@article {pmid40858626,
year = {2025},
author = {Hu, Y and Liu, Y and Hou, Y and Pan, Y and Gao, X},
title = {Dataset of natural conversations about appearance using fNIRS.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1486},
doi = {10.1038/s41597-025-05574-9},
pmid = {40858626},
issn = {2052-4463},
abstract = {Self-objectification, marked by an overemphasis on how one's appearance is viewed by others, promotes increased body surveillance and dissatisfaction. Natural conversations centered around appearance, such as "fat talk"-where individuals, often women, engage in negative or self-deprecating remarks about their bodies or weight-are commonly used to induce a state of self-objectification. However, there is a notable lack of public datasets on brain signals during fat talk. In this dataset, we collected brain data from 31 female participants (aged 19.55 ± 0.89 years) using a 40-channel portable near-infrared device during fat talk and non-fat talk (topics such as travel and home decoration), primarily covering the frontal and parietal areas. Data analyses of subjective reports and fNIRS data revealed an increase in body surveillance and dissatisfaction, suggesting a significant activation of the self-objectification state. This dataset can be utilized to explore fNIRS data processing during natural interpersonal conversations and to gain insights into emotional and cognitive responses under conditions of self-dysregulation.},
}

@article {pmid40858497,
year = {2025},
author = {Tong, JQ and Binder, JR and Conant, LL and Mazurchuk, S and Anderson, AJ and Fernandino, L},
title = {A Common Representational Code for Event and Object Concepts in the Brain.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.2166-24.2025},
pmid = {40858497},
issn = {1529-2401},
abstract = {Events and objects are two fundamental ways in which humans conceptualize their experience of the world. Despite the significance of this distinction for human cognition, it remains unclear whether the neural representations of object and event concepts are categorically distinct or, instead, can be explained in terms of a shared representational code. We investigated this question by analyzing fMRI data acquired from human participants (males and females) while they rated their familiarity with the meanings of individual words (all nouns) denoting object and event concepts. Multivoxel pattern analyses indicated that both categories of lexical concepts are represented in overlapping fashion throughout the association cortex, even in the areas that showed the strongest selectivity for one or the other type in univariate contrasts. Crucially, in these areas, a feature-based model trained on neural responses to individual event concepts successfully decoded object concepts from their corresponding activation patterns (and vice versa), showing that these two categories share a common representational code. This code was effectively modeled by a set of experiential feature ratings, which also accounted for the mean activation differences between these two categories. These results indicate that neuroanatomical dissociations between events and objects emerge from quantitative differences in the cortical distribution of more fundamental features of experience. Characterizing this representational code is an important step in the development of theory-driven brain-computer interface technologies capable of decoding conceptual content directly from brain activity.Significance Statement We investigated how word meaning is encoded in the brain by examining the neural representations of individual lexical concepts from two distinct categories-objects and events. We found that both kinds of concepts were encoded in neural activity patterns in terms of a shared representational space characterized by different modalities of perceptual and emotional experience. This indicates that individual concepts from a wide variety of semantic categories can, at least in principle, be decoded from neural activity using a generative model of concept representation based on interpretable semantic features. Furthermore, both object and event concepts could be decoded from cortical regions previously hypothesized to encode category-specific representations, suggesting that the two categories are jointly represented in these areas.},
}

@article {pmid40857922,
year = {2025},
author = {Hu, W and Zhang, D and Chen, W},
title = {ITSEF: Inception-based two-stage ensemble framework for P300 detection.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {193},
number = {},
pages = {108014},
doi = {10.1016/j.neunet.2025.108014},
pmid = {40857922},
issn = {1879-2782},
abstract = {To address the problems of low signal-to-noise ratio, significant individual differences between subjects, and class imbalance in P300-based brain-computer interface (BCI), this paper proposes a novel Inception-based two-stage ensemble framework (ITSEF) to improve detection accuracy. Firstly, an Inception-based convolutional neural network (ICNN) is designed to extract multi-scale features and conduct cross-channel learning. In addition, a two-stage ensemble framework (TSEF) combined with a pre-training and fine-tuning strategy is developed, aiming to enhance the classification performance of the minority class and improve the generalization ability of the model. The framework comprises a conventional learning branch and a re-balancing branch, each based on an ICNN pre-trained with a different loss function. The prediction results of both branches are dynamically weighted by a cumulative learning strategy, so that the model gradually shifts its learning focus from the majority class to the minority class, comprehensively improving the identification ability for both classes. Experimental results on two datasets, Dataset II of BCI Competition III and BCIAUT-P300, demonstrate that the proposed ITSEF achieves state-of-the-art performance in the P300 classification task, with average classification accuracies of 86.16 % and 92.13 %, respectively. Compared with the existing state-of-the-art methods, the ITSEF achieves improvements of 4.61 % and 1.01 % on the two datasets, respectively. Furthermore, it exhibits significant improvements compared to baseline models and widely used class re-balancing strategies. The proposed ITSEF method provides an innovative deep learning framework for P300 signal analysis and has application potential in the field of P300-BCI.},
}

@article {pmid40857524,
year = {2025},
author = {Golabchi, A and Wu, B and Du, ZJ and Cui, XT},
title = {Long-Term Neural Recording Performance of PEDOT/CNT/Dexamethasone Coated Electrode Array Implanted in Visual Cortex of Rats.},
journal = {Advanced nanobiomed research},
volume = {},
number = {},
pages = {},
doi = {10.1002/anbr.202400114},
pmid = {40857524},
issn = {2699-9307},
abstract = {Implantable neural electrode arrays can be inserted in the brain to provide single-cell electrophysiology recording for neuroscience research and brain-machine interface applications. However, maintaining signal quality over time is complicated by inflammatory tissue responses and degradation of electrode materials. Organic electrode coatings offer a solution by enhancing recording and stimulation capabilities, including reduced impedance, increased charge injection capacity, and the ability to incorporate and release anti-inflammatory drugs. In this study, acid-functionalized multi-walled carbon nanotubes (CNTs) loaded with dexamethasone (Dex) were incorporated into poly (3,4-ethylendioxythiophene) (PEDOT) as electrode coatings. We investigated the electrochemical stability and recording performance of the PEDOT/CNT/Dex coating over an extended period of approximately 18 months. Cyclic voltammetric (CV) stimulation was used to trigger Dex release in half of the recording sites during the first 11 days of implantation to reduce the acute inflammation. The PEDOT/CNT/Dex coated floating microelectrode arrays demonstrated stable in vivo electrode impedance and successful detection of visually evoked neural activity from the rat visual cortex even at chronic time points. Additionally, the CV-stimulated sites exhibited higher single-unit recording yield, amplitudes, and signal-to-noise ratio compared to unstimulated sites. These results highlight the potential of anti-inflammatory treatments to improve the quality and longevity of chronic neural recordings.},
}

@article {pmid40857498,
year = {2025},
author = {Zhao, B and Huggins, JE and Kang, J},
title = {Bayesian Inference on Brain-Computer Interfaces via GLASS.},
journal = {Journal of the American Statistical Association},
volume = {},
number = {},
pages = {},
doi = {10.1080/01621459.2025.2498088},
pmid = {40857498},
issn = {0162-1459},
abstract = {Brain-computer interfaces (BCIs), particularly the P300 BCI, facilitate direct communication between the brain and computers. The fundamental statistical problem in P300 BCIs lies in classifying target and non-target stimuli based on electroencephalogram (EEG) signals. However, the low signal-to-noise ratio (SNR) and complex spatial/temporal correlations of EEG signals present challenges in modeling and computation, especially for individuals with severe physical disabilities-BCI's primary users. To address these challenges, we introduce a novel Gaussian Latent channel model with Sparse time-varying effects (GLASS) under a Bayesian framework. GLASS is built upon a constrained multinomial logistic regression particularly designed for the imbalanced target and non-target stimuli. The novel latent channel decomposition efficiently alleviates strong spatial correlations between EEG channels, while the soft-thresholded Gaussian process (STGP) prior ensures sparse and smooth time-varying effects. We demonstrate GLASS substantially improves BCI's performance in participants with amyotrophic lateral sclerosis (ALS) and identifies important EEG channels (PO8, Oz, PO7, and Pz) in parietal and occipital regions that align with existing literature. For broader accessibility, we develop an efficient gradient-based variational inference (GBVI) algorithm for posterior computation and provide a user-friendly Python module available at https://github.com/BangyaoZhao/GLASS.},
}

@article {pmid40856919,
year = {2025},
author = {Arns, M and Sokhadze, E and Birbaumer, N},
title = {Neurofeedback and Brain-Machine Interfaces: Where are We Now?.},
journal = {Applied psychophysiology and biofeedback},
volume = {},
number = {},
pages = {},
pmid = {40856919},
issn = {1573-3270},
}

@article {pmid40856229,
year = {2025},
author = {Yuan, Y and Gao, Z and Xiao, W},
title = {The Role of Oxytocin in Parental Care.},
journal = {Endocrinology},
volume = {166},
number = {9},
pages = {},
doi = {10.1210/endocr/bqaf129},
pmid = {40856229},
issn = {1945-7170},
support = {2021R52021//This work was supported by the Leading Talents in Science and Technology of Zhejiang Province/ ; 2021ZD0202700//National Science and Technology Innovation 2030-Major Projects/ ; 2025ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; },
mesh = {*Oxytocin/physiology/metabolism ; Humans ; Animals ; *Maternal Behavior/physiology ; *Paternal Behavior/physiology ; Female ; },
abstract = {Parental behaviors are essential for offspring survival and shaped by hormonal changes and adaptations in the neural circuits. Oxytocin, a nonapeptide, has been shown to play an important role in promoting parental behaviors. Using cutting-edge tools, studies have recently uncovered how oxytocin mediates parental behaviors through modulation of different neural circuits. We highlight recent advances in identifying neural pathways contributing to the role of oxytocin in parental care, focusing on how infant-related cues activate the oxytocin system and how oxytocin enhances the salience of sensory cues to enable parental behaviors in this review. We also discuss future challenges to further elucidate mechanisms involved.},
}

*Oxytocin/physiology/metabolism
Humans
Animals
*Maternal Behavior/physiology
*Paternal Behavior/physiology
Female

@article {pmid40855086,
year = {2025},
author = {Xiao, Q and Fan, LH and Ma, Q and Ning, YM and Gu, Z and Chen, L and Li, L and You, JW and Niu, YF and Cui, TJ},
title = {Secure wireless communication of brain-computer interface and mind control of smart devices enabled by space-time-coding metasurface.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {7914},
pmid = {40855086},
issn = {2041-1723},
support = {62288101//National Natural Science Foundation of China (National Science Foundation of China)/ ; 92167202//National Natural Science Foundation of China (National Science Foundation of China)/ ; 72171044//National Natural Science Foundation of China (National Science Foundation of China)/ ; K201924//State Key Laboratory of Millimeter Waves (State Key Lab of Millimeter Waves)/ ; BK20230822//Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation)/ ; 2021M700761//China Postdoctoral Science Foundation/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Wireless Technology/instrumentation ; *Brain/physiology ; Electroencephalography ; *Computer Security ; Photic Stimulation ; Algorithms ; Brainwashing ; },
abstract = {Brain-computer interface (BCI) provides an interconnected pathway between the human brain and external devices and paves a potential route for mind manipulations. However, most existing BCI technologies are based on simple signal transmission and are independent of other interface devices, with limited consideration for the reliability and security of the human brain's information interaction in complicated wireless environments. Here, we propose a deep fusion coding scheme that combines the BCI visual stimulation coding with metasurface space-time coding at the physical layer, enabling reliable and secure information transfers between the human brain and external devices. A brain space-time-coding metasurface platform is designed to implement a secure wireless communication system by using harmonic-encrypted beams. We design and fabricate a proof-of-principle prototype and experimentally show that the proposed wireless BCI scheme can establish a remote but safeguarded paradigm for human-machine interactions and intelligent metasurfaces, providing a potential direction in future secure wireless communications.},
}

*Brain-Computer Interfaces
Humans
*Wireless Technology/instrumentation
*Brain/physiology
Electroencephalography
*Computer Security
Photic Stimulation
Algorithms
Brainwashing

@article {pmid36895960,
year = {2023},
author = {Johnson, KA and Cagle, JN and Lopes, JL and Wong, JK and Okun, MS and Gunduz, A and Wagle Shukla, A and Hilliard, JD and Foote, KD and de Hemptinne, C},
title = {Globus pallidus internus deep brain stimulation evokes resonant neural activity in Parkinson's disease.},
journal = {Brain communications},
volume = {5},
number = {2},
pages = {fcad025},
pmid = {36895960},
issn = {2632-1297},
abstract = {Globus pallidus internus deep brain stimulation is an established therapy for patients with medication-refractory Parkinson's disease. Clinical outcomes are highly dependent on applying stimulation to precise locations in the brain. However, robust neurophysiological markers are needed to determine the optimal electrode location and to guide postoperative stimulation parameter selection. In this study, we evaluated evoked resonant neural activity in the pallidum as a potential intraoperative marker to optimize targeting and stimulation parameter selection to improve outcomes of deep brain stimulation for Parkinson's disease. Intraoperative local field potential recordings were acquired in 22 patients with Parkinson's disease undergoing globus pallidus internus deep brain stimulation implantation (N = 27 hemispheres). A control group of patients undergoing implantation in the subthalamic nucleus (N = 4 hemispheres) for Parkinson's disease or the thalamus for essential tremor (N = 9 patients) were included for comparison. High-frequency (135 Hz) stimulation was delivered from each electrode contact sequentially while recording the evoked response from the other contacts. Low-frequency stimulation (10 Hz) was also applied as a comparison. Evoked resonant neural activity features, including amplitude, frequency and localization were measured and analysed for correlation with empirically derived postoperative therapeutic stimulation parameters. Pallidal evoked resonant neural activity elicited by stimulation in the globus pallidus internus or externus was detected in 26 of 27 hemispheres and varied across hemispheres and across stimulating contacts within individual hemispheres. Bursts of high-frequency stimulation elicited evoked resonant neural activity with similar amplitudes (P = 0.9) but a higher frequency (P = 0.009) and a higher number of peaks (P = 0.004) than low-frequency stimulation. We identified a 'hotspot' in the postero-dorsal pallidum where stimulation elicited higher evoked resonant neural activity amplitudes (P < 0.001). In 69.6% of hemispheres, the contact that elicited the maximum amplitude intraoperatively matched the contact empirically selected for chronic therapeutic stimulation by an expert clinician after 4 months of programming sessions. Pallidal and subthalamic nucleus evoked resonant neural activity were similar except for lower pallidal amplitudes. No evoked resonant neural activity was detected in the essential tremor control group. Given its spatial topography and correlation with postoperative stimulation parameters empirically selected by expert clinicians, pallidal evoked resonant neural activity shows promise as a potential marker to guide intraoperative targeting and to assist the clinician with postoperative stimulation programming. Importantly, evoked resonant neural activity may also have the potential to guide directional and closed-loop deep brain stimulation programming for Parkinson's disease.},
}

@article {pmid40853583,
year = {2025},
author = {Wang, S and Yang, Y and Hao, S and Sun, Y and Wang, H},
title = {Glutamatergic Periaqueductal Gray Projections to the Locus Coeruleus Orchestrate Adaptive Arousal States in Threatening Contexts.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {40853583},
issn = {1995-8218},
abstract = {The locus coeruleus (LC), a norepinephrine nucleus governing arousal states through tonic activity, requires precise regulatory mechanisms to maintain its dynamic activation levels. However, the neural circuitry underlying LC activity maintenance remains unclear. Here, we identify a glutamatergic projection from the ventrolateral periaqueductal gray (vlPAG) to the LC in mice as a critical regulator of arousal dynamics. Fiber photometry recordings revealed stress-induced Ca[2+] dynamics in vlPAG[CaMKIIα]-LC axon terminals across diverse threat paradigms. Slice electrophysiology demonstrated that this pathway mediates LC-norepinephrine (LC-NE) neuronal activity via glutamatergic transmission. Low-frequency pathway activation (1 Hz) mainly induced anxiety-like behaviors, whereas high-frequency stimulation (10 Hz) evoked more panic-like hyperlocomotion, establishing a frequency-dependent continuum of arousal states. Conversely, pathway inhibition reduced pupil size, a reliable biomarker for arousal, concurrently suppressing threat avoidance behaviors and alleviating anxiety-related behaviors without altering environmental preference. These findings reveal that the vlPAG[CaMKIIα]-LC pathway maintains baseline arousal while dynamically scaling threat-induced hyperarousal.},
}

@article {pmid40853288,
year = {2025},
author = {Rahman, MM and Banik, N and Sunny, MSH and Zarif, MII and Bedolla-Martinez, D and Schultz, K and Ahamed, SI and Rahman, MH},
title = {Wheelchair-mounted robotic arms: a systematic review of technical design and activities of daily living outcomes.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {},
number = {},
pages = {1-25},
doi = {10.1080/17483107.2025.2547042},
pmid = {40853288},
issn = {1748-3115},
abstract = {PURPOSE: This review examines wheelchair-mounted robotic arms (WMRAs) as an emerging assistive technology that enhances independence and quality of life for individuals with upper- and lower-limb disabilities. By enabling independent performance of activities of daily living (ADLs), WMRAs hold significant promise for disability and rehabilitation. The article aims to critically evaluate the state of the art in WMRA research and development, identifying persistent challenges and highlighting promising innovations.

MATERIALS AND METHODS: The review systematically analyzes literature on WMRAs published between 2001 and 2025. The analysis emphasizes design specifications, degrees of freedom, actuation methods, control strategies, and performance evaluations. A comparative synthesis is conducted to assess how existing systems support ADL execution, while also integrating technical considerations with user-centered outcomes.

RESULTS AND CONCLUSIONS: The findings indicate that current WMRA designs face significant limitations, including restricted workspace coverage, inadequate gripper dexterity, suboptimal kinematic configurations, limited payload capacity, high cost, and lack of modularity. Safety mechanisms remain underdeveloped, creating barriers to broader adoption. Nevertheless, advancements in AI-driven control systems, modular design strategies, and integration with complementary assistive technologies demonstrate promising progress. The review concludes that WMRAs have substantial potential to improve autonomy and daily functioning for individuals with disabilities. Addressing technical and practical shortcomings is essential to ensure successful real-world deployment. These insights contribute to disability and rehabilitation research, as they highlight pathways to enhance accessibility, safety, and cost-effectiveness in assistive technologies that support independent living.},
}

@article {pmid40852670,
year = {2025},
author = {Zubayr, MO and Obimakinde, AM and Popoola, OA},
title = {PARENTAL RESPONSE AND COPING STRATEGIES FOR ADOLESCENTS' BEHAVIOURAL PROBLEMS: A COMMUNITY-BASED CROSS-SECTIONAL STUDY.},
journal = {Annals of Ibadan postgraduate medicine},
volume = {23},
number = {1},
pages = {15-23},
pmid = {40852670},
issn = {1597-1627},
abstract = {BACKGROUND: Adolescent behavioural problems can be burdensome for parental figures. The lack of good parental responses and coping strategies may worsen adolescent mental health issues. Research in this domain can be informative for effective management of adolescents' behavioural problems in resourcelimited settings like Nigeria.

AIM: We assessed parental responses and coping strategies for adolescents with behavioural problems.

METHODS: A cross-sectional community-based survey with cluster sampling was conducted. Coping strategies were assessed using the Brief Cope Inventory (BCI), dichotomized into Emotional-Based Strategies (EBS) and Problem- Based Strategies (PBS) coping. The Strength and Difficulty Questionnaire (SDQ) assessed adolescent behavioural problems. Data were analyzed using descriptive and inferential statistics.

RESULTS: Four hundred and ten (410) parental figures of adolescents aged 14.8±2.3 years were recruited. Parental response to adolescent problem behaviours included corporal punishment in 44% and few (5.8%) sought medical or spiritual help for the adolescent. The most deployed parental coping strategy was 'active' coping (69%) while 'instrumental support' was the least adopted coping strategy. The age, gender, educational level and income of parental figures, were associated with the choice of utilizing PBS coping.

CONCLUSION: Parental figures employed more corporal punishment and utilized active coping, and planning as coping strategies when dealing with adolescents' problem behaviours. Interventions to discourage corporal punishment and promote more effective parental coping are needed.},
}

@article {pmid40852573,
year = {2025},
author = {Chen, L and Yang, T and Liu, R and Xu, Q and Ge, Q and Wu, M and Yu, H},
title = {Sensory and neural responses to flavor compound 3-Methylbutanal in dry fermented sausages: Enhancing perceived overall aroma.},
journal = {Food chemistry: X},
volume = {29},
number = {},
pages = {102769},
pmid = {40852573},
issn = {2590-1575},
abstract = {This study investigated the impact of 3-methylbutanal (0, 60, 120, 180, 240, and 300 μg/kg) on aroma and neural responses in fermented sausages. Among 33 volatiles identified, 3-methylbutanal exhibited the highest odor activity value of 868, indicating its dominant contribution. Sensory analysis showed that samples with 180 μg/kg received the highest ratings for savory (7.0), caramelized (7.1), and nutty (4.4) notes, whereas the 300 μg/kg group showed the lowest overall aroma intensity. EEG analysis indicated global power and α-band activity peaked at 180 μg/kg, increasing by 65.8 % and 73.2 % over baseline, then declined at higher doses. Time-resolved topographies showed odor decoding began at 100 ms and peaked at 500 ms. Source localization identified increased activity in dorsolateral, orbitofrontal, and ventromedial prefrontal cortices at 180 μg/kg. These results demonstrate that moderate levels of 3-methylbutanal enhance aroma perception and evoke heightened neural activity in brain regions associated with olfactory processing and emotion.},
}

@article {pmid40852333,
year = {2025},
author = {An, J and Goyal, P and Luft, AR and Schönhammer, JG},
title = {Functional near-infrared spectroscopy short-channel regression improves cortical activation estimates of working memory load.},
journal = {Neurophotonics},
volume = {12},
number = {3},
pages = {035009},
pmid = {40852333},
issn = {2329-423X},
abstract = {SIGNIFICANCE: Functional near-infrared spectroscopy (fNIRS) is a noninvasive technique commonly used to examine cognitive functions such as working memory (WM). However, fNIRS signals are often interfered with by extracerebral activity, such as scalp hemodynamics. Short separation channels (SSCs) allow direct measurement of these signals. Short-channel regression (SCR) is widely used to reduce scalp interference, but its added value in WM paradigms remains underexplored.

