@article {pmid41605144,
year = {2026},
author = {Wei, S and Yu, H and Huang, Y and Meng, J and Xu, R and Jung, TP and Xu, M and Ming, D},
title = {Predicting Attention Decline: An Integrated Beta-Band and SSVEP Approach for Visual Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2026.3658740},
pmid = {41605144},
issn = {1558-0210},
abstract = {Real-time monitoring of sustained attention fluctuations during continuous complex tasks is vital for enhancing task performance and preventing accidents. Attention modulates neurons in the visual cortex in various ways to improve the visual sensitivity at an attended location. EEG-based brain-computer interfaces (BCIs) offer one of the most effective approaches for monitoring the state of human individuals. Whether transient responses evoked by brief stimuli, steady-state responses elicited by prolonged stimuli, or spontaneous neural oscillations, researchers can extract recognized electrophysiological features that reflect attention levels. However, unimodal features face inherent limitations, such as the low signal-to-noise ratio of transient responses and susceptibility of spontaneous rhythms to electrophysiological interference. Nevertheless, few studies have explored multimodal feature fusion for attention state monitoring. Here, we developed an innovative continuous go/no-go task to concurrently evoke both event-related potential (ERP) and steady-state visual evoked potential (SSVEP), while modulating spontaneous oscillatory activities through attentional engagement. To maximize the attentional modulation effect, we integrated the contrast-response functions of the modulation effect of attention on SSVEP and implemented 12 stimulus contrast levels to identify optimal visual stimulation intensity. Results from 25 subjects demonstrated that the decline in sustained attention during a continuous task was predictable before behavioral mistakes. Classification performance peaked at 31.60% stimulus contrast condition using the fused features combining spontaneous beta-band oscillations and SSVEP responses (average: 74.48%; best: 90.83%). These findings advance the development of more robust real-time attention monitoring systems based on BCI technology.},
}

@article {pmid41603967,
year = {2026},
author = {Schumacher, X and Frazzini, V and Adam, C and Dupont, S and Bielle, F and Guesdon, A and Mere, M and Nguyen-Michel, VH and Navarro, V and Mathon, B},
title = {Safety and efficacy of sEEG-guided resective surgery in patients with MRI-negative drug-resistant epilepsy.},
journal = {Neurosurgical review},
volume = {49},
number = {1},
pages = {166},
pmid = {41603967},
issn = {1437-2320},
}

@article {pmid41603128,
year = {2026},
author = {Shimizu, S and Osawa, T and Sato, M and Yamada, S and Harabayashi, T and Miki, J and Kobayashi, T and Hashine, K and Kawashima, A and Matsumoto, T and Mochizuki, T and Taoka, R and Urabe, F and Tatarano, S and Sawada, A and Kojima, T and Takahashi, A and Yokomizo, A and Suekane, S and Hashimoto, K and Hashimoto, Y and Yatsuda, J and Morita, K and Kobayashi, K and Satake, Y and Sazawa, A and Matsui, Y and Ito, YM and Nishiyama, H and Kitamura, H and Shinohara, N and Fukuhara, S and , },
title = {Validation of the 7-Item Quality of Life Disease-Specific Impact Scale in Patients Undergoing Radical Cystectomy for Bladder Cancer: A Cross-Sectional Study.},
journal = {International journal of urology : official journal of the Japanese Urological Association},
volume = {33},
number = {2},
pages = {e70364},
doi = {10.1111/iju.70364},
pmid = {41603128},
issn = {1442-2042},
mesh = {Humans ; Cross-Sectional Studies ; *Quality of Life ; Male ; *Urinary Bladder Neoplasms/surgery/psychology ; *Cystectomy/psychology/adverse effects ; Female ; Aged ; Middle Aged ; Reproducibility of Results ; Surveys and Questionnaires ; Psychometrics ; Body Image ; Aged, 80 and over ; Factor Analysis, Statistical ; Self Report ; },
abstract = {OBJECTIVES: To validate, for the first time in patients with bladder cancer who underwent radical cystectomy, the recently developed 7-item Quality of Life Disease-specific Impact Scale (QDIS-7), a brief, unidimensional instrument designed for cross-condition comparisons.

METHODS: In this cross-sectional study conducted at 24 facilities, patients aged ≥ 20 years who were 3 months post-radical cystectomy for bladder cancer completed self-reported questionnaires. The enrollment period was from January 2020 to October 2022. Quality of life measures included the QDIS-7, the Bladder Cancer Index (BCI), and the Body Image Scale (BIS). Confirmatory factor analysis was performed to test the hypothesized one-factor structure of the QDIS-7. Internal consistency reliability was assessed using Cronbach's alpha coefficient. Criterion-based validity was evaluated using Spearman's correlation coefficients (ρ) between the QDIS-7 scores and the BCI bother subdomains and BIS scores.

RESULTS: In total, 205 patients (median age, 71 years; 78.5% male) were included. The QDIS-7 score showed no floor or ceiling effects. Confirmatory factor analysis supported the one-factor model (factor loadings, 0.71-0.94). Internal consistency reliability was high (Cronbach's alpha, 0.94). The QDIS-7 score showed moderate correlations with the BIS and the BCI urinary and bowel bother subdomain scores (ρ = 0.654, -0.560, and -0.475, respectively).

CONCLUSIONS: The QDIS-7 effectively captured urinary and bowel symptom burden and body image impairment in patients undergoing radical cystectomy for bladder cancer. Its brevity, strong psychometric properties, and capacity for comparisons across conditions support its use in patient-centered research.

TRAIL REGISTRATION: UMIN-CTR (UMIN000039538).},
}

Humans
Cross-Sectional Studies
*Quality of Life
Male
*Urinary Bladder Neoplasms/surgery/psychology
*Cystectomy/psychology/adverse effects
Female
Aged
Middle Aged
Reproducibility of Results
Surveys and Questionnaires
Psychometrics
Body Image
Aged, 80 and over
Factor Analysis, Statistical
Self Report

@article {pmid41600429,
year = {2026},
author = {Falk, M and Shleev, S},
title = {Advances in (Bio)Sensors for Physiological Monitoring: A Special Issue Review.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {2},
pages = {},
doi = {10.3390/s26020633},
pmid = {41600429},
issn = {1424-8220},
mesh = {Humans ; Monitoring, Physiologic/methods/instrumentation ; Wearable Electronic Devices ; *Biosensing Techniques/methods ; Brain-Computer Interfaces ; },
abstract = {Physiological monitoring has become an inherently interdisciplinary field, merging advances in engineering, chemistry, biology, medicine, and data analytics to create sensors that continuously track the vital signals of the body. These developments are enabling more personalized and preventive healthcare, as wearable (bio)sensors and intelligent algorithms can detect subtle physiological changes in real-time. In the Special Issue 'Advances in (Bio)Sensors for Physiological Monitoring', researchers from diverse domains contributed 18 papers showcasing cutting-edge sensor technologies and applications for health and performance monitoring. In this review, we summarize these contributions by grouping them into logical themes based on their focus: (1) cardiovascular and autonomic monitoring, (2) glucose and metabolic monitoring, (3) wearable sensors for movement and musculoskeletal health, (4) neurophysiological monitoring and brain-computer interfaces, and (5) innovations in sensor technology and methods. This thematic organization highlights the breadth of the research, spanning from fundamental sensor hardware to data-driven analytics, and underscores how modern (bio)sensors are breaking traditional boundaries in healthcare.},
}

Humans
Monitoring, Physiologic/methods/instrumentation
Wearable Electronic Devices
*Biosensing Techniques/methods
Brain-Computer Interfaces

@article {pmid41600372,
year = {2026},
author = {Zhang, B and You, X and Liu, Y and Xu, J and Xu, S},
title = {Multi-Level Perception Systems in Fusion of Lifeforms: Classification, Challenges and Future Conceptions.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {2},
pages = {},
doi = {10.3390/s26020576},
pmid = {41600372},
issn = {1424-8220},
support = {2024YFC3406302//the National Key R&D Program of China/ ; 12204273//the National Natural Science Foundation of China/ ; ZR2024MF107//the Natural Science Foundation of Shandong Province, China/ ; 2017YFA0701302//the National Key R&D Program of China/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Perception/physiology ; },
abstract = {The emerging paradigm of "fusion of lifeforms" represents a transformative shift from conventional human-machine interfaces toward deeply integrated symbiotic systems, where biological and artificial components co-adapt structurally, energetically, informationally, and cognitively. This review systematically classifies multi-level perception systems within fusion of lifeforms into four functional categories: sensory and functional restoration, beyond-natural sensing, endogenous state sensing, and cognitive enhancement. We survey recent advances in neuroprosthetics, sensory augmentation, closed-loop physiological monitoring, and brain-computer interfaces, highlighting the transition from substitution to fusion. Despite significant progress, critical challenges remain, including multi-source heterogeneous integration, bandwidth and latency limitations, power and thermal constraints, biocompatibility, and system-level safety. We propose future directions such as layered in-body communication networks, sustainable energy strategies, advanced biointerfaces, and robust safety frameworks. Ethical considerations regarding self-identity, neural privacy, and legal responsibility are also discussed. This work aims to provide a comprehensive reference and roadmap for the development of next-generation fusion of lifeforms, ultimately steering human-machine integration from episodic functional repair toward sustained, multi-level symbiosis between biological and artificial systems.},
}

Humans
*Brain-Computer Interfaces
*Perception/physiology

@article {pmid41600345,
year = {2026},
author = {Dalgaard, KS and Lavesen, ER and Sulkjær, CS and Stevenson, AJT and Jochumsen, M},
title = {Specificity of Pairing Afferent and Efferent Activity for Inducing Neural Plasticity with an Associative Brain-Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {2},
pages = {},
doi = {10.3390/s26020549},
pmid = {41600345},
issn = {1424-8220},
mesh = {Humans ; *Neuronal Plasticity/physiology ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; Evoked Potentials, Motor/physiology ; Transcranial Magnetic Stimulation ; Wrist/physiology ; Electric Stimulation ; Movement/physiology ; Young Adult ; },
abstract = {Brain-computer interface-based (BCI) training induces neural plasticity and promotes motor recovery in stroke patients by pairing movement intentions with congruent electrical stimulation of the affected limb, eliciting somatosensory afferent feedback. However, this training can potentially be refined further to enhance rehabilitation outcomes. It is not known how specific the afferent feedback needs to be with respect to the efferent activity from the brain. This study investigated how corticospinal excitability, a marker of neural plasticity, was modulated by four types of BCI-like interventions that varied in the specificity of afferent feedback relative to the efferent activity. Fifteen able-bodied participants performed four interventions: (1) wrist extensions paired with radial nerve peripheral electrical stimulation (PES) (matching feedback), (2) wrist extensions paired with ulnar nerve PES (non-matching feedback), (3) wrist extensions paired with sham radial nerve PES (no feedback), and (4) palmar grasps paired with radial nerve PES (partially matching feedback). Each intervention consisted of 100 pairings between visually cued movements and PES. The PES was triggered based on the peak of maximal negativity of the movement-related cortical potential associated with the visually cued movement. Before, immediately after, and 30 min after the intervention, transcranial magnetic stimulation-elicited motor-evoked potentials were recorded to assess corticospinal excitability. Only wrist extensions paired with radial nerve PES significantly increased the corticospinal excitability with 57 ± 49% and 65 ± 52% immediately and 30 min after the intervention, respectively, compared to the pre-intervention measurement. In conclusion, maximizing the induction of neural plasticity with an associative BCI requires that the afferent feedback be precisely matched to the efferent brain activity.},
}

Humans
*Neuronal Plasticity/physiology
*Brain-Computer Interfaces
Male
Female
Adult
Evoked Potentials, Motor/physiology
Transcranial Magnetic Stimulation
Wrist/physiology
Electric Stimulation
Movement/physiology
Young Adult

@article {pmid41600232,
year = {2026},
author = {Wu, W and Liu, L and Chen, W and Chen, Y and Wang, X and Cichocki, A and Lu, Y and Jin, J},
title = {MS-TSEFNet: Multi-Scale Spatiotemporal Efficient Feature Fusion Network.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {2},
pages = {},
doi = {10.3390/s26020437},
pmid = {41600232},
issn = {1424-8220},
support = {2022ZD0208900//STI 2030-major projects/ ; 62176090//National Natural Science Foundation of China/ ; 2021SHZDZX//Shanghai Municipal Science and Technology Major Project/ ; BE2022064-1//Project of Jiangsu Province Science and Technology Plan Special Fund in 2022/ ; },
mesh = {*Electroencephalography/methods ; Humans ; Brain-Computer Interfaces ; Algorithms ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Deep Learning ; Brain/physiology ; },
abstract = {Motor imagery signal decoding is an important research direction in the field of brain-computer interfaces, which aim to judge the motor imagery state of an individual by analyzing electroencephalogram (EEG) signals. Deep learning technology has been gradually applied to EEG classification, which can automatically extract features. However, when processing complex EEG signals, the existing decoding models cannot effectively fuse features at different levels, resulting in limited classification performance. This study proposes a multi-scale spatiotemporal efficient feature fusion network (MS-TSEFNet), which learns the dynamic changes in EEG signals at different time scales through multi-scale convolution modules and combines the spatial attention mechanism to efficiently capture the spatial correlation between electrodes in EEG signals. In addition, the network adopts an efficient feature fusion strategy to deeply fuse features at different levels, thereby improving the expression ability of the model. In the task of motor imagery signal decoding, MS-TSEFNet shows higher accuracy and robustness. We use the public BCIC-IV2a, BCIC-IV2b and ECUST datasets for evaluation. The experimental results show that the average classification accuracy of MS-TSEFNet reaches 80.31%, 86.69% and 71.14%, respectively, which is better than the current state-of-the-art algorithms. We conducted an ablation experiment to further verify the effectiveness of the model. The experimental results showed that each module played an important role in improving the final performance. In particular, the combination of the multi-scale convolution module and the feature fusion module significantly improved the model's ability to extract the spatiotemporal features of EEG signals.},
}

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

@article {pmid41598333,
year = {2026},
author = {Wankner, MC and Visser-Vandewalle, V and Andrade, P and Heiden, P},
title = {Cervical Spinal Cord Stimulation for Functional Rehabilitation After Spinal Cord Injury: A Systematic Review of Preclinical and Clinical Studies.},
journal = {Life (Basel, Switzerland)},
volume = {16},
number = {1},
pages = {},
doi = {10.3390/life16010179},
pmid = {41598333},
issn = {2075-1729},
abstract = {Cervical spinal cord injury causes severe functional impairment with limited spontaneous recovery, and while spinal cord stimulation has emerged as a promising neuromodulatory strategy, evidence for cervical applications remains fragmented. To address this gap, we conducted a systematic review synthesizing preclinical and clinical evidence on cervical spinal cord stimulation for functional rehabilitation following spinal cord injury. The review was registered on PROSPERO (CRD420251088804) and conducted in accordance with PRISMA guidelines, with PubMed, Embase, IEEE Xplore, and Web of Science searched from inception to July 2025 for animal and human studies of cervical spinal cord stimulation, including epidural, intraspinal, and transcutaneous approaches, reporting functional neurological outcomes. Risk of bias was assessed using the Cochrane RoB 2 and ROBINS-I tools, and due to substantial heterogeneity, results were synthesized narratively. Thirty-one studies comprising 119 animals and 156 human participants, met inclusion criteria. Across studies, outcome measures such as GRASSP, ISNCSCI, and dynamometry consistently demonstrated improvements in hand strength, dexterity, and voluntary motor activation. Several studies also reported gains in sensory and autonomic function, whereas respiratory outcomes were infrequently assessed. Adjunctive interventions, including cortical stimulation, brain-computer interface priming, and task-specific training frequently augmented recovery. Adverse events were generally mild, although overall risk of bias was predominantly serious. Overall, cervical spinal cord stimulation demonstrates preliminary assistive and therapeutic effects on motor recovery, with additional sensory, autonomic, and potential respiratory benefits.},
}

@article {pmid41597853,
year = {2026},
author = {Ding, Y and Ding, J and Yang, Z and Fan, X and Chen, W},
title = {A Surface-Mount Substrate-Integrated Waveguide Bandpass Filter Based on MEMS Process and PCB Artwork for Robotic Radar Applications.},
journal = {Micromachines},
volume = {17},
number = {1},
pages = {},
doi = {10.3390/mi17010072},
pmid = {41597853},
issn = {2072-666X},
support = {F2025508015//Natural Science Foundation of Hebei Province/ ; 3142025013//Fundamental Research Funds for the Central Universities/ ; 2025011037//Science and Technology Support Project of Langfang/ ; 3142023023//Fundamental Research Funds for the Central Universities/ ; },
abstract = {To address the pressing need for compact and highly reliable perception systems in autonomous mobile robots, a compact bandpass filter (BPF) integrating slot-line resonator with substrate-integrated waveguide (SIW) technology for robotic millimeter-wave radar front ends was proposed. By integrating slot-line resonators between adjacent SIW cavities, the proposed design effectively increases the filtering order without increasing the layout area. This approach not only generates extra transmission poles but also creates a sharp transmission zero at the upper stopband, thereby significantly enhancing out-of-band rejection. This characteristic is crucial for robotic radar operating in complex and dynamic environments, as it effectively suppresses out-of-band interference and improves the system signal-to-noise ratio and detection reliability. To validate the performance, a prototype filter operating in the 24.25-27.5 GHz passband was fabricated. The measured results show good agreement with simulations, demonstrating low insertion loss, compact size, and wide stopband. Finally, to validate its compatibility with robotic radar modules, the chip was assembled onto a PCB using surface-mount technology. The responses of the bare die and the packaged module were then compared to evaluate the impact of integration on the overall RF performance. The proposed design offers a key filtering solution for next-generation high-performance, miniaturized robotic perception platforms.},
}

@article {pmid41596724,
year = {2026},
author = {Yen, C and Chiang, MC},
title = {Neuroimaging-Guided Insights into the Molecular and Network Mechanisms of Chronic Pain and Neuromodulation.},
journal = {International journal of molecular sciences},
volume = {27},
number = {2},
pages = {},
doi = {10.3390/ijms27021080},
pmid = {41596724},
issn = {1422-0067},
support = {NSTC 113-2314-B-030-007, NSTC 113-2515-S-030-001, and NSTC 114-2918-I-030-001//National Science and Technology Council/ ; },
mesh = {Humans ; *Chronic Pain/diagnostic imaging/metabolism/therapy/physiopathology ; *Neuroimaging/methods ; Brain/diagnostic imaging/metabolism/physiopathology ; Transcranial Magnetic Stimulation ; Deep Brain Stimulation ; Animals ; Positron-Emission Tomography ; },
abstract = {Chronic pain is a pervasive and debilitating condition that affects millions of individuals worldwide. Unlike acute pain, which serves a protective physiological role, chronic pain persists beyond routine tissue healing and often arises without a discernible peripheral cause. Accumulating evidence indicates that chronic pain is not merely a symptom but a disorder of the central nervous system, underpinned by interacting molecular, neurochemical, and network-level alterations. Molecular neuroimaging using PET and MR spectroscopy has revealed dysregulated excitatory-inhibitory balance (glutamate/GABA), altered monoaminergic and opioidergic signaling, and neuroimmune activation (e.g., TSPO-indexed glial activation) in key pain-related regions such as the insula, anterior cingulate cortex, thalamus, and prefrontal cortex. Converging multimodal imaging-including functional MRI, diffusion MRI, and EEG/MEG-demonstrates aberrant activity and connectivity across the default mode, salience, and sensorimotor networks, alongside structural remodeling in cortical and subcortical circuits. Parallel advances in neuromodulation, including transcranial magnetic stimulation (TMS), transcranial electrical stimulation (tES), deep brain stimulation (DBS), and emerging biomarker-guided closed-loop approaches, provide tools to perturb these maladaptive circuits and to test mechanistic hypotheses in vivo. This review integrates neuroimaging findings with molecular and systems-level mechanistic insights into chronic pain and its modulation, highlighting how imaging markers can link biochemical signatures to neural dynamics and guide precision pain management and individualized therapeutic strategies.},
}

Humans
*Chronic Pain/diagnostic imaging/metabolism/therapy/physiopathology
*Neuroimaging/methods
Brain/diagnostic imaging/metabolism/physiopathology
Transcranial Magnetic Stimulation
Deep Brain Stimulation
Animals
Positron-Emission Tomography

@article {pmid41596014,
year = {2026},
author = {Liang, J and Zhou, Y and Ma, K and Jia, Y and Zhang, Y and Han, B and Xiang, M},
title = {Generative Adversarial Networks for Modeling Bio-Electric Fields in Medicine: A Review of EEG, ECG, EMG, and EOG Applications.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {13},
number = {1},
pages = {},
doi = {10.3390/bioengineering13010084},
pmid = {41596014},
issn = {2306-5354},
support = {U23A20485//National Natural Science Foundation of China/ ; 62501039//National Natural Science Foundation of China/ ; 2021ZD0300503//Innovation Program for Quantum Science and Technology/ ; BX20240465//China National Postdoctoral Program for Innovative Talents/ ; LMS25H180004//Zhejiang Provincial Natural Science Foundation Exploration Project/ ; },
abstract = {Bio-electric fields-manifested as Electroencephalogram (EEG), Electrocardiogram (ECG), Electromyogram (EMG), and Electrooculogram (EOG)-are fundamental to modern medical diagnostics but often suffer from severe data imbalance, scarcity, and environmental noise. Generative Adversarial Networks (GANs) offer a powerful, nonlinear solution to these modeling hurdles. This review presents a comprehensive survey of GAN methodologies specifically tailored for bio-electric signal processing. We first establish a theoretical foundation by detailing GAN principles, training mechanisms, and critical structural variants, including advancements in loss functions and conditional architectures. Subsequently, the paper extensively analyzes applications ranging from high-fidelity signal synthesis and noise reduction to multi-class classification. Special attention is given to clinical anomaly detection, specifically covering epilepsy, arrhythmia, depression, and sleep apnea. Furthermore, we explore emerging applications such as modal transformation, Brain-Computer Interfaces (BCI), de-identification for privacy, and signal reconstruction. Finally, we critically evaluate the computational trade-offs and stability issues inherent in current models. The study concludes by delineating prospective research avenues, emphasizing the necessity of interdisciplinary synergy to advance personalized medicine and intelligent diagnostic systems.},
}

