@article {pmid40746851,
year = {2025},
author = {Aziz, NA and Ng, K and Alifrangis, C and Tran, B and Conduit, C and Liow, E and Ackerman, C and Georgescu, R and Jamal, T and Relton, C and Mayer, E and Nicol, D and Cazzaniga, W and Huddart, R and Reid, A and Shamash, J and Rajan, P},
title = {Therapy de-escalation for testicular cancer (THERATEST): A multi-centre observational cohort feasibility study of de-escalation therapies for good prognosis stage II germ cell tumours.},
journal = {BJUI compass},
volume = {6},
number = {8},
pages = {e70057},
pmid = {40746851},
issn = {2688-4526},
abstract = {BACKGROUND: Standard of care (SOC) treatments for International Germ Cell Cancer Collaborative Group (IGCCCG) good prognosis stage II germ cell tumours (GCT) involve primary orchidectomy followed by combination chemotherapy for both seminoma and non-seminomatous germ cell tumours (NSGCT). Alternatively, external beam radiotherapy may be used for seminoma and retroperitoneal lymph node dissection (RPLND) for NSGCT. While these treatments achieve high cure rates, they are associated with significant toxicities. De-escalation strategies including three cycles of Carboplatin AUC10 or robotic RPLND with or without adjuvant chemotherapy have demonstrated potential to reduce treatment-related toxicity in stage II seminoma while preserving oncological efficacy. However, these approaches are not widely adopted due to limited prospective comparative trials.

STUDY DESIGN: The THERATEST trial is a prospective multicentre observational feasibility study evaluating participants receiving SOC treatments for good prognosis stage II seminoma and NSGCT or de-escalated treatments for stage II seminoma.

ENDPOINTS: The primary endpoints are to assess feasibility of recruitment and retention. Secondary endpoints include assessing health-related quality of life (HRQOL), sexual function and satisfaction, progression-free survival (PFS), overall survival (OS) and safety and treatment-related complications.

PATIENTS AND METHODS: Thirty participants with good prognosis stage II seminoma or NSGCTs will be recruited over 18 months into two cohorts: de-escalation arm and SOC arm. The de-escalation cohort will receive either Carboplatin AUC10 or robotic RPLND with or without adjuvant therapy depending on institutional SOC. Participants who decline or are ineligible for de-escalation will receive SOC treatment: combination chemotherapy or radiotherapy for seminoma and combination chemotherapy for NSGCT. All participants will be followed for two years post-treatment or until withdrawal. Data collection includes recruitment and retention rates, disease status, surgical outcomes, adverse events and patient-reported outcomes using validated questionnaire: EORTC QLQ-TC26, EORTC QLQ-C30, Brief Male Sexual Function Inventory (BMSFI) and additional enquiries on anejaculation.

COORDINATING CENTRE: THERATEST Trial Coordinator, Centre for Experimental Cancer Medicine, Barts Cancer Institute, Queen Mary University of London, Old Anatomy Building, Charterhouse Square, London, EC1M 6BQ|T: 0207882 8497|E: bci-theratest@qmul.ac.uk.

TRIAL REGISTRATION NUMBER: ISRCTN61007118.},
}

@article {pmid40746199,
year = {2025},
author = {Madhavan, S},
title = {Harnessing Neuroplasticity: The Role of Priming in Enhancing Post Stroke Motor Function.},
journal = {Restorative neurology and neuroscience},
volume = {},
number = {},
pages = {9226028251358162},
doi = {10.1177/09226028251358162},
pmid = {40746199},
issn = {1878-3627},
abstract = {Stroke remains a leading cause of disability worldwide, highlighting the need for innovative neurorehabilitation strategies to enhance recovery. Recent advancements emphasize neuroplasticity-the brain's ability to reorganize and form new connections-through targeted interventions. Among these, cortical priming has emerged as a promising approach to enhance neuroplasticity and improve motor recovery post-stroke by modulating brain excitability for optimal motor learning. This review explores the role of cortical priming in stroke rehabilitation, highlighting its ability to enhance neural excitability and plasticity in motor-related brain regions. Various priming techniques, including non-invasive brain stimulation (rTMS, tDCS), deep brain stimulation (DBS), vagus nerve stimulation (VNS), brain-computer interfaces (BCIs), movement-based priming, aerobic exercise, and sensory stimulation, are examined. Despite promising findings, challenges remain in optimizing protocols and addressing individual variability. Future directions focus on biomarker-driven rehabilitation, personalized strategies, and large-scale trials to integrate cortical priming into clinical practice.},
}

@article {pmid40745321,
year = {2025},
author = {Chen, J and Liu, Q and Chen, G and Cai, G and Jiang, J and Yang, X and Tan, C and Zhang, C and Xu, G and Lan, Y},
title = {iTBS on RDLPFC improves performance of motor imagery: a brain-computer interface study combining EEG and fNIRS.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {172},
pmid = {40745321},
issn = {1743-0003},
support = {82072548//National Science Foundation of China/ ; 82472619//National Science Foundation of China/ ; 2022YFC2009700//Natural Key Research and Development Program of China/ ; 202206010197 and 202201020378//Guangzhou Municipal Science and Technology Program/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Spectroscopy, Near-Infrared ; Electroencephalography/methods ; *Imagination/physiology ; Adult ; Young Adult ; *Transcranial Magnetic Stimulation/methods ; *Dorsolateral Prefrontal Cortex/physiology ; Neuronal Plasticity/physiology ; Psychomotor Performance/physiology ; },
abstract = {BACKGROUND: Some individuals using brain-computer interfaces (BCIs) exhibit ineffective control during motor imagery-based BCI (MI-BCI) training. MI-BCI performance correlates with the activation in the frontoparietal attention network, premotor-parietal network, and supplementary motor area (SMA). This study aimed to enhance motor imagery ability and MI-BCI performance by modulating the excitability of the right dorsolateral prefrontal cortex (RDLPFC) through intermittent theta-burst stimulation (iTBS), inducing neuroplastic changes.

METHODS: Fifty-two healthy right-handed participants were randomly assigned to either the iTBS or sham group. They undertook two MI-BCI training sessions, with electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) used to assess acute neuroplasticity changes. The intervention was administered between sessions. Corticospinal excitability and motor imagery vividness were assessed using single-pulse transcranial magnetic stimulation (spTMS) and the Kinesthetic and Visual Imagery Questionnaire-20 (KVIQ-20) before and following the trial.

RESULTS: The iTBS group significantly improved motor state percentage (MSP). Significant µ event-related desynchronization (µ-ERD) was observed at the F4 electrode in the iTBS group. Functional connectivity (FC) analyses revealed decreased connectivity among several electrodes during the post-intervention period. The hemodynamic response function (HRF) indicated significant activation in the right PMC and SMA, with reduced FC among motor areas. No significant differences in MEP, CSP, and KVIQ-20 scores were found between groups.

CONCLUSION: iTBS targeting the RDLPFC may improve MI-BCI training performance and address the "BCI inefficiency" problem. RDLPFC stimulation induced changes in FC of brain regions associated with motor imagery and increased the activation of motor areas, suggesting that the RDLPFC could be a promising target for enhancing motor imagery and optimizing BCI systems.},
}

Humans
*Brain-Computer Interfaces
Male
Female
Spectroscopy, Near-Infrared
Electroencephalography/methods
*Imagination/physiology
Adult
Young Adult
*Transcranial Magnetic Stimulation/methods
*Dorsolateral Prefrontal Cortex/physiology
Neuronal Plasticity/physiology
Psychomotor Performance/physiology

@article {pmid40745252,
year = {2025},
author = {Ke, Y and Han, Y and Liu, P and Ming, D},
title = {Dataset of binocularly coded steady-state visual evoked potentials recorded with an augmented reality headset.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1338},
pmid = {40745252},
issn = {2052-4463},
support = {62276184 and 81925020//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Electroencephalography ; *Augmented Reality ; Vision, Binocular ; },
abstract = {Steady-state visually evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have shown significant promise for practical applications. The integration of SSVEP-BCIs with head-mounted augmented-reality (AR) displays is expected to foster wearable, portable systems; nevertheless, empirical resources for such configurations are scarce, especially for paradigms employing innovative stimulation paradigms. Here we present a curated SSVEP dataset recorded with a binocular AR headset that independently modulates the visual input to each eye and a lightweight electroencephalography recorder. Beyond the conventional binocular-congruent single-frequency stimulation adopted in AR-SSVEP studies, the dataset systematically explores binocular-incongruent dual-frequency encoding whereby the two lenses render flickers with distinct frequencies and/or phases. We report comparative analyses of SSVEP characteristics and BCI performance under congruent versus incongruent protocols, and delineate the influence of inter-ocular frequency and phase disparities. The results substantiate the feasibility of wearable AR-SSVEP-BCIs and highlight binocular-incongruent dual-frequency stimulation as a compelling strategy for improving target separability. The dataset should accelerate research on portable SSVEP-BCIs, novel encoding schemes, and the neural mechanisms of binocular vision.},
}

Humans
*Evoked Potentials, Visual
*Brain-Computer Interfaces
Electroencephalography
*Augmented Reality
Vision, Binocular

@article {pmid40744250,
year = {2025},
author = {Li, Q and Ping, A and Feng, Y and Xu, B and Zhang, B and Roe, AW and Gao, L and Li, X},
title = {Mesoscale functional connectivity of amygdala to the auditory and prefrontal cortex of macaque monkeys revealed by INS-fMRI.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121406},
doi = {10.1016/j.neuroimage.2025.121406},
pmid = {40744250},
issn = {1095-9572},
abstract = {Mammals rely heavily on their auditory system to perceive environmental threats, socially communicate, and care for the young. As an extension of the multiple sensory system including the auditory system, the amygdala evaluates the emotional salience of acoustic stimuli, and mediates its impact on sensory, cognitive, and physiological aspects of emotional processing via the lateral amygdala (LA), basal amygdala (BA), and central amygdala (CeA) nuclei of the amygdala in acoustic domain. However, the functional connections of LA, BA, and CeA with the auditory cortex (AC) and the prefrontal cortex (PFC) remain unclear, particularly at the mesoscale level. Here we employed a novel method called INS-fMRI (Infrared Neural Stimulation combined with high-resolution functional magnetic resonance imaging) in Macaque monkeys; this method permits stimulation of multiple sites within single animals in vivo, so that the relative organization of auditory networks can be studied. We found that: (1) Focal INS stimulation of the amygdala elicited robust and reliable responses in both the AC and the PFC; (2) Amygdala stimulation mainly activated ipsilateral AC and PFC; (3) The stimulation of the amygdala mainly activated the secondary AC, and the dorsolateral PFC; (4) The connection between the amygdala and the cortex is mainly mediated by neurons in LA and BA connection area. Our study further revealed the functional connectivity among the amygdala subnucleus, the auditory cortex and the prefrontal cortex, and will shed light on the research for processing biologically meaningful complex sounds.},
}

@article {pmid40744238,
year = {2025},
author = {Wen, Z and Yang, D and Yang, Y and Hu, J and Parviainen, A and Chen, X and Li, Q and VanDeusen, E and Ma, J and Tay, F},
title = {The path to biotechnological singularity: Current breakthroughs and outlook.},
journal = {Biotechnology advances},
volume = {84},
number = {},
pages = {108667},
doi = {10.1016/j.biotechadv.2025.108667},
pmid = {40744238},
issn = {1873-1899},
abstract = {Fueled by rapid advances in gene editing, synthetic biology, artificial intelligence, regenerative medicine, and brain-computer interfaces, biotechnology is approaching a transformative era often referred to as biotechnological singularity. CRISPR-based gene editing has revolutionized genetic engineering, enabling precise modifications for treating hereditary diseases and cancer. Synthetic biology facilitates sustainable biomaterial production and innovative therapeutic applications. Artificial intelligence accelerates drug discovery, enhances diagnostic accuracy, and personalizes treatment through deep learning models. Driven by stem cell research, regenerative medicine offers promising avenues for reversing aging and treating degenerative diseases. Brain-computer interfaces merge human cognition with technology, enabling direct neural control of prosthetics and expanding human-machine interactions. These breakthroughs, however, raise ethical, regulatory, and societal concerns, including equitable access, biosecurity risks, and the implications of human enhancement. The convergence of biological and computational technologies challenges traditional boundaries, necessitating comprehensive governance frameworks. By embracing responsible innovation, society can harness these advancements for transformative health interventions, environmental sustainability, and extended longevity. The realization of biotechnological singularity depends on interdisciplinary collaboration among scientists, policymakers, and the public to ensure that progress aligns with the well-being of humanity and ethical considerations.},
}

@article {pmid40743699,
year = {2025},
author = {Amiri, G and Shalchyan, V},
title = {Decoding muscle activity via CNN-LSTM from 3D spatiotemporal EEG.},
journal = {Computer methods and programs in biomedicine},
volume = {271},
number = {},
pages = {108983},
doi = {10.1016/j.cmpb.2025.108983},
pmid = {40743699},
issn = {1872-7565},
abstract = {OBJECTIVE: Reconstructing muscle activity from electromyogram (EMG) data using non-invasive electroencephalogram (EEG) signals could lead to significant advancements in brain-computer interfaces (BCIs). However, extracting muscle-related signals from EEG poses considerable challenges due to the mixed nature of signals captured by EEG sensors from various cortical regions.

APPROACH: This study introduces a new method for estimating muscle activity from non-invasive EEG signals while participants performed the grasp and lift (GAL) task. Envelopes of the delta, theta, alpha, beta, and gamma frequency bands were chosen as EEG features for the decoding models, computed similarly to muscle activity (EMG envelopes). These were converted into three-dimensional spatiotemporal matrices based on EEG electrode locations. A deep learning model, combining convolutional neural networks (CNN) for spatial and long short-term memory (LSTM) network for temporal EEG information extraction, was applied. This model was compared with two linear and nonlinear decoding methods: multivariate linear regression (mLR) and multilayer perceptron (MLP).

MAIN RESULTS: The average ± standard deviation of the normalized root mean square error (nRMSE), coefficient of determination (R²), and correlation coefficient (CC) between the estimated and actual muscle activity of two muscles in five participants were 0.21 ± 0.05, 0.54 ± 0.17, and 0.76 ± 0.10, respectively. The CNN-LSTM model outperformed both mLR and MLP approaches (p-value < 0.016), with higher frequencies proving more effective for decoding.

SIGNIFICANCE: The proposed model effectively captures nonlinear relationships between brain and muscle activities, indicating its potential to enhance the accuracy and reliability of non-invasive BCIs.},
}

@article {pmid40742862,
year = {2025},
author = {Heo, D and Kim, SP},
title = {Freeing P300-Based Brain-Computer Interfaces from Daily Recalibration by Extracting Daily Common ERPs.},
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.2025.3594341},
pmid = {40742862},
issn = {1558-0210},
abstract = {When people use brain-computer interfaces (BCIs) based on event-related potentials (ERPs) over different days, they often need to repeatedly calibrate BCIs every day using ERPs acquired on the same day. This cumbersome recalibration procedure would make it difficult to use BCIs daily. We aim to address the daily recalibration issue by examining across-day variations of the BCI performance and proposing a method to avoid daily recalibration. To this end, we implemented a P300-based BCI system designed to control a home appliance over five days. We first examined how the BCI performance varied across days with or without daily recalibration. On each day, the BCIs were tested using recalibration-based and recalibration-free decoders (RB and RF), with an RB or an RF decoder being built on the training data on each day or those on the first day, respectively. Using the RF decoder resulted in lower BCI performance on subsequent days compared to the RB decoder. Then, we developed a method to extract daily common ERP patterns from observed ERP signals using the sparse dictionary learning algorithm. We applied this method to the RF decoder and retested the BCI performance over days. Using the proposed method improved the RF decoder performance on subsequent days; the performance was closer to the level of the RB decoder compared to the original RF decoder. The method may provide a novel approach to addressing the daily-recalibration issue for P300-based BCIs, which is essential to implementing BCIs into daily life.},
}

@article {pmid40741299,
year = {2025},
author = {Si, Y and Sun, Y and Wu, K and Gao, L and Wang, S and Xu, M and Qi, X},
title = {Effects of ASMR on mental fatigue recovery revealed by EEG power and brain network analysis.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1619424},
pmid = {40741299},
issn = {1662-5161},
abstract = {INTRODUCTION: Mental fatigue, resulting from prolonged cognitive tasks or sleep deprivation, significantly impacts safety and performance, particularly in high-risk environments. However, effective intervention methods are limited, highlighting the urgent need for new approaches to alleviate mental fatigue. This study explores the effectiveness of Autonomous Sensory Meridian Response (ASMR) as a novel intervention for alleviating mental fatigue.

METHODS: A within-subject design was employed in this work, where 28 healthy young subjects (M/F = 17/11, age = 21.82 ± 0.37 years) were requested to perform a continuous 30 min sustained attention task (named No-Break session) and a 30 min task with a 4-min mid-task ASMR break (named ASMR-Break session) at a counterbalanced order. The immediate effect and general effect of ASMR were quantitatively assessed on behavioral performance and EEG characteristics.

RESULTS: Behaviorally, only significant immediate effect was revealed as showing in reduced reaction time. Further interrogation of brain dynamics showed complex patterns of spatio-spectrum alterations and an interaction in small-world metric in theta band. Specifically, the ASMR intervention prevented an increase in small-worldness, and the correlation between changes in small-worldness and reaction times diminished after the intervention.

DISCUSSION: In sum, this preliminary investigation provides insight into ASMR's neural mechanisms and suggests it may help attenuate fatigue. Further research in larger, more diverse samples will be necessary to confirm its utility for mental fatigue management in real-world settings.},
}

@article {pmid39885677,
year = {2025},
author = {Sara Wilson, K and Saravanan, KK},
title = {Artificial intelligent based control strategy for reach and grasp of multi-objects using brain-controlled robotic arm system.},
journal = {Network (Bristol, England)},
volume = {36},
number = {3},
pages = {1253-1281},
doi = {10.1080/0954898X.2025.2453620},
pmid = {39885677},
issn = {1361-6536},
mesh = {Humans ; *Robotics/methods/instrumentation ; Electroencephalography/methods ; *Hand Strength/physiology ; *Brain/physiology ; *Artificial Intelligence ; *Brain-Computer Interfaces ; *Arm/physiology ; Algorithms ; },
abstract = {Brain-controlled robotic arm systems are designed to provide a method of communication and control for individuals with limited mobility or communication abilities. These systems can be beneficial for people who have suffered from a spinal cord injury, stroke, or neurological disease that affects their motor abilities. The ability of a person to control a robotic arm to reach and grasp multiple objects using their brain signals. This technology involves the use of an electroencephalogram (EEG) cap that captures the electrical activity in the user's brain, which is then processed by an artificial intelligent to translate it into commands that control the movements of the robotic arm. With this technology, individuals who are unable to move their limbs due to paralysis or other conditions can still perform daily activities such as feeding themselves, drinking from a glass, or grasping objects. In this paper, we propose an artificial intelligent-based control strategy for reach and grasp of multi-objects using brain-controlled robotic arm system. The proposed control strategy consists of threefold process: feature extraction, feature optimization, and control strategy classification. Initially, we design an improved ResNet pre-trained architecture for deep feature extraction from the given EEG signal.},
}

Humans
*Robotics/methods/instrumentation
Electroencephalography/methods
*Hand Strength/physiology
*Brain/physiology
*Artificial Intelligence
*Brain-Computer Interfaces
*Arm/physiology
Algorithms

@article {pmid40741298,
year = {2025},
author = {Zhu, T and Tang, H and Jiang, L and Li, Y and Li, S and Wu, Z},
title = {A study of motor imagery EEG classification based on feature fusion and attentional mechanisms.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1611229},
pmid = {40741298},
issn = {1662-5161},
abstract = {INTRODUCTION: Motor imagery EEG-based action recognition is an emerging field arising from the intersection of brain science and information science, which has promising applications in the fields of neurorehabilitation and human-computer collaboration. However, existing methods face challenges including the low signal-to-noise ratio of EEG signals, inter-subject variability, and model overfitting.

METHODS: We propose HA-FuseNet, an end-to-end motor imagery action classification network. This model integrates feature fusion and attention mechanisms to classify left hand, right hand, foot, and tongue movements. Its innovations include: (1) multi-scale dense connectivity, (2) hybrid attention mechanism, (3) global self-attention module, and (4) lightweight design for reduced computational overhead.

RESULTS: On BCI Competition IV Dataset 2A, HA-FuseNet achieved 77.89% average within-subject accuracy (8.42% higher than EEGNet) and 68.53% cross-subject accuracy.

CONCLUSION: The model demonstrates robustness to spatial resolution variations and individual differences, effectively mitigating key challenges in motor imagery EEG classification.},
}

@article {pmid40741296,
year = {2025},
author = {Degirmenci, M and Yuce, YK and Perc, M and Isler, Y},
title = {Classification of finger movements through optimal EEG channel and feature selection.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1633910},
pmid = {40741296},
issn = {1662-5161},
abstract = {INTRODUCTION: Electrencephalography (EEG)-based brain-computer interfaces (BCIs) have become popular as EEG is accepted as the simplest and non-invasive neuroimaging modality to record the brain's electrical activity. In the current BCI research context, apart from predicting extremity movements, recent BCI studies have been interested in accurately predicting finger movements of the same hand using different pattern recognition methods over EEG data collected based on motor imagery (MI), through which a mental image of the desired action is generated when a person ideally simulates or imagines carrying out a certain motor task. Although several pattern recognition methods have already been recommended in literature, majority of the studies focusing on classifying five finger movements, were based on study designs that neglected or excluded the idle state of brain (i.e., no mental task state) during which brain does not carry out any MI task. This study design may result in an increasing number of false positives and a significant decrease in the prediction rates and classification performance. Moreover, recent studies have focused on improving prediction performance using complex feature extraction and machine learning algorithms while ignoring comprehensive EEG channels and feature investigation in the prediction of finger movements from EEGs. Therefore, the objectives of this study are threefold: (i) to develop a more viable and practical system to predict the movements of five fingers and the no mental task (NoMT) state from EEG signals (ii) to analyze the effects of the statistical-significance based feature selection method over four different feature domains (nonlinear domain, time-domain, frequency-domain and time-frequency domain) and their combinations, and (iii) to test these feature sets with different and prominent classifiers.