AIM: We aimed to examine the effect of SCR on improving the validity of fNIRS measurements for WM load (WML).

APPROACH: We used the N -Back task to induce WML-dependent brain activation by varying the " n " level. Data from 20 participants were collected using fNIRS with SSC. Hemodynamic responses were analyzed with generalized linear models and linear mixed models to assess SCR's effect on the sensitivity of cortical activation measures.

RESULTS: SCR enhanced the statistical effects of N -Back levels on measured hemodynamic responses at both group and subject levels, improving the validity and sensitivity of fNIRS.

CONCLUSIONS: SCR improves fNIRS measurement sensitivity and validity, even in tasks with minimal motor requirements.},
}

@article {pmid40850941,
year = {2025},
author = {Zhang, X and Yang, Y},
title = {Gut: The gate and key to brain.},
journal = {Chinese medical journal},
volume = {},
number = {},
pages = {},
pmid = {40850941},
issn = {2542-5641},
abstract = {Brain science is the frontier of modern science, and new advances have been made in brain-like designs and brain-computer interfaces to simulate or develop brain functions. However, given that the brain is hermetically sealed within the skull, exploration and deciphering of the brain structure and functions are limited. Growing evidence suggests that the gut is not just a digestive organ. It not only provides essential nutrients and electrolytes for brain neurodevelopment and the maintenance of brain function, but it also transmits external environmental and intestinal wall signals from the intestinal lumen to the central nervous system through multiple pathways to regulate brain activity, function, and structure. A variety of gut-brain interaction pathways have been identified, including neural pathways, neuroimmune signaling, endocrine pathways, and biochemical messengers produced by gut microbes. Gut microbes interact with food and the gut to modulate gut-brain communication. The gut's important role and potential in neurodevelopment, maintenance of normal function, and disease development make it an increasingly important area of research in brain science and neuropsychiatric disorders. The gut's unique role in brain functions and its accessibility for research (compared to direct brain studies) establish it as a critical gate to understanding the mysteries of brain science. Crucially, intestinal nutrients and microbes provide two unique keys to unlock this gate-enabling neural regulation and novel treatments for neuropsychiatric diseases.},
}

@article {pmid40850267,
year = {2025},
author = {Li, J and Zhang, W and Liao, Y and Qiu, Y and Zhu, Y and Zhang, X and Wang, C},
title = {Neural decoding reliability: Breakthroughs and potential of brain-computer interfaces technologies in the treatment of neurological diseases.},
journal = {Physics of life reviews},
volume = {55},
number = {},
pages = {1-40},
doi = {10.1016/j.plrev.2025.08.007},
pmid = {40850267},
issn = {1873-1457},
abstract = {Neurological disorders such as Parkinson's disease, stroke, and epilepsy frequently result in irreversible disability. Brain-computer interface (BCI) technologies offer the promise of recovering or replacing impaired sensory, motor, and cognitive functions by directly stimulating cortical activity or by converting self-generated cortical activity into commands for external assistive devices. In-depth studies of cerebral cortex connectivity, function and neural hierarchical coding mechanisms can provide novel solutions for BCI-based treatments. This review summarizes the fundamental principles and history of BCI technology and current research progress, including the utilization of known cortical functions and the potential impact of newly discovered cortical functions on the future development of BCI-based applications. The article then systematically reviews the application of BCI technology for the treatment of motor, cognitive, and psychiatric disorders, innovative uses of hydrogels and carbon nanomaterials in BCI systems, and the current limitations and future research directions of BCI systems with respect to the reliability of neural decoding. This article aims to provide clinicians and researchers with the latest progress and a comprehensive overview of BCI applications for diagnosing and treating neurological diseases from in-depth studies on cerebral cortex structure and function, and to propose potential future applications based on interdisciplinary approaches, especially in enhancing the reliability of neural decoding.},
}

@article {pmid40848671,
year = {2025},
author = {Weng, Y and He, B and Zhou, J and Luo, P and Xu, Z and Yan, H and Yang, B and He, Q and Lu, J and Yang, X},
title = {Potential saviour of pulmonary fibrosis: multi-pathway treatment of natural products.},
journal = {Phytomedicine : international journal of phytotherapy and phytopharmacology},
volume = {147},
number = {},
pages = {157174},
doi = {10.1016/j.phymed.2025.157174},
pmid = {40848671},
issn = {1618-095X},
abstract = {BACKGROUND: Pulmonary fibrosis (PF), a terminal manifestation of diverse interstitial lung diseases, remains incompletely understood in its pathogenesis. Natural products possess multifaceted biological activities and relatively favorable safety profiles, showing great advantage in treating complex disease including PF, though bioavailability limitations require formulation optimization.

PURPOSE: This review systematically consolidates insights into the underlying mechanisms of natural products and prospects several promising targets for the treatment of PF.

METHODS: A comprehensive literature search was conducted in PubMed, Web of Science, and specialized pharmacology texts using key terms related to pulmonary fibrosis, natural products (e.g., alkaloids, terpenoids, flavonoids, saponins), inflammation, and oxidative stress. The information was reviewed to emphasize the potential mechanisms of natural products in the treatment of PF.

RESULTS: Natural products ameliorate PF through multi-pathway interventions, including suppression of inflammation, antagonism of oxidative stress, inhibition of epithelial-mesenchymal transition and endothelial-to-mesenchymal transition, targeting of fibroblast activation, modulation of metabolic homeostasis, promotion of autophagy and repression of senescence and apoptosis. These effects are mediated by modulating intricate pathways such as the TGF-β1/SMAD, PI3K/Akt/mTOR, NOX4-Nrf2, AMPK, NF-κB and STAT3 signaling pathways. In addition, the toxicology and side effects of natural products for the treatment of pulmonary fibrosis, and various clinical questions and limitations are discussed.

CONCLUSION: These unveiling mechanisms provide robust support for the exploration of novel applications of existing medications. This review aims to contribute novel insights towards the further studies of natural products for the prevention and treatment of PF.},
}

@article {pmid40848318,
year = {2025},
author = {Yu, SH and Park, HY and Lee, E and Kam, TE and Jeong, JH},
title = {DeepSMR: Decoding high-complex motor imagery via subject-dependent multi-feature refinement in deep convolutional networks.},
journal = {Computers in biology and medicine},
volume = {197},
number = {Pt A},
pages = {110920},
doi = {10.1016/j.compbiomed.2025.110920},
pmid = {40848318},
issn = {1879-0534},
abstract = {Electroencephalography (EEG) is a noninvasive neuroimaging technique that records electrical activity in the brain using electrodes placed on the scalp. It is widely used in neuroscience, clinical diagnosis, and brain-computer interface (BCI) applications to analyze brain signals in real time. This study proposes an advanced EEG-based BCI framework designed to decode and classify individual finger movements within a single hand during a finger-tapping task involving all five fingers. Our method employs a subject-dependent multi-feature refinement framework called DeepSMR, a novel deep convolutional network architecture optimized for feature extraction from EEG signals is introduced. This approach integrates spectral, temporal, and spatial analyses, leveraging event-related desynchronization/event-related synchronization (ERD/ERS), common spatial pattern (CSP), and power spectral density (PSD) techniques. Further, a subject-dependent multi-feature refinement framework. The DeepSMR achieved high classification accuracy for fine-motor tasks, achieving an average accuracy of 0.7471 (±0.0270) for the thumb and 0.7485 (±0.0314) for the index finger during motor execution tasks. DeepSMR outperformed EEGNet and DeepConvNet across all finger classes, showing an improvement of up to 15% in accuracy compared with the baseline models. Spectral feature analysis confirmed increased activity in the sensorimotor rhythm (SMR) frequency bands (8-13 Hz and 13-30 Hz), whereas temporal analysis revealed distinct patterns during the active and relaxed states. Spatial feature analysis highlighted class-specific features, further enhancing model performance. In the motor imagery session, DeepSMR maintained a superior performance, achieving the highest accuracy of 0.6984 (±0.0324) for the index finger. The results show that DeepSMR improves BCI performance by increasing the classification accuracy and computational efficiency, particularly for challenging finger-movement tasks. The framework could provide applications in neuroprosthetics, assistive robotics, and rehabilitation. In future work, the method could be expanded to include more motor tasks and integrate additional data types to further enhance the decoding accuracy for specific users and complex actions.},
}

@article {pmid40847250,
year = {2025},
author = {Tan, H and Jin, S and Lv, W and Guo, L and Jiang, P and Li, Y and Shi, M and Wang, D and Wang, Y and Bao, A},
title = {Hypothalamic Oxytocin Neuronal Activation Induces Bipolar-Like Mood Changes in Mice in a Sex- and Dosage-Dependent Manner.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {40847250},
issn = {1995-8218},
abstract = {Clinical studies have suggested that increased plasma oxytocin (OT) levels are a promising biomarker for bipolar disorder (BD), and our earlier post-mortem study found increased OT activity in the hypothalamic paraventricular nucleus (OT[PVN]) in BD. However, the potential contribution of the supraoptic nucleus (SON, OT[SON]), a major part of the central OT system, to BD remains unknown. We therefore systematically performed independent acute or chronic chemogenetic activation of OT[PVN], OT[SON], or OT[PVN+SON] experiments in OT-cre mice. We found that acute activation of OT[PVN+SON] neurons led to slight mania-like (anti-depression-like) behaviors both in male and female mice, while chronic activation of OT[PVN] or OT[PVN+SON] led to sex-dependent behavioural changes from depression/anxiety-like to mania-like, accompanied by stress-related molecular changes in a sex- dependent manner in the medial prefrontal cortex. Our findings imply that OT may be involved in bipolar-like mood changes in a sex- and dosage-dependent manner.},
}

@article {pmid40844935,
year = {2025},
author = {Zhu, Y and Chen, J and Cheng, L and Zhu, F and Zhang, X and Liu, Q},
title = {A Sparse-Integrated Filtering Residual Spiking Neural Network for High-Accuracy Spike Sorting and Co-optimization on Memristor Platforms.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBCAS.2025.3601403},
pmid = {40844935},
issn = {1940-9990},
abstract = {Brain-computer interfaces rely on precise decoding of neural signals, where spike sorting is a critical step to extract individual neuronal activities from complex neural data. This works presents a spiking neural network (SNN) framework for efficient spike sorting, named SIFT-RSNN. In the SIFT-RSNN, raw neural signals are encoded into spike trains using a threshold-based temporal encoding strategy, then a sparse-integrated filtering module refines misfiring spikes, enhancing data sparsity for pattern learning. The RSNN module with a membrane shortcut structure ensures efficient feature transfer and improves generalization performance of the overall system. The SIFT-RSNN achieves an accuracy of 96.2% and 99.6% on the Difficult1 and Difficult2 subset of Leicester dataset, surpassing state-of-the-art methods. Also, we conducted it on a compute-in-memory platform with 8k memristor cells utilizing quantization-free mapping method and propose two algorithm-hardware co-optimization strategies to mitigate non-ideal hardware effects: weight outlier pre-constraint (WOP) and noise adaptation training (NAT). After optimization, our algorithm continues to outperform existing spike sorting methods, achieving accuracies of 94.2% and 99.7%, while also demonstrating improved robustness. The memristor platform only exhibits a 2% and 1.5% accuracy drop compared to software results on the two difficult subsets. Additionally, it achieves 3.52 μJ energy consumption and 0.5 ms latency per inference. This work offers promising solutions for brain-computer interfaces systems and neural prosthesis applications in the future.},
}

@article {pmid40843108,
year = {2025},
author = {Hao, Y and Cheng, S},
title = {Motor imagery EEG classification method using 3D CNN and LSTM for rehabilitation application.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {131},
pmid = {40843108},
issn = {1871-4080},
abstract = {Due to the limitations in the accuracy and robustness of current EEG classification methods, applying motor imagery for practical Brain-Computer Interface applications remains challenging. Therefore, an EEG classification method with high accuracy and strong robustness is of significant importance. This paper proposed a method called 3D CNN and LSTM for Motor Imagery (3D-CLMI), which combines 3D CNN and LSTM network with attention to classify MI-EEG signals. This method combined MI-EEG signals from different channels into 3D features and extracted spatial features through convolution operations with multiple 3D convolutional kernels of different scales. At the same time, in order to ensure the integrity of the extracted temporal features of the MI-EEG signal, 3D-CLMI adopted a parallel structure to obtain spatial and temporal features respectively, and then combined the obtained features for classification. Experimental results showed that this method achieved a classification accuracy of 92.7% and an F1-score of 0.91 on BCI Competition IV 2a, which were both higher than the state-of-the-art methods in the field of MI tasks. Additionally, 12 participants were invited to complete a four-class MI task, and experiments on the collected dataset showed that our method also maintained the highest classification accuracy and F1-score. Our proposed method achieved the best results on both datasets, and we then demonstrated the effectiveness of each part of the proposed method through ablation experiments. Additionally, we designed a rehabilitation application system in a VR environment based on the proposed method, and the experimental results validated that it could assist patients with impaired hand motor function.},
}

@article {pmid40843107,
year = {2025},
author = {Ding, P and Wang, F and Zhao, L and Gong, A and Fu, Y},
title = {HWI encoding/decoding of a non-invasive HWI-BCI paradigm based on temporal variation abundance scale.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {130},
pmid = {40843107},
issn = {1871-4080},
abstract = {The performance of non-invasive Handwriting Imagery (HWI) input in Brain-computer interface (BCI) systems is highly dependent on the paradigms employed, yet there is limited research on interpretable scales to measure how HWI-BCI paradigms and neural encoding designs affect performance. This study introduces the "Temporal Variation Abundance" metric and utilizes it to design two classes of handwriting imagery paradigms: Low Temporal Variation Abundance (LTVA) and High Temporal Variation Abundance (HTVA). A dynamic time warping algorithm based on random templates (rt-DTW) is proposed to align HWI velocity fluctuations using EEG. Comprehensive comparisons of these experimental paradigms are conducted in terms of feature space distance, offline and online classification accuracy, and cognitive load assessment using functional near-infrared spectroscopy. Results indicate that HTVA-HWI exhibits lower velocity stability but demonstrates higher spatial distance, offline classification accuracy, online testing classification accuracy, and lower cognitive load. This study provides deep insights into paradigm design for non-invasive HWI-BCI and scales of neural encoding, offering new theoretical support and methodological insights for future advancements in brain-computer interaction.},
}

@article {pmid40843078,
year = {2025},
author = {Deb, N and Khan, Z and Sulaiman, M and Abu Bakar, M},
title = {Editorial: Interdisciplinary synergies in neuroinformatics, cognitive computing, and computational neuroscience.},
journal = {Frontiers in computational neuroscience},
volume = {19},
number = {},
pages = {1657167},
pmid = {40843078},
issn = {1662-5188},
}

@article {pmid40842871,
year = {2025},
author = {Wang, Y and Wang, X},
title = {Entheogen: an evolutionary medicine for neuropsychiatric disorders.},
journal = {National science review},
volume = {12},
number = {8},
pages = {nwaf168},
pmid = {40842871},
issn = {2053-714X},
}

@article {pmid40841966,
year = {2025},
author = {Yang, W and Lu, J and Luo, P and Xu, Z and Yan, H and Yang, B and He, Q and Zhou, J and Yang, X},
title = {An exhaustive examination of the research progress in identifying potential JAK inhibitors from natural products: a comprehensive overview.},
journal = {Chinese medicine},
volume = {20},
number = {1},
pages = {130},
pmid = {40841966},
issn = {1749-8546},
support = {No.2020YFE0204300//the National Key Research and Development Program/ ; No.82404960//Youth Fund of the National Natural Science Foundation of China/ ; No. 82274018//the National Natural Science Foundation of China/ ; },
abstract = {The JAK-STAT signaling pathway serves as a central regulator of diverse cellular processes encompassing proliferation, apoptosis, inflammation, and differentiation. Specifically, extracellular ligands such as interleukins, and colony-stimulating factors induce JAKs phosphorylation, subsequently triggering dimerization and nuclear translocation of STATs protein. In this way, the JAK-STAT pathway modulates target gene expression. Dysregulation of the JAK-STAT pathways has been implicated in the pathogenesis of multiple diseases, including inflammatory diseases, autoimmune diseases, malignant tumors. Therefore, JAK inhibitors have been considered promising therapeutic candidates with substantial clinical potential. While previous reviews have primarily focused on natural products targeting JAK-STAT signaling pathways for the specific disease application, this paper comprehensively collected 88 natural products demonstrating JAKs inhibitory activity across multiple pathological conditions. We mainly referenced nearly 20 years of literature from 2005 to 2025, comprising 294 different types of publications including review articles and research papers. Through systematic analysis of the compounds, we further classified these phytochemicals according to their structural characteristics (flavonoids, alkaloids, terpenoids) and molecular targets within the signaling cascades. This study provides novel insights into the pathophysiological relationships between diseases and JAK kinases, while offering valuable guidance for developing next-generation JAK inhibitors with improved therapeutic profiles.},
}

@article {pmid40839508,
year = {2025},
author = {Liyanage, KA and Yoo, PE and Grayden, DB and Opie, NL and Oxley, TJ},
title = {Artifact removal in electrocorticography devices with cardiac contamination.},
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.2025.3601445},
pmid = {40839508},
issn = {1558-0210},
abstract = {OBJECTIVE: Electrocorticography (ECoG) devices with electronics housed near the chest are susceptible to artifacts of a differing nature to electroencephalography (EEG) and standard ECoG. Using data obtained via an endovascular neural interface, we compared different artifact removal techniques in an offline setting with the aim of improving the quality and usefulness of clinically acquired data.

METHODS: Three different methods of filtration were applied and assessed: Common Average Referencing (CAR), Independent Component Analysis (ICA) with automated ECG channel selection, and Template-Based Removal (TBR). The automated ECG channel selection method was compared to manual selection. Methods were compared using signal-to-artifact root-mean-squared (RMS) values.

RESULTS: The automated ECG source channel selection had high concordance with manual selection. All filtration methods decreased post-artifact RMS amplitudes and improved signal-to-artifact ratios. ICA took the most time to compute but had the most improved signal-to-artifact ratio. In regions with no ECG artifact, TBR preserved the underlying electrocorticography data better than the other methods.

CONCLUSION: ICA with an automated method of ECG channel selection is the preferred method out of the three tested to remove ECG artifact while preserving the underlying signal.

SIGNIFICANCE: We establish methods that can be used to improve neural data of electrocorticography devices susceptible to cardiac contamination to facilitate translation as brain-computer interfaces.},
}

@article {pmid40837818,
year = {2025},
author = {Si, JY and Lin, ZY and Gan, DG and Zhang, XY and Liu, YN and Hu, YX and Bao, YP and Wang, XQ and Sun, HQ and Yu, X and Lu, L},
title = {Informed consent competency assessment for brain-computer interface clinical research and application in psychiatric disorders: A systematic review.},
journal = {World journal of psychiatry},
volume = {15},
number = {8},
pages = {107593},
pmid = {40837818},
issn = {2220-3206},
abstract = {BACKGROUND: Brain-computer interface (BCI) technology is rapidly advancing in psychiatry. Informed consent competency (ICC) assessment among psychiatric patients is a pivotal concern in clinical research.

AIM: To analyze the assessment of ICC and form a framework with multi-dimensional elements involved in ICC of BCI clinical research among psychiatric disorders.

METHODS: A systematic review of studies regarding ICC assessments of BCI clinical research in patients with six kinds of psychiatric disorders was conducted. A systematic literature search was performed using PubMed, ScienceDirect, and Web of Science. Peer-reviewed articles and full-text studies were included in the analysis. There were no date restrictions, and all studies published up to February 27, 2025, were included.

RESULTS: A total of 103 studies were selected for this review. Fifty-eight studies included ICC factors, and forty-five were classified in ICC related ethical issues of BCI research in six kinds of psychiatric disorders. Executive function impairment is widely recognized as the most significant factor impacting ICC, and processing speed deficits are observed in schizophrenia, mood disorders, and Alzheimer's disease. Memory dysfunction, particularly episodic and working memory, contributes to compromised ICC. Five core ethical issues in BCI research should be addressed: BCI specificity, vulnerability, autonomy, dynamic ICC, comprehensiveness, and uncertainty.

