@article {pmid41299162,
year = {2025},
author = {Meng, L and Song, Z and Lu, J},
title = {Brain-imager: a multimodal framework for image reconstruction and captioning from human brain activity.},
journal = {Brain informatics},
volume = {12},
number = {1},
pages = {32},
pmid = {41299162},
issn = {2198-4018},
support = {62073061//National Natural Science Foundation of China/ ; 2025A1515011602//Guangdong Basic and Applied Basic Research Foundation/ ; },
abstract = {OBJECTIVE: The reconstruction of visual stimuli and captions from brain activity offers a distinctive viewpoint on how perception reconstructs the external world within neural dynamics. Despite considerable advancements in deep generative models in recent years, simultaneously generating images and captions with both detailed accuracy and semantic consistency remains a significant challenge.

METHODS: We introduce panoptic segmentation and generative semantics for the first time, offering enhanced, multi-level data support and a novel perspective in the domain of brain decoding. Using multi-scale fusion techniques, we integrate pixel features from natural images with structural features from panoptic segmentation, creating a state-of-the-art "initial guess." Building upon the neural paradigm that we discovered, we propose an innovative semantic connection strategy to guide image reconstruction. Additionally, by fine-tuning visual semantics within the encoded space compressed by a language model and further combining our advanced retrieval module with the comprehension capabilities of large language models (LLMs), we generate high-quality brain captions.

RESULTS: Experimental results demonstrate that we surpass current methods in visual decoding and brain captioning tasks. We offer a webpage to showcase the results: www.neuai4science.cn:5001/brain_visual_decode .

CONCLUSION: Our proposed Brain-Imager framework, which incorporates multi-level data and semantic guidance, sets a new standard in the domain.

SIGNIFICANCE: This work provides a novel perspective on the relationship between text and image semantics and the visual pathways of the human brain, with potential applications in downstream tasks such as brain-computer interfaces. Additionally, our code is publicly available at https://github.com/songqianyi01/Brain-Imager .},
}

@article {pmid41298548,
year = {2025},
author = {Ciferri, M and Ferrante, M and Toschi, N},
title = {Reconstructing music perception from brain activity using a prior guided diffusion model.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {42108},
pmid = {41298548},
issn = {2045-2322},
support = {101070908//European Union's European Innovation Council/ ; 101017727//European Union's Horizon 2020 Research and Innovation Programme/ ; },
mesh = {Humans ; *Music ; *Auditory Perception/physiology ; *Brain/physiology/diagnostic imaging ; Magnetic Resonance Imaging ; Bayes Theorem ; Male ; Female ; Adult ; Brain Mapping/methods ; Young Adult ; Models, Neurological ; Acoustic Stimulation ; },
abstract = {Reconstructing music directly from brain activity provides insight into the neural representations underlying auditory processing and paves the way for future brain-computer interfaces. We introduce a fully data-driven pipeline that combines cross-subject functional alignment with bayesian decoding in the latent space of a diffusion-based audio generator. Functional alignment projects individual fMRI responses onto a shared representational manifold, increasing the performance of cross-participant accuracy with respect to anatomically normalized baselines. A bayesian search over latent trajectories then selects the most plausible waveform candidate, stabilizing reconstructions against neural noise. Crucially, we bridge CLAP's multi-modal embeddings to music-domain latents through a dedicated aligner, eliminating the need for hand-crafted captions and preserving the intrinsic structure of musical features. Evaluated on ten diverse genres, the model achieves a cross-subject-averaged identification accuracy of [Formula: see text], and produces audio that human listeners recognize above chance in 85.7% of trials. Voxel-wise analyses locate the predictive signal within a bilateral circuit spanning early auditory, inferior-frontal, and premotor cortices, consistent with hierarchical and sensorimotor theories of music perception. The framework establishes a principled bridge between generative audio models and cognitive neuroscience.},
}

Humans
*Music
*Auditory Perception/physiology
*Brain/physiology/diagnostic imaging
Magnetic Resonance Imaging
Bayes Theorem
Male
Female
Adult
Brain Mapping/methods
Young Adult
Models, Neurological
Acoustic Stimulation

@article {pmid41296974,
year = {2025},
author = {Zhang, J and Zhang, L and Mu, F and Huang, Z and Zou, C and Huang, R and Wang, C and Cheng, H},
title = {Spatiotemporal Dynamics Modeling of Brain Activity for Human-Robot Cognitive Interaction: ADistributed-Lumped Parameter System Framework.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3631130},
pmid = {41296974},
issn = {2162-2388},
abstract = {This article investigates the system modeling problem for the dynamical process of human brain activity in human-robot cognitive interaction (HRCI). An important novelty of the proposed approaches is to build a computational model of a human-distributed robot-lumped parameter system (HDRLPS) that describes the inherent dynamical principle of human brain activity (with spatiotemporal-varying characteristic) undergoing the interaction between the intrinsic cognitive dynamics and extrinsic robot stimuli. A deterministic learning (DL)-based spatiotemporal dynamics identification scheme is proposed to accurately identify the spatiotemporal dynamics of HDRLS and obtain the associated knowledge as a constant radial basis functional neural network (RBF NN) model. A spatiotemporal dynamics estimator is designed with this model, which can accurately evaluate and monitor the dynamical process of human brain activity in real-time HRCI by the generated dynamics-synchronized state. The effectiveness and practicability of the approaches in the dynamics identification and evaluation for the human brain activity in HRCI are validated by the thorough analysis, including the mathematical proof, the simulation study, and the brain-computer interface (BCI) experiment using publicly available datasets. Our method is compared with state-of-the-art (SOTA) methods, such as LGGNet, EEGNet, Tsception, EEG-Deformer, EEG-Transformer, and EEGViT. The results show that our method can outperform these methods with better recognition accuracy and macro- $F1$ scores. The source code can be found at: https://github.com/alonexing/source_code/tree/master.},
}

@article {pmid41296962,
year = {2025},
author = {Wang, J and Bi, L and Wei, Y and Fei, W and Liu, H and Miao, D},
title = {EEG-Based Movement Decoding in Motor-Impaired Patients by Extracting and Aligning Neural Patterns with Healthy Individuals.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3637053},
pmid = {41296962},
issn = {2168-2208},
abstract = {Decoding human movement intentions from electroencephalography (EEG) signals is critical for brain-computer interface (BCI) applications in motor neurorehabilitation, active assistance, and functional augmentation. However, current BCI models face two challenges for motor-impaired patients: 1) prolonged EEG data collection from patients is difficult; 2) differences in brain functional structures and motor behaviors between healthy individuals and patients limit the generalizability of models trained on healthy individuals' EEG data. To address these challenges, this study proposes a transfer learning-based model, TL-ME, to bridge the gap between healthy individuals' and patients' EEG data and improve movement decoding accuracy for patients. TL-ME integrates an attention-based feature extractor, adversarial domain discriminator, multi-source selection, and movement classifier to transfer knowledge from healthy individuals' EEG data (source domain) to patients' EEG data (target domain). Temporal and spectral visualizations are used to inspect brain activation patterns for shared motor tasks between healthy individuals and patients. Experimental results show a 10.8% improvement in upper-limb movement decoding's accuracy using TL-ME, with each module contributing to performance gains. Visualization analyses also demonstrate similar brain activation patterns across domains, validating the transferability of healthy individuals' EEG data to patient-specific models. This work introduces a novel cross-population transfer learning approach that leverages healthy individuals' EEG data to enhance neural decoding for motor-impaired patients, bridging the gap between experimental studies and real-world applications in BCI-based neurorehabilitation.},
}

@article {pmid41295901,
year = {2025},
author = {Turner, S and Yadav, P and Morrin, H and Bhat, A},
title = {The future of psychiatry: clinical practice, diagnosis, and treatment.},
journal = {International review of psychiatry (Abingdon, England)},
volume = {},
number = {},
pages = {1-22},
doi = {10.1080/09540261.2025.2594523},
pmid = {41295901},
issn = {1369-1627},
abstract = {This paper overviews the future of clinical practice in psychiatry, covering diagnosis, treatment, and public health. We consider recent advances and new controversies as psychiatry moves from a categorical to a dimensional approach to diagnosing and classifying mental illness; as well as the potential pitfalls of overdiagnosis, underdiagnosis, and misdiagnosis. We also review some of the most exciting new developments in treatment modalities, such as psychedelic treatments, ketamine, and new antipsychotics. The potential of interventional psychiatry using technology, and review techniques including neuromodulation, neurofeedback, brain-computer interfaces, AI-assisted psychotherapy, and virtual reality is also discussed in the context of future of public mental health strategy, including the important issue of online disinformation and how it can influence the public's understanding of mental health. Finally, we consider the evolving understanding of addiction, particularly behavioural and technological addictions. We conclude with a brief discussion of how best to influence the political leadership in using these new advances to develop evidence-based, scientifically-informed healthcare policy.},
}

@article {pmid41294772,
year = {2025},
author = {Qi, L and Wang, Y and Liang, X},
title = {Emerging Implantable Sensor Technologies at the Intersection of Engineering and Brain Science.},
journal = {Biosensors},
volume = {15},
number = {11},
pages = {},
doi = {10.3390/bios15110762},
pmid = {41294772},
issn = {2079-6374},
support = {32200882//National Natural Science Foundation of China/ ; 5250071402//National Natural Science Foundation of China/ ; 2024YQB048//Young Doctoral Program of Xinqiao Hspital/ ; 0//Chongqing Brain Science Key Project/ ; },
mesh = {Humans ; *Biosensing Techniques ; *Brain/physiology ; Brain-Computer Interfaces ; *Prostheses and Implants ; Transistors, Electronic ; Animals ; },
abstract = {Advances in implantable sensor technologies are revolutionizing the landscape of brain science by enabling chronic, precise, and multimodal interfacing with neural tissues. With the convergence of material science, electronics, and neurobiology, flexible, wireless, bioresorbable, and multimodal sensors are expanding the frontiers of diagnosis, therapy, and brain-machine interfacing. This review presents the latest breakthroughs in implantable neural sensor technologies, emphasizing bio-integration, signal fidelity, and functional adaptability. We highlight innovations such as CMOS-integrated flexible probes, internal ion-gated organic electrochemical transistors (IGTs), multimodal neurotransmitter-electrophysiology sensors, and wireless energy systems. Finally, we discuss the clinical potential, translational challenges, and future directions for brain science and neuroengineering. We further benchmark transduction and analytical performance in physiological media and outline in vivo calibration, antifouling/packaging, and on-node data-efficient processing for long-term stability.},
}

Humans
*Biosensing Techniques
*Brain/physiology
Brain-Computer Interfaces
*Prostheses and Implants
Transistors, Electronic
Animals

@article {pmid41294401,
year = {2025},
author = {Gkintoni, E and Halkiopoulos, C},
title = {Mapping EEG Metrics to Human Affective and Cognitive Models: An Interdisciplinary Scoping Review from a Cognitive Neuroscience Perspective.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {11},
pages = {},
doi = {10.3390/biomimetics10110730},
pmid = {41294401},
issn = {2313-7673},
abstract = {Background: Electroencephalography (EEG) offers millisecond-precision measurement of neural oscillations underlying human cognition and emotion. Despite extensive research, systematic frameworks mapping EEG metrics to psychological constructs remain fragmented. Objective: This interdisciplinary scoping review synthesizes current knowledge linking EEG signatures to affective and cognitive models from a neuroscience perspective. Methods: We examined empirical studies employing diverse EEG methodologies, from traditional spectral analysis to deep learning approaches, across laboratory and naturalistic settings. Results: Affective states manifest through distinct frequency-specific patterns: frontal alpha asymmetry (8-13 Hz) reliably indexes emotional valence with 75-85% classification accuracy, while arousal correlates with widespread beta/gamma power changes. Cognitive processes show characteristic signatures: frontal-midline theta (4-8 Hz) increases linearly with working memory load, alpha suppression marks attentional engagement, and theta/beta ratios provide robust cognitive load indices. Machine learning approaches achieve 85-98% accuracy for subject identification and 70-95% for state classification. However, significant challenges persist: spatial resolution remains limited (2-3 cm), inter-individual variability is substantial (alpha peak frequency: 7-14 Hz range), and overlapping signatures compromise diagnostic specificity across neuropsychiatric conditions. Evidence strongly supports integrated rather than segregated processing, with cross-frequency coupling mechanisms coordinating affective-cognitive interactions. Conclusions: While EEG-based assessment of mental states shows considerable promise for clinical diagnosis, brain-computer interfaces, and adaptive technologies, realizing this potential requires addressing technical limitations, standardizing methodologies, and establishing ethical frameworks for neural data privacy. Progress demands convergent approaches combining technological innovation with theoretical sophistication and ethical consideration.},
}

@article {pmid41293812,
year = {2025},
author = {Ivanov, N and Wong, M and Chau, T},
title = {A Multi-Class Intra-Trial Trajectory Analysis Technique to Visualize and Quantify Variability of Mental Imagery EEG Signals.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2550075},
doi = {10.1142/S0129065725500753},
pmid = {41293812},
issn = {1793-6462},
abstract = {High inter- and intra-individual variation is a prominent characteristic of electroencephalography (EEG) signals and a significant inhibitor to the practical implementation of brain-computer interfaces (BCIs) outside of research laboratories. However, a few methods exist to assess EEG signal variability. Here, a novel multi-class intra-trial trajectory (MITT) analysis to study EEG variability for mental imagery BCIs is presented. The methods yield insight into different aspects of signal variation, specifically (i) inter-individual, (ii) inter-task, (iii) inter-trial, and (iv) intra-trial. A novel representation of the time evolution of EEG signals was developed. Task trials were segmented into short temporal windows and represented in a feature space derived from unsupervised clustering of trial covariance matrices. Using this representation, temporal trajectories through the feature space were constructed. Two metrics were defined to assess user performance based on these trajectories: (1) InterTaskDiff, based on time-varying distances between the mean trajectories of different tasks, and (2) InterTrialVar, which measured the inter-trial variation of the temporal trajectories along the feature dimensions. Analysis of three-class BCI data from 14 adolescents revealed both metrics correlated significantly with classification results. Further analysis of intra-trial trajectories suggested the existence of characteristic task- and user-specific temporal dynamics. The participant-specific insights provided by MITT analysis could be used to overcome EEG-variability challenges impeding practical implementation of BCIs by elucidating avenues to improve user training feedback or selection of user-optimal classifiers and hyperparameters.},
}

@article {pmid41293116,
year = {2025},
author = {Zheng, M and Qian, Z and Zhao, T},
title = {Motor imagery EEG classification via wavelet-packet synthetic augmentation and entropy-based channel selection.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1689647},
pmid = {41293116},
issn = {1662-4548},
abstract = {INTRODUCTION: Motor-imagery (MI) brain-computer interfaces often suffer from limited EEG datasets and redundant channels, hampering both accuracy and clinical usability. We address these bottlenecks by presenting a unified framework that simultaneously boosts classification performance, reduces the number of required sensors, and eliminates the need for extra recordings.

METHODS: A three-stage pipeline is proposed. (1) Wavelet-packet decomposition (WPD) partitions each MI class into low-variance "stable" and high-variance "variant" trials; sub-band swapping between matched pairs generates synthetic trials that preserve event-related desynchronization/synchronization signatures. (2) Channel selection uses wavelet-packet energy entropy (WPEE) to quantify both spectral-energy complexity and class-separability; the top-ranked leads are retained. (3) A lightweight multi-branch network extracts multi-scale temporal features through parallel dilated convolutions, refines spatial patterns via depth-wise convolutions, and feeds the fused spatiotemporal tensor to a Transformer encoder with multi-head self-attention; soft-voted fully-connected layers deliver robust class labels.

RESULTS: On BCI Competition IV 2a and PhysioNet MI datasets the proposed method achieves 86.81 and 86.64% mean accuracies, respectively, while removing 27% of sensors. These results outperform the same network trained on all 22 channels, and paired t-tests confirm significant improvements (p < 0.01).

DISCUSSION: Integrating WPD-based augmentation with WPEE-driven channel selection yields higher MI decoding accuracy with fewer channels and without extra recordings. The framework offers a computationally efficient, clinically viable paradigm for enhanced EEG classification in resource-constrained settings.},
}

@article {pmid41292958,
year = {2025},
author = {Lichenstein, SD and Weng, Y and Robinson, H and Rodriguez, L and Babaeianjelodar, M and Maynard, J and Metayer, M and Suneja, S and Horien, C and Greene, AS and Constable, RT and Moore, TM and Barzilay, R and Yip, SW and Keller, AS},
title = {Multivariate environmental exposures are reflected in whole-brain functional connectivity and cognition in youth.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.11.13.688261},
pmid = {41292958},
issn = {2692-8205},
abstract = {Each individual's complex, multidimensional environment, known as their 'exposome', plays an essential role in shaping cognitive neurodevelopment. Understanding the mechanisms whereby children's exposome influences their development is crucial to facilitate the design of interventions to foster positive developmental trajectories for all youth. Recent work has identified a general exposome factor associated with socio-economic inequality that is strongly related to cognition and individual differences in the spatial organization of functional brain networks in youth. Building on these findings, the current study explores whether alterations in functional connectivity may represent a potential mechanism linking variation in the exposome to cognitive performance. We apply a data-driven, cross-validated, whole-brain machine learning approach, connectome-based statistical inference, to identify patterns of functional connectivity associated with exposome scores among early adolescents enrolled in the Adolescent Brain Cognitive Development (ABCD) Study using data collected during three cognitive tasks and during rest. Additionally, we investigate whether the identified patterns of functional connectivity relate to individual differences in cognitive performance across three domains: General Cognition, Executive Functioning, and Learning/Memory. Models incorporating 10-fold cross-validation over 100 iterations identified consistent functional connections associated with the exposome across task and rest conditions (model performance: ns = 6,137-8,391, rs = 0.34 - 0.44, ps <.001). Results were robust across data collection sites and functional connections common across all significant models were associated with cognitive performance across domains (ps < 0.0009). Collectively, these findings reveal that multidimensional environmental exposures are reflected in patterns of functional connectivity and relate to cognitive functioning among youth.},
}

@article {pmid41248548,
year = {2025},
author = {Lian, K and Liu, H and Fang, Z and Peng, Y and Padfield, N and Yang, B and Kong, W and Cichocki, A},
title = {P[2]CSL: cross-subject EEG classification by subspace class prototype-based progressive confident target sample labeling.},
journal = {Journal of neural engineering},
volume = {22},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ae204c},
pmid = {41248548},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods/classification ; Male ; Adult ; *Brain/physiology ; Female ; Algorithms ; Young Adult ; },
abstract = {Objective.Domain adaptation (DA) has achieved remarkable performance in cross-subject electroencephalogram (EEG) decoding by mitigating the inter-subject data distribution discrepancies. However, when exploring the feature alignment subspace and performing self-supervised pseudo-labeling in an iterative way, two difficulties are often encountered: one is that unreliable target labeling results inevitably mislead the domain-free feature learning process in the early stage and the other is that the contribution of source and target samples should be balanced in the later stage.Approach.To address both issues, this paper proposes prototype-based progressive confident target sample labeling (P[2]CSL) method to use subspace class prototypes to assist in labeling target samples under the unified framework of domain-invariant EEG feature learning and the self-supervised target sample labeling, and progressively incorporate confident target samples into DA model fitting. The underlying rationality is that early-stage pseudo-labels from unconverged models are prone to error propagation, requiring auxiliary mechanisms to ensure their reliability and stabilize training. With the gradual alignment of cross-subject features, the estimated pseudo-label information of target domain will be more reliable, meaning that more target samples should be involved in model training.Main results.Experiments on emotion recognition and inner speech decoding demonstrate the competitive performance of P[2]CSL in cross-subject EEG classification in comparison with SOTA methods.Significance.Our study indicates the effectiveness of jointly considering the reliability of target samples and their contribution to model training in the context of DA. In addition, some fine-grained results including the sample confidence allocation strategy, the DA effects, and the dynamic model optimization process are provided to further illustrate the model execution details.},
}

Humans
*Electroencephalography/methods/classification
Male
Adult
*Brain/physiology
Female
Algorithms
Young Adult

@article {pmid41289136,
year = {2025},
author = {Liu, Y and Wu, W and Gui, Z and Yan, D and Wang, Z and Han, N and Gao, R and Zhang, Z and Cui, L and Wu, J and Ming, D},
title = {The Enhancement Efficacy of Motor Imagery Based on Gait Phase Encoding Sequential Electrical Stimulation in Stroke Patients.},
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.2025.3637128},
pmid = {41289136},
issn = {1558-0210},
abstract = {Motor imagery-based brain-computer interface (MI-BCI) has been widely used to promote stroke rehabilitation. However, the conventional lower limb MI paradigm can only induce weak brain activation in stroke patients and cannot effectively guide patients to generate pronounced features during MI tasks, limiting the widespread application of MI-BCI. In this study, we applied a novel walking MI paradigm based on gait phase encoding sequential sensory threshold electrical stimulation (SES-MI) in stroke patients, and systematically explored the efficacy of SES-MI in enhancing brain response patterns and improving classification accuracy, compared with the MI paradigm only with text cues (Non-MI) and with invariable electrical stimulation (IES-MI). Thirteen stroke patients were recruited for this experiment. Event-related spectral perturbation (ERSP) was utilized to supply details about the event-related desynchronization (ERD) phenomenon. Brain activation region, intensity and functional connectivity were compared among the three paradigms. SES-MI induced stronger and wider-area ERD activation than Non-MI and IES-MI. In the somatosensory cortex, the ERD amplitudes of SES-MI increased by a maximum of 115% in contrast to Non-MI. The enhancement of activation in bilateral sensorimotor cortex and prefrontal cortex was observed in SES-MI. The increased brain excitability only occurred in the alpha frequency band. Compared with Non-MI, decreased functional connectivity between different brain regions was found in SES-MI and IES-MI, especially in SES-MI. In the alpha+beta bands, the 2-class classification accuracy for SES-MI vs. SES-Idle (81.30%) was significantly improved compared with the other two paradigms. This work demonstrates that SES-MI is a more efficient paradigm for the modulation of the brain activation patterns, having the potential to promote the development of MI-BCI in stroke lower limb rehabilitation.},
}

@article {pmid41289134,
year = {2025},
author = {Cai, X and Xue, C and Cao, L and Guo, Z and Xu, H and Zhang, S and Fan, C and Jia, J},
title = {A Novel Brain-Computer Interface Application: Precise Decoding of Urination and Defecation Motor Attempts in Spinal Cord Injury Patients.},
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.2025.3637066},
pmid = {41289134},
issn = {1558-0210},
abstract = {Patients with spinal cord injury (SCI) often face urinary and defecation dysfunction, and existing treatments have limited effectiveness. Brain-computer interface (BCI) technology has been shown to have positive effects on the rehabilitation of SCI patients, but its application in promoting the recovery of urinary and defecation functions has not been explored. This study proposes a new BCI application approach and develops an accurate decoding model targeted at urination and defecation motor attempt tasks. Specifically, we designed a Bidirectional Temporal Convolutional Network (UDCNN-BiTCN) to decode both the suppressed urination and defecation (S-UD) task and the urination and defecation (UD) task. Seventy-one participants (including 44 healthy controls and 27 SCI patients) were recruited for the experiment. The results showed that UDCNN-BiTCN achieved an average accuracy of 91.47% on the S-UD task and 91.81% on the UD task. The study also conducted within-subject cross-task transfer learning and cross-subject experiments, further validating the superiority of the model. In addition, we conducted a comprehensive analysis of this new paradigm from the perspective of classification performance. The research approach and findings in this study provide a valuable new perspective for BCI applications in the recovery of urinary and defecation functions.},
}

@article {pmid41288610,
year = {2025},
author = {Shu, L and Zhuang, D and Tang, J and Zhao, J and Shao, W and Guan, X and Zhang, D},
title = {DemuxTrans: Transformer and temporal convolution network for accurate barcode demultiplexing in nanopore sequencing.},
journal = {Bioinformatics (Oxford, England)},
volume = {41},
number = {11},
pages = {},
doi = {10.1093/bioinformatics/btaf612},
pmid = {41288610},
issn = {1367-4811},
support = {62136004//National Natural Science Foundation of China/ ; 62276130//National Natural Science Foundation of China/ ; 2023YFF1204803//National Key R&D Program of China/ ; BE2022842//Key Research and Development Plan of Jiangsu Province/ ; },
mesh = {*Nanopore Sequencing/methods ; *Deep Learning ; *Sequence Analysis, RNA/methods ; *Software ; Nanopores ; },
abstract = {MOTIVATION: Oxford Nanopore Technologies (ONT) direct RNA sequencing (dRNA-seq) offers high-resolution, single-molecule analysis but is hindered by the lack of robust multiplex barcoding methods. Existing approaches struggle to accurately demultiplex raw nanopore signals, failing to capture both local patterns and long-range dependencies. This limitation underscores the requirement for advanced solutions to improve accuracy, efficiency, and adaptability in sequencing workflows. We present DemuxTrans, a hybrid deep learning framework that integrates Multi-Layer Feature Fusion, Transformers, and Temporal Convolutional Networks (TCN) for precise barcode demultiplexing.