METHODS: In this study, our major goal is not to explore the best machine algorithm performance, but to investigate the best EEG channels and features that can be used in the classification of finger movements. Hence, the comprehensive analysis of the effectiveness of EEG channels and features is performed utilizing a statistically significant feature distribution over 19 EEG channels for each feature set independently. A bulky dataset of electroencephalographic MI for EEG-based BCIs is used in this study. A total of 1102 EEG features supplied from different feature domains have been investigated. Subsequently, these features were tested with eight well-known classifiers, comprising Decision tree, Discriminant analysis, Naive Bayes, Support vector machine, k-nearest neighbor, Ensemble learning, Neural networks, and Kernel approximation.

RESULTS: For subject-dependent analysis, the maximum accuracy of 59.17% was obtained using the EEG features that were selected the most (including (i) energy and variance of five frequency bands in frequency-domain feature set, (ii) all feature types in time-domain, time-frequency domain, and nonlinear domain feature sets) and all EEG channels by the Support vector machine algorithm. For subject-independent analysis, the maximum accuracy of 39.30% was obtained using the mostly selected EEG features (which are (i) all feature types excluding the waveform length, average amplitude change value, absolute difference in standard deviation, and slope-change value feature types in time-domain feature set, (ii) the energy and variance values of all frequency bands except gamma frequency band in frequency-domain feature set, (iii) the entropy value of five frequency bands in time-frequency-domain feature set, and (iv) SD 2 and SD 1/SD 2 values where lag = 1 in nonlinear feature set) and EEG channels (which are (i) some definite EEG channels including 2nd, 3rd, 7th, 11th, 13th, 14th, and 15th channels in time-frequency-domain feature set and (ii) all EEG channels in time-domain, frequency-domain, and nonlinear feature sets) by the Support vector machine classifier.

DISCUSSION: Experimental results demonstrate that despite the high-class number, the proposed approach obtained a modest yet considerable advancement in finger movement prediction when the results are compared to the results of similar studies. Additionally, for almost all feature sets, the statistical significance-based feature reduction method improves the prediction performance in the most of classifiers, contributing elaborate EEG channel and feature analysis. Nonetheless, in this study, we used an EEG dataset recorded from only 13 healthy subjects; therefore, a dataset covering more subjects is necessary to reach a more general conclusion.},
}

@article {pmid40740060,
year = {2025},
author = {Kumar, A and Kumar, A},
title = {BiLSTM-Based Human Emotion Classification Using EEG Signal.},
journal = {Clinical EEG and neuroscience},
volume = {},
number = {},
pages = {15500594251364017},
doi = {10.1177/15500594251364017},
pmid = {40740060},
issn = {2169-5202},
abstract = {Emotion recognition using electroencephalography (EEG) signals has garnered significant attention due to its applications in affective computing, human-computer interaction, and healthcare. This study employs a Bidirectional Long Short-Term Memory (BiLSTM) network to classify emotions using EEG data from four well-established datasets: SEED, SEED-IV, SEED-V, and DEAP. By leveraging the temporal dependencies inherent in EEG signals, the BiLSTM model demonstrates robust learning of emotional states. The model achieved notable classification accuracies, with 92.30% for SEED, 99.98% for SEED-IV, 99.97% for SEED-V, and 88.33% for DEAP, showcasing its effectiveness across datasets with varying class distributions. The superior performance on SEED-IV and SEED-V underscores the BiLSTM's capability to capture bidirectional temporal information, which is crucial for emotion recognition tasks. Moreover, this work highlights the importance of utilizing diverse datasets to validate the generalizability of EEG-based emotion recognition models. The integration of both dimensional and discrete emotion models in the study demonstrates the framework's flexibility in addressing various emotion representation paradigms. Future directions include optimizing the framework for real-world applications, such as wearable EEG devices, and exploring transfer learning techniques to enhance cross-subject and cross-cultural adaptability. Overall, this study advances EEG-based emotion recognition methodologies, establishing a robust foundation for integrating affective computing into various domains and paving the way for real-time, reliable emotion recognition systems.},
}

@article {pmid40739107,
year = {2025},
author = {Jia, O and Tan, Q and Zhang, S and Jia, K and Gong, M},
title = {The precision of attention selection during reward learning influences the mechanisms of value-driven attention.},
journal = {NPJ science of learning},
volume = {10},
number = {1},
pages = {49},
doi = {10.1038/s41539-025-00342-1},
pmid = {40739107},
issn = {2056-7936},
support = {2023-PT310-01//Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; 32371087//National Natural Science Foundation of China/ ; 32300855//National Natural Science Foundation of China/ ; 226-2024-00118//Fundamental Research Funds for the Central University/ ; 2021ZD0200409//National Science and Technology Innovation 2030-Major Project/ ; },
abstract = {Reward-predictive items capture attention even when task-irrelevant. While value-driven attention typically generalizes to stimuli sharing critical reward-associated features (e.g., red), recent evidence suggests an alternative generalization mechanism based on feature relationships (e.g., redder). Here, we investigated whether relational coding of reward-associated features operates across different learning contexts by manipulating search mode and target-distractor similarity. Results showed that singleton search training induced value-driven relational attention regardless of target-distractor similarity (Experiments 1a-1b). In contrast, feature search training produced value-driven relational attention only when targets and distractors were dissimilar, but not when they were similar (Experiments 2a-2c). These findings indicate that coarse selection training (singleton search or feature search among dissimilar items) promotes relational coding of reward-associated features, while fine selection (feature search among similar items) engages precise feature coding. The precision of target selection during reward learning thus critically determines value-driven attentional mechanisms.},
}

@article {pmid40737169,
year = {2025},
author = {Xiong, D and Hu, L and Jin, J and Ding, Y and Tan, C and Zhang, J and Tian, Y},
title = {Interpretable Cross-Modal Alignment Network for EEG Visual Decoding With Algorithm Unrolling.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3592646},
pmid = {40737169},
issn = {2162-2388},
abstract = {Accurate decoding in electroencephalography (EEG) technology, particularly for rapid visual stimuli, remains challenging due to the low signal-to-noise ratio (SNR). Additionally, existing neural networks struggle with issues related to generalization and interpretability. This article proposes a cross-modal aligned network, E2IVAE, which leverages shared information from multiple modalities for self-supervised alignment of EEG to images for extracting visual perceptual information and features a novel EEG encoder, ISTANet, based on algorithm unrolling. This network framework significantly enhances the accuracy and stability of EEG decoding for object recognition in novel classes while reducing the extensive neural data typically required for training neural decoders. The proposed ISTANet employs algorithm unrolling to transform the multilayer sparse coding algorithm into an end-to-end format, extracting features from noisy EEG signals while incorporating the interpretability of traditional machine learning. The experimental results demonstrate that our method achieves SOTA top-1 accuracy of 62.39% and top-5 accuracy of 88.98% on a comprehensive rapid serial visual presentation (RSVP) dataset for public comparison in a 200-class zero-shot neural decoding task. Additionally, ISTANet enables visualization and analysis of multiscale atom features and overall reconstruction features, exploring biological plausibility across temporal, spatial, and spectral dimensions. On another more challenging RSVP large-scale dataset, the proposed framework also achieves significantly above chance-level performance, proving its robustness and generalization. This research provides critical insights into neural decoding and brain-computer interfaces (BCIs) within the fields of cognitive science and artificial intelligence.},
}

@article {pmid40735361,
year = {2025},
author = {Zhang, Q and Zhang, C and Ji, H and Chen, J and Wang, X and Zhang, T and Liu, P and Wang, Z and Xu, Y},
title = {Ethical governance of clinical research on the brain-computer interface for mental disorders: a modified Delphi study.},
journal = {General psychiatry},
volume = {38},
number = {4},
pages = {e101755},
pmid = {40735361},
issn = {2517-729X},
abstract = {BACKGROUND: Clinical brain-computer interface (BCI) for mental disorders is an emerging interdisciplinary research field, posing new ethical concerns and challenges, yet lacking practical ethical governance guidelines for stakeholders and the entire community.

AIMS: This study aims to establish a multidisciplinary consensus of principles for ethical governance of clinical BCI research for mental disorders and offer practical ethical guidance to stakeholders involved.

METHODS: A systematic literature review, symposium and roundtable discussions, and a pre-Delphi (round 0) survey were conducted to form the questionnaire for the three-round modified Delphi study. Two rounds of surveys, followed by a third round of independent interviews of 25 experts from BCI-related research domains, were involved. We conducted quantitative analysis of responses and agreements among experts to reveal the consensus and differences regarding the ethical governance of mental BCI research from a multidisciplinary perspective.

RESULTS: The Delphi panel emphasised important concerns of ethical review practices and ethical principles within the BCI context, identified qualified and highly influential institutions and personnel in conducting and advancing clinical BCI research, and recognised prioritised aspects in the risk-benefit evaluation. Experts expressed diverse opinions on specific ethical concerns, including concerns about invasive technology, its impact on humanity and potential social consequences. Agreement was reached that the practices of ethical governance of clinical BCI for mental disorders should focus on patient voluntariness, autonomy, long-term effects and related assessments of BCI interventions, as well as privacy protection, transparent reporting and ensuring that the research is conducted in qualified institutions with strong data security.

CONCLUSIONS: Ethical governance of clinical research on BCI for mental disorders should include interdisciplinary experts to balance various needs and incorporate the expertise of different stakeholders to avoid serious ethical issues. It requires scientifically grounded approaches, continuous monitoring and interdisciplinary collaboration to ensure evidence-based policies, comprehensive risk assessments and transparency, thereby promoting responsible innovations and protecting patient rights and well-being.},
}

@article {pmid40735214,
year = {2025},
author = {Ran, J and Xu, J and Luo, D and Li, T and Xu, J},
title = {Problematic internet use and aggression in Chinese middle school students: mediation effect of reality social connectedness.},
journal = {Frontiers in public health},
volume = {13},
number = {},
pages = {1587400},
pmid = {40735214},
issn = {2296-2565},
mesh = {Humans ; *Aggression/psychology ; China/epidemiology ; Male ; Female ; Cross-Sectional Studies ; *Students/psychology/statistics & numerical data ; Adolescent ; Surveys and Questionnaires ; *Internet Addiction Disorder/psychology/epidemiology ; *Internet Use/statistics & numerical data ; East Asian People ; },
abstract = {INTRODUCTION: Problematic internet use (PIU) has become a prevalent concern worldwide and is associated with increased aggression. However, the underlying effect of PIU on aggression remains unclear. In this study, we aimed to investigate the potential influence of reality social connectedness (RSC) on the relationship between PIU and aggression.

METHODS: We used cross-sectional data from a large survey conducted among middle school students in four provinces of China between September 2022 and March 2023. PIU, RSC, and aggression were assessed using Young's 20-item Internet Addiction Test (IAT-20), the modified Social Connectedness Scale-Revised (SCS-R), and the Buss-Perry Aggression Questionnaire (BPAQ), respectively.

RESULTS: We found that students who experienced PIU had significantly higher scores on the BPAQ, which reflects the aggression levels, compared to students without PIU. Specifically, all four dimensions of aggression-verbal aggression, physical aggression, hostility, and anger-were elevated in the PIU group. Additionally, RSC was significantly reduced among individuals with PIU. Notably, RSC significantly mediated the relationship between PIU and aggression, accounting for 18.89% of the total effect. Among the four dimensions of aggression, the mediating effect of RSC was strongest for hostility, followed by anger and physical aggression, with the weakest observed for verbal aggression.

DISCUSSION: RSC significantly mediated the relationship between PIU and aggression, suggesting that reduced RSC partially explains how PIU exacerbates aggression. This result highlights the importance of fostering RSC as a strategy to reduce aggression related to PIU.},
}

Humans
*Aggression/psychology
China/epidemiology
Male
Female
Cross-Sectional Studies
*Students/psychology/statistics & numerical data
Adolescent
Surveys and Questionnaires
*Internet Addiction Disorder/psychology/epidemiology
*Internet Use/statistics & numerical data
East Asian People

@article {pmid40734822,
year = {2025},
author = {Liu, ZY and Zhang, L and Wang, ZD and Huang, ZQ and Li, MC and Lu, Y and Hu, JP and Chen, QL and Chen, XY},
title = {Magnetic resonance imaging for spinocerebellar ataxia: a bibliometric analysis based on web of science.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1512800},
pmid = {40734822},
issn = {1664-2295},
abstract = {The objective of this study was to review the history of magnetic resonance imaging (MRI) research on spinocerebellar ataxia (SCA) over the last 16 years. We conducted a comprehensive bibliometric analysis of relevant scientific literature that explores the use of MRI in studying SCA using CiteSpace. A total of 761 scientific manuscripts, published between January 2009 and March 2025 and available in the Web of Science (WoS) database, were included in this analysis. A total of 197 out of 761 articles were analyzed using CiteSpace to determine the number and centrality of publications, countries, institutions, journals, authors, cited references, and keywords related to MRI and SCA. Overall, the number of publications that use MRI to study SCA has gradually increased over the years. The United States, China, Italy, Germany, and Brazil are at the forefront in this research field; a total of 420 authors from 317 research institutions in these nations have published articles in neuroscience-related journals. Among the most cited publications are an article by Rezende et al. on brain structural damage in SCA3 patients and an review by Klockgether et al. on spinocerebellar ataxia. The keyword "spinocerebellar ataxia" has the highest frequency of occurrence. However, "feature" may become a research hotspot in the coming years based on the analysis of the keyword's citation burst. The findings of this bibliometric study provide a summary of the last 16 years of SCA research using MRI technology. More importantly, the present study identifies current trends and future research hotspots in the field, helping researchers to identify new and unexplored research areas.},
}

@article {pmid40731219,
year = {2025},
author = {Motiwala, A and Soldado-Magraner, J and Batista, AP and Smith, MA and Yu, BM},
title = {Brain-computer interfaces as a causal probe for scientific inquiry.},
journal = {Trends in cognitive sciences},
volume = {},
number = {},
pages = {},
pmid = {40731219},
issn = {1879-307X},
abstract = {Establishing causal relationships between neural activity and brain function requires experimental perturbations of neural activity. Many existing perturbation methods modify activity by directly applying external signals to the brain. We review an alternative approach where brain-computer interfaces (BCIs) leverage volitional control of neural activity to manipulate and causally perturb it. We highlight the potential of BCIs to manipulate neural activity in ways that are flexible, accurate, and adhere to intrinsic biophysical and network-level constraints to investigate the consequences of configuring neural population activity in specified ways. We discuss the advantages and disadvantages of using BCIs as a perturbation tool compared with other perturbation methods and how BCIs can expand the scope of questions that can be addressed about brain function.},
}

@article {pmid40731189,
year = {2025},
author = {Zhao, X and Yu, J and Xu, B and Xu, Z and Lei, X and Han, S and Luo, S and Zhang, C and Peng, G and Li, J and Yu, J and Ling, Y and Fan, Z and Mo, W and Yang, Y and Zhang, J},
title = {Gut-derived bacterial vesicles carrying lipopolysaccharide promote microglia-mediated synaptic pruning.},
journal = {Alzheimer's & dementia : the journal of the Alzheimer's Association},
volume = {21},
number = {8},
pages = {e70331},
pmid = {40731189},
issn = {1552-5279},
support = {82020108012//National Natural Science Foundation of China/ ; 82371250//National Natural Science Foundation of China/ ; 2024C03098//Key Research and Development Program of Zhejiang Province/ ; 2024SSYS0018//Key Research and Development Program of Zhejiang Province/ ; LZ23H090002//Natural Science Foundation of Zhejiang Province/ ; LY24H090006//Natural Science Foundation of Zhejiang Province/ ; //Innovative Institute of Basic Medical Science of Zhejiang University/ ; },
mesh = {*Lipopolysaccharides/metabolism ; *Microglia/metabolism ; Humans ; *Gastrointestinal Microbiome/physiology ; Animals ; Mice ; *Extracellular Vesicles/metabolism ; *Alzheimer Disease/metabolism ; *Neuronal Plasticity/physiology ; Blood-Brain Barrier/metabolism ; Male ; Female ; Brain/metabolism ; },
abstract = {INTRODUCTION: Growing evidence links gut microbiota (GM) to Alzheimer's disease (AD). Elevated lipopolysaccharide (LPS) levels, a Gram-negative bacteria component, are found in AD brains, but how LPS breaches the blood-brain barrier (BBB) remains unclear. Hypotheses suggest that bacteria-derived extracellular vesicles (bEVs) may transport LPS across the BBB.

METHODS: bEVs were extracted from human and mouse feces and blood, and LPS levels were measured. In vivo imaging and immunofluorescence confirmed the transport of blood LPS-carrying bEVs across the BBB. The role of these bEVs in microglia was investigated both in vivo and in vitro.

RESULTS: Elevated LPS-containing bEVs were detected in the plasma of AD patients compared to healthy individuals. These bEVs activated microglial Piezo1, consequently precipitating an excessive synaptic pruning process mediated by the C1q-C3 complement pathway.

DISCUSSION: These findings illuminate the complex interplay between the gut microbiota, bEVs, neuroinflammation, and synaptic plasticity - a key early event in AD - offering insights for potential therapeutic interventions.

HIGHLIGHTS: GM-derived bEVs can traverse the BBB. LPS was necessary for bEVs' penetration into the brain, and bEVs might be closely related to AD progression. bEVs mediated microglial activation and synaptic pruning via C1q-C3 complement pathway. Microglia Piezo1 was involved in bEV-induced excessive synaptic pruning.},
}

*Lipopolysaccharides/metabolism
*Microglia/metabolism
Humans
*Gastrointestinal Microbiome/physiology
Animals
Mice
*Extracellular Vesicles/metabolism
*Alzheimer Disease/metabolism
*Neuronal Plasticity/physiology
Blood-Brain Barrier/metabolism
Male
Female
Brain/metabolism

@article {pmid40667167,
year = {2025},
author = {Sun, L and Qin, W and Liang, X and Wang, C and Men, W and Duan, Y and Fan, XR and Cai, Q and Qiu, S and Wang, M and Gong, Q and Tian, Y and Liang, P and Liu, Z and Zhang, X and Song, H and Ye, Z and Zhang, P and Dong, Q and Tao, S and Zhu, W and Zhang, J and Xie, F and Feng, J and Zhang, J and Liu, C and Qian, Q and Zhang, B and Meng, M and Hu, L and Gao, JH and Jiang, T and Zhu, X and Zhang, Y and Liu, L and Liu, H and Liao, W and Wang, D and Wang, H and Guo, T and Dai, Z and Lui, S and Xu, K and Li, L and Xie, P and Feng, C and Cui, G and Wu, J and Yin, X and Ding, G and Xian, J and Zhao, L and Lu, J and Liu, Z and Han, Y and Yuan, Z and Zhang, X and Si, T and Zhou, F and Bi, Y and Wu, D and Gao, F and Wang, F and Qin, S and Wang, G and Chen, F and Zhang, Z and Sui, J and Chen, H and Cai, J and Liu, S and Geng, Z and Zhang, C and Mao, N and Yin, H and Liu, B and Ma, H and Gao, B and Miao, Y and Kong, XZ and Zhou, Y and Liu, L and Hu, J and Wang, L and Zhang, Q and Shu, H and Wang, P and Lee, TMC and Cao, Q and Yang, L and Zhang, X and Luo, W and Liang, M and Yao, H and Li, M and Huang, H and Peng, Y and Han, Z and Zhou, C and Xu, H and Feng, M and Shen, W and Hu, Y and Chen, H and Wang, Y and Gong, G and Yan, Z and Xu, X and Liu, J and Chen, G and Wang, P and Yang, Y and Yao, D and Han, T and He, H and Chen, C and Zou, Q and Liu, H and Zhang, H and Chai, C and Lu, C and Tu, Y and Liu, Y and Lin, D and Zhao, W and Xu, X and Liu, X and Cui, Z and Wang, Z and Huang, R and Li, Z and Liu, Y and Li, X and Yang, X and Zhang, N and Chen, A and Zhang, B and Qin, P and Liu, C and Yao, Z and Wei, Y and Yuan, H and Wang, F and Zhang, Y and Zhang, Q and Hu, F and Xie, H and Wu, X and Wang, J and Fan, G and Wang, Z and Zhang, D and Zhong, H and Wang, Y and Bai, L and Li, Y and Wei, X and Wang, J and Zhang, Y and He, H and Li, S and Zhang, T and Jiang, F and Yang, J and Chen, F and Liu, F and Liu, H and Chen, N and Yang, J and Hou, B and Huang, CC and Zhu, J and Cai, H and Wei, D and Chen, Q and Wei, Y and Miao, P and Li, Y and Liu, Y and Yang, N and Gao, X and Liu, Y and Shen, Y and Huang, X and Ji, GJ and , and Zhang, L and Qiu, J and Yu, Y and Lin, CP and Feng, F and Li, K and Yu, C and He, Y},
title = {Population-specific brain charts reveal Chinese-Western differences in neurodevelopmental trajectories.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40667167},
issn = {2692-8205},
abstract = {Human brain charts provide unprecedented opportunities for decoding neurodevelopmental milestones and establishing clinical benchmarks for precision brain medicine [1-7]. However, current lifespan brain charts are primarily derived from European and North American cohorts, with Asian populations severely underrepresented. Here, we present the first population-specific brain charts for China, developed through the Chinese Lifespan Brain Mapping Consortium (Phase I) using neuroimaging data from 43,037 participants (aged 0-100 years) across 384 sites nationwide. We establish the lifespan normative trajectories for 296 structural brain phenotypes, encompassing global, subcortical, and cortical measures. Cross-population comparisons with Western brain charts (based on data from 56,339 participants aged 0-100 years) reveal distinct neurodevelopmental patterns in the Chinese population, including prolonged cortical and subcortical maturation, accelerated cerebellar growth, and earlier development of sensorimotor regions relative to paralimbic regions. Crucially, these Chinese-specific charts outperform Western-derived models in predicting healthy brain phenotypes and detecting pathological deviations in Chinese clinical cohorts. These findings highlight the urgent need for diverse, population-representative brain charts to advance equitable precision neuroscience and improve clinical validity across populations.},
}