@article {pmid41594981,
year = {2025},
author = {Jiao, M and Ding, Z and Huang, C and Xu, Y and Zhong, B and Chen, H},
title = {The Effects of Computerized Cognitive Training via Tablet and Computer Platforms on Cognitive Function in Patients with Mild Cognitive Impairment: A Systematic Review and Meta-Analysis.},
journal = {Behavioral sciences (Basel, Switzerland)},
volume = {16},
number = {1},
pages = {},
doi = {10.3390/bs16010040},
pmid = {41594981},
issn = {2076-328X},
support = {No.2022ZD0210800//the Science and Technology Innovation 2030-"Brain Science and Brain-like Research" Major Project/ ; No. 20&ZD045//the Emerging Enhancement Technology under Perspective of Humanistic Philosophy, supported by the National Office for Philosophy and Social Science/ ; No.32171046//the Emerging Enhancement Technology under Perspective of Humanistic Philosophy, supported by the National Office for Philosophy and Social Science/ ; 226-2024-00207, 226-2024-00118//the Fundamental Research Funds for the Central Universities/ ; WX23A99//the Wuhan Medical Research Project 2023 (Healthy Development)/ ; 2024020801020405//the 2024 Wuhan Natural Science Foundation Exploration Plan Municipal Medical Institutions Clinical Research Key Project/ ; },
abstract = {Background: Mild cognitive impairment (MCI) is a high-risk prodromal stage of dementia. While tablet/computer-based computerized cognitive training (CCT) is widely used, its efficacy and gamification's role need clarification. Objective: This study aimed to evaluate the effect of tablet/computer-based CCT on global cognition in older adults with MCI and explore the impact of gamification. Methods: We systematically searched five databases for RCTs (through October 2025) involving individuals aged ≥55 with MCI. The intervention was task-based CCT via tablets/computers. Primary outcome was global cognition. We used random-effects meta-analysis and subgroup analyses. Results: Nineteen RCTs (1013 participants) were included. CCT demonstrated a significant, moderate positive effect on global cognition (Hedges' g = 0.57, 95% CI [0.36, 0.78]). A trend suggesting greater benefits with higher gamification was observed: high (g = 0.71), medium (g = 0.46), and low (g = 0.45) degrees. However, subgroup differences were not statistically significant (p = 0.4333). Results were robust in sensitivity analyses. Conclusions: Tablet/computer-based CCT effectively improves global cognition in MCI. The potential additive value of gamification highlights its promise for enhancing engagement and effects, warranting further investigation in larger trials. This systematic review was registered with PROSPERO (CRD420251231618).},
}

@article {pmid41594816,
year = {2026},
author = {Li, M and Xia, J and Pan, J and Zhao, S and Zhang, X and Jin, H and Dong, S},
title = {SleepMFormer: An Efficient Attention Framework with Contrastive Learning for Single-Channel EEG Sleep Staging.},
journal = {Brain sciences},
volume = {16},
number = {1},
pages = {},
doi = {10.3390/brainsci16010095},
pmid = {41594816},
issn = {2076-3425},
support = {2021ZD0200401//STI2030-Major projects/ ; 2024C03001//Zhejiang Province Key R & D programs/ ; 2025C01137//Zhejiang Province Key R & D programs/ ; 2022R52042//Zhejiang Province high level talent special support plan/ ; 2025ZFJH01//Fundamental Research Funds for the Central Universities/ ; },
abstract = {BACKGROUND/OBJECTIVES: Sleep stage classification is crucial for assessing sleep quality and diagnosing related disorders. Electroencephalography (EEG) is currently recognized as a primary method for sleep stage classification. High-performance automatic sleep staging methods based on EEG leverage the powerful contextual modeling capabilities of Transformer Encoder architectures. However, the global self-attention mechanism in Transformers incurs significant computational overhead, substantially hindering the training and inference efficiency of automatic sleep staging algorithms.

METHODS: To address these issues, we introduce an end-to-end framework for automatic sleep stage classification using single-channel EEG: SleepMFormer. At the algorithmic level, SleepMFormer adopts a task-driven simplification of the Transformer encoder to improve attention efficiency while preserving sequence modeling capability. At the training level, supervised contrastive learning is incorporated as an auxiliary strategy to enhance representation robustness. From an engineering perspective, these design choices enable efficient training and inference under resource-constrained settings.

RESULTS: When integrated with the SleePyCo backbone, the proposed framework achieves competitive performance on three widely used public datasets: Sleep-EDF, PhysioNet, and SHHS. Notably, SleepMFormer reduces training and inference time by up to 33% compared to conventional self-attention-based models. To further validate the generalizability of MaxFormer, we conduct additional experiments using DeepSleepNet and TinySleepNet as alternative feature extractors. Experimental results demonstrate that MaxFormer consistently maintains performance across different model architectures.

CONCLUSIONS: Overall, SleepMFormer introduces an efficient and practical framework for automatic sleep staging, demonstrating strong potential for related clinical applications.},
}

@article {pmid41594762,
year = {2025},
author = {Liu, Y and Xue, W and Yang, L and Li, M},
title = {Deep Learning-Based EEG Emotion Recognition: A Review.},
journal = {Brain sciences},
volume = {16},
number = {1},
pages = {},
doi = {10.3390/brainsci16010041},
pmid = {41594762},
issn = {2076-3425},
support = {252102311095//Key Scientific and Technological Projects of Henan Province/ ; 2025T180781//China Postdoctoral Science Foundation/ ; 62301496//National Natural Science Foundation of China/ ; GZC20232447//Postdoctoral Fellowship Program of China Postdoctoral Science Foundation/ ; },
abstract = {Affective Computing and emotion recognition hold significant importance in healthcare, identity verification, human-computer interaction, and related fields. Accurate identification of emotion is crucial for applications in medicine, education, psychology, and military domains. Electroencephalographic (EEG) signals have gained widespread application in emotion recognition due to their inherent characteristics of being non-concealable and directly reflecting brain activity. In recent years, with the establishment of open datasets and advancements in deep learning, an increasing number of researchers have integrated EEG with deep learning methods for emotion recognition studies. This review summarizes commonly used deep learning models in EEG-based emotion recognition along with their applications in this field, including the design of different network architectures, optimization strategies, and model designs based on EEG signal features. We also discuss limitations from the perspectives of commonality-individuality (C-I) and suggest improvements. The review outlines future research directions and provided a minimal C-I framework to assess models. Through this review, we aim to provide researchers in this field with a comprehensive reference and approach to balance universality and personalization to promote the development of deep learning-based EEG emotion recognition methods.},
}

@article {pmid41594729,
year = {2025},
author = {Tyler, WJ},
title = {Transcutaneous Auricular Vagus Nerve Stimulation for Treating Emotional Dysregulation and Inflammation in Common Neuropsychiatric Disorders.},
journal = {Brain sciences},
volume = {16},
number = {1},
pages = {},
doi = {10.3390/brainsci16010008},
pmid = {41594729},
issn = {2076-3425},
support = {FA8650-18-2-5402//United States Air Force Research Laboratory/ ; },
abstract = {Development of new therapeutic approaches and strategies for common neuropsychiatric disorders, including Major Depressive Disorder, anxiety disorders, and Post-Traumatic Stress Disorder, represent a significant global health challenge. Recent research indicates that emotional dysregulation and persistent inflammation are closely linked and serve as key pathophysiological features of these conditions. Emotional dysregulation is mechanistically coupled to locus coeruleus and norepinephrine (LC-NE) or noradrenergic system activity. Stemming from chronic stress, persistently elevated activity of the LC-NE system leads to hypervigilance, anxious states, and depressed mood. Concurrently, these symptoms are marked by systemic inflammation as indicated by elevated pro-inflammatory cytokines, and central neuroinflammation indicated by microglial activation in brain regions and networks involved in mood regulation and emotional control. In turn, chronic inflammation increases sympathetic tone and LC-NE activity resulting in a vortex of psychoneuroimmunological dysfunction that worsens mental health. Transcutaneous auricular vagus nerve stimulation (taVNS) in a non-invasive neuromodulation method uniquely positioned to address both noradrenergic dysfunction and chronic inflammation in neuropsychiatric applications. Evidence spanning the past decade demonstrates taVNS works via two complementary mechanisms. An ascending pathway engages vagal afferents projecting to the LC-NE system in the brain stem, which has been shown to modulate cortical arousal, cognitive function, mood, and stress responses. Through descending circuits, taVNS also modulates the cholinergic anti-inflammatory pathway to suppress the production of pro-inflammatory cytokines like TNF-α and IL-6 mitigating poor health outcomes caused by inflammation. By enhancing both central brain function and peripheral immune responses, taVNS has shown significant potential for recalibrating perturbed affective-cognitive processing. The present article describes and discusses recent evidence suggesting that taVNS offers a promising network-based paradigm for restoring psychoneuroimmunological homeostasis in common neuropsychiatric conditions.},
}

@article {pmid41594269,
year = {2026},
author = {Wang, H and Xu, S and Guo, R and Han, J and Huang, MC},
title = {Neurosense: Bridging Neural Dynamics and Mental Health Through Deep Learning for Brain Health Assessment via Reaction Time and p-Factor Prediction.},
journal = {Diagnostics (Basel, Switzerland)},
volume = {16},
number = {2},
pages = {},
doi = {10.3390/diagnostics16020293},
pmid = {41594269},
issn = {2075-4418},
support = {N/A//Kunshan Municipal Government research funding/ ; },
abstract = {Background/Objectives: Cognitive decline and compromised attention control serve as early indicators of neurodysfunction that manifest as broader psychopathological symptoms, yet conventional mental health assessment relies predominantly on subjective self-report measures lacking objectivity and temporal granularity. We propose Neurosense, an AI-driven brain health assessment framework using electroencephalography (EEG) to non-invasively capture neural dynamics. Methods: Our Dual-path Spatio-Temporal Adaptive Gated Encoder (D-STAGE) architecture processes temporal and spatial EEG features in parallel through Transformer-based and graph convolutional pathways, integrating them via adaptive gating mechanisms. We introduce a two-stage paradigm: first training on cognitive task EEG for reaction time prediction to acquire cognitive performance-related representations, then featuring parameter-efficient adapter-based transfer learning to estimate p-factor-a transdiagnostic psychopathology dimension. The adapter-based transfer achieves competitive performance using only 1.7% of parameters required for full fine-tuning. Results: The model achieves effective reaction time prediction from EEG signals. Transfer learning from cognitive tasks to mental health assessment demonstrates that cognitive efficiency representations can be adapted for p-factor prediction, outperforming direct training approaches while maintaining parameter efficiency. Conclusions: The Neurosense framework reveals hierarchical relationships between neural dynamics, cognitive efficiency, and mental health dimensions, establishing foundations for a promising computational framework for mental health assessment applications.},
}

@article {pmid41593817,
year = {2026},
author = {Bahadir, S and Robinson, JT and Morse, LR and Yoo, PB and Kern, R and Makowski, NS and Kozai, TDY and Fudim, M and Barbe, MF and Lim, HH and Chang, EH and Zanos, S},
title = {The sixth bioelectronic medicine summit: Neurotechnologies for individuals and communities.},
journal = {Bioelectronic medicine},
volume = {12},
number = {1},
pages = {3},
pmid = {41593817},
issn = {2332-8886},
support = {NIH SPARC U41 - NS129436/NH/NIH HHS/United States ; NIH-NIGMS R01GM143362/NH/NIH HHS/United States ; NIH-NINDS 1R01NS136685/NH/NIH HHS/United States ; },
}

@article {pmid41592346,
year = {2026},
author = {Suo, X and Li, W and Liao, X and Wu, Y and Zhang, H},
title = {A study of cortical activation and corticomuscular coupling enhancement following pre-task electrical stimulation in motor imagery.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae3e17},
pmid = {41592346},
issn = {1741-2552},
abstract = {OBJECTIVE: Motor imagery-based brain-computer interfaces (MI-BCIs) have been extensively studied. However, their widespread application is limited by the difficulty in extracting motor intentions from electroencephalography (EEG) signals, leading to low recognition rates. Additionally, the phenomenon of motor imagery blindnes s in some individuals further limits its applicability. Previous studies have attempted to improve motor imagery ability through electrical stimulation. However, applying electrical stimulation during motor imagery may introduce EEG artifacts and interfere with participants' concentration. The goal of this study is to investigate a new experimental paradigm. The new experimental paradigm improves motor imagery ability through pre-task electrical stimulation while preventing participant distraction or EEG artifacts.

APPROACH: This study implemented two paradigms: motor imagery with pre-task electrical stimulation (MI-ES) and motor imagery-only (MI-Only). Electrical stimulation was applied over hand muscle groups. Electromyography (EMG) and 64-channel EEG signals were simultaneously recorded under two experimental conditions.

MAIN RESULTS: We analyzed cortical activities and correlations between different brain regions under the two experimental conditions. Participants in the MI-ES condition exhibited a higher level of brain activation compared to the MI-Only condition. Additionally, in the MI-ES condition, the correlation between participants' EEG and EMG signals increased after electrical stimulation, indicating that the activation level of the motor-related cortex increased. A novel convolutional spiking neural network was applied to classify motor intentions, with participants achieving higher accuracy under the MI-ES condition, demonstrating enhanced motor imagery ability through pre-task electrical stimulation.

SIGNIFICANCE: This research demonstrates that pre-task electrical stimulation significantly enhances motor imagery ability, while also increasing cortical activation and corticomuscular coupling without introducing EEG artifacts or attentional interference.},
}

@article {pmid41590267,
year = {2025},
author = {Mehmood, F and Rehman, SU and Mehmood, A and Kim, YJ},
title = {Advances in AI-Driven EEG Analysis for Neurological and Oculomotor Disorders: A Systematic Review.},
journal = {Biosensors},
volume = {16},
number = {1},
pages = {},
doi = {10.3390/bios16010015},
pmid = {41590267},
issn = {2079-6374},
support = {20022793//KEIT/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Nervous System Diseases/diagnosis ; *Artificial Intelligence ; *Ocular Motility Disorders/diagnosis ; Machine Learning ; Deep Learning ; },
abstract = {Electroencephalography (EEG) has emerged as a powerful, non-invasive modality for investigating neurological and oculomotor disorders, particularly when combined with advances in artificial intelligence (AI). This systematic review examines recent progress in machine learning (ML) and deep learning (DL) techniques applied to EEG-based analysis for the diagnosis, classification, and monitoring of neurological conditions, including oculomotor-related disorders. Following the PRISMA guidelines, a structured literature search was conducted across major scientific databases, resulting in the inclusion of 15 peer-reviewed studies published over the last decade. The reviewed works encompass a range of neurological and ocular-related disorders and employ diverse AI models, from conventional ML algorithms to advanced DL architectures capable of learning complex spatiotemporal representations of neural signals. Key trends in feature extraction, signal representation, model design, and validation strategies are synthesized here to highlight methodological advancements and common challenges. While the reviewed studies demonstrate the growing potential of AI-enhanced EEG analysis for supporting clinical decision-making, limitations such as small sample sizes, heterogeneous datasets, and limited external validation remain prevalent. Addressing these challenges through standardized methodologies, larger multi-center datasets, and robust validation frameworks will be essential for translating EEG-driven AI approaches into reliable clinical applications. Overall, this review provides a comprehensive overview of current methodologies and future directions for AI-driven EEG analysis in neurological and oculomotor disorder assessment.},
}

Humans
*Electroencephalography/methods
*Nervous System Diseases/diagnosis
*Artificial Intelligence
*Ocular Motility Disorders/diagnosis
Machine Learning
Deep Learning

@article {pmid41590101,
year = {2026},
author = {He, M and Huang, Y and Cui, Z and Cheng, Z and Cao, W and Wang, G and Yao, W and Feng, M},
title = {Construction of Flexible Kaolin/Chitin Composite Aerogels and Their Properties.},
journal = {Gels (Basel, Switzerland)},
volume = {12},
number = {1},
pages = {},
doi = {10.3390/gels12010076},
pmid = {41590101},
issn = {2310-2861},
support = {253A7631D//Hebei Science and Technology Department Project/ ; 51503177//National Natural Science Foundation of China/ ; No. CNP-C-240204//Sichuan Province Engineering Technology Research Center of Novel CN Polymeric Materials/ ; },
abstract = {In this work, kaolin/chitin (K/Ch) composite aerogels with different mass ratios were successfully fabricated via a freeze-drying approach. The influence of kaolin content on the microstructure, properties and hemostatic performance of the composite aerogels was systematically investigated. The results demonstrated that the incorporation of kaolin endowed the chitin-based aerogels with tunable porous structures, excellent water absorption capacity (up to 4282% for K0.25/Ch2), and enhanced water retention (73.7% for K2/Ch2 at 60 min). Moreover, the K/Ch composite aerogels exhibited good biodegradability, no cytotoxicity (cell viability > 91.9%), and no hemolysis (hemolysis rate < 1.5% at all test concentrations). In vitro hemostatic evaluations revealed that the composite aerogels exhibited rapid blood coagulation (blood clotting time of 16 s for K2/Ch2) with a blood coagulation index (BCI) as low as 0.5%, which was attributed to the synergistic effect of the physical adsorption of chitin and the coagulation cascade activation by kaolin. These findings indicated that the K/Ch composite aerogels could be used as novel natural hemostatic materials for potential effective and rapid hemostasis.},
}

@article {pmid41587494,
year = {2026},
author = {Jia, J and Zhang, R and Yuan, D and Yu, D and Li, P},
title = {Theoretical and applied research on spatio-temporal graph attention networks for single-trial P300 detection.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae3d68},
pmid = {41587494},
issn = {1741-2552},
abstract = {Accurate detection of single-trial P300 ERPs (event-related potentials) is crucial for developing high-performance non-invasive BCIs (brain-computer interfaces). However, this task remains challenging because of the low SNR (signal-to-noise ratio) of EEG (electroencephalography) and the limited ability of existing models to concurrently capture the complex non-Euclidean spatiotemporal dynamics of brain signals. Approach: We propose a novel ST-GraphTRNet (spatiotemporal graph transformer network). This architecture synergis-tically integrates temporal convolutions for local feature extraction, graph convolutions to explicitly model the neurophysio-logical spatial relationships between EEG electrodes, and a temporal transformer with a self-attention mechanism to capture global, long-range temporal dependencies across the entire signal. Main Results: Extensive evaluation of four public P300 datasets demonstrates that ST-GraphTRNet significantly outper-forms SOTA (state-of-the-art) benchmarks under both within-subject and cross-subject paradigms. Crucially, interpretability analyses via t-SNE (T-distributed Stochastic Neighbor Embedding) and Grad-CAM (Gradient-weighted Class Activation Mapping) revealed that the model's decisions aligned with established neurophysiological priors, focusing on parietal elec-trodes approximately 300 ms post-stimulus. Significance: This study provides a powerful and interpretable framework for single-trial ERPs decoding. By effectively integrating the strengths of CNNs (Convolutional Neural Networks), GNNs (Craph Neural Networks), and Transformers, a new benchmark for building high-accuracy, generalizable, and clinically viable BCIs is established, moving closer to the goal of plug-and-play systems that require minimal user-specific calibration. .},
}

@article {pmid41586177,
year = {2026},
author = {Chin, AHB and Roslan, R and Alsomali, N and Al-Balas, Q and Salhab, BBS and Muhsin, SM},
title = {Islamic Bioethics Viewpoint on Elective Brain Chip Implants and Brain-Computer Interfaces for Enhancing Academic Performance in Competitive Examinations.},
journal = {Asian bioethics review},
volume = {18},
number = {1},
pages = {79-92},
pmid = {41586177},
issn = {1793-9453},
abstract = {The first implantation of a brain chip into a human paralysis patient by Neuralink demonstrated much potential for treating debilitating neurological diseases and injuries. Nevertheless, brain chips can also be implanted in healthy people to provide an interface between the human brain with computers, robotic machines, and novel artificial intelligence platforms, which generates new ethical issues. The focus here is on the development of brain chip implants that can significantly improve memory, intelligence, and cognition, thereby boosting performance in national examinations for university admissions and securing civil service jobs, thus providing a "game-changer" and "shortcut" for many students and parents. Given that Islam is a major world religion, constituting a significant portion of the global population, it is crucial for the biomedical industry to comprehend Islamic perspectives on emerging medical technologies, which will enable it to more effectively cater to a substantial and growing demographic. We thus critically examine whether the application of brain chip technology to enhance academic performance in highly competitive examinations is consistent with Islamic principles. Based on the Islamic jurisprudential framework, such an application for intellectual enhancement of normal and healthy people without any mental impairment may conflict with the injunction to preserve intellect (Hifz al-Aql) and "consideration of consequences" (murā'āt al-ma'ālāt) in Islam. It may also be viewed as tampering with Allah's creation (Taghyir Khalq Allah). Gaining such unfair advantages in competitive examinations will likely be viewed as unethical, by transgressing the core Islamic precepts of Amanah (trustworthiness), Al-'Adl (justice), Ikhlas (sincerity), and Mujahadah (striving).},
}

@article {pmid41585948,
year = {2025},
author = {Wang, S and Wang, R and Chang, L and Wu, J and Hu, L},
title = {AMANet: a data-augmented multi-scale temporal attention convolutional network for motor imagery classification.},
journal = {Frontiers in neurorobotics},
volume = {19},
number = {},
pages = {1704111},
pmid = {41585948},
issn = {1662-5218},
abstract = {Motor imagery brain-computer interface (MI-BCI) has garnered considerable attention due to its potential for neural plasticity. However, the limited number of MI-EEG samples per subject and the susceptibility of features to noise and artifacts posed significant challenges for achieving high decoding performance. To address this problem, a Data-Augmented Multi-Scale Temporal Attention Convolutional Network (AMANet) was proposed. The network mainly consisted of four modules. First, the data augmentation module comprises three steps: sliding-window segmentation to increase sample size, Common Spatial Pattern (CSP) extraction of discriminative spatial features, and linear scaling to enhance network robustness. Then, multi-scale temporal convolution was incorporated to dynamically extract temporal and spatial features. Subsequently, the ECA attention mechanism was integrated to realize the adaptive adjustment of the weights of different channels. Finally, depthwise separable convolution was utilized to fully integrate and classify the deep extraction of temporal and spatial features. In 10-fold cross-validation, the results show that AMANet achieves classification accuracies of 84.06 and 85.09% on the BCI Competition IV Datasets 2a and 2b, respectively, significantly outperforming baseline models such as Incep-EEGNet. On the High-Gamma dataset, AMANet attains a classification accuracy of 95.48%. These results demonstrate the excellent performance of AMANet in motor imagery decoding tasks.},
}

