using a portable, low-cost amplifier (Backyard Brains, Ann Arbor, MI) that connects to smartphones and PCs, independent of their operating system. Using open-source software (SpikeRecorder) and analysis tools (Python, Google Colaboratory), we demonstrate tractable and robust laboratory exercises for students to gain insights into the scientific method and discover multidisciplinary neuroscience research. We developed 2 laboratory exercises and ran them on participants within our research lab (N = 17, development group). In our first protocol, we analyzed power differences in the alpha band (8-13 Hz) when participants alternated between eyes open and eyes closed states (n = 137 transitions). We could robustly see an increase of over 50% in 59 (43%) of our sessions, suggesting this would make a reliable introductory experiment. Next, we describe an exercise that uses a SpikerBox to evoke an event-related potential (ERP) during an auditory oddball task. This experiment measures the average EEG potential elicited during an auditory presentation of either a highly predictable ("standard") or low-probability ("oddball") tone. Across all sessions in the development group (n=81), we found that 64% (n=52) showed a significant peak in the standard response window for P300 with an average peak latency of 442ms. Finally, we tested the auditory oddball task in a university classroom setting. In 66% of the sessions (n=30), a clear P300 was shown, and these signals were significantly above chance when compared to a Monte Carlo simulation. These laboratory exercises cover the two methods of analysis (frequency power and ERP), which are routinely used in neurology diagnostics, brain-machine interfaces, and neurofeedback therapy. Arming students with these methods and analysis techniques will enable them to investigate this laboratory exercise's variants or test their own hypotheses.}, }
@article {pmid39355516, year = {2024}, author = {von Groll, VG and Leeuwis, N and Rimbert, S and Roc, A and Pillette, L and Lotte, F and Alimardani, M}, title = {Large scale investigation of the effect of gender on mu rhythm suppression in motor imagery brain-computer interfaces.}, journal = {Brain computer interfaces (Abingdon, England)}, volume = {11}, number = {3}, pages = {87-97}, pmid = {39355516}, issn = {2326-263X}, abstract = {The utmost issue in Motor Imagery Brain-Computer Interfaces (MI-BCI) is the BCI poor performance known as 'BCI inefficiency'. Although past research has attempted to find a solution by investigating factors influencing users' MI-BCI performance, the issue persists. One of the factors that has been studied in relation to MI-BCI performance is gender. Research regarding the influence of gender on a user's ability to control MI-BCIs remains inconclusive, mainly due to the small sample size and unbalanced gender distribution in past studies. To address these issues and obtain reliable results, this study combined four MI-BCI datasets into one large dataset with 248 subjects and equal gender distribution. The datasets included EEG signals from healthy subjects from both gender groups who had executed a right- vs. left-hand motor imagery task following the Graz protocol. The analysis consisted of extracting the Mu Suppression Index from C3 and C4 electrodes and comparing the values between female and male participants. Unlike some of the previous findings which reported an advantage for female BCI users in modulating mu rhythm activity, our results did not show any significant difference between the Mu Suppression Index of both groups, indicating that gender may not be a predictive factor for BCI performance.}, }
@article {pmid39354145, year = {2024}, author = {Lian, YN and Cao, XW and Wu, C and Pei, CY and Liu, L and Zhang, C and Li, XY}, title = {Deconstruction the feedforward inhibition changes in the layer III of anterior cingulate cortex after peripheral nerve injury.