@article {pmid40730254,
year = {2025},
author = {Alouani, Z and Gannour, OE and Saleh, S and El-Ibrahimi, A and Daanouni, O and Cherradi, B and Bouattane, O},
title = {A novel contrastive Dual-Branch Network (CDB-Net) for robust EEG-Based Alzheimer's disease diagnosis.},
journal = {Brain research},
volume = {1865},
number = {},
pages = {149863},
doi = {10.1016/j.brainres.2025.149863},
pmid = {40730254},
issn = {1872-6240},
abstract = {Alzheimer's Disease (AD) is neurodegenerative disorder that causes cognitive decline, memory loss, confusion, and changes in behavior. Early and accurate detection is important for timely intervention, current diagnostic methods can be slow, expensive, and have limited sensitivity. Electroencephalography (EEG) offers a simple and non-invasive way to measure brain activity, and it has shown promise in supporting AD diagnosis. However, EEG signals are often affected by noise-such as muscle movement, blinking, or electrical interference-which can make it harder for models to give reliable results. To address these challenges, we introduce CDB-Net (Contrastive Dual-Branch Network), a deep learning model built to improve the accuracy and robustness of EEG-based AD classification. The model uses two parallel branches: one processes clean EEG data, while the other processes a noisy version of the same data. By training these branches together using contrastive learning, the model learns to focus on features that stay consistent even when the signal is distorted by noise. A classification head is trained jointly using cross-entropy loss for downstream diagnosis. We tested our method on a public EEG dataset and found that CDB-Net achieved 97.92% accuracy on clean data and 83.41% accuracy even under adversarial attacks (FGSM), outperforming traditional machine learning classifiers and deep learning baselines models. These results highlight the effectiveness of contrastive dual-branch learning in enhancing model generalization and robustness, positioning CDB-Net as a promising tool for reliable EEG-based clinical decision support in the context of Alzheimer's Disease diagnosis.},
}

@article {pmid40728869,
year = {2025},
author = {Kawaguchi, N and Koyano, K and Morita, H and Pengiran Mohamad Fadly, DNRAC and Shinabe, Y and Noguchi, Y and Arioka, M and Nakao, Y and Ozaki, M and Nakamura, S and Kondo, S and Konishi, Y and Kuboi, T and Okada, H and Yasuda, S and Itoh, S and Murao, K and Kusaka, T},
title = {Quantitative effects of bilirubin photoisomers on the measurement of direct bilirubin by the enzymatic bilirubin oxidase method.},
journal = {Annals of clinical biochemistry},
volume = {},
number = {},
pages = {45632251367245},
doi = {10.1177/00045632251367245},
pmid = {40728869},
issn = {1758-1001},
abstract = {BACKGROUND: Bilirubin photoisomers, generated during phototherapy or through inadvertent light exposure, may interfere with the measurement of direct bilirubin (DB) using the bilirubin oxidase method. This interference is particularly relevant in neonates, who physiologically exhibit elevated levels of unconjugated bilirubin.

METHODS: Residual serum samples from 30 neonates were irradiated under controlled conditions to selectively produce bilirubin configurational isomers (BCIs) and structural isomers (BSIs). DB and total bilirubin (TB) values were measured before and after irradiation using the bilirubin oxidase method. The concentrations of BCIs and BSIs were quantified using high-performance liquid chromatography (HPLC). Linear and multiple regression analyses were performed to evaluate the extent to which these photoisomers contributed to the DB values.

RESULTS: Following irradiation, DB values significantly increased in correlation with BCI and BSI concentrations. Approximately 11% of BCI and 32% of BSI were quantified as DB using the bilirubin oxidase method. These findings were consistent across both individual and multiple regression models.

CONCLUSIONS: Bilirubin photoisomers can significantly elevate DB values measured by the bilirubin oxidase method, leading to a potential overestimation of conjugated bilirubin. In neonatal clinical practice, careful interpretation of DB values is warranted, particularly under conditions involving light exposure. Accurate sample handling and an awareness of photoisomer interference are essential for reliable assessment of hyperbilirubinemia in newborns.},
}

@article {pmid40727297,
year = {2025},
author = {Ebrahimibasabi, S and Golshahi, M and Shahraki, N and Tamjid Shabestari, D and Sajjadi, M and Hashemi, S and Borchert, A and Baker, I and Khalifehzadeh, L and Arami, H},
title = {Designing parylene coating for implantable brain-machine interfaces.},
journal = {RSC advances},
volume = {15},
number = {33},
pages = {26660-26672},
pmid = {40727297},
issn = {2046-2069},
abstract = {Parylene is widely recognized as an effective candidate for encapsulating implantable bioelectronics due to its outstanding chemical stability, conformity and biocompatibility. However, its weak adhesion to inorganic substrates remains a significant challenge. Here, we explored various pre- and post-deposition treatments to enhance adhesion and stability of parylene coating for implantable brain-machine interfaces (BMIs). We utilized 0%, 0.5%, 1%, and 1.5% (v/v) 3-(trimethoxysilyl)propyl-methacrylate as an adhesion promoter for substrate treatment prior to deposition. Deposited samples were subsequently subjected to post-heat treatments at various temperatures. Samples were exposed to an in vitro accelerated aging bath at 87 °C for 7 days to assess their post-implantation durability. Cytotoxicity and in vivo biocompatibility were also investigated to further evaluate biocompatibility and encapsulation efficiency of parylene coatings on commonly used rigid and flexible bioelectronic substrates. The emergence of carboxyl groups in FTIR and chlorine abstraction in EDS analyses, indicated that the as-deposited samples were degraded during aging. The chemical stability of these coatings was improved in heat-treated samples due to their higher crystallinity. Additionally, delamination and microcrack initiation/growth reduced due to post-heat treatments. We found the optimal heat treatment temperature to be 150 °C; any increase beyond this compromised coating quality by increasing delamination and defect formation. Increasing the concentration of adhesion promoter enhanced coating adhesion to the substrates in both as-deposited samples and the ones heat-treated at 150 °C. In contrast, the adhesion strength decreased when heat-treatment was performed at higher temperatures, even when the concentration of adhesion promoter was increased. Numerical analysis was used to assess the effect of parylene coating on the electrical performance of a typical implantable, wirelessly powered model device. The results demonstrated that the presence of the parylene layer not only preserved the wireless coupling between this device and the pickup probe, but also enhanced it. In addition to these favourable physiochemical improvements, parylene also promoted general in vivo brain compatibility and cell viability of the devices. This study revealed the synergistic effects of pre- and post-deposition treatments and systematically optimized adhesion and stability of parylene coatings for implantable BMIs for the first time.},
}

@article {pmid40722830,
year = {2025},
author = {Ga, YJ and Go, YY and Yeh, JY},
title = {Small Interfering RNAs Targeting VP4, VP3, 2B, or 3A Coding Regions of Enterovirus A71 Inhibit Viral Replication In Vitro.},
journal = {Biomedicines},
volume = {13},
number = {7},
pages = {},
pmid = {40722830},
issn = {2227-9059},
support = {2019//Incheon National University/ ; },
abstract = {Background: Enterovirus A71 (EV-A71) is considered as the primary causative agent of hand, foot, and mouth disease (HFMD) in young children, leading to severe neurological complications and contributing to substantial mortalities in recent HFMD outbreaks across Asia. Despite this, there is currently no effective antiviral treatment available for EV-A71. RNA interference (RNAi) is a powerful mechanism of post-transcriptional gene regulation that utilizes small interfering RNA (siRNA) to target and degrade specific RNA sequences. Objectives: The aim of this study was to design various siRNAs targeting EV-A71 genomic regions and evaluate the RNAi efficacy against a novel, previously genetically uncharacterized EV-A71 strain. Methods: A novel EV-A71 strain was first sequenced to design target-specific siRNAs. The viral titers, viral protein expression, cytopathic effects, and cell viability of EV-A71-infected HeLa cells were examined to evaluate the specific viral inhibition by the siRNAs. Results: A substantial reduction in viral titers and viral protein synthesis was observed in EV-A71-infected HeLa cells treated with specific siRNAs targeting the VP4, VP3, 2B, and 3A genes. siRNAs delayed cytopathic effects and increased cell viability of EV-A71-infected HeLa cells. Nonspecific interferon induction caused by siRNAs was not observed in this study. In contrast, replication of coxsackievirus B3, another important member of the Enterovirus genus, remained unaffected. Conclusions: Overall, the findings demonstrate that RNAi targeting genomic regions of EV-A71 VP4, VP3, 2B, or 3A could become a potential strategy for controlling EV-A71 infection, and this promising result can be integrated into future anti-EV-A71 therapy developments.},
}

@article {pmid40722467,
year = {2025},
author = {Zhan, H and Li, X and Song, X and Lv, Z and Li, P},
title = {MCTGNet: A Multi-Scale Convolution and Hybrid Attention Network for Robust Motor Imagery EEG Decoding.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {7},
pages = {},
pmid = {40722467},
issn = {2306-5354},
support = {No. 2108085MF207//Anhui Natural Science Foundation/ ; No. 2024AH050054//Natural Science Research Project of Anhui Educational Committee under Grant/ ; No. 2208085J05//Distinguished Youth Foundation of Anhui Scientific Committee/ ; No. 62476004//National Natural Science Foundation of China (NSFC)/ ; },
abstract = {Motor imagery (MI) EEG decoding is a key application in brain-computer interface (BCI) research. In cross-session scenarios, the generalization and robustness of decoding models are particularly challenging due to the complex nonlinear dynamics of MI-EEG signals in both temporal and frequency domains, as well as distributional shifts across different recording sessions. While multi-scale feature extraction is a promising approach for generalized and robust MI decoding, conventional classifiers (e.g., multilayer perceptrons) struggle to perform accurate classification when confronted with high-order, nonstationary feature distributions, which have become a major bottleneck for improving decoding performance. To address this issue, we propose an end-to-end decoding framework, MCTGNet, whose core idea is to formulate the classification process as a high-order function approximation task that jointly models both task labels and feature structures. By introducing a group rational Kolmogorov-Arnold Network (GR-KAN), the system enhances generalization and robustness under cross-session conditions. Experiments on the BCI Competition IV 2a and 2b datasets demonstrate that MCTGNet achieves average classification accuracies of 88.93% and 91.42%, respectively, outperforming state-of-the-art methods by 3.32% and 1.83%.},
}

@article {pmid40722306,
year = {2025},
author = {Deniz, SM and Ademoglu, A and Duru, AD and Demiralp, T},
title = {Application of Graph-Theoretic Methods Using ERP Components and Wavelet Coherence on Emotional and Cognitive EEG Data.},
journal = {Brain sciences},
volume = {15},
number = {7},
pages = {},
pmid = {40722306},
issn = {2076-3425},
abstract = {Background/Objectives: Emotion and cognition, two essential components of human mental processes, have traditionally been studied independently. The exploration of emotion and cognition is fundamental for gaining an understanding of human mental functioning. Despite the availability of various methods to measure and evaluate emotional states and cognitive processes, physiological measurements are considered to be one of the most reliable methods due to their objective approach. In particular, electroencephalography (EEG) provides unique insight into emotional and cognitive activity through the analysis of event-related potentials (ERPs). In this study, we discriminated pleasant/unpleasant emotional moods and low/high cognitive states using graph-theoretic features extracted from spatio-temporal components. Methods: Emotional data were collected at the Physiology Department of Istanbul Medical Faculty at Istanbul University, whereas cognitive data were obtained from the DepositOnce repository of Technische Universität Berlin. Wavelet coherence values for the N100, N200, and P300 single-trial ERP components in the delta, theta, alpha, and beta frequency bands were investigated individually. Then, graph-theoretic analyses were performed using wavelet coherence-based connectivity maps. Global and local graph metrics such as energy efficiency, strength, transitivity, characteristic path length, and clustering coefficient were used as features for classification using support vector machines (SVMs), k-nearest neighbor(K-NN), and linear discriminant analysis (LDA). Results: The results show that both pleasant/unpleasant emotional moods and low/high cognitive states can be discriminated, with average accuracies of up to 92% and 89%, respectively. Conclusions: Graph-theoretic metrics based on wavelet coherence of ERP components in the delta band with the SVM algorithm allow for the discrimination of emotional and cognitive states with high accuracy.},
}

@article {pmid40722299,
year = {2025},
author = {Zhang, Z and Lu, G},
title = {Multimodal Knowledge Distillation for Emotion Recognition.},
journal = {Brain sciences},
volume = {15},
number = {7},
pages = {},
doi = {10.3390/brainsci15070707},
pmid = {40722299},
issn = {2076-3425},
abstract = {Multimodal emotion recognition has emerged as a prominent field in affective computing, offering superior performance compared to single-modality methods. Among various physiological signals, EEG signals and EOG data are highly valued for their complementary strengths in emotion recognition. However, the practical application of EEG-based approaches is often hindered by high costs and operational complexity, making EOG a more feasible alternative in real-world scenarios. To address this limitation, this study introduces a novel framework for multimodal knowledge distillation, designed to improve the practicality of emotion decoding while maintaining high accuracy, with the framework including a multimodal fusion module to extract and integrate interactive and heterogeneous features, and a unimodal student model structurally aligned with the multimodal teacher model for better knowledge alignment. The framework combines EEG and EOG signals into a unified model and distills the fused multimodal features into a simplified EOG-only model. To facilitate efficient knowledge transfer, the approach incorporates a dynamic feedback mechanism that adjusts the guidance provided by the multimodal model to the unimodal model during the distillation process based on performance metrics. The proposed method was comprehensively evaluated on two datasets based on EEG and EOG signals. The accuracy of the valence and arousal of the proposed model in the DEAP dataset are 70.38% and 60.41%, respectively. The accuracy of valence and arousal in the BJTU-Emotion dataset are 61.31% and 60.31%, respectively. The proposed method achieves state-of-the-art classification performance compared to the baseline method, with statistically significant improvements confirmed by paired t-tests (p < 0.05), and the framework effectively transfers knowledge from multimodal models to unimodal EOG models, enhancing the practicality of emotion recognition while maintaining high accuracy, thus expanding the applicability of emotion recognition in real-world scenarios.},
}

@article {pmid40722278,
year = {2025},
author = {Yazıcı, M and Ulutaş, M and Okuyan, M},
title = {Effect of EEG Electrode Numbers on Source Estimation in Motor Imagery.},
journal = {Brain sciences},
volume = {15},
number = {7},
pages = {},
doi = {10.3390/brainsci15070685},
pmid = {40722278},
issn = {2076-3425},
abstract = {The electroencephalogram (EEG) is one of the most popular neurophysiological methods in neuroscience. Scalp EEG measurements are obtained using various numbers of channels for both clinical and research applications. This pilot study explores the effect of EEG channel count on motor imagery classification using source analysis in brain-computer interface (BCI) applications. Different channel configurations are employed to evaluate classification performance. This study focuses on mu band signals, which are sensitive to motor imagery-related EEG changes. Common spatial patterns are utilized as a spatiotemporal filter to extract signal components relevant to the right hand and right foot extremities. Classification accuracies are obtained using configurations with 19, 30, 61, and 118 electrodes to determine the optimal number of electrodes in motor imagery studies. Experiments are conducted on the BCI Competition III Dataset Iva. The 19-channel configuration yields lower classification accuracy when compared to the others. The results from 118 channels are better than those from 19 channels but not as good as those from 30 and 61 channels. The best results are achieved when 61 channels are utilized. The average accuracy values are 83.63% with 19 channels, increasing to 84.70% with 30 channels, 84.73% with 61 channels, and decreasing to 83.95% when 118 channels are used.},
}

@article {pmid40721281,
year = {2025},
author = {Ognard, J and Douri, D and El Hajj, G and Ghozy, S and Rohleder, M and Gentric, JC and Kadirvel, R and Kallmes, DF and Brinjikji, W},
title = {Future is Ven(o)us: A 5-year narrative update on the venous route for therapeutics in Neurointervention.},
journal = {AJNR. American journal of neuroradiology},
volume = {},
number = {},
pages = {},
doi = {10.3174/ajnr.A8942},
pmid = {40721281},
issn = {1936-959X},
abstract = {Over the past five years, transvenous (TV) techniques have rapidly expanded the neurointerventional landscape, offering new diagnostic and therapeutic strategies for a range of cerebrovascular conditions. This narrative review synthesizes contemporary evidence and technical advances across multiple venous applications, including TV embolization for arteriovenous malformations and dural fistulas, treatment of cerebrospinal fluid-venous fistulas, and venous sinus stenting for pulsatile tinnitus, intracranial hypertension, and skull-base leaks. Recent data underscore high efficacy rates and favorable safety profiles in carefully selected patients, often matching or surpassing traditional arterial approaches. Innovations such as fetal vein of Galen embolization, vein-targeted brain-computer interface implantation, and endovascular cerebrospinal fluid shunting exemplify the therapeutic versatility of venous access. However, procedural challenges, such as venous anatomy, access, and embolic control, require meticulous planning and advanced skillsets. Trials like TATAM and DIVE-IIN are and will shape evidence-based indications for TV therapy. With expanding indications and growing operator expertise, the venous route is evolving from a niche adjunct into a cornerstone of neurovascular care.ABBREVIATIONS: bAVM(s)＝ brain arteriovenous malformation(s); CVF(s)＝ cerebrospinal fluid-venous fistula(s); CVT＝ cerebral venous thrombosis; DAVF(s)＝ dural arteriovenous fistula(s); EVT＝ endovascular therapy; EVOH＝ ethylene-vinyl alcohol copolymer; IIH＝ idiopathic intracranial hypertension; JR-NET3＝ Japanese Registry of NeuroEndovascular Therapy; PT＝ pulsatile tinnitus; RPCT＝ retrograde pressure-cooker technique; SIH＝ spontaneous intracranial hypotension; sCSFL＝ spontaneous cerebrospinal fluid leak; SSWA＝ sigmoid sinus wallabnormality/abnormalities; TV＝ transvenous; TVE＝ transvenous embolization; VSS＝ venous sinus stenting.},
}

@article {pmid40720979,
year = {2025},
author = {Silva, AB and Liu, JR and Anderson, VR and Kurtz-Miott, CM and Hallinan, IP and Littlejohn, KT and Brosler, S and Tu-Chan, A and Ganguly, K and Moses, DA and Chang, EF},
title = {Implications of shared motor and perceptual activations on the sensorimotor cortex for neuroprosthetic decoding.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adf50e},
pmid = {40720979},
issn = {1741-2552},
abstract = {OBJECTIVE: Neuroprostheses can restore communicative ability to people with paralysis by decoding intended speech motor movements from the sensorimotor cortex (SMC). However, overlapping neural populations in the SMC are also engaged in visual and auditory perceptual processing. The nature of these shared motor and perceptual activations and their potential to interfere with decoding are particularly relevant questions for speech neuroprostheses, as reading and listening are essential daily functions.

APPROACH: In two participants with vocal-tract paralysis and anarthria (ClinicalTrials.gov; NCT03698149), we developed an online electrocorticography (ECoG) based speech-decoding system that maintained accuracy and specificity to intended speech, even during common daily tasks like reading and listening. Offline, we studied the spectrotemporal characteristics and spatial distribution of reading, listening, and attempted-speech responses across our participants' ECoG arrays.

MAIN RESULTS: Across participants, the speech-decoding system had zero false-positive activations during 63.2 minutes of attempted speech and perceptual tasks, maintaining accuracy and specificity to volitional speech attempts. Offline, though we observed shared neural populations that responded to attempted speech, listening, and reading, we found they leveraged different neural representations with differentiable spectrotemporal responses. Shared populations localized to the middle precentral gyrus and may have a distinct role in speech-motor planning.

