@article {pmid41836667,
year = {2026},
author = {Shang, W and Choi, B and Zhan, Q and Wu, J and Xu, D},
title = {Neuromodulation and rehabilitation of post-stroke cognitive impairment: challenges and prospects.},
journal = {Frontiers in psychiatry},
volume = {17},
number = {},
pages = {1780907},
pmid = {41836667},
issn = {1664-0640},
abstract = {It is essential to recognize the significant daily impact that post-stroke cognitive impairment (PSCI) has on patients and their families. Neuromodulation strategies have been increasingly applied in the clinical management of PSCI. This review outlines the mechanisms and brain function detection approaches through which neuromodulation promotes cognitive enhancement in stroke patients. For cognitive recovery, transcranial magnetic stimulation, transcranial electrical stimulation, vagus nerve stimulation, and brain-computer interfaces have shown promising results in clinical and preclinical studies. However, their efficacy remains unproven in large-scale pivotal trials. Preliminary clinical trials have shown that photobiomodulation enhances cognitive performance, but further investigation is required into the issue of skull attenuation of light. Transcranial ultrasound stimulation, a novel technology that overcomes the limitation of requiring deep electrode implantation for focal deep brain stimulation, still lacks scientific evidence. Chemogenetics and optogenetics provide methods for monitoring, disrupting, and regulating neural circuits after a stroke. To enhance the effectiveness of neuromodulation, it is recommended to implement multi-target stimulation, strengthen active participation in rehabilitation, and leverage cognitive-motor interactions to promote holistic recovery after stroke. Finally, we propose that neuromodulation will evolve toward brain-machine interaction neuromodulation, using artificial intelligence to develop a closed-loop strategy encompassing stimulation, detection, optimization, and re-stimulation.},
}

@article {pmid41838473,
year = {2026},
author = {Searls, WC and Roderique, TJ and Cominos, ND and Khalil, LS},
title = {Editorial Commentary: Bio-Inductive Collagen Implant Augmentation Shows Long-Term Cost-Effectiveness, But Clinical Patient Outcomes and Careful Patient Selection Must Guide the Path Forward.},
journal = {Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association},
volume = {42},
number = {1},
pages = {83-86},
doi = {10.1002/arj.70031},
pmid = {41838473},
issn = {1526-3231},
mesh = {Humans ; Cost-Benefit Analysis ; *Collagen/economics ; *Patient Selection ; *Rotator Cuff Injuries/surgery/economics ; *Arthroscopy/methods/economics ; Treatment Outcome ; *Prostheses and Implants/economics ; },
abstract = {Arthroscopic rotator cuff repairs (ARCR) are fraught with low healing rates despite improvements in surgical techniques and constructs. Several studies have emerged showing significant improvements in failure to heal rates when incorporating bioinductive collagen implants (BCI) in the short term. Structural integrity following ARCR is paramount, as retear places exorbitant costs on the health care system and long-term studies have established that clinical outcomes are significantly worse in patients with structural retear. The up-front costs of biologic augmentation is cost-prohibitive in ambulatory surgery centers, where a large portion of ARCR occurs, despite the efficacy of improving rotator cuff repair tendon quality and integrity. This short-sighted, bundled reimbursement paradigm that omits BCI from Current Procedural Terminology coding must be revised considering the long-term cost effectiveness of reducing retear risk following ARCR. As BCI augmentation is established as a dominant strategy, strongly recommended by the American Academy of Orthopaedic Surgeons, to reduce retears and improve patient outcomes, it is critical that long-term clinical studies evaluating patient outcomes drive the indications for implementation of BCI in patients with high risk of repair failure.},
}

Humans
Cost-Benefit Analysis
*Collagen/economics
*Patient Selection
*Rotator Cuff Injuries/surgery/economics
*Arthroscopy/methods/economics
Treatment Outcome
*Prostheses and Implants/economics

@article {pmid41838553,
year = {2026},
author = {Hurley, ET and Ibán, MÁR and Oeding, JF and Navlet, MG and Lafuente, JLÁ and Klifto, CS},
title = {Bio-Inductive Collagen Implant Augmentation for Arthroscopic Rotator Cuff Repair Is Cost-Effective in Medium to Large Tears for Reducing Retears: A Secondary Analysis of a Randomized Controlled Trial.},
journal = {Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association},
volume = {42},
number = {1},
pages = {73-82},
doi = {10.1002/arj.70000},
pmid = {41838553},
issn = {1526-3231},
mesh = {Humans ; Cost-Benefit Analysis ; *Rotator Cuff Injuries/surgery/economics ; *Arthroscopy/economics/methods ; Markov Chains ; Quality-Adjusted Life Years ; *Collagen/economics/therapeutic use ; Monte Carlo Method ; *Prostheses and Implants/economics ; Recurrence ; },
abstract = {PURPOSE: To perform a Markov model-based cost-effectiveness analysis comparing arthroscopic rotator cuff repair (ARCR) and bio-inductive collagen implant (BCI) to ARCR for symptomatic, medium-to-large rotator cuff tears.

METHODS: A Markov chain Monte Carlo probabilistic model was developed to evaluate the outcomes and costs of 1000 simulated patients undergoing ARCR + BCI versus ARCR for isolated, symptomatic, reparable, full-thickness, medium-to-large posterosuperior nonacute rotator cuff tears, with fatty infiltration ≤2. Health utility values, transition probabilities, and costs were derived from the published literature. Outcome measures included costs, quality-adjusted life years (QALYs), and the incremental cost-effectiveness ratio (ICER). Ten-year costs for each patient in the microsimulation model were averaged by initial treatment strategy to capture costs of any subsequent treatments patients underwent as a result of retears. Cycle length was defined as 1 year, with all costs and utilities discounted at 3% annually. Disutility was applied to patient health states involving conversion to reverse shoulder arthroplasty (RSA) for retears and postoperative complications.