}, journal = {Communications biology}, volume = {7}, number = {1}, pages = {1237}, pmid = {39354145}, issn = {2399-3642}, mesh = {Animals ; *Gyrus Cinguli/physiopathology ; *Peripheral Nerve Injuries/physiopathology ; Mice ; Male ; Mice, Inbred C57BL ; Neural Inhibition ; Neurons/physiology ; Peroneal Nerve/injuries/physiopathology ; Thalamus/physiopathology ; }, abstract = {The anterior cingulate cortex (ACC) is one of the critical brain areas for processing noxious information. Previous studies showed that peripheral nerve injury induced broad changes in the ACC, contributing to pain hypersensitivity. The neurons in layer 3 (L3) of the ACC receive the inputs from the mediodorsal thalamus (MD) and form the feedforward inhibition (FFI) microcircuits. The effects of peripheral nerve injury on the MD-driven FFI in L3 of ACC are unknown. In our study, we record the enhanced excitatory synaptic transmissions from the MD to L3 of the ACC in mice with common peroneal nerve ligation, affecting FFI. Chemogenetically activating the MD-to-ACC projections induces pain sensitivity and place aversion in naive mice. Furthermore, chemogenetically inactivating MD-to-ACC projections decreases pain sensitivity and promotes place preference in nerve-injured mice. Our results indicate that the peripheral nerve injury changes the MD-to-ACC projections, contributing to pain hypersensitivity and aversion.}, }
@article {pmid39352734, year = {2024}, author = {Gao, Y and Cai, YC and Liu, DY and Yu, J and Wang, J and Li, M and Xu, B and Wang, T and Chen, G and Northoff, G and Bai, R and Song, XM}, title = {GABAergic inhibition in human hMT+ predicts visuo-spatial intelligence mediated through the frontal cortex.}, journal = {eLife}, volume = {13}, number = {}, pages = {}, pmid = {39352734}, issn = {2050-084X}, support = {2021ZD0200401//STI 2030 - Major Projects/ ; 2022ZD0206000//STI 2030 - Major Projects/ ; 61876222//The National Natural Science Foundation of China/ ; 82222032//The National Natural Science Foundation of China/ ; U1909205//The National Natural Science Foundation of China/ ; 785907//Horizon 2020 Framework Programme/ ; 20YJC880095//Humanities and Social Sciences Ministry of Education/ ; 18YJA190001//Humanities and Social Sciences Ministry of Education/ ; 2022C03096//The Key R&amp;D Program of Zhejiang/ ; 2022ZJJH02-06//The Key R&amp;D Program of Zhejiang/ ; 32000761//The National Natural Science Foundation of China/ ; }, mesh = {Humans ; *Frontal Lobe/physiology/diagnostic imaging ; Male ; *gamma-Aminobutyric Acid/metabolism ; *Intelligence/physiology ; Female ; Young Adult ; *Magnetic Resonance Imaging ; Adult ; Magnetic Resonance Spectroscopy/methods ; Space Perception/physiology ; }, abstract = {The prevailing opinion emphasizes fronto-parietal network (FPN) is key in mediating general fluid intelligence (gF). Meanwhile, recent studies show that human MT complex (hMT+), located at the occipito-temporal border and involved in 3D perception processing, also plays a key role in gF. However, the underlying mechanism is not clear, yet. To investigate this issue, our study targets visuo-spatial intelligence, which is considered to have high loading on gF. We use ultra-high field magnetic resonance spectroscopy (MRS) to measure GABA/Glu concentrations in hMT+ combining resting-state fMRI functional connectivity (FC), behavioral examinations including hMT+ perception suppression test and gF subtest in visuo-spatial component. Our findings show that both GABA in hMT+ and frontal-hMT+ functional connectivity significantly correlate with the performance of visuo-spatial intelligence. Further, serial mediation model demonstrates that the effect of hMT+ GABA on visuo-spatial gF is fully mediated by the hMT+ frontal FC. Together our findings highlight the importance in integrating sensory and frontal cortices in mediating the visuo-spatial component of general fluid intelligence.}, }
@article {pmid39351695, year = {2024}, author = {Wang, PS and Yang, XX and Wei, Q and Lv, YT and Wu, ZY and Li, HF}, title = {Clinical characterization and founder effect analysis in Chinese amyotrophic lateral sclerosis patients with SOD1 common variants.}, journal = {Annals of medicine}, volume = {56}, number = {1}, pages = {2407522}, pmid = {39351695}, issn = {1365-2060}, mesh = {Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Age of Onset ; *Amyotrophic Lateral Sclerosis/genetics ; China/epidemiology ; East Asian People ; Exome Sequencing ; *Founder Effect ; Genetic Association Studies ; Haplotypes ; Mutation ; Phenotype ; *Superoxide Dismutase-1/genetics ; }, abstract = {OBJECTIVE: In the Asian population, SOD1 variants are the most common cause of amyotrophic lateral sclerosis (ALS). To date, more than 200 variants have been reported in SOD1. This study aimed to summarize the genotype-phenotype correlation and determine whether the patients carrying common variants derive from a common ancestor.