SIGNIFICANCE: Potential neuroprosthesis users strongly desire reliable systems that will retain specificity to volitional speech attempts during daily use. These results demonstrate a decoding framework for speech neuroprostheses that maintains this specificity and further our understanding of shared perceptual and motor activity on the SMC.},
}

@article {pmid40720264,
year = {2025},
author = {Cobilean, V and Mavikumbure, HS and Drake, D and Stuart, M and Manic, M},
title = {Investigating Membership Inference Attacks against CNN Models for BCI Systems.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3593443},
pmid = {40720264},
issn = {2168-2208},
abstract = {As Deep Learning (DL) algorithms become more widely adopted in healthcare applications, there is a greater emphasis on understanding and addressing potential privacy risks associated with these models. The purpose of this study is to investigate the privacy vulnerabilities of the Convolutional Neural Network (CNN) classifiers for Electroencephalogram (EEG) data in the Brain-Computer Interfaces (BCIs). Specifically, it focuses on the Membership Inference Attack (MIA), which seeks to determine if data from an individual were used in model training. The novelty of this work lies in its empirical analysis of MIA, specifically by addressing two key challenges that are less common in other domains: 1) datasets that are heterogeneous and 2) spatial-temporal design choices. Motivated by these challenges, we investigate the susceptibility to MIA based on: 1) specifics of the training dataset (number of participants, demographics), and 2) specifics of the CNN (such as architecture, regularization). Our experiments revealed that an adversary with limited knowledge of the model and its training process can compromise the privacy of training participants, noting that the same attack is not effective against deep learning models trained on image and tabular datasets. Some of our findings are: 1) training on diverse participant datasets improves the privacy of the most participants but increases risks of memorization and vulnerabilities for underrepresented groups; 2) regularization is less effective in defending against the MIA for EEG data CNN classifiers when compared to other types of input data; 3) depth and width of model architecture has no impact on membership attack effectiveness. We hope that the presented insights will assist future researchers develop more privacy-aware deep learning based BCI systems.},
}

@article {pmid40720262,
year = {2025},
author = {Wang, K and Liu, Y and Tian, F and Yi, W and Zhang, Y and Jung, TP and Xu, M and Ming, D},
title = {Adaptive Neurofeedback Training Using a Virtual Reality Game Enhances Motor Imagery Performance in Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3592988},
pmid = {40720262},
issn = {1558-0210},
abstract = {Neurofeedback training (NFT) has been widely used in motor rehabilitation. However, NFT combined with motor imagery-based brain-computer interface (MI-BCI) faces challenges such as mental fatigue and non-personalized training strategies. Therefore, we proposed an adaptive NFT based on a VR game that simulates real-life motor tasks to improve training efficiency. We conducted a detailed comparative analysis of the efficiency of the VR-based NFT and traditional Graz-based NFT. Forty-eight healthy subjects were randomly assigned to five groups and underwent various NFT protocols. Among them, the subjects in the four experimental groups were required to perform the NFT three times over five days, including virtual or real scenarios, as well as unilateral or bilateral hands training. We evaluated training effects by analyzing EEG features and classification performance, while online recognition duration served as the primary measure for assessing the adaptive NFT strategy. EEG analysis showed that VR-based NFT significantly enhanced the Event-related desynchronization (ERD) activations in the sensorimotor cortices over five days. The VR-based NFT group achieved a classification accuracy of 81.85%, representing a 10.14% improvement from baseline, which exceeded the 6.43% increase observed in the Graz-based NFT group. Furthermore, implementing the adaptive NFT strategy reduced the mean task duration by over 30% compared to the fixed-time training protocol. The results demonstrated that the adaptive MI-BCI-based NFT in a VR game achieves superior training outcomes while reducing training duration. These findings suggest the promising potential for applying MI-BCI NFT with VR games in motor rehabilitation following a stroke.},
}

@article {pmid40719991,
year = {2025},
author = {Luo, X and Dong, J and Li, T},
title = {The Role of CCL11-CCR3 Induced Mitochondrial Dysfunction and Oxidative Stress in Cognitive Impairment in Early-onset Schizophrenia: Insights from Preclinical Studies.},
journal = {Inflammation},
volume = {},
number = {},
pages = {},
pmid = {40719991},
issn = {1573-2576},
support = {81920108018//National Nature Science Foundation of China Key Project/ ; },
abstract = {Abnormal cytokine expression has been implicated as a potential contributor to neurodegeneration. This study aimed to investigate the plasma cytokine profiles in patients with early-onset schizophrenia (SCZ) and to explore the molecular mechanisms underlying the role of the key cytokine CCL11 in contributing to cognitive impairment. Plasma concentrations of 44 cytokines were quantified in individuals with SCZ. The effects of CCL11 on mitochondrial function were examined in vitro using primary hippocampal neurons. An in vivo model was subsequently developed by administering CCL11 into the lateral ventricle. The impact of the CCL11-CCR3 signaling pathway on mitochondrial function, oxidative stress, and cognitive function within the hippocampus was assessed using a combination of behavioral testing, molecular biology experiments, transcriptomic analysis, and non-targeted metabolomics. In individuals with SCZ, CCL11 and IL-13 levels were notably higher than in controls. In vitro, CCL11 exposure caused mitochondrial dysfunction and increased reactive oxygen species in hippocampal neurons. In vivo, CCL11-treated mice showed cognitive deficits, mitochondrial fission, and neuroinflammation in the hippocampus. Comprehensive integration of transcriptomic and metabolomic data revealed that CCL11 significantly disrupted the Glucokinase/Glucose-6-phosphate metabolism pathway, coinciding with elevated metabolites indicative of oxidative damage. Finally, downregulation of the CCR3 receptor in the hippocampus mitigated CCL11-induced oxidative stress, mitochondrial dysfunction, and cognitive impairment. CCL11 causes cytotoxicity in neurons by increasing oxidative stress and mitochondrial dysfunction. In a mouse model, knockout of the CCR3 receptor alleviates CCL11-induced cognitive impairment, mitochondrial dysfunction, and oxidative stress.},
}

@article {pmid40666891,
year = {2025},
author = {Wang, Y and Cheng, L and Li, D and Lu, Y and Hopkins, WD and Sherwood, CC and Xu, T and Liu, C and Paxinos, G and Jiang, T and Chu, C and Fan, L},
title = {Evolutionary Convergence of the Arcuate Fasciculus in Marmosets and Humans.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40666891},
issn = {2692-8205},
abstract = {The marmoset is a highly vocal platyrrhine monkey that shares key anatomical and functional features with humans, offering insights into the evolution of brain connectivity. Although similarities in vocalization features with humans have been reported, it remains unclear whether marmosets possess an arcuate fasciculus (af) homolog. This study delineated white matter tracts in marmosets, establishing homologies with those observed in other primates, including macaques, chimpanzees, and humans. The presence of an af homolog in marmosets was confirmed by tracer and ultra-high-resolution diffusion magnetic resonance imaging datasets. We compared cortical connectivity patterns across these species and found the af in marmosets terminates in the ventral frontal cortex, with greater similarity to humans than macaques. Furthermore, we linked af connectivity with vocalization-related brain activation in both marmosets and humans. Collectively, our findings suggest that a dorsal pathway, which emerged early in marmoset evolution, has evolved convergently with humans, despite their distant phylogenetic kinship.},
}

@article {pmid40661574,
year = {2025},
author = {Hou, X and Iacobacci, C and Card, NS and Wairagkar, M and Singer-Clark, T and Kunz, EM and Fan, C and Kamdar, F and Hahn, N and Hochberg, LR and Henderson, JM and Willett, FR and Brandman, DM and Stavisky, SD},
title = {Error encoding in human speech motor cortex.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40661574},
issn = {2692-8205},
abstract = {Humans monitor their actions, including detecting errors during speech production. This self-monitoring capability also enables speech neuroprosthesis users to recognize mistakes in decoded output upon receiving visual or auditory feedback. However, it remains unknown whether neural activity related to error detection is present in the speech motor cortex. In this study, we demonstrate the existence of neural error signals in speech motor cortex firing rates during intracortical brain-to-text speech neuroprosthesis use. This activity could be decoded to enable the neuroprosthesis to identify its own errors with up to 86% accuracy. Additionally, we observed distinct neural patterns associated with specific types of mistakes, such as phonemic or semantic differences between the person's intended and displayed words. These findings reveal how feedback errors are represented within the speech motor cortex, and suggest strategies for leveraging these additional cognitive signals to improve neuroprostheses.},
}

@article {pmid40719383,
year = {2025},
author = {Greenbaum, D},
title = {Enhancing the Warfighter: Ethical, Legal, and Strategic Implications of Brain-Machine Interface-Enabled Military Exoskeletons.},
journal = {AJOB neuroscience},
volume = {},
number = {},
pages = {1-26},
doi = {10.1080/21507740.2025.2530952},
pmid = {40719383},
issn = {2150-7759},
abstract = {The integration of brain-machine interfaces (BMIs) with military exoskeletons represents a significant development in human-machine interaction, raising complex ethical, legal, and strategic challenges. Unlike conventional human enhancement technologies, BMI-exoskeleton systems translate neural intent directly into mechanical movement, generating new concerns regarding agency, accountability, long-term health outcomes, and the governance of neuroadaptive changes. This paper offers a structured interdisciplinary analysis, developing taxonomies of current technologies, tracing the historical trajectory of military exoskeleton development, and critically assessing the emerging convergence between exoskeletal augmentation and neural interface systems. We argue that BMI-exoskeletons constitute a distinct category of augmentation that blurs traditional boundaries between operator and tool, requiring governance frameworks attentive to both operational effectiveness and the ethical implications for individual service members, military institutions, and broader society. Drawing on research in engineering, neuroscience, military studies, and bioethics, we outline a comprehensive ethical-legal framework designed to guide the entire lifecycle of human enhancement-from recruitment and informed consent processes through active service, operational deployment, and post-discharge reintegration. Particular attention is given to autonomy, cybersecurity vulnerabilities, distributive justice, gender equity, and the risks associated with de-enhancement and neuroplastic adaptation. Recognizing the preliminary and rapidly evolving nature of empirical evidence in this domain, we emphasize the need for anticipatory, adaptive policy approaches that safeguard the dignity, rights, and long-term welfare of enhanced warfighters while ensuring that technological innovation proceeds with responsible, ethically-informed oversight.},
}

@article {pmid40719065,
year = {2025},
author = {Xu, G and Wang, Z and Xu, K and Zhu, J and Zhang, J and Wang, Y and Hao, Y},
title = {Decoding Handwriting Trajectories from Intracortical Brain Signals for Brain-to-Text Communication.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e05492},
doi = {10.1002/advs.202505492},
pmid = {40719065},
issn = {2198-3844},
abstract = {The potential to decode handwriting trajectories from brain signals has yet to be fully explored in clinical brain-computer interfaces (BCIs). Here, intracortical neural signals are recorded from a paralyzed individual during attempted handwriting of complex characters. An innovative decoding framework is introduced to address both shape and temporal distortions between neural activity and movement, effectively resolving the misalignment issue commonly encountered in clinical BCIs due to the lack of accurate movement labels. The results demonstrated the reconstruction of highly accurate and human-recognizable handwriting trajectories, significantly outperforming conventional methods. Furthermore, the new framework enabled effective multi-day data fusion, leading to additional improvements in trajectory quality. By employing a dynamic time warping approach to translate trajectories into text, a recognition rate up to 91.1% is achieved within a 1000-character database. Additionally, the framework is applied to reconstruct single-trial trajectories of English letters using a previously published dataset, achieving similarly high recognition rates. Collectively, these findings present a novel BCI decoding scheme capable of accurately reconstructing handwriting trajectories, demonstrating its applicability to both alphabetic and logographic brain-to-text translation. This approach has the potential to revolutionize communication for individuals with motor impairments by enabling accurate brain-to-text translation across diverse languages.},
}

@article {pmid40718756,
year = {2025},
author = {Kim, R and Liu, Y and Zhang, J and Xie, C and Luan, L},
title = {Towards Precise Synthetic Neural Codes: High-dimensional Stimulation with Flexible Electrodes.},
journal = {Npj flexible electronics},
volume = {9},
number = {1},
pages = {},
doi = {10.1038/s41528-025-00447-y},
pmid = {40718756},
issn = {2397-4621},
abstract = {Neural representations arise from the spatiotemporally structured activity of neuron populations, inherently residing in high-dimensional spaces. Writing specific information into the central nervous system requires precisely manipulating neural states within this framework. However, current neuromodulation methods lack the precision to fully address this complexity, presenting a significant challenge for advancing effective bidirectional interfaces. In this perspective, we advocate for high-dimensional stimulation as a systematic approach capable of approximating the high dimensionality of natural neural code for brain-machine interface applications. We outline key technological requirements on resolution, coverage, and safety, review recent advances in critical application areas, and highlight the promise of flexible electrode technology in enabling a transformative leap towards precise synthetic neural codes.},
}

@article {pmid40718596,
year = {2025},
author = {Chang, L and Yang, B and Zhang, J and Li, T and Feng, J and Xu, W},
title = {DSTA-Net: dynamic spatio-temporal feature augmentation network for motor imagery classification.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {118},
doi = {10.1007/s11571-025-10296-0},
pmid = {40718596},
issn = {1871-4080},
abstract = {Accurate decoding and strong feature interpretability of Motor Imagery (MI) are expected to drive MI applications in stroke rehabilitation. However, the inherent nonstationarity and high intra-class variability of MI-EEG pose significant challenges in extracting reliable spatio-temporal features. We proposed the Dynamic Spatio-Temporal Feature Augmentation Network (DSTA-Net), which combines DSTA and the Spatio-Temporal Convolution (STC) modules. In DSTA module, multi-scale temporal convolutional kernels tailored to the α and β frequency bands of MI neurophysiological characteristics, while raw EEG serve as a baseline feature layer to retain original information. Next, Grouped Spatial Convolutions extract multi-level spatial features, combined with weight constraints to prevent overfitting. Spatial convolution kernels map EEG channel information into a new spatial domain, enabling further feature extraction through dimensional transformation. And STC module further extracts features and conducts classification. We evaluated DSTA-Net on three public datasets and applied it to a self-collected stroke dataset. In tenfold cross-validation, DSTA-Net achieved average accuracy improvements of 6.29% (p < 0.01), 3.05% (p < 0.01), 5.26% (p < 0.01), and 2.25% over the ShallowConvNet on the BCI-IV-2a, OpenBMI, CASIA, and stroke dataset, respectively. In hold-out validation, DSTA-Net achieved average accuracy improvements of 3.99% (p < 0.01) and 4.2% (p < 0.01) over the ShallowConvNet on the OpenBMI and CASIA datasets, respectively. Finally, we applied DeepLIFT, Common Spatial Pattern, and t-SNE to analyze the contributions of individual EEG channels, extract spatial patterns, and visualize features. The superiority of DSTA-Net offers new insights for further research and application in MI. The code is available in https://github.com/CL-Cloud-BCI/DSTANet-code.},
}

@article {pmid40718569,
year = {2025},
author = {Lopez Blanco, C and Tyler, WJ},
title = {The vagus nerve: a cornerstone for mental health and performance optimization in recreation and elite sports.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1639866},
doi = {10.3389/fpsyg.2025.1639866},
pmid = {40718569},
issn = {1664-1078},
abstract = {Decades of physiological and psychological research into human performance and wellness have established a critical role for vagus nerve signaling in peak physical and cognitive performance. We outline models and perspectives that have emerged through neuroscience and psychophysiology studies to elucidate how the vagus nerve governs human performance through its influence on central nervous system functions and autonomic nervous system activity. These functions include the monitoring and regulation of cardio-respiratory activity, emotional responses, inflammation and physical recovery, cognitive control, stress resilience, and team cohesion. We briefly review some useful interventions such as transcutaneous auricular vagus nerve stimulation, heart-rate variability biofeedback, and controlled breathing as accessible tools for enhancing vagal tone, improving executive functioning under pressure, and mitigating fatigue and burnout. We describe how these approaches and their biological underpinnings are rooted by psychological models like the Yerkes-Dodson law and Polyvagal theory to contextualize their effects on athletic performance. These perspectives suppor recent shifts in sports science toward integrating vagal-centered approaches as scalable, evidence-based strategies that can enhance human performance and wellness.},
}

@article {pmid40717726,
year = {2025},
author = {Otarbay, Z and Kyzyrkanov, A},
title = {SVM-enhanced attention mechanisms for motor imagery EEG classification in brain-computer interfaces.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1622847},
doi = {10.3389/fnins.2025.1622847},
pmid = {40717726},
issn = {1662-4548},
abstract = {Brain-Computer Interfaces (BCIs) leverage brain signals to facilitate communication and control, particularly benefiting individuals with motor impairments. Motor imagery (MI)-based BCIs, utilizing non-invasive electroencephalography (EEG), face challenges due to high signal variability, noise, and class overlap. Deep learning architectures, such as CNNs and LSTMs, have improved EEG classification but still struggle to fully capture discriminative features for overlapping motor imagery classes. This study introduces a hybrid deep neural architecture that integrates Convolutional Neural Networks, Long Short-Term Memory networks, and a novel SVM-enhanced attention mechanism. The proposed method embeds the margin maximization objective of Support Vector Machines directly into the self-attention computation to improve interclass separability during feature learning. We evaluate our model on four benchmark datasets: Physionet, Weibo, BCI Competition IV 2a, and 2b, using a Leave-One-Subject-Out (LOSO) protocol to ensure robustness and generalizability. Results demonstrate consistent improvements in classification accuracy, F1-score, and sensitivity compared to conventional attention mechanisms and baseline CNN-LSTM models. Additionally, the model significantly reduces computational cost, supporting real-time BCI applications. Our findings highlight the potential of SVM-enhanced attention to improve EEG decoding performance by enforcing feature relevance and geometric class separability simultaneously.},
}

@article {pmid40715543,
year = {2025},
author = {Xu, H and Huang, Q and Song, P and Chen, Y and Li, Q and Zhai, Y and Du, X and Ye, H and Bao, X and Mehmood, I and Tanigawa, H and Niu, W and Tu, Z and Chen, P and Zhang, T and Zhang, L and Zhao, X and Zhang, L and Wen, W and Cao, L and Yu, X},
title = {EEG neural indicator of temporal integration in the human auditory brain with clinical implications.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {1109},
doi = {10.1038/s42003-025-08540-8},
pmid = {40715543},
issn = {2399-3642},
support = {32171044//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32100827//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32271078//National Natural Science Foundation of China (National Science Foundation of China)/ ; LGF22H170006//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
abstract = {Temporal integration, the process by which the auditory system combines sound information over a certain period to form a coherent auditory experience, is essential for auditory perception, yet its neural mechanisms remain underexplored. We use a "transitional click train" paradigm, which concatenates two click trains with slightly differing inter-click intervals (ICIs), to investigate temporal integration in the human brain. Using a 64-channel electroencephalogram (EEG), we recorded responses from healthy participants exposed to regular and irregular transitional click trains and conducted change detection tasks. Regular transitional click trains elicited significant change responses in the human brain, indicative of temporal integration, whereas irregular trains did not. These neural responses were modulated by length, contrast, and regularity of ICIs. Behavioral data mirrored EEG findings, showing enhanced detection for regular conditions compared to irregular conditions and pure tones. Furthermore, variations in change responses were associated with decision-making processes. Temporal continuity was critical, as introducing gaps between click trains diminished both behavioral and neural responses. In clinical assessments, 22 coma patients exhibited diminished or absent change responses, effectively distinguishing them from healthy individuals. Our findings identify distinct neural markers of temporal integration and highlight the potential of transitional click trains for clinical diagnostics.},
}

@article {pmid40715225,
year = {2025},
author = {Das, A and Singh, S and Kim, J and Ahanger, TA and Pise, AA},
title = {Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {27161},
pmid = {40715225},
issn = {2045-2322},
support = {No.RS-2022-00155857//Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.RS-2022-00155857, Artificial Intelligence Convergence Innovation Human Resources Development (Chungnam National University). Also supported part by Woosong university research fund 2024./ ; No.RS-2022-00155857//Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.RS-2022-00155857, Artificial Intelligence Convergence Innovation Human Resources Development (Chungnam National University). Also supported part by Woosong university research fund 2024./ ; },
abstract = {Brain-computer interfaces (BCIs) establish a communication pathway between the human brain and external devices by decoding neural signals. This study focuses on enhancing the classification of Motor Imagery (MI) within BCI systems by leveraging advanced machine learning and deep learning techniques. The accurate classification of electroencephalogram (EEG) data is crucial for enhancing BCI performance. The BCI architecture processes electroencephalography signals through three critical stages: data pre-processing, feature extraction, and classification. The research evaluates the performance of five traditional machine learning classifiers- K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB)-using the "PhysioNet EEG Motor Movement/Imagery Dataset". This dataset encompasses EEG data from various motor tasks, including both actual and imagined movements. Among the traditional classifiers, Random Forest achieved the highest accuracy of 91%, underscoring its efficacy in motor imagery classification within BCI systems. In addition to conventional approaches, the study also explores deep learning techniques, with Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks yielding accuracies of 88.18% and 16.13%, respectively. However, the proposed hybrid model, which synergistically combines CNN and LSTM, significantly surpasses both traditional machine learning and individual deep learning methods, achieving an exceptional accuracy of 96.06%. This substantial improvement highlights the potential of hybrid deep learning models to advance the state of the art in BCI systems, offering a more robust and precise approach to motor imagery classification.},
}

@article {pmid40714477,
year = {2025},
author = {Wang, J and Zhao, S and Luo, Z and Zhou, Y and Li, S and Pan, G},
title = {EEGMamba: An EEG foundation model with Mamba.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {192},
number = {},
pages = {107816},
doi = {10.1016/j.neunet.2025.107816},
pmid = {40714477},
issn = {1879-2782},
abstract = {Electroencephalography (EEG) captures brain activity and has been widely used in clinic and brain-computer interfaces (BCIs). Classic EEG decoding methods rely on supervised learning, limiting their performance and generalizability. Inspired by the revolutionary impact of large models in other fields, researchers are now investigating EEG foundation models. Recently, state space models (SSMs), such as Mamba, have demonstrated strong sequence modeling capabilities, which may be suitable to model the spatiotemporal dependencies of EEG signals. However, the application of Mamba for EEG representation learning remains largely unexplored. In this paper, we investigate the potential of Mamba for learning generic EEG representations and propose a novel EEG foundation model, EEGMamba. Specifically, we employ Mamba encoder as the backbone of EEGMamba to model the spatiotemporal dependencies among EEG patches. Meanwhile, we use patch-based masked EEG reconstruction to learn generic EEG representations. EEGMamba is pre-trained on a large and diverse EEG corpus (16,724 h) from five datasets. We evaluate EEGMamba on up to six downstream BCI tasks using six public datasets. EEGMamba achieves the state-of-the-art performance across all the tasks, demonstrating its strong capability and generalizability.},
}