@article {pmid41585347,
year = {2025},
author = {Zhang, M and Wang, T and Zhu, Z},
title = {Bridging neuromorphic computing and deep learning for next-generation neural data interpretation.},
journal = {Frontiers in computational neuroscience},
volume = {19},
number = {},
pages = {1737839},
pmid = {41585347},
issn = {1662-5188},
}

@article {pmid41584816,
year = {2026},
author = {Kim, DU and Yoo, MA and Choi, SI and Kim, MY and Kim, SP},
title = {Toward zero-calibration MEG brain-computer interfaces based on event-related fields.},
journal = {Biomedical engineering letters},
volume = {16},
number = {1},
pages = {67-76},
pmid = {41584816},
issn = {2093-985X},
abstract = {Magnetoencephalography (MEG) offers high spatiotemporal resolution, but its application in practical brain-computer interface (BCI) systems remains limited partially due to the need for user-specific calibration and inter-subject variability. We present a zero-calibration MEG-based BCI based on event-related fields (ERFs) by leveraging spatial filters and deep learning techniques. First, we developed an on-line ERF-based MEG BCI with a visual oddball paradigm, achieving the mean classification accuracy of 94.29% and an information transfer rate (ITR) of 20.47 bits/min. Using the resulting multi-subject dataset, we applied xDAWN spatial filtering and trained a deep convolutional neural network (DeepConvNet) to classify target versus non-target responses. To simulate real-world plug-and-play use, zero-calibration performance was evaluated using a leave-one-subject-out (LOSO) cross-validation approach. The combination of xDAWN and DeepConvNet achieved the average accuracy of 80.32% and ITR of 12.75 bits/min, respectively, demonstrating cross-subject generalization. These results underscore the feasibility of zero-calibration MEG BCIs for more practical use.},
}

@article {pmid41582168,
year = {2026},
author = {Mannan, MMN and Palipana, DB and Mulholland, K and Jurd, E and Lloyd, ECR and Quinn, ARJ and Crossley, CB and Rabbi, MF and Lloyd, DG and Teng, YD and Pizzolato, C},
title = {Virtual reality mediated brain-computer interface training improves sensorimotor neuromodulation in unimpaired and post spinal cord injury individuals.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-36431-3},
pmid = {41582168},
issn = {2045-2322},
abstract = {Real-time brain-computer interfaces (BCIs) that decode electroencephalograms (EEG) during motor imagery (MI) are powerful adjuncts to rehabilitation after neurotrauma. Further, immersive virtual reality (VR) could complement BCIs by delivering visual and auditory sensory feedback (VR biofeedback) congruent to user's MI, enabling task-oriented therapies. Yet, therapeutic outcomes rely on user's proficiency in evoking MI to attain volitional BCI-commanded VR interaction. While previous studies have explored multi-session BCIs, we investigated the impact of longitudinal training on sensorimotor neuromodulation using BCI combined with VR-mediated externally-cued and self-paced lower-limb MI tasks. The EEG-based BCI was coupled with real-time VR biofeedback congruent with the MI task. Over multiple training sessions in laboratory conditions, five unimpaired individuals progressively learnt to improve control over their EEG during MI virtual walking, corresponding with increased BCI classification accuracy. Further, similar improvements were found with four individuals with chronic complete spinal cord injury (SCI) using the system in real-world neurorehabilitation settings. These findings demonstrate that unimpaired and SCI impaired individuals learnt to control their sensorimotor EEG associated with MI tasks through VR-mediated BCI training, which was associated with improved BCI classification accuracy. Our findings highlight the potential of VR-mediated BCIs in enhancing neuromodulation, providing a foundation for future rehabilitation therapies.},
}

@article {pmid41581018,
year = {2026},
author = {Liao, X and Gao, G},
title = {Strategies for improving recovery of consciousness after acute brain injury.},
journal = {Current opinion in critical care},
volume = {},
number = {},
pages = {},
doi = {10.1097/MCC.0000000000001365},
pmid = {41581018},
issn = {1531-7072},
abstract = {PURPOSE OF REVIEW: Advances in critical care have improved survival rates after severe brain injuries, yet many patients experience prolonged disorders of consciousness, resulting in significant care burdens and ethical challenges. Therefore, a systematic review of current treatment strategies for these disorders following acute brain injury is essential to provide evidence-based guidance for clinicians, ultimately aiming to enhance patient prognosis and quality of life.

RECENT FINDINGS: Research has rapidly evolved beyond traditional drugs like amantadine and zolpidem, with significant breakthroughs in neuromodulation techniques such as spinal cord stimulation, transcranial direct current stimulation, and brain-computer interfaces. These innovations are reshaping clinical practice by transitioning from theoretical concepts to validated interventions, enabling more precise, individualized treatment protocols. This shift moves clinical management from empirical medication toward targeted neural circuit modulation, while technologies detecting covert consciousness are helping redefine diagnostic standards. The differential effects of these interventions are also advancing fundamental research, deepening understanding of consciousness networks and shifting focus from single targets to whole brain dynamic regulation.

SUMMARY: These developments collectively highlight the need for integrated multimodal assessment and multilevel interventions, pointing toward a future of personalized, precision medicine for arousal promotion that offers tangible hope for improving patient recovery outcomes and quality of life.},
}

@article {pmid41580794,
year = {2026},
author = {Niu, X and Yuan, M and Zhang, J and Yang, J and Yu, Q and Wang, D},
title = {Noninvasive BCI-based cognitive rehabilitation in poststroke cognitive impairment: study protocol for a randomized controlled trial.},
journal = {Trials},
volume = {},
number = {},
pages = {},
doi = {10.1186/s13063-026-09449-1},
pmid = {41580794},
issn = {1745-6215},
abstract = {BACKGROUND: Poststroke cognitive impairment (PSCI) significantly reduces quality of life and survival rates. Current interventions face challenges in efficacy and accessibility. Noninvasive brain-computer interface (BCI) technology may enhance neural plasticity and cognitive recovery through real-time neurofeedback, offering a safe and accessible approach for poststroke cognitive rehabilitation. This trial aims to evaluate the efficacy of BCI-based cognitive training and explore its neural mechanisms.

METHODS: A prospective, randomized, double-blind, controlled, single-center trial will enroll 66 PSCI patients. Participants will be randomized into the intervention group or control group. Interventions will be administered 5 days/week for 4 weeks. Primary outcome is as follows: The 4-week post-intervention MoCA scores; secondary outcomes are as follows: 3-month follow-up MoCA scores, attention, memory, executive function, neurophysiological markers, and daily living function. Assessments will be conducted at baseline (T0W), post-intervention (T4W), and 3-month follow-up (T16W).

DISCUSSION: Results will provide evidence for BCI's clinical utility and neuroplasticity mechanisms, guiding personalized neurorehabilitation strategies.

TRIAL STATUS: The protocol version used for this study is Version 3.0, dated May 8, 2025. Recruitment is scheduled to begin on June 10, 2025, and is expected to be completed by May 8, 2026.

TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2500107318. Registered on 8 August 2025.},
}

@article {pmid41243805,
year = {2026},
author = {Colucci, A and Vermehren, M and Angerhöfer, C and Peekhaus, N and Kim, WS and Chang, WK and Hömberg, V and Paik, NJ and Soekadar, SR},
title = {Hybrid Brain/Neural Exoskeleton Enables Bimanual ADL Training in Routine Stroke Rehabilitation.},
journal = {Stroke},
volume = {57},
number = {2},
pages = {505-510},
pmid = {41243805},
issn = {1524-4628},
mesh = {Humans ; Middle Aged ; *Stroke Rehabilitation/methods/instrumentation ; Male ; Aged ; Female ; *Activities of Daily Living ; Adult ; Aged, 80 and over ; *Exoskeleton Device ; *Stroke/physiopathology/complications ; Pilot Projects ; Electroencephalography ; Young Adult ; Electrooculography ; *Brain/physiopathology ; Adolescent ; },
abstract = {BACKGROUND: Severe upper limb motor impairment is one of the most disabling consequences of stroke. Although brain-controlled rehabilitation technologies, such as brain/neural exoskeletons (B/NE), have been shown to be effective in promoting motor recovery, their clinical adoption remains limited because of insufficient integration of B/NE into existing clinical workflows. Here, we introduce and validate a fully portable B/NE system that overcomes this limitation by relying on brain (electroencephalography) and ocular (electrooculography) signals to restore bimanual activities of daily living within a novel therapeutic framework.

METHODS: In this pilot study, we tested the feasibility of the novel approach in 5 stroke survivors (mean age, 51 years; SD=14.8) undergoing inpatient neurorehabilitation. Stroke survivors aged 18 to 80 years, who exhibited hemiparesis and sufficient cognitive ability to understand and follow instructions, were invited to participate in a 1-hour training session. This session included system setup and calibration, followed by performing B/NE-supported, self-paced bimanual activities of daily living. As primary outcome measures, we assessed control accuracy, the ability to reliably modulate electroencephalography and electrooculography signals, and time to initialize, defined as the time required to react to cues and initiate the task, serving as a measure of control intuitiveness. In addition, participants' B/NE control performance during assisted training of bimanual activities of daily living, as well as setup preparation time, were assessed via direct observation.

RESULTS: Participants demonstrated reliable control accuracy in using both brain (mean, 83%; SD=15.36) and ocular (mean=100%) signals, as well as intuitive control (time to initialize <2 s). All participants reliably controlled the B/NE performing a battery of 10 bimanual activities of daily living. Moreover, setup and calibration times remained below 20 minutes.

CONCLUSIONS: These findings highlight the compatibility of the novel B/NE with existing clinical workflows and its feasibility to enable B/NE-supported stroke neurorehabilitation by facilitating seamless integration into clinical practice.},
}

Humans
Middle Aged
*Stroke Rehabilitation/methods/instrumentation
Male
Aged
Female
*Activities of Daily Living
Adult
Aged, 80 and over
*Exoskeleton Device
*Stroke/physiopathology/complications
Pilot Projects
Electroencephalography
Young Adult
Electrooculography
*Brain/physiopathology
Adolescent

@article {pmid41585048,
year = {2023},
author = {Rodriguez-Calienes, A and Oliver, M and Hassan, AE and Vivanco-Suarez, J and Divani, AA and Ribo, M and Petersen, N and Abraham, M and Fifi, J and Guerrero, WR and Malik, AM and Siegler, JE and Nguyen, T and Sheth, S and Yoo, A and Linares, G and Janjua, N and Quispe-Orozco, D and Galecio-Castillo, M and Alhajala, H and Malaga, M and Farooqui, M and Jovin, T and Jumaa, M and Ortega-Gutierrez, S},
title = {Safety of Intravenous Cangrelor Versus Dual Oral Antiplatelet Loading Therapy in Endovascular Treatment of Tandem Lesions: An Observational Cohort Study.},
journal = {Stroke (Hoboken, N.J.)},
volume = {3},
number = {6},
pages = {e001020},
pmid = {41585048},
issn = {2694-5746},
abstract = {BACKGROUND: Procedural intravenous cangrelor has been proposed as an effective platelet inhibition strategy for stenting in acute ischemic stroke. We aimed to compare the safety profile of low-dose intravenous cangrelor versus dual oral antiplatelet therapy (DAPT) loading in patients with acute cervical tandem lesions.

METHODS: We retrospectively identified cases from an international multicenter cohort who underwent intraprocedural administration of intravenous cangrelor (15 μg/kg followed by an infusion of 2 μg/kg per min) or DAPT loading during acute tandem lesions intervention. Safety outcomes included rates of symptomatic intracranial hemorrhage, parenchymal hematoma type 2, petechial hemorrhage, and in-stent thrombosis. Inverse probability of treatment weighting matching was used to reduce confounding.

RESULTS: From 691 patients, we included 195 patients, 30 of whom received intravenous cangrelor and 165 DAPT. The DAPT regimens were aspirin+clopidogrel (93.3%) or aspirin+ticagrelor (6.6%). After inverse probability of treatment weighting, the patients treated with cangrelor were not at greater odds of symptomatic intracranial hemorrhage (odds ratio [OR], 1.30 [95% CI, 0.09-17.3]; P=0.837), symptomatic intracranial hemorrhage-parenchymal hematoma type 2 (OR, 0.54 [95% CI, 0.05-4.98]; P=0.589), or petechial hemorrhage (OR, 1.11 [95% CI, 0.38-3.28]; P=0.836). Similarly, the rate of in-stent thrombosis was not significantly different between the 2 groups (1.8% versus 0%; P=0.911).

CONCLUSION: Cangrelor at the half dose of the myocardial infarction protocol showed a similar safety profile compared with the commonly used DAPT loading protocols in patients with acute tandem lesions. Further studies with larger samples are warranted to elucidate the safety of antiplatelet therapy in tandem lesions.},
}

@article {pmid41580462,
year = {2026},
author = {Huang, Q and Chen, D and Pereira, AC and Leonard, A and Ellis, CL and Velthuis, H and Dimitrov, M and Ponteduro, FM and Wong, NML and Kowalewski, L and Pretzsch, CM and Daly, E and Murphy, DGM and McAlonan, GM},
title = {Differential GABA dynamics across brain functional networks in autism.},
journal = {Communications biology},
volume = {},
number = {},
pages = {},
doi = {10.1038/s42003-026-09563-5},
pmid = {41580462},
issn = {2399-3642},
support = {2024A1515011690//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Differences in the ϒ-aminobutyric acid (GABA) system contribute to an excitatory-inhibitory imbalance in autism, particularly affecting sensory processing. However, the brain's broader response to interventions targeting GABA pathways in individuals with autism remains poorly understood. This study tested the hypothesis that GABAergic control of information transfer across large-scale brain functional networks is altered in autism. We conducted a phase-amplitude coupling (PAC) analysis of resting-state EEG signals within and between these networks. Responses were compared after double-blind, randomized oral administration of either a placebo or 15/30 mg of arbaclofen, a GABAB receptor agonist. Twenty-four non-autistic (9 males; 19-53 years) and 15 autistic participants (9 males; 20-51 years) completed 93 study visits. Autistic participants exhibited significantly higher theta-beta PAC, especially within the limbic network. High-dose arbaclofen shifted PAC metrics in visual and somatomotor networks towards non-autistic levels, but had minimal effects on networks related to higher cognitive functions. Interestingly, altered PAC within and between networks in the limbic system of autistic participants was normalized by low-dose arbaclofen, yet reemerged after high-dose administration. These findings provide compelling evidence for altered GABAergic responsivity in autism, helping explain some of the challenges in prescribing medications for autistic individuals, such as paradoxical reactions and dose sensitivity.},
}

@article {pmid41579859,
year = {2026},
author = {Huang, C and Tao, H and Zhou, Y and Wu, Q and Li, M and Liu, A and Zhu, T and Yu, C and Li, P and Huang, S and Guo, H and Hu, J and Wang, G},
title = {Pregnenolone promotes immune evasion through blocking endogenous retrovirus expression.},
journal = {Cell metabolism},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cmet.2025.12.020},
pmid = {41579859},
issn = {1932-7420},
abstract = {Research into steroid hormones shaping tumor biology has gained increasing attention. Using multiple mouse tumor models, we show that pregnenolone promoted tumor progression and reduced sensitivity to immunotherapy. Pregnenolone levels were markedly elevated in maternal mice experiencing mating deficiency. Mechanistically, pregnenolone directly binds Kap1 and inhibits Trim39-mediated ubiquitination at K750, leading to Kap1 stabilization and repression of endogenous retrovirus (ERV) expression and type-I interferon production. Furthermore, pharmacological antagonism of pregnenolone effectively suppressed tumor growth and enhanced immunotherapy efficacy. These findings reveal a previously unrecognized link between mating-associated steroid metabolism and tumor immune regulation and identify pregnenolone signaling as a potential therapeutic target in cancer.},
}

@article {pmid41576955,
year = {2026},
author = {Ning, C and Fu, G and Zhang, YY and Meyniel, F and Wang, L},
title = {Macaque prefrontal cortex integrates multiple components for metacognitive judgments of working memory.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2025.11.014},
pmid = {41576955},
issn = {1097-4199},
abstract = {The ability to evaluate one's own memory is known as metamemory. Whether metamemory is inherent to memory strength or requires additional computation in the brain remains largely unknown. We investigated the metacognitive mechanism of working memory (WM) using two-photon calcium imaging in the prefrontal cortex (PFC) of macaque monkeys, memorizing spatial sequences of varying difficulties. In some trials, after viewing the sequence, monkeys could opt out of retrieval for a smaller reward, reflecting their confidence in WM (meta-WM). We discovered that PFC neurons encoded WM strength by jointly representing the remembered locations and their associated uncertainties. Additional factors-trial history and arousal-encoded in baseline activity also predicted opt-out decisions, serving as cues for meta-WM. We further identified a code for meta-WM itself that integrated WM strength with these cues. Thus, the PFC neural geometry implements metacognitive computations, integrating WM strength with cues into a meta-WM signal to guide behavior.},
}

@article {pmid41574639,
year = {2026},
author = {Guo, Z and Ye, R and Guan, L and He, W and Qiu, S and Shao, X and Fang, J and Fang, J and Du, J},
title = {Differential roles of EA-TRAPed cells in the anterior cingulate cortex across various intervention times in inflammatory pain.},
journal = {Animal models and experimental medicine},
volume = {},
number = {},
pages = {},
doi = {10.1002/ame2.70118},
pmid = {41574639},
issn = {2576-2095},
support = {//The National Natural Science Fund of China (82374561, 82174490, 81873360), the Research Project of Zhejiang Chinese Medical University (2022JKZKTS44, 2022FSYYZZ07), and the Zhejiang Medical and Health Science and Technology Program (2021RC098)/ ; },
abstract = {BACKGROUND: The analgesic effects of multiple electroacupuncture (EA) sessions and single EA sessions differ significantly in pain management. Area 24b (A24b) of the anterior cingulate cortex (ACC) is crucial in pain processing. EA relieves pain by targeting and modulating the neuronal activity within this subregion. However, whether the cumulative effect of EA antinociception is connected to A24b mechanisms has remained unclear.

METHODS: In our study, we used the Complete Freund's Adjuvant (CFA) model to induce inflammatory pain and the Spared Nerve Injury (SNI) model to induce neuropathic pain, and adult male C57BL/6, FosTRAP, and FosTRAP:Ai9 mice were used as experimental subjects to investigate the cumulative effect of EA antinociception and whether multiple EA sessions and a single EA session regulate different neuronal populations in the A24b.

RESULTS: We observed that EA effectively alleviated pain in mice, with three EA sessions yielding superior analgesic effects compared to a single session. Using chemical genetics combined with FosCreER technology to activate EA-TRAPed cells in the A24b, we found that pain relief was more pronounced with three EA sessions. Moreover, chemogenetic inhibition of EA-TRAPed cells in the A24b reversed the analgesic effects of a single EA session but not those of three EA sessions. Fluorescent in situ hybridization results indicated that three EA sessions significantly increased the number of GABAergic neurons in the A24b compared with a single session. Additionally, retrograde tracing revealed that the A24b circuit that monosynaptically innervates EA-TRAPed cells included projections from the central lateral nucleus (CL), lateral mediodorsal thalamic nucleus (MDL), lateral habenula (LHb), dorsal raphe nucleus (DR), caudal linear nucleus of the raphe (CLi), dorsal tuberomamillary nucleus (DTM), periventricular hypothalamic nucleus (Pe) and hippocampal fields CA1, CA2, and CA3. These findings suggest that multiple EA sessions and single EA sessions activated different neuronal populations in the A24b. The enhanced analgesic effect of multiple EA sessions may be attributed to an increase in the proportion of GABAergic neurons within the A24b.