RESULTS: Over the 10-year time horizon, mean total costs resulting from ARCR + BCI and ARCR were $49,240 ± $8516 and $56,358 ± $8665, respectively. On average, ARCR + BCI was associated with 5.6 ± 0.4 QALYs, while ARCR alone was associated with 4.3 ± 0.4 QALYs. Overall, ARCR + BCI was determined the preferred cost-effective strategy in 100% of patients included in the microsimulation model. Deterministic sensitivity analysis on the risk of retear associated with ARCR + BCI found that the recurrence risk associated with ARCR + BCI would need to be greater than 26.5% in order for ARCR without BCI augmentation to be more cost-effective than ARCR + BCI at a willingness-to-pay threshold of $50,000/QALY.

CONCLUSIONS: ARCR + BCI was determined to be the dominant, cost-effective treatment strategy to reduce retears for symptomatic, medium-to-large rotator cuff tears based on the Monte Carlo microsimulation and probabilistic sensitivity analysis. Patients treated ARCR alone faced higher retear rates, leading to greater downstream costs that ultimately exceeded those of the ARCR + BCI group.

LEVEL OF EVIDENCE: Level I, economic and decision analysis.},
}

Humans
Cost-Benefit Analysis
*Rotator Cuff Injuries/surgery/economics
*Arthroscopy/economics/methods
Markov Chains
Quality-Adjusted Life Years
*Collagen/economics/therapeutic use
Monte Carlo Method
*Prostheses and Implants/economics
Recurrence

@article {pmid41838798,
year = {2026},
author = {Zhao, H and Zhang, X and Marin-Llobet, A and Lin, X and Liu, J},
title = {Benchmarking spike source localization algorithms in high density probes.},
journal = {PLoS computational biology},
volume = {22},
number = {3},
pages = {e1014059},
doi = {10.1371/journal.pcbi.1014059},
pmid = {41838798},
issn = {1553-7358},
abstract = {Estimating neuron location from extracellular recordings is essential for developing advanced brain-machine interfaces. Accurate neuron localization improves spike sorting, which involves detecting action potentials and assigning them to individual neurons. It also assists in monitoring probe drift, which affects long-term probe reliability. Although several localization algorithms are currently in use, the field is nascent and arguments for using one algorithm over another are largely theoretical or based on visual inspection of clustering results. We present a first-of-its-kind benchmarking of commonly used neuron localization algorithms. We assess these algorithms using two ground truth datasets: a biophysically realistic simulated dataset, and an experimental dataset pairing patch-clamp and extracellular Neuropixels recording data. We systematically evaluate the accuracy, robustness, and runtime of these algorithms in ideal recording conditions and long-term recording conditions with electrode degradation. Our findings highlight significant performance differences; while more complex and physically realistic models perform better in ideal conditions, models relying on simpler heuristics demonstrate superior robustness to noise and electrode degradation, making them more suitable for long-term neural recordings. This work provides a framework for assessing localization algorithms and developing robust, biologically grounded algorithms to advance the development of brain-machine interfaces.},
}

@article {pmid41839845,
year = {2026},
author = {Wang, M and Jiang, H and Ni, C and Zhou, X and Xu, Y and Shang, S and You, X and Wang, W and Zhou, C and Zhang, W and Wang, X and Zhang, S and Shi, L and Ji, B},
title = {Conformal bumped electrode web for chronic ECoG recordings in swine.},
journal = {Microsystems & nanoengineering},
volume = {12},
number = {1},
pages = {},
pmid = {41839845},
issn = {2055-7434},
support = {62204204//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {The acquisition of high-quality electrocorticogram (ECoG) signal is of great significance for the diagnosis and treatment of neurological diseases such as high amputation, visual injury, epilepsy and Parkinson's disease. Currently, flexible ECoG electrodes have received attention due to their low mechanical mismatch and minimally invasive characteristics. However, the traditional ECoG electrodes are non-stretchable planar structures that cannot be conformal with the cerebral cortex, which is in constant motion and has sulci and gyri structure. In this work, a flexible stretchable ECoG electrode with bumped electrodes was developed to alleviate these problems. Firstly, the mechanical simulation results show that this stretchable electrode structure can effectively reduce the stress mismatch between electrode and tissue interface. Secondly, the results of cyclic voltammetry scanning and mechanical tensile experiments show that the stretchable ECoG electrode structure can be conformally attached to the surface of the cerebral cortex and maintain good electrochemical stability during continuous stretching. Third, the bumped electrode has a larger adhesive force than the planar electrode and can significantly reduce the background noise by conformal attachment and electrochemical modification of PEDOT:PSS. Most importantly, in vivo animal experiments showed that the stretchable ECoG electrode can continuously record high-quality ECoG signals on the surface of the cerebral cortex of swine over an area of 22 × 22 mm[2] for more than 5 weeks.},
}