@article {pmid40714230,
year = {2025},
author = {Gao, C and Wu, X and Ma, L and Li, D and Wang, Y and Guo, C and Li, W and Wang, H and Chu, C and Madsen, KH and Fan, L},
title = {Iterative Prior-Guided Parcellation (iPGP) for Capturing Inter-Subject and Inter-Nuclei Variability in Thalamic Mapping.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121399},
doi = {10.1016/j.neuroimage.2025.121399},
pmid = {40714230},
issn = {1095-9572},
abstract = {The thalamus, a critical relay station in the brain, consists of multiple nuclei that play essential roles in various brain circuits. Identifying these nuclei is crucial for understanding how thalamic structures influence cognitive functions. However, genetic and environmental factors introduce substantial variability in thalamic parcellation patterns, posing both challenges and opportunities for individualized mapping of thalamic function. This study proposes an iterative prior-guided parcellation (iPGP) framework to construct individualized thalamic parcellations. The iPGP method utilizes the Morel histological atlas as prior guidance, incorporates spatially constrained local diffusion characteristics as features, and employs an iterative framework to optimize an individual-specific parcellation model. As a result, iPGP automatically adapts to individual thalamic contrast variations, producing personalized and anatomically consistent parcellations. Through test-retest assessments, iPGP demonstrated a high degree of intra-subject reproducibility. By evaluating inter-subject and inter-nuclei variability, iPGP exhibited strong adaptability across different age groups while capturing subject-specific and region-specific variability. Furthermore, thalamic parcellations generated by iPGP showed significant associations with adolescent age and adult behavioral-cognitive scores. Our findings suggest that iPGP effectively captures inter-subject and inter-nuclei variability in thalamic parcellation, highlighting its potential for advancing thalamic mapping in exploring brain function.},
}

@article {pmid40712594,
year = {2025},
author = {Huang, J and Yang, P and Xiong, B and Lv, Y and Wang, Q and Wan, B and Zhang, Z},
title = {Mixup-based data augmentation for enhancing few-shot SSVEP detection performance.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adf467},
pmid = {40712594},
issn = {1741-2552},
abstract = {Few-shot steady-state visual evoked potential (SSVEP) detection remains a major challenge in brain-computer interface (BCI) systems, as limited calibration data often leads to degraded performance. This study aims to enhance few-shot SSVEP detection through an effective data augmentation strategy. Approach: We propose a mixup-based data augmentation method that generates synthetic trials by linearly interpolating between real SSVEP signals extracted using a sliding window strategy. The interpolation weight is optimized by maximizing the similarity between the mixed signal and both the template and reference signals. The augmented data is then used to train spatial filters for improved SSVEP detection. Main results: The proposed method was evaluated on two benchmark SSVEP datasets using task-related component analysis (TRCA) and INS-SF as spatial filters. Results demonstrate that the mixup-based augmentation significantly improves detection accuracy under few-shot conditions, outperforming existing augmentation and baseline methods. Significance: The mixup-based method offers an effective and practical solution for enhancing SSVEP decoding with limited data, reducing calibration time, and improving BCI systems' usability in real-world scenarios.},
}

@article {pmid40712572,
year = {2025},
author = {Wang, J and Liu, Y and Ma, Y and Feng, Y and Lin, L and Ping, A and Tian, F and Zhang, X and Berman, AJL and Bollmann, S and Polimeni, JR and Roe, AW},
title = {In vivo 7 Tesla MRI of non-human primate intracortical microvascular architecture.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2025.05.028},
pmid = {40712572},
issn = {1097-4199},
abstract = {Intracortical arterioles are key locations for blood flow regulation and oxygen supply in the brain and are critical to brain health and disease. However, imaging such small (<100-μm-sized) vessels in humans is challenging. Here, using non-human primates as a model, we developed a capability for imaging microvasculature in vivo with a clinical 7 T MRI scanner. Using simulations, we identified parameters for imaging intracortical vessels with slow flow and combined this with high-resolution imaging (64 × 64 μm[2] in-plane). Across large swaths of occipital, parietal, and temporal cortex, arrays of intracortical arterioles and venules were observed in gyral crowns and deep within sulcal folds. Systematic arteriole-venule patterns revealed potential architecture of input-output flow relationships. Even single vessels could be followed across cortical laminae. As a first step toward imaging microvasculature in humans, this method introduces a new technology and animal model for understanding relationships between functional and vascular architectures.},
}

@article {pmid40712216,
year = {2025},
author = {Li, S and Xu, R and Wang, X and Cichocki, A and Jin, J},
title = {Dual branch neural network with dynamic learning mechanism for P300-based brain-computer interfaces.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {192},
number = {},
pages = {107876},
doi = {10.1016/j.neunet.2025.107876},
pmid = {40712216},
issn = {1879-2782},
abstract = {Brain-computer interface (BCI) system offers an alternative or supplementary means of interaction for individuals with disabilities. P300 speller is a commonly utilized BCI system due to its high stability, and reliability and without intensive user training. Nevertheless, the inherent class imbalance within P300 datasets predisposes the system to overfit, potentially impacting the classification performances. Existing class rebalancing methods mainly rely on resampling or adjusting the class weight with a fixed value, thus it is still tricky to ensure that the output is evenly balanced. To mitigate the above class imbalance issue, this study proposes a dual branch learning (DBL) method that concurrently considers feature representation and class imbalance. This approach involves the ingestion of two distinct sample types-uniformly sampled and reverse-sampled data-into the feature extraction and classification modules during the training phase. Furthermore, a dynamic learning mechanism is implemented to incrementally emphasize minority class samples (specifically the P300 component) as training progresses. The effectiveness of the proposed DBL method is proved using both publicly accessible and self-collected datasets in a subject-dependent scheme. The proposed DBL method can achieve an accuracy of 97.37 % and 88.72 % in the above datasets. Besides, it provides superior and more reliable results compared with several deep learning and rebalancing methods. These findings highlight the promising potential of the proposed DBL framework in P300-based BCI.},
}

@article {pmid40710265,
year = {2025},
author = {Ma, S and Situ, Z and Peng, X and Li, Z and Huang, Y},
title = {Multi-Class Classification Methods for EEG Signals of Lower-Limb Rehabilitation Movements.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {7},
pages = {},
pmid = {40710265},
issn = {2313-7673},
support = {2024ZD0715801//The National Science and Technology Major Project of China/ ; },
abstract = {Brain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices by decoding motor intentions from EEG signals. However, the existing multi-class classification methods for motor imagery EEG (MI-EEG) signals are hindered by low signal quality and limited accuracy, restricting their practical application. This study focuses on rehabilitation training scenarios, aiming to capture the motor intentions of patients with partial or complete motor impairments (such as stroke survivors) and provide feedforward control commands for exoskeletons. This study developed an EEG acquisition protocol specifically for use with lower-limb rehabilitation motor imagery (MI). It systematically explored preprocessing techniques, feature extraction strategies, and multi-classification algorithms for multi-task MI-EEG signals. A novel 3D EEG convolutional neural network (3D EEG-CNN) that integrates time/frequency features is proposed. Evaluations on a self-collected dataset demonstrated that the proposed model achieved a peak classification accuracy of 66.32%, substantially outperforming conventional approaches and demonstrating notable progress in the multi-class classification of lower-limb motor imagery tasks.},
}

@article {pmid40709513,
year = {2025},
author = {Guo, W and Wang, H and Deng, W and Dong, Z and Liu, Y and Luo, S and Yu, J and Huang, X and Chen, Y and Ye, J and Song, J and Jiang, Y and Li, D and Wang, W and Sun, X and Kuang, W and Qiu, C and Cheng, N and Li, W and Zhang, W and Liu, Y and Tang, Z and Du, X and Greenshaw, AJ and Zhang, L and Li, T},
title = {Impact of early detection and management of emotional distress on length of stay in non-psychiatric inpatients: A retrospective hospital-based cohort study.},
journal = {Chinese medical journal},
volume = {},
number = {},
pages = {},
pmid = {40709513},
issn = {2542-5641},
abstract = {BACKGROUND: While emotional distress, encompassing anxiety and depression, has been associated with negative clinical outcomes, its impact across various clinical departments and general hospitals has been less explored. Previous studies with limited sample sizes have examined the effectiveness of specific treatments (e.g., antidepressants) rather than a systemic management strategy for outcome improvement in non-psychiatric inpatients. To enhance the understanding of the importance of addressing mental health care needs among non-psychiatric patients in general hospitals, this study retrospectively investigated the impacts of emotional distress and the effects of early detection and management of depression and anxiety on hospital length of stay (LOS) and rate of long LOS (LLOS, i.e., LOS >30 days) in a large sample of non-psychiatric inpatients.

METHODS: This retrospective cohort study included 487,871 inpatients from 20 non-psychiatric departments of a general hospital. They were divided, according to whether they underwent a novel strategy to manage emotional distress which deployed the Huaxi Emotional Distress Index (HEI) for brief screening with grading psychological services (BS-GPS), into BS-GPS (n = 178,883) and non-BS-GPS (n = 308,988) cohorts. The LOS and rate of LLOS between the BS-GPS and non-BS-GPS cohorts and between subcohorts with and without clinically significant anxiety and/or depression (CSAD, i.e., HEI score ≥11 on admission to the hospital) in the BS-GPS cohort were compared using univariable analyses, multilevel analyses, and/or propensity score-matched analyses, respectively.

RESULTS: The detection rate of CSAD in the BS-GPS cohort varied from 2.64% (95% confidence interval [CI]: 2.49%-2.81%) to 20.50% (95% CI: 19.43%-21.62%) across the 20 departments, with a average rate of 5.36%. Significant differences were observed in both the LOS and LLOS rates between the subcohorts with CSAD (12.7 days, 535/9590) and without CSAD (9.5 days, 3800/169,293) and between the BS-GPS (9.6 days, 4335/178,883) and non-BS-GPS (10.8 days, 11,483/308,988) cohorts. These differences remained significant after controlling for confounders using propensity score-matched comparisons. A multilevel analysis indicated that BS-GPS was negatively associated with both LOS and LLOS after controlling for sociodemographics and the departments of patient discharge and remained negatively associated with LLOS after controlling additionally for the year of patient discharge.

CONCLUSION: Emotional distress significantly prolonged the LOS and increased the LLOS of non-psychiatric inpatients across most departments and general hospitals. These impacts were moderated by the implementation of BS-GPS. Thus, BS-GPS has the potential as an effective, resource-saving strategy for enhancing mental health care and optimizing medical resources in general hospitals.},
}

@article {pmid40708811,
year = {2025},
author = {Tsytsarev, V and Volnova, A and Rojas, L and Sanabria, P and Ignashchenkova, A and Ortiz-Rivera, J and Alves, J and Inyushin, M},
title = {Vectorial principles of sensorimotor decoding.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1612626},
pmid = {40708811},
issn = {1662-5161},
abstract = {This review explores the vectorial principles underlying sensorimotor decoding across diverse biological systems. From the encoding of light wavelength in retinal cones to direction-specific motor cortex activity in primates, neural representations frequently rely on population vector coding-a scheme, in which neurons with directional or modality-specific preferences integrate their activity to encode stimuli or motor commands. Early studies on color vision and motor control introduced concepts of vector summation and neuronal tuning, evolving toward more precise models such as the von Mises distribution. Research in invertebrates, including leeches and snails, reveals that even simple nervous systems utilize population vector principles for reflexes and coordinated movements. Furthermore, analysis of joint limb motion suggests biomechanical optimization aligned with Fibonacci proportions, facilitating efficient neural and mechanical control. The review highlights that motor units and neurons often display multimodal or overlapping tuning fields, reinforcing the need for population-based decoding strategies. These findings suggest a unifying vectorial framework for sensory and motor coding, with implications for periprosthetic and brain-machine interface.},
}

@article {pmid40708808,
year = {2025},
author = {Chowdhury, AT and Hassanein, A and Al Shibli, AN and Khanafer, Y and AbuHaweeleh, MN and Pedersen, S and Chowdhury, MEH},
title = {Neural signals, machine learning, and the future of inner speech recognition.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1637174},
pmid = {40708808},
issn = {1662-5161},
abstract = {Inner speech recognition (ISR) is an emerging field with significant potential for applications in brain-computer interfaces (BCIs) and assistive technologies. This review focuses on the critical role of machine learning (ML) in decoding inner speech, exploring how various ML techniques improve the analysis and classification of neural signals. We analyze both traditional methods such as support vector machines (SVMs) and random forests, as well as advanced deep learning approaches like convolutional neural networks (CNNs), which are particularly effective at capturing the dynamic and non-linear patterns of inner speech-related brain activity. Also, the review covers the challenges of acquiring high-quality neural signals and discusses essential preprocessing methods for enhancing signal quality. Additionally, we outline and synthesize existing approaches for improving ISR through ML, that can lead to many potential implications in several domains, including assistive communication, brain-computer interfaces, and cognitive monitoring. The limitations of current technologies were also discussed, along with insights into future advancements and potential applications of machine learning in inner speech recognition (ISR). Building on prior literature, this work synthesizes and organizes existing ISR methodologies within a structured mathematical framework, reviews cognitive models of inner speech, and presents a detailed comparative analysis of existing ML approaches, thereby offering new insights into advancing the field.},
}

@article {pmid40707971,
year = {2025},
author = {Ji, X and Lu, X and Xu, Y and Zhang, W and Yang, H and Yin, C and Wang, H and Ren, C and Ji, Y and Li, Y and Huang, G and Shen, Y},
title = {Effects and neural mechanisms of a brain-computer interface-controlled soft robotic glove on upper limb function in patients with subacute stroke: a randomized controlled fNIRS study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {171},
pmid = {40707971},
issn = {1743-0003},
support = {No.Q202414//Youth Project of the Wuxi Municipal Health Commission/ ; No.2022YFC2009700//National Key Research & Development Program of China/ ; No.BE2023023-2//the Key Project of Jiangsu Province's Key Research and Development Program/ ; No.BE2023034//the Competitive Project of Jiangsu Province's Key Research and Development Program/ ; No.JBGS202414//Jiangsu Province Hospital clinical diagnosis and treatment of technological innovation "Open bidding for selecting the best candidates" project/ ; 2025-K10//Open Research Fund of State Key Laboratory of Digital Medical Engineering/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Middle Aged ; *Stroke Rehabilitation/methods/instrumentation ; *Upper Extremity/physiopathology ; Spectroscopy, Near-Infrared ; *Robotics/instrumentation ; Aged ; *Stroke/physiopathology/complications ; *Paresis/rehabilitation/physiopathology/etiology ; Adult ; },
abstract = {BACKGROUND AND PURPOSE: The brain-computer interface-based soft robotic glove (BCI-SRG) holds promise for upper limb rehabilitation in subacute stroke patients, yet its efficacy and neural mechanisms are unclear. This study aimed to investigate the therapeutic effects and neural mechanisms of BCI-SRGs by functional near-infrared spectroscopy (fNIRS).

METHODS: Forty subacute stroke patients with left-sided hemiparesis were randomized into the BCI-SRG (n = 20) and soft robotic glove (SRG) (n = 20) groups. Both groups received 20 sessions of intervention over 4 weeks in addition to conventional rehabilitation. The BCI-SRG group was trained using a soft robotic glove controlled by a brain‒computer interface (BCI), whereas the SRG group used the same soft robotic glove without BCI control. The clinical outcomes included the Action Research Arm Test (ARAT), the Fugl-Meyer Assessment Upper Limb (FMA-UL), and Modified Barthel Index (MBI) scores. In addition, fNIRS was used to explore potential clinical brain mechanisms. All assessments were performed before treatment and after 4 weeks of treatment.

RESULTS: A total of 39 participants completed the intervention and clinical assessments (BCI-SRG: n = 20; SRG: n = 19). Compared with the SRG group, the BCI-SRG group showed greater improvements in the ARAT (Z = - 2.139, P = 0.032) and FMA-UL (Z = - 2.588, P = 0.010), with no notable difference in the MBI (Z = - 1.843, P = 0.065). fNIRS data were available for 35 participants (BCI-SRG: n = 17; SRG: n = 18). Within-group comparisons revealed significant postintervention increases in cortical activation in the bilateral sensorimotor cortex (SMC) and medial prefrontal cortex (MPFC) in the BCI-SRG group, whereas no significant changes were observed in the SRG group. Between-group comparisons further revealed significantly greater changes in HbO concentrations in the BCI-SRG group than in the SRG group across the same cortical regions. Moreover, changes in prefrontal activation (post-pre) were positively correlated with improvements in ARAT scores, with significant correlations observed in the left dorsal lateral prefrontal cortex (LDLPFC) (Ch9, r = 0.592, P = 0.012; Ch25, r = 0.488, P = 0.047) and right dorsal lateral prefrontal cortex (RDLPFC) (Ch19, r = 0.671, P = 0.003).

CONCLUSIONS: BCI-SRG training significantly enhances upper limb function and facilitates bilateral motor and sensory cortical reorganization. PFC activation is correlated with functional improvements, suggesting a potential mechanism underlying the benefits of rehabilitation in stroke patients.

TRIAL REGISTRATION: This trial was registered under the Chinese Clinical Trial Registry (ChiCTR2400082786) and was retrospectively registered on April 8, 2024.},
}

Humans
*Brain-Computer Interfaces
Male
Female
Middle Aged
*Stroke Rehabilitation/methods/instrumentation
*Upper Extremity/physiopathology
Spectroscopy, Near-Infrared
*Robotics/instrumentation
Aged
*Stroke/physiopathology/complications
*Paresis/rehabilitation/physiopathology/etiology
Adult

@article {pmid40707673,
year = {2025},
author = {Zhang, X and Li, M and Chen, Y and Liu, J and Zhang, J and Shao, C and Deng, B and Zhang, J and Wang, T and Cao, J and Xu, X and He, Q and Yang, B and Shao, X and Ying, M},
title = {Deubiquitinase USP6 stabilizes oncogenic RUNX1 fusion proteins to promote the leukemic potential and malignant progression.},
journal = {Leukemia},
volume = {},
number = {},
pages = {},
pmid = {40707673},
issn = {1476-5551},
support = {No. 82273942//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {RUNX1-rearranged leukemia is one of the most common subtypes of leukemia associated with genetic abnormalities. Although the majority of patients respond to chemotherapy, relapse and long-term adverse effects remain significant challenges. RUNX1 fusions, resulting from chromosomal rearrangements, are pivotal oncogenic drivers, with over 70 distinct variants identified. Therefore, elucidating their regulatory mechanisms may help to develop novel therapeutic strategies. Herein, we identify a universal deubiquitinase, USP6, that stabilizes RUNX1 fusion proteins with different partners. Importantly, USP6 is specifically upregulated in RUNX1-rearranged leukemia and strongly correlates with poor patient outcomes. Mechanistically, USP6 stabilizes RUNX1 fusions to facilitate the formation of phase separation, leading to robust transcriptional activation of the fusions. Depletion of USP6 dramatically inhibits proliferation and induces differentiation of RUNX1-rearranged leukemic cells. The marketed drug auranofin is identified as a potential USP6 inhibitor, which induces degradation of different RUNX1 fusions, further triggering myeloid differentiation and arresting xenograft tumor growth. Notably, auranofin exhibits selective therapeutic efficacy in patient-derived leukemia blasts from RUNX1-rearranged cases. Together, we not only uncover a new biological function of USP6 in regulating the transcriptional activity of RUNX1 fusions but also validate USP6 as a promising drug target and auranofin as a candidate therapy for RUNX1-rearranged leukemia.},
}

@article {pmid40707358,
year = {2025},
author = {Odermatt, IA and Schulthess-Lutz, M and Mihelj, E and Howell, P and Heimhofer, C and McMackin, R and Ruddy, K and Freund, P and Kikkert, S and Wenderoth, N},
title = {TMS-based neurofeedback training of mental finger individuation induces neuroplastic changes in the sensorimotor system.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.2189-24.2025},
pmid = {40707358},
issn = {1529-2401},
abstract = {Neurofeedback (NF) training based on motor imagery is increasingly used in neurorehabilitation with the aim to improve motor functions. However, the neuroplastic changes underpinning these improvements are poorly understood. Here, we used mental 'finger individuation', i.e., the selective facilitation of single finger representations without producing overt movements, as a model to study neuroplasticity induced by NF. To enhance mental finger individuation, we used transcranial magnetic stimulation (TMS)-based NF training. During motor imagery of individual finger movements, healthy female and male human participants were provided visual feedback on the size of motor evoked potentials, reflecting their finger-specific corticospinal excitability. We found that TMS-NF improved the mental activation of finger-specific representations. First, intracortical inhibitory circuits in the primary motor cortex were tuned after training such that inhibition was selectively reduced for the finger that was mentally activated. Second, motor imagery finger representations in areas of the sensorimotor system assessed with functional MRI became more distinct after training. Together, our results indicate that the neural underpinnings of finger individuation, a well-known model system for neuroplasticity, can be modified using TMS-NF guided motor imagery training. These findings demonstrate that TMS-NF induces neuroplasticity in the sensorimotor system, highlighting the promise of TMS-NF on the recovery of fine motor function.Significance statement The activation of sensorimotor representations through motor imagery can be used to control brain-computer interfaces (BCIs) as assistive devices or training interventions. Here, we investigated how improvements in BCI control may change sensorimotor representations activated through motor imagery. We used BCI-neurofeedback based on TMS that allows for finger-specific feedback on corticospinal excitability. Therefore, this training can be used to practice and improve mental finger individuation, providing a model to study neuroplasticity. We demonstrate that motor imagery representations became more finger-specific after training, as evident in the tuning of intracortical inhibition and more distinct fMRI activation patterns in the sensorimotor system. These findings show that BCI training induces neuroplasticity in the sensorimotor system and shapes sensorimotor representations activated through motor imagery.},
}