CONCLUSIONS: Multiple and single EA sessions recruit distinct neuronal populations in A24b, with the stronger analgesic effect of repeated EA linked to a higher proportion of GABAergic neurons in this region.},
}

@article {pmid41573300,
year = {2025},
author = {Nieves-Méndez, C},
title = {From neurotechnology to the classroom: the promise of brain-computer interfaces for training systems engineers.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1733768},
pmid = {41573300},
issn = {1662-5161},
abstract = {This perspective article explores the transformative potential of brain-Computer Interfaces (BCI) in undergraduate systems engineering programs, a domain characterized by high attrition and a widening gap between rapid technological innovation and slower pedagogical change. I argue that BCI, by enabling real-time detection of cognitive states such as mental workload, attention, and frustration, can evolve from laboratory tools to central pedagogical instruments for adaptive, student-centered education. I review the state-of-the-art methods, which demonstrate the technical feasibility of low-cost electroencephalography (EEG) devices and machine learning algorithms that classify cognitive states with high accuracy in controlled settings. Building on this evidence, I outline concrete applications in three dimensions: formative assessment, dynamic curricular adaptation, and cognitive inclusion, with a specific emphasis on preventing dropout in foundational courses such as algorithms. I also examine ethical, technical, and pedagogical challenges, and propose a scalable, ethically grounded pilot model tailored for universities, particularly in Latin America. This study reports no empirical results. It synthesizes the existing evidence and proposes a roadmap for research and educational action.},
}

@article {pmid41571161,
year = {2026},
author = {Artigas, R and Ruiz, S and Montalba, C and Peñafiel, C and Figueroa, R and Irarrazaval, P},
title = {Decreased levels of N-Acetylaspartyglutamate, myo-inositol, and syllo-inositol, in cortical brain regions of women exposed to adverse childhood experiences.},
journal = {Magnetic resonance imaging},
volume = {128},
number = {},
pages = {110621},
doi = {10.1016/j.mri.2026.110621},
pmid = {41571161},
issn = {1873-5894},
abstract = {Adverse Childhood Experiences (ACE), including abuse and neglect, can have lasting negative effects on health, decreasing lifespan and increasing the risk of chronic diseases. While research on ACE's impact on brain biochemistry is limited, Magnetic Resonance Spectroscopy (MRS) provides a non-invasive way to study these alterations. This study aims to identify neurochemical patterns linked to ACE exposure using J-edited MRS methods. 43 female participants (18 Low-ACE and 25 High-ACE), aged 19 to 31, were recruited. ACE exposure was assessed using the Maltreatment and Abuse Chronology of Exposure (MACE) test. MRS was conducted on a 3.0 T scanner, with J-edited single-voxel 1H-MRS from the Anterior Cingulate Cortex (ACC), Pre-Frontal Cortex (PFC), and hippocampus. Metabolite quantification was carried out using the Osprey pipeline and analyzed using univariate and multivariate methods. Univariate analysis showed reduced N-Acetylaspartyglutamate (NAAG) and syllo-Inositol (sI) levels in the ACC (p = 0.06) and PFC (p = 0.057), respectively, among High-ACE participants. Logistic Regression identified lower NAAG, GABA, glutathione (GSH), and myo-Inositol (mI) in the ACC, and differences in sI, lactate, NAAG, and GSH in the PFC, within the High-ACE group. Random Forest and Support Vector Machines confirmed NAAG, mI, and sI as possible ACE biomarkers. Throughout this study, cortical regions consistently showed reduced levels of NAAG, mI, and sI in the High-ACE group, suggesting a potential link to ACE. These findings improve our understanding of neurochemical changes associated with ACE, aiding in the identification of at-risk individuals and in the development of strategies to prevent long-term health effects.},
}

@article {pmid41570674,
year = {2026},
author = {Fu, X and Jiang, W and Liu, R and Müller-Putz, GR and Guan, C},
title = {EEG-to-gait decoding via phase-aware representation learning.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {198},
number = {},
pages = {108608},
doi = {10.1016/j.neunet.2026.108608},
pmid = {41570674},
issn = {1879-2782},
abstract = {Accurate decoding of lower-limb motion from EEG signals is essential for advancing brain-computer interface (BCI) applications in movement intent recognition and control. This study presents NeuroDyGait, a two-stage, phase-aware EEG-to-gait decoding framework that explicitly models temporal continuity and domain relationships. To address challenges of causal, phase-consistent prediction and cross-subject variability, Stage I learns semantically aligned EEG-motion embeddings via relative contrastive learning with a cross-attention-based metric, while Stage II performs domain relation-aware decoding through dynamic fusion of session-specific heads. Comprehensive experiments on two benchmark datasets (GED and FMD) show substantial gains over baselines, including a recent 2025 model EEG2GAIT. The framework generalizes to unseen subjects and maintains inference latency below 5 ms per window, satisfying real-time BCI requirements. Visualization of learned attention and phase-specific cortical saliency maps further reveals interpretable neural correlates of gait phases. Future extensions will target rehabilitation populations and multimodal integration.},
}

@article {pmid41570350,
year = {2026},
author = {Sun, J and Xie, R and Yu, J and Ji, L and Jia, T and Pan, Y and Li, C},
title = {Dynamic modulation of corticomuscular coherence during ankle dorsiflexion after stroke: towards hybrid BCI for lower-limb rehabilitation.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae3c41},
pmid = {41570350},
issn = {1741-2552},
abstract = {OBJECTIVE: Hybrid brain-computer interface (BCI) systems incorporate electroencephalography (EEG) and electromyography (EMG) signals to extract corticomuscular coherence (CMC) features, enabling self-modulation of neural communication. While promising for stroke rehabilitation, the neurophysiological mechanism underlying hybrid BCI therapy remains poorly understood. To address this gap, we characterized post-stroke CMC dynamics during ankle dorsiflexion and further established their relationship with functional motor recovery.

APPROACH: We acquired synchronous EEG and high-density EMG (HD-EMG) recordings from 13 subacute stroke patients (with their affected limb) before and after three-week rehabilitation, and 9 age-matched healthy controls (using their dominant limb) during isometric ankle dorsiflexion. Using multivariate coupling analysis, we computed EEG and EMG projection vectors to identify optimal coupling patterns. Subsequently, we derived CMC spectra and topographies through coherence analysis to characterize corticomuscular interactions at spatial and spectral scales.

MAIN RESULTS: Compared to healthy controls, stroke patients demonstrated reduced beta-band CMC patterns, particularly within the sensorimotor areas involved in the foot movement. No significant differences in CMC patterns were observed between stroke patients before and after rehabilitation training. Further analysis revealed significant correlation between betaband CMC changes and clinical improvements measured by the Berg Balance Scale (BBS).

SIGNIFICANCE: Beta-band CMC is a potential neurophysiological biomarker of motor recovery following stroke. These findings provide novel insights into the disrupted corticomuscular communication underlying post-stroke motor dysfunction, while offering mechanistic evidence to guide the design and implementation of hybrid BCI systems that target these specific biomarkers for therapeutic intervention.},
}

@article {pmid41529302,
year = {2026},
author = {Yue, H and Ruan, H and Zhao, Y},
title = {LMSA-net: a lightweight multi-scale attention network for eeg-based emotion recognition.},
journal = {Biomedical physics & engineering express},
volume = {12},
number = {1},
pages = {},
doi = {10.1088/2057-1976/ae3763},
pmid = {41529302},
issn = {2057-1976},
mesh = {*Electroencephalography/methods ; Humans ; *Emotions/physiology ; Algorithms ; *Signal Processing, Computer-Assisted ; *Attention ; *Neural Networks, Computer ; },
abstract = {Electroencephalogram (EEG)-based emotion recognition holds great potential in affective computing, mental health assessment, and human-computer interaction. However, EEG signals are non-stationary, noisy, and composed of multiple frequency bands, making direct feature learning from raw data particularly challenging. While end-to-end models alleviate the need for manual feature engineering, advancing the performance frontier of lightweight architectures remains a crucial and complex challenge for practical deployment. To address these issues, we propose LMSA-Net (Lightweight Multi-Scale Attention Network), a lightweight, interpretable, and end-to-end model that directly learns spatio-temporal features from raw EEG signals. The architecture integrates learnable channel weighting for adaptive spatial encoding, multi-scale temporal separable convolution for rhythm-specific feature extraction, and Sim Attention Module for parameter-free saliency enhancement. Our proposed LMSA-Net is evaluated on three benchmark datasets, SEED, SEED-IV, and DEAP, under subject-dependent protocols. It achieves top performance on SEED (65.53% accuracy), competitive results on SEED-IV (48.52% accuracy), and strong performance in arousal classification on DEAP, demonstrating good generalization. Ablation studies confirm the critical role of each proposed module. Frequency analysis reveals that our multi-scale temporal kernels inherently specialize in distinct EEG rhythms, validating their neurophysiological alignment. Lightweight design is evidenced by minimal parameters (7.64K) and low latency, ideal for edge deployment. Interpretability analysis further shows the model's focus on emotion-related brain regions. LMSA-Net thus delivers an efficient, interpretable, and high-performing solution. The code is available athttps://github.com/rhr0411/LMSA-Net.git.},
}

*Electroencephalography/methods
Humans
*Emotions/physiology
Algorithms
*Signal Processing, Computer-Assisted
*Attention
*Neural Networks, Computer

@article {pmid41566808,
year = {2026},
author = {Huang, C and Bai, J and Lin, K and Li, X and Guo, D and Song, J and Wang, J and Chen, Z and Wang, C},
title = {Exploring Back Muscle Activities in Chronic Low Back Pain Patients Using a Large-Area Stretchable Electrode Array With Full-Back Coverage.},
journal = {Advanced healthcare materials},
volume = {},
number = {},
pages = {e04815},
doi = {10.1002/adhm.202504815},
pmid = {41566808},
issn = {2192-2659},
support = {12302223//National Natural Science Foundation of China/ ; 2024E10108//Zhejiang Key Laboratory of Intelligent Rehabilitation and Translational Neuroelectronics/ ; 2022C03038//Key R&D Program of Zhejiang Province/ ; BLB19J014//Key R&D Program of Zhejiang Province/ ; 2022M710126//China Postdoctoral Science Foundation/ ; LGF20H170004//Zhejiang Province Basic Public Welfare Research Project/ ; BX20220268//National Postdoctoral Program for Innovative Talents/ ; },
abstract = {Exploring the back muscle activity in chronic low back pain (CLBP) patients is crucial for the quantitative assessment of their neuromuscular function. However, hindered by the lack of stretchable, large-area electrode arrays capable of spanning the entire back, existing studies have primarily focused on the erector spinae and multifidus muscles in the lower back. Here, we report a large-area, stretchable high-density surface electromyography (HD-sEMG) electrode array designed to cover both lower and upper back regions, enabling comprehensive characterization of back muscle activity in CLBP patients. The array comprises 64 channels arranged in a half-dumbbell configuration, inspired by the anatomical distribution of the erector spinae and multifidus muscles. Notably, this array demonstrates unprecedented operability, scalability for mass production, and reliable data acquisition capabilities. 128-channel HD-sEMG signals were acquired from both healthy controls and CLBP patients during the Biering-Sørensen test, a standardized lumbar endurance protocol. Statistical analyses of sEMG-derived metrics and topographic maps revealed significant intergroup differences in muscle activity, as well as regional variations between the lower back, upper back, and full-back segments, particularly in contraction time and fatigue-related metric changes. These findings offer novel insights into the neuromuscular dysfunction in CLBP, potentially illuminating the underlying physiological adaptations associated with CLBP.},
}

@article {pmid41566501,
year = {2026},
author = {Prasad, NK and Perry, NJ and Goldring, AL and Fleisher, LA and Petrossian, L and Leuthardt, EC and Souders, L and Wilk, SJ},
title = {A retrospective analysis of post-stroke rehabilitation with real world use of brain-computer interface.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-026-01880-4},
pmid = {41566501},
issn = {1743-0003},
}

@article {pmid41565807,
year = {2026},
author = {Wang, Z and Chen, K and Shi, X and Du, Q and Ai, Y and Li, P and Yong, L and Sun, X and Wang, N and Hu, X and Lu, C and Tang, C and Wang, L and Zheng, Y and Zhang, Y and Guo, H and Pu, Z and Wang, X and Zhang, Y and Jiang, H and Liu, Y and Tang, Z and You, L and Deng, J and Che, R and Gao, Y and Zhang, S and Wang, B and Sun, X and Qin, J and Huang, Y and Shen, L and Ge, J and Zeng, X and Chen, L and Chen, P and Peng, H},
title = {Fibre integrated circuits by a multilayered spiral architecture.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {41565807},
issn = {1476-4687},
abstract = {Fibre electronic devices are transforming traditional fibres and garments into new-generation wearables that can actively interact with human bodies and the environment to shape future life[1-5]. Fibre electronic devices have achieved almost all of the desired functions, such as powering[6,7], sensing[8,9] and display[10,11] functions. However, viable information-processing fibres, which lie at the heart of building intelligent interactive fibre systems similar to any electronic product, remain the missing piece of the puzzle[12-15]. Here we fill this gap by creating a fibre integrated circuit (FIC) with unprecedented microdevice density and multimodal processing capacity. The integration density reaches 100,000 transistors per centimetre, which effectively satisfies the requirements for interactive fibre systems. The FICs can not only process digital and analogue signals similar to typical commercial arithmetic chips but also achieve high-recognition-accuracy neural computing similar to that of the state-of-the-art in-memory image processors. The FICs are stable under harsh service conditions that bulky and planar counterparts have difficulty withstanding, such as repeated bending and abrasion for 10,000 cycles, stretching to 30%, twisting at an angle of 180° cm[-1] and even crushing by a container truck weighing 15.6 tons. The realization of FICs enables closed-loop systems in a single fibre, without the need for any external rigid and bulky information processors. We demonstrate that this fully flexible fibre system paves the way for the interaction pattern desired in many cutting-edge applications, for example, brain-computer interfaces, smart textiles and virtual-reality wearables. This work presents new insights that can promote the development of fibre devices towards intelligent systems.},
}

@article {pmid41565681,
year = {2026},
author = {Zhu, L and Li, R and Qian, M and Lv, F and Hong, H and Li, Y and Zhou, Y and Li, Z and Lei, J and Zou, W and Guan, MX and Zhang, Y and Zhao, G and Ma, H and Gong, J and Kang, L},
title = {A Glial Hub-and-Spoke Circuitry in C. elegans orchestrates bidirectional thermosensation.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-68766-w},
pmid = {41565681},
issn = {2041-1723},
support = {2021ZD0203303//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; },
abstract = {Thermosensation is evolutionarily conserved for survival, yet the roles of glia in temperature coding and circuit dynamics remain poorly understood. Here, we identify C. elegans AMsh glia as dual-mode thermosensory hubs that autonomously detect temperature fluctuations by co-expressing the heat receptor GCY-28 (guanylate cyclase) and cold receptor GLR-3 (ionotropic glutamate receptor). Thermal changes induce spatiotemporal calcium dynamics in glia, driving GABA release to bidirectionally modulate neural circuits: enhancing AFD-mediated warmth detection through the excitatory receptor EXP-1 and suppressing ASH-dependent cold avoidance via the inhibitory receptor LGC-38. This GABAergic hub-and-spoke architecture regulates a broad range of thermal behaviors, including thermal avoidance, thermal resistance, and thermal preference. These findings establish glia as critical interpreters of environmental cues, highlighting their dual roles as sensors and modulators in sensory processing and providing a paradigm for understanding conserved glial mechanisms in neural circuit dynamics and behavioral plasticity.},
}

@article {pmid41565527,
year = {2026},
author = {Sun, Y and Liu, W and Zhang, H and Du, Z and Liu, H and Ma, K and Li, D and Wang, S and Fan, S and Li, L and Zheng, D and Shen, G},
title = {An ultrasoft, breathable, and multichannel ear-computer interface patch.},
journal = {Science bulletin},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.scib.2025.12.042},
pmid = {41565527},
issn = {2095-9281},
abstract = {Brain-computer interface (BCI) presented by the non-invasive electroencephalography (EEG) cap/band or implantable chips enabling people to fast and reliable control computers or mobile devices with thoughts has redefined the boundaries of human capabilities. However, the existing cap/band-adhered sticky gel usually needs to be tightly fixed on the scalp through the hair to ensure intimate contact, which inconveniences the user. And the implantable chips represented by Neuralink gave a living example of how BCI can make quadriplegic live better, but the destructive unacceptable for healthy people. Here we proposed a multichannel wearable ear-computer interface (ECI) patch worn behind the ears for direct communication and control via brain activity. The 8-channel ECI patch based on MXene electrode was prepared by a facile direct inject print approach on the soft, thin, and breathable medical film that enables superior adherence. The fatigue induction experiments tested by the ECI patch offer an average classification accuracy of 90.5%, showing effective monitoring of the fatigue state. Participants wearing the ECI patch also perform the 4-target steady state visual evoked potential (SSVEP) BCI classification offline and online experiment, the online 4-route tasks reap a comparable average accuracy of 93.5% to the commercial cap. Moreover, the complex route task relied on the subjects who gave commands while observing the unmanned vehicle completed 3 times, demonstrating the reliability and possibility of the ECI patch.},
}

@article {pmid41564094,
year = {2025},
author = {Zafar, R and Abdulrab, H},
title = {Deep Learning Unveils Health Predictions From EEG and MRI Data.},
journal = {IEEE pulse},
volume = {16},
number = {5},
pages = {27-34},
doi = {10.1109/MPULS.2025.3618430},
pmid = {41564094},
issn = {2154-2317},
mesh = {Humans ; *Deep Learning ; *Electroencephalography/methods ; *Magnetic Resonance Imaging/methods ; *Brain/diagnostic imaging/physiology ; Neuroimaging/methods ; Brain-Computer Interfaces ; },
abstract = {The field of neuroscience and neuroimaging has been revolutionized with the use of artificial intelligence (AI), as it helps in enhancing the detection of brain activities and accurately diagnosing neurological disorders using various modalities. There are different modalities that help in measuring brain activities, but the most common and widely used are functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). The advanced AI approaches, like deep learning (DL) models, give a new opportunity to various fields, including brain research. This research investigates various AI-driven techniques used for the detection and exploration of the human brain using fMRI and EEG. The AI methods include different machine learning (ML) and DL techniques used to interpret neural activities. Basically, the AI-based models, which also include ML and DL, identify the patterns and detect the abnormalities with higher accuracy, which is helpful in many applications, including brain decoding, monitoring cognitive states, brain-computer interface (BCI), and diagnosis of various diseases. This research provides a comprehensive overview of AI applications in neuroimaging, highlights key applications in cognitive neuroscience and medical imaging, along with a discussion of challenges and future directions. The AI impact of the transformation of neuroimaging research is comprehensively discussed with examples to enhance comprehension.},
}

Humans
*Deep Learning
*Electroencephalography/methods
*Magnetic Resonance Imaging/methods
*Brain/diagnostic imaging/physiology
Neuroimaging/methods
Brain-Computer Interfaces

@article {pmid41561967,
year = {2025},
author = {Sebastián-Romagosa, M and Cho, W and Ortner, R and Sieghartsleitner, S and Guger, M and von Oertzen, TJ and Meuth, SG and Laureys, S and Allison, BZ and Guger, C},
title = {Toward Brain-Computer Interface motor rehabilitation for people with Multiple Sclerosis.},
journal = {Frontiers in medicine},
volume = {12},
number = {},
pages = {1661972},
pmid = {41561967},
issn = {2296-858X},
abstract = {BACKGROUND: Multiple Sclerosis (MS) is a chronic neurodegenerative disease in which the immune system attacks the myelin sheaths around nerves. People with MS (pwMS) often experience pain, fatigue, cognitive dysfunction, and reduced mobility. Today, MS is incurable, and treatments can at best slow the progression of the disease and manage symptoms. We conducted a preliminary, single-arm study using a motor-imagery brain-computer interface (MI-BCI) with functional electrical stimulation (FES) and virtual reality avatar targeting gait in pwMS.

METHODS: Twenty-six pwMS were enrolled; 24 completed 30 BCI sessions. Outcomes were assessed at Baseline, immediately post-treatment (Post1, week 13) and during follow-up (Post2, week 17; Post3, week 37). Change from baseline was analyzed using mixed models for repeated measures (with log-ratio models for skewed measures) and multiplicity control. This uncontrolled study is hypothesis-generating.

RESULTS: Patients treated with the BCI-based intervention obtained significant improvements that were largely maintained to 6 months after the therapy. The walking endurance, assessed by the 6-minute walking test (6MWT), increased by 37.3 m (95% CI 21.50-53.10) after the treatment (p < 0.001), exceeding the minimal clinically important difference (MCID). This improvement in the walking endurance was maintained during the following 6 months after the intervention. Mobility/speed improved: TUG and T25FW times decreased by -15.5% and -16.4% after the last BCI session (both p < 0.001), with benefits persisting after 6 months. Spasticity (MAS) declined by about 1 point, and patient-reported outcomes improved statistically and clinically (MSIS-29 10.18 points, MFIS 7.29 points). Pairwise post-visit contrasts were not significant, consistent with maintenance. Exploratory models found no consistent MS-subtype effect on 6MWT change and suggested larger gains with higher baseline EDSS. Two discontinuations were due to participant availability, not concerns with fatigue or safety.

CONCLUSION: In this preliminary, single-arm study, a MI-BCI + FES system was associated with statistically significant, clinically meaningful gains in gait endurance, mobility/speed, spasticity, and patient-reported outcomes, sustained up to 6 months after the intervention.},
}

@article {pmid41560829,
year = {2026},
author = {Li, Z and Li, T and Ge, R and Chen, F and Du, C and Wang, D and Wang, L},
title = {Thermo-responsive, on-demand adhesive and tissue-conformal hydrogel electrodes for organ repair and brain-computer interfaces.},
journal = {Materials today. Bio},
volume = {36},
number = {},
pages = {102705},
pmid = {41560829},
issn = {2590-0064},
abstract = {Implantable bioelectronic devices, such as brain-computer interfaces (BCIs), face persistent challenges in achieving stable, rapid, and reversible adhesion on wet tissues due to hydration layers and mechanical mismatch, which can cause interfacial failure and unstable signals. Here, we report a conductive hydrogel interface with tissue-adaptive, temperature-controllable adhesion. The material is synthesized via dynamic co-entanglement of poly(acrylic acid) and poly(lipoic acid) with LA-NHS, establishing a dual physico-chemical anchoring mechanism that enables efficient tissue integration in aqueous environments. The hydrogel penetrates tissue microstructures within 5 s, withstands burst pressures >213 mmHg, exhibits <10 % swelling, ∼2784 % extensibility, and a low modulus of 41 kPa, thereby conforming to soft, irregular surfaces and reducing interfacial mismatch. Its temperature-triggered adhesion allows safe detachment and repositioning without apparent tissue damage, supporting repeated applications. In vivo and ex vivo tests confirm rapid hemostasis in mouse liver and tail injury models, effective sealing of porcine gastric, bladder, and intestinal defects, and stable electrocorticography and electrocardiography recordings. Moreover, the hydrogel demonstrates high cytocompatibility (>90 %), <5 % hemolysis, reactive oxygen species scavenging, and ∼90 % antibacterial efficiency. By integrating rapid wet adhesion, mechanical compliance, electrical functionality, and bioprotective features, this hydrogel provides a versatile platform for next-generation bioelectronic interfaces and soft therapeutic devices.},
}

@article {pmid41559987,
year = {2026},
author = {Xu, Q and Shao, Z and Ma, D and Zhai, X and Wang, Y and Dou, W and Pan, Y},
title = {Predicting rehabilitation outcomes of unilateral stroke after brain-computer interface training based on magnetic resonance imaging data.},
journal = {Medicine},
volume = {105},
number = {3},
pages = {e46280},
doi = {10.1097/MD.0000000000046280},
pmid = {41559987},
issn = {1536-5964},
support = {2022YFC3601100//National Key R&D Program of China/ ; 2022YFC3601105//National Key R&D Program of China/ ; Z221100003522016//Beijing Science and Technology Program/ ; 2022-2Z-2242//Capital’s Funds for Health Improvement and Research/ ; 1001020124//Tsinghua University Precision Medicine Research Program/ ; },
mesh = {Humans ; *Stroke Rehabilitation/methods ; Male ; *Brain-Computer Interfaces ; *Magnetic Resonance Imaging/methods ; Female ; Middle Aged ; Aged ; *Stroke/diagnostic imaging/complications/physiopathology ; Prognosis ; Recovery of Function ; Adult ; Treatment Outcome ; China ; *Hemiplegia/rehabilitation/etiology ; },
abstract = {Stroke remains a significant cause of disability globally, with a noticeable prevalence in China. Post-stroke rehabilitation, particularly through brain-computer interface (BCI) methods, plays a vital role in enhancing motor function recovery. However, the efficacy of BCI rehabilitation might be hindered by challenges in individualized program of prognosis prediction. This study aimed to develop prognostic prediction models for unilateral hemiplegia after BCI rehabilitation, utilizing both clinical and functional magnetic resonance imaging (fMRI) data, in order to enhance treatment efficiency and optimize patient outcomes. The study included 40 stroke patients (22 left hemisphere affected and 18 right hemisphere affected) who underwent BCI rehabilitation training at the Beijing Tsinghua Changgung Hospital (Beijing, China). Data related to patients' demographics, disease duration, and assessment scores were collected. Based on the improvement in the Fugl-Meyer assessment of the upper extremity (FMA-UE) rating scale, patients were categorized into responder and non-responder groups. Linear regression and its variants, including multivariate logistic regression and optimal subset regression, were utilized to predict the post-treatment scores based on both fMRI and clinical data. The accuracy and R-squared value of the models were assessed using leave-one-out cross-validation (LOOCV). The linear regression model using imaging data exhibited a remarkable performance with a classification accuracy of 100% and R2 (LOOCV) exceeding 0.94. In contrast, the model relying solely on clinical data achieved a classification accuracy of <80%. These results clearly demonstrated the potential of employing imaging data and machine learning methods to effectively predict the effectiveness of BCI rehabilitation. This study assessed the effectiveness of neuroimaging in predicting the efficacy of BCI rehabilitation for unilateral stroke patients. The developed model could serve as a foundation for enhancing our comprehension of rehabilitation outcomes, especially in uniqueness of left and right stroke, and ultimately improving patient well-being. The findings underscored the potential of neuroimaging data in optimizing BCI rehabilitation, leading to the enhanced recovery of motor function in unilateral stroke patients.},
}