CONCLUSION: A Five-Dimensional evaluative framework, including clinical, ethical, sociocultural, legal, and procedural dimensions, is constructed and proposed for future ICC research in BCI clinical research involving psychiatric disorders.},
}

@article {pmid40837785,
year = {2025},
author = {Wang, P and Dai, AL and Guo, XR and Jiang, HT},
title = {Portable electroencephalography in early detection of depression: Progress and future directions.},
journal = {World journal of psychiatry},
volume = {15},
number = {8},
pages = {107725},
pmid = {40837785},
issn = {2220-3206},
abstract = {Traditional diagnostic tools for depression, such as the Patient Health Questionnaire-9, are susceptible to subjective bias, increasing the risk of misdiagnosis and emphasizing the critical need for objective biomarkers. This minireview evaluates the emerging role of portable electroencephalography (EEG) as a cost-effective, accessible solution for early depression detection. By synthesizing findings from 45 studies (selected from 764 screened articles), we highlight EEG's capacity to identify aberrant neural oscillations associated with core depressive symptoms, including anhedonia, excessive guilt, and persistent low mood. Advances in portable systems demonstrate promising classification accuracy when integrated with machine learning algorithms, with long short-term memory models achieving > 90% accuracy in recent trials. However, persistent challenges, such as signal quality variability, motion artifacts, and limited clinical validation, hinder widespread adoption. Further innovation in sensor optimization, multimodal data integration, and real-world clinical trials is essential to translate portable EEG into a reliable diagnostic tool. This minireview underscores the transformative potential of neurotechnology in psychiatry while advocating for rigorous standardization to bridge the gap between research and clinical practice.},
}

@article {pmid40836680,
year = {2025},
author = {Riemann, D and Nissen, C and Geoffroy, PA and Feige, B and Ellis, J},
title = {Sleep and Dreams as Reflected by Science Fiction Literature and Films-Anything to Learn From?.},
journal = {Journal of sleep research},
volume = {},
number = {},
pages = {e70183},
doi = {10.1111/jsr.70183},
pmid = {40836680},
issn = {1365-2869},
abstract = {Sleep and dreams are frequent themes in science fiction (Sci-Fi) literature and films, often used to explore questions about consciousness, reality, technology and the human experience. Sci-Fi authors and filmmakers utilise the enigmatic nature of sleep and dreams to blur the boundaries between reality and imagination, raising philosophical questions or extrapolating the effects of futuristic technologies on human life. In this article, we want to highlight some areas that have been recurring themes relating to sleep and dreams in Sci-Fi. These will include the concepts of so-called hypno-paedagogics, space hibernation, brain machine interfaces, electrostimulation, genetic engineering and the impact of substances (viruses, bacteria, drugs, toxins) on sleep and dreams. We will then confront Sci-Fi concepts with what is known from contemporary sleep science and judge what might be feasible, or not, in the future. A question we also want to address is how the relationship between sleep science and sleep Sci-Fi can be conceptualised: whether novel concepts have been instigated by Sci-Fi and taken up by sleep science or whether Sci-Fi merely reflects state of the art topics of sleep science, with just adding a touch of fiction.},
}

@article {pmid40836202,
year = {2025},
author = {Agarwal, P and Kumar, S and Singh, R},
title = {Motor imagery-based neural networks for assisting tetraplegic patients.},
journal = {Medical & biological engineering & computing},
volume = {},
number = {},
pages = {},
pmid = {40836202},
issn = {1741-0444},
abstract = {Nowadays, deep network-based classification algorithms are used in a myriad of applications for brain-computer interfaces (BCIs). These interfaces can enhance the daily lives of quadriplegic patients. Electroencephalography (EEG) based motor imagery (MI) is an integral part of BCI, and the performance of the available deep classifiers is still limited. This paper presents a novel convolutional neural network (CNN) architecture designed to enhance the multiclass classification accuracy of motor imagery (MI) signals acquired through EEG-based sensing. We have selected the electrodes over the sensorimotor cortex region of the brain in the 8-30 Hz EEG frequency band. Further, we have computed the classification accuracy and kappa scores in an end-to-end deep classification network. Our framework surpasses the contemporary literature algorithms in classifying BCI competition IV-2a, a four-class MI dataset of nine subjects (left hand, right hand, both feet, tongue). The proposed network architecture has achieved an average and maximum accuracy of 95.19% and 99.28%, respectively. We have outperformed state-of-the-art accuracies of the individual subjects S1, S2, S3, S4, S5, S6, S8, and the average accuracy of the dataset by 8.28%, 40.97%, 5.54%, 14.83%, 19.09%, 25.5%, 10.43%, and 12.82% respectively.},
}

@article {pmid40835615,
year = {2025},
author = {Zuo, C and Yin, Y and Wang, H and Zheng, Z and Ma, X and Yang, Y and Wang, J and Wang, S and Huang, ZG and Ye, C},
title = {Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1451},
pmid = {40835615},
issn = {2052-4463},
support = {NO.2024M764330//China Postdoctoral Science Foundation/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography ; *Lower Extremity/physiopathology ; *Osteoarthritis, Knee/physiopathology/rehabilitation ; Algorithms ; Deep Learning ; Male ; Imagery, Psychotherapy ; Middle Aged ; },
abstract = {Chronic knee osteoarthritis pain significantly impacts patients' quality of life and motor function. While motor imagery (MI)-based brain-computer interface (BCI) systems have shown promise in rehabilitation, their application to lower-limb conditions, particularly in pain patients, is underexplored. This study evaluates the feasibility of applying an MI-BCI model to a large dataset of knee pain patients, utilizing a novel deep learning algorithm for signal decoding. This EEG data was collected and analysed from 30 knee pain patients, revealing significant event-related (de)synchronization (ERD/ERS) during MI tasks. Traditional decoding algorithms achieved accuracies of 51.43%, 55.71%, and 76.21%, while the proposed OTFWRGD algorithm reached an average accuracy of 86.41%. This dataset highlights the potential of lower-limb MI in enhancing neural plasticity and offers valuable insights for future MI-BCI applications in lower-limb rehabilitation, especially for patients with knee pain.},
}

Humans
*Brain-Computer Interfaces
*Electroencephalography
*Lower Extremity/physiopathology
*Osteoarthritis, Knee/physiopathology/rehabilitation
Algorithms
Deep Learning
Male
Imagery, Psychotherapy
Middle Aged

@article {pmid40835596,
year = {2025},
author = {Molokanova, E and Zhou, T and Vasupal, P and Cherkas, VP and Narute, P and Ferraz, MSA and Reiss, M and Almenar-Queralt, A and Chaldaiopoulou, G and de Souza, JS and Hemati, H and Downey, F and Olajide, OO and Thörn Perez, C and Puppo, F and Mesci, P and Pfaff, SL and Kireev, D and Muotri, AR and Savchenko, A},
title = {Non-genetic neuromodulation with graphene optoelectronic actuators for disease models, stem cell maturation, and biohybrid robotics.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {7499},
pmid = {40835596},
issn = {2041-1723},
support = {1R43MH124563//U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)/ ; 1R01MH128365//U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)/ ; MH123828//U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)/ ; DISC2-13866//California Institute for Regenerative Medicine (CIRM)/ ; 1R43AG076088//U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ ; 5R44DA050393//U.S. Department of Health & Human Services | NIH | National Institute on Drug Abuse (NIDA)/ ; 1R43NS122666//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01NS123642//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; 1R01ES033636//U.S. Department of Health & Human Services | NIH | National Institute of Environmental Health Sciences (NIEHS)/ ; },
mesh = {*Graphite/chemistry ; *Robotics/methods/instrumentation ; Induced Pluripotent Stem Cells/cytology ; Humans ; Organoids/cytology ; Neurons/cytology ; Brain/cytology ; Alzheimer Disease/pathology/therapy ; Optogenetics/methods ; Cell Differentiation ; Animals ; },
abstract = {Light can serve as a tunable trigger for neurobioengineering technologies, enabling probing, control, and enhancement of brain function with unmatched spatiotemporal precision. Yet, these technologies often require genetic or structural alterations of neurons, disrupting their natural activity. Here, we introduce the Graphene-Mediated Optical Stimulation (GraMOS) platform, which leverages graphene's optoelectronic properties and its ability to efficiently convert light into electricity. Using GraMOS in longitudinal studies, we found that repeated optical stimulation enhances the maturation of hiPSC-derived neurons and brain organoids, underscoring GraMOS's potential for regenerative medicine and neurodevelopmental studies. To explore its potential for disease modeling, we applied short-term GraMOS to Alzheimer's stem cell models, uncovering disease-associated alterations in neuronal activity. Finally, we demonstrated a proof-of-concept for neuroengineering applications by directing robotic movements with GraMOS-triggered signals from graphene-interfaced brain organoids. By enabling precise, non-invasive neural control across timescales from milliseconds to months, GraMOS opens new avenues in neurodevelopment, disease treatment, and robotics.},
}

*Graphite/chemistry
*Robotics/methods/instrumentation
Induced Pluripotent Stem Cells/cytology
Humans
Organoids/cytology
Neurons/cytology
Brain/cytology
Alzheimer Disease/pathology/therapy
Optogenetics/methods
Cell Differentiation
Animals

@article {pmid40835360,
year = {2025},
author = {Xiao, X and Li, H},
title = {Improving brain-computer interface performance with optimized frequency interaction and enhancement techniques: CFC-PSO-XGBoost (CPX).},
journal = {Medical engineering & physics},
volume = {143},
number = {},
pages = {104392},
doi = {10.1016/j.medengphy.2025.104392},
pmid = {40835360},
issn = {1873-4030},
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography ; Male ; Female ; Adult ; *Signal Processing, Computer-Assisted ; Young Adult ; Imagination ; Boosting Machine Learning Algorithms ; },
abstract = {PURPOSE: This work aims to increase the classification accuracy of motor imagery-based brain-computer interface (MI-BCI) by employing Cross-Frequency Coupling (CFC) and using spontaneous EEG as an input for the features to increase the system's robustness.

METHODS: Using a benchmark MI-BCI dataset, we examined 25 participants who completed two trials of a motor imagery task split into two classes. Our methodology involved preprocessing EEG data, using Phase-Amplitude Coupling (PAC) to extract CFC characteristics and Particle Swarm Optimization (PSO) to identify the optimal channels. The XGBoost method was utilized to classify the data, and 10-fold cross-validation was employed to verify the results. They are integrated into a single pipeline, named CFC-PSO-XGBoost (CPX).

RESULTS: With an average classification accuracy of 76.7 % ± 1.0 %, with only eight EEG channels, the suggested approach (CPX) outperformed cutting-edge techniques like CSP (60.2 % ± 12.4 %), FBCSP (63.5 % ± 13.5 %), FBCNet (68.8 % ± 14.6 %), and EEGNet. This significant improvement demonstrates the effectiveness of CFC features and PSO for channel selection in MI-BCI classification. Furthermore, the method was evaluated on the public BCI Competition IV-2a dataset, achieving an average multi-class classification accuracy of 78.3 % (95 % CI: 74.85-81.76 %), confirming the scalability and robustness of CPX on external benchmarks.

CONCLUSION: CPX leveraging spontaneous EEG signals and CFC features significantly improves classification accuracy. We anticipate this methodology will be a robust and practical solution in BCI applications, providing better brain-to-device communication with low-channel utilization and considerable performance metrics.},
}

*Brain-Computer Interfaces
Humans
Electroencephalography
Male
Female
Adult
*Signal Processing, Computer-Assisted
Young Adult
Imagination
Boosting Machine Learning Algorithms

@article {pmid40834866,
year = {2025},
author = {Gupta, D and Brangaccio, JA and Mojtabavi, H and Wolpaw, JR and Hill, NJ},
title = {Extracting Robust Single-Trial Somatosensory Evoked Potentials for Non-Invasive Brain Computer Interfaces.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adfd8a},
pmid = {40834866},
issn = {1741-2552},
abstract = {Reliable extraction of single-trial somatosensory evoked potentials (SEPs) is essential for developing brain-computer interface (BCI) applications to support rehabilitation after brain injury. For real-time feedback, these responses must be extracted prospectively on every trial, with minimal post-processing and artifact correction. However, noninvasive SEPs elicited by electrical stimulation at recommended parameter settings (0.1-0.2 msec pulse width, stimulation at or below motor threshold, 2-5 Hz frequency) are typically small and variable, often requiring averaging across multiple trials or extensive processing. Here, we describe and evaluate ways to optimize the stimulation setup to enhance the signal-to-noise ratio (SNR) of noninvasive single-trial SEPs, enabling more reliable extraction. Approach: SEPs were recorded with scalp electroencephalography in tibial nerve stimulation in thirteen healthy people, and two people with CNS injuries. Three stimulation frequencies (lower than recommended: 0.2 Hz, 1 Hz, 2 Hz) with a pulse width longer than recommended (1 msec), at a stimulation intensity based on H-reflex and M-wave at Soleus muscle were evaluated. Detectability of single-trial SEPs relative to background noise was tested offline and in a pseudo-online analysis, followed by a real-time demonstration. Results: SEP N70 was observed predominantly at the central scalp regions. Online decoding performance was significantly higher with Laplacian filter. Generalization performance showed an expected degradation, at all frequencies, with an average decrease of 5.9% (multivariate) and 6.5 % (univariate), with an AUC score ranging from 0.78 - 0.90. The difference across stimulation frequencies was not significant. In individuals with injuries, AUC of 0.86 (incomplete spinal cord injury) and 0.81 (stroke) was feasible. Real-time demonstration showed SEP detection with AUC of 0.89. Significance: This study describes and evaluates a system for extracting single-trial SEPs in real-time, suitable for a BCI-based operant conditioning. It enhances SNR of individual SEPs by alternate electrical stimulation parameters, dry headset, and optimized signal processing. .},
}

@article {pmid40834823,
year = {2025},
author = {Zhang, Z and Meng, W and Sun, H and Pan, G},
title = {CausalCOMRL: Context-based offline meta-reinforcement learning with causal representation.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {193},
number = {},
pages = {107955},
doi = {10.1016/j.neunet.2025.107955},
pmid = {40834823},
issn = {1879-2782},
abstract = {Context-based offline meta-reinforcement learning (OMRL) methods have achieved appealing success by leveragingpre-collected offline datasets to develop task representations that guide policy learning. However, current context-based OMRL methods often introduce spurious correlations, where task components are incorrectly correlated due to confounders. These correlations can degrade policy performance when the confounders in the test taskdiffer from those in the training task. To address this problem, we propose CausalCOMRL, a context-based OMRL method that integrates causal representation learning. This approach uncovers causal relationships among the task components and incorporates the causal relationships into task representations, enhancing the generalizability of RL agents. We further improve the distinction of task representations from different tasks by using mutual information optimization and contrastive learning. Utilizing these causal task representations, we employSAC to optimize policies on meta-RL benchmarks. Experimental results show that CausalCOMRL achieves better performance than other methods on most benchmarks.},
}

@article {pmid40833932,
year = {2025},
author = {Foster, MW and Sanhueza, C and Bahr, E and Kuo, JL and Wu, Y and Komolafe, DO and Blanchette, V and Brinza, T and Morgan-Daniel, J and Oshodi, Y and Sodimu, KA and Omuku, N and Akisanya, E and Trinder, L and Willmoth, S and Simpson, N and White, N and Shaw, TA and Moyse Fenning, H and Runefelt, A and Kolnik, M and Pokorn, M and Fietje, N and Sajnani, N},
title = {The effects of viewing visual artwork on patients, staff, and visitors in healthcare settings: A scoping review.},
journal = {PloS one},
volume = {20},
number = {8},
pages = {e0328215},
doi = {10.1371/journal.pone.0328215},
pmid = {40833932},
issn = {1932-6203},
mesh = {Humans ; *Health Personnel/psychology ; *Art ; *Patients/psychology ; Delivery of Health Care ; },
abstract = {BACKGROUND: The integration of visual art in healthcare settings has been demonstrated to contribute to well-being. However, the impact of visual arts in healthcare has been primarily evaluated among patients. Viewing visual art could be a health resource to a greater number of people in healthcare settings, including patients, staff, and visitors.

METHODS: We conducted a scoping review to synthesize literature on the impact of viewing visual artwork among patients, staff, and visitors in healthcare settings related to the reported outcomes of well-being, wellness, and belonging. The review was informed by Arksey and O'Malley and Joanna Briggs Institute frameworks with masked pairs of reviewers. Included studies were in English, with no restrictions on geographical settings or publication dates. Nine academic databases and twelve gray literature sources were searched, in addition to a hand search and global call for submissions.

RESULTS: From an initial 25,222 records, 68 publications met inclusion criteria across 20 locations. 35 were peer-reviewed studies and 33 constituted gray literature. Included publications that reported sample sizes reflected a total of 6,006 participants with the majority being patients (3,133) followed by staff (1,343), visitors (32), and other/unspecified participants (996). Reported outcomes for patients indicated that visual arts in hospitals reduced heart rates, improved reported mental health outcomes, increased well-being, and provided a positive distraction. Reported outcomes for healthcare staff included an increased well-being, belonging, and capacity to prioritize patient needs. Reported outcomes for visitors consisted of an improved experience in healthcare environments and increased well-being.

CONCLUSIONS: Our synthesis of evidence indicates that integration of visual arts within healthcare settings has positive outcomes for its viewers. Our findings are useful to promote the generation of evidence that can reliably inform the design and experience of healthcare environments.},
}

Humans
*Health Personnel/psychology
*Art
*Patients/psychology
Delivery of Health Care

@article {pmid40833894,
year = {2025},
author = {Liu, T and Wang, Z and Shakil, S and Tong, RK},
title = {Uncovering Low-dimensional Manifolds of Neural Dynamics for Motor-Imagery based Stroke Rehabilitation: An EEG-based Brain-Computer Interface study.},
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.2025.3600824},
pmid = {40833894},
issn = {1558-0210},
abstract = {Stroke rehabilitation aims to repair neural circuits and dynamics through the remapping of neuronal functions. However, there is currently a gap in understanding the alteration of neural population dynamics-the fundamental computational unit driving functions-under clinical settings. In this study, we introduced a novel method to identify stable low-dimensional structures of neural population dynamics in stroke patients during motor tasks. Using whole-brain EEG recordings from chronic stroke patients performing motor imagery (MI) tasks before and after brain-computer interface (BCI) training, as well as a public EEG dataset of acute stroke patients performing MI tasks, we projected EEG signals from sensor space to voxel space via source localization (eLORETA), simulating neural population activity in regions of interest. By applying dimensionality reduction, we successfully obtained low-dimensional neural manifolds to represent neural population dynamics. Our analysis revealed three key findings: (1) For right-handed patients, task-related low-dimensional dynamics in the related brain regions remain stable across subjects, with their features holding potential as biomarkers for stroke rehabilitation; (2) BCI training promotes global and sustained restoration of neural population dynamics; (3) EEG theta-band oscillations show strong correlation with these dynamics, highlighting their macroscopic nature. This study proposes a new, simple, and powerful tool for comprehension and validation of stroke rehabilitation mechanisms confirming the effectiveness of BCI training in restoring neural dynamics.},
}

@article {pmid40832972,
year = {2025},
author = {Wu, X and Tan, S and Zhang, Y and Yin, Y and Hsu, YC and Xue, R and Bai, R},
title = {Feasibility of relaxation-exchange magnetic resonance imaging (REXI) for measuring water exchange across the blood-CSF barrier in the human choroid plexus.},
journal = {Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism},
volume = {},
number = {},
pages = {271678X251369218},
doi = {10.1177/0271678X251369218},
pmid = {40832972},
issn = {1559-7016},
abstract = {The choroid plexus (CP) is important for cerebrospinal fluid (CSF) secretion and forms the blood-CSF barrier (BCSFB), which is essential for brain homeostasis. However, noninvasive methods for evaluating BCSFB function remain limited. Previously, we introduced a novel magnetic resonance imaging (MRI) technique, relaxation-exchange MRI (REXI), to quantify water exchange between CP and CSF in rats by leveraging the substantial difference in transverse relaxation times between CP tissue and CSF. Here, we adapted REXI to human applications by implementing segmented echo-planar imaging readout for enhanced acquisition speed, optimizing key parameters based on the Cramér-Rao lower bound, and refining the analysis methodology. We conducted simulations and phantom experiments for methodological validation. Subsequently, we performed a scan-rescan experiment in healthy volunteers (n = 6, mean-age ∼22 years), revealing relatively good repeatability in measurements of the apparent water exchange rate kBCSFB (intraclass correlation coefficient = 0.84). REXI detected a 34% decrease in kBCSFB among middle-aged healthy adults (n = 6, mean-age ∼55 years) compared with young healthy adults (n = 9, mean-age ∼23 years, p = 0.0048). These results demonstrate the feasibility of REXI in quantifying water exchange in human CP in vivo, providing a promising tool for future investigations of BCSFB function.},
}

@article {pmid40832808,
year = {2025},
author = {Ma, C and Li, W and Gao, C and Li, X and She, J and Zou, Z and Zhang, D and Jin, Y and Xu, C and Liu, B and Luo, Z},
title = {Multifunctional Hydrogel Materials for Advanced Neural Interfaces.},
journal = {Small methods},
volume = {},
number = {},
pages = {e01134},
doi = {10.1002/smtd.202501134},
pmid = {40832808},
issn = {2366-9608},
support = {32471387//National Natural Science Foundation of China/ ; 2024BCB002//Key Research and Development Program of Hubei Province/ ; },
abstract = {Conventional rigid neural electrodes mismatch the soft, wet nature of neural tissue, hindering long-term stable interfaces. Multifunctional hydrogels, with their tissue-like compliance, ionic conductivity, and biocompatibility, offer a promising solution to bridge bioelectronic systems and neural tissues. This review systematically examines critical hydrogel properties-mechanical compliance, adhesion, biocompatibility, conductivity, and injectability-for neural interfacing. It summarizes recent advances in hydrogel-based technologies, including hydrogel coatings, conductive hydrogel electrodes, and integrated hydrogel electronics. Future challenges involve balancing biodegradation with long-term stability, developing advanced fabrication strategies, and ensuring chronic performance stability. Key future directions include optimizing hydrogel properties for chronic applications, creating smart-responsive hydrogels, integrating artificial intelligence, and advancing wireless systems. Leveraging materials science, bioengineering, and nanotechnology, hydrogel-based neural interfaces are poised to unlock unprecedented capabilities in brain-computer interfaces, neural prosthetics, neuromodulation, and regenerative therapies, heralding a paradigm shift in neurotechnology.},
}

@article {pmid40832231,
year = {2025},
author = {Jude, JJ and Haro, S and Levi-Aharoni, H and Hashimoto, H and Acosta, AJ and Card, NS and Wairagkar, M and Brandman, DM and Stavisky, SD and Williams, ZM and Cash, SS and Simeral, JD and Hochberg, LR and Rubin, DB},
title = {Decoding intended speech with an intracortical brain-computer interface in a person with longstanding anarthria and locked-in syndrome.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.08.12.668516},
pmid = {40832231},
issn = {2692-8205},
abstract = {Intracortical brain-computer interfaces (iBCIs) for decoding intended speech have provided individuals with ALS and severe dysarthria an intuitive method for high-throughput communication. These advances have been demonstrated in individuals who are still able to vocalize and move speech articulators. Here, we decoded intended speech from an individual with longstanding anarthria, locked-in syndrome, and ventilator dependence due to advanced symptoms of ALS. We found that phonemes, words, and higher-order language units could be decoded well above chance. While sentence decoding accuracy was below that of demonstrations in participants with dysarthria, we are able to attain an extensive characterization of the neural signals underlying speech in a person with locked-in syndrome and through our results identify several directions for future improvement. These include closed-loop speech imagery training and decoding linguistic (rather than phonemic) units from neural signals in middle precentral gyrus. Overall, these results demonstrate that speech decoding from motor cortex may be feasible in people with anarthria and ventilator dependence. For individuals with longstanding anarthria, a purely phoneme-based decoding approach may lack the accuracy necessary to support independent use as a primary means of communication; however, additional linguistic information embedded within neural signals may provide a route to augment the performance of speech decoders.},
}

@article {pmid40831738,
year = {2025},
author = {},
title = {Corrigendum to "A Fuzzy Shell for Developing an Interpretable BCI Based on the Spatiotemporal Dynamics of the Evoked Oscillations".},
journal = {Computational intelligence and neuroscience},
volume = {2025},
number = {},
pages = {9842516},
doi = {10.1155/cone/9842516},
pmid = {40831738},
issn = {1687-5273},
abstract = {[This corrects the article DOI: 10.1155/2021/6685672.].},
}