RESULTS: DemuxTrans achieves state-of-the-art performance across multiple datasets by effectively balancing local feature extraction, global context modeling, and long-term dependency capture, excelling in metrics such as accuracy, recall and F1-score. These results demonstrate DemuxTrans as a scalable, efficient solution for barcode demultiplexing in nanopore sequencing, enabling precise identification of multiplexed RNA samples and improving throughput in transcriptomic and epigenomic analyses.

The code and datasets are publicly available on https://github.com/LiyuanShu116/Demuxtrans.},
}

*Nanopore Sequencing/methods
*Deep Learning
*Sequence Analysis, RNA/methods
*Software
Nanopores

@article {pmid41286920,
year = {2025},
author = {Sun, Z and Hu, S and Zhu, J and Ye, Z and Ma, M and Ma, G},
title = {The impact of non-invasive brain-computer interface technology on the therapeutic effect of patients with spinal cord injury: a summary of evidence based on meta-analysis.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {250},
pmid = {41286920},
issn = {1743-0003},
support = {No. YC2024022//Graduate Student Research and Innovation Fund of Jilin Sport University/ ; No. YC2024022//Graduate Student Research and Innovation Fund of Jilin Sport University/ ; No. 2019B122//Social Science Foundation of Jilin Province/ ; },
mesh = {Humans ; *Spinal Cord Injuries/rehabilitation ; *Brain-Computer Interfaces ; Activities of Daily Living ; },
abstract = {BACKGROUND: The objective of this study is to systematically evaluate the effects of non-invasive brain-computer interface technology on motor and sensory functions and daily living abilities of patients with spinal cord injuries. In addition, the study will investigate the related modifying factors. Ultimately, the study will provide evidence-based recommendations for clinical practice.

METHODS: A systematic search was conducted on PubMed, Web of Science, Scopus, Wiley Online Library, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang Data Resource System, and VIP Database for relevant literature from database inception to February 2025. The quality of the studies was assessed using Review Manager 5.4, with the risk of bias visually represented. The presence of publication bias was assessed through the utilization of the "metafor" package (version 4.6-0) in R (version 4.4.1). The certainty of the evidence was evaluated using the GRADE framework.

RESULTS: A total of 9 papers were included, including 4 randomized controlled trials and 5 self-controlled trials with 109 spinal cord injury patients. Compared with the control group, the non-invasive brain-computer interface intervention had a significant impact on patients' motor function (SMD = 0.72, 95% CI: [0.35,1.09], P < 0.01, I[2] = 0%, medium level of evidence), sensory function (SMD = 0.95, 95% CI: [0.43,1.48], P < 0.01, I[2] = 0%, medium level of evidence), activities of daily living (SMD = 0.85, 95% CI: [0.46,1.24], P < 0.01, I[2] = 0%, low level of evidence) reached statistical significance. Subgroup analyses showed that for the current summary of evidence, noninvasive brain-computer interface interventions in patients with subacute stage spinal cord injuries showed statistically stronger effects on motor function, sensory function, and ability to perform activities of daily living than in patients with slow chronic stage spinal cord injuries.

CONCLUSION: As far as the existing literature is concerned, non-invasive brain-computer interface technology shows the potential to improve motor and sensory functioning as well as the ability to perform activities of daily living in patients with spinal cord injury. However, the conclusions are preliminary and hypothetical, and as the current evidence for non-invasive BCI interventions for people with spinal cord injury remains limited, this paper does not recommend the application of the conclusions to clinical practice until future large-sample RCTs.},
}

Humans
*Spinal Cord Injuries/rehabilitation
*Brain-Computer Interfaces
Activities of Daily Living

@article {pmid41287227,
year = {2021},
author = {Glannon, W},
title = {Ethical and social aspects of neural prosthetics.},
journal = {Progress in biomedical engineering (Bristol, England)},
volume = {4},
number = {1},
pages = {},
doi = {10.1088/2516-1091/ac23e6},
pmid = {41287227},
issn = {2516-1091},
abstract = {Neural prosthetics are devices or systems that bypass, modulate, supplement, or replace regions of the brain and its connections to the body that are damaged and dysfunctional from congenital abnormalities, brain and spinal cord injuries, limb loss, and neuropsychiatric disorders. Some prosthetics are implanted in the brain. Others consist of implants and systems outside the brain to which they are connected. Still others are completely external to the brain. But they all send inputs to and receive outputs from neural networks to modulate or improve connections between the brain and body. As artificial systems, neural prosthetics can improve but not completely restore natural sensory, motor and cognitive functions. This review examines the main ethical and social issues generated by experimental and therapeutic uses of seven types of neural prosthetics: auditory and visual prosthetics for deafness and blindness; deep brain stimulation for prolonged disorders of consciousness; brain-computer and brain-to-brain interfaces to restore movement and communication; memory prosthetics to encode and retrieve information; and optogenetics to modulate or restore neural function. The review analyzes and discusses how recipients of neural prosthetics can benefit from them in restoring autonomous agency, how they can be harmed by trying and failing to use or adapt to them, how these systems affect their identities, how to protect people with prosthetics from external interference, and how to ensure fair access to them. The review concludes by emphasizing the control these systems provide for people and a brief exploration of the future of neural prosthetics.},
}

@article {pmid41285817,
year = {2025},
author = {Li, S and Chen, J and Zhang, C and Tang, S and Xie, Y and Wang, L},
title = {Flexible Use of Limited Resources for Sequence Working Memory in Macaque Prefrontal Cortex.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {10386},
pmid = {41285817},
issn = {2041-1723},
mesh = {Animals ; *Prefrontal Cortex/physiology ; *Memory, Short-Term/physiology ; Neurons/physiology ; Macaca mulatta/physiology ; Male ; Behavior, Animal/physiology ; },
abstract = {Our brain is remarkably limited in how many items it can hold simultaneously, but it can also represent unbounded novel items through generalization. How the brain rationally uses limited resources in working memory (WM) remains unexplored. We investigated mechanisms of WM resource allocation using calcium imaging and electrophysiological recording in the prefrontal cortex of monkeys performing sequence WM (SWM) tasks. We found that changes in the neural representation of SWM, including geometry, generalizable and separate rank subspaces, reflected WM load. SWM resources, represented by neurons' signal strength and spatial tuning projected onto each rank subspace, were shared flexibly between ranks. Crucially, the prefrontal cortex dynamically utilized shared tuning neurons to ensure generalization, while engaging disjoint and spatially shifted neurons to minimize interference, thus achieving a trade-off between behavioral and neural costs within capacity. The allocated resources can predict monkeys' behavior. Thus, the geometry of compositionality underlies the flexible use of limited resources in SWM.},
}

Animals
*Prefrontal Cortex/physiology
*Memory, Short-Term/physiology
Neurons/physiology
Macaca mulatta/physiology
Male
Behavior, Animal/physiology

@article {pmid41285049,
year = {2025},
author = {Lampert, F and Baker, MR and Jensen, MA and Ayyoubi, AH and Bentler, C and Bowersock, JL and Esteller, R and Herron, JA and Johnson, GW and Kipke, DR and Kovach, CK and Kremen, V and Mivalt, F and Neimat, JS and Netoff, TI and Opri, E and Rockhill, AP and Rosenow, JM and Sellers, KK and Staff, NP and Swamy, CP and Viswanathan, A and Schalk, G and Denison, TJ and Hermes, D and Ince, NF and Brunner, P and Worrell, GA and Miller, KJ},
title = {Adaptive neuromodulation dialogues: navigating current challenges and emerging innovations in neuromodulation system development.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae2359},
pmid = {41285049},
issn = {1741-2552},
abstract = {Adaptive neuromodulation systems and implantable brain-computer interfaces have made notable strides in recent years, translating experimental prototypes into clinical applications and garnering substantial attention from the public. This surge in interest is accompanied by increased scrutiny related to the safety, efficacy, and ethical implications of these systems, all of which must be directly addressed as we introduce new neurotechnologies. In response, we have synthesized the insights resulting from discussions between groups of experts in the field and summarized them into five key domains essential to therapeutic device development: (1) analyzing current landscape of neuromodulation devices and translational platforms (2) identifying clinical need, (3) understanding neural mechanisms, (4) designing viable technologies, and (5) addressing ethical concerns. The role of translational research platforms that allow rapid, iterative testing of hypotheses in both preclinical and clinical settings is emphasized. These platforms must balance experimental flexibility with patient safety and clear clinical benefit. Furthermore, requirements for interoperability, modularity, and wireless communication protocols are explored to support long-term usability and scalability. The current regulatory processes and funding models are examined alongside the ethical responsibilities of researchers and device manufacturers. Special attention is given to the role of patients as active contributors to research and to the long-term obligations we have to them as the primary burden-bearers of the implanted neurotechnologies. This article represents a synthesis of scientific, engineering, and clinical viewpoints to inform key stakeholders in the neuromodulation and brain-computer interface spaces.},
}

@article {pmid41284455,
year = {2025},
author = {Baradaran, Y and Rojas, RF and Goecke, R and Ghahramani, M},
title = {Exploring Prefrontal Cortex Involvement in Postural Control Across Degraded Sensory Conditions Using fNIRS and Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3636169},
pmid = {41284455},
issn = {2168-2208},
abstract = {The prefrontal cortex (PFC) of the brain is involved in processing visual, vestibular, and somatosensory inputs to stabilise postural balance. However, the PFC's activation map for a standing person and different sensory inputs remains unclear. This study aimed to explore the PFC activity map and distinct haemodynamic responses during postural control when sensory inputs change. To this end, functional near-infrared spectroscopy (fNIRS) was employed to capture the haemodynamic responses throughout the PFC from a group of young adults standing in four sensory conditions. The results revealed distinct PFC activation patterns supporting sensory processing, motor planning, and cognitive control to maintain balance under different degraded sensory conditions. Furthermore, by applying machine learning classifiers and multivariate feature selection, the PFC locations and haemodynamic responses indicative of different sensory conditions were identified. The findings of this study offer valuable insights for optimising rehabilitation approaches, enhancing the design of fNIRS studies, and advancing brain-computer interface technologies for balance assessment and training.},
}

@article {pmid41284444,
year = {2025},
author = {Liu, J and Li, M and Li, Z and Yang, Y and Qi, Q},
title = {DA-META: A Dual Attention Meta-Learning Framework for Unsupervised Motor Imagery Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3636462},
pmid = {41284444},
issn = {2168-2208},
abstract = {Motor imagery electroencephalography (MI-EEG) decoding demonstrates significant potential for paralysis rehabilitation, and its generalization capability is often compromised by intersubject variability and scarcity of labeled target domain data. Meta-learning has emerged as a promising approach for unsupervised domain adaptation problem. However, existing implementations suffer from two critical limitations: insufficient feature extraction and overlooking the guiding role of unlabeled target data. To overcome these challenges, we propose a dual-attention meta-learning framework (DA-META) with model-agnostic architecture in this paper. The framework comprises three stages: meta-task construction, guided meta-training, and fine-tuning-free meta-testing. In the guided meta-training stage, DA-META incorporates two key attention mechanisms: an enhanced temporal attention module for effective feature extraction, and a cosine similarity-based attention module to leverage the guidance of target domain. Using EEGNet as the backbone network, DA-META achieves mean classification accuracies of 68.04% and 76.61% on self-collected datasets from patients and healthy subjects, and 73.29% and 80.93% on the public BCI Competition IV 2a and 2b datasets, outperforming state-of-the-art methods. When employing EEGNet, DeepConvNet, and EEG Conformer as backbone networks respectively, the framework achieves accuracy improvements of 5.17%, 2.56%, and 0.85% on the 2a dataset, compared to the baseline. These results demonstrate the framework's superior ability to handle inter-subject variability and its significant potential to improve practical applicability.},
}

@article {pmid41282818,
year = {2025},
author = {Johnson, TR and Foli, C and Conlan, EC and Koenig, KA and Lowe, MJ and Memberg, WD and Kirsch, RF and Herring, EZ and Bazarek, SF and Graczyk, EL and Taylor, DM and Ajiboye, AB and Sweet, J},
title = {Targeting Optimal Grasp-Related Cortical Areas for Intracortical Brain-Machine Interfaces after Spinal Cord Injury.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.10.10.25337598},
pmid = {41282818},
abstract = {OBJECTIVE: This study aimed to optimize intracortical microelectrode array implantation sites for grasp-related motor decoding by integrating anatomical, functional, and vascular imaging with preoperative 3D modeling.

METHODS: A participant with C5 tetraplegia underwent anatomical magnetic resonance imaging (MRI), diffusion-weighted imaging, and task-based functional MRI (fMRI) to identify grasp-related cortical regions while avoiding vasculature and speech-critical areas. Quicktome software was used to refine target selection by integrating structural connectivity and functional activation data. A 3D-printed skull and cortical model enabled preoperative planning, including craniotomy and electrode positioning simulations. Electrode placement was validated post-operatively using neural data collected from the implanted arrays during attempted movements of the arm and hand.

RESULTS: Functional imaging identified distinct grasp-related activation in anterior intraparietal area (AIP), ventral premotor cortex (PMv), and inferior frontal gyrus (IFG). AIP was selected based on its strong connectivity with motor cortex and distinct functional activation. Subregions 6v and 6r of PMv, which exhibited robust grasp-related activity and were surgically accessible, were chosen over the posterior IFG region, which extended into a sulcus making implantation difficult. Post-surgically, the arrays enabled high-fidelity decoding of arm/hand movements, achieving a combined accuracy of 96%.

CONCLUSION: This study presents a multi-modal approach for optimizing intracortical electrode placement by combining MRI-based anatomical mapping, fMRI-guided functional localization, connectivity information, and 3D surgical modeling. These findings demonstrate an effective method for identifying surgically feasible grasp network implant locations in a paralyzed individual. This is an essential step for brain-machine interface (BMI) systems that use grasp-related brain activity to command devices, such as neuromuscular stimulation systems for restoring upper limb function in individuals with spinal cord injury (SCI).},
}

@article {pmid41281720,
year = {2025},
author = {Gan, L and Yuan, S and Guo, M and Wang, Q and Deng, Z and Jia, B},
title = {Triboelectric nanogenerators for neural data interpretation: bridging multi-sensing interfaces with neuromorphic and deep learning paradigms.},
journal = {Frontiers in computational neuroscience},
volume = {19},
number = {},
pages = {1691017},
pmid = {41281720},
issn = {1662-5188},
abstract = {The rapid growth of computational neuroscience and brain-computer interface (BCI) technologies require efficient, scalable, and biologically compatible approaches for neural data acquisition and interpretation. Traditional sensors and signal processing pipelines often struggle with the high dimensionality, temporal variability, and noise inherent in neural signals, particularly in elderly populations where continuous monitoring is essential. Triboelectric nanogenerators (TENGs), as self-powered and flexible multi-sensing devices, offer a promising avenue for capturing neural-related biophysical signals such as electroencephalography (EEG), electromyography (EMG), and cardiorespiratory dynamics. Their low-power and wearable characteristics make them suitable for long-term health and neurocognitive monitoring. When combined with deep learning models-including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and spiking neural networks (SNNs)-TENG-generated signals can be efficiently decoded, enabling insights into neural states, cognitive functions, and disease progression. Furthermore, neuromorphic computing paradigms provide an energy-efficient and biologically inspired framework that naturally aligns with the event-driven characteristics of TENG outputs. This mini review highlights the convergence of TENG-based sensing, deep learning algorithms, and neuromorphic systems for neural data interpretation. We discuss recent progress, challenges, and future perspectives, with an emphasis on applications in computational neuroscience, neurorehabilitation, and elderly health care.},
}

@article {pmid41281344,
year = {2025},
author = {Xu, M and He, Z and Zhou, J and Zhao, J and Tian, X and Cheng, Q and Lin, Y and Xin, H and Mou, C and Xue, Q and Luo, B},
title = {Altered oral microbiomes in patients with prolonged disorders of consciousness.},
journal = {Journal of oral microbiology},
volume = {17},
number = {1},
pages = {2577220},
pmid = {41281344},
issn = {2000-2297},
abstract = {BACKGROUND: The host microbiome is increasingly recognized as a key modulator of brain function and disease progression, yet the role of the oral microbiome in patients with prolonged disorders of consciousness remains underexplored.

METHODS: This study characterized oral microbiota differences among pDoC patients (n = 89) in the vegetative state (VS), the minimally conscious state (MCS), and emerging from the MCS (EMCS), with a particular focus on the impact of antibiotic use. We used 16S ribosomal RNA sequencing to profile oral microbiota in patients with different levels of consciousness.

RESULTS: β-diversity was significantly reduced in the VS group compared to the EMCS group. Differential abundance analysis identified five taxa (i.e., species Streptococcus danieliae, species Corynebacterium durum, family Lachnospiraceae, species Phocaeicola abscessus, and species Campylobacter showae) that exhibited significant differences between VS and EMCS, suggesting they were potentially involved in regulating oral microbial dysbiosis and brain-microbiome interactions. Antibiotic treatment induced pronounced microbial shifts in the VS group, while no such effect was observed in the MCS or EMCS groups. Prognostic models built using differential and dominant microbiota panels demonstrated strong predictive performance, achieving areas under the curve of 0.820 and 0.920, respectively.

CONCLUSIONS: These findings highlight oral microbiome alterations in pDoC and their potential relevance to disease progression, emphasizing the importance of microbiome-informed clinical strategies.},
}

@article {pmid41280332,
year = {2025},
author = {Wu, Z and Yu, S and Tian, D and Cheng, L and Jing, J},
title = {Microglial TREM2 and cognitive impairment: insights from Alzheimer's disease with implications for spinal cord injury and AI-assisted therapeutics.},
journal = {Frontiers in cellular neuroscience},
volume = {19},
number = {},
pages = {1705069},
pmid = {41280332},
issn = {1662-5102},
abstract = {Cognitive impairment is a frequent but underrecognized complication of neurodegenerative and traumatic central nervous system disorders. Although research on Alzheimer's disease (AD) revealed that microglial triggering receptor expressed on myeloid cells 2 (TREM2) plays a critical role in inhibiting neuroinflammation and improving cognition, its contribution to cognitive impairment following spinal cord injury (SCI) is unclear. Evidence from AD shows that TREM2 drives microglial activation, promotes pathological protein clearance, and disease-associated microglia (DAM) formation. SCI patients also experience declines in attention, memory, and other functions, yet the specific mechanism of these processes remains unclear. In SCI, microglia and TREM2 are involved in inflammation and repair, but their relationship with higher cognitive functions has not been systematically examined. We infer that TREM2 might connect injury-induced neuroinflammation in the SCI with cognitive deficits, providing a new treatment target. Artificial intelligence (AI) offers an opportunity to accelerate this endeavor by incorporating single-cell transcriptomics, neuroimaging, and clinical data for the identification of TREM2-related disorders, prediction of cognitive trajectories, and applications to precision medicine. Novel approaches or modalities of AI-driven drug discovery and personalized rehabilitation (e.g., VR, brain-computer interface) can more precisely steer these interventions. The interface between lessons learned from AD and SCI for generating new hypotheses and opportunities for translation.},
}

@article {pmid41279978,
year = {2025},
author = {Canfield, RA and Ouchi, T and Fang, H and Macagno, B and Smith, LI and Scholl, LR and Orsborn, AL},
title = {The spatiotemporal structure of neural activity in motor cortex during reaching.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {41279978},
issn = {2692-8205},
abstract = {UNLABELLED: Intracortical brain-computer interfaces (BCI) leverage knowledge about neural representations to translate movement-related neural activity into actions. BCI implants have targeted broad cortical regions known to have relevant motor representations, but emerging technologies will allow flexible targeting to specific neural populations. The structure of motor representations at this scale, however, has not been well characterized across frontal motor cortices. Here, we investigate how motor representations and population dynamics (temporal coordination) vary across a large expanse of frontal motor cortices. We used high-density, laminar, microelectrode arrays to simultaneously record many neurons and then sampled neural populations across frontal motor cortex in two monkeys while they performed a reaching task. Our experiments allowed us to map neuronal activity across three spatial dimensions and relate them to movement. Target decoding analysis revealed that task information was heterogeneously distributed across the cortical surface and in depth. Similarly, we found that the temporal dynamics of different neural populations were heterogeneous, but that the amount of task information predicted which neural populations had similar dynamics. The neural populations with the most similar dynamics were composed of neurons with high task information regardless of spatial location. Our results highlight the spatiotemporal complexity of motor representations across frontal motor cortex at the level of neurons and neural populations, where well-learned movements consistently recruit a spatially distributed subset of neurons. Further insights into the spatiotemporal structure of neural activity patterns across frontal motor cortex will be critical to guide future implants for improved BCI performance.