Humans
*Stroke Rehabilitation/methods
Male
*Brain-Computer Interfaces
*Magnetic Resonance Imaging/methods
Female
Middle Aged
Aged
*Stroke/diagnostic imaging/complications/physiopathology
Prognosis
Recovery of Function
Adult
Treatment Outcome
China
*Hemiplegia/rehabilitation/etiology

@article {pmid41559146,
year = {2026},
author = {Du, M and Shi, P and Liu, Z and Xu, Y and Liu, X and Liu, W and Liu, S and Ming, D},
title = {Naturalistic facial dynamics enable quantitative clinical assessment of atypical expression phenotypes in children with autism spectrum disorder.},
journal = {NPJ digital medicine},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41746-026-02375-1},
pmid = {41559146},
issn = {2398-6352},
support = {23JCZDJC01030//Natural Science Foundation of Tianjin/ ; 23JCZDJC01030//Natural Science Foundation of Tianjin/ ; 23JCZDJC01030//Natural Science Foundation of Tianjin/ ; 23JCZDJC01030//Natural Science Foundation of Tianjin/ ; 23JCZDJC01030//Natural Science Foundation of Tianjin/ ; 24HHNJSS00012//Autonomous Project of Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration/ ; 24HHNJSS00012//Autonomous Project of Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration/ ; 24HHNJSS00012//Autonomous Project of Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration/ ; 24HHNJSS00012//Autonomous Project of Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration/ ; },
abstract = {Existing facial-expression studies in children with autism spectrum disorder (ASD) rely mainly on discrete, task-driven measures that overlook the sustained emotional fluctuations and ambiguous expressions in naturalistic interactions. This study quantified atypical facial expression patterns in ASD during spontaneous, unscripted interactions. We analyzed 184 naturalistic video sessions from 99 children with ASD and 85 typically developing (TD) peers and extracted three features capturing spontaneous dynamics: emotion variation (temporal stability of emotional states), expression intensity (magnitude of facial muscle activation), and facial coordination (synchrony across facial muscles). These features integrated holistic and processual representations across coarse- and fine-grained levels, enabling detailed quantification of facial patterns. Compared with TD peers, the ASD group exhibited increased prominence of anger, altered emotion transition probabilities, heightened activation in non-core facial muscles, and atypical facial coordination (p < 0.05). These findings reveal subtle facial dynamics inaccessible to traditional approaches and provide a quantitative explanation for the hard-to-describe atypical expressions. Using the fused feature set, ASD classification reached 92.4% accuracy and 0.977 AUC. Regression analyses further predicted symptom severity with mean absolute errors of 13.94 on the ABC scale and 3.84 on the CABS scale. These quantitative and interpretable markers show promise for large-scale ASD screening in naturalistic settings.},
}

@article {pmid41559106,
year = {2026},
author = {Qamar, WUR and Abibullaev, B},
title = {Multi-scale EEG feature decoding with Swin Transformers for subject independent motor imagery BCIs.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {2503},
pmid = {41559106},
issn = {2045-2322},
support = {040225FD4727//Nazarbayev University under Faculty development competitive research grants program/ ; 040225FD4727//Nazarbayev University under Faculty development competitive research grants program/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Signal Processing, Computer-Assisted ; *Imagination/physiology ; Algorithms ; },
abstract = {High inter-subject variability and the non-stationary nature of EEG signals pose significant challenges for subject-independent Brain-Computer Interfaces (BCIs) leading to poor model generalization. Differences in neural activity patterns, electrode placements, and external noise further degrade performance making it difficult to develop BCIs that remain reliable across users without extensive recalibration. This study presents a Compact Convolutional Swin Transformer (CCST) to address this issue by using hierarchical window based self-attention combined with convolutional feature extraction to efficiently capture both local electrode interactions and global temporal dependencies. This multi-scale feature representation enhances generalization across subjects, a critical factor for real world BCI deployment. We evaluated CCST on the BCI Competition IV (2a, 2b) and PhysioNet MI datasets using Leave-One-Subject-Out (LOSO) cross-validation achieving state-of-the-art classification accuracies of 68.27%, 76.61%, and 71.70% respectively. Our statistical analysis using the Wilcoxon signed-rank test with Bonferroni correction confirms significant performance improvements over benchmark models. Additionally, CCST achieves a reduction in parameters and a decrease in FLOPs compared to full self-attention models making it more efficient for real-time BCI applications. These results establish CCST as a scalable and efficient framework for adaptive subject-independent BCIs with promising applications in neurorehabilitation, assistive technology, and cognitive training.},
}

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

@article {pmid41557503,
year = {2026},
author = {Deng, GC and Liu, L and Liu, BY and Jing, R and Wu, C and Li, YY and Feng, X and Li, KY and Wang, JH and Liu, YJ and Yu, YQ and Chen, JD and Yang, HB and Li, XY and Duan, S and Sun, L},
title = {Transthyretin-mediated regulation of neuropathic pain and anxiety-like behavior in the lateral parabrachial nucleus.},
journal = {Cell reports},
volume = {45},
number = {1},
pages = {116860},
doi = {10.1016/j.celrep.2025.116860},
pmid = {41557503},
issn = {2211-1247},
abstract = {Neuropathic pain presents a complex challenge in clinical treatment due to its multifaceted etiology and frequent comorbidities with anxiety. Despite its prevalence, the underlying molecular, cellular, and circuit mechanisms remain poorly understood. The lateral parabrachial nucleus (LPBN) is a critical center that regulates both pain perception and the emotional aspects. In this study, single-cell sequencing shows upregulated transthyretin (TTR) in neuropathic pain models. Through bidirectional conditional knockout (cKO) and overexpression of TTR in LPBN neurons of mice, we confirm that TTR in LPBN glutamatergic neurons serves as a necessary and sufficient regulator of pain-anxiety comorbidity. Furthermore, TTR plays a pivotal role in pain regulation by binding to its receptor, receptor for advanced glycation end products (RAGE), thereby influencing neuroinflammation and neuronal excitability through the NF-κB signaling pathway. These results highlight potential molecular targets for the treatment of neuropathic pain.},
}

@article {pmid40768444,
year = {2026},
author = {Scholten, K and Xu, H and Lu, Z and Jiang, W and Jin, Z and Ortigoza-Diaz, J and Petrossians, A and Orler, S and Gallonio, R and Liu, X and Song, D and Meng, E},
title = {A Comprehensive Research Dissemination Model for Polymer-Based Neural Interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {73},
number = {2},
pages = {934-944},
doi = {10.1109/TBME.2025.3596222},
pmid = {40768444},
issn = {1558-2531},
support = {U24 NS113647/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Rats ; *Polymers/chemistry ; Microelectrodes ; *Electrodes, Implanted ; Electroencephalography/instrumentation ; Equipment Design ; Rats, Sprague-Dawley ; *Brain-Computer Interfaces ; Hippocampus/physiology ; },
abstract = {OBJECTIVE: Implantable polymer microelectrode arrays (pMEAs) offer stable integration with neural tissue but are not widely available. An academic resource model is explored as a means of standardizing and disseminating pMEAs.

METHODS: The resource is based on a multi-project wafer model, originally developed in the semiconductor industry, allowing the simultaneous microfabrication of pMEAs with arbitrary designs. This model leverages innovations in design, manufacturing, and packaging to produce custom penetrating, surface, and cuff-type form-factors in batch and at low cost. Device quality is verified through benchtop testing and chronic electrophysiological recording in rats.

RESULTS: To date, over 1000 pMEAs (more than 50 designs) were provided to 45 academic labs. Implanted penetrating arrays in the hippocampus achieved high quality, chronic recordings from freely moving rats. Surface arrays reliably recorded electroencephalogram signals from the cortex and evoked potentials from the somatosensory cortex in awake rats.

CONCLUSION: Efficient production of custom pMEAs for research is possible through a unique resource model inspired by the semiconductor industry.

SIGNIFICANCE: Greater access to pMEAs enables researchers to conduct new experiments across different regions of the nervous system, accelerating discoveries.},
}

Animals
Rats
*Polymers/chemistry
Microelectrodes
*Electrodes, Implanted
Electroencephalography/instrumentation
Equipment Design
Rats, Sprague-Dawley
*Brain-Computer Interfaces
Hippocampus/physiology

@article {pmid41557476,
year = {2026},
author = {Kristen, R and Lenarz, T and Keintzel, T and Sprinzl, G and Köstler, C and Knoelke, N and Busch, S and Raffelsberger, T and Magele, A and Schörg, P and Wiek, RJ},
title = {Lifetime Real-World Evidence on Safety and Performance of the First Active Transcutaneous Bone Conduction Implant (BCI), the Bonebridge Covering Conductive to Mixed Hearing Loss (CMHL), and Single-Sided Deafness (SSD): Results From a Long-Term Retrospective Analysis.},
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.0000000000004837},
pmid = {41557476},
issn = {1537-4505},
abstract = {OBJECTIVE: Confirm the safety and performance of the first partially implantable active transcutaneous Bone Conduction Implant (tBCI) in patients who have been implanted for a minimum of 5 years before 2023.

SETTING: Otolaryngology departments of 4 German and Austrian hospitals.

STUDY DESIGN: Retrospective, multicenter, longitudinal, open-label case series study. Patients: 186 ears treated for conductive and mixed hearing loss (CMHL), or single-sided deafness (SSD) implanted for 5 years (151 aged 18 y or older, 35 aged 5 to 17 y) at the time of implantation.

INTERVENTION: Implantation of the Bonebridge (BB) BCI 601, a partially implantable active middle ear implant (AMEI).

MAIN OUTCOME MEASURES: Patients' audiometric pure-tone average (PTA4) (0.5, 1, 2, 4 kHz) thresholds (bone conduction, sound field) and speech perception (word recognition scores) were retrospectively collected up to 10 years 10 months postoperatively. Complications were recorded with focus on revision surgery and explantations. Subgroups were adults and children.

RESULTS: Safety was established by stable bone conduction (BC) thresholds 5 years after implantation or later with mean paired differences of -5.33 dB for adults and -8.05 dB for children and underscored by a low number of technical failures and high survival rates 10 years after implantation. Paired mean sound field PTA4 thresholds and word recognition scores significantly improved as tested by post hoc analysis 5 years or later after implantation, with functional gains for CMHL of 23.44 dB (adults), 27.69 dB (children), and word recognition scores of 58.22% (adults), 80.00% (children). Furthermore, mean sound field PTA4 thresholds and word recognition scores remain significantly improved over time at 36.37 dB HL and 68.75% 5 years or later after implantation as tested with linear mixed-effects model.

CONCLUSIONS: The findings of this study demonstrate that this tBCI remains safe and effective for up to 10 years.},
}

@article {pmid41555272,
year = {2026},
author = {An, Q and Cao, M and Zhang, J and Liu, D and Jialin, A and Chen, D and Wang, J and Wang, C and Zhao, X and Wang, D and Li, K and Zhang, D and Deng, W},
title = {Spatiotemporal disruption of prefrontal dynamics during affective association in depression: an fNIRS case-control study.},
journal = {BMC psychiatry},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12888-026-07800-z},
pmid = {41555272},
issn = {1471-244X},
}

@article {pmid41554257,
year = {2026},
author = {Althoff, J and Nogueira, W},
title = {Selective auditory attention decoding in bilateral cochlear implant users to music instruments.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae3a1a},
pmid = {41554257},
issn = {1741-2552},
abstract = {Electroencephalography (EEG) data can be used to decode an attended sound source in normal-hearing (NH) listeners, even for music stimuli. This information could steer the sound processing strategy for cochlear implants (CIs) users, potentially improving their music listening experience. The aim of this study was to investigate whether selective auditory attention decoding (SAAD) could be performed in CI users for music stimuli. Approach: High-density EEG was recorded from 8 NH and 8 CI users. Duets containing a clarinet and cello were dichotically presented. A linear decoder was trained to reconstruct audio features of the attended instrument from EEG data. The estimated attended instrument was selected based on which of the two instruments had a higher correlation to the reconstructed instrument. EEG recordings are challenging in CI users, as these devices introduce strong electrical artifacts. We also propose a new artifact rejection technique that employs ICA calculating ICs and automating their selection for removal, which we termed ASICA. Main results: We showed that it was possible to perform SAAD for music in CI users. The decoding accuracies were 59.4 \% for NH listeners and 60 \% for CI users with the proposed algorithm. Using the proposed algorithm, the correlation coefficients between the reconstructed audio feature and the attended audio feature were improved in conditions where artifact was dominating. Significance: Results indicate that selective auditory attention to musical instruments can be effectively decoded, and that this decoding is enhanced by the new artifact reduction algorithm, particularly in scenarios where the cochlear implant's electrical artifact has greater influence. Moreover, these results could be relevant as an objective measure of music perception or for a brain computer interface that improves music enjoyment. Additionally we showed that the stimulation artifact can be suppressed. The ethic's committee of the MHH approved this study (8874_BO_K_2020).},
}

@article {pmid41553892,
year = {2026},
author = {Lim, H and Choi, H and Ahmed, B and Park, Y and Ku, J},
title = {Attention-Adaptive BCI-AOT System Enhances Motor Recovery and Neural Engagement After Stroke.},
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.2026.3654935},
pmid = {41553892},
issn = {1558-0210},
abstract = {Stroke frequently results in long-term motor deficits that impair quality of life. Action observation therapy (AOT) has shown promise for motor recovery through engagement of the mirror neuron system (MNS), yet its passive nature and lack of attentional tracking limit its neuroplasticity efficacy. To address these limitations, we developed a closed-loop Brain-Computer Interface-integrated AOT (BCI-AOT) system employing real-time Steady-State Visual Evoked Potential (SSVEP)-based attention monitoring to dynamically control therapy delivery. In a within-subject crossover study, 22 individuals with hemiplegic stroke completed both BCI-AOT and conventional AOT conditions, each consisting of five daily sessions and separated by a one-week washout. In BCI-AOT, video playback depended on sustained attentional engagement detected via SSVEPs. Behavioral outcomes (Box and Block Test [BBT], Action Research Arm Test [ARAT]) and physiological measures (Motor Evoked Potential [MEP] amplitude and latency, EEG power) were assessed pre- and post-intervention. Significant Condition × Day interactions were found for both BBT and ARAT, indicating greater functional gains over time in the BCI-AOT condition. Both conditions showed increased corticospinal excitability over time, while BCI-AOT additionally exhibited distinct mu and theta modulation over time. Participants also reported greater motivation and attention after BCI-AOT compared to conventional AOT. These results suggest that BCI-AOT elicits stronger neuroplasticity responses and user engagement than standard AOT. This study supports the feasibility and clinical potential of closed-loop, attention-adaptive neurorehabilitation for stroke recovery.},
}

@article {pmid41553833,
year = {2026},
author = {Yang, Y and Su, Z and Liu, X and Song, J and Li, C and Xu, Y and Ye, J and Lin, LL},
title = {A flexible plasmonic SERS hydrogel patch for metabolite sensing on bio-interfaces.},
journal = {Nanoscale},
volume = {},
number = {},
pages = {},
doi = {10.1039/d5nr04403k},
pmid = {41553833},
issn = {2040-3372},
abstract = {The growing demand for real-time, non-invasive monitoring of biochemical molecules has driven the development of advanced, flexible sensing materials. Surface-enhanced Raman spectroscopy (SERS) offers high molecular specificity and ultralow detection limits. While rigid SERS substrates based on plasmonic nanoparticle arrays provide strong signal enhancements, they lack the mechanical compatibility and conformal adhesion required for dynamic biological surfaces, such as human skin or neural tissues. Here, we present a flexible SERS hydrogel patch for the label-free detection of metabolites at bio-interfaces. The patch integrates a self-assembled silver nanoparticle film with an ultrathin polyvinyl alcohol (PVA) hydrogel layer to achieve good plasmonic enhancement, mechanical durability, conformity and reliable SERS stability. The SERS patch allows the detection of metabolites within 6 min upon analyte exposure, enabling the label-free detection of key metabolites, such as glucose, uric acid and urea with concentrations down to 1 μM, 50 μM and 1 mM, respectively. We demonstrate the versatility of this platform by performing ex vivo experiments on porcine brain and muscle tissues to simulate real-world application scenarios in brain-machine interfaces and implantable sensors. This work demonstrates the feasibility of SERS hydrogel-based flexible platforms for the in situ monitoring of metabolites at bio-interfaces.},
}

@article {pmid41553800,
year = {2026},
author = {Jafar, R},
title = {Dimensions of Transparency: How Dys-Appearance Affects BCI Embodiment.},
journal = {AJOB neuroscience},
volume = {17},
number = {1},
pages = {25-27},
doi = {10.1080/21507740.2025.2606297},
pmid = {41553800},
issn = {2150-7759},
}

@article {pmid41553799,
year = {2026},
author = {Zilio, F},
title = {A Multi-Criteria Framework for Transparency in the Design and Use of Brain-Computer Interfaces.},
journal = {AJOB neuroscience},
volume = {17},
number = {1},
pages = {22-25},
doi = {10.1080/21507740.2025.2606296},
pmid = {41553799},
issn = {2150-7759},
}

@article {pmid41553797,
year = {2026},
author = {Barnhart, AJ},
title = {A Phenomenological Photo Finish: Testing Transparency at the Cybathlon Brain-Computer Interface Race.},
journal = {AJOB neuroscience},
volume = {17},
number = {1},
pages = {20-22},
doi = {10.1080/21507740.2025.2606294},
pmid = {41553797},
issn = {2150-7759},
}

@article {pmid41553197,
year = {2026},
author = {Bhargava, EK and Arvaneh, M},
title = {Expanding the olfactory implant paradigm through recent advances in brain-computer interface technology.},
journal = {Rhinology},
volume = {},
number = {},
pages = {},
doi = {10.4193/Rhin25.554},
pmid = {41553197},
issn = {0300-0729},
abstract = {The international opinion paper by Whitcroft et al. provides invaluable guidance for the emerging field of olfactory implants (1). While the authors thoroughly address clinical considerations and current technological approaches, we would like to expand upon Statements 9.1 and 9.3 regarding electrode technology limitations and highlight recent advances in brain-computer interface (BCI) technology that could address key technological challenges around electrode longevity and biocompatibility.},
}

@article {pmid41552876,
year = {2026},
author = {Ding, Y and Lu, Y and Zhao, G and Gong, Z},
title = {Drosophila Larvae Generate Force to Counteract External Mechanical Pressures.},
journal = {The Journal of experimental biology},
volume = {},
number = {},
pages = {},
doi = {10.1242/jeb.250849},
pmid = {41552876},
issn = {1477-9145},
support = {T2293720/ T2293721, 32271041//National Natural Science Foundation of China/ ; 2021ZD0200405//National Major Science and Technology Projects of China/ ; 2022C01022//Key Research and Development Program of Zhejiang Province/ ; },
abstract = {To counteract or to retreat presents a fundamental dilemma for biological organisms when facing adverse abiotic environmental conditions. In many cases, the predominant strategy animals adopt is to retreat. However, if counteraction is possible, and how the choice between counteraction and retreat is decided, are not clear. Here, we report that Drosophila larvae can actively counteract external mechanical pressure, inspired by Drosophila larval cleft-squeezing behaviour. We developed a behavioural paradigm to investigate the counteracting force of larvae in response to external pressures. Instead of retreating by crawling backward, a portion of Drosophila larvae could crawl forward and counteract against the external physical pressure. Under externally applied pressing forces of 25mN, 93.9% of forward peristaltic movements increased the counterforce, while 88.2% of backward peristaltic movements decreased it. The activeness in counteraction force was reflected by the longer inter-wave delay, more oscillation work and longer force wave period during consecutive forward peristaltic waves. As the external pressing force was increased from 25mN to 50mN, 75mN and 100mN, counteraction by forward peristalsis was less frequent, while retreat by backward peristalsis was more frequent when pressure is high. A reduction of the external pressure immediately following the counteracting forward peristalsis, which might serve as rewarding signal, could reinforce the counteraction and induce more ensuing forward peristalsis. The rewarding effect of reducing external pressure by forward crawling was much more than that by backward crawling. Our study sheds light on the intricate mechanisms underlying animal proactive responses to adverse abiotic environmental conditions.},
}

@article {pmid41551232,
year = {2025},
author = {Lin, Z and Choi, J and Mao, R and Zhao, B and Kang, J},
title = {Spatial Adaptive Selection using Binary Conditional Autoregressive Model with Application to Brain-Computer Interface.},
journal = {Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America},
volume = {},
number = {},
pages = {},
pmid = {41551232},
issn = {1061-8600},
abstract = {In medical imaging studies, scalar-on-image regression presents significant challenges due to limited sample sizes and the high-dimensionality of datasets. Additionally, imaging predictors often exhibit spatially heterogeneous activation patterns and complex nonlinear associations with the response variable. To address these issues, we propose a novel Bayesian scalar-on-image regression model with the Spatial Adaptive Selection using Binary Conditional Autoregressive Model (SAS-BCAR) prior. The proposed approach leverages a binary conditional autoregressive model to capture spatial dependencies among feature selection indicators, effectively identifying spatially structured sparsity patterns within image data, while addressing nonlinear relationships between image predictors and the response variable. Furthermore, our SAS-BCAR incorporates an adaptive feature selection mechanism that adjusts to varying spatial dependencies across different image regions, ensuring a more precise and robust feature selection process. Through extensive numerical simulations on benchmark computer vision datasets and analysis of electroencephalography data in brain-computer interface applications, we demonstrate that the SAS-BCAR model achieves superior predictive performance compared to state-of-the-art alternatives, particularly in scenarios with limited training data. Supplementary materials including computer code, R packages, datasets, and additional figures are available online.},
}