<br><br>METHODS: A total of 103 sporadic ALS (SALS) and 11 familial ALS (FALS) probands were included and variants were screened by whole exome sequencing. Functional analyses were performed on fibroblasts derived from patients with SOD1 p.V48A and control. Haplotype analysis was performed in the probands with p.H47R or p.V48A and their familial members.<br><br>RESULTS: A total of 25 SOD1 variants were identified in 44 probands, in which p.H47R, p.V48A and p.C112Y variants were the most common variants. 94.3% and 60% of patients with p.H47R or p.V48A had lower limb onset with predominant lower motor neurons (LMNs) involvement. Patients with p.H47R had a slow progression and prolonged survival time, while patients with p.V48A exhibited a duration of 2-5&#xa0;years. Patients with p.C112Y variant showed remarkable phenotypic variation in age at onset and disease course. SOD1[V48A] fibroblasts showed mutant SOD1 aggregate formation, enhanced intracellular reactive oxygen species level, and decreased mitochondrial membrane potential compared to the control fibroblast. Haplotype analysis showed that seven families had two different haplotypes. p.H47R and p.V48A variants did not originate from a common founder.<br><br>CONCLUSIONS: Our study expanded the understanding of the genotype-phenotype correlation of ALS with SOD1 variants and revealed that the common p.H47R or p.V48A variant did not have a founder effect.}, }
@article {pmid39350409, year = {2024}, author = {Pang, M and Yao, H and Bao, K and Xu, R and Xi, R and Peng, R and Zhi, H and Zhang, K and He, R and Du, Y and Su, Y and Liu, X and Ming, D}, title = {Phenolic Glycoside Monomer from Reed Rhizome Inhibits Melanin Production via PI3K-Akt and Ras-Raf-MEK-ERK Pathways.}, journal = {Current medicinal chemistry}, volume = {}, number = {}, pages = {}, doi = {10.2174/0109298673341645240919072455}, pmid = {39350409}, issn = {1875-533X}, abstract = {INTRODUCTION: Melanogenesis, the process responsible for melanin production, is a critical determinant of skin pigmentation. Dysregulation of this process can lead to hyperpigmentation disorders.<br><br>METHOD: In this study, we identified a novel Reed Rhizome extract, (1'S, 2'S)-syringyl glycerol 3'-O-&#x3b2;-D-glucopyranoside (compound 5), and evaluated its anti-melanogenic potential in zebrafish models and in vitro assays. Compound 5 inhibited melanin synthesis by 36.66% &#xb1; 14.00% and tyrosinase in vivo by 48.26% &#xb1; 6.94%, surpassing the inhibitory effects of arbutin. Network pharmacological analysis revealed key targets, including HSP90AA1, HRAS, and PIK3R1, potentially involved in the anti-melanogenic effects of compound 5.<br><br>RESULTS: Molecular docking studies supported the interactions between compound 5 and these targets. Further, gene expression analysis in zebrafish indicated that compound 5 up-regulates hsp90aa1.1, hrasa, and pik3r1, and subsequently down-regulating mitfa, tyr, and tyrp1, critical genes in melanogenesis.<br><br>CONCLUSION: These findings suggest that compound 5 inhibits melanin production via PI3K-Akt and Ras-Raf-MEK-ERK signaling pathways, positioning it as a promising candidate for the treatment of hyperpigmentation.}, }
@article {pmid39350194, year = {2024}, author = {Du, YC and Ma, LH and Li, QF and Ma, Y and Dong, Y and Wu, ZY}, title = {Genotype-phenotype correlation and founder effect analysis in southeast Chinese patients with sialidosis type I.}, journal = {Orphanet journal of rare diseases}, volume = {19}, number = {1}, pages = {362}, pmid = {39350194}, issn = {1750-1172}, support = {82230062, 82071260//National Natural Science Foundation of China/ ; }, mesh = {Adolescent ; Child ; Child, Preschool ; Female ; Humans ; Infant ; Male ; China/epidemiology ; East Asian People ; *Founder Effect ; Genetic Association Studies ; Genotype ; Haplotypes ; *Mucolipidoses/genetics ; Mutation ; Neuraminidase/genetics ; Polymorphism, Single Nucleotide ; }, abstract = {BACKGROUND: Sialidosis type 1 (ST-1) is a rare autosomal recessive disorder caused by mutation in the NEU1 gene. However, limited reports on ST-1 patients in the Chinese mainland are available.