SIGNIFICANCE STATEMENT: Motor brain-computer interfaces (BCI) translate neural activity into movement, but how to target implants within motor cortices to maximize performance remains unclear. We used high-density recordings of neural activity spanning a large cortical area and related them to movement to map the spatial distribution of task information and the evolution of neural population activity over time. Our measurements revealed that neurons with the most task information were heterogeneously distributed across cortex yet also evolved coherently in time, suggesting that spatially distributed neurons coordinate to control movements. Our results provide new links between neuron- and population-level maps of motor representations, and highlight the complex spatiotemporal structure of activity that may need to be considered when designing next-generation BCIs.},
}

@article {pmid41278188,
year = {2025},
author = {Li, Z and Kambara, H and Koike, Y},
title = {Neural signatures of engagement in driving: comparing active control and passive observation.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1698625},
pmid = {41278188},
issn = {1662-4548},
abstract = {Understanding how the human brain differentiates between active engagement and passive observation is a fundamental question in cognitive neuroscience. Using a matched-stimulus driving paradigm to isolate engagement from sensory input, we recorded whole-brain EEG while participants performed a manual control task and passively viewed a replay of their own performance. Manual control elicited distinct spectral signatures, including stronger frontal midline theta power and, paradoxically, greater occipital alpha power, consistent with heightened cognitive control and active attentional filtering. While a classifier could distinguish these states with high within-subject accuracy, performance declined in cross-subject validation, highlighting inter-individual variability. These findings delineate the distinct neural signatures of active versus passive engagement under controlled conditions. This work establishes a foundational neurophysiological baseline that can inform research on cognitive state monitoring and the design of neuroadaptive systems in complex human-machine interaction.},
}

@article {pmid40936379,
year = {2025},
author = {Shin, CJ and Lee, K and Langford, L and Bai, W},
title = {Conductive and Semiconductive 2D Materials for Neural Interfaces, Biosensors, and Therapeutic Modulation.},
journal = {Small methods},
volume = {9},
number = {11},
pages = {e01330},
doi = {10.1002/smtd.202501330},
pmid = {40936379},
issn = {2366-9608},
support = {CCSS-2443105//Division of Electrical, Communications and Cyber Systems/ ; 1R01EB034332/EB/NIBIB NIH HHS/United States ; 1R01EB034332/EB/NIBIB NIH HHS/United States ; },
mesh = {Humans ; *Biosensing Techniques/methods/instrumentation ; *Semiconductors ; Graphite/chemistry ; Electric Conductivity ; Animals ; *Brain-Computer Interfaces ; },
abstract = {Due to population aging, the surge in chronic diseases, and recent pandemics, healthcare is increasingly shifting from hospital-centered models toward digital care. However, widespread adoption is impeded by signal degradation under physiological motion, biofouling, stringent power and data constraints. Effectively overcoming these challenges will require clinically robust devices providing precise, reliable, and reproducible performance. 2D materials address these demands through high carrier mobility that can improve signal-to-noise ratios, low-defect lattices for uniformity, and mechanical pliability that maintains intimate tissue contact and stable impedance during motion. These traits have fueled the rapid growth of 2D-material bioelectronics for remote care in lightweight, stretchable devices. This review surveys flexible, low-impedance neural electrodes of graphene, transition-metal dichalcogenides, and MXenes that integrate electrophysiological recording with optical imaging to provide high-resolution brain interfaces. It then examines their roles in biosensing and autonomous therapy, including sub-picomolar biomarker detection in complex fluids and photothermal, genetic, and antibacterial interventions. Open questions regarding long-term biocompatibility, scalable manufacturing, and protocol harmonization are highlighted. By aligning recent breakthroughs with persistent challenges, the review outlines the prospects of conductive and semiconductive 2D materials for neural interfacing, biosensing, and therapeutic delivery, and maps a pathway toward practical clinical translation.},
}

Humans
*Biosensing Techniques/methods/instrumentation
*Semiconductors
Graphite/chemistry
Electric Conductivity
Animals
*Brain-Computer Interfaces

@article {pmid41275665,
year = {2025},
author = {Li, T and An, X and Di, Y and Wang, H and Yan, Y and Liu, S and Dong, Y and Ming, D},
title = {Fuzzy symbolic convergent cross mapping: A causal coupling measure for EEG signals in disorders of consciousness patients.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {195},
number = {},
pages = {108318},
doi = {10.1016/j.neunet.2025.108318},
pmid = {41275665},
issn = {1879-2782},
abstract = {Accurate and timely diagnosis in disorders of consciousness (DOC) patients remains a core clinical challenge. Electroencephalography (EEG) shows strong potential for detecting physiological biomarkers of consciousness, and brain network analysis serves as an effective technique. Therefore, a robust approach to brain network construction is of great significance. The convergent cross mapping (CCM) is a powerful tool for capturing the coupling relationship between two signals. However, a major drawback of CCM is its sensitivity to noise. To address this problem, we proposed a symbolic method that combines fuzzy membership functions called fuzzy symbolic convergent cross mapping (FuzzSCCM). Through the simulation results, we verified its robustness to noise, sensitivity to coupling, and data length. Building on this coupling measure, we constructed EEG brain networks and validated the approach on real DOC EEG datasets. In patients with DOC, FuzzSCCM identified distinct network features between vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state (MCS). Specifically, compared with the MCS group, the VS group showed greater asymmetry between the left hemisphere and the right hemisphere in the α band, and was relatively less active in the anterior in the θ band. Moreover, our results demonstrate spontaneous transitions between distinct brain network states, suggesting these dynamic reconfigurations may constitute a fundamental mechanism underlying consciousness modulation. These findings provide novel insights into the dynamic neural signatures of DOC, while establishing a potential diagnostic tool.},
}

@article {pmid41274911,
year = {2025},
author = {Hardstone, R and Ostrowski, LM and Dusang, AN and López-Larraz, E and Jesser, J and Cash, SS and Cramer, SC and Hochberg, LR and Ramos-Murguialday, A and Lin, DJ},
title = {Extension of voxel-based lesion mapping to multidimensional neurophysiological data.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-025-17247-z},
pmid = {41274911},
issn = {2045-2322},
support = {FMD clinical research fellowship//MGH ECOR/ ; Clinical research Training Scholar//American Academy of Neurology/ ; 1IK2RX004237//U.S. Department of Veterans Affairs/ ; },
abstract = {Focal brain lesions cause neurophysiological changes in local and distributed neural systems. While electroencephalography (EEG) has a long history in post-stroke neurophysiological assessment, the observed changes have rarely been linked to specific lesion locations, leaving neuroanatomical-neurophysiological relationships after stroke unclear. Current data-driven methods, such as voxel-based lesion symptom mapping (VLSM), relate lesion locations to single-feature "symptoms" but currently cannot associate anatomical injury with multidimensional data such as EEG, with its rich spatiotemporal information. To overcome this limitation, we introduce MD-VLM, an extension of VLSM to multidimensional "symptoms" that identifies relationships between lesion locations and neurophysiology. MD-VLM is data-agnostic, compatible with various lesion (e.g., lesion maps, lesion network maps) and neurophysiological (e.g., channel-level or source-localized EEG) inputs, and uses robust statistics to test for the existence of significant neuroanatomical-neurophysiological relationships. We demonstrate MD-VLM's feasibility by applying it to EEG from chronic stroke patients performing a cued-movement task. MD-VLM revealed significant associations between frontal white-matter lesions and reduced ipsilesional parietal cue-evoked responses, consistent with damage to known fronto-parietal networks. MD-VLM is a novel data-driven extension to VLSM for multidimensional "symptoms". Applying MD-VLM to link lesions to neurophysiological data can improve mechanistic understanding of post-stroke neurological impairments and guide future biomarker development.},
}

@article {pmid41274087,
year = {2025},
author = {Xu, H and Lin, N},
title = {Neurovista: A bidirectional masked cross-Modal fusion network for robust EEG-to-Image decoding.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {195},
number = {},
pages = {108297},
doi = {10.1016/j.neunet.2025.108297},
pmid = {41274087},
issn = {1879-2782},
abstract = {Electroencephalography (EEG)-based visual decoding has significant potential in brain-computer interfaces but faces substantial challenges due to noise, inter-subject variability, and limited fine-grained alignment between neural signals and visual representations. Existing approaches predominantly utilize global EEG embeddings and static fusion methods, restricting their capability to capture nuanced cross-modal interactions. To address these limitations, We propose NeuroVista, a novel framework that integrates localized EEG masking with dynamic bidirectional cross-modal attention, achieving state-of-the-art EEG-to-image decoding performance. Specifically, NeuroVista employs a channel-level EEG masking strategy during training, encouraging the model to learn robust, context-sensitive neural features, thus significantly improving generalization and noise resistance. Simultaneously, our bidirectional cross-modal attention module dynamically aligns EEG embeddings with corresponding visual features, enhancing semantic coherence across modalities. Extensive experiments on standard EEG-to-image benchmarks demonstrate that NeuroVista consistently outperforms state-of-the-art methods, achieving up to +16.0 % top-1 accuracy improvement in both subject-dependent and subject-independent settings. Our results validate the effectiveness of combining localized masking and interactive cross-modal attention, establishing NeuroVista as a robust, interpretable, and highly generalizable approach for EEG-based visual decoding tasks.},
}

@article {pmid41273580,
year = {2026},
author = {Miloulis, ST and Kakkos, I and Zorzos, I and Karampasi, A and Anastasiou, A and Asvestas, P and Ventouras, EC and Kalatzis, I and Matsopoulos, GK},
title = {Deep Learning Discrimination for BCI Implementation Using 3D Convolutional Neural Network and EEG Topographic Maps.},
journal = {Advances in experimental medicine and biology},
volume = {1487},
number = {},
pages = {405-413},
pmid = {41273580},
issn = {0065-2598},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Deep Learning ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Adult ; Male ; Convolutional Neural Networks ; },
abstract = {The growing interest in improved rehabilitation systems and assistive technologies for individuals with motor impairments necessitates the need for new applications of Deep Learning approaches for Brain-Computer Interface (BCI) implementation. This study investigates the application of Deep Learning techniques, specifically the Hierarchical 3D Convolutional Neural Network (H3DCNN) model, for enhancing classification systems utilizing electroencephalography (EEG) data. As such, topographic maps were extracted from EEG signals in a real motion task experiment integrating 4 different motions. The H3DCNN model was then employed in a step-wise fashion to classify and decode the EEG signals, demonstrating its effectiveness in distinguishing between different movement intentions. Moreover, three different optimizers were implemented, including RMSprop, Adam, and Stochastic Gradient Descent (SGD), to further assess and enhance the model performance. The findings indicate that the integration of advanced deep learning techniques can significantly enhance the accuracy and reliability of BCI systems, with RMSprop and SGD showing superior results in terms of accuracy. Moreover, our results illustrate the possibility of decoding neural mechanisms via deep learning paradigms, paving the way for future developments in BCI applications, thus aiming to improve the quality of life for individuals with motor impairments.},
}

*Brain-Computer Interfaces
*Electroencephalography/methods
Humans
*Deep Learning
*Neural Networks, Computer
Signal Processing, Computer-Assisted
Adult
Male
Convolutional Neural Networks

@article {pmid41272829,
year = {2025},
author = {Shi, Y and Ma, J and Zhao, X and Zhao, H and Wang, D and Zhang, X and Zhu, X and Meng, L and Ming, D},
title = {Bilateral intermittent theta-burst stimulation as a priming strategy to enhance action observation and imitation training in early parkinson's disease: a proof-of-concept study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {247},
pmid = {41272829},
issn = {1743-0003},
support = {82372083//National Natural Science Foundation of China/ ; 2022YFF1202500//National Key Research and Development Program of China/ ; },
mesh = {Humans ; *Parkinson Disease/rehabilitation/physiopathology ; *Transcranial Magnetic Stimulation/methods ; Male ; Female ; Double-Blind Method ; Middle Aged ; Aged ; Cross-Over Studies ; Proof of Concept Study ; Evoked Potentials, Motor/physiology ; *Gait Disorders, Neurologic/rehabilitation/physiopathology/etiology ; Motor Cortex/physiopathology ; *Imitative Behavior/physiology ; Postural Balance/physiology ; Theta Rhythm/physiology ; },
abstract = {BACKGROUND: Action observation and imitation training (AOIT) is an evidence-based cognitive-motor rehabilitation strategy for Parkinson's disease (PD), particularly for the postural instability and gait disorder (PIGD) subtype. However, its effectiveness may decline with disease-related impairments in neuroplasticity. Intermittent theta burst stimulation (iTBS), a patterned repetitive transcranial magnetic stimulation protocol, can induce LTP-like plasticity and may enhance responsiveness to rehabilitation. This study investigated whether iTBS priming augments AOIT effects on gait and cognition in early-stage PIGD and explored underlying neurophysiological mechanisms.

METHODS: Fifteen patients with early-stage PIGD participated in a randomized, double-blind, sham-controlled crossover trial. Each phase included five consecutive days of AOIT preceded by either real or sham iTBS applied over the bilateral leg region of the primary motor cortex, separated by a washout period of more than four weeks. Pre- and post-intervention assessments included dual-task gait analysis, cognitive tests, clinical scales, neurophysiological measures (motor evoked potentials, cortical silent period), and resting-state EEG power spectral density.

RESULTS: Both conditions improved balance and gait measures. However, real iTBS significantly enhanced dual-task gait automaticity (F = 5.558, P = 0.026) and global cognition (F = 5.294, P = 0.026) compared to sham. Real iTBS also increased cortical silent period (F = 4.655, P = 0.040) and MEP-based cortical plasticity response (F = 6.131, P = 0.020). Improvements in cortical plasticity were significantly correlated with better gait performance (r = - 0.429, P = 0.020) and motor scores (r = - 0.463, P = 0.011). No adverse events were reported.

CONCLUSIONS: Bilateral iTBS targeting the leg representation of the primary motor cortex can potentiate AOIT effects in early-stage PIGD by enhancing cortical plasticity and motor learning. These findings support the integration of iTBS as a priming strategy within cognitive-motor rehabilitation protocols for PD. Trial registration Chinese Clinical Trial Registry, ChiCTR2300067657. Registered January 17, 2023.},
}

Humans
*Parkinson Disease/rehabilitation/physiopathology
*Transcranial Magnetic Stimulation/methods
Male
Female
Double-Blind Method
Middle Aged
Aged
Cross-Over Studies
Proof of Concept Study
Evoked Potentials, Motor/physiology
*Gait Disorders, Neurologic/rehabilitation/physiopathology/etiology
Motor Cortex/physiopathology
*Imitative Behavior/physiology
Postural Balance/physiology
Theta Rhythm/physiology

@article {pmid41268354,
year = {2025},
author = {Haro, S and Beauchene, C and Quatieri, TF and Smalt, CJ},
title = {A Brain-Computer Interface for Improving Auditory Attention in Multi-Talker Environments.},
journal = {IEEE access : practical innovations, open solutions},
volume = {13},
number = {},
pages = {189903-189914},
pmid = {41268354},
issn = {2169-3536},
abstract = {There is significant research in accurately determining the focus of a listener's attention in a multi-talker environment using auditory attention decoding (AAD) algorithms. These algorithms rely on neural signals to identify the intended speaker, assuming that these signals consistently reflect the listener's focus. However, some listeners struggle with this competing talkers task, leading to suboptimal tracking of the desired speaker due to potential interference from distractors. The goal of this study was to enhance a listener's attention to the target speaker in real time and investigate the underlying neural bases of this improvement. This paper describes a closed-loop neurofeedback system that decodes the auditory attention of the listener in real time, utilizing data from a non-invasive, wet electroencephalography (EEG) brain-computer interface (BCI). Fluctuations in the listener's real-time attention decoding accuracy were used to provide acoustic feedback. As accuracy improved, the ignored talker in the two-talker listening scenario was attenuated; making the desired talker easier to attend to due to the improved attended talker signal-to-noise ratio (SNR). A one-hour session was divided into a 10-minute decoder training phase, with the rest of the session allocated to observing changes in neural decoding. In this study, we found evidence of suppression of (i.e., reduction in) net neural tracking and decoding of the unattended talker when comparing the first and second half of the neurofeedback session (p = 0.02, Cohen's d = -1.29, 95% CI [-0.02, -0.01] and p = 0.01, Cohen's d = -1.56, 95% CI [-7.25, -3.44], respectively). We did not find a statistically significant increase in the neural tracking or decoding of the attended talker. These results establish a single session performance benchmark for a time-invariant, non-adaptive attended talker linear decoder utilized to extract attention from a listener integrated within a closed-loop neurofeedback system. This research lays the engineering and scientific foundation for prospective multi-session clinical trials of an auditory attention training paradigm.},
}

@article {pmid41266624,
year = {2025},
author = {Sen, O and Soni, R and Virmani, D and Parekh, A and Lehman, P and Jena, S and Katikhaneni, A and Khalifa, A and Chatterjee, B},
title = {A low-latency neural inference framework for real-time handwriting recognition from EEG signals on an edge device.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {41040},
pmid = {41266624},
issn = {2045-2322},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Handwriting ; Male ; Female ; Adult ; Signal-To-Noise Ratio ; Machine Learning ; Young Adult ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interfaces (BCIs) hold significant promise for restoring communication in individuals with severe motor or speech impairments. Imagined handwriting, as a form of motor imagery, offers an intuitive paradigm for character-level neural decoding. While invasive techniques such as electrocorticography (ECoG) offer high decoding accuracy, their surgical requirements pose clinical risks and hinder scalability. Non-invasive alternatives like electroencephalography (EEG) are safer and more accessible but suffer from low signal-to-noise ratio (SNR) and spatial resolution, limiting their effectiveness in high-resolution decoding. Here, we investigate how advanced machine learning, combined with informative feature extraction, can overcome these limitations, enabling EEG-based decoding performance that approaches invasive methods, while supporting real-time inference on edge devices. We present the first real-time, low-latency, high-accuracy system for decoding imagined handwriting from non-invasive EEG signals on a portable edge device. EEG data were collected from 15 participants using a 32-channel headcap and preprocessed with bandpass filtering and artifact subspace reconstruction (ASR). We extracted 85 time domain, frequency domain, and graphical features, then applied Pearson correlation coefficient-based feature selection to reduce latency while preserving accuracy. We developed a hybrid architecture, EEdGeNet, which integrates a Temporal Convolutional Network (TCN) with a multilayer perceptron (MLP), trained on the extracted features and deployed on the NVIDIA Jetson TX2 for real-time inference. The system achieved [Formula: see text] accuracy with 914.18 ms per-character inference latency. By selecting only ten key features, the model incurred a minimal accuracy loss of [Formula: see text], while achieving a [Formula: see text] reduction in inference latency (202.62 ms) compared to the full 85-feature set. These findings show that non-invasive EEG, combined with efficient feature and model design, can enable accurate, real-time neural decoding on low-power edge devices, paving the way for practical, portable BCIs.},
}

Humans
*Electroencephalography/methods
*Brain-Computer Interfaces
*Handwriting
Male
Female
Adult
Signal-To-Noise Ratio
Machine Learning
Young Adult
Signal Processing, Computer-Assisted

@article {pmid41265571,
year = {2025},
author = {Luo, R and Meng, J and Wei, Y and Mai, X and Li, G},
title = {Outcome processing response coupled to feedback-related EEG dynamics during discrete and continuous performance monitoring.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110629},
doi = {10.1016/j.jneumeth.2025.110629},
pmid = {41265571},
issn = {1872-678X},
abstract = {BACKGROUND: Error-related potential (ErrP) reflects the inconsistency between internal expectation and external feedback outcome. Despite the exploration of numerous experimental paradigms, ErrP components exhibit distinct latency and amplitude across different paradigms. However, previous studies have not quantitatively correlated potential influencing factors with this ErrP variability. Additionally, these qualitatively analyzed factors offer limited predictions for ErrP in new paradigms.

NEW METHOD: We proposed that a neutral condition removing goal-directed outcome expectations reflects cross-paradigm variability in correct and erroneous outcome responses. This neutral condition was designed as a control condition for each paradigm. Three different paradigms were designed to provide discrete and continuous varied feedback outcomes. Correlations were assessed between neutral condition responses and correct and erroneous outcome responses in latency and amplitude. The predictive effectiveness of neutral condition responses for new paradigms was further evaluated through single-trial cross-paradigm classification.

RESULTS: Correct and erroneous outcome responses were observed to have significant latency and amplitude coupling with these neutral condition responses in the middle frontal and bilateral parietal regions. Results from source reconstruction, pupillometry data, and workload score confirm that the neutral condition serves as the baseline response for outcome processing responses. This baseline relationship explains the cross-paradigm ErrP variability.

The single-trial decoding results show that utilizing neutral condition responses can significantly increase the accuracy of cross-paradigm classification by up to 7% and 17% with covariance-based and amplitude-based approaches.

CONCLUSION: These findings provide a quantitative physiological explanation for cross-paradigm ErrP variability and promote transfer learning applications in ErrP-based BCIs.},
}

@article {pmid41264938,
year = {2025},
author = {Fang, S and Zhao, X and Wang, Z and Si, Y and Haifeng, L and Hu, H and Xu, T and Zhou, T},
title = {Enhancing SSVEP-BCI performance through multi-stimulus discriminant fusion analysis.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae220d},
pmid = {41264938},
issn = {1741-2552},
abstract = {To enhance frequency recognition in Steady-State Visual Evoked Potential (SSVEP)-based Brain-Computer Interfaces (BCIs), particularly under short data acquisition and complex environmental conditions. Approach. We propose Multi-Stimulus Discriminant Fusion Analysis (MSDFA), a novel method that integrates multi-stimulus strategies with discriminant modeling. MSDFA was evaluated on two public datasets (Benchmark and BETA) and compared with conventional approaches including eCCA, eTRCA, and their variants. Main results. MSDFA consistently outperformed existing methods across different data lengths and training block quantities. It achieved maximum information transfer rates of 247.17±10.15 bpm on the Benchmark dataset and 192.72±9.44 bpm on the BETA dataset, demonstrating superior robustness and efficiency. Significance. By combining complementary algorithmic strengths, MSDFA improves adaptability to individual variability and complex environments, advancing the practical utility and reliability of SSVEP-BCI systems. .},
}

@article {pmid41262557,
year = {2025},
author = {Zhang, X and Wang, S and Gao, Y and Wang, Y and Qiu, S and He, H},
title = {Enhancing visual brain-computer interface through V1-targeted RTMS by modulating visual attention.},
journal = {Imaging neuroscience (Cambridge, Mass.)},
volume = {3},
number = {},
pages = {},
pmid = {41262557},
issn = {2837-6056},
abstract = {Brain-computer interfaces (BCIs) enable users to control devices directly through brain activity. Despite recent advancements in machine-learning algorithms, the signal-to-noise ratio (SNR) of the brain's responses still limits decoding performance, highlighting the necessity for targeted neuromodulation techniques to overcome this limitation. To evaluate whether 5 Hz repetitive transcranial magnetic stimulation (rTMS) targeting the primary visual cortex (V1) can enhance SSVEP-based BCI performance by improving neural signal SNR and modulating visual network dynamics. Twenty-four healthy subjects underwent both real and sham rTMS in a randomized order. The rTMS was precisely implemented through magnetic resonance imaging (MRI)-guided navigation to stimulate V1 in participants. Electroencephalograms (EEGs) were recorded during SSVEP tasks and resting-state before, immediately after, and 20 min after rTMS. SSVEP tasks were conducted across four frequency bands: low frequency (LF: 8-12 Hz), middle frequency (MF: 18-22 Hz), high frequency (HF: 28-32 Hz), and super high frequency (SHF: 38-42 Hz). The discriminability of BCI commands in the MF (+7.53%) and HF (+11.4%) bands significantly improved (p < 0.001), driven by enhanced prominence of both fundamental and harmonic components (p < 0.01). Quantitative analysis indicated that the improved SNR was due to the suppression of the background activity (p < 0.05). This effect was linked to rTMS-induced enhancements in visual attention, evidenced by increased occurrence and contribution of microstate B during the SSVEP task (p < 0.01). This study highlights the potential of 5 Hz rTMS as an effective neuromodulatory tool for optimizing BCI performance, particularly through facilitating visual attention.},
}

@article {pmid41261122,
year = {2025},
author = {Gobert, F and Merida, I and Maby, E and Seguin, P and Jung, J and Morlet, D and André-Obadia, N and Dailler, F and Berthomier, C and Otman, A and Le Bars, D and Scheiber, C and Hammers, A and Bernard, E and Costes, N and Bouet, R and Mattout, J},
title = {Disorder of consciousness rather than complete Locked-In Syndrome for end stage Amyotrophic Lateral Sclerosis: a case series.},
journal = {Communications medicine},
volume = {5},
number = {1},
pages = {482},
pmid = {41261122},
issn = {2730-664X},
abstract = {BACKGROUND: The end-stage of amyotrophic lateral sclerosis (ALS) is commonly regarded as a complete Locked-In Syndrome (cLIS). Shifting the perspective from cLIS (assumed consciousness) to Cognitive Motor Dissociation (potentially demonstrable consciousness), we aimed to assess the preservation of covert awareness (internally preserved but externally inaccessible) using a multimodal battery.