@article {pmid41839864,
year = {2026},
author = {Yan, X and Li, Y and Zhao, Y and Pan, C and Yan, S and Yang, D and Ruan, GJ and Zhao, H and Chen, F and Yangdong, XJ and Wang, P and Yu, W and Yang, Y and Wang, C and Cheng, B and Liang, SJ and Miao, F},
title = {Light-programmable mechanical computing via polyaniline composite film.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-70425-z},
pmid = {41839864},
issn = {2041-1723},
abstract = {Mechanical computing represents a highly promising paradigm for environment-adaptive information processing. However, existing implementations are generally constrained by limited architectural scalability, and their modes of application in practical scenarios remain insufficiently defined. Here, we develop a light-programmable mechanical computing system that not only performs scalable logic operations but also enables environment-adaptive optical camouflage. The system is based on a polyaniline composite film (PCF) that integrates light-responsive expansion-contraction elements with a flexible conductive layer. Light illumination dynamically modulates the conductive pathways, giving rise to optically controlled single-pole single-throw (SPST) and single-pole double-throw (SPDT) relays that reconfigure signal transmission routes. Interconnecting these relays enables the construction of basic logic gates and 2-bit full-adder circuits, establishing a scalable paradigm for light-programmable mechanical computation. Moreover, we implement an adaptive camouflage function that senses environmental textures and generates matching optical patterns, demonstrating potential for intelligent skin applications capable of environmental interaction. This work establishes a light-programmable, pathway-reconfigurable mechanical computing framework, expanding possibilities for autonomous and adaptive intelligent systems.},
}

@article {pmid41839891,
year = {2026},
author = {Li, H and Wang, S and Yu, Q and Zhao, H and Tang, Z and Lv, L and Han, F and Yang, R and Zhao, Y and Fu, Z and Shi, B and Li, G and Wang, C and Zhang, J and Song, K and Li, Y and Liu, Z},
title = {Implantable soft bladder-machine interface for neurogenic bladder dysfunction.},
journal = {Nature communications},
volume = {17},
number = {1},
pages = {},
pmid = {41839891},
issn = {2041-1723},
support = {//International Partnership Program of Chinese Academy of Sciences/ ; //Guangdong Provincial Key Laboratory of Multimodality Non-Invasive Brain-Computer Interfaces/ ; //Shenzhen Science and Technology Program/ ; },
mesh = {*Urinary Bladder, Neurogenic/therapy/physiopathology ; Animals ; *Urinary Bladder/physiopathology ; Rats ; Electromyography ; *Electric Stimulation Therapy/instrumentation/methods ; Female ; Rats, Sprague-Dawley ; *Prostheses and Implants ; Electric Stimulation ; Disease Models, Animal ; Muscle Contraction/physiology ; },
abstract = {Neurogenic bladder dysfunction impairs bladder sensation and contraction, causing severe renal complications. The bladder's large isotropic expansion hinders the development of implantable bioelectronic devices for monitoring and electrical stimulation. Addressing this, we report an implantable soft bladder-machine interface (BdMI) that integrates seamlessly with the bladder, providing monitoring and electrical stimulation. This BdMI features a conductive thin film capable of keeping functions under isotropic stretch up to 800%, created without the complex pre-stretching of its elastic substrate. We elucidate its stretchability mechanism and validate the BdMI in rat models, which enables simultaneous intravesical pressure detection, detrusor electromyographic monitoring, and electrical stimulation therapy. Implanted for 7 days, the BdMI operates efficiently and markedly reduces involuntary bladder contraction frequency post-stimulation. These findings validate the potential of BdMI in offering real-time, physiological feedback and electrical stimulation-based regulation for neurogenic bladder pathologies, marking a significant advancement in the field.},
}

*Urinary Bladder, Neurogenic/therapy/physiopathology
Animals
*Urinary Bladder/physiopathology
Rats
Electromyography
*Electric Stimulation Therapy/instrumentation/methods
Female
Rats, Sprague-Dawley
*Prostheses and Implants
Electric Stimulation
Disease Models, Animal
Muscle Contraction/physiology

@article {pmid41831590,
year = {2026},
author = {Yang, W and Yuan, J and Ding, L and Keung Chow, SK},
title = {A neurofeedback-guided EEG and BCI framework for personalized attention rehabilitation in ADHD.},
journal = {Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuroscience.2026.03.010},
pmid = {41831590},
issn = {1873-7544},
abstract = {The integration of game-based cognitive training with electroencephalography (EEG)-based brain-computer interaction (BCI) has demonstrated potential for enhancing attention among individuals with attention-deficit hyperactivity disorder (ADHD). However, existing systems often lack adaptive difficulty regulation and rely solely on single-modal assessments, thereby limiting personalization and sustained engagement. This study developed and assessed an adaptive, multi-task EEG-BCI training system that combines real-time neurofeedback with machine learning-driven customization to bolster attentional capabilities. Fifty participants (25 with ADHD and 25 controls) completed attention-enhancement sessions utilizing SkiSport, a Unity-based skiing game that adjusts difficulty levels according to EEG-derived attention metrics obtained from the NeuroSky TGAM sensor. Support Vector Regression, XGBoost, and Multi-Layer Perceptron models were trained on behavioral and EEG data to predict optimal difficulty parameters. Attention and behavioural metrics were compared before and after personalisation. The findings indicated that EEG attention scores increased by an average of 15% (7.85% in controls, 21.5% in ADHD participants). The adaptive multi-task games yielded an additional 10% increase following personalization. Behavioral indices on reaction accuracy, game score, and completion time showed an overall improvement of 19%. XGBoost achieved the highest predictive accuracy on a held-out test set (R[2] value of 0.9826, RMSE of 0.8560, and MAE of 0.6417) for within-subject, window-level attention prediction. The proposed EEG-BCI game facilitated short-term enhancements in attention-related metrics among individuals with ADHD. The incorporation of machine learning-driven personalization into serious games offers a scalable, non-pharmacological strategy for short-term cognitive training and attentional modulation.},
}