@article {pmid40706724,
year = {2025},
author = {Kim, YS and Kim, CU and Han, H and Kim, MY and Choi, SI and Im, CH},
title = {Performance enhancement of steady-state visual evoked field-based brain-computer interfaces using spatial distribution of synchronization index in MEG channel space.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121391},
doi = {10.1016/j.neuroimage.2025.121391},
pmid = {40706724},
issn = {1095-9572},
abstract = {The development of helmet-type magnetoencephalography (MEG) systems that do not require liquid helium (e.g., OPM-MEG) has sparked growing interest in steady-state visual evoked field (SSVEF)-based brain-computer interfaces (BCIs). Unlike electroencephalography (EEG), MEG records less distorted signals with a high spatial resolution, covering the entire head without requiring cumbersome electrode attachment. However, conventional algorithms, such as the filter bank-driven multivariate synchronization index (FBMSI), are prone to misclassification in ambiguous cases where the differences between synchronization indices (S indices) are minimal. Additionally, these algorithms fail to fully exploit high spatial resolution and whole-head coverage of MEG. To address these limitations, this study proposes a novel, calibration-free SSVEF classification algorithm termed Spatial Distribution Analysis (SDA). The SDA algorithm utilizes the center of gravity of the S index distribution in the MEG channel space to enhance classification accuracy. Experimental evaluations with 20 participants using a helmet-type SQUID MEG system demonstrated that the proposed SDA algorithm achieved significantly higher classification accuracy and information transfer rate (ITR) across all window sizes. Notably, the largest improvements of 5.76% in accuracy and 4.87 bits/min in ITR were reported for a window size of 2.5 s. Furthermore, the generalizability of the SDA algorithm was validated on an OPM-MEG dataset, showing performance improvements across all window sizes. The SDA algorithm also mitigated misclassification due to adjacent stimuli and showed short time delay of 0.0907 s, enough to be used for real-time BCIs. These findings highlight the potential of SDA algorithm to enhance the overall performance of SSVEF-based BCI.},
}

@article {pmid40705590,
year = {2025},
author = {Ko, BK and Lee, SH and Lee, SW},
title = {Imagined Speech Detection Using Multi-Receptive CNN for Asynchronous BCI Communication and Neurorehabilitation.},
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.2025.3592312},
pmid = {40705590},
issn = {1558-0210},
abstract = {Imagined speech-based brain-computer interface (BCI) facilitates brain signal-driven intuitive communication which holds great promise as an effective speech rehabilitation tool, enabling real-time, hands-free interaction for individuals with speech and motor impairments. While speech-based assistant systems rely on wake-word detection (e.g., "Hey Siri"), BCI-based communication system must capture imagined onset from EEG signals to turn on the 'brain switch' to further convey user's imagined command. Nevertheless, the absence of reliable ground truth for the endogenous paradigm adds to the complexity to train the model to capture exact onset from continuous EEG. To address these issues, we introduce a multi-receptive field convolutional neural network, designed to capture speech and idle states based on behaviorally-aligned EEG features. We propose a voice-based ground truth alignment method with voting strategy that aims to synchronize imagined speech with overt speech onset and offset, providing a structured approach for capturing speech events in asynchronous BCI systems. Furthermore, spectral and phonological analyses revealed that beta and alpha bands, as well as syllable count, appear to influence speech state discriminability. Evaluations on imagined and overt speech tasks, including pseudo-online experiments, demonstrate the potential to enhance asynchronous BCI systems, supporting real-time communication for both healthy and impaired individuals.},
}

@article {pmid40703721,
year = {2025},
author = {Alkhoury, L and O'Sullivan, J and Scanavini, G and Dou, J and Arora, J and Hamill, L and Patchell, A and Radanovic, A and Watson, WD and Lalor, EC and Schiff, ND and Hill, NJ and Shah, SA},
title = {Leveraging meaning-induced neural dynamics to detect covert cognition via EEG during natural language listening-a case series.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1616963},
pmid = {40703721},
issn = {1664-1078},
abstract = {At least a quarter of adult patients with severe brain injury in a disorder of consciousness may have cognitive abilities that are hidden due to motor impairment. In this case series, we developed a tool that extracted acoustic and semantic processing biomarkers from electroencephalography recorded while participants listened to a story. We tested our method on two male adolescent survivors of severe brain injury and showed evidence of acoustic and semantic processing. Our method identifies cognitive processing while obviating demands on attention, memory, and executive function. This lays a foundation for graded assessments of cognition recovery across the spectrum of covert cognition.},
}

@article {pmid40703668,
year = {2025},
author = {Wang, F and Luo, Z and Lv, W and Zhu, X},
title = {DTCNet: finger flexion decoding with three-dimensional ECoG data.},
journal = {Frontiers in computational neuroscience},
volume = {19},
number = {},
pages = {1627819},
pmid = {40703668},
issn = {1662-5188},
abstract = {ECoG signals are widely used in Brain-Computer Interfaces (BCIs) due to their high spatial resolution and superior signal quality, particularly in the field of neural control. ECoG enables more accurate decoding of brain activity compared to traditional EEG. By obtaining cortical ECoG signals directly from the cerebral cortex, complex motor commands, such as finger movement trajectories, can be decoded more efficiently. However, existing studies still face significant challenges in accurately decoding finger movement trajectories. Specifically, current models tend to confuse the movement information of different fingers and fail to fully exploit the dependencies within time series when predicting long sequences, resulting in limited decoding performance. To address these challenges, this paper proposes a novel decoding method that transforms 2D ECoG data samples into 3D spatio-temporal spectrograms with time-stamped features via wavelet transform. The method further enables accurate decoding of finger bending by using a 1D convolutional network composed of Dilated-Transposed convolution, which together extract channel band features and temporal variations in tandem. The proposed method achieved the best performance among three subjects in BCI Competition IV. Compared with existing studies, our method made the correlation coefficient between the predicted multi-finger motion trajectory and the actual multi-finger motion trajectory exceed 80% for the first time, with the highest correlation coefficient reaching 82%. This approach provides new insights and solutions for high-precision decoding of brain-machine signals, particularly in precise command control tasks, and advances the application of BCI systems in real-world neuroprosthetic control.},
}

@article {pmid40703402,
year = {2025},
author = {Borra, D and Ma, M and Martinez-Martin, E and Xia, L},
title = {Editorial: Methods in brain-computer interfaces: 2023.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1647584},
pmid = {40703402},
issn = {1662-5161},
}

@article {pmid40703200,
year = {2025},
author = {Li, K and Zhang, J and Yu, B and Ward, MP and Liu, M and Liu, Y and Wang, Z and Chen, Z and Li, W and Wang, N and Zhao, Y and Yang, X and Yang, F and Wang, P and Zhang, Z},
title = {Meteorological, Socioeconomic, and Environmental Factors Influencing Human Brucellosis Occurrence in Yunnan, China, 2006-2021: A Bayesian Spatiotemporal Modeling Study.},
journal = {Transboundary and emerging diseases},
volume = {2025},
number = {},
pages = {8872434},
pmid = {40703200},
issn = {1865-1682},
mesh = {Humans ; China/epidemiology ; *Brucellosis/epidemiology ; Bayes Theorem ; Socioeconomic Factors ; Spatio-Temporal Analysis ; Risk Factors ; Meteorological Concepts ; Environment ; },
abstract = {Background: Brucellosis epidemics in Yunnan Province in southern China have increased and caused more impact in recent years. However, the epidemiological characteristics and driving factors for brucellosis have not been clearly described. The aim of this study was to analyze the spatiotemporal distribution and potential factors for human brucellosis (HB) in Yunnan Province, 2006-2021. Methods: HB data were obtained from the China National Notifiable Infectious Diseases Reporting Information System. Global spatial autocorrelation and spatial scanning statistics were used to analyze the spatial patterns of brucellosis. Zero-inflated negative binomial (ZINB) Bayesian spatiotemporal models were applied to the analysis of potential risk factors, including environmental, meteorological, and socioeconomic factors. Findings: Between 2006 and 2021, a total of 2794 brucellosis cases were reported. The central and western regions were the most severely affected. GDP showed a positive correlation with brucellosis risk when in the range 0-30.9 billion RMB, peaking with a relative risk (RR) of 13.64 (95% Bayesian credible interval [BCI]: 4.10, 49.10) at around 2.3 billion RMB. Conversely, a negative correlation was observed for GDP between 101 and 135 billion RMB, with the RR dropping to 0.14 (95% BCI: 0.01, 0.89) at 135 billion RMB. Brucellosis cases increased by 4.90% (95% BCI: 1.82%, 7.95%) per 1°C increase in temperature, while a 1° increase in slope reduced cases by 17.06% (95% BCI: 4.01%, 28.81%). Interpretation: Our findings suggest that socioeconomic factors play the greatest role in the occurrence of brucellosis in both northern and southern China; however, the effects of the environmental factors may be different between these areas. Differences in factors affecting each region need to be fully considered, and brucellosis prevention and control need to be adapted to these differences.},
}

Humans
China/epidemiology
*Brucellosis/epidemiology
Bayes Theorem
Socioeconomic Factors
Spatio-Temporal Analysis
Risk Factors
Meteorological Concepts
Environment

@article {pmid40702984,
year = {2025},
author = {Yu, Y and Wang, RM and Dong, Y and Jia, XZ and Wu, ZY},
title = {Neuroimaging correlates of genetics in patients with Wilson's disease.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {35},
number = {7},
pages = {},
doi = {10.1093/cercor/bhaf186},
pmid = {40702984},
issn = {1460-2199},
support = {81125009//National Natural Science Foundation of China/ ; 81701126//National Natural Science Foundation of China/ ; 188020-193810101/089//Research Foundation for Distinguished Scholars of Zhejiang University/ ; },
mesh = {Humans ; *Hepatolenticular Degeneration/genetics/diagnostic imaging/pathology/physiopathology ; Male ; Female ; Adult ; *Brain/pathology/diagnostic imaging/physiopathology ; Young Adult ; *Mutation/genetics ; Magnetic Resonance Imaging ; Neuroimaging ; Copper-Transporting ATPases/genetics ; Adolescent ; Middle Aged ; Atrophy ; },
abstract = {Wilson's disease is an inherited disorder of copper metabolism. Despite significant advancements in neuroimaging studies, prior research into the pathological mechanism of Wilson's disease has ignored the crucial impact of mutation on the disease. This study examined brain imaging in relation to mutation in patients with Wilson's disease. A total of 57 Wilson's disease patients and 25 healthy controls were recruited in the current research. Patients were classified as having either the p.R778L or the p.P992L mutation (N = 43) or other mutations (N = 14). Utilizing the amplitude of low-frequency fluctuations, fractional amplitude of low-frequency fluctuations, and voxel-based morphology, the brain function and structure of Wilson's disease were explored. Compared to healthy controls, Wilson's disease patients with the p.R778L or p.P992L mutation showed greater atrophy in the bilateral putamen, caudate, globus pallidus, thalamus, amygdala, insula, and hippocampus. And these patients showed altered spontaneous neural activity in many more brain regions than healthy controls in three frequency bands. Significant correlation was found between altered brain volume and Unified Wilson's Disease Rating Scale neurological subscale scores. These findings reveal the functional and structural characteristics of Wilson's disease and emphasize the importance of exploring the neuroimaging correlation of genetic mutations in Wilson's disease.},
}

Humans
*Hepatolenticular Degeneration/genetics/diagnostic imaging/pathology/physiopathology
Male
Female
Adult
*Brain/pathology/diagnostic imaging/physiopathology
Young Adult
*Mutation/genetics
Magnetic Resonance Imaging
Neuroimaging
Copper-Transporting ATPases/genetics
Adolescent
Middle Aged
Atrophy

@article {pmid40702747,
year = {2025},
author = {Yang, A and Lv, X and Wang, H and Wang, X},
title = {Psychedelics, Spirituality, and Fundamentalism: A Brain Network Approach to Cognitive Flexibility and Rigidity.},
journal = {ACS chemical neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1021/acschemneuro.5c00509},
pmid = {40702747},
issn = {1948-7193},
abstract = {This viewpoint reconceptualizes mysticism and fundamentalism as brain network disorders, with psychedelics like psilocybin, lysergic acid diethylamide, and N,N-dimethyltryptamine offering potential to modulate these states. By disrupting rigid neural patterns, psychedelics may foster cognitive flexibility, challenge inflexible belief systems, and offer therapeutic value for extremism and mental health disorders.},
}

@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 {pmid40701672,
year = {2025},
author = {Kundi, H and Popma, JJ and Granada, JF and Leon, MB and Kodesh, A and Ascione, G and George, I and Latib, A and Thompson, JB and Popma, A and Alu, MC and Cohen, DJ},
title = {Outcomes in Older Patients Undergoing Surgical Aortic Valve Replacement With Concomitant Procedures.},
journal = {Journal of the American College of Cardiology},
volume = {86},
number = {4},
pages = {280-283},
doi = {10.1016/j.jacc.2025.05.021},
pmid = {40701672},
issn = {1558-3597},
}

@article {pmid40700800,
year = {2025},
author = {Peng, J and Jia, S and Zhang, J and Wang, Y and Yu, Z and Liu, JK},
title = {Decoding natural visual scenes via learnable representations of neural spiking sequences.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {192},
number = {},
pages = {107863},
doi = {10.1016/j.neunet.2025.107863},
pmid = {40700800},
issn = {1879-2782},
abstract = {Visual input underpins cognitive function by providing the brain with essential environmental information. Neural decoding of visual scenes seeks to reconstruct pixel-level images from neural activity, a vital capability for vision restoration via brain-computer interfaces. However, extracting visual content from time-resolved spiking activity remains a significant challenge. Here, we introduce the Wavelet-Informed Spike Augmentation (WISA) model, which applies multilevel wavelet transforms to spike trains to learn compact representations that can be directly fed into deep reconstruction networks. When tested on recorded retinal spike data responding to natural video stimuli, WISA substantially improves reconstruction accuracy, especially in recovering fine-grained details. These results emphasize the value of temporal spike patterns for high-fidelity visual decoding and demonstrate WISA as a promising model for visual decoding.},
}

@article {pmid40700312,
year = {2025},
author = {Correia, P and Quintão, C and Quaresma, C and Vigário, R},
title = {A Framework for Corticomuscle Control Studies Using a Serious Gaming Approach.},
journal = {Methods and protocols},
volume = {8},
number = {4},
pages = {},
doi = {10.3390/mps8040074},
pmid = {40700312},
issn = {2409-9279},
support = {UI/BD/151321/2021//Fundação para a Ciência e Tecnologia (FCT, Portugal)/ ; },
abstract = {Sophisticated voluntary movements are essential for everyday functioning, making the study of how the brain controls muscle activity a central challenge in neuroscience. Investigating corticomuscular control through non-invasive electrophysiological recordings is particularly complex due to the intricate nature of neuronal signals. To address this challenge, we present a novel experimental methodology designed to study corticomuscular control using electroencephalography (EEG) and electromyography (EMG). Our approach integrates a serious gaming biofeedback system with a specialized experimental protocol for simultaneous EEG-EMG data acquisition, optimized for corticomuscular studies. This work introduces, for the first time, a method for assessing brain-muscle functional connectivity during the execution of a demanding motor task. By identifying neuronal sources linked to muscular activity, this methodology has the potential to advance our understanding of motor control mechanisms. These insights could contribute to improving clinical practices and fostering the development of novel brain-computer interface technologies.},
}

@article {pmid40699544,
year = {2025},
author = {Pan, H and Chen, Z and Xu, N and Wang, B and Hu, Y and Zhou, H and Perry, A and Kong, XZ and Shen, M and Gao, Z},
title = {Dissecting Social Working Memory: Neural and Behavioral Evidence for Externally and Internally Oriented Components.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {40699544},
issn = {1995-8218},
abstract = {Social working memory (SWM)-the ability to maintain and manipulate social information in the brain-plays a crucial role in social interactions. However, research on SWM is still in its infancy and is often treated as a unitary construct. In the present study, we propose that SWM can be conceptualized as having two relatively independent components: "externally oriented SWM" (e-SWM) and "internally oriented SWM" (i-SWM). To test this external-internal hypothesis, participants were tasked with memorizing and ranking either facial expressions (e-SWM) or personality traits (i-SWM) associated with images of faces. We then examined the neural correlates of these two SWM components and their functional roles in empathy. The results showed distinct activations as the e-SWM task activated the postcentral and precentral gyri while the i-SWM task activated the precuneus/posterior cingulate cortex and superior frontal gyrus. Distinct multivariate activation patterns were also found within the dorsal medial prefrontal cortex in the two tasks. Moreover, partial least squares analyses combining brain activation and individual differences in empathy showed that e-SWM and i-SWM brain activities were mainly correlated with affective empathy and cognitive empathy, respectively. These findings implicate distinct brain processes as well as functional roles of the two types of SWM, providing support for the internal-external hypothesis of SWM.},
}

@article {pmid40697162,
year = {2025},
author = {Dohle, E and Swanson, E and Jovanovic, L and Yusuf, S and Thompson, L and Horsfall, HL and Muirhead, W and Bashford, L and Brannigan, J},
title = {Toward the Clinical Translation of Implantable Brain-Computer Interfaces for Motor Impairment: Research Trends and Outcome Measures.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e01912},
doi = {10.1002/advs.202501912},
pmid = {40697162},
issn = {2198-3844},
support = {FC001153/WT_/Wellcome Trust/United Kingdom ; FC001153/WT_/Wellcome Trust/United Kingdom ; FC001153/WT_/Wellcome Trust/United Kingdom ; //Rosetrees Trust and Stoneygate Trust/ ; },
abstract = {Implantable brain-computer interfaces (iBCIs) decode neural signals to control external effectors, offering potential to restore function in individuals with severe motor impairments, such as loss of limb function or speech. This systematic review examines the evolution of iBCI research and key bottlenecks to clinical translation, particularly the absence of standardized, clinically meaningful outcome measures. A comprehensive search of MEDLINE, Embase, and CINAHL identifies 112 studies, nearly half (49.1%) published since 2020. Eighty unique iBCI participants were identified, providing the most up-to-date estimate of global users. Research remains concentrated in the United States (83%), with growing contributions from Europe, China, and Australia. Electrocorticography (ECoG)-based devices increasingly emerge alongside micro-electrode arrays. iBCI devices are now being used to control a broader range of effectors, including robotic prosthetics and digital technologies. Although most (69.6%) studies reported outcome measures prospectively, these primarily related to decoding (69.6%) and task performance (62.5%), with only 17.9% assessing clinical outcomes. When cassessed, clinical outcomes were highly heterogeneous due to varied approaches across target populations. iBCIs show potential to restore functional independence at scale. However, challenges remain around cross-subject generalization, scalable implantation, and outcome standardization. Novel measures should be developed collaboratively with engineers, clinicians, and individuals with lived experience of motor impairment.},
}

@article {pmid40696184,
year = {2025},
author = {Yang, L and Guo, C and Zheng, Z and Dong, Y and Xie, Q and Lv, Z and Li, M and Lu, Y and Guo, X and Deng, R and Liu, Y and Feng, Y and Mu, R and Zhang, X and Ma, H and Chen, Z and Zhang, Z and Dong, Z and Yang, W and Zhang, X and Cui, Y},
title = {Author Correction: Stress dynamically modulates neuronal autophagy to gate depression onset.},
journal = {Nature},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41586-025-09404-1},
pmid = {40696184},
issn = {1476-4687},
}

@article {pmid40695313,
year = {2025},
author = {de Borman, A and Wittevrongel, B and Van Dyck, B and Van Rooy, K and Carrette, E and Meurs, A and Van Roost, D and Van Hulle, MM},
title = {Speech mode classification from electrocorticography: transfer between electrodes and participants.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adf2de},
pmid = {40695313},
issn = {1741-2552},
abstract = {Objective Speech brain-computer interfaces aim to restore communication for individuals who have lost the ability to speak by interpreting their brain activity and decoding the intended speech. As an initial component of these decoders, speech detectors have been developed to distinguish between the intent to speak and silence. However, it is important that these detectors account for real-life scenarios in which users may engage language-related brain areas-such as during reading or listening-without any intention to speak. Approach In this study, we analyze the interplay between different speech modes: speaking, listening, imagining speaking, reading and mouthing. We gathered a large dataset of 29 participants implanted with electrocorticography electrodes and developed a speech mode classifier. We also assessed how well classifiers trained on data from a specific participant transfer to other participants, both in the case of a single- and multi-electrode classifier. Main Results High accuracy was achieved using linear classifiers, for both single-electrode and multi-electrode configurations. Single-electrode classification reached 88.89% accuracy and multi-electrode classification 96.49% accuracy in distinguishing among three classes (speaking, listening, and silence). The best performing electrodes were located on the superior temporal gyrus and sensorimotor cortex. We found that single-electrode classifiers could be transferred across recording sites. For multi-electrode classifiers, we observed that transfer performance was higher for binary classifiers compared to multiclass classifiers, with the optimal source subject of the binary classifiers depending on the speech modes being classified. Significance Accurately detecting speech from brain signals is essential to prevent spurious outputs from a speech brain-computer interface and to advance its use beyond lab settings. To achieve this objective, the transfer between participants is particularly valuable as it can reduce training time, especially in cases where subject training is challenging.},
}