METHODS: We evaluate two end-stage ALS patients using neurophysiological testing, passive and active auditory oddball paradigms, an auditory Brain-Computer Interface (BCI), functional activation-task imaging, long-term EEG, brain morphology, and resting-state metabolism to characterize underlying brain function.

RESULTS: Patient 1 initially follows simple commands but fails twice at BCI control. At follow-up, command following is no longer observed and his oddball cognitive responses disappear. Patient 2, at a single evaluation, is unable to follow commands or control the BCI. Both patients exhibit altered wakefulness, brain atrophy, and a global cortico-subcortical hypometabolism pattern consistent with a disorder of consciousness, regarded as an extreme manifestation of ALS-associated fronto-temporal dementia.

CONCLUSIONS: Although it is not possible to firmly prove the absence of awareness, each independent measure concurred with suggesting that a "degenerative disorder of consciousness" rather than a cLIS may constitute the final stage of ALS. This condition appears pathophysiologically distinct from typical tetraplegia and anarthria, in which behavioural communication and BCI use persist to enhance quality of life. Identifying the neuroimaging signatures of this condition represents a substantial milestone in understanding end-stage ALS. Large-scale longitudinal investigations are warranted to determine the prevalence of this profile among patients whose communication appears impossible.},
}

@article {pmid41260504,
year = {2025},
author = {Wang, J and Gan, X and Han, M and Dong, W and He, J and Fu, K and Bore, MC and Xu, T and Klugah-Brown, B and Ferraro, S and Becker, B},
title = {Effects of exogenous oxytocin on human brain function are regulated by oxytocin gene expression: a meta-analysis of 20 years of oxytocin neuroimaging and transcriptomic analyses.},
journal = {Neuroscience and biobehavioral reviews},
volume = {},
number = {},
pages = {106478},
doi = {10.1016/j.neubiorev.2025.106478},
pmid = {41260504},
issn = {1873-7528},
abstract = {Over the past two decades, numerous pharmaco-imaging studies have examined the role of oxytocin (OT) in human cognition and behavior, yet results remain highly heterogeneous and the link between large-scale functional effects and molecular architecture is unclear. To address this, we conducted a comprehensive analysis combining neuroimaging meta-analysis, meta-analytic connectivity modeling, and transcriptomics. Across 75 experiments (n=2,247), consistent, domain-general effects of OT emerged in the left thalamus, pallidum, caudate, and insula. Connectivity modeling showed these regions form an integrated thalamus-striatum-insula circuit directly modulated by OT. Transcriptomic analyses revealed that the expression of three OT pathway genes (CD38, OXT, and OXTR) is enriched in these subcortical regions and associated with the observed neural effects. OT's neural effects were also strongly linked with acetylcholinergic, dopaminergic, and opioidergic gene distributions, potentially reflecting functional interactions with these systems. Findings provide convergent evidence that OT exerts robust effects on human brain function via a biologically-plausible core circuit and can inform effective pharmacotherapeutic targets.},
}

@article {pmid41259181,
year = {2025},
author = {Qin, C and Yang, R and Zhu, L and Chen, Z and Huang, M and Alsaadi, FE and Wang, Z},
title = {EEG-Infinity: A Mathematical Modeling-Inspired Architecture for Addressing Cross-Device Challenges in Motor Imagery.},
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.2025.3635018},
pmid = {41259181},
issn = {1558-0210},
abstract = {The distribution of electroencephalogram (EEG) data generally varies across datasets due to the huge difference between the physical structure of brain-computer interface devices, known as cross-device variability. Such variability poses great challenges in EEG decoding and hinders the standardized utilization of EEG datasets. In this study, we explore a new issue concerning the cross-device variability problem, pointing to the gap in the existing studies facing cross-device variability. To tackle this challenge, our paper is the first to model the cross-device variability problem through a "sequentially comprehensive formula" and a "spatial comprehensive formula". Inspired by this modeling, a novel deep domain adaptation network named EEG-Infinity is proposed, incorporating replaceable EEG feature extraction backbones with a novel structure named "alignment head". To show the effectiveness of the proposed EEG-Infinity, systematic experiments are conducted across four different EEG-based motor imagery datasets under 48 cases. The experimental results highlight the superior performance of the proposed EEG-Infinity over commonly used approaches with an average classification accuracy improvement of 1.51% across 34 cases, laying a foundation for research in large-scale EEG models. The code can be assessed at https://github.com/Baizhige/cd-infinity.},
}

@article {pmid41257892,
year = {2025},
author = {Xiong, W and Ma, L and Li, H},
title = {Adaptive EEG preprocessing to mitigate electrode shift variability for robust motor imagery classification.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {40808},
pmid = {41257892},
issn = {2045-2322},
mesh = {*Electroencephalography/methods/instrumentation ; Humans ; Electrodes ; Brain-Computer Interfaces ; *Imagination/physiology ; Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {Electrode placement variability poses a critical challenge in EEG-based motor imagery tasks, often resulting in reduced classification robustness. We present the Adaptive Channel Mixing Layer (ACML), a plug-and-play preprocessing module that dynamically adjusts input signal weights through a learnable transformation matrix based on inter-channel correlations. By leveraging the inherent spatial structure of EEG caps, ACML effectively compensates for electrode misalignments and noise, enhancing resilience to signal distortion. Experimental validation on two motor imagery datasets with varying channel counts demonstrated consistent improvements in accuracy (up to 1.4%), kappa scores (up to 0.018), and robust performance across subjects, using five neural network architectures including a state-of-the-art model (ATCNet). Notably, ACML requires minimal computational overhead and no task-specific hyperparameter tuning, ensuring compatibility with diverse applications. This method offers a robust and efficient solution for advancing EEG-based motor imagery classification, with potential applications in real-time brain-computer interface systems and neurorehabilitation.},
}

*Electroencephalography/methods/instrumentation
Humans
Electrodes
Brain-Computer Interfaces
*Imagination/physiology
Neural Networks, Computer
*Signal Processing, Computer-Assisted
Algorithms

@article {pmid41257158,
year = {2025},
author = {Ali, U and Khan, JA and Ahsan, MT and Altaf, B and Azreen, S and Alamu, OS and Rana, MS},
title = {Brain-Computer Interfaces in the Rehabilitation of Stroke and Spinal Cord Injury: A Systematic Review and Meta-Analysis of Clinical Efficacy.},
journal = {Cureus},
volume = {17},
number = {10},
pages = {e94833},
pmid = {41257158},
issn = {2168-8184},
abstract = {Brain-computer interfaces (BCIs) have emerged as innovative tools for neurorehabilitation, enabling patients with stroke and spinal cord injury (SCI) to engage in task-specific training through direct neural control of external devices. Despite growing evidence, the overall clinical efficacy of BCIs in functional recovery remains debated. This systematic review and meta-analysis evaluated the effectiveness of BCI-based rehabilitation on motor recovery in stroke and SCI, with a focus on upper and lower limb function. We systematically searched PubMed, EMBASE, Web of Science, and Cochrane CENTRAL for clinical trials published between January 2008 and October 2025, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Eligible studies included randomized controlled trials and controlled interventional trials employing BCI interventions for motor rehabilitation. Risk of bias was assessed with RoB-2 and ROBINS-I. Meta-analysis was performed using a random-effects model. Seventeen studies met the inclusion criteria, comprising both stroke (acute, subacute, and chronic phases) and SCI populations. The pooled analysis demonstrated a significant mean difference of 3.26 points on the Fugl-Meyer Assessment for Upper Extremity (FMA-UE) in favour of BCI interventions (95% CI: 2.73-3.78, p < 0.001). Heterogeneity was negligible (I[2] = 0%). Subgroup analyses suggested that combining BCI with functional electrical stimulation or robotics yielded larger gains. BCI-based rehabilitation significantly improves motor function in stroke and SCI populations, with effect sizes exceeding the minimal clinically important difference for FMA-UE. These findings highlight the translational potential of BCIs as adjunctive therapies in neurorehabilitation. Larger, multicenter trials with standardised protocols are warranted to establish long-term efficacy and guide clinical integration.},
}

@article {pmid41256527,
year = {2025},
author = {Shah, NP and Krasa, BA and Kunz, E and Hahn, N and Kamdar, F and Avansino, D and Hochberg, LR and Henderson, JM and Sussillo, D},
title = {Improved interpretability in LFADS models using a learned, context-dependent per-trial bias.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.10.03.680303},
pmid = {41256527},
issn = {2692-8205},
abstract = {The computation-through-dynamics perspective argues that biological neural circuits process information via the continuous evolution of their internal states. Inspired by this perspective, Latent Factor Activity using Dynamical systems (LFADS, [1]) identifies a generative model consistent with the neural activity recordings. LFADS models neural dynamics with a recurrent neural network (RNN) generator, which results in excellent fit to the data. However, it has been difficult to understand the dynamics of the LFADS generator. In this work, we show that this poor interpretability arises in part because the generator implements complex, multi-stable dynamics. We introduce a simple modification to LFADS that ameliorates issues with interpretability by providing an inferred per-trial bias (modeled as a constant input) to the RNN generator, enabling it to contextually adapt a simpler dynamical system to individual trials. In both simulated neural recordings from pendulum oscillations and real recordings during arm movements in nonhuman primates, we observed that the standard LFADS learned complex, multi-stable dynamics, whereas the modified LFADS learned easier-to-understand contextual dynamics. This enabled direct analysis of the generator, which reproduced at a single-trial level previous results shown only through more complex analyses at the trial average. Finally, we applied the per-trial inferred bias LFADS model to human intracortical brain computer interface recordings during attempted finger movements and speech. We show that modifying neural dynamics using linear operations of the per-trial bias addresses non-stationarity and identifies the extent of behavioral variability, problems known to plague BCI. We call our modification to LFADS as "contextual LFADS".},
}

@article {pmid41256173,
year = {2025},
author = {Candrea, DN and Angrick, M and Luo, S and Ganji, R and Coogan, C and Milsap, GW and Rosenblatt, KR and Uchil, A and Clawson, L and Maragakis, NJ and Vansteensel, MJ and Tenore, FV and Ramsey, NF and Fifer, MS and Crone, NE},
title = {Longitudinal study of gesture decoding in a clinical trial participant with ALS.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.09.26.25335804},
pmid = {41256173},
abstract = {Brain-computer interfaces (BCIs) have the potential to preserve or restore communication and device control in people with paralysis from a variety of causes. For people living with amyotrophic lateral sclerosis (ALS), however, the progressive loss of cortical motor neurons could theoretically pose a challenge to the stability of BCI performance. Here we tested the stability of gesture decoding with a chronic electrocorticographic (ECoG) BCI in a man living with ALS and participating in a clinical trial (ClinicalTrials.gov , NCT03567213). We evaluated offline decoding performance of attempted gestures over two periods: a 5-week period beginning roughly 2 years post-implant and a 6-week period ending roughly 5 months later. Decoder sensitivity was high in both periods (90 - 98%), while classification accuracy was 37 - 68% in the first period and worsened to 23 - 39% in the second. We investigated multiple frequency bands that were used as model features in both periods, and we observed reductions in high gamma band power (70 - 110 Hz) and between-class separation during the second period compared to the first. Over the 5-month period motor function did not appreciably decline. These results, albeit preliminary, suggest that declines in the neural population responses that drive ECoG BCI performance can occur without overt signs of disease progression in people living with ALS, and could serve as a biomarker for disease progression in the future.},
}

@article {pmid41255819,
year = {2025},
author = {Vooijs, M and Bassil, K and van den Brink, A and van Stuijvenberg, OC and Ramsey, NF and Jongsma, KR},
title = {Ethical, legal, and sociocultural considerations in neural device explantation: a systematic review.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1568800},
pmid = {41255819},
issn = {1662-4548},
abstract = {INTRODUCTION: Implantable neural devices, including brain-computer interfaces and spinal cord stimulators, hold significant therapeutic promise for conditions such as paralysis and chronic pain. However, the novelty of these technologies introduces unique ethical challenges. While much of the existing literature emphasizes development-related concerns such as device safety, the ethical issues surrounding explantation remain relatively underexplored.

METHODS: We conducted a systematic review to identify ethical, legal, and sociocultural considerations relevant to the explantation of neural devices. The review applied the IEEE BRAIN Neuroethics framework as a guiding structure for the categorization of the themes. A subsequent thematic analysis was performed to categorize and synthesize findings across studies.

RESULTS: Thematic analysis revealed that medical motives were the predominant factor in discussions of explantation, with 83% of studies citing medical complications as a central concern. Additional themes identified included changes in cognition and behavior, emotional well-being, lack of therapeutic benefit, identity, financial issues, autonomy, post-trial considerations, and neurorights.

DISCUSSION: Our findings underscore the multifaceted nature of neural device explantation, extending beyond purely medical considerations to include psychological, financial, legal, and sociocultural dimensions. These results highlight the necessity of interdisciplinary approaches to adequately address the broad spectrum of challenges associated with explantation.},
}

@article {pmid41255549,
year = {2025},
author = {Hyung, W and Kim, M and Kim, Y and Im, CH},
title = {DeepAttNet: deep neural network incorporating cross-attention mechanism for subject-independent mental stress detection in passive brain-computer interfaces using bilateral ear-EEG.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1685087},
pmid = {41255549},
issn = {1662-5161},
abstract = {INTRODUCTION: Electroencephalography (EEG)-based mental stress detection has the potential to be applied in diverse real-world scenarios, including workplace safety, mental health monitoring, and human-computer interaction. However, most previous passive brain-computer interface (BCI) studies have employed EEG recorded during the performance of specific tasks, making the classification results susceptible to task engagement effects rather than reflecting stress alone. To address this limitation, we introduce a rest-versus-rest paradigm that compares resting EEG recorded immediately after exposure to a stressor with that recorded after meditation, thereby isolating mental stress from the task-related confounds. EEG recording setups were designed under the assumption of bilateral ear-EEG, a compact and discreet form factor suitable for real-world applications. Furthermore, we developed a novel subject-independent deep learning classifier tailored to model interhemispheric neural dynamics for enhanced mental stress detection performance.

METHODS: Thirty-two adults participated in the experiment. To classify mental stress status in a subject-independent manner, we proposed DeepAttNet, a deep learning model based on cross-attention and pointwise temporal compression, specifically designed to effectively capture left and right hemispherical interactions. Classification performance was assessed using eight-fold subject-level cross-validation against conventional deep learning models, including EEGNet, ShallowConvNet, DeepConvNet, and TSception. Ablation studies evaluated the impact of the cross-attention and/or pointwise compression modules.

RESULTS: DeepAttNet achieved the highest average accuracy and macro-F1 values, with performance declining when either the cross-attention or pointwise compression module was removed in the ablation studies. Explainability analyses indicated lower cross-attention entropy with stronger directional ear-to-ear asymmetry under stress, and temporal occlusion identified mid-late windows supporting stress decisions. Moreover, six of seven canonical scalp-EEG markers were FDR-significant for post-stressor vs. post-relaxation rest.

CONCLUSION: The proposed rest-versus-rest paradigm and DeepAttNet enabled robust, subject-independent mental stress detection with a fairly high accuracy using only two-channel EEG recordings. This approach is expected to offer a practical solution for continuous stress monitoring, potentially advancing passive BCI applications outside laboratory settings.},
}

@article {pmid41253791,
year = {2025},
author = {Shi, J and Chen, D and Zhao, X and Zhao, Z and Li, S and Xu, Y and Ding, T and Zhu, Z and Zhang, P and Ye, Q and Tang, Y and Zhang, P and Tao, B and Tang, Z},
title = {HEFMI-ICH: a hybrid EEG-fNIRS motor imagery dataset for brain-computer interface in intracerebral hemorrhage.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1816},
pmid = {41253791},
issn = {2052-4463},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography ; *Cerebral Hemorrhage/rehabilitation/physiopathology ; Spectroscopy, Near-Infrared ; },
abstract = {This study introduces the first hybrid brain-computer interface dataset specifically designed for research on intracerebral hemorrhage (ICH) rehabilitation. It offers a novel data source through the synchronized acquisition of electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals. The dataset innovatively incorporated neural recordings from 17 normal subjects and 20 patients with ICH under standardized left-right hand motor imagery (MI) paradigms, featuring systematically collected and preprocessed dual-modality neural data. Beyond raw neural signals, the resource provides feature-engineered data optimized for classification algorithms and multidimensional signal decoding. The public availability of this dataset can facilitate the validation and optimization of MI decoding algorithms and advance the development of precision rehabilitation systems based on multimodal neural feedback.},
}

Humans
*Brain-Computer Interfaces
*Electroencephalography
*Cerebral Hemorrhage/rehabilitation/physiopathology
Spectroscopy, Near-Infrared

@article {pmid41253750,
year = {2025},
author = {Sun, X and Dias, L and Peng, C and Zhang, Z and Ge, H and Wang, Z and Jin, J and Jia, M and Xu, T and Guo, W and Zheng, W and He, Y and Wu, Y and Cai, X and Agostinho, P and Qu, J and Cunha, RA and Zhou, X and Bai, R and Chen, JF},
title = {Author Correction: 40 Hz light flickering facilitates the glymphatic flow via adenosine signaling in mice.},
journal = {Cell discovery},
volume = {11},
number = {1},
pages = {92},
doi = {10.1038/s41421-025-00845-6},
pmid = {41253750},
issn = {2056-5968},
}

@article {pmid41253390,
year = {2025},
author = {Ji, X and Deng, S},
title = {Cognitive Change as an Early Warning for Late-Life Depression: Implications for Population Health Screening Strategies.},
journal = {Population health management},
volume = {},
number = {},
pages = {},
doi = {10.1177/19427891251395738},
pmid = {41253390},
issn = {1942-7905},
abstract = {Cognitive decline and late-life depression are intertwined public health challenges for aging populations globally. To inform effective prevention, the current study investigated the dynamic temporal associations between multidimensional cognitive functions and depressive symptoms. Using four waves of longitudinal data (2013-2020) from a large panel study of older adults, the current study employed an integrated framework combining optimized dynamic time warping, cross-lagged panel models, and network analysis to model complex, lagged relationships. Results provided strong empirical support for the "cognition-first" hypothesis, with declines in several cognitive domains-notably temporal orientation, calculation, and immediate recall-acting as significant upstream predictors of subsequent depressive symptoms. A modest but significant protective feedback effect from positive affect to cognitive maintenance was also identified, while negative affect showed no significant predictive role sample of older adults who were cognitively and emotionally healthy at baseline. These findings offer preliminary empirical support for a strategic shift in population health management from reactive treatment toward proactive prevention. Based on these results, the current study discusses a conceptual framework for integrating cognitive screening into primary care to identify at-risk older adults, an approach that warrants further investigation and validation. This proactive approach could enable timely, low-cost interventions aimed at promoting positive affect and cognitive resilience, offering a potentially cost-effective strategy to mitigate the long-term burden of mental illness and advance the goals of healthy aging.},
}

@article {pmid41253019,
year = {2025},
author = {Fan, YS and Ye, M and Xu, Y and Xu, Y and Guo, J and Yang, M and Huang, W and Chen, H},
title = {Spatio-temporal information transition abnormalities across brain functional networks in early-onset schizophrenia.},
journal = {Schizophrenia research},
volume = {287},
number = {},
pages = {37-45},
doi = {10.1016/j.schres.2025.11.007},
pmid = {41253019},
issn = {1573-2509},
abstract = {Schizophrenia is a complex neurodevelopmental disorder characterized by widespread functional dysconnectivities across the brain. While disturbed temporal dynamics have been reported in schizophrenia, the information flow involving both temporal and spatial dynamics remains unclear. To capture spatio-temporal transition of brain information and to investigate these processes from a neurodevelopmental perspective, we collected resting-state functional MRI (rs-fMRI) data from 86 early-onset schizophrenia (EOS) patients (onset before age 18) and 91 demographically matched typically developing (TD) controls. We employed a non-homogeneous Markov model (NHMM) on dynamic functional connectivities derived from fMRI data. By means of transition probabilities, we modeled the switching of information flow in brain functional networks over time. Stationary probability vectors were used to describe the information convergence distribution of each network, while optimal reachable steps were used to characterize inter-network transmission efficiency. Compared to controls, EOS patients showed significantly increased stationary transition probabilities in the ventral attention network (VAN) and the dorsal attention network (DAN) but decreased probabilities in the default mode network (DMN). In terms of the dynamic interaction characteristics between networks, patients showed increased optimal reachable steps relative to controls, particularly in the VAN-DMN pathway. By integrating NHMM with neuroimaging data, this study revealed VAN- and DMN-involved information transition abnormalities in the early stage of schizophrenia spatio-temporal dynamics, offering novel insights into the developmental pathophysiology of the disorder. Our approach thus provides a novel analytical framework for quantifying spatio-temporal brain dynamics in neurodevelopmental disorders.},
}

@article {pmid41252716,
year = {2025},
author = {Yi, L and Yang, Y and Zeng, BF and Liu, X and Edel, JB and Ivanov, AP and Tang, L},
title = {Single-molecule quantum tunnelling sensors.},
journal = {Chemical Society reviews},
volume = {},
number = {},
pages = {},
doi = {10.1039/d4cs00375f},
pmid = {41252716},
issn = {1460-4744},
abstract = {Single-molecule sensors are pivotal tools for elucidating chemical and biological phenomena. Among these, quantum tunnelling sensors occupy a unique position, due to the exceptional sensitivity of tunnelling currents to sub-ångström variations in molecular structure and electronic states. This capability enables simultaneous sub-nanometre spatial resolution and sub-millisecond temporal resolution, allowing direct observation of dynamic processes that remain concealed in ensemble measurements. This review outlines the fundamental principles of electron tunnelling through molecular junctions and highlights the development of key experimental architectures, including mechanically controllable break junctions and scanning tunnelling microscopy-based approaches. Applications in characterising molecular conformation, supramolecular binding, chemical reactivity, and biomolecular function are critically examined. Furthermore, we discuss recent methodological advances in data interpretation, particularly the integration of statistical learning and machine learning techniques to enhance signal classification and improve throughput. This review highlights the transformative potential of quantum-tunnelling-based single-molecule sensors to advance our understanding of molecular-scale mechanisms and to guide the rational design of functional molecular devices and diagnostic platforms.},
}

@article {pmid41250658,
year = {2025},
author = {Gonzalez-Astudillo, J and de Vico Fallani, F},
title = {Feature Interpretability in Motor Imagery Brain Computer Interfaces: A Meta-Analysis Across Connectivity, Spatial Filtering, and Riemannian Methods.},
journal = {Brain connectivity},
volume = {},
number = {},
pages = {},
doi = {10.1177/21580014251392230},
pmid = {41250658},
issn = {2158-0022},
abstract = {Introduction: Brain-computer interfaces (BCIs) translate brain activity into commands, enabling applications in communication, control, and neurorehabilitation. A major challenge in noninvasive BCIs is balancing classification performance with interpretability, as many approaches prioritize accuracy while overlooking the neural mechanisms underlying their predictions. Methods: In this study, we conduct a meta-analysis of feature interpretability across widely used methods in motor imagery (MI)-based BCIs, including power spectral density, common spatial patterns (CSP), Riemannian geometry, and functional connectivity. Specifically, we explore how network topology and spatial organization contribute to MI decoding by investigating brain network lateralization. Results: Through evaluations on multiple EEG-based BCI datasets, our results confirm the superior classification performance of CSP and Riemannian methods. However, network lateralization provides stronger neurophysiological plausibility, revealing robust lateralization patterns in sensorimotor and frontal regions contralateral to imagined movements. Discussion: These findings underscore the potential of connectivity-based features as a complementary tool for enhancing interpretability, supporting the development of more transparent and clinically relevant MI-based BCIs. Impact Statement This study addresses a critical gap in motor imagery-based brain-computer interfaces (BCIs) by systematically evaluating and comparing the interpretability of widely used methods, including power spectral density, common spatial pattern, Riemannian geometry, and functional connectivity. By analyzing these approaches across wide-ranging datasets, we offer valuable insights into the underlying neural mechanisms driving their performance. Our findings contribute to enhancing the transparency and biological relevance of BCI systems, ultimately advancing the development of more clinically meaningful and neurophysiologically interpretable BCIs.},
}

@article {pmid41250191,
year = {2025},
author = {Plontke, SK and Lenarz, T and Toner, J and Keintzel, T and Sprinzl, G and Baumgartner, WD and Koitschev, A and Schmutzhard, J and Götze, G and Rahne, T and Knoelke, N and Busch, S and Corkill, S and Raffelsberger, T and Niederwanger, L and Magele, A and Schörg, P and Honeder, C and Liepins, R and Berger, N and Koci, V and Wiek, R},
title = {The Bonebridge BCI 602 Safety and Performance 1 Year Post-Implantation in Adults and Children: A Multicentric Post-Market Study.},
journal = {Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {},
number = {},
pages = {},
doi = {10.1097/MAO.0000000000004688},
pmid = {41250191},
issn = {1537-4505},
abstract = {OBJECTIVE: To confirm the long-term safety and performance of the Bonebridge BCI 602 in patients suffering from conductive or mixed hearing loss (CMHL) or single-sided deafness (SSD) over a 12-month period post-implantation.