@article {pmid41551041,
year = {2025},
author = {Otarbay, Z and Kyzyrkanov, A},
title = {Transfer learning for subject-independent motor imagery EEG classification using convolutional relational networks.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1691929},
pmid = {41551041},
issn = {1662-4548},
abstract = {Motor imagery (MI) based electroencephalography (EEG) classification is central to brain-computer interface (BCI) research but practical deployment remains challenging due to poor generalization across subjects. Inter-individual variability in neural activity patterns significantly limits the development of subject-independent BCIs for healthcare and assistive technologies. To address this limitation, we present a transfer learning framework based on Convolutional Relational Networks (ConvoReleNet) designed to extract subject-invariant neural representations while minimizing the risk of catastrophic forgetting. The method integrates convolutional feature extraction, relational modeling, and lightweight recurrent processing, combined with pretraining on a diverse subject pool followed by conservative fine-tuning. Validation was conducted on two widely used benchmarks, BNCI IV-2a (four-class motor imagery) and BNCI IV-2b (binary motor imagery), to evaluate subject-independent classification performance. Results demonstrate clear improvements over training from scratch: accuracy on BNCI IV-2a increased from 72.22 (±20.49) to 79.44% (±11.09), while BNCI IV-2b improved from 75.10 (±17.17) to 83.85% (±10.30). The best-case performance reached 87.55% on BNCI IV-2a with Tanh activation and 83.85% on BNCI IV-2b with ELU activation, accompanied by reductions in inter-subject variance of 45.9 and 40.0%, respectively. These findings establish transfer learning as an effective strategy for subject-independent MI-EEG classification. By enhancing accuracy, reducing variability, and maintaining computational efficiency, the proposed framework strengthens the feasibility of robust and user-friendly BCIs for rehabilitation, clinical use, and assistive applications.},
}

@article {pmid41548826,
year = {2026},
author = {Gwon, Y and Chung, CK},
title = {Distinct Post-Sentence Neural Patterns Representing Lexical Items vs. Sentence Integration.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121703},
doi = {10.1016/j.neuroimage.2026.121703},
pmid = {41548826},
issn = {1095-9572},
abstract = {While comprehension marks the starting point in daily communication, the process is only fulfilled when suitable responses or inferences are followed. Listeners retain sentence information after initial comprehension. Although comprehension during listening has been widely studied, comparatively little is understood about how and where the brain retains linguistic information beyond the end of a sentence (EOS). A key question is whether the brain retains not only a holistic, sentence-level representation but also independent traces of individual lexical items-and, if so, how and where these dissociable signals are encoded in the brain. By analyzing the high gamma envelope in electrocorticography (ECoG) data from 15 patients with epilepsy, we directly investigated how neural signals encode and retain information about individual lexical items as well as the integrated sentence representation after the EOS. To this end, we employed a question-and-answer paradigm in which participants heard one of four sentences ("Is it alive?", "Is it not alive?", "Is it a part of body?" or "Is it not a part of the body?"), followed by a response prompt. To answer correctly, subjects must retain the relevant linguistic information, so we could trace retained neural representations in post-question periods, that respond either to each lexical item independently-content ("alive" vs. "part of the body") and negation ("positive" vs. "negative")-or to sentence-specific representations integrating both lexical items. Label-based encoding models were fit to predict neural responses from each label, and encoding strength was quantified by the correlation between predicted and observed signals. We found that channels selectively encoding lexical information were distributed across widespread cortical areas. In contrast, sentence-specific encoding was highly localized in the left posterior superior temporal gyrus (pSTG). Furthermore, by applying the same encoding model to neural signals recorded during the subsequent response-preparation period, we found that both lexical-item and integrated sentence information can persist significantly while participants prepared their responses. These findings provide direct evidence for the distinct spatial organization of lexical and sentence-level representations in the human brain after the end of a sentence.},
}

@article {pmid41548791,
year = {2026},
author = {Liu, Y and Wang, S and Zhang, Y and Wan, L and Wang, H and Zhang, L},
title = {Oxidized alginate-based interpenetrated dual-network antibacterial hydrogel for enhanced diabetic wound healing.},
journal = {International journal of biological macromolecules},
volume = {},
number = {},
pages = {150335},
doi = {10.1016/j.ijbiomac.2026.150335},
pmid = {41548791},
issn = {1879-0003},
abstract = {Plagued by a prolonged healing process and recurrent bacterial infections, diabetic wounds pose a significant clinical challenge. This underscores the urgent need to develop advanced dressings to address microbial resistance and dysfunctional healing processes. Herein, we present a self-healing double-network hydrogel that integrates antibacterial activity with enhanced tissue regenerative potential, offering a promising strategy to accelerate diabetic wound repair. The hydrogel was constructed by interpenetrating a stable polyacrylamide (PAM) network into a dynamically crosslinked oxidized alginate-polydopamine (OSPB) network. Owing to the multiple dynamic interactions, including ionic chelation, Schiff base coordination, and hydrogen bonding, the hydrogel exhibits intrinsic self-healing behavior. The compact crosslinked double-network architecture imparted reduced swelling and enhanced mechanical strength while maintaining tissue conformity. Its high stretchability, toughness, and rapid recovery under repetitive stress ensured the hydrogel for dynamic wound protection and long-term wound management. To maximize antibacterial potency, the hydrogel incorporates the antimicrobial Jelleine-1 peptide (J-1), which was deposited at the tissue-adhesive interfaces, imparting strong antibacterial activity. Besides, the enhanced transdermal penetration was confirmed using bovine serum albumin - fluorescein isothiocyanate (BSA-FITC) as the macromolecular model. In vivo studies demonstrated an accelerated wound closure with promoted cell proliferation, migration, and angiogenesis, which consequently improves granulation tissue formation and collagen deposition. Collectively, our work presents a multifunctional hydrogel system for promising clinical treatment of diabetic wounds.},
}

@article {pmid41548584,
year = {2026},
author = {Lim, JH and Kuo, PC},
title = {Enhancing Brain-Computer interface performance through source-level attention mechanism: An EEG motor imagery study.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110666},
doi = {10.1016/j.jneumeth.2025.110666},
pmid = {41548584},
issn = {1872-678X},
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) enable direct communication between humans and machines by translating brain signals into control commands. Electroencephalography (EEG) is a commonly used modality in BCI systems due to its non-invasiveness and high temporal resolution. However, EEG-based BCIs often suffer from low signal-to-noise ratios and limited spatial resolution, primarily due to the small number of recording electrodes. Although source estimation techniques can improve spatial specificity, they typically require subject-specific information such as individual brain anatomy or electrode positions, which may not always be available. This study aims to address these challenges by proposing a framework that enhances task-relevant EEG signals using an attention-guided source estimation approach based on coarse predefined brain regions.

NEW METHOD: We developed an attention-guided neural network that estimates source-level activity most relevant to the BCI task, without requiring subject-specific structural data. The model uses predefined regions of interest to guide attention mechanisms toward informative spatial features.

RESULTS: The framework was validated using publicly available motor imagery EEG datasets, achieving strong performance.

Comparative analyses were conducted against baseline models using traditional EEG signals and standard feature extraction methods. This study presents an effective approach for improving EEG-based BCI performance by integrating an attention-guided source estimation network into the decoding pipeline. The method does not rely on subject-specific anatomical information, making it broadly applicable.

CONCLUSION: By emphasizing task-relevant source activity, the framework enhances signal quality and classification accuracy, thereby advancing the potential of BCIs for precise and practical applications.},
}

@article {pmid41547354,
year = {2026},
author = {Gao, X and Liu, X and Wang, N and Cui, C and Liu, W and Yang, M and Li, Q and Ou, Y and Ning, A and Wei, X and Zhang, M and Qiu, S and Lei, Y and Fu, D and Li, H and Sun, L and Lu, M and Zhang, M and Wang, Y},
title = {Nanoparticles hijack calvarial immune cells for CNS drug delivery and stroke therapy.},
journal = {Cell},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cell.2025.12.008},
pmid = {41547354},
issn = {1097-4172},
abstract = {The rapid accessibility of calvarial immune cells to the brain, in principle, may offer transformative opportunities for overcoming drug delivery barriers in central nervous system (CNS) disorders. Here, we hijacked calvarial immune cells using drug-loaded nanoparticles (NPs) and leveraged their unique migration mechanism through skull-meninges microchannels to bypass the blood-brain barrier (BBB) for CNS drug delivery. We constructed NP-loaded immune cells in situ via intracalvariosseous (ICO) injection, validated their prompt migration in response to CNS perturbation, and targeted therapeutic delivery to CNS lesions. Compared with conventional delivery approaches, this strategy achieved promising therapeutic efficacy in improving both short- and long-term outcomes in preclinical stroke models. Our prospective clinical trial further supports the translational feasibility of ICO immune access in treating malignant stroke. These findings establish skull-based delivery as a promising, clinically translatable route for CNS drug delivery and highlight immune-assisted transport as a potentially transformative strategy for improving therapeutic outcomes in neurological disorders.},
}

@article {pmid41546096,
year = {2026},
author = {Zhang, H and Song, X and Huang, N and Xiong, K and Shao, N and Su, Y and Bian, S and Sawan, M},
title = {A programmable peptide interface for on-demand neural culturing platforms.},
journal = {Journal of nanobiotechnology},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12951-026-04032-x},
pmid = {41546096},
issn = {1477-3155},
support = {W2431058//the National Natural Science Foundation of China/ ; 2024C03002//the "Pioneer" and "Leading Goose" Research and Development Program of Zhejiang/ ; 2023GD004//Project of Westlake Institute for Optoelectronics/ ; },
abstract = {The precise spatial organization of neural cells into two-dimensional networks or three-dimensional spheroids is crucial for advancing neuroscience research and drug discoveries, yet remains challenging with conventional, single-function coatings. Here, we propose a programmable bifunctional peptide that integrates a silica-binding domain with a tunable cell-adhesive Arginine-Glycine-Aspartate (RGD) tripeptide. By systematically improving the RGD variant and linker rigidity, we introduced a single coating material that enables on-demand switching between two distinct functions: guiding the patterned growth of functional neural circuits on glass and facilitating the high-throughput formation of uniform neural spheroids. The latter exhibited high viability, extensive neurite outgrowth, and spontaneous electrophysiological activity, which validates their functional maturity. We establish by this work a versatile and reliable platform for advanced neural interface research, with significant potential for drug discovery and disease modeling.},
}

@article {pmid41545509,
year = {2026},
author = {Li, Y and Li, W and Liu, Y and Chen, Q and Guo, X and Tan, M and Yang, R and Xu, X and Qin, H and Chen, L},
title = {HRV features as potential biomarkers for auxiliary diagnosis in epilepsy.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-025-34682-0},
pmid = {41545509},
issn = {2045-2322},
support = {2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; },
abstract = {Epilepsy affects around 70 million people worldwide, and diagnosis is often difficult and delayed, exposing patients to avoidable morbidity and psychosocial burden. Heart rate variability (HRV) is a non-invasive marker of autonomic nervous system function that may be altered in epilepsy and may support clinical decision-making. In this single-center case-control study, we recorded short-term HRV during a standardized cardiovascular autonomic reflex test including supine resting, deep-breathing and three challenges (active standing, Valsalva manoeuvre and sustained handgrip) in 200 adults with epilepsy and 200 age- and sex-matched healthy controls. Patients with epilepsy showed consistently lower HRV than controls. Using HRV and demographic features, we developed logistic regression models to distinguish epilepsy from health in an independent test set. A model integrating rest and sustained handgrip achieved the highest performance, although still only moderate (area under the curve 0.68; sensitivity 0.821; specificity 0.484). Standardized multi-paradigm HRV assessment may therefore provide a feasible, low-cost adjunct to support, but not replace, conventional diagnostic evaluation. However, the single-center design, relatively short recordings and inclusion of only healthy controls limit generalizability, and larger multicenter studies including patients with paroxysmal conditions that mimic epilepsy are needed to determine clinical utility.},
}

@article {pmid41544906,
year = {2026},
author = {Hong, T and Su, C and Zhou, H and Geng, F and Hu, Y},
title = {Brain activity inhibition during Short Video Viewing: neurochemical insights.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121722},
doi = {10.1016/j.neuroimage.2026.121722},
pmid = {41544906},
issn = {1095-9572},
abstract = {Cognitive control enables individuals to adapt to the ever-changing environmental demands. The dorsal anterior cingulate cortex (dACC) and the dorsolateral prefrontal cortex (dlPFC) are key regions of the cognitive control network, activated during cognitively demanding tasks. In contrast, the entertaining and habitual nature of short-video consumption for leisure shifts neural processing toward emotional engagement and immediate gratification, contributing to excessive use and diminished self-control in some individuals. This raises a critical question: Does short-video viewing suppress cognitive control regions, and what neurochemical factors may underlie individual differences in this process? To address this question, this preregistered study used proton magnetic resonance spectroscopy ([1]H-MRS) to measure glutamate and γ-aminobutyric acid (GABA) concentrations in the dACC at rest, and employed functional magnetic resonance imaging (fMRI) to examine dACC and dlPFC activity during free viewing of short videos in 56 young adults. We found that both the dACC and the dlPFC exhibited significant deactivation in response to preferred videos that were watched to completion, compared to less-preferred videos that were terminated early. Moreover, resting-state glutamate levels in the dACC were associated with the magnitude of this deactivation, with higher glutamate concentrations associated with less suppression of both dACC and dlPFC activity. Additionally, functional connectivity between the dACC and dlPFC increased during video viewing, particularly for preferred videos. By integrating fMRI with [1]H-MRS, our study provides novel evidence that immersive viewing of preferred short videos deactivates the cognitive control network and that individual differences in this deactivation are linked to glutamate metabolism. These findings enhance our understanding of how digital media consumption interacts with neurochemical processes to influence self-regulation. Our study offers new insights into the neural mechanisms underlying short-video engagement and has implications for understanding excessive digital media use.},
}

@article {pmid32413878,
year = {2020},
author = {Del Campo-Vera, RM and Gogia, AS and Chen, KH and Sebastian, R and Kramer, DR and Lee, MB and Peng, T and Tafreshi, A and Barbaro, MF and Liu, CY and Kellis, S and Lee, B},
title = {Beta-band power modulation in the human hippocampus during a reaching task.},
journal = {Journal of neural engineering},
volume = {17},
number = {3},
pages = {036022},
pmid = {32413878},
issn = {1741-2552},
support = {KL2 TR001854/TR/NCATS NIH HHS/United States ; R25 NS099008/NS/NINDS NIH HHS/United States ; },
mesh = {*Beta Rhythm ; Cerebral Cortex ; Electroencephalography ; Hippocampus ; Humans ; *Movement ; },
abstract = {OBJECTIVE: Characterize the role of the beta-band (13-30 Hz) in the human hippocampus during the execution of voluntary movement.

APPROACH: We recorded electrophysiological activity in human hippocampus during a reach task using stereotactic electroencephalography (SEEG). SEEG has previously been utilized to study the theta band (3-8 Hz) in conflict processing and spatial navigation, but most studies of hippocampal activity during movement have used noninvasive measures such as fMRI. We analyzed modulation in the beta band (13-30 Hz), which is known to play a prominent role throughout the motor system including the cerebral cortex and basal ganglia. We conducted the classic 'center-out' direct-reach experiment with nine patients undergoing surgical treatment for medically refractory epilepsy.

MAIN RESULTS: In seven of the nine patients, power spectral analysis showed a statistically significant decrease in power within the beta band (13-30 Hz) during the response phase, compared to the fixation phase, of the center-out direct-reach task using the Wilcoxon signed-rank hypothesis test (p < 0.05).

SIGNIFICANCE: This finding is consistent with previous literature suggesting that the hippocampus may be involved in the execution of movement, and it is the first time that changes in beta-band power have been demonstrated in the hippocampus using human electrophysiology. Our findings suggest that beta-band modulation in the human hippocampus may play a role in the execution of voluntary movement.},
}

*Beta Rhythm
Cerebral Cortex
Electroencephalography
Hippocampus
Humans
*Movement

@article {pmid41544497,
year = {2026},
author = {Luo, H and Ran, X and Li, Z and Xue, H and Jiang, T and Shen, J and Kärkkäinen, T and Xu, Q and Cong, F},
title = {Key-value pair-free continual learner via task-specific prompt-prototype.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {198},
number = {},
pages = {108576},
doi = {10.1016/j.neunet.2026.108576},
pmid = {41544497},
issn = {1879-2782},
abstract = {Continual learning aims to enable models to acquire new knowledge while retaining previously learned information. Prompt-based methods have shown remarkable performance in this domain; however, they typically rely on key-value pairing, which can introduce inter-task interference and hinder scalability. To overcome these limitations, we propose a novel approach employing task-specific Prompt-Prototype (ProP), thereby eliminating the need for key-value pairs. In our method, task-specific prompts facilitate more effective feature learning for the current task, while corresponding prototypes capture the representative features of the input. During inference, predictions are generated by binding each task-specific prompt with its associated prototype. Additionally, we introduce regularization constraints during prompt initialization to penalize excessively large values, thereby enhancing stability. Experiments on several widely used datasets demonstrate the effectiveness of the proposed method. In contrast to mainstream prompt-based approaches, our framework removes the dependency on key-value pairs, offering a fresh perspective for future continual learning research.},
}

@article {pmid41544454,
year = {2026},
author = {Zheng, L and Lu, Y and Lyu, H and Li, T and Cui, S and Xu, Y and Cai, Z and Hou, Y and Li, Y and Yang, Q and Ye, Z and Yang, G and Xu, K},
title = {Laser fabrication of flexible electrodes for bioelectronics.},
journal = {Biosensors & bioelectronics},
volume = {298},
number = {},
pages = {118386},
doi = {10.1016/j.bios.2026.118386},
pmid = {41544454},
issn = {1873-4235},
abstract = {Bioelectronics lies at the intersection of electronics and biology, enabling real-time signal exchange between living systems and machines. As next-generation applications such as wearable diagnostics, brain-computer interfaces, and closed-loop therapeutic systems desire for soft, miniaturized, and biocompatible platforms, the role of bioelectrodes becomes even more critical. Direct laser writing (DLW) has emerged as a powerful microscale fabrication approach, capable of directly patterning functional electrodes with high spatial resolution on diverse materials. In addition, DLW uniquely offers localized material processing and property modulation, enabling controlled synthesis, phase transition, and surface functionalization. This review presents a comprehensive overview of the underlying mechanisms and advanced material systems that enable DLW. We highlight how DLW enables structural design that impart stretchability and tissue conformity, and how such electrodes are integrated into wearable and implantable bioelectronic systems. Finally, we discuss key challenges and future opportunities for DLW-based bioelectrodes, which are poised to become foundational components of intelligent and adaptive biomedical interfaces.},
}

@article {pmid41544325,
year = {2026},
author = {Gong, C and Zou, L and Li, P and Wu, X and Qiao, Y and Hu, Z and Wang, X and Zhou, Y and Wang, K and Hu, Y and Wang, H},
title = {Rapid spatio-temporal MR fingerprinting using physics-informed implicit neural representation.},
journal = {Medical image analysis},
volume = {109},
number = {},
pages = {103935},
doi = {10.1016/j.media.2026.103935},
pmid = {41544325},
issn = {1361-8423},
abstract = {The potential of Magnetic Resonance Fingerprinting (MRF), which allows for rapid and simultaneous multi-parametric quantitative MRI, is often limited by severe aliasing artifacts caused by aggressive undersampling. Conventional MRF approaches typically treat these artifacts as detrimental noise and focus on their removal, often at the cost of either reduced reconstruction speed or increased reliance on large training datasets. Building on the insight that structured aliasing can be leveraged as an informative spatial encoding mechanism, we propose to extend MRF's encoding capacity to the global spatio-temporal domain by introducing a novel Physics-informed implicit neural MRF (πMRF) framework. πMRF integrates physics-informed spatio-temporal fingerprint modeling with implicit neural representations (INRs), enabling unsupervised, gradient-driven joint estimation of quantitative tissue parameters and coil sensitivity maps (CSMs) with enhanced accuracy and robustness. Specifically, πMRF leverages a scalable component based on physics-informed neural networks (PINNs) to facilitate accurate high-dimensional signal modeling and memory-efficient optimization. In addition, a subspace-guided sensitivity regularization is developed to improve the robustness of CSM estimation in highly undersampled scenarios. Experimental results on simulated, phantom, and in vivo datasets demonstrate that πMRF achieves improved quantitative accuracy and robustness even under highly accelerated acquisitions, outperforming state-of-the-art MRF methods.},
}

@article {pmid41543945,
year = {2026},
author = {Ju, J and Li, H},
title = {Neural Signatures and Multi-Cognitive Decoding of EEGSignals Induced by Shared Stimulus: A Paradigm Study.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3654639},
pmid = {41543945},
issn = {1558-2531},
abstract = {Multi-task decoding from electroencephalogram (EEG) signals is valuable for brain-computer interface (BCI) applications in naturalistic settings. Most existing studies focus on decoding distinctly different tasks, leaving the diversity of cognitive responses elicited by a single stimulus underexplored. We introduced a novel experimental paradigm where a common visual stimulus elicits five distinct cognitive processes: single reach, interception reach, sequence reach, attention reach, and inhibition reach. EEG signatures were analyzed using temporal and spectral methods. A regularized linear discriminant analysis (RLDA) classifier was employed for decoding, utilizing both temporal and event-related spectral perturbation (ERSP) features. Significant neural activation differences (p < 0.05) were observed across tasks and brain regions. The RLDA classifier achieved high decoding accuracy: 91.72% ± 6.10% for classifying the five cognitive states using ERSP features. Furthermore, for the sequence reach task, temporal features enabled classification of normal versus catch trials with 77.96% ± 7.03% accuracy. All these results demonstrate the potential for EEG-based BCI applications to distinguish diverse cognitive states elicited by identical stimuli, offering new insights for improving the naturalness and intelligence of BCI systems. Future work will focus on enhancing decoding performance and extending this research to online applications.},
}