<br><br>METHODS: This study reported the genetic and clinical characteristics of 10 ST-1 patients from southeastern China. A haplotype analysis was performed using 21 single nucleotide polymorphism (SNP) markers of 500&#xa0;kb flanking the recurrent c.544&#xa0;A&#x2009;>&#x2009;G in 8 families harboring the mutation. Furthermore, this study summarized and compared previously reported ST-1 patients from Taiwan and mainland China.<br><br>RESULTS: Five mutations within NEU1 were found, including two novel ones c.557&#xa0;A&#x2009;>&#x2009;G and c.799&#xa0;C&#x2009;>&#x2009;T. The c.544&#xa0;A&#x2009;>&#x2009;G mutation was most frequent and identified in 9 patients, 6 patients were homozygous for c.544&#xa0;A&#x2009;>&#x2009;G. Haplotype analysis revealed a shared haplotype surrounding c.544&#xa0;A&#x2009;>&#x2009;G was identified, suggesting a founder effect presenting in southeast Chinese population. Through detailed assessment, 52 ST-1 patients from 45 families from Taiwan and mainland China were included. Homozygous c.544&#xa0;A&#x2009;>&#x2009;G was the most common genotype and found in 42.2% of the families, followed by the c.544&#xa0;A&#x2009;>&#x2009;G/c.239&#xa0;C&#x2009;>&#x2009;T compound genotype, which was observed in 22.2% of the families. ST-1 patients with the homozygous c.544&#xa0;A&#x2009;>&#x2009;G mutation developed the disease at a later age and had a lower incidence of cherry-red spots significantly.<br><br>CONCLUSION: The results contribute to gaps in the clinical and genetic features of ST-1 patients in southeastern mainland China and provide a deeper understanding of this disease to reduce misdiagnosis.}, }
@article {pmid39349841, year = {2024}, author = {Fan, J and Gao, Z}, title = {Promoting glymphatic flow: A non-invasive strategy using 40 Hz light flickering.}, journal = {Purinergic signalling}, volume = {}, number = {}, pages = {}, pmid = {39349841}, issn = {1573-9546}, support = {2070974//National Natural Science Foundation of China/ ; 2070974//National Natural Science Foundation of China/ ; 2021R52021//Key Research and Development Program of Zhejiang Province/ ; 2021R52021//Key Research and Development Program of Zhejiang Province/ ; }, abstract = {The glymphatic system is critical for brain homeostasis by eliminating metabolic waste, whose disturbance contributes to the accumulation of pathogenic proteins in neurodegenerative diseases. Promoting glymphatic clearance is a potential and attractive strategy for several brain disorders, including neurodegenerative diseases. Previous studies have uncovered that 40 Hz flickering augmented glymphatic flow and facilitated sleep (Zhou et al. in Cell Res 34:214-231, 2024) since sleep drives waste clearance via glymphatic flow (Xie et al. in Science 342:373-377, 2013). However, it remains unclear whether 40 Hz light flickering directly increased glymphatic flow or indirectly by promoting sleep. A recent article published in Cell Discovery by Chen et al. (Sun et al. in Cell Discov 10:81, 2024) revealed that 40 Hz light flickering facilitated glymphatic flow, by promoting the polarization of astrocytic aquaporin-4 (AQP4) and vasomotion through upregulated adenosine-A2A receptor (A2AR) signaling, independent of sleep. These findings suggest that 40 Hz light flickering may be used as a non-invasive approach to control the function of the glymphatic-lymphatic system, to help remove metabolic waste in the brain, thereby presenting a potential strategy for neurodegenerative disease treatment.}, }
@article {pmid39349588, year = {2024}, author = {Chen, X and Cao, L and Haendel, BF}, title = {Right visual field advantage in orientation discrimination is influenced by biased suppression.}, journal = {Scientific reports}, volume = {14}, number = {1}, pages = {22687}, pmid = {39349588}, issn = {2045-2322}, support = {201908060012//China Scholarship Council/ ; STI 2030-Major Projects 2021ZD0200409//Ministry of Science and Technology of the People's Republic of China/ ; 32271078//National Natural Science Foundation of China/ ; 677819/ERC_/European Research Council/International ; }, mesh = {Humans ; *Visual Fields/physiology ; Male ; Female ; Adult ; *Reaction Time/physiology ; *Electroencephalography/methods ; Young Adult ; Orientation/physiology ; Orientation, Spatial/physiology ; Evoked Potentials/physiology ; Photic Stimulation ; Visual Perception/physiology ; Functional Laterality/physiology ; Walking/physiology ; Attention/physiology ; }, abstract = {Visual input is not equally processed over space. In recent years, a right visual