@article {pmid41832156,
year = {2026},
author = {Duhay, V and Tian, M and Kosieradzka, K and Ebner, M and Lo, WT and Krauss, M and Sprengel, HL and Voss, M and Riechmann, M and Savas, JN and Schwake, M and Haucke, V and Damme, M},
title = {Control of lysosome function by the GTPase-activating protein TBC1D9B and its binding partner TMEM55B.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-70345-y},
pmid = {41832156},
issn = {2041-1723},
support = {DA 1785/2-2//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; SCHW866/6-1//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; SCHW866/7-1//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; TRR186/A08//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; HA2686/26-1//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; },
abstract = {Lysosomes are highly dynamic organelles that serve antagonistic functions as terminal catabolic stations for the degradation of macromolecules and as central metabolic decision centers for anabolic growth signaling. Lysosome dysfunction is implicated in various human diseases. The physiological roles of lysosomes are linked to the control of lysosome position and dynamics via the activity of the kinesin-activating small GTPase ARL8. How the activity of ARL8 is regulated remains poorly understood. Here, we identify the GTPase-activating Tre-2/Bub2/Cdc16 (TBC) domain protein TBC1D9B as a critical negative regulator of ARL8B function. We demonstrate that TBC1D9B is associated with the lysosomal membrane protein TMEM55B, directly binds to ARL8B-GTP, and stimulates its GTPase activity. Knockout of TBC1D9B or its binding partner TMEM55B causes lysosome dispersion, defective autophagic flux, and impairs the adaptive degradative response of cells to limiting nutrient supply. These lysosomal phenotypes of TBC1D9B loss are occluded by concomitant depletion of ARL8 in cells. Collectively, our data unravel a key role for TBC1D9B in controlling lysosome function by serving as a negative regulator of ARL8 activity.},
}

@article {pmid41832195,
year = {2026},
author = {Wang, Z and Xu, G and Yu, B and Xu, K and Zhu, J and Pan, G and Zhang, J and Wang, Y and Hao, Y},
title = {Cortical representation of multidimensional handwriting movement and implications for neuroprostheses.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-70536-7},
pmid = {41832195},
issn = {2041-1723},
abstract = {Handwriting brain-computer interfaces (BCIs) have enabled high performance brain-to-text communication for paralyzed individuals. However, the detailed parameters of handwriting movement and their cortical representations remain incompletely understood. Here, we recorded intracortical neural activity from a paralyzed subject and found distinct neural representations for strokes and pen lifts with respect to two-dimensional (2D) velocity on the writing plane, indicating that 2D kinematics alone cannot fully account for the observed neural variance. To address this, we acquired multidimensional handwriting data from healthy subjects, including 3D velocity, grip force, writing pressure, and multi-channel electromyographic (EMG) signals. Incorporating these additional dimensions beyond 2D velocity significantly improved the interpretability of neural signals for both strokes and pen lifts. We further leveraged these additional dimensions to enhance handwriting decoding performance. Together, our findings indicate the motor cortex encodes handwriting as multidimensional movement and highlight the importance of multidimensional features for improving the performance of handwriting BCIs.},
}

@article {pmid41832543,
year = {2026},
author = {Hu, G and Tang, H and Zeng, F and Wen, X and Hou, W and Zhang, X},
title = {Brain responses to different action observation paradigms and assessing transferable cross-paradigm decoding.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-026-01946-3},
pmid = {41832543},
issn = {1743-0003},
support = {62206032//the National Natural Science Foundation of China/ ; CSTB2025TIAD-JM011//Chongqing Key Project for Technology Innovation and Application Development/ ; },
}

@article {pmid41834060,
year = {2026},
author = {Zhao, Z and Duan, X and Huang, H and Zhang, Y and Wang, M and Qin, J and Lin, S and Chen, H},
title = {Single-Nucleus Transcriptomics Reveals Microglial State Transitions and Astrocytic Trajectory Divergence During Glial Remodeling Induced by Intracortical Electrode Implantation.},
journal = {Glia},
volume = {74},
number = {5},
pages = {e70148},
doi = {10.1002/glia.70148},
pmid = {41834060},
issn = {1098-1136},
support = {32201095//National Natural Science Foundation of China/ ; 32127801//National Natural Science Foundation of China/ ; 62104051//National Natural Science Foundation of China/ ; },
mesh = {Animals ; *Microglia/metabolism ; *Electrodes, Implanted/adverse effects ; Rats ; *Astrocytes/metabolism ; *Transcriptome/physiology ; Male ; *Motor Cortex/metabolism ; *Neuroglia/metabolism ; Rats, Sprague-Dawley ; },
abstract = {The foreign body response to intracortical electrodes, characterized by chronic neuroinflammation and glial scar formation, remains a primary cause of long-term functional failure. However, neurons and glial cells' heterogeneity and intercellular signaling mechanisms following electrode implantation remain poorly resolved, which is responsible for direct dysfunction. Here, we applied single-nucleus RNA sequencing (snRNA-seq) to profile the peri-implant microenvironment in rat motor cortex tissue at 3, 25, and 50 days post-electrode implantation. Integrated bioinformatic analyses, including clustering, pseudotemporal trajectory reconstruction, and cell-cell communication inference, revealed a coordinated cellular response. We identified a pathologic microglial subpopulation (marked by Gpnmb, SPP1, and CD63) and a scar-associated astrocytic subtype (characterized by Mctp1 and Lrrc7) that progressively dominate the peri-implant niche. Crucially, we reveal that neurons orchestrate these processes via CX3CL1-CX3CR1 signaling, modulating microglial polarization and PTN-ALK/Ptpprz1 interaction, promoting astrogliosis and scar formation. These findings define the dynamic neuron-glia signaling landscape surrounding chronically implanted electrodes and provide mechanistic insight into how modulating cell-cell communication may improve the long-term biocompatibility of neural interfaces.},
}