@article {pmid40831229,
year = {2025},
author = {Chen, D and Shi, J and Tao, B and Zhao, X and Zhao, Z and Li, S and Xu, Y and Ding, T and Zhang, P and Ye, Q and Chen, K and Wu, Z and Tang, Y and Jiang, W and Shu, K and Huang, L and You, Z and Zhang, P and Tang, Z},
title = {A Novel Transfer Learning-Based Hybrid EEG-fNIRS Brain-Computer Interface for Intracerebral Hemorrhage Rehabilitation.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e05426},
doi = {10.1002/advs.202505426},
pmid = {40831229},
issn = {2198-3844},
support = {2023BAA005//Major Program (JD) of Hubei Province/ ; YCJJ20251401//Fundamental Research Funds for the Central Universities/ ; 92148206//National Natural Science Foundation of China/ ; 2024020702030123//Key Research and Development Program of Wuhan/ ; 2024JCYJ044//Huazhong University of Science and Technology/ ; 2022ZHFY01//Research Fund of Tongji Hospital/ ; AI2024B03//Research Fund of Tongji Hospital/ ; },
abstract = {Motor imagery (MI)-based neurorehabilitation shows promise for intracerebral hemorrhage (ICH) recovery, yet conventional unimodal brain-computer interfaces (BCIs) face critical limitations in cross-subject generalization. This study presents a multimodal electroencephalography (EEG)-functional near-infrared spectroscopy (fNIRS) fusion framework incorporating a Wasserstein metric-driven source domain selection method that quantifies inter-subject neural distribution divergence. Through comparative neuroactivation analysis of 17 normal controls and 13 ICH patients during MI tasks, the transfer learning model achieved 74.87% mean classification accuracy on patient data when trained with optimally selected normal templates. Cross-validation on two public hybrid EEG-fNIRS datasets demonstrated generalizability, increasing baseline accuracy to 82.30% and 87.24%, respectively. The proposed system synergistically combines the millisecond temporal resolution of EEG with the hemodynamic spatial specificity of fNIRS, establishing the first clinically viable multimodal analytical protocol for ICH rehabilitation. This paradigm advances neurotechnology translation by paving the way for personalized rehabilitation regimens through robust cross-subject neural pattern transfer while addressing the critical barrier of neurophysiological heterogeneity in post-ICH populations.},
}

@article {pmid40830580,
year = {2025},
author = {Li, Y and Li, H and Wang, H and Wang, X},
title = {Exploring the therapeutic potential of psychedelics in treating substance use disorders.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {40830580},
issn = {1476-5578},
support = {T2350008//National Natural Science Foundation of China (National Science Foundation of China)/ ; 22207103//National Natural Science Foundation of China (National Science Foundation of China)/ ; T2341003//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Psychedelics, particularly psilocybin, have garnered significant attention as potential therapeutic tools for treating substance use disorders (SUDs), such as those related to alcohol, nicotine, heroin (an opioid), or cocaine. Traditional treatments often fall short, leading to high relapse rates and an urgent need for innovative approaches. This article explores the emerging role of psychedelics in SUDs therapy, highlighting their ability to disrupt maladaptive neural circuits, promote neuroplasticity, and facilitate profound psychological insights that address the root causes of SUDs. Clinical trials demonstrate promising results across various forms of SUDs, with psilocybin-assisted therapy showing significant reductions in substance use and improved mental health outcomes. Despite the potential, challenges such as legal barriers, safety concerns, and the need for more rigorous research remain. The future of psychedelics in SUDs treatment is cautiously optimistic, with the possibility of transforming the field of SUDs therapy and offering hope to millions of individuals struggling with SUDs.},
}

@article {pmid40830488,
year = {2025},
author = {Schippers, A and Vansteensel, MJ and Freudenburg, ZV and Luo, S and Crone, NE and Ramsey, NF},
title = {Don't put words in my mouth: speech perception can falsely activate a brain-computer interface.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {181},
pmid = {40830488},
issn = {1743-0003},
support = {SGW-406-18-GO-086//Nederlandse Organisatie voor Wetenschappelijk Onderzoek/ ; SGW-406-18-GO-086//Nederlandse Organisatie voor Wetenschappelijk Onderzoek/ ; SGW-406-18-GO-086//Nederlandse Organisatie voor Wetenschappelijk Onderzoek/ ; SGW-406-18-GO-086//Nederlandse Organisatie voor Wetenschappelijk Onderzoek/ ; UGT7685//Stichting voor de Technische Wetenschappen/ ; UGT7685//Stichting voor de Technische Wetenschappen/ ; UGT7685//Stichting voor de Technische Wetenschappen/ ; UGT7685//Stichting voor de Technische Wetenschappen/ ; ERC-Advanced 'iConnect' project, grant ADV 320708/ERC_/European Research Council/International ; ERC-Advanced 'iConnect' project, grant ADV 320708/ERC_/European Research Council/International ; ERC-Advanced 'iConnect' project, grant ADV 320708/ERC_/European Research Council/International ; ERC-Advanced 'iConnect' project, grant ADV 320708/ERC_/European Research Council/International ; U01DC016686/DC/NIDCD NIH HHS/United States ; U01DC016686/DC/NIDCD NIH HHS/United States ; U01DC016686/DC/NIDCD NIH HHS/United States ; U01DC016686/DC/NIDCD NIH HHS/United States ; U01DC016686/DC/NIDCD NIH HHS/United States ; U01DC016686/DC/NIDCD NIH HHS/United States ; UH3NS114439/NS/NINDS NIH HHS/United States ; UH3NS114439/NS/NINDS NIH HHS/United States ; UH3NS114439/NS/NINDS NIH HHS/United States ; UH3NS114439/NS/NINDS NIH HHS/United States ; UH3NS114439/NS/NINDS NIH HHS/United States ; UH3NS114439/NS/NINDS NIH HHS/United States ; PPS-2021-02//Dutch Brain Foundation/ ; PPS-2021-02//Dutch Brain Foundation/ ; 101070939//HORIZON EUROPE European Innovation Council/ ; 101070939//HORIZON EUROPE European Innovation Council/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Speech Perception/physiology ; Male ; Adult ; Electrocorticography ; Female ; Speech/physiology ; *Sensorimotor Cortex/physiology ; Support Vector Machine ; },
abstract = {BACKGROUND: Recent studies have demonstrated that speech can be decoded from brain activity which in turn can be used for brain-computer interface (BCI)-based communication. It is however also known that the area often used as a signal source for speech decoding BCIs, the sensorimotor cortex (SMC), is also engaged when people perceive speech, thus making speech perception a potential source of false positive activation of the BCI. The current study investigated if and how speech perception may interfere with reliable speech BCI control.

METHODS: We recorded high-density electrocorticography (HD-ECoG) data from five subjects while they performed a speech perception and a speech production task. We first evaluated whether speech perception and production activated the SMC. Second, we trained a support-vector machine (SVM) on the speech production data (including rest). To test the occurrence of false positives, this decoder was then tested on speech perception data where every perception segment that was classified as a produced syllable rather than rest was considered a false positive. Finally, we investigated whether perceived speech could be distinguished from produced speech and rest.

RESULTS: Our results show that both the perception and production of speech activate the SMC. In addition, we found that decoders that are highly reliable at detecting self-produced syllables from brain signals may generate false positive BCI activations during the perception of speech and that it is possible to distinguish perceived speech from produced speech and rest, with high accuracy.

CONCLUSIONS: We conclude that speech perception can interfere with reliable BCI control, and that efforts to limit the occurrence of false positives during daily-life BCI use should be implemented in BCI design to increase the likelihood of successful adoptation by end users.},
}

Humans
*Brain-Computer Interfaces
*Speech Perception/physiology
Male
Adult
Electrocorticography
Female
Speech/physiology
*Sensorimotor Cortex/physiology
Support Vector Machine

@article {pmid40830054,
year = {2025},
author = {Brannigan, J and Kian, A and Eiber, C and Tarigoppula, VSA and Bogard, J and Siddiqui, AH and Rind, G and Berenstein, A and Majidi, S and Oxley, TJ},
title = {Characterizing superficial cerebral cortical venous anatomy for endovascular device implantation: a cross-sectional imaging study.},
journal = {Journal of neurointerventional surgery},
volume = {},
number = {},
pages = {},
doi = {10.1136/jnis-2025-023532},
pmid = {40830054},
issn = {1759-8486},
abstract = {BACKGROUND: Neurovascular electronic devices, including brain-computer interfaces (BCIs), offer a minimally invasive approach to diagnosing and treating neurological disorders. Implanting BCIs in superficial cortical veins, owing to their proximity to sensorimotor cortices, may improve motor function restoration. However, marked anatomical variability and the complex anteriorly directed connection with the superior sagittal sinus (SSS) complicate device navigation. This exploratory study aimed to characterize cortical venous anatomy to inform device design and procedural planning.

METHODS: Retrospective imaging data from 25 patients were analyzed using magnetic resonance venography (MRV) and computed tomography venography (CTV). Vessel segmentation and analysis quantified parameters such as vein presence, diameter, length, angulation, and tortuosity. In 12 patients, T1-weighted magnetic resonance imaging (MRI) was used to extract cortical gyri and sulci, assessing vessel-cortex relationships.

RESULTS: The superior anastomotic vein (vein of Trolard) was identified bilaterally in 84% of patients, with a mean entrance diameter of 4.4 mm. Frequent transient constrictions (<2 mm) were reported. The precentral vein was present bilaterally in 52% of cases. Most cortical veins exhibited take-off angles >90 degrees from the SSS, presenting challenges for endovascular navigation, with overall considerable anatomical variability observed.

CONCLUSION: The vein of Trolard shows promise as a target for endovascular BCIs given its consistent presence and favorable dimensions. Nonetheless, constrictions and steep angulation at the SSS confluence pose challenges for device deployment. A new framework is necessary for the classification of cortical venous anatomy, to guide patient selection and procedural planning, which will require further development and validation.},
}

@article {pmid40829351,
year = {2025},
author = {Yadav, H and Maini, S},
title = {Decoding brain signals: A comprehensive review of EEG-Based BCI paradigms, signal processing and applications.},
journal = {Computers in biology and medicine},
volume = {196},
number = {Pt C},
pages = {110937},
doi = {10.1016/j.compbiomed.2025.110937},
pmid = {40829351},
issn = {1879-0534},
abstract = {Brain-computer interface (BCI) based on electroencephalography (EEG) is a fast-developing field with a wide range of applications such as assistive technology, neurorehabilitation, entertainment, cognitive enhancement, etc. Since EEG is a non-invasive technique that captures brain activity in real time, it is ideally suited for developing interfaces that enable direct brain-to-device communication. The different paradigms utilised in EEG-based BCIs, such as Motor Imagery (MI), Steady-State Visual Evoked Potentials (SSVEP), P300 Event-related Potentials (ERP), and Hybrid paradigms that integrate several strategies for enhanced performance, are the main emphasis of this systematic review. This paper also explores the signal processing techniques, feature extraction strategies, and classification algorithms necessary for handling low-amplitude and noisy EEG recordings. The applications of BCI in different fields, as well as the challenges and possible solutions of EEG-based BCIs, are also covered in this article. Overall, the state-of-the-art in EEG-based BCIs is thoroughly reviewed in this comprehensive review article, which also identifies important areas for further study and technological advancement.},
}

@article {pmid40829175,
year = {2025},
author = {Liu, N and Wang, J and Wang, H and Gao, B and Lin, Z and Xu, TL and Duan, S and Xu, H},
title = {A noncanonical parasubthalamic nucleus-to-extended amygdala circuit converts chronic social stress into anxiety.},
journal = {The Journal of clinical investigation},
volume = {135},
number = {16},
pages = {},
doi = {10.1172/JCI188246},
pmid = {40829175},
issn = {1558-8238},
mesh = {Animals ; Mice ; *Stress, Psychological/physiopathology/metabolism/pathology ; *Anxiety/physiopathology/metabolism/pathology ; *Amygdala/physiopathology/metabolism/pathology ; Male ; *Septal Nuclei/physiopathology/metabolism ; Chronic Disease ; *Thalamus/physiopathology ; Shal Potassium Channels/metabolism/genetics ; Neurons/metabolism ; },
abstract = {Anxiety disorders pose a substantial threat to global mental health, with chronic stress identified as a major etiologic factor. Over the past few decades, extensive studies have revealed that chronic stress induces anxiety states through a distributed neuronal network of interconnected brain structures. However, the precise circuit mechanisms underlying the transition from chronic stress to anxiety remain incompletely understood. Employing the chronic social defeat stress (CSDS) paradigm in mice, we uncovered a critical role of the parasubthalamic nucleus (PSTh) in both the induction and expression of anxiety-like behavior. The anxiogenic effect was mediated by an excitatory trisynaptic circuitry involving the lateral parabrachial nucleus (LPB), PSTh, and bed nucleus of the stria terminalis (BNST). Furthermore, CSDS downregulated Kv4.3 channels in glutamatergic neurons of the PSTh. Reexpression of these channels dampened neuronal overexcitability and alleviated anxiety-like behavior in stressed animals. In parallel with the well-known anxiety network centered on the amygdala, here we identify a noncanonical LPB-PSTh-BNST pathway in the transformation of stress into anxiety. These findings suggest that the PSTh may serve as a potential therapeutic target for anxiety-related disorders.},
}

Animals
Mice
*Stress, Psychological/physiopathology/metabolism/pathology
*Anxiety/physiopathology/metabolism/pathology
*Amygdala/physiopathology/metabolism/pathology
Male
*Septal Nuclei/physiopathology/metabolism
Chronic Disease
*Thalamus/physiopathology
Shal Potassium Channels/metabolism/genetics
Neurons/metabolism

@article {pmid40828021,
year = {2025},
author = {Martinez-Addiego, F and Liu, Y and Moon, K and Shytle, E and Amaral, L and O'Brien, C and Sen, S and Riesenhuber, M and Culham, JC and Striem-Amit, E},
title = {Action-type mapping principles extend beyond evolutionarily conserved actions, even in people born without hands.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {34},
pages = {e2503188122},
doi = {10.1073/pnas.2503188122},
pmid = {40828021},
issn = {1091-6490},
support = {22YF1454200//Shanghai Youth Science and Technology Innovation Plan/ ; },
abstract = {How are actions represented in the motor system? Although the sensorimotor system is broadly organized somatotopically, higher-level sensorimotor areas encode action-type information for reaching and grasping actions-regardless of the acting body part. Does the brain similarly support generalization across acting body parts for more evolutionarily recent actions, such as tool-use? We tested whether there is a body-part-independent action-type organization in sensorimotor areas by examining fMRI responses for tool-use actions that participants performed with their hands or feet. We additionally included individuals born without hands to test whether hand sensorimotor experience is necessary for the development of this action-type organization. Across analyses, we found a consistent dissociation in the motor system. The primary sensorimotor cortices encoded concrete, body-part specific information in both groups. In contrast, higher-level motor areas within the tool-use network represent abstract, action-type information independent of the body part for both groups. Together, our results suggest that the hierarchical organization of the motor system is not dependent on a long evolutionary history of an action. Further, this organization is not dependent on an individual's manual sensorimotor experience. Our results also show that the functional reorganization in congenital handlessness follows the hierarchical organization of the intact cortex, revealing the limitations of brain plasticity. Finally, the results support using a readout of a more abstract code for hierarchical brain-computer interfaces.},
}

@article {pmid40827135,
year = {2025},
author = {Qiao, MX and Yu, H and Fu, Z and Wei, W and Li, XJ and Deng, W and Guo, WJ and Li, T},
title = {Combination Therapy Against Mood and Anxiety Disorders: Association Between Efficacy and White Blood Cell Count.},
journal = {Neuropsychiatric disease and treatment},
volume = {21},
number = {},
pages = {1655-1668},
pmid = {40827135},
issn = {1176-6328},
abstract = {BACKGROUND: Numerous studies suggest that hyperactivation of the immuno-inflammatory system, as reflected in cytokine levels, is associated with more severe symptoms in mood and anxiety disorders and weaker response to treatment. Here we examined whether the efficacy of a combination of bright light therapy, repetitive transcranial magnetic stimulation and medication is associated with another immuno-inflammatory index, white blood cell count, before and/or after treatment, in a retrospective observational study.

METHODS: We retrospectively analyzed 467 inpatients with major depressive, bipolar, or generalized anxiety disorder who were treated with combination therapy for at least one week at Hangzhou Seventh People's Hospital between April 2022 and April 2024. Potential associations between remission incidences within four weeks after treatment and white blood cell count both before treatment and post-treatment were explored. We used mixed-effects linear modeling to examine the association between treatment characteristics and changes in white blood cell count and depressive symptoms.

RESULTS: Bipolar and major depressive disorders were associated with significantly higher white blood cell counts at baseline than generalized anxiety disorder as well as with significantly lower remission incidences. Bright light therapy's effects depended on baseline inflammation, more sessions led to greater reductions in the Hamilton Depression Rating Scale score with low baseline white blood cell count, and greater decreases in white blood cell count with high baseline count. In contrast, repetitive transcranial magnetic stimulation sessions showed no association with white blood cell count.

CONCLUSION: These results highlight the need to account for an individual's immuno-inflammatory state when personalizing treatment for mental health disorders.},
}

@article {pmid40821552,
year = {2025},
author = {Priya, S and Mohan, S and Kuppusamy, R and Suyambulingam, I and Baby, B and Ramesh, R and Han, SS},
title = {Advances in Bio-Microelectromechanical System-Based Sensors for Next-Generation Healthcare Applications.},
journal = {ACS omega},
volume = {10},
number = {31},
pages = {34088-34105},
pmid = {40821552},
issn = {2470-1343},
abstract = {Microelectromechanical system (MEMS)-based sensors have become essential in various fields, including healthcare, automotive, and industrial applications. These sensors integrate mechanical structures and electronics on a single chip, allowing precise, compact, and efficient measurements of parameters like pressure, force, acceleration, and chemical reactions. In this context, this review article presents the essential role of MEMS sensors in healthcare applications. In healthcare, MEMS sensors are widely used for monitoring vital signs, detecting glucose levels, managing cardiovascular and intracranial pressure, and enhancing drug delivery systems. They are also key in tactile sensing during surgeries and in improving neuromuscular monitoring through electromyography (EMG). Despite their advantages, such as small size, low energy consumption, and high performance, MEMS sensors face challenges like sensitivity drift, durability concerns, and long-term calibration stability. This article addresses these limitations and highlights ongoing advancements aimed at improving sensor accuracy, energy efficiency, and adaptability to diverse environments. By examining current trends and innovations, this review provides insights into how MEMS technology is driving breakthroughs in biomedical research, early cancer diagnosis, and bioimaging treatment. We have discussed inertial sensors, MEMS-based glucose sensors, intraocular pressure (IOP) sensors, intracranial pressure sensors, cardiovascular pressure sensors, tactile sensors, and smart inhalers. In addition, we have explored recent advancements in MEMS technologies applied to healthcare, particularly in microfluidic MEMS chips and brain-machine interfaces, with a focus on developments from the last five years. Future research directions focus on enhancing the flexibility, reliability, and energy efficiency of MEMS sensors, positioning them as key components in the next generation of healthcare and medical devices.},
}

@article {pmid40819087,
year = {2025},
author = {Kinreich, S},
title = {Neural transmission in the wired brain, new insights into an encoding-decoding-based neuronal communication model.},
journal = {Translational psychiatry},
volume = {15},
number = {1},
pages = {288},
pmid = {40819087},
issn = {2158-3188},
support = {AA029448//U.S. Department of Health & Human Services | NIH | National Institute on Alcohol Abuse and Alcoholism (NIAAA)/ ; },
abstract = {Brain activity is known to be rife with oscillatory activity in different frequencies, which are suggested to be associated with intra-brain communication. However, the specific role of frequencies in neuronal information transfer is still an open question. To this end, we utilized EEG resting state recordings from 5 public datasets. Overall, data from 1668 participants, including people with MDD, ADHD, OCD, Parkinson's, Schizophrenia, and healthy controls aged 5-89, were part of the study. We conducted a running window of Spearman correlation between the two frontal hemispheres' Alpha envelopes. The results of this analysis revealed a unique pattern of correlation states alternating between fully synchronized and desynchronized several times per second, likely due to the interference pattern between two signals of slightly different frequencies, also named "Beating". Subsequent analysis showed this unique pattern in every pair of ipsilateral/contralateral, across frequencies, either in eyes closed or open, and across all ages, underscoring its inherent significance. Biomarker analysis revealed significantly lower synchronization and higher desynchronization for people older than 50 compared to younger ones and lower ADHD desynchronization compared to age-matched controls. Importantly, we propose a new brain communication model in which frequency modulation creates a binary message encoded and decoded by brain regions for information transfer. We suggest that the binary-like pattern allows the neural information to be coded according to certain physiological and biological rules known to both the sender and recipient. This digital-like scheme has the potential to be exploited in brain-computer interaction and applied technologies such as robotics.},
}

@article {pmid40816597,
year = {2025},
author = {Xu, T and Yu, L and Zheng, Y and Huang, S},
title = {BrainVision: Cross-domain EEG decoding for visual content retrieval and reconstruction.},
journal = {Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuroscience.2025.07.047},
pmid = {40816597},
issn = {1873-7544},
abstract = {Understanding human visual intent through brain signals remains a fundamental challenge in neuroscience and artificial intelligence. Despite recent advances in brain decoding, existing approaches typically operate within isolated datasets and modalities, limiting their generalization capabilities. This paper introduces BrainVision, a novel framework that bridges visual recognition and emotional EEG datasets to enable comprehensive visual content generation through cross-domain learning. BrainVision addresses the critical challenge of leveraging complementary information across heterogeneous EEG sources by implementing a unified cross-domain alignment strategy. Our framework maps neural patterns from the THINGS-EEG visual recognition dataset and the DEAP emotional response dataset into a shared representation space, enabling three distinct visual output capabilities: (1) accurate content retrieval and classification, (2) detailed linguistic descriptions through adapter-enhanced large language models, and (3) high-fidelity image reconstruction via stable diffusion models. Experimental results demonstrate that BrainVision significantly outperforms single-domain approaches, achieving a 15.3% increase in retrieval accuracy and a 12.7% improvement in structural similarity for reconstructed images compared to state-of-the-art methods. Furthermore, our framework demonstrates robust zero-shot generalization, maintaining 82% of its performance when applied to novel stimuli not seen during training. The multi-modal outputs provide complementary interpretations of neural activity, offering a more comprehensive understanding of visual intent. Our findings establish that integrating diverse neural datasets substantially enhances the capabilities of brain decoding systems, providing a promising direction for developing more intuitive and versatile brain-computer interfaces. BrainVision represents an important step toward bridging the gap between neural activity and rich visual experiences across different cognitive domains.},
}

@article {pmid40816112,
year = {2025},
author = {Chen, J and Chen, X and Tang, Z and Lei, L and Zhan, Y and Liu, S and Zhou, H and Wan, J and Chen, Z and Wu, Y and Luo, Z},
title = {Influence of eHealth literacy on acceptance of healthcare services with risks in China: chain-mediating effect of general risk propensity and self-efficacy.},
journal = {Public health},
volume = {247},
number = {},
pages = {105891},
doi = {10.1016/j.puhe.2025.105891},
pmid = {40816112},
issn = {1476-5616},
abstract = {OBJECTIVES: To investigate factors associated with the acceptance of healthcare services with risks among Chinese public.