field advantage during free walking and standing in orientation discrimination and contrast detection task was reported. The current study investigated the underlying mechanism of the previously reported right visual field advantage. It particularly tested if the advantage is driven by a stronger suppression of distracting input from the left visual field or improved processing of targets from the right visual field. Combing behavioural and electrophysiological measurements in a mobile EEG and augmented reality setup, human participants (n = 30) in a standing and a walking condition performed a line orientation discrimination task with stimulus eccentricity and distractor status being manipulated. The right visual field advantage, as demonstrated in accuracy and reaction time, was influenced by the distractor status. Specifically, the right visual field advantage was only observed when the target had an incongruent line orientation with the distractor. Neural data further showed that the right visual field advantage was paralleled by a strong modulation of neural activity in the right hemisphere (i.e. contralateral to the distractor). A significant positive correlation between this right hemispheric event related potential (ERP) and behavioural measures (accuracy and reaction time) was found exclusively for trials in which a target was presented on the right and an incongruent distractor was presented on the left. The right hemispheric ERP component further predicted the strength of the right visual field advantage. Notably, the lateralised brain activity and the right visual field advantage were both independent of stimulus eccentricity and the movement state of participants. Overall, our findings suggest an important role of spatially biased suppression of left distracting input in the right visual field advantage as found in orientation discrimination.}, }
@article {pmid39349502, year = {2024}, author = {Fan, Y and Tao, Y and Wang, J and Gao, Y and Wei, W and Zheng, C and Zhang, X and Song, XM and Northoff, G}, title = {Irregularity of visual motion perception and negative symptoms in schizophrenia.}, journal = {Schizophrenia (Heidelberg, Germany)}, volume = {10}, number = {1}, pages = {82}, pmid = {39349502}, issn = {2754-6993}, support = {LR23E070001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; 82001410//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Schizophrenia (SZ) is a severe psychiatric disorder characterized by perceptual, emotional, and behavioral abnormalities, with cognitive impairment being a prominent feature of the disorder. Recent studies demonstrate irregularity in SZ with increased variability on the neural level. Is there also irregularity on the psychophysics level like in visual perception? Here, we introduce a methodology to analyze the irregularity in a trial-by-trial way to compare the SZ and healthy control (HC) subjects. In addition, we use an unsupervised clustering algorithm K-means + + to identify SZ subgroups in the sample, followed by validation of the subgroups based on intraindividual visual perception variability and clinical symptomatology. The K-means + + method divided SZ patients into two subgroups by measuring durations across trials in the motion discrimination task, i.e., high, and low irregularity of SZ patients (HSZ, LSZ). We found that HSZ and LSZ subgroups are associated with more negative and positive symptoms respectively. Applying a mediation model in the HSZ subgroup, the enhanced irregularity mediates the relationship between visual perception and negative symptoms. Together, we demonstrate increased irregularity in visual perception of a HSZ subgroup, including its association with negative symptoms. This may serve as a promising marker for identifying and distinguishing SZ subgroups.}, }
@article {pmid39347924, year = {2024}, author = {Yan, ZN and Liu, PR and Zhou, H and Zhang, JY and Liu, SX and Xie, Y and Wang, HL and Yu, JB and Zhou, Y and Ni, CM and Huang, L and Ye, ZW}, title = {Brain-computer Interaction in the Smart Era.}, journal = {Current medical science}, volume = {44}, number = {6}, pages = {1123-1131}, pmid = {39347924}, issn = {2523-899X}, mesh = {Humans ; Artificial Intelligence ; *Brain/physiology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Machine Learning ; }, abstract = {The brain-computer interface (BCI) system serves as a critical