STUDY DESIGN: Multicentric, multinational, ambidirectional, observational Post-Market Clinical Follow-Up (PMCF) study.

SETTING: Eight tertiary referral hospitals.

PARTICIPANTS: Fifty-two participants in 3 categories: adults CMHL (N=24), children CMHL (N=17), and SSD (N=11; 9 adults and 2 children).

INTERVENTION: Participants were implanted with the Bonebridge BCI 602 device.

MAIN OUTCOME MEASURES: Outcome measures focused on sound field thresholds (SF), word recognition scores (WRS), speech reception thresholds (SRT) in both quiet and noise, adverse events, and subjective satisfaction (SSQ and AQoL questionnaires) at initial activation and at 3-month and 12-month post-implantation.

RESULTS: Safety was established by stable bone conduction (BC) thresholds and a low adverse event rate with no unanticipated events. Safety was underlined by clinically relevant improvements in the health-related assessment of Quality of Life (AQoL) questionnaire of mean+0.1 (adults and children CMHL) and +0.07 (SSD). Hearing significantly improved in sound field thresholds with mean functional gains of 24.05±8.68 dB (adults CMHL), 21.34±25.43 dB (children CMHL), and 32.89±25.87 dB (SSD). Mean word recognition scores improved by 65.83±28.62 percent points (PP) for adult CMHL and 65.77±27.53 PP for children CMHL and speech reception thresholds (SRT) in quiet by 15.4±9.34 dB and 19.96±14.66 dB, respectively. SRT in noise improved by -5.57±4.23 dB (adults; S0°N0°), -5.12±5.08 dB (children, S0°N0°), and -3.05±3.06 dB (SSD, SSSDNNH). Subjective hearing ability tested with the Speech, Spatial, and Qualities (SSQ) of Hearing questionnaire improved and was clinically relevant for the adult (+2.23) and children (+1.51) CMHL groups.

CONCLUSIONS: The Bonebridge BCI 602 demonstrates significant enhancements in hearing and speech understanding 12 months postoperatively, showing high user satisfaction and safety.},
}

@article {pmid41248054,
year = {2025},
author = {Xie, X and Mou, H and Chen, W and Zhang, S and Xu, Y and Cheng, R and Wang, M},
title = {Noninvasive Temporal Interference Electrical Stimulation for Spinal Cord Rehabilitation.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {224},
pages = {},
doi = {10.3791/68574},
pmid = {41248054},
issn = {1940-087X},
mesh = {*Spinal Cord Injuries/rehabilitation ; Humans ; *Spinal Cord Stimulation/methods/instrumentation ; *Electric Stimulation Therapy/methods/instrumentation ; },
abstract = {Spinal cord injury (SCI) can lead to permanent loss of motor, sensory, and autonomic functions, presenting a significant clinical challenge for rehabilitation. In addition to conventional rehabilitation approaches, epidural spinal cord stimulation (eSCI) is often used to enhance recovery. However, the invasive nature of eSCI limits patient acceptance and widespread application. Compared to traditional spinal cord stimulation, temporal interference (TI) stimulation offers a noninvasive approach to stimulate deep spinal cord regions, making it a promising technique for SCI treatment. A critical factor in achieving effective TI stimulation for SCI rehabilitation is the accurate placement of two electrode pairs on the skin surface to generate a high electric field envelope within the targeted spinal cord area. We propose a unique protocol that utilizes electric field simulations and parameter optimization to determine the optimal electrode placement for specific SCI regions. Additionally, this protocol provides a systematic description of how to efficiently implement the optimized electrode placement strategy in clinical TI stimulation.},
}

*Spinal Cord Injuries/rehabilitation
Humans
*Spinal Cord Stimulation/methods/instrumentation
*Electric Stimulation Therapy/methods/instrumentation

@article {pmid41245956,
year = {2025},
author = {Lu, Y and Jin, Z and Jian, Y and Kong, D and Zhou, H and Xu, Y and Cao, R and Xia, Z and Yang, F and Wu, Q and Gao, Y and Cui, A and Yang, S and Zheng, N and Bang, J and Yang, G and Ko, SH and Yang, H and Xu, K},
title = {Metal-hydrogel chelation interfaces for ultrasoft and bidirectional bioelectronics.},
journal = {National science review},
volume = {12},
number = {11},
pages = {nwaf399},
doi = {10.1093/nsr/nwaf399},
pmid = {41245956},
issn = {2053-714X},
abstract = {Emerging demand in soft bioelectronic systems poses critical challenges in stiffness control and end-to-end connections due to the huge modulus difference in various components. Here, a bidirectional electrical interface of hydrogel and metal electrodes to bridge soft skin/tissue and data collection circuits is enabled by coordination interactions. The dual-mode chelation including internal chelation and surface chelation effectively configures the cross-linking structure of hydrogel, as well as enhances the binding interface of metal-hydrogel complex surfaces. Internally, strong chelation competes with esterification, yielding tissue-like softness of hydrogel with an ultra-low modulus of ∼339.9 Pa. Externally, the hydrogel passivates the combined metal surfaces, promoting the formation of interlocked structures between metal oxide nanoislands, achieving a high binding strength of ∼1.95 MPa without compromising electrical conductivity. The stable electrical interconnections via hybrid interfacial bonding enable high signal-to-noise ratio signal recordings from the skin, neural surfaces and brain, maintaining reliable performance, even under mechanical disturbances. This work provides an effective strategy for achieving mechanically and electrically robust hybrid bioelectronic interfaces, advancing their applications in capturing both in vitro and in vivo electrical signals.},
}

@article {pmid41245196,
year = {2025},
author = {Wang, KJ and Vinjamuri, R and Alimardani, M and Kumar Reddy, T and Mao, ZH},
title = {Editorial: NeuroDesign in human-robot interaction: the making of engaging HRI technology your brain can't resist.},
journal = {Frontiers in robotics and AI},
volume = {12},
number = {},
pages = {1699371},
doi = {10.3389/frobt.2025.1699371},
pmid = {41245196},
issn = {2296-9144},
}

@article {pmid41242443,
year = {2025},
author = {Mir, M and Badea, I and Wilson, LD},
title = {Hierarchical chitosan-lignocellulosic duplex system: An in vitro evaluation of controlled release, anti-pathogenic and hemostatic effects.},
journal = {International journal of biological macromolecules},
volume = {},
number = {},
pages = {149018},
doi = {10.1016/j.ijbiomac.2025.149018},
pmid = {41242443},
issn = {1879-0003},
abstract = {Critical design challenges that affect novel drug delivery systems concern chemical processing and tissue compatibility of source materials, which highlight the need for sustainable, biocompatible materials and versatile manufacturing methods. This investigation leverages the unique surface chemistry of biomass-derived lignocellulose substrates to form biocomposite frameworks with effective antipathogenic and hemostatic properties via noncovalent synthesis. Complementary electrostatic interactions support a highly porous biocomposite framework, according to spectral (IR, Raman and NMR) and microscopy results. Biocomposite complexes with antipathogenic agents (gentamicin and rifamycin salts) are revealed by kinetic release profiles, as noted by an initial burst and sustained release profile under physiological conditions. In vitro biocompatibility was demonstrated by MTT cell viability assays (ca. 90 % after 48 h). Anti-pathogenic effects are revealed by agar diffusion assays with E. coli (up to 16 mm inhibition zones). In vitro blood sorption, cell adhesion and blood clotting index (BCI) results of biocomposites reveal impressive blood absorption capacity (ca. 10-fold; w/w) with good cell adhesion and efficient hemostatic properties with BCI below 2 %. This study challenges the current limits of specialized biomedical applications of lignocellulose fiber-chitosan biocomposites via in-vitro results at bioactive interfaces for anti-infective targeted drug delivery and trauma management.},
}

@article {pmid41242344,
year = {2025},
author = {Huang, Y and Lin, Z and Huang, J and Chen, T and Liu, T},
title = {Correlating the Evans index and bicaudate index with ventricle volume at the three kinds of scanning baselines.},
journal = {Brain research bulletin},
volume = {},
number = {},
pages = {111641},
doi = {10.1016/j.brainresbull.2025.111641},
pmid = {41242344},
issn = {1873-2747},
abstract = {PURPOSE: Evans' Index (EI) and Bicaudate Index (BCI), as two-dimensional linear indexes, are commonly used to evaluate ventricle size. This study is investigated the differences in linear measures at the three kinds of scanning baselines and their correlations with ventricle volume.

METHODS: In 186 healthy volunteers,117 hydrocephalus patients with complete skull and 72 hydrocephalus patients without complete skull, the linear indexes, intracranial volume and ventricle volume were calculated by 3D Slicer. Wilcoxon rank test was used for comparisons of the linear indexes at the scanning baselines respectively. Spearman analysis was applied for the correlations between linear indexes and ventricle volume respectively.

RESULTS: There were statistical differences in the linear indexes of the three scanning baselines. Comparison of the linear indexes in people from three groups, the difference of linear indexes was minimum at Reid's base line (RBL), but max at supraorbitomeatal line (SML). Compared with the third and the fourth ventricle, the linear indexes had a stronger correlation with lateral ventricle volume or total ventricle volume. On the other hand, EI at RBL had a stronger correlation with ventricle volume, compared with other two kinds of scanning baselines.

CONCLUSION: For consistent and representative linear measurements, we recommend using the Evans Index at Reid's baseline (RBL). However, the correlations between linear indices and ventricular volume were only modest, underscoring the limitation of 2D indices for precise volumetric assessment.},
}

@article {pmid41241353,
year = {2025},
author = {Li, G and Said, FM and Liang, J and Li, Y and Jing, Z},
title = {Fabrication of dual physically cross-linked agarose-based double network composite hydrogels with antibacterial and hemostatic properties for infected wound healing.},
journal = {International journal of biological macromolecules},
volume = {},
number = {},
pages = {149011},
doi = {10.1016/j.ijbiomac.2025.149011},
pmid = {41241353},
issn = {1879-0003},
abstract = {An agarose-based double network composite hydrogel with good mechanical, antibacterial, and hemostatic properties was synthesized to accelerate the healing of infected wounds. The double network composite hydrogel was fabricated by hydrogen bonding between poly(ACG-co-NBAA) chains generated by free radical polymerization and helical conformation formed by the agarose-graft-gelatin chains in the presence of Zn-MOF. The synthesized hydrogels exhibited a three-dimensional network structure and excellent pH sensitivity. The disintegration of hydrogen bonds in the hydrogel network caused the increase of swelling ratio of the hydrogels as the pH rose. The mechanical and antibacterial properties of agarose-based composite hydrogels can be well adjusted by changing their composition. The special structure of the hydrogels and Zn-MOF embedding endowed them with good antibacterial properties against S. aureus and E. coli. The results of the hemostasis experiment found that the agarose-based composite hydrogels had a lower BCI value, and the mice treated with the hydrogel sample had lower blood loss and shorter hemostasis time, indicating that the synthesized hydrogels had good hemostatic performance. In addition, a full-layer skin wound infection model demonstrated that the agarose-based composite hydrogels can accelerate the healing of infected wounds, and the wound healing rate of mice treated with the hydrogel sample can reach 97.6 ± 0.8 % at 14 days. Therefore, a biocompatible agarose-based double network composite hydrogel with good mechanical, antibacterial, and hemostatic properties, is expected to be used as a medical dressing to promote the healing of infected wounds.},
}

@article {pmid41241070,
year = {2025},
author = {Kong, L and Wang, H and Sang, R and Saeed, S and Shen, Y and Lai, J and Hu, S},
title = {Down-regulated expressions of LOC151174, GSTT1, and IFI27L1 in the peripheral blood exhibit the biological and immunological features of major depressive disorder.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {120687},
doi = {10.1016/j.jad.2025.120687},
pmid = {41241070},
issn = {1573-2517},
abstract = {BACKGROUND: Mechanism of major depressive disorder (MDD), especially the associations between genetic and peripheral immune changes remain to be elucidated.

METHODS: Databases including Gene Expression Omnibus and GWAS Catalog were investigated and analyzed via differential analyses and summary data-based Mendelian randomization to identify feature genes. Functional annotations, gene-gene interaction network were performed, with immune functions and immune infiltration further analyzed.

RESULTS: Three RNA sequencing datasets and seven genome-wide association study datasets were considered eligible. Genes including LOC151174 (logFC = -0.704, Padjusted = 0.024), GSTT1 (logFC = -0.713, Padjusted = 0.028), and IFI27L1 were identified (logFC = -0.138, Padjusted = 0.043; betaSMR = -0.018, PSMR = 6.714e[-13], PHEIDI = 0.058), and all showed a down-regulated trend in the background of MDD. Functioning pathways including cytokine receptor interaction, ABC transporters, and Ca[2+] signaling pathways were shared by more than one feature gene. As for immune function, scores of antigen presenting cell co-inhibition, natural killer cells, and T cell co-inhibition were significantly higher in the group with low-expression of GSTT1 (P < 0.001), score of T cell co-inhibition was higher in the group with high-expression of IFI27L1 (P < 0.01), and score of dendritic cells was higher in the group with high-expression of both LOC151174 (P < 0.01) and IFI27L1 (P < 0.05). Macrophages M0 showed the highest significance of immune infiltration (P < 0.001). Moreover, expression of GSTT1 showed significant correlation with the activity of plasma cells (R = -0.2, P = 0.041) and activated memory CD4(+) T cells (R = -0.2, P = 0.045).

CONCLUSION: Our work indicates that peripheral expressions of LOC151174, GSTT1, and IFI27L1 might be correlated with MDD particularly through peripheral immune abnormalities.},
}

@article {pmid41240747,
year = {2025},
author = {Rosenblum, D and Karandinos, G and Unick, J and Cauchon, D and Ciccarone, D},
title = {Early evidence of the effects of xylazine-adulterated fentanyl in Ohio.},
journal = {The International journal on drug policy},
volume = {146},
number = {},
pages = {105066},
doi = {10.1016/j.drugpo.2025.105066},
pmid = {41240747},
issn = {1873-4758},
abstract = {BACKGROUND: Xylazine is becoming a prevalent fentanyl adulterant in the US. It has been associated with severe wounds and withdrawal symptoms. However, its impact on fatal overdose rates is poorly understood.

METHODS: Poisson and ordinary least squares regression analyses are used to estimate the relationship between xylazine prevalence and unintentional overdose death and death rates at the county-month level in Ohio from April through December 2023. Xylazine prevalence is calculated from the Ohio Bureau of Criminal Investigation's (BCI) Crime Lab Data, and mortality data is from the Ohio Department of Health.

RESULTS: Xylazine prevalence is positively correlated with overdose deaths and death rates in large population counties. Xylazine adulteration is associated with 319 more overdose deaths [95 percent CI: 147-491 deaths], 10 percent of all unintentional overdose deaths in Ohio, over the nine-month period. Our estimates predict that if all fentanyl had been adulterated with xylazine over these nine months, this would have led to an additional 519 deaths.

DISCUSSION: Although the data covers a limited time period, our estimates provide evidence that xylazine-adulterated fentanyl is likely to lead to additional overdose deaths as it continues to spread across the US, blunting the initial signs of a declining trend in overdose deaths. If the findings can be extrapolated to the rest of the country, it is likely that overdose deaths would have fallen more substantially in 2023 if xylazine had not already been so prevalent in large parts of the US.},
}

@article {pmid41239595,
year = {2025},
author = {Deng, X and Lai, K and Huang, W and Liao, F},
title = {A retrospective study on the clinical efficacy of pneumatic hand rehabilitation devices in managing post-stroke chirospasm following ischemic stroke.},
journal = {Medicine},
volume = {104},
number = {46},
pages = {e45389},
doi = {10.1097/MD.0000000000045389},
pmid = {41239595},
issn = {1536-5964},
support = {2022A01146//Research on Wearable Brain-Computer Interface for Robotic Rehabilitation of Hand Function after Stroke/ ; },
mesh = {Humans ; Retrospective Studies ; Male ; Female ; Middle Aged ; Aged ; *Ischemic Stroke/complications ; *Hand/physiopathology ; *Stroke Rehabilitation/methods/instrumentation ; *Muscle Spasticity/etiology/rehabilitation ; Activities of Daily Living ; Treatment Outcome ; Quality of Life ; *Intermittent Pneumatic Compression Devices ; },
abstract = {Chirospasm is a common sequela of ischemic stroke (IS), often resulting in substantial impairment of hand function and quality of life. Although conventional rehabilitation can partially improve motor recovery, it is often insufficient in effectively reducing spasticity and edema, thereby necessitating adjunctive interventions. This retrospective study aimed to evaluate the effectiveness of a pneumatic hand rehabilitation device in improving hand function and alleviating spasticity in IS patients with chirospasm. Clinical data from 76 patients with chirospasm following IS, treated at our institution between March 2022 and March 2024, were retrospectively analyzed. Patients were divided into 2 groups based on treatment modality: a control group receiving standard rehabilitation therapy and an intervention group receiving additional treatment with a pneumatic hand rehabilitation device. Key evaluation indicators included metacarpophalangeal joint circumference, finger swelling volume, hand function scores (STEF, Fugl-Meyer, MFT), spasticity grading (Ashworth and MAS), neurological deficit indices, pain scores (Visual Analogue Scale), and activities of daily living (ADL). Clinical efficacy was assessed at baseline and after 8 weeks of treatment. Both groups demonstrated improvements after treatment; however, the intervention group showed significantly greater reductions in joint circumference, finger swelling, and muscle tone, as well as higher improvements in hand function scores (P < .05). Notably, Visual Analogue Scale scores were lower and ADL scores were higher in the intervention group. Furthermore, the total effective rate in the intervention group (94.74%) was significantly higher than that in the control group (76.32%). This retrospective analysis suggests that pneumatic hand rehabilitation devices, when integrated with conventional therapy, are more effective in reducing spasticity, alleviating hand edema, improving hand motor function, and enhancing quality of life in post-IS patients with chirospasm. These findings support the broader clinical application of such devices in stroke rehabilitation programs.},
}

Humans
Retrospective Studies
Male
Female
Middle Aged
Aged
*Ischemic Stroke/complications
*Hand/physiopathology
*Stroke Rehabilitation/methods/instrumentation
*Muscle Spasticity/etiology/rehabilitation
Activities of Daily Living
Treatment Outcome
Quality of Life
*Intermittent Pneumatic Compression Devices

@article {pmid41238552,
year = {2025},
author = {Zou, Q and Zou, G and Wang, S and Wang, Y and Xu, J and Long, Y and Zhou, S and Wu, X and Yang, G and Qin, L and Su, ZH and Cui, Z and Zuo, XN and Tang, X and Rao, H and Gao, JH},
title = {Cortical hierarchy underlying homeostatic sleep pressure alleviation.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {10014},
pmid = {41238552},
issn = {2041-1723},
mesh = {Humans ; Adult ; *Homeostasis/physiology ; Magnetic Resonance Imaging ; Male ; *Sleep/physiology ; Female ; Electroencephalography ; Sleep Deprivation/physiopathology ; *Cerebral Cortex/physiology/diagnostic imaging ; Young Adult ; Wakefulness/physiology ; Oxygen/blood ; Brain Mapping ; Middle Aged ; },
abstract = {Sleep dissipates accumulated sleep pressure and restores brain function, yet how this recovery unfolds across the cortical hierarchy remains unclear. Here, we record simultaneous electroencephalogram (EEG) and blood oxygen level-dependent (BOLD) functional magnetic resonance imaging data from 130 healthy adults to map spatial patterns underlying sleep pressure alleviation. Compared to wakefulness, sleep elicits spatially heterogeneous changes in BOLD fluctuation along a sensory-association cortical gradient. The magnitude of these sleep-wake differences correlates with individual slow-wave activity and is most pronounced during the first hour of sleep. As slow waves dissipates, these hierarchical differences are progressively downscaled, implicating homeostatic regulation in sculpting cortical plasticity. In addition, the homeostatic regulation of BOLD fluctuation amplitude is spatially associated with the regional distribution of glycolysis. Finally, recovery sleep reinstates hierarchical BOLD dynamics after sleep loss in an independent sleep deprivation study. These findings consistently suggest a cortical hierarchy underlying the dynamic changes in sleep homeostasis.},
}