@article {pmid41539931,
year = {2026},
author = {Beste, C and Slagter, HA and Herff, C and Kamitani, Y and Coninx, S and van Wezel, R and Frings, C},
title = {Moving intentions from brains to machines.},
journal = {Trends in cognitive sciences},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.tics.2025.12.003},
pmid = {41539931},
issn = {1879-307X},
abstract = {Brain-computer interface (BCI) research has achieved remarkable technical progress but remains limited in scope, typically relying on motor and visual cortex signals in limited patient populations. We propose a paradigm shift in BCI design rooted in ideomotor theory, which conceptualizes voluntary action as driven by internally represented sensory outcomes. This underused framework offers a principled basis for next-generation BCIs that align closely with the brain's natural intentional and action-planning architecture. We suggest a more intuitive, generalizable, and scalable path by reorienting BCIs around the 'what for' of action-user goals and anticipated effects. This shift is timely and feasible, enabled by advances in neural recording and artificial intelligence-based decoding of sensory representations. It may help resolve challenges of usability and generalizability in BCI design.},
}

@article {pmid41537311,
year = {2026},
author = {Shu, L and Tang, J and Guan, X and Zhang, D},
title = {A comprehensive survey of genome language models in bioinformatics.},
journal = {Briefings in bioinformatics},
volume = {27},
number = {1},
pages = {},
pmid = {41537311},
issn = {1477-4054},
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 = {*Computational Biology/methods ; *Natural Language Processing ; Humans ; *Genomics/methods ; Deep Learning ; *Genome ; },
abstract = {Large language models have revolutionized natural language processing by effectively modeling complex semantics and capturing long-range contextual relationships. Inspired by these advancements, genome language models (gLMs) have recently emerged, conceptualizing DNA and RNA sequences as biological texts and enabling the identification of intricate genomic grammar and distant regulatory interactions. This review examines the need for gLMs, emphasizing their capacity to overcome the limitations of traditional deep learning approaches in genomic sequence characterization. We comprehensively survey contemporary gLM architectures, including Transformer models, Hyena convolutions, and state space models, as well as various sequence tokenization strategies, assessing their applicability, and effectiveness across diverse genomic applications. Additionally, we discuss foundational pretraining strategies and provide an overview of genomic pretraining datasets spanning multiple species and functional domains. We critically analyze evaluation methodologies, including supervised, zero-shot, and few-shot learning paradigms, as well as fine-tuning approaches. An extensive taxonomy of downstream tasks is presented, alongside a summary of existing benchmarks and emerging trends. Finally, we contemplate key challenges such as data scarcity, interpretability, and the computational demands of genomic modeling, and propose a roadmap to guide future advances in genome language modeling.},
}

*Computational Biology/methods
*Natural Language Processing
Humans
*Genomics/methods
Deep Learning
*Genome

@article {pmid41532318,
year = {2026},
author = {Sun, X and Wang, T and Gong, H and Qiu, Y and Zhang, Y and Chen, M and Xue, J and Ye, G and Mou, R and Teng, P and Li, W and Chen, T and Zhang, L and Guo, X and Mao, W and Zhao, H and Ma, L and Xu, Q},
title = {Circulating CD34[+] Fibroblast Progenitors Engaged in Heart Fibrosis of Allograft.},
journal = {Circulation research},
volume = {},
number = {},
pages = {},
doi = {10.1161/CIRCRESAHA.125.326558},
pmid = {41532318},
issn = {1524-4571},
abstract = {BACKGROUND: Fibrosis is one of the major causes of cardiac allograft malfunction and is mainly driven by fibroblasts. However, the role of recipient-derived cells in generating allograft fibroblasts and the underlying mechanisms remain to be explored.

METHODS: We analyzed human heart allograft samples and used murine transplant models (C57BL/6J, Cd34-CreER[T2]; R26-tdTomato, mRFP mice, Rosa26-iDTR, Postn-CreER[T2]; R26-tdTomato, double-tdTomato, and immunodeficient mice with BALB/c donors). Human progenitor cells were cultivated from blood. Single-cell RNA sequencing, Western blotting, quantitative polymerase chain reaction, and immunohistochemistry, whole-mount staining with 3-dimensional reconstruction, and in vivo/in vitro experiments were applied to characterize allograft cellular composition and communication.

RESULTS: Single-cell RNA sequencing was introduced to delineate the allograft cell atlas of patients and mice. Y chromosome analysis identified that recipient-derived cells contributed to allograft fibroblasts in both patients and murine models. Combining the genetic cell lineage tracing technique, we found that recipient-derived CD34[+] cells could give rise to activated fibroblasts. Bone marrow transplantation and parabiosis models revealed that the recipient's circulating non-bone marrow Cd34[+] cells could generate allograft fibroblasts. Human CD34[+] cells could differentiate into fibroblasts both in vivo and in vitro. CD34[+] fibroblast progenitors were recruited by CXCL12-ACKR3 and MIF-ACKR3 interactions and differentiated via the TGFβ (transforming growth factor beta)/GFPT2 (glutamine-fructose-6-phosphate transaminase 2)/SMAD2/4 axis. Ablation of recipient Cd34[+] cells reduced activated fibroblasts and alleviated allograft fibrosis.

CONCLUSIONS: We identify circulating CD34[+] cells as a novel source of fibroblast progenitors that contribute to cardiac allograft fibrosis, suggesting that targeting recipient CD34[+] cells could be a novel therapeutic potential for treating cardiac fibrosis after heart transplantation.},
}

@article {pmid41531435,
year = {2026},
author = {Lu, X and Chen, Y and Li, Z and Zhao, J and Zhou, Y and Wu, D and Zhang, M},
title = {Electroencephalography Enables Continuous Decoding of Hand Motion Angles in Polar Coordinates.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {7},
number = {},
pages = {0469},
pmid = {41531435},
issn = {2692-7632},
abstract = {Hand movements in task space are typically represented using either Cartesian or polar coordinate systems. While Cartesian coordinates are commonly used in electroencephalography (EEG)-based brain-computer interface (BCI) studies, polar coordinates offer a more natural representation for circular motion by directly encoding angular information. This study investigates the feasibility of continuous decoding of hand motion angles in polar coordinates using EEG signals. In the paradigm, human participants engaged in bimanual circular tracing with a fixed radius while their EEG signals were recorded. To evaluate the feasibility of this approach, 6 deep learning models, including commonly used EEGNet, DeepConvNet, and ShallowConvNet, and their variants incorporating long short-term memory (LSTM) layers, were employed. Performance was assessed using mean squared error (MSE), mean absolute error (MAE), and correlation coefficient (CC) between decoded and actual angles. Across 8 participants, all 6 models significantly outperformed the chance level (P < 0.01), with the best model achieving an MSE of 1.012 rad[2], an MAE of 0.627 rad, and a CC of 0.895. These results demonstrate the feasibility of continuous angular decoding of circular hand motion in polar coordinates using EEG signals. This approach offers a promising alternative to traditional Cartesian-based decoding methods, particularly for applications involving circular or rotational movements.},
}

@article {pmid41520740,
year = {2026},
author = {Liu, Q and Zhang, X and Niu, J and Chen, K and Xia, J and He, Y and Xu, S and Li, W and Chen, H and Zhang, D and Liao, W and Li, J},
title = {Uniformity in happiness and uniqueness in sadness: Naturalistic emotional representation in major depression.},
journal = {NeuroImage},
volume = {326},
number = {},
pages = {121712},
doi = {10.1016/j.neuroimage.2026.121712},
pmid = {41520740},
issn = {1095-9572},
abstract = {Humans develop shared concepts of others' emotions to support adaptive social functioning, yet how these concepts are dynamically represented in major depressive disorder (MDD) during naturalistic movie viewing is not yet fully established. Using functional MRI, we examined patients with MDD (n = 55) and healthy controls (HCs; n = 62) as they freely viewed movie clips depicting happy and sad emotions. Neural similarity was quantified with inter-subject correlation at whole-brain, network, and regional levels, and its association with emotional traits was assessed using inter-subject representational similarity analysis. Compared with HCs, patients with MDD showed significantly reduced whole-brain similarity, particularly during sad contexts. Network analyses revealed that HCs exhibited increased similarity in the limbic network during sadness, reflecting a shared "sadness resonance," whereas patients with higher depressive severity showed widespread disruptions across visual, limbic, dorsal attention, and default mode networks. At the regional level, similarity in the inferior temporal gyrus and lateral occipital cortex was closely linked to individual differences in emotional awareness, with pronounced context- and region-specificity. These findings highlight neural decoupling and heterogeneity as core features of MDD and provide new evidence for potential biomarkers to inform risk assessment and personalized interventions.},
}

@article {pmid41398114,
year = {2025},
author = {Evans, NG and L Gross, M and Shandler, R},
title = {Enhancing Soldiers for Future Warfare: Good Science; Bad Ethics?.},
journal = {Science and engineering ethics},
volume = {32},
number = {1},
pages = {5},
pmid = {41398114},
issn = {1471-5546},
support = {FA9550-21-1-0142//Air Force Office of Scientific Research/ ; W911NF-24-1-0361//Army Research Office/ ; },
abstract = {UNLABELLED: Ethical concerns dog emerging technologies designed to enhance warfighter performance. Brain-computer interfaces, exoskeletons, and mind- or body-altering drugs raise fears about risky, invasive, and experimental medical procedures that offer armies physically and cognitively superior soldiers that will dictate and disrupt the course of future war. What counts as enhancement, however, has been subject to longstanding and passionate debate. This study aims to put an end to this dispute by employing a conjoint experimental design to survey a group of military and professional experts from across the world to explore how definitional perceptions of enhancement influence ethical acceptability. Two main findings emerge. First, we find that there already exists a broad agreement about what constitutes enhancement, and this consensus spans countries, discipline, political orientation, and age. Future policy may now be able to accommodate a definition of enhancement that is widely shared among members of the international community. Second, across the board, ethical acceptability diminishes as medical technologies aim for transhuman warfighting capabilities. Enhancement research and development for military purposes must navigate the conflicting ethical demands of medical experimentation and lawful war. Human enhancement is not morally unacceptable but ethically precarious, requiring regulation, oversight, and transparency.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11948-025-00573-w.},
}

@article {pmid41529886,
year = {2026},
author = {Zhu, H and Gan, Y and Ye, J and Li, Y and Yu, JZ and Li, X},
title = {Effectiveness of brain-computer interface interventions in autism spectrum disorder rehabilitation: a systematic review and meta-analysis protocol.},
journal = {BMJ open},
volume = {16},
number = {1},
pages = {e102277},
doi = {10.1136/bmjopen-2025-102277},
pmid = {41529886},
issn = {2044-6055},
mesh = {Humans ; *Autism Spectrum Disorder/rehabilitation ; *Brain-Computer Interfaces ; Systematic Reviews as Topic ; Meta-Analysis as Topic ; Research Design ; },
abstract = {BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterised by impairments in social interaction, communication and the presence of repetitive behaviours. Recent advancements in brain-computer interface (BCI) technologies have demonstrated potential benefits in enhancing cognitive, social and communication skills in individuals with ASD. However, the effectiveness of BCI-based interventions in ASD rehabilitation remains inconsistent across studies. Therefore, this protocol outlines a systematic review and meta-analysis to synthesise the evidence on the effectiveness of BCI-based interventions for ASD rehabilitation.

METHODS: We will conduct a comprehensive literature search across multiple databases, including MEDLINE Ovid, Embase Ovid, Cochrane Central Register of Controlled Trials (CENTRAL), Conference Proceedings Citation Index-Science (CPCI-S), Science Citation Index Expanded (SCI-EXPANDED) and so on, to identify relevant studies published from inception to the present. The search will be supplemented by screening the reference lists of included studies and relevant systematic reviews. Two independent reviewers will screen the titles, abstracts and full texts of identified studies for eligibility based on predefined criteria. Data extraction will be performed using a standardised form, and the risk of bias (RoB) will be assessed using the Cochrane RoB tool. Heterogeneity will be evaluated using the I² statistic, and a random-effects or fixed-effects model will be selected for meta-analysis based on the degree of heterogeneity. Subgroup analyses will be conducted to explore potential sources of heterogeneity, including participant age, ASD severity, type of BCI intervention and duration of the intervention. The review will be conducted from January 2026 to April 2026.

ETHICS AND DISSEMINATION: Ethical approval is not required for this study, as it does not involve the collection of primary data from individual patients. Findings will be disseminated through peer-reviewed publication and conference presentations.

PROSPERO REGISTRATION NUMBER: CRD420251010496.},
}

Humans
*Autism Spectrum Disorder/rehabilitation
*Brain-Computer Interfaces
Systematic Reviews as Topic
Meta-Analysis as Topic
Research Design

@article {pmid41528907,
year = {2026},
author = {Zhao, Y and Cao, D and Yu, H and Liang, G and Chen, Z},
title = {MSHANet: A Multiscale Hybrid Attention Network for Motor Imagery EEG Decoding.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3653824},
pmid = {41528907},
issn = {1558-2531},
abstract = {Brain-computer interface (BCI) technology has significant applications in neuro rehabilitation and motor function restoration, especially for patients with stroke or spinal cord injury. Motor imagery electroencephalog-raphy (MI-EEG) is widely used in BCIs, but its nonlinear dynamics and inter-subject variability limit decoding accuracy. In this paper, a multiscale hybrid attention network (MSHANet) for MI-EEG decoding, which consists of spatiotemporal feature extraction (STFE), talking head self-attention (THSA), dynamic squeeze-and-excitation attention (DSEA), and a temporal convolutional network (TCN), is proposed. MSHANet was evaluated via within-subject experiments using BCI Competition IV Datasets 2a and 2b, as well as EEGMMID, achieving decoding accuracies of 83.56%, 89.75%, and 75.66%, respectively. In cross-subject experiments on the three datasets, the mode lattained accuracies of 69.93% on BCI-2a, 81.85% on BCI-2b, and 79.67% on EEGMMID. In addition, we propose an electrode spatial structure-aware encoder. This technique encodes the spatial positions of electrodes in the original data, enabling the model to obtain richer spatial electrode information at the input stage. In within-subject and cross-subject tasks on BCI-2a, this encoding improved the decoding performance by 2.83% and 2.91%, respectively. Visualization was also employed to elucidate feature distributions and the effec tiveness of its attention mechanisms. Experimental results demonstrate that MSHANet performs exceptionally well in MI-EEG decoding tasks and has high potential for clinical applications, particularly in neurorehabilitation and motor function reconstruction.},
}

@article {pmid41528455,
year = {2026},
author = {Becker, L and Krüger, L and Wolf, M and Alfen, K and Theysohn, J and Lefering, R and Dudda, M and Kamp, O and , },
title = {The necessity of CT scans on pediatric carotid injury after blunt trauma - An analysis of the traumaregister DGU[®].},
journal = {European journal of trauma and emergency surgery : official publication of the European Trauma Society},
volume = {52},
number = {1},
pages = {13},
pmid = {41528455},
issn = {1863-9941},
mesh = {Humans ; Child ; *Wounds, Nonpenetrating/diagnostic imaging/epidemiology ; Child, Preschool ; Adolescent ; Male ; Registries ; Female ; Infant ; Germany/epidemiology ; *Carotid Artery Injuries/diagnostic imaging/epidemiology ; *Tomography, X-Ray Computed ; Injury Severity Score ; Infant, Newborn ; Prevalence ; Risk Factors ; },
abstract = {PURPOSE: Blunt carotid injuries (BCI) in pediatric trauma patients are rare. Using data from the TraumaRegister DGU[®][,] this study aims to identify screening parameters and calculate the prevalence of pediatric BCI. By proposing potential risk factors for a BCI, this research seeks to reduce unnecessary radiation exposure in pediatric trauma cases. These findings may enhance understanding of pediatric BCI and highlight the necessity of cautious diagnostic approaches that balance clinical needs with radiation risks.

METHODS: The TraumaRegister DGU[®] is a multicenter database established in 1993 to document the treatment of severely injured patients from initial injury to hospital discharge. Data are collected in four phases: demographics, injury patterns, treatments, and outcomes. Almost 700 hospitals, primarily from Germany, contribute to the registry annually. Statistical analysis was conducted using SPSS. For analysis, the dataset was divided into two groups: trauma patients diagnosed with BCI and trauma patients without BCI. The complete dataset from the TraumaRegister DGU[®] for 2006-2020 was screened for relevant cases. The dataset was limited to patients between 0 and 15 years old.

RESULTS: Out of 9070 severely injured pediatric trauma patients analysed, 50 cases of pediatric BCI were identified, representing a prevalence of 0.6%. Patients with BCI presented with higher injury severity scores (ISS), lower Glasgow Coma Scale (GCS) scores, and a greater prevalence of head injuries, as well as thoracic, abdominal, and extremity injuries. These patients also experienced higher in-hospital mortality rates (34%) and required more frequent blood transfusions. Full-body CT scans were more commonly performed in patients with BCI.

CONCLUSION: This study highlights the rarity and severity of BCI in pediatric trauma patients, with a prevalence of 0.6%. Significant risk factors for a BCI include high injury severity, head trauma, neurological deficits, and pre-hospital hypotension. The findings emphasise the importance of early diagnosis and targeted diagnostic strategies to balance the need for prompt intervention with reducing unnecessary radiation exposure.},
}

Humans
Child
*Wounds, Nonpenetrating/diagnostic imaging/epidemiology
Child, Preschool
Adolescent
Male
Registries
Female
Infant
Germany/epidemiology
*Carotid Artery Injuries/diagnostic imaging/epidemiology
*Tomography, X-Ray Computed
Injury Severity Score
Infant, Newborn
Prevalence
Risk Factors

@article {pmid41527472,
year = {2026},
author = {Niu, J and Xia, J and He, Y and Li, W and Chen, K and Liu, Q and Li, W and Qiu, J and Chen, H and Li, J and Liao, W},
title = {Controllability of morphometric network colocalize with underlying neurobiology in major depression.},
journal = {Psychological medicine},
volume = {56},
number = {},
pages = {e15},
doi = {10.1017/S0033291725103140},
pmid = {41527472},
issn = {1469-8978},
support = {62473082, 62571105, 82121003, 62036003, 62333003//National Natural Science Foundation of China/ ; ZYGX2022YGRH008, ZYGX2024XJ054//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; *Depressive Disorder, Major/diagnostic imaging/physiopathology/metabolism/pathology ; Female ; Adult ; Male ; Middle Aged ; Case-Control Studies ; *Brain/diagnostic imaging/metabolism/pathology ; *Nerve Net/diagnostic imaging ; Magnetic Resonance Imaging ; Diffusion Tensor Imaging ; Young Adult ; },
abstract = {BACKGROUND: Cognitive and behavioral symptoms of major depressive disorder (MDD) are linked to aberrant changes in the controllability of brain networks. However, previous studies examined network controllability using white matter tractography, neglecting the contributions of gray matter. We aimed to examine differences in the controllability of morphometric networks between patients with MDD and demographic-matched healthy controls and identify the associated neurobiological signatures.

METHODS: Based on the structural and diffusion MRI data from two independent cohorts, we calculated the controllability of morphometric similarity networks for each participant. A generalized additive model was used to investigate the case-control differences in regional controllability and their cognitive and behavioral associations. We investigated the associations between imaging-derived controllability and neurotransmitters, brain metabolism, and gene transcription profiles using multivariate linear regression and partial least squares regression analyses.

RESULTS: In both cohorts, depression-related abnormalities of morphometric network controllability were primarily located in the prefrontal, cingulate, and visual cortices, contributing to memory, sensation, and perception processes. These abnormalities in network controllability were spatially aligned with the distributions of serotonergic transmission pathways as well as with altered oxygen and glucose metabolism. In addition, these abnormalities spatially overlapped with differentially expressed genes enriched in annotations related to protein catabolism and mitochondria in neuronal cells and were disproportionately located on chromosome 22.

CONCLUSIONS: Collectively, neuroimaging evidence revealed aberrant morphometric network controllability underlying MDD-related cognitive and behavioral deficits, and the associated genetic and molecular signatures may help identify the neurobiological mechanisms underlying MDD and provide feasible therapeutic targets.},
}

Humans
*Depressive Disorder, Major/diagnostic imaging/physiopathology/metabolism/pathology
Female
Adult
Male
Middle Aged
Case-Control Studies
*Brain/diagnostic imaging/metabolism/pathology
*Nerve Net/diagnostic imaging
Magnetic Resonance Imaging
Diffusion Tensor Imaging
Young Adult

@article {pmid41526383,
year = {2026},
author = {Wang, D and Shi, Y and Pang, J and Zhu, X and Meng, L and Ming, D},
title = {Data-driven subtyping of early Parkinson's disease via mutual cross-attention fusion of EEG and dual-task gait features.},
journal = {NPJ Parkinson's disease},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41531-026-01258-2},
pmid = {41526383},
issn = {2373-8057},
support = {82372083//National Natural Science Foundation of China/ ; },
abstract = {Parkinson's disease (PD) exhibits marked clinical heterogeneity, which poses challenges for diagnosis, prognosis, and therapeutic precision, especially for early-stage PD patients. Existing subtyping approaches often rely on subjective clinical scales and single-modality data, which limits their sensitivity in capturing subtle but clinically relevant differences across patients. To reveal clinically meaningful PD subtypes, we propose a data-driven multimodal framework that integrates resting-state electroencephalography (EEG) and dual-task gait features using mutual cross-attention (MCA) fusion. Forty idiopathic early-stage PD patients were enrolled in a prospective study. EEG biomarkers were encoded via a convolutional neural network for the prediction of motor severity (MDS-UPDRS-III), while dual-task gait features were derived to capture subtle motor dysfunctions. The MCA enabled bidirectional attention-guided integration of EEG and gait features, which were then clustered using an unsupervised method. The analysis revealed three distinct subtypes, with dual-task-based fusion providing superior clinical separation. Subtype I was characterized by pronounced motor deficits; Subtype II showed moderate symptoms with relatively preserved quality of life; and Subtype III presented mild motor impairments but exhibited poorer cognitive and psychosocial outcomes. Feature contribution analyses highlighted central beta and theta EEG activity, along with dual-task gait metrics (e.g., stride length during turning), as key drivers of subtype differentiation. Longitudinal follow-up demonstrated subtype-specific rehabilitation responses, with Subtype II showing an insufficient response compared to other subtypes. In conclusion, this study enables digital phenotyping of PD with prognostic implications for personalized rehabilitation strategies and accelerates precision medicine.},
}

@article {pmid41525762,
year = {2026},
author = {Ding, W and Chen, X and Liu, A},
title = {Breaking the performance barrier in deep learning-based SSVEP-BCIs: A joint frequency-phase training strategy.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae36f6},
pmid = {41525762},
issn = {1741-2552},
abstract = {OBJECTIVE: Deep learning exhibits considerable potential for steady-state visual evoked potential (SSVEP) classification in electroencephalography (EEG)-based brain-computer interfaces (BCIs). SSVEP signals contain both frequency and phase characteristics that correspond to the visual stimuli. However, existing deep learning training strategies typically focus on either frequency or phase information alone, thus failing to fully exploit these dual inherent properties and substantially limiting classification accuracy.