Animals
*Microglia/metabolism
*Electrodes, Implanted/adverse effects
Rats
*Astrocytes/metabolism
*Transcriptome/physiology
Male
*Motor Cortex/metabolism
*Neuroglia/metabolism
Rats, Sprague-Dawley

@article {pmid41834064,
year = {2026},
author = {Wang, X and Ciarlo, A and Lührs, M and Atanasyan, A and Böken, D and Roßmann, J and Schluse, M and Jäger, M and Nordt, M and Cong, F and Mathiak, K and Linden, DEJ and Goebel, R and Mehler, DMA and Zweerings, J},
title = {A Feasibility Study of Navigating Emotional States Using Real-Time Representational Similarity Analysis fMRI Neurofeedback.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2650018},
doi = {10.1142/S0129065726500188},
pmid = {41834064},
issn = {1793-6462},
abstract = {Real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-NF) is a promising noninvasive brain computer interface (BCI) technique for enhancing self-regulation of affective brain states. However, conventional univariate rt-fMRI-NF approaches struggle to discriminate distributed neural patterns underlying distinct emotions. This study implemented an rt-fMRI semantic neurofeedback (rt-fMRI-sNF) paradigm incorporating real-time representational similarity analysis (rt-RSA) to enable navigation among emotional states. Four emotion-specific base patterns were first derived from functional localizer runs and then used as target patterns during neurofeedback. Using an RSA-informed circular semantic map (CSM), participants received real-time visual feedback indicating both the similarity and intensity of their current brain activity relative to target patterns. Participants were instructed to use mental imagery to shift their brain activity toward the specific target pattern and enhance its intensity. Analyses of localizer data revealed overlapping regional activations across emotions and demonstrated that RSA reliably distinguished between emotional states. Group-level mixed-effects modeling of neurofeedback performance indicated significant within-run improvements and higher initial performance in the second run. Together, these results demonstrate the methodological feasibility of an RSA-informed rt-fMRI-NF framework for multivariate brain-state modulation and establish a foundation for future studies examining its transferability and clinical relevance.},
}

@article {pmid41835943,
year = {2026},
author = {Zhang, Q and Cao, Z and Tian, S and Cai, Z and Shi, L and Qi, X},
title = {Comparative study of SSVEP characteristics in mixed versus virtual reality across varying depths.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1713018},
doi = {10.3389/fnins.2026.1713018},
pmid = {41835943},
issn = {1662-4548},
abstract = {Steady-state visually evoked potentials (SSVEP), owing to their high signal-to-noise ratio and low training cost, are widely regarded as an effective approach for constructing visually driven brain-computer interfaces (BCI), particularly in neurorehabilitation applications. However, the accommodation-vergence conflict (VAC) commonly present in mixed reality (MR) and virtual reality (VR) head-mounted displays may attenuate neural responses in the visual cortex, thereby compromising the long-term usability of such systems. This study aims to systematically evaluate the effects of MR and VR environments under different virtual depth conditions on SSVEP signal quality, classification performance, and visual comfort, providing parameter guidelines for the design of immersive visual BCIs in rehabilitation contexts. Green flickering stimuli at 7.5, 11.25, and 18 Hz were presented at three virtual depths of 0.4, 1.0, and 1.8 m. Feature extraction and classification were performed using canonical correlation analysis (CCA), Filter-Bank Canonical Correlation Analysis (FBCCA), and task-related component analysis (TRCA).The results showed a negative correlation between stimulus distance and SSVEP classification accuracy, with FBCCA achieving the highest accuracy at the 0.4 m depth (71.8% ± 33.8%). Overall, the signal-to-noise ratio (SNR) in the MR environment was higher than that in the VR environment, with the most pronounced difference observed under the 1.8 m condition, suggesting that MR is more effective in alleviating VAC and maintaining stable visual cortical responses. Among the three stimulation frequencies, 11.25 Hz elicited the highest SSVEP amplitude and SNR, indicating it as the optimal frequency band. Subjective visual fatigue assessments revealed higher scores for VR in terms of diplopia and fixation difficulty, with trends consistent with the observed SNR reduction. This study elucidates the interactive modulation effects of virtual depth, display modality, and flicker frequency on SSVEP, and demonstrates that MR outperforms VR in terms of signal stability, visual comfort, and potential rehabilitation usability. The derived parameters provide experimentally validated optimization strategies for stimulus depth and frequency in vision-based attention training, spatial orientation training, upper-limb interactive tasks, and immersive feedback systems in neurorehabilitation, thereby contributing to improved long-term adherence and clinical translational value of future rehabilitation BCI.},
}