@article {pmid40694675,
year = {2025},
author = {Izac, M and N'Kaoua, B and Pillette, L and Jeunet-Kelway, C},
title = {[Improve athletes' performance with neurofeedback].},
journal = {Biologie aujourd'hui},
volume = {219},
number = {1-2},
pages = {51-58},
doi = {10.1051/jbio/2025001},
pmid = {40694675},
issn = {2105-0686},
mesh = {Humans ; *Neurofeedback/methods/physiology ; *Athletic Performance/physiology/psychology ; *Athletes/psychology ; Electroencephalography ; Cognition/physiology ; },
abstract = {In order to optimise their performance, athletes are looking for innovative, efficient and reliable training approaches. The development of electroencephalography and neurofeedback (NF) offers the opportunity to create innovative cognitive training procedures. Indeed, these technologies allow athletes to benefit from a feedback during mental training sessions and to objectively assess performance and progress. In addition, NF makes it possible to guide the athletes towards optimal cognitive strategies according to their objectives, and has a motivational dimension that pushes them to engage in the sessions. We first introduce the usefulness of NF to improve sports performance. Then, we review the current results concerning its efficiency. Finally, we provide an overview of the literature showing the heterogeneity of the studies published on the subject, focusing mainly on the aspects that could explain the variability of reported data.},
}

Humans
*Neurofeedback/methods/physiology
*Athletic Performance/physiology/psychology
*Athletes/psychology
Electroencephalography
Cognition/physiology

@article {pmid40694476,
year = {2025},
author = {Rangwani, R and Abbasi, A and Gulati, T},
title = {Effective cerebellar neuroprosthetic control after stroke.},
journal = {Cell reports},
volume = {44},
number = {8},
pages = {116030},
doi = {10.1016/j.celrep.2025.116030},
pmid = {40694476},
issn = {2211-1247},
abstract = {Brain-machine interfaces (BMIs) offer a viable option for restoring function in patients with motor disabilities post-stroke. Most BMI systems rely on signals from the motor cortex (M1), which is often compromised after stroke. The cerebellum, a subcortical structure involved in motor control, remains an underexplored source for neuroprosthetic control. Using chronic electrophysiological recordings in a rat stroke model, we show that cerebellar neural activity can effectively drive BMI control, performing comparably to M1-driven control. We observed this even in animals with motor impairments post-stroke. Simultaneous M1-cerebellum recordings during cerebellar BMI control revealed that cerebellar "direct" neurons driving the interface were influenced by both local cerebellar and distant M1 neurons. While cerebellar influence remained stable, M1's interaction with cerebellar direct neurons shifted from longer to shorter timescales after stroke. These findings highlight that cerebellar direct neural control is possible in the stroke brain and reveal changes in M1-cerebellar network dynamics post-stroke.},
}

@article {pmid40694466,
year = {2025},
author = {Zhang, C and Li, G and Wu, X and Gao, X},
title = {A Novel Hybrid Brain-Computer Interface Integrating Motor Imagery and Multiple Visual Stimuli.},
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.2025.3591616},
pmid = {40694466},
issn = {1558-0210},
abstract = {Brain-Computer Interface (BCI) that integrate Motor Imagery (MI) with Steady-State Visual Evoked Potentials (SSVEP) or Overt Spatial Attention (OSA) have demonstrated superior performance compared to MI only BCI. Nonetheless, the exploration of BCI that combine MI with visual tasks remains limited, and the synchronization between MI and visual tasks is often weak. To address this gap, our study introduces a novel BCI paradigm that combines MI with two visual tasks: SSVEP and OSA. In this paradigm, dynamic images depicting left and right arm movements flash at distinct frequencies, serving as visual stimuli positioned on both sides of the screen. Four classification methods are used for testing. The MI+SSVEP+OSA paradigm achieves higher average accuracy than the MI, MI+SSVEP, and MI+OSA paradigms. This validates the effectiveness of our novel paradigm and confirms the feasibility of simultaneously integrating MI with two visual stimuli. Moreover, we observe that the integration of SSVEP offers significant improvements, especially for participants who exhibit limited performance in the MI only paradigm. Additionally, our results indicate comparable performance between the MI+SSVEP and MI+OSA paradigms. Overall, this study offers valuable insights that can guide future research in hybrid BCI development, paving the way for more efficient and user-friendly BCI.},
}

@article {pmid40694230,
year = {2025},
author = {Afkhaminia, F and Shamsollahi, MB and Bahraini, T},
title = {A distributed adaptive network framework for ERP-Based classification of multichannel EEG signals.},
journal = {Physical and engineering sciences in medicine},
volume = {},
number = {},
pages = {},
pmid = {40694230},
issn = {2662-4737},
abstract = {Understanding brain function is one of the most challenging areas in brain signal processing. This study introduces a novel framework for electroencephalography (EEG) signal classification based on distributed adaptive networks using diffusion strategy. Our approach models the brain as a multitask network, where EEG electrodes are considered as nodes of this network. The network parameters are dynamically optimized based on the data from the nodes and inter-node cooperation. The proposed framework, which comprises network modeling and diffusion-based adaptation using the adapt then combine (ATC) algorithm, has been validated on different types of data. Experimental results indicate that the proposed framework outperforms common methods in classifying EEG data based on event-related potential (ERP) pattern identification, particularly in scenarios where machine learning-based models struggle with limited data. Furthermore, its ability to adapt to the non-stationary and dynamic nature of EEG signals and its efficient real-time implementation make this approach ideal for brain-computer interface (BCI), cognitive neuroscience, and clinical applications.},
}

@article {pmid40694026,
year = {2025},
author = {Guérin, V},
title = {Veteran and Brain-Computer Interfaces: The Duty to Care.},
journal = {AJOB neuroscience},
volume = {},
number = {},
pages = {1-9},
doi = {10.1080/21507740.2025.2530948},
pmid = {40694026},
issn = {2150-7759},
abstract = {Anticipated by science fiction, the enhanced soldier crystallized in the United States at the dawn of the 21st century within the Pentagon's scientific agency, the Defense Advanced Research Projects Agency (DARPA). Fueled by the fear of being overtaken by the enemy, and then by its own technology, this agency's new vision produced a "bifurcation" within anthropotechnics: the modification of humans for war. The soldier is now at the heart of a process of radical innovation, with as yet unknown implications. Emblematic of this enhancement, the use of the brain-computer interfaces (BCIs) will not only expose the soldier to previously unknown psychocognitive and emotional effects, but also offer the enemy potential access to his/her inner self. By giving birth to a new kind of veteran, this hybridization will generate new responsibilities for military commanders and politicians, as well as a new type of care.},
}

@article {pmid40694018,
year = {2025},
author = {Fan, C and Ding, Y and Zhang, H},
title = {A commentary on "Brain-computer interfaces: the innovative to unlocking neurological conditions".},
journal = {International journal of surgery (London, England)},
volume = {},
number = {},
pages = {},
doi = {10.1097/JS9.0000000000003094},
pmid = {40694018},
issn = {1743-9159},
}

@article {pmid40691442,
year = {2025},
author = {Wang, X and Chen, S and Li, J and Gao, Y and Li, S and Li, M and Liu, X and Liu, S and Ming, D},
title = {Enhanced theta oscillations in the left temporoparietal region associated with refractory positive symptoms in schizophrenia.},
journal = {Schizophrenia (Heidelberg, Germany)},
volume = {11},
number = {1},
pages = {104},
pmid = {40691442},
issn = {2754-6993},
abstract = {Positive symptoms are a prominent feature of schizophrenia. Despite antipsychotic treatment, ~30% of patients develop refractory positive symptoms (RPSs). Current research fails to elucidate the potential neurophysiological mechanisms underlying RPSs, thereby hindering the development of additional treatments. This study, which included 37 patients with RPSs and 40 with non-refractory positive symptoms (NRPSs), aimed to explore their underlying neural mechanisms. Outcome measures were relative power spectrum density and interregional synchronization across frequency bands and theta-gamma phase-amplitude coupling (θ-γ PAC). The single-frequency analysis indicated that RPSs exhibited elevated theta power and reduced lateralization in the left temporal lobe and temporo-parietal junction, along with enhanced functional connectivity in the left frontocentral region. The cross-frequency analysis revealed that RPSs exhibited slightly higher θ-γ coupling at the left temporo-parietal junction compared to NRPSs. Correlation analysis revealed significant associations among theta power, the lateralization index, functional connectivity, and the severity of positive symptoms. The aberrant activation of the theta rhythm in the left temporo-parietal region may lead to increased functional asymmetry in the brain, impeding interregional and inter-frequency information transmission and thus significantly impairing the normal processing of auditory information. These findings offer potential insights into the neurophysiological basis of positive symptoms in schizophrenia and may inform future clinical interventions.},
}

@article {pmid40690341,
year = {2025},
author = {Wang, J and Yao, L and Wang, Y},
title = {Enhanced Online Continuous Brain-Control by Deep Learning-based EEG Decoding.},
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.2025.3591254},
pmid = {40690341},
issn = {1558-0210},
abstract = {OBJECTIVE: A growing amount of deep learning models for motor imagery (MI) decoding from electroencephalogram (EEG) have demonstrated their superiority over traditional machine learning approaches in offline dataset analysis. However, current online MI-based brain-computer interfaces (BCIs) still predominantly adopt machine learning decoders while falling short of high BCI performance. Yet, the generalization and advantages of deep learning-based EEG decoding in realistic BCI systems remain far unclear.

METHODS: We conduct a randomized and cross-session online MI-BCI study with 2D center-out tasks in 15 BCI-naive subjects. A newly proposed deep learning model named interactive frequency convolutional neural network (IFNet) is leveraged and rigorously compared with the prevailing benchmark namely filter-bank common spatial pattern (FBCSP) for online MI decoding.

RESULTS: Through extensive online analysis, the deep learning decoder consistently outperforms the classical counterpart across various performance metrics. In particular, IFNet significantly improves the average online task accuracy by 20% and 27% in two sessions compared with FBCSP, respectively. Moreover, a significant cross-session training effect is observed by the IFNet model (P = 0.017) while not for the controlled method (P = 0.337). Further offline evaluations also demonstrate the superior performance of IFNet over state-of-the-art deep learning models. Moreover, we present unique behavioral and neurophysiological insights underlying online brain-machine interaction.

CONCLUSION: We present one of the first studies about online MI-BCIs using deep learning, achieving substantially enhanced online performance for continuous BCI control.

SIGNIFICANCE: This study suggests the good utility of deep learning in MI-BCIs and has implications for clinical applications such as stroke rehabilitation.},
}

@article {pmid40688356,
year = {2025},
author = {Sonntag, J and Yu, L and Wang, X and Schack, T},
title = {Neurophysiological predictors of deep learning based unilateral upper limb motor imagery classification.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1617748},
pmid = {40688356},
issn = {1662-5161},
abstract = {INTRODUCTION: Motor imagery-based brain-computer interfaces (BCIs) are a technique for decoding and classifying the intention of motor execution, solely based on imagined (rather than executed) movements. Although deep learning techniques have increased the potential of BCIs, the complexity of decoding unilateral upper limb motor imagery remains challenging. To understand whether neurophysiological features, which are directly related to neural mechanisms of motor imagery, might influence classification accuracy, most studies have largely leveraged traditional machine learning frameworks, leaving deep learning-based techniques underexplored.

METHODS: In this work, three different deep learning models from the literature (EEGNet, FBCNet, NFEEG) and two common spatial pattern-based machine learning classifiers (SVM, LDA) were used to classify imagined right elbow flexion and extension from participants using electroencephalography data. From two recorded resting states (eyes-open, eyes-closed), absolute and relative alpha and beta power of the frontal, fronto-central and central electrodes were used to predict the accuracy of the different classifiers.

RESULTS: The prediction of classifier accuracies by neurophysiological features revealed negative correlations between the relative alpha band and classifier accuracies and positive correlations between the absolute and relative beta band and classifiers accuracies. Most ipsilateral EEG channels yielded significant correlations with classifier accuracies, especially for the machine learning classifier.

DISCUSSION: This pattern contrasts with previous findings from bilateral MI paradigms, where contralateral alpha and beta activity were more influential. These inverted correlations suggest task-specific neurophysiological mechanisms in unilateral MI, emphasizing the role of ipsilateral inhibition and attentional processes.},
}

@article {pmid40685778,
year = {2025},
author = {Niu, X and Jiang, L and Hu, J and Jia, Y and Zhao, S and Ma, Y and Qiu, Z and Lian, Y and Zhu, E and Ni, J},
title = {Femtosecond Laser-Engineered Multifunctional Bio-Metasurface for the Inhibition of Thrombosis and Bacterial Infections.},
journal = {ACS applied materials & interfaces},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsami.5c05001},
pmid = {40685778},
issn = {1944-8252},
abstract = {Surface engineering is an effective strategy for addressing thrombosis and bacterial infection associated with blood-contacting implants (BCIs). However, most functional surfaces rely on a single mechanism and surface engineering poses substantial processing challenges for chemically inert and difficult-to-process materials such as pyrolytic carbon. Herein, a multifunctional bio-metasurface (LDT surface) synergizing liquid-repellent (L), drag-reduction (D), and turbulence-attenuation (T) strategies is proposed. The LDT surface is achieved through the synergistic interplay of surface texture-mediated flow control and interfacial lubrication effects. The textured LDT surface with microgrooves exhibits a hemodynamic modulation capability, exhibiting an effective turbulence-attenuation effect. The slippery coating on the LDT surface exhibits liquid-repellent and drag-reduction effects, regulating bio (blood and bacteria)-material interfacial interactions. The complex, hierarchical micro-groove, micro-hole, and nano-ripples/gaps/protrusions structures on the surface are fabricated on pyrolytic carbon via temporally shaped femtosecond laser texturing, followed by functional coating. The LDT surface exhibits excellent stability under continuous turbulent flow, with no toxic byproducts generated during processing. The computational fluid dynamics simulation results confirm that the streamwise microgrooves on the wall significantly attenuate turbulence. Compared to the pristine sample surface, the experimental results reveal a 98.2% reduction in platelet adhesion on the LDT surface, with a platelet adhesion rate of only 0.22% and no detected activated platelets, while denatured fibrinogen adhesion decreases by 55.3%. Moreover, the antiadhesion capacities of the LDT surface against Staphylococcus aureus and Escherichia coli improve by 99.4% and 98.4%, respectively, relative to the pristine sample surface, without viable residual bacteria or biofilm formation. The study offers a promising strategy to mitigate BCI-associated thrombosis and bacterial infection on BCIs, particularly those made from difficult-to-machine materials.},
}

@article {pmid40683976,
year = {2025},
author = {Joshi, A and Matharu, PS and Malviya, L and Kumar, M and Jadhav, A},
title = {Advancing EEG based stress detection using spiking neural networks and convolutional spiking neural networks.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {26267},
pmid = {40683976},
issn = {2045-2322},
mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; Brain-Computer Interfaces ; Machine Learning ; Wavelet Analysis ; Signal Processing, Computer-Assisted ; Algorithms ; Deep Learning ; },
abstract = {Accurate and efficient analysis of Electroencephalogram (EEG) signals is crucial for applications like neurological diagnosis and Brain-Computer Interfaces (BCI). Traditional methods often fall short in capturing the intricate temporal dynamics inherent in EEG data. This paper explores the use of Convolutional Spiking Neural Networks (CSNNs) to enhance EEG signal classification. We apply Discrete Wavelet Transform (DWT) for feature extraction and evaluate CSNN performance on the Physionet EEG dataset, benchmarking it against traditional deep learning and machine learning methods. The findings indicate that CSNNs achieve high accuracy, reaching 98.75% in 10-fold cross-validation, and an impressive F1 score of 98.60%. Notably, this F1-score represents an improvement over previous benchmarks, highlighting the effectiveness of our approach. Along with offering advantages in temporal precision and energy efficiency, CSNNs emerge as a promising solution for next-generation EEG analysis systems.},
}

*Electroencephalography/methods
Humans
*Neural Networks, Computer
Brain-Computer Interfaces
Machine Learning
Wavelet Analysis
Signal Processing, Computer-Assisted
Algorithms
Deep Learning

@article {pmid40683565,
year = {2025},
author = {Wu, Y and Lv, K and Zhao, Y and Yang, G and Hao, X and Zheng, B and Lv, C and An, Z and Zhou, H and Yuan, Q and Song, T},
title = {Prediction model for detrusor underactivity via noninvasive clinical parameters in men with benign prostatic hyperplasia.},
journal = {Urology},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.urology.2025.07.021},
pmid = {40683565},
issn = {1527-9995},
abstract = {OBJECTIVE: To develop a clinical prediction model for detrusor underactivity (DU) in patients with benign prostatic hyperplasia (BPH).

METHODS: A retrospective review was conducted on 546 individuals with BPH who had undergone urodynamic testing between January 2012 and May 2022. The bladder contractility index (BCI) was assessed using a pressure-flow study (PFS). Patients were categorized into DU (BCI <100, n=196) and non-DU (BCI ≥100, n=350) groups. Univariate logistic regression was initially performed to identify potential DU-related factors, followed by multivariate analysis to determine independent risk factors.

RESULTS: A predictive model for DU in patients with BPH was developed using the coefficient of these independent risk factors. Among the 546 cases, 196 (35.9%) were diagnosed with DU. Older age, smaller prostate volume, lower urgency symptom score, lower incomplete emptying symptom score, higher straining symptom score, and lower maximum flow rate (Qmax) were identified as independent predictors of DU in patients with BPH. The model demonstrated an area under the curve (AUC) of 0.78 (95% CI, 0.74-0.82), with internal validation yielding 0.75 (95% CI, 0.74-0.75).

CONCLUSIONS: We developed a predictive model that effectively estimates the DU probability in patients with BPH without requiring invasive PFS.},
}

@article {pmid40683191,
year = {2025},
author = {Li, Y and Sun, Y and Wan, F and Yuan, Z and Jung, TP and Wang, H},
title = {MetaNIRS: A general decoding framework for fNIRS based motor execution/imagery.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {192},
number = {},
pages = {107873},
doi = {10.1016/j.neunet.2025.107873},
pmid = {40683191},
issn = {1879-2782},
abstract = {Functional near-infrared spectroscopy (fNIRS) is a crucial brain activity monitoring tool with remarkable potential applications in brain-computer interfaces (BCI), particularly in rehabilitation therapy for disabilities. However, the performance of fNIRS-based BCI systems remains suboptimal, such as motor execution (ME) and motor imagery (MI) decoding. Inspired by the successful application of the PoolFormer framework in visual tasks, we first proposed a novel long-range dilation multilayer perceptron (LongDilMLP) to utilize the hemodynamic characteristics of fNIRS. Furthermore, the LongDilMLP was integrated with the PoolFormer framework, called as MetaNIRS in this study. The proposed framework MetaNIRS was employed for both ME and MI classification tasks, achieving rigorous validation of its effectiveness and practical applicability. To evaluate the performance of MetaNIRS, two publicly available ME datasets (A and C) and one self-collected MI dataset (B) were employed. The experimental results demonstrated that the average accuracy were 76.00 %, 57.45 %, and 84.14 %, with cross-subject accuracy of 77.24 %, 58.55 %, and 85.52 %, respectively. Moreover, sensitivity experiments of model parameters showed the robustness. Ablation experiments highlighted the significance of each MetaNIRS component and the efficacy of LongDilMLP over traditional MLP. Additionally, visualization techniques enhanced the interpretability of MetaNIRS, indicating the main contribution of the first half signals for classification. Using the first half of signals, the average accuracy only reduced 4.30 %, 1.69 %, and 1.11 %, respectively. These findings suggest that the superior performance of MetaNIRS, which provide an efficient general decoding framework for ME and MI.},
}

@article {pmid40683189,
year = {2025},
author = {Chen, H and Zeng, W and Chen, C and Cai, L and Wang, F and Shi, Y and Wang, L and Zhang, W and Li, Y and Yan, H and Siok, WT and Wang, N},
title = {EEG Emotion Copilot: Optimizing lightweight LLMs for emotional EEG interpretation with assisted medical record generation.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {192},
number = {},
pages = {107848},
doi = {10.1016/j.neunet.2025.107848},
pmid = {40683189},
issn = {1879-2782},
abstract = {In the fields of affective computing (AC) and brain-computer interface (BCI), the analysis of physiological and behavioral signals to discern individual emotional states has emerged as a critical research frontier. While deep learning-based approaches have made notable strides in EEG emotion recognition, particularly in feature extraction and pattern recognition, significant challenges persist in achieving end-to-end emotion computation, including rapid processing, individual adaptation, and seamless user interaction. This paper presents the EEG Emotion Copilot, a system optimizing a lightweight large language model (LLM) with 0.5B parameters operating in a local setting, which first recognizes emotional states directly from EEG signals, subsequently generates personalized diagnostic and treatment suggestions, and finally supports the automation of assisted electronic medical records. Specifically, we demonstrate the critical techniques in the novel data structure of prompt, model pruning and fine-tuning training, and deployment strategies aiming at improving performance and computational efficiency. Extensive experiments show that our optimized lightweight LLM-based copilot achieves an enhanced intuitive interface for participant interaction, superior accuracy of emotion recognition and assisted electronic medical records generation, in comparison to such models with similar scale parameters or large-scale parameters such as 1.5B, 1.8B, 3B and 7B. In summary, through these efforts, the proposed copilot is expected to advance the application of AC in the medical domain, offering innovative solution to mental health monitoring. The codes will be released at https://github.com/NZWANG/EEG_Emotion_Copilot.},
}

@article {pmid40681665,
year = {2025},
author = {Schrag, E and Comaduran Marquez, D and Kirton, A and Kinney-Lang, E},
title = {An investigation into the comfort and neural response of textured visual stimuli in pediatric SSVEP-based BCI.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {26168},
pmid = {40681665},
issn = {2045-2322},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Female ; Male ; Adolescent ; Child ; *Photic Stimulation/methods ; Electroencephalography/methods ; Child, Preschool ; Signal-To-Noise Ratio ; },
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) are widely used due to their reliability and possible training-free setup. Common SSVEP stimuli are high contrast and solidly colored, potentially causing discomfort and visual fatigue, particularly when high stimulation frequencies are employed. To address this, textured stimuli, which may evoke visual responses in higher processing systems, have been proposed as an alternative to conventional flashing stimuli. We evaluate the effectiveness of textured stimuli for SSVEP-based BCIs by examining both user comfort and neural responses across different EEG channel subsets. Neurotypical participants aged 5-18 (n = 35, 57% female) were exposed to traditional and textured stimuli at three frequencies (9, 14, and 33 Hz) and asked to report perceived comfort. While textured stimuli were consistently rated as more comfortable, especially at lower frequencies, signal-to-noise ratio analysis indicated that they did not enhance neural responses compared to conventional stimuli. Classification accuracy was driven primarily by stimulation frequency rather than stimulus type and there was a sharp decline in accuracy at 33 Hz. These findings suggest that while textured stimuli improve user comfort, their utility in enhancing BCI performance remains unclear, warranting further investigation into stimulus design for SSVEP-based BCIs.},
}

Humans
*Brain-Computer Interfaces
*Evoked Potentials, Visual/physiology
Female
Male
Adolescent
Child
*Photic Stimulation/methods
Electroencephalography/methods
Child, Preschool
Signal-To-Noise Ratio

@article {pmid40681115,
year = {2025},
author = {Deepika, D and Rekha, G},
title = {Multi-class mental Task Classification based Brain-Computer Interface using Improved Remora depthwise convolutional adaptive neuro-fuzzy inference network model.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110536},
doi = {10.1016/j.jneumeth.2025.110536},
pmid = {40681115},
issn = {1872-678X},
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) offer a promising avenue for individuals with severe motor disabilities to interact with the world. By decoding brain signals, BCIs can enable users to control devices and communicate thoughts. However, challenges such as noise in EEG signals and limited data availability hinder the development of accurate and reliable BCI systems. Nonetheless, problems persist, including limited data availability, noisy EEG signals, real-time performance limitations, and reduced classification accuracy.