STUDY DESIGN: This national cross-sectional study used data from the 2023 Psychology and Behavior Investigation of Chinese Residents.

METHODS: Structural equation modelling was used to analyse the chain-mediated pathways of e-health literacy acting through general risk propensity and self-efficacy on the acceptability of five risky healthcare services (COVID-19 vaccine booster shots, mixed vaccination with COVID-19 vaccine, telemedicine, internet-based home care, and brain-computer interface technology). Subgroup analyses were performed by gender, region, and age.

RESULTS: Mean acceptance ratings for the five services ranged from 53.01 to 65.62. eHealth literacy was positively associated with self-efficacy, general risk propensity, and acceptance of five services (r = 0.012-0.048, P < 0.05). General risk propensity was positively associated with mixed vaccination with COVID-19 vaccine, telemedicine, and brain-computer interface technology (r = 0.009 to 0.041, P < 0.05). After adjusting for covariates, the correlation between general risk propensity and acceptance of the COVID-19 vaccine booster shots and telemedicine was non-significant. eHealth literacy had a significant positive effect on five services, self-efficacy, and general risk propensity (P < 0.05). Subgroup analyses showed that self-efficacy and general risk propensity acted as mediators in the relationship between e-health literacy and acceptance of four health services in addition to the mixed neocoronary vaccine in both male and urban populations.

CONCLUSIONS: This finding shows general risk propensity and self-efficacy mediate the link between eHealth literacy and risky healthcare acceptance, deepening understanding and providing practical guidance for promoting innovative healthcare services in China.},
}

@article {pmid40811166,
year = {2025},
author = {Zhang, X and Zheng, W and Li, Z and Yang, Y and Liu, W and Cai, H and Zhu, J and Liu, J and Hu, B and Dong, Q},
title = {Constraint-Driven Causal Representation Learning for Vigilance Robust Estimation in Brain-Computer Interface.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3594434},
pmid = {40811166},
issn = {2162-2388},
abstract = {Vigilance estimation is a critical task within the field of brain-computer interfaces, extensively applied in monitoring and optimizing user states during human-machine interaction using electroencephalography (EEG). However, most existing vigilance prediction frameworks are prone to spurious correlations stemming from inherent biases in collected data. These biases involve relevant but vigilance-independent information, which may lack robustness when applied to different data distributions, i.e., out-of-distribution (OOD) scenarios. The core idea of this study is to learn constraints that capture causal information from the input based on the assumed underlying data generating process. Leveraging the disentanglement and invariance principles behind the assumptions, we propose a constraint-driven causal representation learning (CCRL) to identify and separate spurious latent variables from biased training data for generalized vigilance estimation. The CCRL training process consists of two phases: self-supervised pretraining and constraint-driven causal information disentanglement. In the first phase, based on the masked autoencoder (MAE) architecture, unlabeled training data are used for reconstructing pretext tasks to capture the comprehensive and intrinsic contextual information from EEG data, which provides a powerful input for downstream disentanglement learning. In the second phase, we propose a novel disentanglement strategy to learn spurious-free latent representations causally related to the vigilance state driven by adversarial and invariance constraints. Comprehensive validation experiments conducted on two well-known public datasets demonstrate the effectiveness and superiority of the proposed framework. In general, this work has promising implications for addressing OOD challenges in vigilance estimation.},
}

@article {pmid40800510,
year = {2024},
author = {Muraoka, Y and Iwama, S and Ushiba, J},
title = {Neurofeedback-induced desynchronization of sensorimotor rhythm elicits pre-movement downregulation of intracortical inhibition that shortens simple reaction time in humans: A double-blind, sham-controlled randomized study.},
journal = {Imaging neuroscience (Cambridge, Mass.)},
volume = {2},
number = {},
pages = {},
pmid = {40800510},
issn = {2837-6056},
abstract = {Sensorimotor rhythm event-related desynchronization (SMR-ERD) is associated with the activities of cortical inhibitory circuits in the motor cortex. The self-regulation of SMR-ERD through neurofeedback training has demonstrated that successful SMR-ERD regulation improves motor performance. However, the training-induced changes in neural dynamics in the motor cortex underlying performance improvement remain unclear. Here, we hypothesized that SMR-neurofeedback based on motor imagery reduces cortical inhibitory activities during motor preparation, leading to shortened reaction time due to the repetitive recruitment of neural populations shared with motor imagery and movement preparation. To test this, we conducted a double-blind, sham-controlled study on 24 participants using neurofeedback training and pre- and post-training evaluation for simple reaction time tests and cortical inhibitory activity using short-interval intracortical inhibition (SICI). The results showed that veritable neurofeedback training effectively enhanced SMR-ERD in healthy male and female participants, accompanied by reduced simple reaction times and pre-movement SICI. Furthermore, SMR-ERD changes correlated with changes in pre-movement cortical disinhibition, and the disinhibition magnitude correlated with behavioral changes. These results suggest that SMR-neurofeedback modulates cortical inhibitory circuits during movement preparation, thereby enhancing motor performance.},
}

@article {pmid40797003,
year = {2025},
author = {Lo, BWY and Fukuda, H},
title = {Advances in Ischemic Stroke Treatment: Current and Future Therapies.},
journal = {Neurology and therapy},
volume = {},
number = {},
pages = {},
pmid = {40797003},
issn = {2193-8253},
abstract = {This review summarizes current concepts in our understanding of stroke anatomy, pathophysiology of cerebral hypoperfusion, and collateral circulation. It also provides an evidence-based update in stroke trials and treatments assessed using PRISMA guidelines. Intravenous thrombolysis, endovascular thrombectomy for anterior circulation strokes, blood pressure control after endovascular thrombectomy, and medical management principles are discussed. Endovascular thrombectomy and medical therapy improves functional independence at 90 days in anterior circulation strokes even in late windows up to 24 h post symptom onset regardless of infarct core size. Intensive systolic blood pressure control acutely post thrombectomy is associated with harm and worse outcomes. This review also provides an evidence-based update on neurorehabilitation strategies with emerging interventions such as brain-computer interface and robotics having the potential to maximize neuroplasticity for potential improvement and recovery post stroke.},
}

@article {pmid40791388,
year = {2025},
author = {Nuñez Ponasso, G and Drumm, DA and Oppermann, H and Wang, A and Noetscher, GM and Maess, B and Knösche, TR and Makaroff, SN and Haueisen, J},
title = {High-Resolution EEG Source Reconstruction from PCA-Corrected BEM-FMM Reciprocal Basis Funcions: A Study with Visual Evoked Potentials from Intermittent Photic Stimulation.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.07.11.664246},
pmid = {40791388},
issn = {2692-8205},
abstract = {Modern automated human head segmentations can generate high-resolution computational meshes involving many non-nested tissues. However, most source reconstruction software is limited to 3 -4 nested layers of low resolution and a small number of dipolar sources∼ 10, 000. Recently, we introduced modeling techniques for source reconstruction of magnetoencephalographic (MEG) signals using the reciprocal approach and the boundary element fast multipole method (BEM-FMM). The technique of BEM-FMM can process both nested and non-nested models with as many as 4 million surface elements. In this paper, we present an analogue technique for source reconstruction of electroencephalographic (EEG) signals based on cortical global basis functions. The present work uses Helmholtz reciprocity to relate the reciprocally-generated lead-field matrices to their direct counterpart, while resolving the issue of possible biases toward the reference electrode. Our methodology is tested with experimental EEG data collected from a cohort of 12, young and healthy, volunteers subjected to intermittent photic stimulation (IPS). Our novel high-resolution source reconstruction models can have impact on mental health screening as well as brain-computer inter-faces.},
}

@article {pmid40789435,
year = {2025},
author = {Tozzi, A and Jaušovec, K},
title = {Takens' theorem to assess EEG traces: Regional variations in brain dynamics.},
journal = {Neuroscience letters},
volume = {865},
number = {},
pages = {138352},
doi = {10.1016/j.neulet.2025.138352},
pmid = {40789435},
issn = {1872-7972},
abstract = {Takens' theorem (TT) proves that the behaviour of a dynamical system can be effectively reconstructed within a multidimensional phase space. This offers a comprehensive framework for examining temporal dependencies, dimensional complexity and predictability of time series data. We applied TT to investigate the physiological regional differences in EEG brain dynamics of healthy subjects, focusing on three key channels: FP1 (frontal region), C3 (sensorimotor region), and O1 (occipital region). We provided a detailed reconstruction of phase spaces for each EEG channel using time-delay embedding. The reconstructed trajectories were quantified through measures of trajectory spread and average distance, offering insights into the temporal structure of brain activity that traditional linear methods struggle to capture. Variability and complexity were found to differ across the three regions, revealing notable regional variations. FP1 trajectories exhibited broader spreads, reflecting the dynamic complexity of frontal brain activity associated with higher cognitive functions. C3, involved in sensorimotor integration, displayed moderate variability, reflecting its functional role in coordinating sensory inputs and motor outputs. O1, responsible for visual processing, showed constrained and stable trajectories, consistent with repetitive and structured visual dynamics. These findings align with the functional specialization of different cortical areas, suggesting that the frontal, sensorimotor and occipital regions operate with autonomous temporal structures and nonlinear properties. This distinction may have significant implications for advancing our understanding of normal brain function and enhancing the development of brain-computer interfaces. In sum, we demonstrated the utility of TT in revealing regional variations in EEG traces, underscoring the value of nonlinear dynamics.},
}

@article {pmid40783421,
year = {2025},
author = {Hashemi, SI and Cheron, G and Demolin, D and Cebolla, AM},
title = {EEG oscillations and related brain generators of phonation phases in long utterances.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {29150},
pmid = {40783421},
issn = {2045-2322},
support = {ANR-20-CE23-0008//Agence Nationale de la Recherche/ ; },
mesh = {Humans ; Male ; Female ; *Electroencephalography ; Adult ; *Phonation/physiology ; *Brain/physiology ; Young Adult ; Electromyography ; *Speech/physiology ; },
abstract = {While the role of brain rhythms in respiratory and speech motor control has been mainly explored during brief utterances, the specific involvement of brain rhythms in the transition of regulating subglottic pressure phases which are concomitant to specific muscle activation during prolonged phonation remains unexplored. This study investigates whether power spectral variations of the electroencephalogram brain rhythms are related specifically to prolonged phonation phases. High-density EEG and surface EMG were recorded in nineteen healthy participants while they repeatedly produced the syllable [pa] without taking a new breath, until reaching respiratory exhaustion. Aerodynamic, acoustic, and electrophysiological signals were analyzed to detect the brain areas involved in different phases of prolonged phonation. Each phase was defined by successive thoracic and abdominal muscle activity maintaining estimated subglottic pressure. The results showed significant changes in power spectrum, with desynchronization and synchronization in delta, theta, low-alpha, and high-alpha bands during transitions among the phases. Brain source analysis estimated that the first phase (P1), associated with vocal initiation and elastic rib cage recoil, involved frontal regions, suggesting a key role in voluntary phonation preparation. Subsequent phases (P2, P3, P4) showed multiband dynamics, engaging motor and premotor cortices, anterior cingulate, sensorimotor regions, thalamus, and cerebellum, indicating progressive adaptation and fine-tuning of respiratory and articulatory muscle control. Additionally, the involvement of temporal and insular regions in delta rhythm suggests a role in maintaining phonetic representation and preventing spontaneous verbal transformations. These findings provide new insights into the mechanisms and brain regions involved in prolonged phonation. These findings pave the way for applications in vocal brain-machine interfaces, clinical biofeedback for respiratory and vocal disorders, and the development of more ecologically valid paradigms in speech neuroscience.},
}

Humans
Male
Female
*Electroencephalography
Adult
*Phonation/physiology
*Brain/physiology
Young Adult
Electromyography
*Speech/physiology

@article {pmid40702190,
year = {2025},
author = {Kaifosh, P and Reardon, TR and , },
title = {A generic non-invasive neuromotor interface for human-computer interaction.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {40702190},
issn = {1476-4687},
abstract = {Since the advent of computing, humans have sought computer input technologies that are expressive, intuitive and universal. While diverse modalities have been developed, including keyboards, mice and touchscreens, they require interaction with a device that can be limiting, especially in on-the-go scenarios. Gesture-based systems use cameras or inertial sensors to avoid an intermediary device, but tend to perform well only for unobscured movements. By contrast, brain-computer or neuromotor interfaces that directly interface with the body's electrical signalling have been imagined to solve the interface problem[1], but high-bandwidth communication has been demonstrated only using invasive interfaces with bespoke decoders designed for single individuals[2-4]. Here, we describe the development of a generic non-invasive neuromotor interface that enables computer input decoded from surface electromyography (sEMG). We developed a highly sensitive, easily donned sEMG wristband and a scalable infrastructure for collecting training data from thousands of consenting participants. Together, these data enabled us to develop generic sEMG decoding models that generalize across people. Test users demonstrate a closed-loop median performance of gesture decoding of 0.66 target acquisitions per second in a continuous navigation task, 0.88 gesture detections per second in a discrete-gesture task and handwriting at 20.9 words per minute. We demonstrate that the decoding performance of handwriting models can be further improved by 16% by personalizing sEMG decoding models. To our knowledge, this is the first high-bandwidth neuromotor interface with performant out-of-the-box generalization across people.},
}

@article {pmid40527666,
year = {2025},
author = {Toner, AA and Eberlin, L and Pichaimuthu, R and Tompkins, T and Szekeres, M},
title = {The use of robotics and artificial intelligence in upper extremity rehabilitation following traumatic injury: A scoping review.},
journal = {Journal of hand therapy : official journal of the American Society of Hand Therapists},
volume = {38},
number = {2},
pages = {254-265},
doi = {10.1016/j.jht.2025.04.009},
pmid = {40527666},
issn = {1545-004X},
mesh = {Humans ; *Artificial Intelligence ; *Robotics ; *Upper Extremity/injuries ; *Arm Injuries/rehabilitation ; },
abstract = {BACKGROUND: With the recent advances in technology and its increased use in society, healthcare practices work to identify areas where technology can be implemented to enhance patient care. Rehabilitation has begun to incorporate the use of robotics and artificial intelligence to facilitate positive outcomes and assist in achieving patient goals following injury. While traumatic upper extremity injuries can result in increased levels of pain and disability for an individual, it is not clear how robotics and artificial intelligence have been used in hand rehabilitation to address these issues.

PURPOSE: The objective of this study is to understand the extent of the use of robotics and artificial intelligence for traumatic upper extremity injuries.

STUDY DESIGN: Scoping review.

METHODS: The search strategy was conducted in Embase, CINAHL, MEDLINE, and PsycINFO and identified 7105 studies published between 2014 and 2024. Following title and abstract screening and removal of duplicates, 122 full-text articles were screened. A total of 13 papers were included that used artificial intelligence, robotics, or other technology in rehabilitation programs for individuals with traumatic upper extremity injuries.

RESULTS: Of the 13 included studies: 11 used robotics such as the KINARM Exoskeleton, the Hybrid Assistive Limb, and the WRISTBOT, and two used artificial intelligence including chatbots and brain-computer interface. Multiple outcomes were reported with the most common including range of motion, strength, pain, function, and joint sense.

CONCLUSIONS: Currently, there is a wide variety of different forms of robotics with very little reported use of artificial intelligence for traumatic upper extremity injuries. There exists opportunities for future research to further investigate how these technologies can influence clinical outcomes for patients with traumatic upper extremity injuries.},
}

Humans
*Artificial Intelligence
*Robotics
*Upper Extremity/injuries
*Arm Injuries/rehabilitation

@article {pmid40522765,
year = {2026},
author = {Kinfe, T and Brenner, S and Etminan, N},
title = {Brain-computer interfaces re-shape functional neurosurgery.},
journal = {Neural regeneration research},
volume = {21},
number = {3},
pages = {1122-1123},
pmid = {40522765},
issn = {1673-5374},
}

@article {pmid40422053,
year = {2025},
author = {Avital, N and Shulkin, N and Malka, D},
title = {Automatic Calculation of Average Power in Electroencephalography Signals for Enhanced Detection of Brain Activity and Behavioral Patterns.},
journal = {Biosensors},
volume = {15},
number = {5},
pages = {},
pmid = {40422053},
issn = {2079-6374},
mesh = {Humans ; *Electroencephalography/methods ; *Brain/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Male ; Female ; Adult ; Young Adult ; },
abstract = {Precise analysis of electroencephalogram (EEG) signals is critical for advancing the understanding of neurological conditions and mapping brain activity. However, accurately visualizing brain regions and behavioral patterns from neural signals remains a significant challenge. The present study proposes a novel methodology for the automated calculation of the average power of EEG signals, with a particular focus on the beta frequency band which is known for its pronounced activity during cognitive tasks such as 2D content engagement. An optimization algorithm is employed to determine the most appropriate digital filter type and order for EEG signal processing, thereby enhancing both signal clarity and interpretability. To validate the proposed methodology, an experiment was conducted with 22 students, during which EEG data were recorded while participants engaged in cognitive tasks. The collected data were processed using MATLAB (version R2023a) and the EEGLAB toolbox (version 2022.1) to evaluate various filters, including finite impulse response (FIR) and infinite impulse response (IIR) Butterworth and IIR Chebyshev filters with a 0.5% passband ripple. Results indicate that the IIR Chebyshev filter, configured with a 0.5% passband ripple and a fourth-order design, outperformed the alternatives by effectively reducing average power while preserving signal fidelity. This optimized filtering approach significantly improves the accuracy of neural signal visualizations, thereby facilitating the creation of detailed brain activity maps. By refining the analysis of EEG signals, the proposed method enhances the detection of specific neural behaviors and deepens the understanding of functional brain regions. Moreover, it bolsters the reliability of real-time brain activity monitoring, potentially advancing neurological diagnostics and insights into cognitive processes. These findings suggest that the technique holds considerable promise for future applications in brain-computer interfaces and advanced neurological assessments, offering a valuable tool for both clinical practice and research exploration.},
}

Humans
*Electroencephalography/methods
*Brain/physiology
Algorithms
Signal Processing, Computer-Assisted
Male
Female
Adult
Young Adult

@article {pmid39880895,
year = {2025},
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 = {Author Correction: Enhanced control of a brain-computer interface by tetraplegic participants via neural-network-mediated feature extraction.},
journal = {Nature biomedical engineering},
volume = {9},
number = {6},
pages = {986},
doi = {10.1038/s41551-025-01355-2},
pmid = {39880895},
issn = {2157-846X},
support = {R01 EY015545/EY/NEI NIH HHS/United States ; UG1 EY032039/EY/NEI NIH HHS/United States ; },
}

@article {pmid38975192,
year = {2024},
author = {Li, Y and Nie, Y and Quan, Z and Zhang, H and Song, R and Feng, H and Cheng, X and Liu, W and Geng, X and Sun, X and Fu, Y and Wang, S},
title = {Brain-machine interactive neuromodulation research tool with edge AI computing.},
journal = {Heliyon},
volume = {10},
number = {12},
pages = {e32609},
pmid = {38975192},
issn = {2405-8440},
abstract = {Closed-loop neuromodulation with intelligence methods has shown great potentials in providing novel neuro-technology for treating neurological and psychiatric diseases. Development of brain-machine interactive neuromodulation strategies could lead to breakthroughs in precision and personalized electronic medicine. The neuromodulation research tool integrating artificial intelligent computing and performing neural sensing and stimulation in real-time could accelerate the development of closed-loop neuromodulation strategies and translational research into clinical application. In this study, we developed a brain-machine interactive neuromodulation research tool (BMINT), which has capabilities of neurophysiological signals sensing, computing with mainstream machine learning algorithms and delivering electrical stimulation pulse by pulse in real-time. The BMINT research tool achieved system time delay under 3 ms, and computing capabilities in feasible computation cost, efficient deployment of machine learning algorithms and acceleration process. Intelligent computing framework embedded in the BMINT enable real-time closed-loop neuromodulation developed with mainstream AI ecosystem resources. The BMINT could provide timely contribution to accelerate the translational research of intelligent neuromodulation by integrating neural sensing, edge AI computing and stimulation with AI ecosystems.},
}

@article {pmid38909175,
year = {2024},
author = {Dai, C and Lin, X and Xue, B and Xi, X and Gao, M and Liu, X and Han, T and Li, Q and Yuan, H and Sun, X},
title = {Correlation of bilateral M1 hand area excitability and overall functional recovery after spinal cord injury: protocol for a prospective cohort study.},
journal = {BMC neurology},
volume = {24},
number = {1},
pages = {213},
pmid = {38909175},
issn = {1471-2377},
support = {82072534//National Natural Science Foundation of China/ ; 82272591//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Spinal Cord Injuries/rehabilitation/physiopathology/therapy ; *Recovery of Function/physiology ; *Hand/physiopathology ; *Transcranial Magnetic Stimulation/methods ; *Motor Cortex/physiopathology ; Prospective Studies ; Evoked Potentials, Motor/physiology ; Male ; Adult ; Female ; Cohort Studies ; Middle Aged ; Spectroscopy, Near-Infrared/methods ; },
abstract = {BACKGROUND: After spinal cord injury (SCI), a large number of survivors suffer from severe motor dysfunction (MD). Although the injury site is in the spinal cord, excitability significantly decreases in the primary motor cortex (M1), especially in the lower extremity (LE) area. Unfortunately, M1 LE area-targeted repetitive transcranial magnetic stimulation (rTMS) has not achieved significant motor improvement in individuals with SCI. A recent study reported that the M1 hand area in individuals with SCl contains a compositional code (the movement-coding component of neural activity) that links matching movements from the upper extremities (UE) and the LE. However, the correlation between bilateral M1 hand area excitability and overall functional recovery is unknown.

OBJECTIVE: To clarify the changes in the excitability of the bilateral M1 hand area after SCI and its correlation with motor recovery, we aim to specify the therapeutic parameters of rTMS for SCI motor rehabilitation.