@article {pmid41836195,
year = {2026},
author = {Kunekar, P and Mankar, S and Cholke, P and Kulkarni, A and Nooji, P and Gadhave, R},
title = {MedIntelliCare: neurodynamic-inspired AI for medical decision support by integrating retrieval-augmented generation with multimodal cognitive processing.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {61},
doi = {10.1007/s11571-026-10429-z},
pmid = {41836195},
issn = {1871-4080},
abstract = {MedIntelliCare is an AI-powered medical assistant designed to enhance diagnostic accuracy, reduce cognitive load on healthcare professionals, and integrate real-time medical data. While current AI-driven medical systems focus on information retrieval and response generation, MedIntelliCare leverages Retrieval-Augmented Generation (RAG) combined with principles from neural computation and decision-making processes. This study explores the system's ability to simulate biologically inspired information processing by integrating brain-like computing, predictive modeling, and multimodal analysis, including EEG and neuroimaging data. By aligning MedIntelliCare with advances in computational neuroscience and intelligent diagnostics, we aim to establish a model that enhances clinical decision support through adaptive information retrieval. The system's future implications include cognitive disorder modeling, brain-computer collaboration, and advanced AI-driven diagnostics inspired by neural processing frameworks. Experimental validation using cosine similarity metrics demonstrates that MedIntelliCare achieves a 73% alignment with expert-generated reports, reinforcing its potential in neuro-inspired medical intelligence.},
}

@article {pmid41828036,
year = {2026},
author = {Gao, Q and Jin, Y and Sun, Y and Jin, M and Tang, L and Chen, Y and She, Y and Li, M},
title = {Transforming Intracerebral Hemorrhage Care with Artificial Intelligence: Opportunities, Challenges, and Future Directions.},
journal = {Diagnostics (Basel, Switzerland)},
volume = {16},
number = {5},
pages = {},
pmid = {41828036},
issn = {2075-4418},
support = {XY2025074//Scientific Research Fund of Zhejiang University/ ; },
abstract = {Spontaneous intracerebral hemorrhage (ICH) is associated with substantial mortality and morbidity. Current management paradigms rely heavily on the rapid interpretation of neuroimaging and clinical data, yet are frequently constrained by limitations in processing speed, diagnostic accuracy, and prognostic precision. Artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), offers transformative potential to circumvent these challenges across the entire continuum of ICH care. This comprehensive review synthesizes the rapidly evolving landscape of AI applications in ICH management. Through a systematic evaluation of recent literature, we examine studies focused on the development, validation, or critical appraisal of AI-driven technologies for ICH care. Our analysis encompasses automated neuroimaging, computer-assisted surgical navigation, brain-computer interfaces (BCIs), prognostic modeling, and fundamental research into disease mechanisms. AI has demonstrated performance comparable to that of clinical experts in automating hematoma segmentation, predicting complications such as hematoma expansion, and refining surgical planning via augmented reality. Furthermore, BCIs present innovative therapeutic avenues for motor rehabilitation. However, the translation of these technological advances into routine clinical practice is impeded by substantial challenges, including data heterogeneity, model opacity ("black-box" issues), workflow integration barriers, regulatory ambiguities, and ethical concerns surrounding accountability and algorithmic bias. The integration of AI into ICH care signifies a paradigm shift from standardized treatment protocols toward dynamic, precision medicine. Realizing this vision necessitates interdisciplinary collaboration to engineer robust, generalizable, and interpretable AI systems. Key priorities include the establishment of large-scale multimodal data repositories, the advancement of explainable AI (XAI) frameworks, the execution of rigorous prospective clinical trials to validate efficacy, and the implementation of adaptive regulatory and ethical guidelines. By systematically addressing these barriers, AI can evolve from a mere analytical tool into an indispensable clinical partner, ultimately optimizing patient outcomes.},
}

@article {pmid41828065,
year = {2026},
author = {Tasci, I and Sercek, I and Talu, Y and Barua, PD and Baygin, M and Tasci, B and Dogan, S and Tuncer, T},
title = {TensorCSBP: A Tensor Center-Symmetric Feature Extractor for EEG Odor Detection.},
journal = {Diagnostics (Basel, Switzerland)},
volume = {16},
number = {5},
pages = {},
pmid = {41828065},
issn = {2075-4418},
support = {123E612//Scientific and Technological Research Council of Turkey/ ; TF.25.35//Scientific Research Projects Coordination Unit of Firat University/ ; },
abstract = {Objective: Accurate odor classification from EEG signals requires informative and interpretable features. Although Local Binary Pattern (LBP) and variants such as the center-symmetric binary pattern are widely used, they lack sufficient explainability and tensor-level implementations. Additionally, neuroscientific understanding of odor processing remains limited. Methods: We propose Tensor Center-Symmetric Binary Pattern (TensorCSBP), a novel tensor-based feature extractor designed for EEG odor analysis. TensorCSBP is integrated into an explainable feature engineering (XFE) pipeline with four steps: (1) TensorCSBP for feature generation, (2) CWNCA for feature selection, (3) tkNN classifier for decision making, and (4) DLob method for symbolic interpretability. Results: TensorCSBP XFE was evaluated on a newly collected 32-channel EEG dataset for odor detection. It achieved 96.68% accuracy under 10-fold cross-validation. Conclusions: The information entropy of the DLob symbol sequence was 3.5675, demonstrating the richness of the interpretability output. Significance: This study presents a high-accuracy, explainable, and computationally efficient model for EEG-based odor classification. TensorCSBP bridges low-level signal patterns with symbolic neuroscience insights, offering real-time potential for BCI and clinical applications.},
}