APPROACH: To tackle this limitation, this study proposes a Joint Frequency-Phase Training Strategy (JFPTS), which comprises two complementary stages with distinct time-window sampling schemes. The first stage adopts a frequency prior-driven sampling scheme to improve frequency component utilization, whereas the second stage employs a phase-locked sampling scheme to enhance intra-category phase consistency. This design enables JFPTS to effectively leverage both frequency and phase properties of SSVEP signals.

MAIN RESULTS: Comprehensive experiments on two well-established public datasets validate the effectiveness of JFPTS. The results demonstrate that the JFPTS-enhanced model achieves a marked superiority over the current state-of-the-art classification approaches, notably surpassing the long-standing performance benchmark set by task discriminative component analysis (TDCA).

SIGNIFICANCE: Overall, JFPTS establishes a new training paradigm that advances deep learning approaches for SSVEP classification and promotes the broader adoption of SSVEP-BCIs.},
}

@article {pmid41525614,
year = {2026},
author = {Jin, J and Wang, C and Xu, R and He, X and Wu, X and Li, J and Chen, W and Wang, X and Cichocki, A},
title = {RUNet: A Zero-Calibration Framework for Cross-Domain EEG Decoding via Riemannian and Unsupervised Representation Learning.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3653024},
pmid = {41525614},
issn = {1558-2531},
abstract = {OBJECTIVE: Inter-session and inter-subject variability in electroencephalography (EEG) signals, resulting from individual differences and environmental factors, poses a major challenge for neural decoding in brain-computer interface (BCI) applications.

METHODS: To address this issue, we propose RUNet, a zero-calibration motor imagery EEG decoding framework based on Riemannian manifold learning and unsupervised representation learning. RUNet incorporates a multi-scale spatiotemporal convolutional module that jointly captures local global spatial and multi-resolution temporal dynamics features. To enhance the robustness of EEG features against non stationarity, a polysynergistic covariance optimization module is employed, which strengthens the covariance matrix representation through multiple regularizations and adaptive fusion. In addition, RUNet integrates the Riemannian Affine Log Mapping layer, based on Affine-Invariant Transformation and Log-Euclidean Mapping, in an end-to-end manner to mitigate cross-domain covariance drift and enhance domain-invariant feature learning. A transfer learning framework is further integrated into RUNet: during pre-training, an unsupervised contrastive loss is applied to resting-state EEG data to learn domain-invariant spatiotemporal features; during retraining, task-specific data are used to enhance discriminability and feature disentanglement.

CONCLUSION: Experimental results on the BCI Competition IV 2a, 2b datasets and a self-collected laboratory dataset show that RUNet achieves average cross-session accuracies of 87.19%, 88.03% and 85.45%, and cross-subject accuracies of 68.09%, 78.29% and 87.25%, respectively. On the PhysioNet dataset, a cross-subject accuracy of 78.14% is achieved. These results demonstrate the effectiveness of RUNet's unified pipeline and its robust cross-domain generalization.},
}

@article {pmid41525559,
year = {2026},
author = {Guan, S and Li, Y and Gao, Y and Yin, R and Luo, Y and Liang, J and Zhang, J and Zhang, Y and Li, R},
title = {Enhanced Mapping of Finger Movement Representations Using Diffuse Optical Tomography: A Systematic Comparison with fNIRS.},
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.2026.3652812},
pmid = {41525559},
issn = {1558-0210},
abstract = {Advancing neuroimaging modalities for motor cortex analysis is critical for understanding the neural mechanisms underlying fine motor tasks and for expanding clinical applications. Functional Near-Infrared Spectroscopy (fNIRS) is widely used for measuring cortical hemodynamic activity due to its portability and accessibility, but its inherent limitations in spatial resolution and noise sensitivity reduce its utility for precise neural mapping. Diffuse Optical Tomography (DOT) has emerged as a promising alternative with superior spatial resolution and sensitivity. In this study, we performed a systematic comparison of DOT and fNIRS in detecting task-evoked neural activation during a finger-tapping paradigm including four conditions varying by finger type (thumb vs. little finger) and frequency (high vs. low). Our results demonstrated that DOT consistently captured robust activation in motor-related brain regions, even during less demanding conditions, while fNIRS exhibited limited sensitivity. Temporal trace analyses revealed that DOT achieved higher contrast-to-noise ratio (CNR) and contrast-to-background ratio (CBR), validating its enhanced signal quality and ability to distinguish subtle hemodynamic responses. Furthermore, statistical comparisons highlighted significant differences in task-related activations detected by the two modalities, particularly in low-effort conditions. These findings underscore the advantages of DOT over fNIRS, particularly in applications requiring high spatial resolution and sensitivity to subtle neural processes. The results contribute to ongoing efforts to refine optical imaging techniques for motor neuroscience and reinforce DOT's potential for clinical translation in motor deficit diagnosis, rehabilitation monitoring, and brain-computer interface development.},
}

@article {pmid41525552,
year = {2026},
author = {Zhu, J and Li, K and Chen, S and Huang, H and Zhang, Y and Hu, L and Li, Y},
title = {Smart Ward Control Based on a Wearable Multimodal Brain-Computer Interface Mouse.},
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.2026.3653138},
pmid = {41525552},
issn = {1558-0210},
abstract = {For patients with severe extremity motor function impairment, traditional smart ward control methods, such as those using joysticks and touchscreens, are frequently unsuitable due to their limited physical abilities. Consequently, developing an effective brain-computer interface (BCI) suitable for their operation has become an immediate concern. This paper presents a wearable multimodal BCI system for smart ward control, which employs a self-designed wearable headband to capture head rotation and blinking movement. By wearing the headband, users can control a computer cursor on the screen only with head rotation and blinking, and further control devices in a smart ward with self-designed graphical user interfaces (GUIs). The system decodes signals from an inertial measurement unit (IMU) to map the head posture to the position of the cursor on the screen and decodes electrooculography (EOG) and electroencephalography (EEG) signals to detect valid blinks for selecting and activating function buttons. Ten participants were recruited to perform two experimental tasks that simulate the daily needs of patients with extremity motor function issues. To our satisfaction, all the participants fully accomplished the simulated tasks, and an average accuracy of 97.0±3.9 % and an average response time of 2.39±0.53 s were achieved. Different from traditional step-controlled BCI nursing beds, we designed a continuous-controlled nursing bed and achieved satisfactory results. Furthermore, workload evaluation using NASA Task Load Index (NASA-TLX) revealed that the participants experienced a low workload when using the system. The experimental results demonstrate the effectiveness of our proposed system, indicating significant potential for practical applications.},
}

@article {pmid41525550,
year = {2026},
author = {Padmaja, GKR and Bhagat, NA and Balasubramani, PP},
title = {Assessing the utility of Fronto-Parietal and Cingulo-Opercular networks in predicting the trial success of brain-machine interfaces for upper extremity stroke rehabilitation.},
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.2026.3653049},
pmid = {41525550},
issn = {1558-0210},
abstract = {Brain-machine interfaces (BMIs) have the potential to improve stroke rehabilitation by actively facilitating sensory-cognitive-motor connections to restore movement. However, individuals with cognitive impairments are often excluded from BMI-based neurorehabilitation due to concerns about impaired cognition, specifically reduced attention and executive control. We propose leveraging the trial-wise dynamics of large-scale cognitive control networks-specifically, the frontoparietal (FPN) and cingulo-opercular (CON) networks-to build neural markers of cognitive control. Using existing BMI datasets, we demonstrate that trial-wise activity within these networks predicts motor task performance, suggesting that cognitive control signals in these networks could serve as adaptive modulations for BMI-based rehabilitation. Our system is able to predict unsuccessful BMI trials at the population level about 84.2% of the time on average, with an overall mean accuracy of 72.2% in a 3-fold cross-validation. Additionally, in a leave-one-subject-out validation, our system achieved 71% specificity on average, with an overall mean accuracy of 68.3%. Notably, model performance varies across subjects, with some individuals showing up to 92% specificity and 100% sensitivity. Unlike previous studies that primarily focus on resting-state data, our findings point toward the untapped potential of incorporating cognitive network state monitoring into BMI systems to optimize online performance through trials. Specifically, we suggest that our pre-trained models can be fine-tuned with subject-specific information to design more targeted rehabilitation programs that enhance motor performance by identifying precise attention and learning tasks to improve the successful response of the network model in patients with significant cognitive impairment.},
}

@article {pmid41525004,
year = {2026},
author = {Yan, Y and Zhang, Y and Zhao, X and Chen, R and Fang, S and Zhou, Y and Huang, J and Wang, F and Chen, C and Lin, Z and Xu, X},
title = {Life-course body shape trajectories and cerebral oxygen metabolism in community-dwelling older adults.},
journal = {GeroScience},
volume = {},
number = {},
pages = {},
pmid = {41525004},
issn = {2509-2723},
support = {NSFC/72274170//Natural Science Foundation of China/ ; NSFC/82201733//Natural Science Foundation of China/ ; },
abstract = {Obesity and lifelong body-shape fluctuation are associated with late-life structural brain damage, suggesting the involvement of metabolic pathways. The cerebral metabolic rate of oxygen (CMRO2) reflects hemodynamic and oxidative stress and precedes structural atrophy, but its role in adiposity-related brain change remains unclear. We examined whether current and life-course adiposity relate to CMRO2 and to structural change. A total of 303 community-dwelling adults aged 50 years and older were included. Body shape was assessed using Body Mass Index (BMI) and Body Roundness Index (BRI). Global CMRO2 was derived from TRUST and phase-contrast MRI. T1-weighted MPRAGE provided volumetry, and medial temporal atrophy (MTA) grading. General linear models estimated associations of BMI and BRI with CMRO2, including age interactions. Age-stratified mediation tested CMRO2 as a mediator of adiposity to MTA associations. Body-shape trajectories at ages 25, 40, 60, and current age were modeled and related to CMRO2 and metabolism-related regions. Adiposity was associated with lower CMRO2: with overweight (β = -1.12 μmol/100 g/min, 95%CI = (-1.96, -0.28)) and higher BRI (β = -1.31, 95%CI = (-2.36, -0.27)) showing stronger effects with advancing age. Among participants aged 70 years, CMRO2 mediated the association between BMI and MTA (indirect β = 0.06, 95%CI = (0.01, 0.14)). Three adulthood body-shape patterns emerged, and CMRO2 was lower in moderate increasing (β = -11.40; 95%CI = (-20.90, -1.90)) and high-rising (β = - 12.23; 95%CI = (-23.56, -0.90)) groups. Metabolism-related regions were larger in higher-risk patterns, particularly the left hypothalamus. Greater and prolonged adiposity is linked to reduced CMRO2 and related structural differences in older adults.},
}

@article {pmid41523970,
year = {2025},
author = {Xu, C and Kong, L and Mou, T and Tang, A and Hu, S and Lai, J},
title = {Vitamin B12 and Affective Disorders: A Focus on the Gut-Brain Axis.},
journal = {Alpha psychiatry},
volume = {26},
number = {6},
pages = {49138},
pmid = {41523970},
issn = {2757-8038},
abstract = {Accumulating evidence highlights the role of Vitamin B12 (VitB12) in the pathophysiology of affective disorders. However, its influence on brain function and the underlying mechanisms remain incompletely understood. In humans, VitB12 is obtained solely from dietary sources, primarily animal-based foods. VitB12 deficiency leads to the accumulation of homocysteine, a known contributor to emotional and behavioral dysregulation. VitB12 plays a critical role in maintaining neuron stability, synapsis plasticity, and regulating neuroinflammation by modulating key bioactive factors. These processes help to alleviate hippocampal damage, mitigate blood-brain barrier disruption, reduce oxidative stress, and enhance both structural and functional connectivity-collectively contributing to resilience against affective disorders. VitB12 from both diet and microbial sources is essential to gut homeostasis. Within the gut lumen, it stabilizes gut microbial communities, promotes short-chain fatty acid (SCFA) production, and supports neurotransmitter metabolism (e.g., serotonin and dopamine) via its role in S-adenosyl-l-methionine biosynthesis. Crucially, VitB12, gut microbiota, SCFAs, intestinal mucosa, and vagal nerve signaling interact synergistically within the gut-brain axis (GBA) to maintain gut microenvironment stability, protect the gut-blood barrier, and suppress neuroinflammatory cascades, eventually reducing the susceptibility to affective disorders. This review synthesizes current evidence on the involvement of VitB12 in the GBA, its association with mood regulation, and its potential as a nutritional adjunct in managing affective disorders. By elucidating this integrative mechanism, we provide new insights into targeting the GBA to improve clinical outcomes in affective disorders.},
}

@article {pmid41523966,
year = {2025},
author = {Wang, R and Hou, X and Li, R and Cheng, B and Zhou, C and Xue, C and Li, K and Deng, W},
title = {Maintenance of Noninvasive Brain Stimulation for Preventing Relapse in Depression: A Systematic Review and Meta-Analysis.},
journal = {Alpha psychiatry},
volume = {26},
number = {6},
pages = {49140},
pmid = {41523966},
issn = {2757-8038},
abstract = {BACKGROUND: Depression relapse rates remain high after acute treatment; this study evaluates the efficacy of maintenance noninvasive brain stimulation in preventing relapse and identifies optimal treatment parameters.

METHODS: This meta-analysis was conducted following PRISMA guidelines. We conducted a systematic search of PubMed, Embase, Web of Science, Cochrane Library, and PsycINFO databases up to January 5, 2025. The primary outcome was relapse rate.

RESULTS: A total of nine randomized controlled trials with 837 participants were included, six studies used electroconvulsive therapy (ECT) and three studies used repetitive transcranial magnetic stimulation (rTMS). Our findings indicate that ECT combined with pharmacotherapy or rTMS alone demonstrated superiority over pharmacotherapy alone in reducing the relapse of depression during 6, 9, 12-month maintenance treatment periods. Interestingly, ECT alone did not show significant results. In terms of stimulation parameters, the ECT combined with pharmacotherapy group mainly received right unilateral stimulation, while the ECT alone group had bitemporal stimulation. The stimulation frequency was similar between the two groups. In contrast, the rTMS-alone group had significantly higher stimulation frequencies than the ECT groups. We did not find any eligible studies on transcranial direct current stimulation, transcranial alternating current stimulation or magnetic seizure therapy, but they also showed potential in the maintenance treatment of depression, which warrants further investigation.

CONCLUSIONS: ECT combined with pharmacotherapy, or rTMS alone, is more effective than pharmacotherapy alone in preventing relapse of depression during 6 to 12 months of maintenance treatment. Future research should prioritize identifying the optimal treatment regimen and exploring the potential of combination therapies.

THE PROSPERO REGISTRATION: CRD42023490546, https://www.crd.york.ac.uk/PROSPERO/view/CRD42023490546.},
}

@article {pmid41523191,
year = {2026},
author = {van Balen, B and Ramsey, NF and Vansteensel, MJ},
title = {Relational personhood: the missing link for evaluating clinical impact of brain-computer interfaces.},
journal = {Brain communications},
volume = {8},
number = {1},
pages = {fcaf470},
pmid = {41523191},
issn = {2632-1297},
}

@article {pmid41521389,
year = {2026},
author = {Yilmaz Kars, M and Akkar, I and Dogan, MH and Turgut, ZI and Cicek, O and Dikmeer, A and Kollu, K and Cakir Ozden, EC and Kizilarslanoglu, MC},
title = {EXPRESS: The CRP/Albumin Ratio (CAR) may be more strongly linked to delirium than other indices derived from laboratory parameters in older patients in an intensive care unit.},
journal = {Journal of investigative medicine : the official publication of the American Federation for Clinical Research},
volume = {},
number = {},
pages = {10815589261415891},
doi = {10.1177/10815589261415891},
pmid = {41521389},
issn = {1708-8267},
abstract = {The aim of this study is to investigate the association of delirium with laboratory-derived indices and ratios in patients staying in an intensive care unit (ICU). Delirium was diagnosed according to DSM-5 criteria, and laboratory data obtained at the time of diagnosis were retrospectively analyzed. The following indices were calculated: C-reactive protein(CRP)/albumin ratio(CAR), CRP-albumin-lymphocyte(CALLY), B12-CRP(BCI), Systemic Immune-Inflammation(SII), Prognostic Nutritional Index(PNI), Advanced Lung Cancer Inflammation (ALI), Systemic Inflammation Response indices (SIRI) and Glasgow Prognostic Score (GPS). In addition, inflammation markers derived from the complete blood count were also analyzed. They were compared between patients with and without delirium. The study included 215 patients, of whom 104 had delirium (median age 76 years, 51.6% female). Patients with delirium were older than those without delirium(p=0.008). The median CAR index was higher in patients with delirium (3.4 mg/g, 0.02-28.23) compared to those without delirium (2.19 mg/g,0.02-16.74), with borderline statistical significance(p=0.071). No statistically significant differences were found in other indices and laboratory parameters between patients with delirium and those without it (p>0.05 for all). When patients were stratified into tertiles based on CAR levels, the occurrence of delirium was significantly higher in the third tertile than in the other two tertiles (p=0.020). Even after adjusting for all significant confounding factors, CAR remained independently associated with delirium [Odds ratio(OR):1.099, 95% confidence interval(CI):1.002-1.205, p=0.046]. The findings of this study suggest that the CAR index may serve as an independent associated factor for delirium compared to other laboratory-derived markers in critically ill patients.},
}

@article {pmid41521257,
year = {2026},
author = {Wang, S and Song, X and Song, X and Gu, Y and Cong, Z and Shen, Y and Yu, L},
title = {Non-Invasive Brain-Computer Interfaces: Converging Frontiers in Neural Signal Decoding and Flexible Bioelectronics Integration.},
journal = {Nano-micro letters},
volume = {18},
number = {1},
pages = {193},
pmid = {41521257},
issn = {2150-5551},
abstract = {The development of non-invasive brain-computer interfaces (BCIs) relies on multidisciplinary integration across neuroscience, artificial intelligence, flexible electronics, and systems engineering. Recent advances in deep learning have significantly improved the accuracy and robustness of neural signal decoding. Parallel progress in electrode design-particularly through the use of flexible and stretchable materials like nanostructured conductors and novel fabrication strategies-has enhanced wearability and operational stability. Nevertheless, key challenges persist, including individual variability, biocompatibility limitations, and susceptibility to interference in complex environments. Further validation and optimization are needed to address gaps in generalization capability, long-term reliability, and real-world operational robustness. This review systematically examines the representative progress in neural decoding algorithms and flexible bioelectronic platforms over the past decade, highlighting key design principles, material innovations, and integration strategies that are poised to advance non-invasive BCI capabilities. It also discusses the importance of multimodal data fusion, hardware-software co-optimization, and closed-loop control strategies. Furthermore, the review discusses the application potential and associated engineering challenges of this technology in clinical rehabilitation and industrial translation, aiming to provide a reference for advancing non-invasive BCIs toward practical and scalable deployment.},
}

@article {pmid41521019,
year = {2026},
author = {Lv, Z and Li, X and Zhang, X},
title = {Commentary on He et al.: From static association to dynamic causation - a methodological leap in understanding and addressing addiction.},
journal = {Addiction (Abingdon, England)},
volume = {},
number = {},
pages = {},
doi = {10.1111/add.70312},
pmid = {41521019},
issn = {1360-0443},
support = {2024YFF0507600//National Key R&D Program of China/ ; 2021ZD0202101//Chinese National Programs for Brain Science and Brain-like Intelligence Technology/ ; 32571266//National Natural Science Foundation of China/ ; 32171080//National Natural Science Foundation of China/ ; 32400919//National Natural Science Foundation of China/ ; 32200914//National Natural Science Foundation of China/ ; ZSYS(2024)001//Project of Guizhou Key Laboratory of Artificial Intelligence and Brain-inspired Computing QianKeHe Platform/ ; 2408085QC081//Natural Science Foundation of Anhui Province/ ; 24YJCZH014//Ministry of Education of China/ ; //Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; },
}

@article {pmid41518865,
year = {2026},
author = {Hu, W and Xiao, J and Li, L and Zhao, W and Feng, Y and Shan, X and Chen, H and Duan, X},
title = {Developmental organization of neural dynamics supporting social processing: Evidence from naturalistic fMRI in children and adults.},
journal = {Developmental cognitive neuroscience},
volume = {78},
number = {},
pages = {101670},
doi = {10.1016/j.dcn.2026.101670},
pmid = {41518865},
issn = {1878-9307},
abstract = {The development of social cognition underpins significant implications for diagnosing and treating neurodevelopmental disorders such as autism spectrum disorder. This study investigates the dynamic neural organization of social cognition in children (n = 60, ages 3-10) and adults (n = 55) using a naturalistic fMRI paradigm that tracks continuous brain activity during real-world social interactions. We identify four distinct co-activation patterns (CAP) that reflect a functional hierarchy, ranging from basic sensory processing to complex social-cognitive integration. These brain state dynamics reveal significant developmental differences: children exhibit immature transitions, often bypassing intermediate states (e.g., salience-driven filtering, State 3) and prematurely shifting from early sensory encoding (State 1) to internally-directed integration (State 2). Moreover, during mentalizing and pain events, children show reduced modulation of sensory and perceptual brain states, indicating limited cognitive flexibility that is essential for social interaction. Structural equation modeling reveals a developmental cascade linking the maturation of sensory (State 1), perceptual filtering (State 3), and social-cognitive (State 2) processing states. This pathway is mediated by individual differences in Theory of Mind (ToM) development and further predicts empathic abilities. These findings advance our understanding of how brain state reorganization supports social cognitive maturation and offer new insights into neurodevelopmental disorders.},
}