METHODS: This study is a 12-month prospective cohort study. The neurophysiological and overall functional status of the participants will be assessed. The primary outcomes included single-pulse and paired-pulse TMS. The second outcome included functional near-infrared spectroscopy (fNIRS) measurements. Overall functional status included total motor score, modified Ashworth scale score, ASIA Impairment Scale grade, spinal cord independence measure and modified Barthel index. The data will be recorded for individuals with SCI at disease durations of 1 month, 2 months, 4 months, 6 months and 12 months. The matched healthy controls will be measured during the same period of time after recruitment.

DISCUSSION: The present study is the first to analyze the role of bilateral M1 hand area excitability changes in the evaluation and prediction of overall functional recovery (including motor function and activities of daily living) after SCI, which will further expand the traditional theory of the predominant role of M1, optimize the current rTMS treatment, and explore the brain-computer interface design for individuals with SCI.

TRIAL REGISTRATION NUMBER: ChiCTR2300068831.},
}

Humans
*Spinal Cord Injuries/rehabilitation/physiopathology/therapy
*Recovery of Function/physiology
*Hand/physiopathology
*Transcranial Magnetic Stimulation/methods
*Motor Cortex/physiopathology
Prospective Studies
Evoked Potentials, Motor/physiology
Male
Adult
Female
Cohort Studies
Middle Aged
Spectroscopy, Near-Infrared/methods

@article {pmid40800520,
year = {2024},
author = {Banville, H and Jaoude, MA and Wood, SUN and Aimone, C and Holst, SC and Gramfort, A and Engemann, DA},
title = {Do try this at home: Age prediction from sleep and meditation with large-scale low-cost mobile EEG.},
journal = {Imaging neuroscience (Cambridge, Mass.)},
volume = {2},
number = {},
pages = {},
pmid = {40800520},
issn = {2837-6056},
abstract = {Electroencephalography (EEG) is an established method for quantifying large-scale neuronal dynamics which enables diverse real-world biomedical applications, including brain-computer interfaces, epilepsy monitoring, and sleep staging. Advances in sensor technology have freed EEG from traditional laboratory settings, making low-cost ambulatory or at-home assessments of brain function possible. While ecologically valid brain assessments are becoming more practical, the impact of their reduced spatial resolution and susceptibility to noise remain to be investigated. This study set out to explore the potential of at-home EEG assessments for biomarker discovery using the brain age framework and four-channel consumer EEG data. We analyzed recordings from more than 5200 human subjects (18-81 years) during meditation and sleep, to predict age at the time of recording. With cross-validated R 2 scores between 0.3 - 0.5 , prediction performance was within the range of results obtained by recent benchmarks focused on laboratory-grade EEG. While age prediction was successful from both meditation and sleep recordings, the latter led to higher performance. Analysis by sleep stage uncovered that N2-N3 stages contained most of the signal. When combined, EEG features extracted from all sleep stages gave the best performance, suggesting that the entire night of sleep contains valuable age-related information. Furthermore, model comparisons suggested that information was spread out across electrodes and frequencies, supporting the use of multivariate modeling approaches. Thanks to our unique dataset of longitudinal repeat sessions spanning 153 to 529 days from eight subjects, we finally evaluated the variability of EEG-based age predictions, showing that they reflect both trait- and state-like information. Overall, our results demonstrate that state-of-the-art machine-learning approaches based on age prediction can be readily applied to real-world EEG recordings obtained during at-home sleep and meditation practice.},
}

@article {pmid38738941,
year = {2024},
author = {Ramezani, Z and André, V and Khizroev, S},
title = {Modeling the effect of magnetoelectric nanoparticles on neuronal electrical activity: An analog circuit approach.},
journal = {Biointerphases},
volume = {19},
number = {3},
pages = {},
doi = {10.1116/5.0199163},
pmid = {38738941},
issn = {1559-4106},
mesh = {*Neurons/physiology/drug effects ; Nanoparticles/chemistry ; Humans ; Models, Neurological ; Action Potentials/drug effects/physiology ; Magnetic Fields ; },
abstract = {This paper introduces a physical neuron model that incorporates magnetoelectric nanoparticles (MENPs) as an essential electrical circuit component to wirelessly control local neural activity. Availability of such a model is important as MENPs, due to their magnetoelectric effect, can wirelessly and noninvasively modulate neural activity, which, in turn, has implications for both finding cures for neurological diseases and creating a wireless noninvasive high-resolution brain-machine interface. When placed on a neuronal membrane, MENPs act as magnetic-field-controlled finite-size electric dipoles that generate local electric fields across the membrane in response to magnetic fields, thus allowing to controllably activate local ion channels and locally initiate an action potential. Herein, the neuronal electrical characteristic description is based on ion channel activation and inhibition mechanisms. A MENP-based memristive Hodgkin-Huxley circuit model is extracted by combining the Hodgkin-Huxley model and an equivalent circuit model for a single MENP. In this model, each MENP becomes an integral part of the neuron, thus enabling wireless local control of the neuron's electric circuit itself. Furthermore, the model is expanded to include multiple MENPs to describe collective effects in neural systems.},
}

*Neurons/physiology/drug effects
Nanoparticles/chemistry
Humans
Models, Neurological
Action Potentials/drug effects/physiology
Magnetic Fields

@article {pmid38513289,
year = {2024},
author = {Sadras, N and Pesaran, B and Shanechi, MM},
title = {Event detection and classification from multimodal time series with application to neural data.},
journal = {Journal of neural engineering},
volume = {21},
number = {2},
pages = {},
pmid = {38513289},
issn = {1741-2552},
support = {R01 MH123770/MH/NIMH NIH HHS/United States ; },
mesh = {*Algorithms ; Neurons/physiology ; Normal Distribution ; Animals ; Models, Neurological ; Action Potentials/physiology ; Computer Simulation ; Humans ; },
abstract = {The detection of events in time-series data is a common signal-processing problem. When the data can be modeled as a known template signal with an unknown delay in Gaussian noise, detection of the template signal can be done with a traditional matched filter. However, in many applications, the event of interest is represented in multimodal data consisting of both Gaussian and point-process time series. Neuroscience experiments, for example, can simultaneously record multimodal neural signals such as local field potentials (LFPs), which can be modeled as Gaussian, and neuronal spikes, which can be modeled as point processes. Currently, no method exists for event detection from such multimodal data, and as such our objective in this work is to develop a method to meet this need. Here we address this challenge by developing the multimodal event detector (MED) algorithm which simultaneously estimates event times and classes. To do this, we write a multimodal likelihood function for Gaussian and point-process observations and derive the associated maximum likelihood estimator of simultaneous event times and classes. We additionally introduce a cross-modal scaling parameter to account for model mismatch in real datasets. We validate this method in extensive simulations as well as in a neural spike-LFP dataset recorded during an eye-movement task, where the events of interest are eye movements with unknown times and directions. We show that the MED can successfully detect eye movement onset and classify eye movement direction. Further, the MED successfully combines information across data modalities, with multimodal performance exceeding unimodal performance. This method can facilitate applications such as the discovery of latent events in multimodal neural population activity and the development of brain-computer interfaces for naturalistic settings without constrained tasks or prior knowledge of event times.},
}

*Algorithms
Neurons/physiology
Normal Distribution
Animals
Models, Neurological
Action Potentials/physiology
Computer Simulation
Humans

@article {pmid38456888,
year = {2024},
author = {Meier, K and de Vos, CC and Bordeleau, M and van der Tuin, S and Billet, B and Ruland, T and Blichfeldt-Eckhardt, MR and Winkelmüller, M and Gulisano, HA and Gatzinsky, K and Knudsen, AL and Hedemann Sørensen, JC and Milidou, I and Cottin, SC},
title = {Examining the Duration of Carryover Effect in Patients With Chronic Pain Treated With Spinal Cord Stimulation (EChO Study): An Open, Interventional, Investigator-Initiated, International Multicenter Study.},
journal = {Neuromodulation : journal of the International Neuromodulation Society},
volume = {27},
number = {5},
pages = {887-898},
doi = {10.1016/j.neurom.2024.01.002},
pmid = {38456888},
issn = {1525-1403},
mesh = {Humans ; *Spinal Cord Stimulation/methods ; Male ; Female ; Middle Aged ; *Chronic Pain/therapy ; Aged ; Adult ; Time Factors ; Prospective Studies ; Pain Measurement/methods ; Treatment Outcome ; Internationality ; Neuralgia/therapy ; },
abstract = {OBJECTIVES: Spinal cord stimulation (SCS) is a surgical treatment for severe, chronic, neuropathic pain. It is based on one to two lead(s) implanted in the epidural space, stimulating the dorsal column. It has long been assumed that when deactivating SCS, there is a variable interval before the patient perceives the return of the pain, a phenomenon often termed echo or carryover effect. Although the carryover effect has been problematized as a source of error in crossover studies, no experimental investigation of the effect has been published. This open, prospective, international multicenter study aimed to systematically document, quantify, and investigate the carryover effect in SCS.

MATERIALS AND METHODS: Eligible patients with a beneficial effect from their SCS treatment were instructed to deactivate their SCS device in a home setting and to reactivate it when their pain returned. The primary outcome was duration of carryover time defined as the time interval from deactivation to reactivation. Central clinical parameters (age, sex, indication for SCS, SCS treatment details, pain score) were registered and correlated with carryover time using nonparametric tests (Mann-Whitney/Kruskal-Wallis) for categorical data and linear regression for continuous data.

RESULTS: In total, 158 patients were included in the analyses. A median carryover time of five hours was found (interquartile range 2.5;21 hours). Back pain as primary indication for SCS, high-frequency stimulation, and higher pain score at the time of deactivation were correlated with longer carryover time.

CONCLUSIONS: This study confirms the existence of the carryover effect and indicates a remarkably high degree of interindividual variation. The results suggest that the magnitude of carryover may be correlated to the nature of the pain condition and possibly stimulation paradigms.

CLINICAL TRIAL REGISTRATION: The Clinicaltrials.gov registration number for the study is NCT03386058.},
}

Humans
*Spinal Cord Stimulation/methods
Male
Female
Middle Aged
*Chronic Pain/therapy
Aged
Adult
Time Factors
Prospective Studies
Pain Measurement/methods
Treatment Outcome
Internationality
Neuralgia/therapy

@article {pmid38278790,
year = {2024},
author = {Wu, H and Hou, Y and Yoon, J and Knoepfel, AM and Zheng, L and Yang, D and Wang, K and Qian, J and Priya, S and Wang, K},
title = {Down-selection of biomolecules to assemble "reverse micelle" with perovskites.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {772},
pmid = {38278790},
issn = {2041-1723},
support = {DE-EE0009364//DOE | Office of Energy Efficiency & Renewable Energy | Solar Energy Technologies Office (SETO)/ ; },
mesh = {*Micelles ; *Oxides/chemistry ; Semiconductors ; Biocompatible Materials ; *Titanium ; *Calcium Compounds ; },
abstract = {Biological molecule-semiconductor interfacing has triggered numerous opportunities in applied physics such as bio-assisted data storage and computation, brain-computer interface, and advanced distributed bio-sensing. The introduction of electronics into biological embodiment is being quickly developed as it has great potential in providing adaptivity and improving functionality. Reciprocally, introducing biomaterials into semiconductors to manifest bio-mimetic functionality is impactful in triggering new enhanced mechanisms. In this study, we utilize the vulnerable perovskite semiconductors as a platform to understand if certain types of biomolecules can regulate the lattice and endow a unique mechanism for stabilizing the metastable perovskite lattice. Three tiers of biomolecules have been systematically tested and the results reveal a fundamental mechanism for the formation of a "reverse-micelle" structure. Systematic exploration of a large set of biomolecules led to the discovery of guiding principle for down-selection of biomolecules which extends the classic emulsion theory to this hybrid systems. Results demonstrate that by introducing biomaterials into semiconductors, natural phenomena typically observed in biological systems can also be incorporated into semiconducting crystals, providing a new perspective to engineer existing synthetic materials.},
}

*Micelles
*Oxides/chemistry
Semiconductors
Biocompatible Materials
*Titanium
*Calcium Compounds

@article {pmid36452175,
year = {2022},
author = {Weisinger, B and Pandey, DP and Saver, JL and Hochberg, A and Bitton, A and Doniger, GM and Lifshitz, A and Vardi, O and Shohami, E and Segal, Y and Reznik Balter, S and Djemal Kay, Y and Alter, A and Prasad, A and Bornstein, NM},
title = {Frequency-tuned electromagnetic field therapy improves post-stroke motor function: A pilot randomized controlled trial.},
journal = {Frontiers in neurology},
volume = {13},
number = {},
pages = {1004677},
pmid = {36452175},
issn = {1664-2295},
abstract = {BACKGROUND AND PURPOSE: Impaired upper extremity (UE) motor function is a common disability after ischemic stroke. Exposure to extremely low frequency and low intensity electromagnetic fields (ELF-EMF) in a frequency-specific manner (Electromagnetic Network Targeting Field therapy; ENTF therapy) is a non-invasive method available to a wide range of patients that may enhance neuroplasticity, potentially facilitating motor recovery. This study seeks to quantify the benefit of the ENTF therapy on UE motor function in a subacute ischemic stroke population.

METHODS: In a randomized, sham-controlled, double-blind trial, ischemic stroke patients in the subacute phase with moderately to severely impaired UE function were randomly allocated to active or sham treatment with a novel, non-invasive, brain computer interface-based, extremely low frequency and low intensity ENTF therapy (1-100 Hz, < 1 G). Participants received 40 min of active ENTF or sham treatment 5 days/week for 8 weeks; ~three out of the five treatments were accompanied by 10 min of concurrent physical/occupational therapy. Primary efficacy outcome was improvement on the Fugl-Meyer Assessment - Upper Extremity (FMA-UE) from baseline to end of treatment (8 weeks).

RESULTS: In the per protocol set (13 ENTF and 8 sham participants), mean age was 54.7 years (±15.0), 19% were female, baseline FMA-UE score was 23.7 (±11.0), and median time from stroke onset to first stimulation was 11 days (interquartile range (IQR) 8-15). Greater improvement on the FMA-UE from baseline to week 4 was seen with ENTF compared to sham stimulation, 23.2 ± 14.1 vs. 9.6 ± 9.0, p = 0.007; baseline to week 8 improvement was 31.5 ± 10.7 vs. 23.1 ± 14.1. Similar favorable effects at week 8 were observed for other UE and global disability assessments, including the Action Research Arm Test (Pinch, 13.4 ± 5.6 vs. 5.3 ± 6.5, p = 0.008), Box and Blocks Test (affected hand, 22.5 ± 12.4 vs. 8.5 ± 8.6, p < 0.0001), and modified Rankin Scale (-2.5 ± 0.7 vs. -1.3 ± 0.7, p = 0.0005). No treatment-related adverse events were reported.

CONCLUSIONS: ENTF stimulation in subacute ischemic stroke patients was associated with improved UE motor function and reduced overall disability, and results support its safe use in the indicated population. These results should be confirmed in larger multicenter studies.

CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/ct2/show/NCT04039178, identifier: NCT04039178.},
}

@article {pmid35725741,
year = {2022},
author = {Nickl, RW and Anaya, MA and Thomas, TM and Fifer, MS and Candrea, DN and McMullen, DP and Thompson, MC and Osborn, LE and Anderson, WS and Wester, BA and Tenore, FV and Crone, NE and Cantarero, GL and Celnik, PA},
title = {Characteristics and stability of sensorimotor activity driven by isolated-muscle group activation in a human with tetraplegia.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {10353},
pmid = {35725741},
issn = {2045-2322},
mesh = {Electromyography ; *Forearm/physiology ; Humans ; Movement/physiology ; *Muscle, Skeletal/physiology ; Quadriplegia ; },
abstract = {Understanding the cortical representations of movements and their stability can shed light on improved brain-machine interface (BMI) approaches to decode these representations without frequent recalibration. Here, we characterize the spatial organization (somatotopy) and stability of the bilateral sensorimotor map of forearm muscles in an incomplete-high spinal-cord injury study participant implanted bilaterally in the primary motor and sensory cortices with Utah microelectrode arrays (MEAs). We built representation maps by recording bilateral multiunit activity (MUA) and surface electromyography (EMG) as the participant executed voluntary contractions of the extensor carpi radialis (ECR), and attempted motions in the flexor carpi radialis (FCR), which was paralytic. To assess stability, we repeatedly mapped and compared left- and right-wrist-extensor-related activity throughout several sessions, comparing somatotopy of active electrodes, as well as neural signals both at the within-electrode (multiunit) and cross-electrode (network) levels. Wrist motions showed significant activation in motor and sensory cortical electrodes. Within electrodes, firing strength stability diminished as the time increased between consecutive measurements (hours within a session, or days across sessions), with higher stability observed in sensory cortex than in motor, and in the contralateral hemisphere than in the ipsilateral. However, we observed no differences at network level, and no evidence of decoding instabilities for wrist EMG, either across timespans of hours or days, or across recording area. While map stability differs between brain area and hemisphere at multiunit/electrode level, these differences are nullified at ensemble level.},
}

Electromyography
*Forearm/physiology
Humans
Movement/physiology
*Muscle, Skeletal/physiology
Quadriplegia

@article {pmid34902609,
year = {2022},
author = {Li, R and Ren, C and Zhang, X and Hu, B},
title = {A novel ensemble learning method using multiple objective particle swarm optimization for subject-independent EEG-based emotion recognition.},
journal = {Computers in biology and medicine},
volume = {140},
number = {},
pages = {105080},
doi = {10.1016/j.compbiomed.2021.105080},
pmid = {34902609},
issn = {1879-0534},
abstract = {Emotion recognition is a vital but challenging step in creating passive brain-computer interface applications. In recent years, many studies on electroencephalogram (EEG)-based emotion recognition have been conducted. Ensemble learning has been widely used in emotion recognition because of its superior accuracy and generalization. In this study, we proposed a novel ensemble learning method based on multiple objective particle swarm optimization for subject-independent EEG-based emotion recognition. First, we used a 4 s sliding time window with a 2 s overlap to extract 13 different features from EEG signals and construct a feature vector. Then, we employed L1 regularization to select effective features. Second, a model selection method was applied to choose the optimal basic analysis submodels. Afterward, we proposed an ensemble operator that converts the classification results of a single model from discrete values to continuous values to better characterize the classification results. Subsequently, multiple objective particle swarm optimization was adopted to confirm the optimal parameters of the ensemble learning model. Finally, we conducted extensive experiments on two public datasets: DEAP and SEED. Considering the generalization of the model, we applied leave-one-subject-out cross-validation to evaluate the performance of the model. The experimental results demonstrate that the proposed method achieves a better recognition performance than single methods, commonly used ensemble learning methods, and state-of-the-art methods. The average accuracies for arousal and valence are 65.70% and 64.22%, respectively, on the DEAP database, and the average accuracy on the SEED database is 84.44%.},
}

@article {pmid34697401,
year = {2021},
author = {Brydges, CR and Fiehn, O and Mayberg, HS and Schreiber, H and Dehkordi, SM and Bhattacharyya, S and Cha, J and Choi, KS and Craighead, WE and Krishnan, RR and Rush, AJ and Dunlop, BW and Kaddurah-Daouk, R and , },
title = {Indoxyl sulfate, a gut microbiome-derived uremic toxin, is associated with psychic anxiety and its functional magnetic resonance imaging-based neurologic signature.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {21011},
pmid = {34697401},
issn = {2045-2322},
support = {P50-MH077083/NH/NIH HHS/United States ; R01 AG046171/AG/NIA NIH HHS/United States ; R01 MH108348/MH/NIMH NIH HHS/United States ; M01 RR000039/RR/NCRR NIH HHS/United States ; R01 MH080880/MH/NIMH NIH HHS/United States ; R01MH108348/NH/NIH HHS/United States ; U01 AG061359/AG/NIA NIH HHS/United States ; P50 MH077083/MH/NIMH NIH HHS/United States ; UL1 RR025008/RR/NCRR NIH HHS/United States ; },
mesh = {Adult ; Aged ; Anxiety/blood/*diagnosis/*etiology ; Biomarkers ; Brain/diagnostic imaging/physiopathology ; Disease Susceptibility ; Female ; Functional Neuroimaging/methods ; *Gastrointestinal Microbiome ; Humans ; Indican/*adverse effects/biosynthesis ; *Magnetic Resonance Imaging/methods ; Male ; Metabolic Networks and Pathways ; Metabolome ; Metabolomics/methods ; Middle Aged ; Symptom Assessment ; Uremic Toxins/*adverse effects/biosynthesis ; Young Adult ; },
abstract = {It is unknown whether indoles, metabolites of tryptophan that are derived entirely from bacterial metabolism in the gut, are associated with symptoms of depression and anxiety. Serum samples (baseline, 12 weeks) were drawn from participants (n = 196) randomized to treatment with cognitive behavioral therapy (CBT), escitalopram, or duloxetine for major depressive disorder. Baseline indoxyl sulfate abundance was positively correlated with severity of psychic anxiety and total anxiety and with resting state functional connectivity to a network that processes aversive stimuli (which includes the subcallosal cingulate cortex (SCC-FC), bilateral anterior insula, right anterior midcingulate cortex, and the right premotor areas). The relation between indoxyl sulfate and psychic anxiety was mediated only through the metabolite's effect on the SCC-FC with the premotor area. Baseline indole abundances were unrelated to post-treatment outcome measures, and changes in symptoms were not correlated with changes in indole concentrations. These results suggest that CBT and antidepressant medications relieve anxiety via mechanisms unrelated to modulation of indoles derived from gut microbiota; it remains possible that treatment-related improvement stems from their impact on other aspects of the gut microbiome. A peripheral gut microbiome-derived metabolite was associated with altered neural processing and with psychiatric symptom (anxiety) in humans, which provides further evidence that gut microbiome disruption can contribute to neuropsychiatric disorders that may require different therapeutic approaches. Given the exploratory nature of this study, findings should be replicated in confirmatory studies.Clinical trial NCT00360399 "Predictors of Antidepressant Treatment Response: The Emory CIDAR" https://clinicaltrials.gov/ct2/show/NCT00360399 .},
}

Adult
Aged
Anxiety/blood/*diagnosis/*etiology
Biomarkers
Brain/diagnostic imaging/physiopathology
Disease Susceptibility
Female
Functional Neuroimaging/methods
*Gastrointestinal Microbiome
Humans
Indican/*adverse effects/biosynthesis
*Magnetic Resonance Imaging/methods
Male
Metabolic Networks and Pathways
Metabolome
Metabolomics/methods
Middle Aged
Symptom Assessment
Uremic Toxins/*adverse effects/biosynthesis
Young Adult