@article {pmid41829691,
year = {2026},
author = {Gao, X and Cao, G and Ma, G},
title = {SFE-GAT: Structure-Feature Evolution Graph Attention Network for Motor Imagery Decoding.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {5},
pages = {},
pmid = {41829691},
issn = {1424-8220},
support = {2020YFB17122//Ministry of Science and Technology of the People's Republic of China/ ; 2021M692457//China Postdoctoral Science Foundation/ ; YDZJ202301ZYTS263//Department of Science and Technology of Jilin Province/ ; YDZJ202301ZYTS423//Department of Science and Technology of Jilin Province/ ; },
mesh = {Humans ; Electroencephalography/methods ; *Neural Networks, Computer ; Brain-Computer Interfaces ; *Motor Cortex/physiology ; Nerve Net/physiology ; *Attention/physiology ; Algorithms ; Brain/physiology ; },
abstract = {Motor imagery EEG decoding often relies on static functional connectivity graphs that cannot capture the dynamic, stage-wise reorganization of brain networks during tasks. This paper aims to develop a graph neural network that explicitly simulates this neurodynamic process to improve decoding and provide computational insights. This paper proposes a Structure-Feature Evolution Graph Attention Network (SFE-GAT). Its inter-layer evolution mechanism dynamically co-adapts graph topology and node features, mimicking functional network reorganization. Initialized with phase-locking value connectivity and spectral features, the model uses a graph autoencoder with Monte Carlo sampling to iteratively refine edges and embeddings. On the BCI Competition IV-2a dataset, SFE-GAT achieved 77.70% (subject-dependent) and 66.59% (subject-independent) accuracy, outperforming baselines. Evolved graphs showed sparsification and strengthening of task-critical connections, indicating hierarchical processing. This paper advances EEG decoding through a dynamic graph architecture, providing a computational framework for studying the hierarchical organization of motor cortex activity and linking adaptive graph learning with neural dynamics.},
}

Humans
Electroencephalography/methods
*Neural Networks, Computer
Brain-Computer Interfaces
*Motor Cortex/physiology
Nerve Net/physiology
*Attention/physiology
Algorithms
Brain/physiology

@article {pmid41830336,
year = {2026},
author = {Momin, M and Feng, L and Chen, X and Ahmed, S and AlMahmood, B and Huang, LP and Ren, J and Wang, X and Lee, H and Cramer, SR and Zhang, N and Zhang, S and Zhou, T},
title = {3D-Printable, Honeycomb-Inspired Tissue-Like Bioelectrodes for Patient-Specific Neural Interface.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e16291},
doi = {10.1002/adma.202516291},
pmid = {41830336},
issn = {1521-4095},
support = {1R01HL171633/NH/NIH HHS/United States ; NTUT-PSU-113-01//National Taipei University of Technology-Penn State Collaborative Seed Grant Program/ ; //National Science Foundation/ ; },
abstract = {The unique gyral patterns of the human brain demand patient-specific neural interfaces to achieve precise neuromodulation, mitigate adverse tissue responses, and optimize therapeutic efficacy and safety. One-size-fits-all, conventional rigid electrocorticography (ECoG) electrodes, standardized for mass production through lithographic techniques, exhibit limited conformability to the brain's heterogeneous cortical topography. This mechanical mismatch results in poor electrode-tissue contact, signal loss, and foreign body responses. To address these limitations, we present an integrated novel platform, synergizing MRI-based anatomical mapping, finite element analysis (FEA)-optimized mechanical design, and direct ink writing (DIW) 3D printing to fabricate electrodes customized to individual gyral patterns. The resulting honeycomb-inspired printable gel electrode (HiPGE) employs a bioinspired honeycomb architecture with ultra-soft hydrogels, engineered to match the bending stiffness of brain tissue (0.1-10 kPa) while maintaining cost-efficiency and long-term durability. This mechanical congruence ensures exceptional cortical conformability and adaptive interfacing, circumventing the geometric and material limitations of traditional rigid electrodes. By combining patient-specific design with scalable fabrication, our platform establishes a transformative framework for neural interface engineering, enhancing precision, biocompatibility, and functional performance in neuromodulation therapies and neuroprosthetic applications.},
}

@article {pmid41826752,
year = {2026},
author = {Li, Y and Si, Y and Pang, X and Li, S and Jiang, L and Yi, C and Yao, D and Li, F and Xu, P},
title = {EEG hyperscanning reveals dynamic interbrain network patterns during interactive social decision-making.},
journal = {Communications biology},
volume = {},
number = {},
pages = {},
doi = {10.1038/s42003-026-09852-z},
pmid = {41826752},
issn = {2399-3642},
abstract = {Social decision-making involves intricate and dynamic interactions between brains, yet prior hyperscanning research primarily concentrated on investigating the overall patterns of interbrain synchrony (IBS), leaving its fine-grained temporal dynamics unveiled. Here, after recording the electroencephalography of proposer-responder pairs who engaged in an iterated ultimatum game, time-varying IBS network architectures were explored by leveraging source-localized wavelet transform coherence and k-means clustering. Results revealed a sequence of temporally and functionally distinct IBS states along the response and feedback periods. Early states, occurring around stimulus onset, were dominated by a posterior parietal modular configuration, likely associated with shared attention and visual processing. In contrast, later states during the decision-feedback stage involved increased IBS in the frontal and temporoparietal regions, reflecting coordinated activity between interacting partners supporting decision execution and adaptive behavioral adjustments. Crucially, advantageous conditions (fair proposal or acceptance feedback) elicited more active and efficient dynamic IBS states than disadvantageous conditions (unfair proposal or rejection feedback), with greater IBS related to increased reciprocal behavior. These findings reveal recurring IBS patterns, suggesting that social decision-making is modulated not only by temporal fluctuations in IBS networks but also by flexible interbrain communication between key cortical regions.},
}