NEW METHOD: To overcome this, the present work proposes an efficient Multi-Class Mental Task Classification based BCI using deep learning techniques. Initially, the obtained EEG data is pre-processed with a Finite Linear Haar wavelet-based Filtering (FLHF) technique to remove disturbances in EEG data. Afterwards, optimal feature extraction utilizes a Hybrid dynamic centre binary pattern and multi-threshold-based ternary pattern (H-DCBP-MTTP) technique to extract characteristics from pre-processed EEG data. Finally, the Improved Remora depthwise convolutional adaptive neuro-fuzzy inference network (IRDCANFIN) model is used to classify the mental tasks. To improve classification results, the model's parameters are fine-tuned using an Improved Remora optimization approach (IROA).

RESULTS: The proposed approach's performance is examined using the BCI laboratory dataset and the EEG Psychiatric Disorders Dataset, which yield accuracy results of 99.3% and 99.56%, respectively. Furthermore, evaluation results show that the proposed approach outperforms existing models.

Compared to existing models, such as DQN with a 1D-CNN-LSTM, GSP-ML, Shallow 1D-CNN, KNN, and SVM, and the proposed approach yields effective results in terms of accuracy, robustness, and computational efficiency.

CONCLUSION: The proposed IRDCANFIN classifier is used to classify multiple classes of mental tasks like baseline, counting, multiplication, letter composing, and rotation.},
}

@article {pmid40681114,
year = {2025},
author = {Zhang, R and Li, Z and Pan, X and Cui, H and Chen, X},
title = {Hybrid BCI for upper limb rehabilitation: integrating MI with peripheral field SSVEP stimulation.},
journal = {Journal of neuroscience methods},
volume = {423},
number = {},
pages = {110537},
doi = {10.1016/j.jneumeth.2025.110537},
pmid = {40681114},
issn = {1872-678X},
abstract = {BACKGROUND: Rehabilitation systems based on brain-computer interfaces (BCIs) hold significant potential for stroke patients. Existing systems, predominantly relying on motor imagery (MI), have room for improvement in both performance and user comfort. This study aims to enhance these aspects by developing a hybrid BCI system integrating MI with steady-state visual evoked potentials (SSVEPs) elicited by peripheral visual field stimulation.

NEW METHODS: The system is coupled with a soft robotic hand for feedback, forming a closed-loop framework. The design incorporates concentric rings with 7° and 10° eccentricities as peripheral stimuli, flashing at frequencies of 34 Hz and 35 Hz for left and right sides, respectively, to evoke SSVEPs. A central video (304 ×304 pixels) of left-hand/right-hand grasping motions guides subjects in performing synchronized MI tasks simply by focusing on it, which could also complete the SSVEP task.

RESULTS: The offline results of 11 subjects showed that the classification result of MI was 70.65 ± 3.38 %, and the SSVEP result was 96.04 ± 3.33 %, and the fusion result reached 96.23 ± 3.21 %, which confirmed the validity of the fusion method. The online experiment of 11 subjects achieved a result of 97.12 ± 2.09 %, validating the feasibility of the system.

The proposed system improves the comfort level while ensuring the performance of the system as compared to the existing systems.

CONCLUSION: The feasibility of the proposed system was verified by offline and online experiments to advance the clinical applications.},
}

@article {pmid40680338,
year = {2025},
author = {Sun, R and Ma, D and Pan, G},
title = {Post-training quantization for efficient ANN-SNN conversion.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {191},
number = {},
pages = {107832},
doi = {10.1016/j.neunet.2025.107832},
pmid = {40680338},
issn = {1879-2782},
abstract = {Spiking Neural Networks (SNNs), inspired by the behavior of biological neurons, offer a promising direction for next-generation neural computing. Two primary methodologies have emerged for training deep SNNs: Direct Training, which optimizes SNNs using surrogate gradients, and ANN-to-SNN Conversion, which derives SNNs from Artificial Neural Networks (ANNs). In this work, we focus on the latter and investigate the conversion error that arises during the transformation. We provide a theoretical analysis showing that channel-wise thresholds are more effective than traditional layer-wise thresholds in mitigating this error. To achieve this efficiently, we leverage post-training quantization (PTQ), which enables calibration using only a small dataset without requiring retraining. Compared to conventional direct training and ANN-to-SNN conversion methods, our approach significantly reduces training time while improving accuracy on both static image and neuromorphic datasets.},
}

@article {pmid40679899,
year = {2025},
author = {Kim, YS and Han, H and Kim, CU and Choi, SI and Kim, MY and Im, CH},
title = {Performance enhancement of steady-state visual evoked field-based brain-computer interfaces incorporating MEG source imaging.},
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.2025.3590576},
pmid = {40679899},
issn = {1558-0210},
abstract = {Recent advancements in helmet-type magnetoencephalography (MEG) systems that operate without liquid helium, such as optically pumped magnetometer (OPM)-based MEG, have increased interest in MEG-based brain-computer interfaces (BCIs). Among various BCI paradigms, steady-state visual evoked field (SSVEF)-based BCIs have been actively studied owing to their high information transfer rate (ITR) and low demand for calibration sessions. Although MEG provides excellent spatial resolution and whole-head coverage, conventional algorithms such as the filter bank-driven multivariate synchronization index (FBMSI) do not fully exploit these advantages. To overcome this limitation, this study employed MEG source imaging to utilize information from whole-head MEG recordings fully and developed a novel weighting method called the averaged source location-based weighting (ASLW). ASLW leverages the averaged source locations of SSVEF signals to enhance BCI performance. Experimental results with 20 participants demonstrated that integrating ASLW with the FBMSI algorithm (ASLW-FBMSI) significantly improved both the classification accuracy and ITR across all tested window sizes. Notably, the largest performance gains included a 13.89% accuracy improvement at a 3-s window size and a 13.12 bits/min increase in ITR at a 2.5-s window size. Additionally, the ASLW-FBMSI algorithm exhibited a short processing delay of 0.107 s at a 4-s data length and was successfully validated in online BCI experiments with 20 participants. Although tested in SQUID-MEG in this study, our findings demonstrate the effectiveness of ASLW in significantly enhancing the overall performance of SSVEF-based BCIs.},
}

@article {pmid40678831,
year = {2025},
author = {Wang, J and Chen, H and Wang, X},
title = {Tirzepatide Induces Ferroptosis in Glioblastoma Cell Lines via the SOX2/SLC7A11 Axis: A Potential Therapeutic Strategy for Glioma Treatment.},
journal = {Journal of biochemical and molecular toxicology},
volume = {39},
number = {8},
pages = {e70392},
doi = {10.1002/jbt.70392},
pmid = {40678831},
issn = {1099-0461},
support = {//This study was supported by the Fifth Affiliated Hospital of Zhengzhou University./ ; },
mesh = {*Ferroptosis/drug effects ; Humans ; Cell Line, Tumor ; *Amino Acid Transport System y+/metabolism ; *SOXB1 Transcription Factors/metabolism ; *Glioblastoma/metabolism/drug therapy/pathology ; Cell Proliferation/drug effects ; Lipid Peroxidation/drug effects ; *Neoplasm Proteins/metabolism ; *Brain Neoplasms/metabolism/drug therapy/pathology ; Cell Movement/drug effects ; Tirzepatide ; },
abstract = {Tirzepatide, a dual agonist for glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) receptors used in type 2 diabetes and obesity management, was investigated for its effects on glioma cells, focusing on its potential to induce ferroptosis. Tirzepatide treatment significantly inhibited glioma cell proliferation and migration, as demonstrated by the CCK-8 and Transwell migration assays. Tirzepatide also induced lipid peroxidation, evidenced by increased ROS levels, elevated MDA production, and reduced SOD activity, while the GSH/GSSG ratio was decreased, reflecting oxidative stress. Ferroptosis was further confirmed by increased Fe[2+] concentrations and alterations in iron metabolism-related genes (Ferritin and TFR1) and lipid metabolism-related genes (ACSL4 and GPX4). Tirzepatide also inhibited the SOX2/SLC7A11 axis, which plays a critical role in resisting ferroptosis. Fer-1, a ferroptosis inhibitor, or SOX2 overexpression, markedly reduced Tirzepatide's effects on proliferation, migration, lipid peroxidation, and ferroptosis, highlighting the critical role of the SOX2/SLC7A11 axis in mediating these effects. These findings indicate that Tirzepatide inhibits glioma cell growth by inducing ferroptosis, presenting a potential therapeutic approach for glioma.},
}

*Ferroptosis/drug effects
Humans
Cell Line, Tumor
*Amino Acid Transport System y+/metabolism
*SOXB1 Transcription Factors/metabolism
*Glioblastoma/metabolism/drug therapy/pathology
Cell Proliferation/drug effects
Lipid Peroxidation/drug effects
*Neoplasm Proteins/metabolism
*Brain Neoplasms/metabolism/drug therapy/pathology
Cell Movement/drug effects
Tirzepatide

@article {pmid40678346,
year = {2025},
author = {Tsay, JJ and Velez, A and Collazo, D and Laniado, I and Bessich, J and Murthy, V and DeMaio, A and Rafeq, S and Kwok, B and Darawshy, F and Pillai, R and Wong, K and Li, Y and Schluger, R and Lukovnikova, A and Roldan, S and Blaisdell, M and Paz, F and Krolikowski, K and Gershner, K and Liu, Y and Gong, J and Borghi, S and Zhou, F and Tsirigos, A and Pass, H and Segal, LN and Sterman, DH},
title = {A Phase I Dose-Escalation Clinical Trial of Bronchoscopic Cryoimmunotherapy in Advanced-Stage NSCLC.},
journal = {JTO clinical and research reports},
volume = {6},
number = {8},
pages = {100849},
pmid = {40678346},
issn = {2666-3643},
abstract = {INTRODUCTION: Outcomes for NSCLC remain suboptimal. Recent data suggest that cryoablation can generate antitumor immune effects. In this first-in-human phase I clinical trial, we investigated the safety and feasibility of bronchoscopic cryoimmunotherapy (BCI) delivered during standard-of-care bronchoscopy and explored associated systemic immune responses.

METHODS: Subjects with known or suspected advanced-stage NSCLC were recruited. BCI was delivered in dose-escalated freeze-thaw cycles to determine maximum dose tolerance. Feasibility assessment was determined with a pre-set goal of achieving successful BCI in more than or equal to 80% of subjects. Safety was assessed by review of BCI-related complications, including grades 2 to 3 bleeding, pneumothorax requiring intervention, and National Cancer Institute Common Terminology Criteria for Adverse Events grade 3 to 5 adverse events. Pre- and post-BCI blood samples were collected to explore changes in the systemic immune profile.

RESULTS: Subjects with predominantly clinical TNM stage 3 or 4 adenocarcinoma or squamous cell carcinoma were enrolled. We reached the maximum dose of 30 seconds with 100% feasibility and no BCI-related adverse events. In peripheral blood analysis, we observed a significant decrease in derived neutrophil-to-lymphocyte ratio in the high-dose BCI group in comparison to the low-dose BCI cohort. We also observed increases in inflammatory cytokines-GM-CSF, IFN-γ, IL-1β, IL-17A, and IL-2-and effector memory T cells post-BCI.

CONCLUSION: BCI is safe and feasible. In addition, we provide preliminary evidence that at higher dose levels there is a systemic immune response consistent with a cytotoxic profile. Further immune analyses will determine the potential of BCI as an adjunctive therapy in combination with immune checkpoint inhibition in NSCLC treatment.},
}

@article {pmid40677333,
year = {2025},
author = {Zheng, ZW and Xu, MH and Fan, LN and Wang, RM and Xu, WQ and Yang, GM and Guo, LY and Liu, C and Dong, Y and Wu, ZY},
title = {Renal Impairment in Wilson's Disease.},
journal = {Kidney international reports},
volume = {10},
number = {7},
pages = {2453-2456},
pmid = {40677333},
issn = {2468-0249},
}

@article {pmid40674496,
year = {2025},
author = {Nair, A},
title = {Unraveling the emergent chorus of the mind: Machine learning reveals how a hidden neural code orchestrates diverse emotion states.},
journal = {Science (New York, N.Y.)},
volume = {389},
number = {6757},
pages = {245},
doi = {10.1126/science.adx7811},
pmid = {40674496},
issn = {1095-9203},
mesh = {*Emotions/physiology ; *Machine Learning ; Humans ; *Brain/physiology ; *Neurons/physiology ; },
abstract = {Machine learning reveals how a hidden neural code orchestrates diverse emotion states.},
}

*Emotions/physiology
*Machine Learning
Humans
*Brain/physiology
*Neurons/physiology

@article {pmid40672704,
year = {2025},
author = {Rekrut, M and Ihl, J and Jungbluth, T and Krüger, A},
title = {How low can you go: evaluating electrode reduction methods for EEG-based speech imagery BCIs.},
journal = {Frontiers in neuroergonomics},
volume = {6},
number = {},
pages = {1578586},
pmid = {40672704},
issn = {2673-6195},
abstract = {Speech imagery brain-computer interfaces (SI-BCIs) aim to decode imagined speech from brain activity and have been successfully established using non-invasive brain measures such as electroencephalography (EEG). However, current EEG-based SI-BCIs predominantly rely on high-resolution systems with 64 or more electrodes, making them cumbersome to set up and impractical for real-world use. In this study, we evaluated several electrode reduction algorithms in combination with various feature extraction and classification methods across three distinct EEG-based speech imagery datasets to identify the optimal number and position of electrodes for SI-BCIs. Our results showed that, across all datasets, the original 64 channels could be reduced by 50% without a significant performance loss in classification accuracy. Furthermore, the relevant areas were not limited to the left hemisphere, widely known to be responsible for speech production and comprehension, but were distributed across the cortex. However, we could not identify a consistent set of optimal electrode positions across datasets, indicating that electrode configurations are highly subject-specific and should be individually tailored. Nonetheless, our findings support the move away from extensive and costly high-resolution systems toward more compact, user-specific setups, facilitating the transition of SI-BCIs from laboratory settings to real-world applications.},
}

@article {pmid40672675,
year = {2025},
author = {Zhang, C and Wang, Y and Wang, X},
title = {Reimagining Neuropsychiatric and Neurological Disorders through the Lens of Brain Network Dynamics: Psychedelics as Catalysts for System-Level Plasticity.},
journal = {ACS pharmacology & translational science},
volume = {8},
number = {7},
pages = {2308-2311},
pmid = {40672675},
issn = {2575-9108},
abstract = {Neuropsychiatric disorders reflect disruptions in brain network dynamics along an "order-complexity-chaos" continuum. Psychedelics may therapeutically increase neural entropy, disrupt maladaptive patterns, and promote network reorganization. This system-level framework emphasizes dynamic connectome remodeling over static molecular correction, offering a novel strategy for treating psychiatric and neurological conditions through controlled neural destabilization and reconnection.},
}

@article {pmid40672502,
year = {2025},
author = {Chaichanasittikarn, O and Diaz, L and Thomas, N and Candrea, D and Luo, S and Nathan, K and Tenore, FV and Fifer, MS and Crone, NE and Christie, B and Osborn, LE},
title = {High-gamma electrocorticography activity represents perceived vibration intensity in human somatosensory cortex.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.07.09.25331186},
pmid = {40672502},
abstract = {Haptic feedback can play a useful role in rehabilitation and brain-computer interface applications by providing users with information about their system or performance. One challenge delivering tactile stimulation is not knowing how the haptic sensation is actually perceived, irrespective of the stimulation amplitude, during real-world use and beyond controlled psychophysical experiments. In a participant with chronically implanted electrocorticography arrays, we observed that perceived intensity of haptic vibration on the fingertips was represented in the high-gamma (HG) frequency band (70-170 Hz) in the somatosensory cortex. The five fingers of the participant's right hand were represented by distinct channels in the implanted array and modulated by the vibration amplitude at the fingertips. Although it reliably varied with the vibration amplitude, we found that HG activity had a stronger relationship with the actual perceived intensity of haptic stimulation (r s = 0.45 , p < 10 [-6]). These results demonstrate that neural signals, specifically HG activity, in the somatosensory cortex can represent qualities of perceived haptic intensity regardless of the stimulation amplitude, which could enable a new way to passively quantify or ensure effective haptic feedback to a user.},
}

@article {pmid40672280,
year = {2025},
author = {Lei, T and Scheid, MR and Glaser, JI and Slutzky, MW},
title = {Active Dissociation of Intracortical Spiking and High Gamma Activity.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.07.10.663559},
pmid = {40672280},
issn = {2692-8205},
abstract = {Cortical high gamma activity (HGA) is used in many scientific investigations, yet its biophysical source is a matter of debate. Two leading hypotheses are that HGA predominantly represents summed postsynaptic potentials or-more commonly- predominantly represents summed local spikes. If the latter were true, the nearest neurons to an electrode should contribute most to HGA recorded on that electrode. We trained subjects to decouple spiking from HGA on a single electrode using a brain-machine interface. Their ability to decouple them indicated that HGA is not primarily generated by summed local spiking. Instead, HGA correlated with neuronal population co-firing of neurons that were widely distributed across millimeters. The neuronal spikes that contributed more to this co-firing also contributed more to, and preceded, spike-triggered HGA. These results suggest that HGA arises predominantly from summed postsynaptic potentials triggered by synchronous co-firing of widely distributed neurons.},
}

@article {pmid40668700,
year = {2025},
author = {Robinson, JT},
title = {Making Heads and Tails of the Coming Era of Neural Devices, Could Moore's Law Address the Declining Mental Health Trend.},
journal = {IEEE pulse},
volume = {16},
number = {3},
pages = {50-55},
doi = {10.1109/MPULS.2025.3572593},
pmid = {40668700},
issn = {2154-2317},
mesh = {Humans ; *Brain-Computer Interfaces/trends ; *Mental Health ; *Transcranial Magnetic Stimulation ; },
abstract = {Despite major advances in medicine and technology, mental health outcomes have declined globally over the past several decades. Fortunately we are in the early phases of exponential growth neurotech similar to Moore's Law. These emerging neural devices may provide a solution to the growing mental health crisis. Clinical data shows promising outcomes from technologies such as transcranial magnetic stimulation (TMS) leading to exponential improvement in performance improvements and cost reductions. As a result, neurotechnology could follow a similar path to personal computing going from a handful of niche markets to ubiquity over the next decade. Indeed, next generation therapeutic brain-computer interfaces (BCIs)-particularly minimally invasive implants-could become mass-market solutions for regulating mental states. The future may be one where neural devices help individuals thrive in an increasingly complex world, not by augmenting human intelligence but by enhancing emotional well-being and preserving the most precious aspects of our humanity.},
}

Humans
*Brain-Computer Interfaces/trends
*Mental Health
*Transcranial Magnetic Stimulation