@article {pmid33828507,
year = {2021},
author = {Mencel, J and Jaskólska, A and Marusiak, J and Kamiński, Ł and Kurzyński, M and Wołczowski, A and Jaskólski, A and Kisiel-Sajewicz, K},
title = {Motor Imagery Training of Reaching-to-Grasp Movement Supplemented by a Virtual Environment in an Individual With Congenital Bilateral Transverse Upper-Limb Deficiency.},
journal = {Frontiers in psychology},
volume = {12},
number = {},
pages = {638780},
pmid = {33828507},
issn = {1664-1078},
abstract = {This study explored the effect of kinesthetic motor imagery training on reaching-to-grasp movement supplemented by a virtual environment in a patient with congenital bilateral transverse upper-limb deficiency. Based on a theoretical assumption, it is possible to conduct such training in this patient. The aim of this study was to evaluate whether cortical activity related to motor imagery of reaching and motor imagery of grasping of the right upper limb was changed by computer-aided imagery training (CAIT) in a patient who was born without upper limbs compared to a healthy control subject, as characterized by multi-channel electroencephalography (EEG) signals recorded before and 4, 8, and 12 weeks after CAIT. The main task during CAIT was to kinesthetically imagine the execution of reaching-to-grasp movements without any muscle activation, supplemented by computer visualization of movements provided by a special headset. Our experiment showed that CAIT can be conducted in the patient with higher vividness of imagery for reaching than grasping tasks. Our results confirm that CAIT can change brain activation patterns in areas related to motor planning and the execution of reaching and grasping movements, and that the effect was more pronounced in the patient than in the healthy control subject. The results show that CAIT has a different effect on the cortical activity related to the motor imagery of a reaching task than on the cortical activity related to the motor imagery of a grasping task. The change observed in the activation patterns could indicate CAIT-induced neuroplasticity, which could potentially be useful in rehabilitation or brain-computer interface purposes for such patients, especially before and after transplantation. This study was part of a registered experiment (ID: NCT04048083).},
}

@article {pmid24109560,
year = {2013},
author = {O'Rawe, JA and Fang, H and Rynearson, S and Robison, R and Kiruluta, ES and Higgins, G and Eilbeck, K and Reese, MG and Lyon, GJ},
title = {Integrating precision medicine in the study and clinical treatment of a severely mentally ill person.},
journal = {PeerJ},
volume = {1},
number = {},
pages = {e177},
pmid = {24109560},
issn = {2167-8359},
support = {R44 HG006579/HG/NHGRI NIH HHS/United States ; },
abstract = {Background. In recent years, there has been an explosion in the number of technical and medical diagnostic platforms being developed. This has greatly improved our ability to more accurately, and more comprehensively, explore and characterize human biological systems on the individual level. Large quantities of biomedical data are now being generated and archived in many separate research and clinical activities, but there exists a paucity of studies that integrate the areas of clinical neuropsychiatry, personal genomics and brain-machine interfaces. Methods. A single person with severe mental illness was implanted with the Medtronic Reclaim(®) Deep Brain Stimulation (DBS) Therapy device for Obsessive Compulsive Disorder (OCD), targeting his nucleus accumbens/anterior limb of the internal capsule. Programming of the device and psychiatric assessments occurred in an outpatient setting for over two years. His genome was sequenced and variants were detected in the Illumina Whole Genome Sequencing Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory. Results. We report here the detailed phenotypic characterization, clinical-grade whole genome sequencing (WGS), and two-year outcome of a man with severe OCD treated with DBS. Since implantation, this man has reported steady improvement, highlighted by a steady decline in his Yale-Brown Obsessive Compulsive Scale (YBOCS) score from ∼38 to a score of ∼25. A rechargeable Activa RC neurostimulator battery has been of major benefit in terms of facilitating a degree of stability and control over the stimulation. His psychiatric symptoms reliably worsen within hours of the battery becoming depleted, thus providing confirmatory evidence for the efficacy of DBS for OCD in this person. WGS revealed that he is a heterozygote for the p.Val66Met variant in BDNF, encoding a member of the nerve growth factor family, and which has been found to predispose carriers to various psychiatric illnesses. He carries the p.Glu429Ala allele in methylenetetrahydrofolate reductase (MTHFR) and the p.Asp7Asn allele in ChAT, encoding choline O-acetyltransferase, with both alleles having been shown to confer an elevated susceptibility to psychoses. We have found thousands of other variants in his genome, including pharmacogenetic and copy number variants. This information has been archived and offered to this person alongside the clinical sequencing data, so that he and others can re-analyze his genome for years to come. Conclusions. To our knowledge, this is the first study in the clinical neurosciences that integrates detailed neuropsychiatric phenotyping, deep brain stimulation for OCD and clinical-grade WGS with management of genetic results in the medical treatment of one person with severe mental illness. We offer this as an example of precision medicine in neuropsychiatry including brain-implantable devices and genomics-guided preventive health care.},
}

@article {pmid23272508,
year = {2012},
author = {Manea, P and Ghiuru, R and Gavrilescu, MC and Munteanu, D},
title = {Long-term prognosis of polyvascular patients with associated chronic obstructive pulmonary disease.},
journal = {Revista medico-chirurgicala a Societatii de Medici si Naturalisti din Iasi},
volume = {116},
number = {3},
pages = {669-673},
pmid = {23272508},
issn = {0048-7848},
mesh = {Aged ; Aged, 80 and over ; Body Mass Index ; Cardiovascular Diseases/*complications/diagnosis/mortality/physiopathology/therapy ; Female ; Follow-Up Studies ; Humans ; Incidence ; Male ; Metabolic Syndrome/complications ; Middle Aged ; Patient Compliance ; Peripheral Vascular Diseases/complications ; Practice Guidelines as Topic ; Prognosis ; Pulmonary Disease, Chronic Obstructive/*complications/diagnosis/mortality/physiopathology/therapy ; Quality of Life ; Risk Factors ; Romania/epidemiology ; Smoking/adverse effects ; Spirometry ; Survival Rate ; },
abstract = {OBJECTIVE: To evaluate by various tools the prognosis of the polyvascular patients (defined as the presence of more than one affected vascular bed), who also associate chronic obstructive pulmonary disease.

MATERIAL AND METHODS: Fifty-eight patients discharged after an episode of acute cardiorespiratory failure were examined at 3 month-intervals for 1, 2 and 3 years (2010-2012). The following were performed: physical examination, biochemical and hematological tests, spirometry, electrocardiography, transthoracic echocardiography, brain computer tomography or magnetic resonance imaging. All patients in our study were smokers with chronic pulmonary obstructive disease. Treatment relied on the European recommendations for cardiac pathology and associated medical conditions.

RESULTS: A favorable clinical course was noticed in compliant patients. Patients with metabolic syndrome and/or old stroke, and peripheral arterial disease have a poor prognosis. A strong link seems to exist between systolic function of the right ventricle and cardiovascular mortality. The association of this condition to ischemic heart disease modifies the right ventricle hemodynamics.

CONCLUSIONS: Polyvascular patients in acute cardiorespiratory failure have a mortality of 36% in the first 3 weeks. After 3 years, 86% of the patients survive. The modern methods of diagnosis and treatment allow improving the quality of life and increasing its duration.},
}

Aged
Aged, 80 and over
Body Mass Index
Cardiovascular Diseases/*complications/diagnosis/mortality/physiopathology/therapy
Female
Follow-Up Studies
Humans
Incidence
Male
Metabolic Syndrome/complications
Middle Aged
Patient Compliance
Peripheral Vascular Diseases/complications
Practice Guidelines as Topic
Prognosis
Pulmonary Disease, Chronic Obstructive/*complications/diagnosis/mortality/physiopathology/therapy
Quality of Life
Risk Factors
Romania/epidemiology
Smoking/adverse effects
Spirometry
Survival Rate

@article {pmid23237418,
year = {2013},
author = {Smits-Bandstra, S and De Nil, LF},
title = {Early-stage chunking of finger tapping sequences by persons who stutter and fluent speakers.},
journal = {Clinical linguistics & phonetics},
volume = {27},
number = {1},
pages = {72-84},
doi = {10.3109/02699206.2012.746397},
pmid = {23237418},
issn = {1464-5076},
mesh = {Efferent Pathways/physiology ; Fingers/*physiology ; Humans ; Learning/physiology ; Male ; Memory/physiology ; Motor Skills/*physiology ; Movement/physiology ; Psychomotor Performance/*physiology ; Reaction Time/physiology ; Speech/*physiology ; Speech Therapy ; Stuttering/*physiopathology/therapy ; },
abstract = {This research note explored the hypothesis that chunking differences underlie the slow finger-tap sequencing performance reported in the literature for persons who stutter (PWS) relative to fluent speakers (PNS). Early-stage chunking was defined as an immediate and spontaneous tendency to organize a long sequence into pauses, for motor planning, and chunks of fluent motor performance. A previously published study in which 12 PWS and 12 matched PNS practised a 10-item finger tapping sequence 30 times was examined. Both groups significantly decreased the duration of between-chunk intervals (BCIs) and within-chunk intervals (WCIs) over practice. PNS had significantly shorter WCIs relative to PWS, but minimal differences between groups were found for the number of, or duration of, BCI. Results imply that sequencing differences found between PNS and PWS may be due to differences in automatizing movements within chunks or retrieving chunks from memory rather than chunking per se.},
}

Efferent Pathways/physiology
Fingers/*physiology
Humans
Learning/physiology
Male
Memory/physiology
Motor Skills/*physiology
Movement/physiology
Psychomotor Performance/*physiology
Reaction Time/physiology
Speech/*physiology
Speech Therapy
Stuttering/*physiopathology/therapy

@article {pmid40825359,
year = {2025},
author = {Voskoboinikov, A and Aliverdiev, M and Nekrasova, Y and Semenkov, I and Skalnaya, A and Sinkin, M and Ossadtchi, A},
title = {Towards stimulation-free automatic electrocorticographic speech mapping in neurosurgery patients.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adfc9c},
pmid = {40825359},
issn = {1741-2552},
abstract = {The precise mapping of speech-related functions is crucial for successful neurosurgical interventions in epilepsy and brain tumor cases. Traditional methods like Electrocortical Stimulation Mapping (ESM) are effective but carry a significant risk of inducing seizures. Methods. To address this, we have prepared a comprehensive ESM+ECM dataset from 14 patients with chronically implanted stereo-EEG electrodes. Then we explored several compact machine learning (ML) approaches to convert the Electrocorticographic Mapping (ECM) signals to the ground truth derived from the risky ESM procedure. Both procedures involved the standard picture naming task. As features, we used gamma-band power within successive temporal windows in the data averaged with respect to picture and voice onsets. We focused on a range of classifiers, including XGBoost, Linear Support Vector Classification, Regularized Logistic Regression, Random Forest, k-Nearest Neighbors, Decision Tree, Multi-Layer Perceptron, AdaBoost and Gaussian Naive Bayes classifiers and equipped them with confidence interval estimates, crucial in a real-life application. We validated the ML approaches using a leave-one-patient-out procedure and computed ROC and Precision-Recall curves for various feature combinations. Results. For Linear Support Vector Classification we achieved ROC-AUC and PR-AUC scores of 0.91 and 0.88, respectively, which effectively distinguishes speech-related from non-related iEEG channels. We have also observed that the use of information on the voice onset moment notably improved the classification accuracy. Significance. We have for the first time rigorously compared the ECM and ESM results and mimicked a real-life use of the ECM technology. We have also provided public access to the comprehensive ECM+ESM dataset to pave the road towards safer and more reliable eloquent cortex mapping procedures.},
}

@article {pmid40824102,
year = {2025},
author = {Fang, P and Li, GH and Rao, YB and Cheng, C and He, WL and Wang, J and Li, XY and Lu, YR},
title = {Serum Cytokines as Biomarkers for Comorbid Anxiety in Postpartum Depression: A Machine Learning Approach.},
journal = {Psychiatry and clinical psychopharmacology},
volume = {35},
number = {3},
pages = {245-252},
doi = {10.5152/pcp.2025.241043},
pmid = {40824102},
issn = {2475-0581},
abstract = {Background: This study aimed to investigate the serum levels of interleukin 2, interleukin 6 (IL-6), interleukin 10, and tumor necrosis factor-alpha in patients with postpartum depression (PPD) and to explore their potential as biomarkers for PPD and comorbid anxiety using machine learning techniques. Methods: Serum samples were collected from 53 patients diagnosed with PPD and 35 healthy controls. Cytokine levels were measured using a flow cytometer analyzer. Machine learning models, including Multinomial Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines (SVMs), were developed to predict PPD and comorbid anxiety based on cytokine levels. Results: Patients with PPD exhibited significantly elevated serum levels of IL-6 compared to the control group. A positive correlation was found between psychological anxiety scores and IL-6 levels (r = 0.483, P < .001). Machine learning models, particularly the Random Forest and SVMs, demonstrated high accuracy in predicting PPD and comorbid anxiety, with IL-6 being identified as a key predictor. Conclusion: The activation of serum cytokines is evident in PPD patients, with IL-6 potentially serving as an auxiliary biomarker for the diagnosis of PPD and comorbid anxiety. The incorporation of machine learning techniques has enhanced the understanding of the complex relationships between cytokines and PPD, with IL-6 levels showing a correlation to the severity of clinical symptoms.},
}

@article {pmid40819304,
year = {2025},
author = {Kontogianni, A and Yang, H and Chen, W},
title = {Brain insulin resistance and neuropsychiatric symptoms in Alzheimer's disease: A role for dopamine signaling.},
journal = {Neural regeneration research},
volume = {},
number = {},
pages = {},
doi = {10.4103/NRR.NRR-D-25-00281},
pmid = {40819304},
issn = {1673-5374},
}

@article {pmid40819020,
year = {2025},
author = {Shao, X and Chung, RS and Cavaleri, JM and Del Campo-Vera, RM and Parra, M and Sundaram, S and Zhang, S and Surabhi, A and McGinn, RJ and Liu, CY and Kellis, SS and Lee, B},
title = {Directional hand movement can be classified from insular cortex SEEG signals using recurrent neural networks and high-gamma band features.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {29993},
pmid = {40819020},
issn = {2045-2322},
abstract = {Motor BCIs, with the help of Artificial Intelligence (AI) and machine learning, have shown promise in decoding neural signals for restoring motor function. Structures beyond motor cortex have provided additional sources for movement signals. New evidence points to the role of the insula in motor control, specifically directional hand-movements. In this study, we applied AI and machine learning techniques to decode directional hand-movements from high-gamma band (70-200 Hz) activity in the insular cortex. Seven participants with medication-resistant epilepsy underwent stereo electroencephalographic (SEEG) implantation of depth electrodes for seizure monitoring in the insula. SEEG data were sampled throughout a cued motor task involving three conditions: left-hand movement, right-hand movement, or no movement. Neural signal processing focused on high-gamma band activity. Demixed Principal Component Analysis (dPCA) was used for dimension reduction (d = 10) and feature extraction from the time-frequency analysis. For movement classification, we implemented a bidirectional Long Short-Term Memory (LSTM) architecture with a single layer, utilizing the capacity to process temporal sequences in forward and back directions for optimal decoding of movement direction. Our findings revealed robust directional-specific high-gamma modulation within the insular cortex during motor execution. Temporal decomposition through dPCA demonstrated distinct spatiotemporal patterns of high-gamma activity across movement conditions. Subsequently, LSTM networks successfully decoded these condition-specific neural signatures, achieving a classification accuracy of 72.6% ± 13.0% (mean ± SD), which significantly exceeded chance-level performance of 33.3% (p < 0.0001, n = 16 sessions). Furthermore, we identified a strong negative correlation between temporal distance of training-testing sessions and decoding performance (r = -0.868, p < 0.0001), indicating temporal difference of the neural representations. Our study highlights the potential role of deep brain structures, such as the insula, in conditional movement discrimination. We demonstrate that LSTM networks and high-gamma band analysis can advance the understanding of neural mechanisms underlying movement. These insights may pave the way for improvements in SEEG-based BCI.},
}

@article {pmid40818100,
year = {2025},
author = {Wei, W and Li, C and Li, W and Jiang, M and Zhang, X and Xing, L and Qian, Z and Jin, X},
title = {Study of a non-water-cooled microwave ablation needle based on a vacuum needle rod to achieve carbonization-free operation: design, simulation, and experimental research.},
journal = {Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy},
volume = {},
number = {},
pages = {1-12},
doi = {10.1080/13645706.2025.2543894},
pmid = {40818100},
issn = {1365-2931},
abstract = {BACKGROUND: At present, the microwave ablation needle used in clinic needs to add water circulation in the needle rod to reduce the rod temperature. However, the water circulation will take away a lot of heat during the ablation process, which requires increasing the ablation dose to achieve the expected thermal coagulation target volume. This undoubtedly increases the risk of carbonization.

METHODS: A design scheme of non-water-cooled microwave ablation needle based on double-layer vacuum structure was proposed. Two types of non-water-cooled microwave ablation needles with long and short needles were designed, and the ablation simulation was carried out by establishing the finite element simulation model.

RESULTS: Simulation and experimental results indicate that, at 20 W power, the long-needle vacuum tube ablation needle can create a carbonization-free solidification zone with a length of 34 mm after 180 s of ablation, whereas the short-needle vacuum tube ablation needle requires 300 s to form a similar zone with a length of 30 mm. Additionally, the axial ratio of the solidification zone created by the long-needle vacuum tube ablation needle exceeds that of the short-needle one. Consequently, the long-needle vacuum tube ablation needle is more apt for creating a larger solidification zone with minimal carbonization, while also achieving a more spherical shape.By comparing the ablation effects of long needle vacuum tube ablation needle and ky-2450b1 under low power,It is verified that the vacuum tube non-water-cooled ablation needle can ensure the effective ablation volume and non carbonization ablation under low-power and short-time ablation, which provides an important technical scheme for clinical optimization of the therapeutic effect of microwave ablation.

CONCLUSIONS: The LPH-W-F-MWA is more adept at creating a larger coagulation zone with minimal carbonization, achieving a more spherical shape to a greater extent. This ensures both an effective ablation volume and char-free ablation, offering a crucial technical solution for optimizing the therapeutic effect of MWA in clinical settings.},
}

@article {pmid40817330,
year = {2025},
author = {Jiang, L and Genon, S and Ye, J and Zhu, Y and Wang, G and He, R and Valdes-Sosa, PA and Wan, F and Yao, D and Eickhoff, SB and Dong, D and Li, F and Xu, P},
title = {Gene transcription, neurotransmitter, and neurocognition signatures of brain structural-functional coupling variability.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {7623},
pmid = {40817330},
issn = {2041-1723},
abstract = {The relationship between brain structure and function, known as structural-functional coupling (SFC), is highly dynamic. However, the temporal variability of this relationship, referring to the fluctuating extent to which functional profiles interact with anatomy over time, remains poorly elucidated. Here, we propose a framework to quantify SFC temporal variability and determine its neurocognitive map, genetic architecture, and neurochemical basis in 1206 healthy human participants. Results reveal regional heterogeneity in SFC variability and a composite emotion dimension co-varying with variability patterns involving the dorsal attention, somatomotor, and visual networks. The transcriptomic signatures of SFC variability are enriched in synapse- and cell cycle-related biological processes and implicated in emotion-related disorders. Moreover, regional densities of serotonin, glutamate, γ-aminobutyric acid, and opioid systems are predictive of SFC variability across the cortex. Collectively, SFC variability mapping provides a biologically plausible framework for understanding how SFC fluctuates over time to support macroscale neurocognitive specialization.},
}

@article {pmid40816538,
year = {2025},
author = {Kong, K and Wang, J and Li, M and Zhang, T and Qi, E and Zhao, Q},
title = {Action sequence guidance with exposure trajectory technology improves performance of motor imagery-based brain-computer interface.},
journal = {Journal of neuroscience methods},
volume = {423},
number = {},
pages = {110553},
doi = {10.1016/j.jneumeth.2025.110553},
pmid = {40816538},
issn = {1872-678X},
abstract = {BACKGROUND: The paradigms greatly influence the performance of motor imagery (MI)-based brain-computer interfaces (BCI) by guiding subjects to imagine. How to make the guidance clear and intuitive is important for MI-BCI to improve performance.

NEW METHODS: This study proposes a novel MI-BCI paradigm based on action sequence (AS) guidance, which visualizes and choreographs sequential actions to support motor imagery. In a drawing task, the action exposure trajectory technique presents a gray nib at the starting point of the next stroke while the current stroke is being drawn, highlighting the order and details of the movement. Ten subjects participated in offline and online experiments under both AS and traditional MI conditions. EEG activation regarding multiple frequencies and periods, and MI-BCI performance are evaluated.

RESULTS: The AS paradigm evokes more significant ERD/ERS features, and improves offline and online BCI accuracies and information transfer rates to 85.69 %, 78.77 %, and 15.60 bits/min, which are 8.37 %, 7.95 %, and 7.13 bits/min higher than the traditional paradigm. In addition, the subjects are demonstrated more comfortable subjective feelings.

The AS paradigm offers clearer and more intuitive guidance, enhances EEG feature activation, and significantly improves MI-BCI performance in both offline and online experiments.