Humans
Adult
*Homeostasis/physiology
Magnetic Resonance Imaging
Male
*Sleep/physiology
Female
Electroencephalography
Sleep Deprivation/physiopathology
*Cerebral Cortex/physiology/diagnostic imaging
Young Adult
Wakefulness/physiology
Oxygen/blood
Brain Mapping
Middle Aged

@article {pmid41237236,
year = {2025},
author = {Li, F and Wang, G and Genon, S and Eickhoff, SB and He, R and Yi, C and Dong, D and Yao, D and Jiang, L and Wu, W and Xu, P},
title = {Mapping neurophysiological and molecular profiles of heterogeneity and homogeneity in schizophrenia-bipolar disorder.},
journal = {Science advances},
volume = {11},
number = {46},
pages = {eadz0389},
pmid = {41237236},
issn = {2375-2548},
mesh = {Humans ; *Bipolar Disorder/physiopathology/metabolism/diagnosis ; *Schizophrenia/physiopathology/metabolism/diagnosis ; Adult ; Male ; Female ; Electroencephalography ; Machine Learning ; Middle Aged ; Psychotic Disorders/physiopathology ; Brain Mapping ; },
abstract = {The heterogeneity of psychotic disorders leads to instability in subjectively defined diagnoses. This study used a machine learning framework termed common orthogonal basis extraction (COBE) to decompose electroencephalography-based functional connectivity (FC) in patients with psychotic bipolar disorder (PBD), schizophrenia (SCZ), and schizoaffective disorder (SAD) into individualized and shared subspaces. The results demonstrated that individualized FCs captured disease heterogeneity and predicted symptom severity more accurately than raw FCs, while shared FCs revealed diagnosis-specific abnormalities and achieved an accuracy of 79.30% in differentiating PBD, SCZ, and SAD. Furthermore, molecular decoding implicated regionally selective serotonin systems and astrocytes in the neurobiological differences among disorders, suggesting disorder-specific pharmacological targets. Critically, these findings were replicated in an independent cohort, confirming the effectiveness of the COBE framework in mining neurophysiological and molecular profiles of schizophrenia-bipolar disorder. These findings advance mechanistic understanding of psychotic disorders and offer a promising avenue toward objective, clinically relevant tools for psychotic evaluation.},
}

Humans
*Bipolar Disorder/physiopathology/metabolism/diagnosis
*Schizophrenia/physiopathology/metabolism/diagnosis
Adult
Male
Female
Electroencephalography
Machine Learning
Middle Aged
Psychotic Disorders/physiopathology
Brain Mapping

@article {pmid41236698,
year = {2025},
author = {Cai, M and Liu, H and Shao, C and Li, T and Jin, J and Liang, Y and Wang, J and Cao, J and Yang, B and He, Q and Shao, X and Ying, M},
title = {Metabolomics and metabolites in cancer diagnosis and treatment.},
journal = {Molecular biomedicine},
volume = {6},
number = {1},
pages = {109},
pmid = {41236698},
issn = {2662-8651},
support = {U23A20534//National Natural Science Foundation of China/ ; LR23H310001//Science Fund for Distinguished Young Scholars of Zhejiang Province/ ; LR24H310001//Science Fund for Distinguished Young Scholars of Zhejiang Province/ ; 2024C03181//"Pioneer" and "Leading Goose" R&D Program of Zhejiang Province/ ; 226-2024-00178//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; *Metabolomics/methods ; *Neoplasms/diagnosis/metabolism/therapy ; Biomarkers, Tumor/metabolism ; *Metabolome ; Prognosis ; },
abstract = {Cancer is a leading cause of death worldwide. Metabolic reprogramming in cancers plays an important role in tumor initiation, malignant progression and therapeutic response. Based on this, significant progress has been made in the development of the metabolite-based early cancer detection and targeted interventions. Over the past decade, metabolomics has been widely applied to detect metabolic alterations in tumor cells as well as their microenvironment. However, an up-to-date systematic review to summarize the current metabolomic and metabolites in cancer, especially their connections to cancer diagnostics/prognostic biomarkers and therapeutic strategies, is lacking. Here, we first introduced the platforms and analytical processes of metabolomics, as well as their application in different biological matrix of tumor patients. Then, we summarized representative cancer studies in which specific metabolites was found to be act as diagnostic or prognostic/stratification biomarkers. Furthermore, we reviewed the current therapeutic strategies targeting cancer metabolism, particularly the drugs/compounds that are either market-approved or in clinical trials, and also analyzed the potential of metabolites in personalizing precision treatment. Finally, we discussed the key challenges in this field, including the technical limitations of metabolomics and the clinical limitations of therapeutic targeting cancer metabolism, and further explored the future directions such as multi-omics perspective and lifestyle interventions. Taken together, we provides a comprehensive overview from technological platforms of metabolomics to translational applications of metabolites, facilitating the discovery of novel biomarkers and targeting strategies for precision oncology.},
}

Humans
*Metabolomics/methods
*Neoplasms/diagnosis/metabolism/therapy
Biomarkers, Tumor/metabolism
*Metabolome
Prognosis

@article {pmid41235174,
year = {2025},
author = {Ge, J and Wang, J and Zheng, X and Li, M and Wang, F and Xu, G},
title = {A multi-domain graph convolutional network-based prediction model for personalized motor imagery action.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1637018},
pmid = {41235174},
issn = {1662-4548},
abstract = {Motor imagery (MI)-based brain-computer interfaces (BCIs) offer a novel method to decode action imagination. Our previous study demonstrated that actions play a key role in causing individual differences. Cognitive EEG signals showed a positive correlation with MI, reflecting these differences and providing a foundation for predicting suitable MI actions for each individual. This study aimed to propose a multi-domain graph convolutional network (M-GCN) for predicting personalized MI action using cognitive data. The M-GCN extracts time, frequency, and spatial domain features from cognitive tasks to construct multi-domain brain networks using different EEG quantization methods according to the characteristics of the three domains. Subsequently, the M-GCN utilizes spectral GCN to learn the topology relationship between EEG channels by analyzing functional connection strength. Finally, for each action, the M-GCN can accurately map cognitive data to the corresponding MI action and output a personalized action for each subject. A subject-independent decoding paradigm with leave-one-subject-out cross-validation is adopted to validate the model on ten subjects. Compared to baseline and single-domain models, the M-GCN achieves the highest prediction accuracy of 73.60% (p = 7.1 × 10[-3]), improving by 15.87% (p = 2.0 × 10[-4]) and by 7.2% (p = 4.0 × 10[-4]), respectively. This study proves that the M-GCN can precisely predict personalized MI actions, reflecting the efficiency of the multi-domain feature fusion based on cognitive tasks and GCN and offering a novel method for personalized BCI.},
}

@article {pmid41235025,
year = {2025},
author = {Chen, ZJ and Huang, XL and Xia, N and Gu, MH and Xu, J and Lu, M and Chen, H and Xiong, CH and Chen, Y},
title = {Next-Generation Neurotechnologies Inspired by Motor Primitive Model for Restoring Human Natural Movement.},
journal = {Research (Washington, D.C.)},
volume = {8},
number = {},
pages = {0942},
pmid = {41235025},
issn = {2639-5274},
abstract = {Advances in neuroengineering and artificial intelligence are transforming the landscape of motor rehabilitation, aiming to restore human movement as natural as possible. In recent decades, more advanced interventions are increasingly achievable via hybrid robotic systems, neuroprosthetics, and brain-computer interfaces. However, a fundamental gap of these neurotechnologies remains in modeling the complexity of neuromotor control, particularly how the central nervous system coordinates high-dimensional motor outputs in naturalistic behaviors. Rooted in theoretical neuroscience, the motor primitive (MP) model proposes an adaptable framework to deconstruct and reproduce motor tasks through low-dimensional modules. Interestingly, recent studies have indicated that the MP model may reform current-generation neurotechnologies by digitally shaping the course of human-machine interaction. In this narrative review, we will critically examine conventional target settings and identify their limitations in guiding biomimetic control in neurotechnologies. We then introduce the MP model with its machine learning and physiological scaffolds for better understanding and replicating human natural movement. Finally, we will present its potential in facilitating the next-generation neurotechnologies across kinematic, muscular, and neural domains. By modeling motor control in human and neuroengineering, we believe that the MP-inspired paradigms can initiate a new era of intelligent, patient-specific, and naturalistic motor restoration for various neurological and traumatic diseases.},
}

@article {pmid41233249,
year = {2025},
author = {Yu, Y and Wang, Z and Kroemer, NB and Zhang, L and Lu, L and Sun, J},
title = {Closed-loop brain-body interface: integrating brain-computer interfaces and peripheral nerve stimulation for adaptive neuromodulation.},
journal = {Science bulletin},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.scib.2025.10.037},
pmid = {41233249},
issn = {2095-9281},
}

@article {pmid41232683,
year = {2025},
author = {Cai, X and Sun, W and Zheng, X and Ding, N and Luo, M and Tu, Y and Meng, D and Liu, Y and Ding, S and Yuan, B and Long, X},
title = {Safety and efficacy of low intensity transcranial ultrasound stimulation for depression: A single-blind randomized controlled clinical study.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {120666},
doi = {10.1016/j.jad.2025.120666},
pmid = {41232683},
issn = {1573-2517},
abstract = {AIMS: This study aimed to confirm the safety and effectiveness of transcranial ultrasound stimulation (TUS) in treating depression by targeting a subregion of the left dorsolateral prefrontal cortex (dlPFC).

METHODS: A single-blind, randomized, sham-controlled clinical study was conducted involving 24 patients with depression in the TUS group and 12 in the sham group. Participants underwent psychiatric assessments and functional MRI scans. We employed an MR-compatible transducer, integrating dual navigation through optical guidance and MR acoustic radiation force imaging, to accurately target Brodmann area 46 (BA46) of the left dlPFC. The treatment group received active TUS, while the sham group received identical treatment without energy output, followed by actual TUS treatment.

RESULTS: Following treatment, the TUS group exhibited significant improvements in depression and anxiety scores, as well as sleep quality, with benefits lasting up to four weeks. The sham group showed minor improvements after sham stimulation, but these became significant after subsequent real TUS treatment. Functional connectivity analysis revealed changes in the TUS group, particularly in connectivity with regions implicated in emotion processing, including the subgenual anterior cingulate cortex, ventral posterior cingulate cortex, and precuneus, all of which correlated with symptom improvements. Adverse effects of TUS were minimal and well-tolerated.

CONCLUSION: This study underscores the potential of low-intensity TUS as a safe and effective treatment for depression, with the capacity to modulate neural activity in targeted brain areas. Future research should emphasize optimizing TUS parameters and exploring its effects on other brain regions linked to depression.},
}

@article {pmid41232376,
year = {2025},
author = {Zhang, H and Xie, J and Liu, K and Liu, Y and Dong, W},
title = {Dual-TTFNet: An end-to-end dual-branch temporal and time-frequency fusion network for auditory attention decoding in steady state motion auditory evoked potential.},
journal = {Computers in biology and medicine},
volume = {199},
number = {},
pages = {111284},
doi = {10.1016/j.compbiomed.2025.111284},
pmid = {41232376},
issn = {1879-0534},
abstract = {Auditory attention decoding based on steady-state motion auditory evoked potential (SSMAEP) offers a promising pathway for developing auditory brain-computer interface (BCI) driven by auditory selective attention. However, achieving high decoding performance with strong interpretability remains a major challenge. To address this issue, we proposed an end-to-end dual-branch neural network that fuses temporal and time-frequency information (Dual-TTFNet) to enhance SSMAEP decoding performance. The model consisted of a temporal convolutional branch and a time-frequency branch with learnable S-transform convolutional kernels for modeling of time-frequency patterns. To further strengthen inter-branch interactions, bidirectional cross-branch EEG channel attention mechanism and attention mechanism-based Transformer was introduced to achieve deep integration of temporal and time-frequency representations. Experiments on two and three-target SSMAEP-BCI datasets demonstrate that Dual-TTFNet consistently outperforms state-of-the-art methods under various tasks, time windows, and EEG channel configurations. It achieved accuracies of 95.08 ± 7.46 % (two-class) and 91.50 ± 4.90 % (three-class) at 5 s, with information transfer rate of 7.94 ± 3.08 bits/min and 11.06 ± 2.35 bits/min, respectively. Ablation studies and visualization analyses further validated the crucial role of the attention mechanisms and S-transform kernels in enhancing feature discriminability and neural interpretability. Dual-TTFNet achieves a synergistic optimization of SSMAEP-BCI decoding performance and interpretability, demonstrating excellent generalization ability and application potential.},
}

@article {pmid41231905,
year = {2025},
author = {Ergün, E and Aydemir, Ö and Korkmaz, OE},
title = {A novel scrolling text reading paradigm for improving the performance of multiclass and hybrid brain computer interface systems.},
journal = {PloS one},
volume = {20},
number = {11},
pages = {e0334784},
pmid = {41231905},
issn = {1932-6203},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Male ; Female ; Adult ; *Reading ; Spectroscopy, Near-Infrared ; Young Adult ; Algorithms ; *Brain/physiology ; },
abstract = {A Brain-Computer Interface (BCI) enables direct communication between the brain and external devices, such as computers or prosthetic limbs. This allows the brain to send commands while receiving sensory feedback from the device. Despite their potential, the performance limitations of existing BCI systems have motivated researchers to improve their efficiency and reliability. To address this challenge, the present study introduces a novel BCI paradigm centered on a cognitive task involving the reading of scrolling text in four different directions: right, left, up and down. The primary objective was to explore the electroencephalography (EEG) and near-infrared spectroscopy (NIRS) signals within this framework and assess the potential of hybrid BCI systems based on this innovative paradigm. The experimental protocol involved eight participants performing tasks across four classes of scrolling text. To optimize system accuracy and speed, EEG and NIRS data were segmented into discrete temporal windows. Features were extracted using the Hilbert Transform, while classification was performed via the k-nearest neighbor algorithm. The proposed approach achieved a classification accuracy of 96.28% [Formula: see text] 1.30% for multi-class tasks, demonstrating the effectiveness of hybrid modalities. This study not only introduces a novel paradigm for hybrid BCI systems, but also validates its performance, providing a promising direction for advancing the field.},
}

Humans
*Brain-Computer Interfaces
Electroencephalography
Male
Female
Adult
*Reading
Spectroscopy, Near-Infrared
Young Adult
Algorithms
*Brain/physiology

@article {pmid41231687,
year = {2025},
author = {Chang, TY and Wang, JB and Tsai, YH and Tsao, Y and Yang, CH},
title = {A 40-nm 3.9mW, 200words/min Neural Signal Processor in Speech Decoding for Brain-Machine Interface.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBCAS.2025.3625650},
pmid = {41231687},
issn = {1940-9990},
abstract = {Brain-machine interface (BMI) technology enables the human brain to communicate directly with machines. This work presents a neural signal processor for real-time BMI, supporting translation from user's speech attempt to sentences. By employing speech attempt detection, the energy consumption is reduced by 46% and the number of channels for speech attempt detection can be decreased from 128 to 16. The proposed weight encoding, which leverages both sparse encoding and mixed-precision arithmetic, reduces the off-chip memory size of the neural network by 80%. Computation reordering decreases the processing latency by 55%. For the partial sum caching technique, the number of neural network operations is reduced by 25%. The processing element (PE) array in the neural network engine exploits both input and weight sparsity to lower the processing latency by 95%. By using the proposed mixed-precision multiplier in the PE array, the area is reduced by 27% compared with the PE array with the full precision. In the beam search engine, the proposed approximate top-k selection architecture exhibits 16× fewer comparators. The neural signal processor achieves speech decoding with a phone error rate of 16.6% and a word error rate of 23.5%. Fabricated in 40-nm CMOS, the chip achieves the maximum communication rate of 200 words/min, which is 16.7-to-42.6× faster than the state-of-the-art designs. This work is able to decode up to 125,000 words, which is not achievable by prior works that can only decode up to 31 characters.},
}

@article {pmid41230997,
year = {2025},
author = {Tang, R and Sun, C and Chang, J and Ju, Z and Si, Y and Yang, Y and Shi, Y and Wu, J and Ye, Y and Bao, K and Deng, Q and Wu, Y and Jian, J and Chen, Z and Wang, Y and Sun, H and Wang, Y and Ji, B and Lin, H and Li, L},
title = {Ambient-Stable NIR Nanolasing: Monolithic Integration of PbS CQDs on a Silicon Photonic Platform.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e16460},
doi = {10.1002/adma.202516460},
pmid = {41230997},
issn = {1521-4095},
support = {2024SDXHDX0005//"Pioneer" and "Leading Goose" R&D Program of Zhejiang Province/ ; 62175202//National Natural Science Foundation of China/ ; 62205274//National Natural Science Foundation of China/ ; 2023GD003/110500Y0022303//Key Project of Westlake Institute for Optoelectronics/ ; 210230006022302/002//Research Center for Industries of the Future (RCIF) at Westlake University/ ; },
abstract = {Nanolasers based on colloidal quantum dots (CQDs), while transformative in the visible spectrum, face critical roadblocks in the near-infrared (NIR) regime due to material instability under ambient conditions and ultrafast Auger recombination in large NIR CQDs. Here, these limitations are addressed through zinc-doped PbS CQDs that suppress nonradiative decay, integrated with compact high-Q silicon nanobeam cavities to leverage the Purcell effect for efficiently guiding spontaneous emission into laser modes, thereby significantly reducing the threshold power. This work demonstrates a monolithic CQD-integrated silicon photonic platform that achieves NIR lasing under pulsed optical pumping, featuring a record narrow linewidth of 0.29 nm (0.15 meV) at 1579.20 nm and an ultralow threshold of 127 µJ cm[-2]. Notably, under continuous-wave (CW) pumping, the device exhibits cavity-filtered spontaneous emission with a sub-nanometer linewidth across the 1350-1600 nm spectrum. This emission showcases <6% peak power decay over 15 h at 300 K, robust performance up to 360 K, and negligible degradation after 250 days of ambient storage. By monolithically integrating solution-processed CQDs with CMOS-compatible silicon photonics, this platform establishes a reliable, scalable, and low-cost route toward multiwavelength on-chip nanolaser arrays in the NIR regime, unlocking transformative potential for compact photonic technologies in imaging, sensing, and communications.},
}

@article {pmid41230692,
year = {2025},
author = {Shao, X and Xia, Z and Cai, M and Shao, C and Bing, S and Wang, T and Du, W and Liu, J and Shen, D and Cao, J and Yang, B and He, Q and Xu, X and Zhang, J and Ying, M},
title = {Chromosomal rearrangement-enhanced mRNA stability drives the oncogenic potential of fusion genes in pediatric leukemia.},
journal = {Haematologica},
volume = {},
number = {},
pages = {},
doi = {10.3324/haematol.2025.288256},
pmid = {41230692},
issn = {1592-8721},
abstract = {Acute lymphoblastic leukemia (ALL), the most common type of pediatric leukemia, is frequently driven by fusion genes generated by chromosomal rearrangements. Compared with wild-type genes, many oncogenic fusions show increased expression and sustained functional activity that drives tumorigenesis. However, the mechanisms by which chromosomal rearrangements lead to functional enhancement remain largely elusive. In addition, although large-scale sequencing has identified numerous fusion events, the functional significance of most remains unclear. Here, we demonstrate that enhanced mRNA stability represents an important tumorigenic mechanism for oncogenic fusions, including classical PAX5 fusions. Based on this mechanism, we characterize a novel oncogenic fusion, STK38-PXT1, which exhibits upregulated STK38 mRNA levels and drives the development of ALL. Mechanistically, the increased mRNA stability results primarily from enhanced m6A modification of oncogenic fusions, which is attributable to "gene truncation" (as in PAX5 fusions) and "partner collaboration" (as in STK38-PXT1). Furthermore, the m6A reader IGF2BP3 is crucial for maintaining the high mRNA stability of oncogenic fusions. We further propose venetoclax as an innovative and clinically available therapy for ALL driven by these oncogenic fusions characterized by high mRNA stability. Our study not only highlights mRNA stabilization as a crucial mechanism by which oncogenic fusions to drive tumorigenesis, but also presents a promising therapeutic strategy for patients with ALL.},
}

@article {pmid41224809,
year = {2025},
author = {Zhang, J and Zhang, ZY and Wang, YL and Zhou, B and Xia, CY and Su, M and Li, CG},
title = {Unveiling the upper-limb functional recovery mechanisms in stroke patients using brain-machine interfaces: a near-infrared functional imaging-based study.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {39704},
pmid = {41224809},
issn = {2045-2322},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Upper Extremity/physiopathology ; *Stroke Rehabilitation/methods ; Middle Aged ; Spectroscopy, Near-Infrared/methods ; Aged ; *Recovery of Function/physiology ; *Stroke/physiopathology/diagnostic imaging ; },
abstract = {Upper limb dysfunction is highly prevalent among patients in the chronic stage of stroke. Brain-computer interface (BCI) technology, which creates a direct link between the brain's electrical signals and external devices, stroke patients with motor disabilities are able to perform BCI tasks for clinical rehabilitation. However, traditional BCI applications are often limited in their capacity to monitor the brain function of patients. In this study, functional near-infrared spectroscopy (fNIRS) was employed to observe changes in brain cortex activation patterns before and after BCI use in ischemic stroke patients with upper limb dysfunction. Thirty-four ischemic stroke patients with upper limb dysfunction meeting the inclusion criteria were selected and randomly assigned to either a treatment group or a control group using a random number table, with 17 patients in each group. During the study, 4 participants dropped out, leaving 30 patients for the final statistical analysis, 15 in each group. Both groups received routine upper limb rehabilitation training. Additionally, the treatment group underwent daily BCI training for 30 min, 5 days a week, for 4 consecutive weeks. Upper limb function was evaluated using the Fugl-Meyer assessment for upper extremity (FM), and daily living activities were assessed with the modified barthel index (MBI). The six regions of interest (ROIs) in the cortex for fNIRS measurement were the ipsilesional and contralesional primary motor cortex (PMC), supplementary motor area (SMA), and somatosensory motor cortex (SMC). The three time points of measurement were baseline (prior to any treatment), 2 weeks of treatment, and 4 weeks of treatment. fNIRS was used to detect the oxygenated hemoglobin values (HbO) in six ROIs at each time point. After treatment, both groups exhibited improvements in FM and MBI scores. The treatment group demonstrated significantly greater functional gains than the control group at both 2 and 4 weeks, as reflected in FM (T1T0: 5.867 ± 3.482 vs. 3.200 ± 2.077, P < 0.01, d = 0.93; T2T0: 13.533 ± 5.705 vs. 7.133 ± 2.503, P < 0.05, d = 1.45) and MBI scores (T1T0: 13.400 ± 7.129 vs. 8.133 ± 4.357, P < 0.05, d = 0.89; T2T0: 27.867 ± 10.106 vs. 16.467 ± 7.010, P < 0.05, d = 1.31). fNIRS data revealed that after 4 weeks, the treatment group showed significantly increased oxygenated hemoglobin levels in PMC and SMA compared to baseline (PMC: P < 0.001, d = 0.62; SMA: P < 0.001, d = 0.89), along with a more pronounced PMC activation and higher brain network efficiency relative to the control group (PMC: 0.019 ± 0.017 vs. 0.007 ± 0.005, P < 0.01, d = 1.01; network efficiency: P < 0.05). Moreover, improvements in brain network efficiency were positively correlated with gains in both FM and MBI scores across the cohort. Our study suggests that BCI treatment combined with conventional medical and rehabilitation therapy can effectively enhance motor function and activities of daily living in stroke patients with upper-limb dysfunction. Additionally, it can promote cortical activation in the ipsilesional PMC and SMA regions and improve the network efficiency between brain regions.},
}

Humans
*Brain-Computer Interfaces
Male
Female
*Upper Extremity/physiopathology
*Stroke Rehabilitation/methods
Middle Aged
Spectroscopy, Near-Infrared/methods
Aged
*Recovery of Function/physiology
*Stroke/physiopathology/diagnostic imaging

@article {pmid41223449,
year = {2025},
author = {Jia, Z and Wang, H and Shen, Y and Hu, F and An, J and Shu, K and Wu, D},
title = {Magnetoencephalography (MEG) based non-invasive Chinese speech decoding.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae1ea2},
pmid = {41223449},
issn = {1741-2552},
abstract = {OBJECTIVE: As an emerging paradigm of brain-computer interfaces (BCIs), speech BCI has the potential to directly reflect auditory perception and thoughts, offering a promising communication alternative for patients with aphasia. Chinese is one of the most widely spoken languages in the world, whereas there is very limited research on speech BCIs for Chinese language.

APPROACH: This paper reports a text-magnetoencephalography (MEG) dataset for non-invasive Chinese speech BCIs. It also proposes a multi-modality assisted speech decoding (MASD) algorithm to capture both text and acoustic information embedded in brain signals during speech activities.

MAIN RESULTS: Experiment results demonstrated the effectiveness of both our text-MEG dataset and our proposed MASD algorithm.

SIGNIFICANCE: To our knowledge, this is the first study on multi-modality assisted decoding for non-invasive Chinese speech BCIs.},
}

@article {pmid41223103,
year = {2025},
author = {Sun, J and Meng, J and Wang, H and He, F and Jung, TP and Xu, M and Yu, H and Ming, D},
title = {Joint-Shrinkage Pattern Matching for Small-Sample and Imbalanced ERP Decoding in Brain-Computer Interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3632096},
pmid = {41223103},
issn = {1558-2531},
abstract = {Event-related potential (ERP)-based brain-computer interface (BCI) systems are approaching sub-microvolt-level resolution, enabling detailed decoding of sophisticated cognitive processes. This progress has increased the demand for robust classifiers. Current algorithms encounter two fundamental challenges when decoding ERPs: data scarcity and class imbalance. To address these challenges, we propose a joint-shrinkage pattern matching (JSPM) algorithm consisting of two modules. First, a novel joint-shrinkage spatial filter is constructed by integrating shrinkage-based regularization with the ℓℓ22,pp norm. This regularization approach effectively bridges the gap between complex structured regularization and implementation simplicity, which introduces automated regularization to enhance module robustness under data-scarce conditions. The ℓℓ22,pp-norm provides a flexible feature distance measurement, enabling adaptation to data quality variability. Second, a weighted template matching module mitigates decision boundary shift caused by class imbalance. Using error-related potentials (ErrPs) as representative signals, we validated the algorithm through comprehensive comparisons. JSPM significantly outperformed 14 state-of-the-art classifiers on one self-collected and two public ErrP datasets. With only 40 imbalanced training samples, it achieved up to 14.84% higher average balanced accuracy (bAcc) than competing methods, maintaining a 4.88% average bAcc advantage over its nearest competitor. Notably, JSPM significantly enhanced inter-class discriminability for ErrP features with approximately 1 μV amplitude, achieving a maximum bAcc enhancement of 8.80%compared to deep learning methods. Overall, JSPM effectively addresses small-sample and imbalanced ERP decoding in BCI systems, facilitating the transition from laboratory research to real-world applications.},
}

@article {pmid41222907,
year = {2025},
author = {Wen, H and Xu, M and Cui, S},
title = {Global research trends in brain-computer interfaces for Alzheimer's disease: a bibliometric perspective.},
journal = {International journal of surgery (London, England)},
volume = {},
number = {},
pages = {},
doi = {10.1097/JS9.0000000000004000},
pmid = {41222907},
issn = {1743-9159},
}

@article {pmid41222817,
year = {2025},
author = {Şahin, E and Özdemir, D},
title = {ThinkSTra: a transformer-driven architecture for decoding imagined speech from EEG with spatial-temporal dynamics.},
journal = {Medical & biological engineering & computing},
volume = {},
number = {},
pages = {},
pmid = {41222817},
issn = {1741-0444},
support = {125E067//Scientific and Technological Research Council of Türkiye (TUBITAK)/ ; },
abstract = {Brain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices without requiring physical movement, offering a transformative solution particularly for individuals with impaired or lost motor functions. By providing an alternative communication pathway, BCIs hold considerable promise for both clinical interventions and cognitive neuroscience research. In this study, we introduce ThinkSTra, a novel Transformer-based framework for classifying inner speech commands from electroencephalography (EEG) signals. Unlike conventional models, ThinkSTra jointly captures the temporal dynamics and spatial distributions of neural activity, thereby enabling a more comprehensive representation of the complex structure inherent in EEG signals. We systematically evaluated ThinkSTra on multiple datasets, including the sentence-level TSEEG dataset and the Kumar EEG datasets encompassing character, digit, and visual object classification. To rigorously examine its robustness and generalizability, we additionally performed region- and channel-wise contribution analyses, conducted pretraining and cross-validation experiments, and visualized the learned feature representations using t-SNE. ThinkSTra consistently surpassed existing state-of-the-art approaches, achieving accuracies of 100% on sentence-level, 98.10% on character recognition, 98.34% on digit classification, and 99.5% on visual object tasks. Overall, this study advances inner speech decoding by introducing a robust Transformer-based framework and uncovering how distinct cortical regions contribute to this process, offering both methodological and neuroscientific insights for future brain-computer interfaces.},
}

@article {pmid41221369,
year = {2025},
author = {Lee, JY and Lee, S and Mishra, A and Yan, X and McMahan, B and Gaisford, B and Kobashigawa, C and Qu, M and Xie, C and Kao, JC},
title = {Brain-computer interface control with artificial intelligence copilots.},
journal = {Nature machine intelligence},
volume = {7},
number = {9},
pages = {1510-1523},
pmid = {41221369},
issn = {2522-5839},
support = {DP2 NS122037/NS/NINDS NIH HHS/United States ; R01 NS121097/NS/NINDS NIH HHS/United States ; },
abstract = {Motor brain-computer interfaces (BCIs) decode neural signals to help people with paralysis move and communicate. Even with important advances in the past two decades, BCIs face a key obstacle to clinical viability: BCI performance should strongly outweigh costs and risks. To significantly increase the BCI performance, we use shared autonomy, where artificial intelligence (AI) copilots collaborate with BCI users to achieve task goals. We demonstrate this AI-BCI in a non-invasive BCI system decoding electroencephalography signals. We first contribute a hybrid adaptive decoding approach using a convolutional neural network and ReFIT-like Kalman filter, enabling healthy users and a participant with paralysis to control computer cursors and robotic arms via decoded electroencephalography signals. We then design two AI copilots to aid BCI users in a cursor control task and a robotic arm pick-and-place task. We demonstrate AI-BCIs that enable a participant with paralysis to achieve 3.9-times-higher performance in target hit rate during cursor control and control a robotic arm to sequentially move random blocks to random locations, a task they could not do without an AI copilot. As AI copilots improve, BCIs designed with shared autonomy may achieve higher performance.},
}

@article {pmid41219281,
year = {2025},
author = {Bhaskara, S and Shabari Girishan, KV and Murugaiyan, S and Dwivedi, AA and Krishnakumaran, R and Pandya, HJ},
title = {An L-shaped flexible neural implant for chronic ECoG signal acquisition in M2 region of control and Parkinsonian rat models.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {39461},
pmid = {41219281},
issn = {2045-2322},
support = {/WT_/Wellcome Trust/United Kingdom ; FG/PTCH-22-2098//Pratiksha Trust BCD-Moonshot project/ ; IA/TSG/23/1/600493//DBT/Wellcome Trust India Alliance (India Alliance) Team Science Grant (TSG)/ ; },
mesh = {Animals ; Rats ; Disease Models, Animal ; *Electrocorticography/methods/instrumentation ; *Electrodes, Implanted ; *Parkinson Disease/physiopathology ; Male ; Rats, Sprague-Dawley ; *Motor Cortex/physiopathology ; },
abstract = {Neural implants help understand neurological disorders and are actively used to study deep and cortical brain surface regions. Dealing with cortical surface regions is less complicated in clinical therapy than deep brain regions. Researchers are interested in identifying cortical surface region/s for a particular neurological disorder. Rodent models are extensively used in preclinical studies. Usually, microwires, screws, and grid-type implants are used for such studies, but they are not designed for specific rodent brain regions. Since the grids are typically standard in size, in some cases, the craniotomy required to implant the grid will be significantly bigger than the region of interest, which may pose challenges for chronic studies due to infection. Additionally, the grids may block the nearby brain regions in multisite studies, posing difficulty for another device to be implanted. In this study, a novel L-shaped surface neural implant with five electrodes (diameter: 400 μm) spanning a 1 mm × 3 mm area is fabricated using biocompatible Polyimide material for cortical surface studies. The overall thickness of the neural implant is around 25 μm. The average impedance of the electrodes is 18.315 kΩ at 1 kHz. A bilateral craniotomy is performed to place the neural implants in the secondary motor area for subdural chronic electrocorticography (ECoG) signal acquisition in control and hemi parkinsonian rats. After the recovery period, the ECoG signals are acquired using the Open BCI Cyton Daisy Biosensing board for two weeks from the rats.},
}

Animals
Rats
Disease Models, Animal
*Electrocorticography/methods/instrumentation
*Electrodes, Implanted
*Parkinson Disease/physiopathology
Male
Rats, Sprague-Dawley
*Motor Cortex/physiopathology

@article {pmid41218505,
year = {2025},
author = {Wu, X and Ge, H and Zhao, W and Thummavichai, K and Bi, L and Chen, B},
title = {Multi-functional 3D printed hydrogel electrodes for brain-computer interfaces and wearable sensing.},
journal = {Journal of colloid and interface science},
volume = {704},
number = {Pt 2},
pages = {139418},
doi = {10.1016/j.jcis.2025.139418},
pmid = {41218505},
issn = {1095-7103},
abstract = {In this study, a 3D printing-based polyvinyl alcohol (PVA)/κ-carrageenan (κ-CA)/ carbon nanotubes (CNTs) hydrogel composite (referred to as PCC) was developed for the fabrication of flexible electrodes, targeting applications in brain-computer interfaces (BCIs) and wearable strain sensors. The hydrogel composite exhibited excellent mechanical properties, including a tensile strength of 633 kPa, an elastic modulus of 243 kPa, and a maximum tensile strain of 283 %. In BCI tests, the PCC hydrogel electrode achieved a scalp contact impedance of 76.08 kΩ across five channels, with signal quality comparable to wet electrodes (3.06 μV at 13 Hz stimulation) and significantly higher than dry electrodes (2.16 μV). The decoding accuracy for the PCC hydrogel electrode was 78.2 % with a 1.25 s window length, comparable to the wet electrode, and the information transfer rate (ITR) reached 71.3 bits/min. Furthermore, the hydrogel demonstrated excellent strain sensing performance, with a gauge factor (GF) of 2.7 in the 0-75 % strain range and fast self-recovery, making it a promising material for dynamic wearable sensing devices. This work highlights the successful integration of material optimization and structural design, offering a new approach for development of next-generation flexible bioelectronic devices.},
}

@article {pmid41218224,
year = {2025},
author = {Kenyeres, B and Helmeczi, A and Pytel, Á},
title = {Efficacy of Transurethral Resection of the Prostate in Male Patients With Impaired Detrusor Contractile Function and Urinary Retention.},
journal = {Lower urinary tract symptoms},
volume = {17},
number = {6},
pages = {e70040},
pmid = {41218224},
issn = {1757-5672},
mesh = {Humans ; Male ; *Urinary Retention/surgery/physiopathology/etiology ; *Transurethral Resection of Prostate/methods/adverse effects ; Retrospective Studies ; Aged ; Urodynamics ; *Urinary Bladder, Underactive/surgery/physiopathology/complications ; Treatment Outcome ; Middle Aged ; *Prostatic Hyperplasia/surgery/complications ; Aged, 80 and over ; },
abstract = {OBJECTIVES: Detrusor underactivity (DUA) increasingly affects aging male patients with voiding symptoms, while its management remains challenging, with less favorable surgical outcomes compared to bladder outlet obstruction. Our aim was to evaluate the efficacy of TURP in male patients with urinary retention and unfavorable urodynamic findings.

MATERIALS AND METHODS: This retrospective, single-center study included 67 male patients undergoing TURP between September 2021 and September 2024 after a failed trial of voiding. Patients were divided into three groups labeled as detrusor acontractility (DA, n = 18, voided without detrusor contraction), DUA (n = 19, voided with BCI < 100 and BOOI < 20), or non-voiders (n = 30, failed to urinate and lacked measurable detrusor contractions on pressure-flow study). Surgical success was defined as successful voiding with post-void residual (PVR) < 150 mL at 3 months. Baseline parameters (PSA, prostate volume, cystoscopy and urodynamic findings), rate of surgical success, Patient Global Impression of Improvement (PGI-I) score and adverse events (subsequent surgeries and urinary tract infection) were registered and analyzed.

RESULTS: Overall 37 (55.2%) patients became catheter-free within 3 months. The mean follow-up duration was 25.4 ± 9.6 months. Surgical success was achieved in DA, DUA, and non-voider groups in 6 (33%), 13 (68.4%), and 18 (60%) cases, respectively, and a PGI-I score greater than 4 was reported by 35 (52.2%) patients. Multivariate analysis showed higher prostate volume as an independent predictor for failure (OR: 1.7; 95% CI: 1.010-2.940; p = 0.046). Two patients developed stress urinary incontinence, and three required additional surgical intervention due to urethral stricture. Urinary tract infections occurred more frequently in the treatment failure group: Nine patients (30%) were hospitalized, and 16 (53%) required more than two antibiotic prescriptions within a 6-month period. In contrast, among the success group, only two patients (5.4%) were hospitalized, and none required frequent antibiotic therapy.

CONCLUSION: TURP offers a reasonable chance for catheter discontinuation in case of unfavorable urodynamic parameters. With careful patient selection in mind, surgery remains a viable option even in this patient population.},
}

Humans
Male
*Urinary Retention/surgery/physiopathology/etiology
*Transurethral Resection of Prostate/methods/adverse effects
Retrospective Studies
Aged
Urodynamics
*Urinary Bladder, Underactive/surgery/physiopathology/complications
Treatment Outcome
Middle Aged
*Prostatic Hyperplasia/surgery/complications
Aged, 80 and over

@article {pmid41217924,
year = {2025},
author = {Liu, D and Li, S and Wang, Z and Li, W and Wu, D},
title = {SDDA: Spatial Distillation based Distribution Alignment for Cross-Headset EEG Classification.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3631604},
pmid = {41217924},
issn = {1558-2531},
abstract = {OBJECTIVE: A non-invasive brain-computer interface (BCI) enables direct interaction between the user and external devices, typically via electroencephalogram (EEG) signals. This paper tackles the problem of decoding EEG signals across different headsets, which is challenging due to differences in the number and locations of the electrodes.

METHODS: We propose a spatial distillation based distribution alignment (SDDA) approach for heterogeneous cross-headset transfer in non-invasive BCIs. SDDA uses first spatial distillation to make use of the full set of electrodes, and then input/feature/output space distribution alignments to cope with the significant differences between the source and target domains.

RESULTS: Extensive experiments on six EEG datasets from two BCI paradigms demonstrated that SDDA achieved superior performance in both offline unsupervised domain adaptation and online supervised domain adaptation scenarios, consistently outperforming 10 classical and state-of-the-art transfer learning algorithms.

SIGNIFICANCE: Our approach enables effective transfer between heterogenous EEG headsets, improving and expediting BCI calibration.},
}

@article {pmid41217916,
year = {2025},
author = {Wu, Z and Chen, Z and He, W and Xie, Q and Pan, J},
title = {Cross-Subject P300-Based Audiovisual BCI System via Continual Learning: A Clinical Application for Disorders of Consciousness.},
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.2025.3631664},
pmid = {41217916},
issn = {1558-0210},
abstract = {This study proposes an advanced cross-subject P300-based audiovisual brain-computer interface (BCI) system to assess consciousness levels and predict clinical outcomes in patients with disorders of consciousness (DOC). The system employs an audiovisual stimulus paradigm, integrating face photos and corresponding name sounds, to enhance the elicitation of P300 signals. It further incorporates a hybrid prototype-based continual learning method (HPC) to improve diagnostic accuracy and robustness. The HPC constructs P300 prototypes for each historical task and selectively integrates both similar and dissimilar prototypes when a new task is introduced. Dissimilar prototypes are hybridized and masked, while similar prototypes are merged via an attention mechanism, effectively preventing catastrophic forgetting. Experimental results demonstrate the efficacy of this approach, with the HPC achieving 98.33% accuracy in a P300 spelling task among healthy subjects and 95.50% accuracy in healthy controls within a clinical setting. Significantly, eight out of ten DOC patients exhibited notable accuracy, underscoring the system's clinical potential. This BCI system thus offers a robust and adaptable solution for assessing consciousness levels and predicting outcomes in DOC patients, contributing to enhanced clinical diagnosis and prognosis.},
}

@article {pmid41217794,
year = {2025},
author = {Filippov, MS and Pogonchenkova, IV and Kostenko, EV and Rassulova, MA and Makarova, MR and Egorov, PD},
title = {[Ideomotor training combining the use with integrated application of electromyostimulation and a robotic brain-computer interface in post-stroke upper limb dysfunction: a randomized controlled trial].},
journal = {Voprosy kurortologii, fizioterapii, i lechebnoi fizicheskoi kultury},
volume = {102},
number = {5},
pages = {5-19},
doi = {10.17116/kurort20251020515},
pmid = {41217794},
issn = {0042-8787},
mesh = {Humans ; Middle Aged ; Male ; Female ; *Upper Extremity/physiopathology ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; *Robotics ; *Stroke/physiopathology/complications/therapy ; *Electric Stimulation Therapy/methods ; Aged ; },
abstract = {UNLABELLED: One of the leading causes that disrupt human interaction with the environment is upper limb (UL) dysfunction, which develops in 48-77% of cases after a stroke. The combination of electromyostimulation (EMS) with neurocomputer interface (NCI) technology demonstrates the greatest clinical effectiveness among various types of sensorimotor BOS, the study of which seems promising.

OBJECTIVE: To study the effect of combined use the integrated of EMS and robotic NCI on the functioning of UL in post-stroke spastic paresis in the early recovery period of ischemic stroke (IS).

MATERIAL AND METHODS: A randomized controlled trial involved 120 patients in the early recovery period of IS with moderate to severe spastic paresis of UL, with an average age of 57.43±3.68 years. By simple randomization, the patients were divided into 4 groups of 30 people each, depending on the medical rehabilitation program (MR). All patients received a basic MR program: therapeutic gymnastics for 30 minutes; magnetic field therapy on the neck and collar area for 20 minutes; therapeutic massage for 20 minutes. The patients of the control group (GC) received only the basic program; The main group (MG) - interval complex multi-purpose EMS of the agonist muscles and antagonist muscles of the forearm in combination with the use of NCI with exoskeletons of both hands; comparison group 1 (CG-1) - training using a robotic NCI; comparison group 2 (CG-2) - EMS. The duration of the MR course is 2 weeks, daily, 5 days a week, 10 treatments for each factor. The effectiveness of MR was evaluated at three control points (T): after completion of 5 procedures (T1) and 10 procedures (T2), 3 months after completion of MR (T3). Assessment tools: Medical Research Committee Scale (MRCs), Modified Ashworth Scale (mAs), The Fugl-Meyer Assessment for upper extremity (FMA-UE), The Action Research Arm Test (ARAT).

RESULTS: Patients with MG demonstrated significant (p<0.05) positive dynamics of recovery of UL function at the end of the MR course and after 3 months. The increase in muscle strength in MG and CG-1 averaged 0.77 and 0.59 points (p<0.05) in the distal muscle group, in CG-2 (0.24 points) and GC (0.21 points), p>0.05 compared with baseline values. Only patients with MG (+7.7 points) achieved a clinically significant difference (Δ) in FMA-UE-total at the end of MR, while patients with CG-1 achieved Δ=+4.9 points. In patients with GC and CG-2, the values of Δ according to FMA-UE-total were comparable (+2.3 and 2.6 points, respectively). According to the ARAT test, only MG patients also achieved a clinically significant difference (+6.2 points). Patients with CG-1 - Δ=+3.5 points. In patients with GC and CG-2, Δ values were comparable (+1.3 and 2.2 points, respectively).