CONCLUSIONS: Dynamic action sequences action with exposure trajectory technique could enhance the subject's brian activation by offering richer content and more intuitive guidance, providing a new way for prompting BCI performance.},
}

@article {pmid40816265,
year = {2025},
author = {Kunz, EM and Abramovich Krasa, B and Kamdar, F and Avansino, DT and Hahn, N and Yoon, S and Singh, A and Nason-Tomaszewski, SR and Card, NS and Jude, JJ and Jacques, BG and Bechefsky, PH and Iacobacci, C and Hochberg, LR and Rubin, DB and Williams, ZM and Brandman, DM and Stavisky, SD and AuYong, N and Pandarinath, C and Druckmann, S and Henderson, JM and Willett, FR},
title = {Inner speech in motor cortex and implications for speech neuroprostheses.},
journal = {Cell},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cell.2025.06.015},
pmid = {40816265},
issn = {1097-4172},
abstract = {Speech brain-computer interfaces (BCIs) show promise in restoring communication to people with paralysis but have also prompted discussions regarding their potential to decode private inner speech. Separately, inner speech may be a way to bypass the current approach of requiring speech BCI users to physically attempt speech, which is fatiguing and can slow communication. Using multi-unit recordings from four participants, we found that inner speech is robustly represented in the motor cortex and that imagined sentences can be decoded in real time. The representation of inner speech was highly correlated with attempted speech, though we also identified a neural "motor-intent" dimension that differentiates the two. We investigated the possibility of decoding private inner speech and found that some aspects of free-form inner speech could be decoded during sequence recall and counting tasks. Finally, we demonstrate high-fidelity strategies that prevent speech BCIs from unintentionally decoding private inner speech.},
}

@article {pmid40815626,
year = {2025},
author = {Padrão, N and Gregoricchio, S and Eickhoff, N and Dong, J and Luzietti, L and Bossi, D and Severson, TM and Siefert, J and Calcinotto, A and Buluwela, L and Donaldson Collier, M and Ali, S and Young, L and Theurillat, JP and Varešlija, D and Zwart, W},
title = {TRIM24 as a therapeutic target in endocrine treatment-resistant breast cancer.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {33},
pages = {e2507571122},
doi = {10.1073/pnas.2507571122},
pmid = {40815626},
issn = {1091-6490},
support = {813599//EC | ERC | HORIZON EUROPE European Research Council (ERC)/ ; 813599//EC | ERC | HORIZON EUROPE European Research Council (ERC)/ ; 813599//EC | ERC | HORIZON EUROPE European Research Council (ERC)/ ; 9171640//KWF Kankerbestrijding (DCS)/ ; 016.156.401//ZonMw (Netherlands Organisation for Health Research and Development)/ ; 2014MayPR234//Breast Cancer Now (BCN)/ ; C37/A18784//Cancer Research UK (CRUK)/ ; 20/FFP-P/8597//Research Ireland/ ; 23/SPP/11783//Research Ireland/ ; 2019AugSF1310//Breast Cancer Now (BCN)/ ; 18239A01//Breast Cancer Ireland (BCI)/ ; 19/FFP/6443//Research Ireland/ ; 23/SPP/11783//Research Ireland/ ; 2021JulyPCC1460//Breast Cancer Now (BCN)/ ; },
abstract = {While Estrogen receptor alpha (ERα)+ breast cancer treatment is considered effective, resistance to endocrine therapy is common. Since ERα is still the main driver in most therapy-resistant tumors, alternative therapeutic strategies are needed to disrupt ERα transcriptional activity. In this work, we position TRIM24 as a therapeutic target in endocrine resistance, given its role as a key component of the ERα transcriptional complex. TRIM24 interacts with ERα and other well-known ERα cofactors to facilitate ERα chromatin interactions and allows for maintenance of active histone marks including H3K23ac and H3K27ac. Consequently, genetic perturbation of TRIM24 abrogates ERα-driven transcriptional programs and reduces tumor cell proliferation capacity. Using a recently developed degrader targeting TRIM24, ERα-driven transcriptional output and growth were blocked, effectively treating not only endocrine-responsive cell lines but also drug-resistant derivatives thereof as well as cell line models bearing activating ESR1 point mutations. Finally, using human tumor-derived organoid models, we could show the efficacy of TRIM24 degrader in the endocrine-responsive and -resistant setting. Overall, our study positions TRIM24 as a central component for the integrity and activity of the ERα transcriptional complex, with degradation-mediated perturbation of TRIM24 as a promising therapeutic avenue in the treatment of primary and endocrine resistance breast cancer.},
}

@article {pmid40815349,
year = {2025},
author = {Li, Z and Li, M and Yang, Y},
title = {Motor imagery decoding network with multisubject dynamic transfer.},
journal = {Brain informatics},
volume = {12},
number = {1},
pages = {20},
pmid = {40815349},
issn = {2198-4018},
support = {Nos. 62173010//National Natural Science Foundation of China/ ; },
abstract = {Brain computer interface (BCI) provides a promising and intelligent rehabilitation method for motor function, and it is crucial to acquire the patient's movement intentions accurately through decoding motor imagery EEG (MI-EEG) . Because of the inter-individual heterogeneity, the decoding model should demonstrate dynamic adaptation abilities.Domain adaptation (DA) is effective to enhance model generalization by reducing the inherent distribution difference among subjects. However, the existing DA methods usually mix the multiple source domains into a new domain, the resulting multi-source domain conflict may cause negative transfer. In this paper, we propose a multi-source dynamic conditional domain adaptation network (MSDCDA). First, a multi-channel attention block is employed in the feature extractor to focus on the channels relevant to the corresponding MI task. Subsequently, the shallow spatial-temporal features are extracted using a spatial-temporal convolution block. And a dynamic residual block is applied in the feature extractor to dynamically adapt specific features of each subject to alleviate conflicts among multiple source domains since each domain is viewed as a distribution of electroencephalogram (EEG) signals. Furthermore, we employ the Margin Disparity Discrepancy (MDD) as the metric to achieve conditional distribution domain adaptation between the source and target domains through adversarial learning with an auxiliary classifier. MSDCDA achieved accuracies of 78.55 % and 85.08 % on Datasets IIa and IIb of BCI Competition IV, respectively. Our experimental results demonstrate that MSDCDA can effectively address multi-source domain conflicts and significantly enhance the decoding performance of target subjects. This study positively facilitates the application of BCI based on motor function rehabilitation.},
}

@article {pmid40813381,
year = {2025},
author = {Hendry, MF and Cruz-Garza, JG and Delgado-Jiménez, EA and Lima-Carmona, YE and Aguilar-Herrera, AJ and Ramírez-Moreno, MA and Ravindran, AS and Paek, AY and Smith, M and Kan, J and Fors, M and Alam, A and Liu, R and Noble, A and Contreras-Vidal, JL},
title = {Mobile Brain-Body Imaging and Visual Data of Theatrical Actors During Rehearsal and Performance.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1421},
pmid = {40813381},
issn = {2052-4463},
support = {1757949//National Science Foundation (NSF)/ ; 2137255//National Science Foundation (NSF)/ ; 2412731//National Science Foundation (NSF)/ ; },
abstract = {This longitudinal Mobile Brain-Body Imaging dataset was acquired during six rehearsal sessions and three public performances of a scene from a play with highly emotional components. Three student actor dyads (N=6), one theatre director (N=1) and three audience members (N=3) participated in this study. The MoBI data recorded includes mobile electroencephalography, electrooculography, blood volume pulse, heart rate, body temperature, electrodermal activity, triaxial arm and head acceleration. The visual data includes five streams of video. This article describes the experimental setup, the multi-modal data streams acquired using a hyperscanning methodology, and an assessment of the data quality.},
}

@article {pmid40813218,
year = {2025},
author = {Xie, J and Xu, G and Yang, Z and Su, H and Zhang, S},
title = {Modeling multiscale time-frequency complex networks on Riemannian manifolds for motor imagery BCI classification with graph convolutional networks.},
journal = {ISA transactions},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.isatra.2025.07.058},
pmid = {40813218},
issn = {1879-2022},
abstract = {Motor imagery brain-computer interface (MI-BCI) classification faces challenges such as low decoding accuracy and difficulty in capturing the spatiotemporal dynamics of EEG signals. The use of Riemannian geometry classifiers for this task has become one of the most popular classification methods. However, current Riemannian geometry classifiers typically compute the covariance matrix over a period of time to capture spatial features, neglecting the multiscale characteristics of EEG signals in both time and frequency, which limits their classification performance. To address these issues, this study proposes a novel framework. Specifically, we introduce graph convolutional network (GCN) on Riemannian geometry (GR) to process multiscale networks, using virtual nodes to capture global topological features and integrating spatial features across time and frequency domains. This method significantly enhances the feature extraction capability of Riemannian geometry classifiers. The proposed method was validated on three public datasets, with average classification accuracies of 91.87 % ± 7.33 %, 87.96 % ± 7.6 %, and 82.50 % ± 7.74 %, respectively. Ablation experiments show that, compared to traditional single-scale methods, the average classification accuracy improved by 9.85 %, highlighting the effectiveness and versatility of the proposed method. This research provides a new perspective for multiscale EEG signal analysis and advances the development of motor imagery BCI classification technology.},
}

@article {pmid40810162,
year = {2025},
author = {Zhang, M and Zhai, H and Yang, L and Li, H and Wang, X},
title = {The Medial Prefrontal Cortex Modulates Psychedelic-like Effects of Psilocin.},
journal = {ACS pharmacology & translational science},
volume = {8},
number = {8},
pages = {2767-2776},
pmid = {40810162},
issn = {2575-9108},
abstract = {Recent advancements in the study of psilocybin and its active metabolite psilocin have highlighted their unique psychedelic properties and potential therapeutic applications, particularly in the rapid and sustained treatment of depression. However, the potent acute psychedelic effects of psilocybin necessitate a deeper understanding of the neural mechanisms underlying its action. In this study, we investigated the psilocin-induced neural activity in male mice using c-Fos immunofluorescent labeling and identified brain regions associated with psychedelic-like activity. Among the medial prefrontal cortex (mPFC), orbitofrontal cortex (OFC), interstitial nucleus of the posterior limb of the anterior commissure (IPAC), and dorsomedial striatum (DMS), only the mPFC was specifically associated with the head twitch response (HTR), a hallmark of psychedelic-like behavior. A picomolar dose of psilocin in the mPFC was sufficient to induce significant HTR, suggesting that c-Fos-positive neurons in this region modulate psychedelic-like activity. To validate this hypothesis, optogenetic activation of these neurons significantly increased spontaneous HTR in TRAP2 mice, whereas acute inhibition suppressed drug-induced HTR. These findings establish the mPFC as a critical regulator of psilocin-induced psychedelic-like activity and provide valuable insights for enhancing the clinical safety and therapeutic application of psychedelics.},
}

@article {pmid40807942,
year = {2025},
author = {Chen, J and Yang, C and Wei, R and Hua, C and Mu, D and Sun, F},
title = {Steady-State Visual-Evoked-Potential-Driven Quadrotor Control Using a Deep Residual CNN for Short-Time Signal Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {15},
pages = {},
pmid = {40807942},
issn = {1424-8220},
support = {BX2021157//Post Doctoral Innovative Talent Support Program under Grants/ ; 62103221//National Natural Science Foundation of China under Grant/ ; },
abstract = {In this paper, we study the classification problem of short-time-window steady-state visual evoked potentials (SSVEPs) and propose a novel deep convolutional network named EEGResNet based on the idea of residual connection to further improve the classification performance. Since the frequency-domain features extracted from short-time-window signals are difficult to distinguish, the EEGResNet starts from the filter bank (FB)-based feature extraction module in the time domain. The FB designed in this paper is composed of four sixth-order Butterworth filters with different bandpass ranges, and the four bandwidths are 19-50 Hz, 14-38 Hz, 9-26 Hz, and 3-14 Hz, respectively. Then, the extracted four feature tensors with the same shape are directly aggregated together. Furthermore, the aggregated features are further learned by a six-layer convolutional neural network with residual connections. Finally, the network output is generated through an adaptive fully connected layer. To prove the effectiveness and superiority of our designed EEGResNet, necessary experiments and comparisons are conducted over two large public datasets. To further verify the application potential of the trained network, a virtual simulation of brain computer interface (BCI) based quadrotor control is presented through V-REP.},
}

@article {pmid40807891,
year = {2025},
author = {Huang, Y and Cao, L and Chen, Y and Wang, T},
title = {Optimization of Dynamic SSVEP Paradigms for Practical Application: Low-Fatigue Design with Coordinated Trajectory and Speed Modulation and Gaming Validation.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {15},
pages = {},
pmid = {40807891},
issn = {1424-8220},
abstract = {Steady-state visual evoked potential (SSVEP) paradigms are widely used in brain-computer interface (BCI) systems due to their reliability and fast response. However, traditional static stimuli may reduce user comfort and engagement during prolonged use. This study proposes a dynamic stimulation paradigm combining periodic motion trajectories with speed control. Using four frequencies (6, 8.57, 10, 12 Hz) and three waveform patterns (sinusoidal, square, sawtooth), speed was modulated at 1/5, 1/10, and 1/20 of each frequency's base rate. An offline experiment with 17 subjects showed that the low-speed sinusoidal and sawtooth trajectories matched the static accuracy (85.84% and 83.82%) while reducing cognitive workload by 22%. An online experiment with 12 subjects participating in a fruit-slicing game confirmed its practicality, achieving recognition accuracies above 82% and a System Usability Scale score of 75.96. These results indicate that coordinated trajectory and speed modulation preserves SSVEP signal quality and enhances user experience, offering a promising approach for fatigue-resistant, user-friendly BCI application.},
}

@article {pmid40807821,
year = {2025},
author = {Aziz, MZ and Yu, X and Guo, X and He, X and Huang, B and Fan, Z},
title = {BCINetV1: Integrating Temporal and Spectral Focus Through a Novel Convolutional Attention Architecture for MI EEG Decoding.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {15},
pages = {},
pmid = {40807821},
issn = {1424-8220},
support = {2025A1515011449//Natural Science Foundation of Guangdong Province/ ; 20240001053007//Aviation Science Foundation Project/ ; 62220106006//National Natural Science Foundation of China/ ; },
abstract = {Motor imagery (MI) electroencephalograms (EEGs) are pivotal cortical potentials reflecting cortical activity during imagined motor actions, widely leveraged for brain-computer interface (BCI) system development. However, effectively decoding these MI EEG signals is often overshadowed by flawed methods in signal processing, deep learning methods that are clinically unexplained, and highly inconsistent performance across different datasets. We propose BCINetV1, a new framework for MI EEG decoding to address the aforementioned challenges. The BCINetV1 utilizes three innovative components: a temporal convolution-based attention block (T-CAB) and a spectral convolution-based attention block (S-CAB), both driven by a new convolutional self-attention (ConvSAT) mechanism to identify key non-stationary temporal and spectral patterns in the EEG signals. Lastly, a squeeze-and-excitation block (SEB) intelligently combines those identified tempo-spectral features for accurate, stable, and contextually aware MI EEG classification. Evaluated upon four diverse datasets containing 69 participants, BCINetV1 consistently achieved the highest average accuracies of 98.6% (Dataset 1), 96.6% (Dataset 2), 96.9% (Dataset 3), and 98.4% (Dataset 4). This research demonstrates that BCINetV1 is computationally efficient, extracts clinically vital markers, effectively handles the non-stationarity of EEG data, and shows a clear advantage over existing methods, marking a significant step forward for practical BCI applications.},
}

@article {pmid40807788,
year = {2025},
author = {Siribunyaphat, N and Tohkhwan, N and Punsawad, Y},
title = {Investigation of Personalized Visual Stimuli via Checkerboard Patterns Using Flickering Circles for SSVEP-Based BCI System.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {15},
pages = {},
pmid = {40807788},
issn = {1424-8220},
support = {WU67260//Research and Innovation Institute of Excellence, Walailak University/ ; },
abstract = {In this study, we conducted two steady-state visual evoked potential (SSVEP) studies to develop a practical brain-computer interface (BCI) system for communication and control applications. The first study introduces a novel visual stimulus paradigm that combines checkerboard patterns with flickering circles configured in single-, double-, and triple-layer forms. We tested three flickering frequency conditions: a single fundamental frequency, a combination of the fundamental frequency and its harmonics, and a combination of two fundamental frequencies. The second study utilizes personalized visual stimuli to enhance SSVEP responses. SSVEP detection was performed using power spectral density (PSD) analysis by employing Welch's method and relative PSD to extract SSVEP features. Commands classification was carried out using a proposed decision rule-based algorithm. The results were compared with those of a conventional checkerboard pattern with flickering squares. The experimental findings indicate that single-layer flickering circle patterns exhibit comparable or improved performance when compared with the conventional stimuli, particularly when customized for individual users. Conversely, the multilayer patterns tended to increase visual fatigue. Furthermore, individualized stimuli achieved a classification accuracy of 90.2% in real-time SSVEP-based BCI systems for six-command generation tasks. The personalized visual stimuli can enhance user experience and system performance, thereby supporting the development of a practical SSVEP-based BCI system.},
}

@article {pmid40807738,
year = {2025},
author = {Leerskov, KS and Spaich, EG and Jochumsen, MR and Andreasen Struijk, LNS},
title = {Design and Demonstration of a Hybrid FES-BCI-Based Robotic Neurorehabilitation System for Lower Limbs.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {15},
pages = {},
pmid = {40807738},
issn = {1424-8220},
support = {A33234//Hartmann Fonden/ ; },
abstract = {BACKGROUND: There are only a few available options for early rehabilitation of severely impaired individuals who must remain bedbound, as most exercise paradigms focus on out-of-bed exercises. To enable these individuals to exercise, we developed a novel hybrid rehabilitation system combining a brain-computer interface (BCI), functional electrical stimulation (FES), and a robotic device.

METHODS: The BCI assessed the presence of a movement-related cortical potential (MRCP) and triggered the administration of FES to produce movement of the lower limb. The exercise trajectory was supported by the robotic device. To demonstrate the system, an experiment was conducted in an out-of-lab setting by ten able-bodied participants. During exercise, the performance of the BCI was assessed, and the participants evaluated the system using the NASA Task Load Index, Intrinsic Motivation Inventory, and by answering a few subjective questions.

RESULTS: The BCI reached a true positive rate of 62.6 ± 9.2% and, on average, predicted the movement initiation 595 ± 129 ms prior to the MRCP peak negativity. All questionnaires showed favorable outcomes for the use of the system.

CONCLUSIONS: The developed system was usable by all participants, but its clinical feasibility is uncertain due to the total time required for setting up the system.},
}

@article {pmid40803174,
year = {2025},
author = {Kamali, S and Baroni, F and Varona, P},
title = {Mu and beta power effects of fast response trait double dissociate during precue and movement execution in the sensorimotor cortex.},
journal = {Computers in biology and medicine},
volume = {196},
number = {Pt C},
pages = {110874},
doi = {10.1016/j.compbiomed.2025.110874},
pmid = {40803174},
issn = {1879-0534},
abstract = {A better understanding of the neural and muscular mechanisms underlying motor responses is essential for advancing neurorehabilitation protocols, brain-computer interfaces (BCI), feature engineering for biosignal classification algorithms, and identifying biomarkers of disease and performance enhancement strategies. In this study, we examined the neuromuscular dynamics of healthy individuals during a sequential finger-pinching task, focusing on the relationships between cortical oscillations and muscle activity in simultaneous electroencephalography (EEG) and electromyography (EMG) recordings. We contrasted two pairs of subsets of the dataset based on the latency of EMG onset: an across-subjects trait-based comparison and a within-subjects state-based comparison. Trait-based analyses showed that fast responders had higher baseline beta power, indicating stronger motor inhibition and efficient resetting of motor networks, and greater mu desynchronization during movement, reflecting enhanced motor cortex activation. Visual association areas also displayed more pronounced changes in different phases of the task in subjects with lower latency. Fast responders exhibited lower baseline EMG activity and stronger EMG power during movement initiation, showing effective motor inhibition and rapid muscle activation. State-based analyses revealed no significant EEG differences between fast and slow trials, while EMG differences were only detected after movement onset. These results highlight that fast response trait is related to electrophysiological differences at specific frequency bands and task phases, offering insights for enhancing motor function in rehabilitation, biomarker identification and BCI applications.},
}

@article {pmid40801596,
year = {2025},
author = {Henderson, FC and Tuchman, K},
title = {Angiogenic Cell Precursors and Neural Cell Precursors in Service to the Brain-Computer Interface.},
journal = {Cells},
volume = {14},
number = {15},
pages = {},
pmid = {40801596},
issn = {2073-4409},
abstract = {The application of artificial intelligence through the brain-computer interface (BCI) is proving to be one of the great advances in neuroscience today. The development of surface electrodes over the cortex and very fine electrodes that can be stereotactically implanted in the brain have moved the science forward to the extent that paralyzed people can play chess and blind people can read letters. However, the introduction of foreign bodies into deeper parts of the central nervous system results in foreign body reaction, scarring, apoptosis, and decreased signaling. Implanted electrodes activate microglia, causing the release of inflammatory factors, the recruitment of systemic inflammatory cells to the site of injury, and ultimately glial scarring and the encapsulation of the electrode. Recordings historically fail between 6 months and 1 year; the longest BCI in use has been 7 years. This article proposes a biomolecular strategy provided by angiogenic cell precursors (ACPs) and nerve cell precursors (NCPs), administered intrathecally. This combination of cells is anticipated to sustain and promote learning across the BCI. Together, through the downstream activation of neurotrophic factors, they may exert a salutary immunomodulatory suppression of inflammation, anti-apoptosis, homeostasis, angiogenesis, differentiation, synaptogenesis, neuritogenesis, and learning-associated plasticity.},
}

@article {pmid40800758,
year = {2025},
author = {Kostorz, K and Nguyen, T and Pan, Y and Melinscak, F and Steyrl, D and Hu, Y and Sorger, B and Hoehl, S and Scharnowski, F},
title = {Investigating short windows of interbrain synchrony: A step toward fNIRS-based hyperfeedback.},
journal = {Imaging neuroscience (Cambridge, Mass.)},
volume = {3},
number = {},
pages = {},
pmid = {40800758},
issn = {2837-6056},
abstract = {Social interaction is of fundamental importance to humans. Prior research has highlighted the link between interbrain synchrony and positive outcomes in human social interaction. Neurofeedback is an established method to train one's brain activity and might offer a possibility to increase interbrain synchrony, too. Consequently, it would be advantageous to determine the feasibility of creating a neurofeedback system for enhancing interbrain synchrony to benefit human interaction. One vital step toward developing a neurofeedback setup is to determine whether the target metric can be determined in relatively short time windows. In this study, we investigated whether the most widely employed metric for interbrain synchrony, wavelet transform coherence, can be assessed accurately in short time windows using functional near-infrared spectroscopy (fNIRS), which is recognized for its mobility and ecological suitability for interactive research. To this end, we have undertaken a comprehensive approach where we created artificial data of different noise levels of a dyadic interaction and re-processed two human-interaction datasets. For both artificial and in vivo data, we computed short windows of interbrain synchrony of varying size and assessed significance at each window size. Our findings indicate that relatively short windows of wavelet transform coherence of integration durations of about 1 minute are feasible. This would align well with the methodology of an intermittent neurofeedback procedure. Our investigation lays a foundational step toward an fNIRS-based system to measure interbrain synchrony in real time and provide participants with information about their interbrain synchrony. This advancement is crucial for the future development of a neurofeedback training system tailored to enhance interbrain synchrony to potentially benefit human interaction.},
}