CONCLUSION: Ideomotor training with EMS in MR of impaired VC function in patients with IS, combining stimulation of visual, vestibular, and proprioceptive analyzers with training of cognitive functions, promotes regression of sensorimotor disorders of UL and restoration of manipulative activity.},
}

Humans
Middle Aged
Male
Female
*Upper Extremity/physiopathology
*Stroke Rehabilitation/methods
*Brain-Computer Interfaces
*Robotics
*Stroke/physiopathology/complications/therapy
*Electric Stimulation Therapy/methods
Aged

@article {pmid41216611,
year = {2025},
author = {Li, J and Chen, H and Liao, W},
title = {Mapping the white-matter functional connectome: a personal perspective.},
journal = {Psychoradiology},
volume = {5},
number = {},
pages = {kkaf028},
pmid = {41216611},
issn = {2634-4416},
abstract = {In contemporary neuroscience, mapping the human brain's functional connectomes is essential to understanding its functional organization. Functional organizations in the brain gray matter have been the subject of previous research, but the functional information in white matter (WM), the other half of the brain, has been relatively underexplored. However, the dynamics of functional magnetic resonance imaging (fMRI) have been reliably identified in the brain WM. This review summarizes current knowledge about task-free (resting-state) fMRI neuroimaging analyses for the WM functional connectome. We present comparative findings of the WM functional connectome, including its mapping, physiological underpinnings, cognitive neuroscience relationships, and clinical applications. Furthermore, we explore the emerging consensus that WM functional networks have valid topological characteristics that can distinguish between individuals with brain diseases and healthy controls, predict general intelligence, and identify inter-subject variabilities. Lastly, we emphasize the need for further studies and the limitations, challenges, and future directions for the WM functional connectome. An overview of these developments could lead to new directions for cognitive neuroscience and clinical neuropsychiatry.},
}

@article {pmid41214430,
year = {2025},
author = {Cheng, X and Zhang, R and Chen, P and Song, Z and Cheng, F and Dikker, S and Pan, Y},
title = {Promoting Social Connectedness Through Interbrain Neurofeedback.},
journal = {Annals of the New York Academy of Sciences},
volume = {},
number = {},
pages = {},
doi = {10.1111/nyas.70135},
pmid = {41214430},
issn = {1749-6632},
abstract = {Humans are inherently driven to form meaningful relationships, yet attempts at social connection often fall short or fail. This study investigates whether social connectedness can be improved by modulating interbrain coupling-a neural correlate of successful social interactions-through neurofeedback. Using a multibrain computer interface that visualized, in real time, the degree to which dyad members' electroencephalography (EEG) signals synchronized, dyads were randomly assigned to receive either neurofeedback or sham feedback generated from random signals. Compared with the sham group, dyads receiving neurofeedback showed greater interbrain coupling, and increases in coupling were associated with stronger feelings of social connectedness. A chain-mediation analysis suggested that the experience of enhanced social connectedness was driven by a sense of joint control and shared intentionality. Together, these findings demonstrate the potential of interbrain neurofeedback to modulate interbrain coupling and support key components of social connectedness.},
}

@article {pmid41214065,
year = {2025},
author = {He, Y and Jan, YH and Yang, F and Ma, Y and Chen, XY and Pei, C},
title = {The fatigue status feature of bicycle movement based on deep learning and signal processing technology.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {39328},
pmid = {41214065},
issn = {2045-2322},
support = {2020J01653//The Upper-Level Project of the Natural Science Foundation of Fujian Province/ ; 2020J01653//The Upper-Level Project of the Natural Science Foundation of Fujian Province/ ; 2023J01323//The Upper-Level Project of the Natural Science Foundation of Fujian Province/ ; 2020J01653//The Upper-Level Project of the Natural Science Foundation of Fujian Province/ ; 22SCZZX009//Fujian Special Financial Project for Research/ ; XRCZX2022010//Start-up funds for scientific research of high-level talents, Fujian Medical University/ ; 2023CDPFAT-02//China Disabled Persons' Federation Research Project on Assistive Devices for Persons with Disabilities/ ; },
mesh = {Humans ; Adult ; Male ; *Bicycling/physiology ; Female ; Middle Aged ; *Deep Learning ; *Fatigue/physiopathology ; Young Adult ; *Signal Processing, Computer-Assisted ; Movement/physiology ; Adolescent ; Algorithms ; Aged ; },
abstract = {Cycling is a common and effective home-based rehabilitation exercise. Accurate and accessible assessment of the onset of fatigue is essential to achieving optimal exercise benefits and preventing overuse injuries. To obtain fatigue-related parameters in different age groups, we applied deep learning algorithms and signal processing technology to analyze cycling movement features for the people aged over 45. 20 healthy adults aged over 45 and 20 aged 18-30 were recruited. Participants were asked to ride a stationary exercise bike at their self-regulated pedaling speeds for 10 min and wear a COSMED K5 device to collect physiological signals. The Keypoint RCNN (KR) algorithm and three signal processing methods (Fourier transform, short-time Fourier transform, and multiscale entropy analysis were used to analyze the cycling movement data. Based on time-frequency analysis, subjects' movement status change points were identified when fatigue occurred. Four movement status parameters were calculated, including the peak frequency before/after the movement status change point and the complexity index average (CIA) before/after the movement status change point. Inter-group and intra-group movement features, movement status, and physiological data were compared to determine fatigue-related features. Results showed that the peak frequency (p = 0.005), the peak frequency before/after the change point (p = 0.008/0.019), the CIA after the change point (p = 0.014), the maximum heart rate, maximal oxygen consumption, metabolic equivalents, and energy efficiency exhibited significant inter-group differences. The KR algorithm demonstrated outstanding performance in keypoint detection, achieving an accuracy of 86.5%, significantly outperforming OpenPose. With an inference speed of 30 FPS, it fulfills the demands for real-time monitoring. In addition, CIA valuses before and after change pointsshowed significant differences in the the middle-aged and elderly people group. After the change point, the CIA canidentify movement status changes in inter-group and intra-group comparisons, suggesting it can be used as a indicator of fatigue status, especially for people aged over 45.},
}

Humans
Adult
Male
*Bicycling/physiology
Female
Middle Aged
*Deep Learning
*Fatigue/physiopathology
Young Adult
*Signal Processing, Computer-Assisted
Movement/physiology
Adolescent
Algorithms
Aged

@article {pmid41213447,
year = {2025},
author = {Calabrò, RS and Calderone, A and Simoncini, L and Naro, A and Haughton, LOS and Quartarone, A and Leochico, CFD},
title = {The potential of robotics: A systematic review of neuroplastic changes following advanced lower limb rehabilitation in neurological disorders.},
journal = {Neuroscience and biobehavioral reviews},
volume = {180},
number = {},
pages = {106459},
doi = {10.1016/j.neubiorev.2025.106459},
pmid = {41213447},
issn = {1873-7528},
abstract = {BACKGROUND: Neurological diseases are among the most common pathologies that strongly influence a person's ability to walk and move, affecting the lower extremities. They disrupt motor brain networks that enable precise movement, leading to deficits in gait, balance, and coordination; while conventional therapies remain essential, advances in robotic technologies show growing promise for rehabilitation.

AIM OF REVIEW: This systematic review aims to investigate the role of robotic rehabilitation in improving neuroplasticity and motor outcomes for individuals with neurological disorders, with a particular focus on studies incorporating neurophysiological or neuroimaging techniques to assess neuroplastic changes and their long-term impact on recovery.

A systematic review was carried out utilizing an online search of articles from 2014 to 2025 on the PubMed, Web of Science, Cochrane Library, Embase, EBSCOhost, and Scopus databases in accordance with PRISMA guidelines. Studies were chosen based on predetermined inclusion criteria, with an emphasis on robotic rehabilitation therapies targeted at improving neuroplasticity in lower limb rehabilitation for people with neurological conditions. This review has been registered on Prospero with the following number: CRD42025640347. The search identified 12,769 records; after screening and eligibility assessment, 25 studies met inclusion criteria. Studies demonstrate that robot-assisted gait training (RAGT) and exoskeleton-based therapies improve motor function, gait, balance, and neuroplasticity across stroke, spinal cord injury, cerebral palsy, and brain injury populations. Adjunctive approaches such as brain-computer interface (BCI) integration, virtual reality feedback, and neuromodulation further enhance outcomes, with increases in cortical activation and improvements in functional connectivity supported by convergent neurophysiological and neuroimaging data; changes in corticospinal excitability are also reported. Taken together, robotic interventions, often combined with neuromodulation or virtual reality (VR), appear to catalyze neuroplasticity in ways that align with clinically meaningful gains. These findings underscore their transformative potential for tailored, multimodal rehabilitation strategies in neurological recovery.},
}

@article {pmid41212978,
year = {2025},
author = {Miller, KJ and Abosch, A},
title = {A Moment of Reckoning for Implanted Brain-Computer Interface Studies.},
journal = {Neurosurgery},
volume = {97},
number = {2},
pages = {277-280},
doi = {10.1227/neu.0000000000003585},
pmid = {41212978},
issn = {1524-4040},
}

@article {pmid41212707,
year = {2025},
author = {Fitriah, N and Zakaria, H and Budikayanti, A and Suksmono, AB and Mengko, TLER},
title = {Decoding Speech Imagery: A Spectro-spatial Approach to Electroencephalography Band Power Analysis.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3631062},
pmid = {41212707},
issn = {2168-2208},
abstract = {Decoding speech imagery from brain signals potentially assists individuals with speech impairments. However, limited data and complex brain activity in recent studies have made accurate decoding challenging. We analyzed both the spectral (frequency) and spatial (location) aspects of brain activity to enhance decoding accuracy in small datasets. To our knowledge, previous studies have mainly used generated features without adequately considering spectro-spatial aspects. We trained machine learning with time-frequency representation (TFR) features using a public dataset from the Brain-Computer Interface (BCI) competition (BCI-DB) and our own recordings (PrimAudio-DB). The results showed prominent feature patterns of speech imagery in the frontal region and Gamma band, achieving accuracies of 98.6% in BCI-DB (exceeding benchmarks) and 81.7% in PrimAudio-DB. Moreover, our analysis revealed insights regarding speech differences (language and semantics). This study contributed to non-invasive speech imagery decoding and offered valuable insights for future speech rehabilitation and assistive technologies.},
}

@article {pmid41212618,
year = {2025},
author = {Liu, X and Cao, L and Du, Z and Cui, Y and Saleem, KS and Zhang, Y and Lu, Y and Zhang, B and Liu, Y and Hou, X and Cheng, L and Li, K and Fan, L and Yang, Z and Jiang, T},
title = {Proteomic insights into the macaque insular parcellation based on structural connectivity gradients.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {35},
number = {11},
pages = {},
doi = {10.1093/cercor/bhaf307},
pmid = {41212618},
issn = {1460-2199},
support = {2021ZD0200200//Science and Technology Innovation 2030 - Brain Science and Brain-Inspired Intelligence Project of China/ ; 62327805//National Natural Science Foundation of China/ ; 62336007//National Natural Science Foundation of China/ ; 82151307//National Natural Science Foundation of China/ ; },
mesh = {Animals ; *Proteomics/methods ; Male ; *Insular Cortex/metabolism/diagnostic imaging/anatomy & histology ; Diffusion Magnetic Resonance Imaging/methods ; Neural Pathways/metabolism/diagnostic imaging ; Macaca mulatta ; Macaca ; },
abstract = {Gradients across microstructure, macro-connectivity, and gene expression scales have been identified in the primate brain, offering a continuous perspective to explore regional heterogeneity. The macaque insula, with its extensive connections with other cortical regions and involvement in diverse functions, exhibits gradient transitions at the microstructural level. However, the gradients of macroscopic structural connectivity (SC) and its relationship with gene expression in the macaque insula remain unclear. We hypothesized that SC gradients are closely associated with gene expression, driving insular parcellation. To test this, we analyzed high-resolution diffusion-weighted MR imaging alongside spatially aligned proteomic data. Our findings revealed a rostrocaudal organization of the dominant SC gradient in the macaque insula, leading to the identification of a four-subregion pattern within the insula based on the first two SC gradients. Proteomic profiles strongly correlated with the dominant SC gradient and the clustering of proteomic similarity aligned with the four-subregion pattern. Notably, the dominant SC gradient more effectively captured spatial protein expression variations than T1w/T2w and cortical thickness maps. Overall, this study demonstrated that the SC gradient analysis revealed a four-subregion pattern of parcellation aligned with the spatial distribution of proteomic profiles along the rostro-caudal axis.},
}

Animals
*Proteomics/methods
Male
*Insular Cortex/metabolism/diagnostic imaging/anatomy & histology
Diffusion Magnetic Resonance Imaging/methods
Neural Pathways/metabolism/diagnostic imaging
Macaca mulatta
Macaca

@article {pmid41211047,
year = {2025},
author = {Zhang, N and Hu, BW and Li, XM and Huang, H},
title = {Rethinking parvalbumin: From passive marker to active modulator of hippocampal circuits.},
journal = {IBRO neuroscience reports},
volume = {19},
number = {},
pages = {760-773},
pmid = {41211047},
issn = {2667-2421},
abstract = {Parvalbumin (PV)-expressing interneurons are critical regulators of neural circuit dynamics, and for decades, the PV protein has served as their definitive molecular marker. This review confronts a central, yet underappreciated, paradox: the incongruity of a kinetically slow Ca[2+] buffer (PV) being the defining feature of the brain's fastest-spiking neurons. We synthesize evidence from molecular biophysics, genetics, in vivo circuit analysis, and disease modeling to dissect the dual role of PV as both a cellular marker and an active functional regulator. We argue that PV's slow kinetics are not a coincidence but a crucial adaptation that shapes short-term synaptic plasticity, protects against metabolic stress during high-frequency firing, and allows the circuit to shift between states of plasticity and stability. This reframing resolves the paradox by demonstrating how a "slow" molecule is essential for "fast" neuronal function. Furthermore, we highlight that dysfunction of the PV system is a convergent hub of pathology in numerous neurological and psychiatric disorders, including schizophrenia, epilepsy, and Alzheimer's disease. By moving beyond its identity as a passive marker, we establish PV as an active modulator of neural computation and a potential therapeutic target for restoring network function in disease.},
}

@article {pmid41209581,
year = {2025},
author = {Zeng, YY and Saeed, S and Hu, SH},
title = {Non-Suicidal Self-Injury: Pain Addiction Mechanisms, Neurophysiological Signatures, and Therapeutic Advances.},
journal = {Journal of clinical medicine research},
volume = {17},
number = {10},
pages = {537-549},
pmid = {41209581},
issn = {1918-3003},
abstract = {The aim of this study was to review the neurobiological mechanisms, epidemiology, and therapeutic interventions for non-suicidal self-injury (NSSI), emphasizing the pain addiction model and electroencephalographic biomarkers as frameworks for precision intervention. A narrative review of the literature was conducted using PubMed, Web of Science, CNKI, and Wanfang Data up to October 2025. Search strategy employed the terms "non-suicidal self-injury," "pain addiction," "electroencephalography," "endogenous opioid system," and "HPA axis." Selection criteria prioritized original human studies, high-quality systematic reviews, and mechanistic investigations. Pain addiction and electroencephalography (EEG) were selected as focal variables based on their explanatory power: pain addiction elucidates NSSI perpetuation through endogenous opioid-mediated reward sensitization and dopaminergic reinforcement, while event-related potentials (ERPs) provide temporal precision in mapping cognitive-affective dysregulation underlying emotional impulsivity and regulatory deficits. Global adolescent NSSI prevalence averages 17.2%, with Chinese rates reaching 24.7% and trends toward earlier onset. Neurobiological substrates include fronto-limbic dysregulation, hypoactive hypothalamic-pituitary-adrenal (HPA) axis function with blunted cortisol reactivity, and endogenous opioid system alterations producing widespread hypoalgesia. EEG/ERP studies demonstrate increased N2 amplitude with decreased P3 amplitude and prolonged latency during negative stimuli processing, reflecting impaired conflict monitoring and attentional resource allocation. Dialectical behavior therapy shows established efficacy, while repetitive transcranial magnetic stimulation and opioid antagonists demonstrate therapeutic potential. NSSI emerges from neurobiological vulnerability within pain-reward-emotion circuits interacting with psychosocial factors. The pain addiction framework and EEG signatures provide translatable targets for biomarker development and personalized intervention. Future research requires multimodal neuroimaging, longitudinal designs, and genetic integration to establish predictive algorithms and precision therapeutics.},
}

@article {pmid41209401,
year = {2025},
author = {Lu, Y and Yang, W and Wu, S and Li, Y and Wei, J and Li, M and Li, Y and Huai, Y},
title = {Exploring neural activity changes during motor imagery-based brain-computer interface training with robotic hand for upper limb rehabilitation in ischemic stroke patients: a pilot study.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1626000},
pmid = {41209401},
issn = {1662-5161},
abstract = {OBJECTIVE: This pilot study aimed to evaluate the feasibility and tolerability of motor imagery (MI)-based brain-computer interface (BCI) training with robotic hand assistance for upper limb rehabilitation, and to explore preliminary neural markers in ischemic stroke patients.

METHODS: Three post-stroke participants performed MI tasks combined with exoskeleton-assisted movements to facilitate rehabilitation training. Electroencephalography (EEG) signals were recorded to assess the neural correlates of MI. Functional outcomes were evaluated using standard assessment tools.

RESULTS: Our results demonstrated significant improvements in motor function across all participants. Additionally, EEG analysis revealed event-related desynchronization (ERD) in the high-alpha band power at motor cortex locations, with individual differences in both the frequency and power of neural activity. However, no significant trends in neural activity were observed across the training sessions.

CONCLUSION: These findings suggest that MI-based BCI training, combined with robotic assistance, offer a promising approach for enhancing upper limb function in ischemic stroke patients.},
}

@article {pmid41209398,
year = {2025},
author = {Milyani, AH and Attar, ET},
title = {Deep learning for inner speech recognition: a pilot comparative study of EEGNet and a spectro-temporal Transformer on bimodal EEG-fMRI data.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1668935},
pmid = {41209398},
issn = {1662-5161},
abstract = {BACKGROUND: Inner speech-the covert articulation of words in one's mind-is a fundamental phenomenon in human cognition with growing interest across BCI. This pilot study evaluates and compares deep learning models for inner-speech classification using non-invasive EEG derived from a bimodal EEG-fMRI dataset (4 participants, 8 words). The study assesses a compact CNN (EEGNet) and a spectro-temporal Transformer using leave-one-subject-out validation, reporting accuracy. Macro-F1, precision, and recall.

OBJECTIVE: This study aims to evaluate and compare deep learning models for inner speech classification using non-invasive electroencephalography (EEG) data, derived from a bimodal EEG-fMRI dataset. The goal is to assess the performance and generalizability of two architectures: the compact convolutional EEGNet and a novel spectro-temporal Transformer.

METHODS: Data were obtained from four healthy participants who performed structured inner speech tasks involving eight target words. EEG signals were preprocessed and segmented into epochs for each imagined word. EEGNet and Transformer models were trained using a leave-one-subject-out (LOSO) cross-validation strategy. Performance metrics included accuracy, macro-averaged F1 score, precision, and recall. An ablation study examined the contribution of Transformer components, including wavelet decomposition and self-attention mechanisms.

RESULTS: The spectro-temporal Transformer achieved the highest classification accuracy (82.4%) and macro-F1 score (0.70), outperforming both the standard and improved EEGNet models. Discriminative power was also substantially improved by using wavelet-based time-frequency features and attention mechanisms. Results showed that confusion patterns of social word categories outperformed those of number concepts, corresponding to different mental processing strategies.

CONCLUSION: Deep learning models, in particular attention-based Transformers, demonstrate great promise in decoding internal speech from EEG. These findings lay the groundwork for non-invasive, real-time BCIs for communication rehabilitation in severely disabled patients. Future work will take into account vocabulary expansion, wider participant variety, and real-time validation in clinical settings.},
}

@article {pmid41207999,
year = {2025},
author = {Yang, J and Xia, F and Jin, H and Mahanand, C and Lin, H and Cao, Y and Bian, J and Wei, D and Nevo, E and Du, J and Duan, S and Guo, F and Zhao, Y and Chen, X},
title = {Variations of Corticotropin-Releasing Factor Receptor 1α Contribute to the Blunted HPA Axis Responses to Hypoxia in Plateau Mammals.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {41207999},
issn = {1995-8218},
abstract = {Corticotropin-releasing factor (CRF) and its receptor (CRFR1) are critical components of the hypothalamic-pituitary-adrenocortical (HPA) axis. Ochotona curzoniae (O. curzoniae), Myospalax baileyi (M. baileyi), and Microtus oeconomus (M. oeconomus) have diversely evolved adaptive strategies to the extreme environment at high altitude. Here, we found blunted HPA axis responsiveness in native Tibetan mammals. CRF was 100% conserved, three amino-acid variations were in M. oeconomus-urocortin (UCN), and unique amino-acid variations in ligand-receptor binding domains of O. curzoniae-, M. baileyi-, and M. oeconomus-CRFR1αs. The native mammals' binding affinity and cAMP production varied depending on different doses of ligand-CRF/UCN treatment. Variations in M. oeconomus-UCN and O. curzoniae-, M. baileyi-, M. oeconomus-CRFR1α were responsible for weaker CRF-CRFR1α binding and higher EC50. They had the same HPA response pattern as that of CRF-CRFR1α binding affinity, cAMP production, and cell permeability. AlphaFold3.0 predicted altered structural interactions for both CRF-CRFR1α and UCN-CRFR1α complexes corroborate our findings. This study reveals that the variations of UCN/CRFR1α contribute to the different responsiveness of the HPA axis to extreme environments.},
}

@article {pmid41207468,
year = {2025},
author = {Upadhyay, PK and Chandra, KA},
title = {Quantum enhanced EEG classifier towards brain-controlled wheelchair navigation.},
journal = {Neuroscience},
volume = {591},
number = {},
pages = {1-20},
doi = {10.1016/j.neuroscience.2025.10.047},
pmid = {41207468},
issn = {1873-7544},
abstract = {Brain-computer interfaces (BCIs) provide a pathway to assistive technologies such as brain-controlled wheelchairs, yet accurate motor imagery (MI) classification from electroencephalography (EEG) remains challenging due to noise and subject variability. In this work, we propose a hybrid Quantum Enhanced CNN-LSTM model EEG Classifier (HQeCL), incorporating a simulated quantum pooling layer for richer feature abstraction. The framework integrates power spectral density (PSD) from the frequency domain, common spatial patterns (CSP) from the spatial domain, and quantum entropy from the non-linear domain to capture complementary EEG characteristics. The model was evaluated using leave-one-subject-out (LOSO) cross-validation on the 8-channel motor imagery dataset, achieving 92.1%±5.9 accuracy, 93.1%±6.2 precision, 91.9%±1.3 recall, 92.5%±1.3 F1-score, and Cohen's κ=0.89±0.02. Compared to existing methods, HQeCL outperformed CSP-LDA (74.5%±1.4), ShallowConvNet (83.3%±1.6), and CNN-LSTM (88.8%±1.2), while remaining competitive with QuEEGNet (91.4%±1.3). Ablation analysis confirmed the contribution of quantum pooling, which provided a +0.7% gain over average pooling, and UMAP, which improved performance by +14.8% over PCA and +29.7% over t-SNE. Complexity analysis further demonstrated the efficiency of HQeCL with only 0.12M parameters, 270.2M FLOPs, and an inference latency of 77.6ms. While these results demonstrate near real-time feasibility in simulation, translation to hardware remains a challenge, positioning HQeCL as a quantum-inspired, Pareto-efficient EEG classifier advancing motor imagery decoding for brain-controlled wheelchair navigation.},
}

@article {pmid41207160,
year = {2025},
author = {Shirodkar, VR and Reddy Edla, D and Kumari, A and Afonso, MM},
title = {Multi-domain feature extraction and Sand Cat Swarm Optimized Broad Learning System for EEG-based Motor Imagery decoding in stroke patients.},
journal = {Computers in biology and medicine},
volume = {199},
number = {},
pages = {111285},
doi = {10.1016/j.compbiomed.2025.111285},
pmid = {41207160},
issn = {1879-0534},
abstract = {Brain-Computer Interfaces (BCIs) enable the translation of brain activity into executable commands, with Motor Imagery (MI)- based systems gaining prominence for their intuitive and non-invasive control. Electroencephalography is widely used due to its portability and time resolution, though its non-stationary and subject-specific nature poses major challenges for reliable classification. This research proposes a lightweight and efficient classification architecture that first selects discriminative filter bands based on Event-Related Desynchronization (ERD) scores. It then integrates Empirical Mode Decomposition (EMD), the Hilbert-Huang Transform (HHT), Riemannian Geometry (RG), and Common Spatial Pattern (CSP)-based feature extraction with a Broad Learning System (BLS) classifier. The BLS parameters are optimized using the Sand Cat Swarm Optimization (SCSO) algorithm to enhance convergence speed, avoid local minima, and improve generalization. EMD separates the EEG signal into a set of Intrinsic Mode Functions, while HHT extracts instantaneous amplitude and frequency features, effectively modeling the nonlinear and dynamic properties of EEG signals. Performance assessment was done on two datasets: the BCI IV 2a dataset and a clinical stroke EEG dataset. It achieved classification accuracies of 90.78% on BCI-IV 2a and 96.41% on the stroke dataset. The proposed approach also showed competitive generalization performance in All-subjects and Leave-One-Subject-Out (LOSO) validation settings. Analysis reveals that the proposed pipeline effectively extracts discriminative features and handles inter-subject variability, illustrating its applicability to real-world BCI systems.},
}