@article {pmid41826709,
year = {2026},
author = {Jones, CT and Hill, ER},
title = {The evolution of speech communication devices for anarthria: a review.},
journal = {Journal of neurology},
volume = {273},
number = {3},
pages = {},
pmid = {41826709},
issn = {1432-1459},
mesh = {Humans ; *Communication Devices for People with Disabilities/trends ; *Facial Paralysis/rehabilitation/etiology ; *Communication Disorders/etiology ; },
abstract = {Anarthria is a lack of verbal communication caused by physiological disturbances in the motor pathway. While affected individuals retain the ability to comprehend and produce speech, orofacial paralysis renders them unable to execute speech. Anarthria can be caused by amyotrophic lateral sclerosis, stroke, traumatic brain injury, and other etiologies that affect the descending motor pathway. A wide range of technologies has been developed and tested to improve communication efficiency for patients with anarthria and accompanying paralysis. This review evaluates three key eras of communication device development. First, before implantation devices gained traction, many communication devices revolved around blinks, head and eye tracking, and non-invasive brain recording. Second, implanted cortical neuroprosthetics were designed to improve accuracy and speed of communication. Finally, the review analyzes the future era, where accessibility, patient comfort, and broader applications of neural analysis elevate communication for patients with anarthria to match fluid communication. Restoring speech communication in patients with anarthria is vital to improve their quality of life. Therefore, understanding communication device efficiency and its future trajectory is of utmost clinical importance.},
}

Humans
*Communication Devices for People with Disabilities/trends
*Facial Paralysis/rehabilitation/etiology
*Communication Disorders/etiology

@article {pmid41825840,
year = {2026},
author = {Miao, M and Fu, W and Zeng, H and Xu, B and Zhang, W and Hu, W},
title = {Meta-Learning Enhanced Multi-Source Domain Adaptation for zero-calibration motor imagery EEG decoding.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110742},
doi = {10.1016/j.jneumeth.2026.110742},
pmid = {41825840},
issn = {1872-678X},
abstract = {BACKGROUND: Motor imagery (MI) based brain-computer interface (BCI) holds promising application prospects for closed-loop neurorehabilitation in stroke recovery. Despite substantial progress, challenges such as inter-subject variability, lack of training data for specific subject, and the need for time-consuming calibration still hinder the practical deployment of MI-BCI systems.

NEW METHOD: In this work, aiming to address these issues, we propose a novel Meta-Learning Enhanced Multi-Source Domain Adaptation (MLEMSDA) framework that unifies cross-task, cross-dataset, and cross-subject domain adaptation with gradient-based meta-learning to enable calibration-free MI-EEG decoding. Specifically, two large public ME and MI EEG datasets are firstly used for pre-training to facilitate cross-task and cross-dataset knowledge transfer. Afterward, to further reduce the differences in feature distribution among different individuals, meta-learning based fine-tuning is performed using data from all subjects in the target dataset except the unseen subject. Finally, the obtained decoding model is tested on the unseen subject.

RESULTS: The proposed MLEMSDA framework was validated on a public stroke MI EEG dataset (CBCIC), our own collected MI EEG dataset, and BCI Competition IV dataset 2b using leave-one-out cross-validation method. DeepConvNet achieved the highest average accuracy of 77.87% on CBCIC dataset, EEGNet yielded the highest average accuracy of 75.54% on our own collected dataset, and ShallowConvNet obtained the highest average accuracy of 72.72% on BCI Competition IV dataset 2b.

With respect to classification accuracy in the zero-calibration scenario, our method outperforms all the competing methods.

CONCLUSION: These results clearly demonstrate the effectiveness and generalizability of our method, paving the way for more practical MI-BCI applications.},
}

@article {pmid41825227,
year = {2026},
author = {Zhou, H and Bao, Y and Xu, J and Wang, D and Geng, F and Guo, W and Hu, Y},
title = {Test-retest reliability and symptom association of personalized depression TMS targets: A comparative study of refined seed-based (RSA) and hierarchical clustering (HCA) approaches.},
journal = {Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics},
volume = {23},
number = {2},
pages = {e00884},
doi = {10.1016/j.neurot.2026.e00884},
pmid = {41825227},
issn = {1878-7479},
abstract = {Personalized transcranial magnetic stimulation (TMS) targeting holds promise for improving depression treatment, but its clinical translation is hindered by limited open-source implementation and systematic comparisons of target reproducibility and clinical relevance. We implemented two leading personalized TMS-target generating approaches, namely refined seed-based (RSA) and hierarchical clustering (HCA) algorithms, and compared them on (1) test-retest reliability of derived targets, and (2) association of target-sgACC connectivity with depressive symptoms. Using resting-state fMRI data from healthy and depressed individuals, spatial reliability was quantified via inter-run Euclidean distances, and clinical relevance was assessed through correlations between depression severity and functional connectivity of targets with sgACC. Effects of global signal regression (GSR) were also evaluated. The results showed that RSA produced targets in more superior and postrior part of DLPFC and demonstrated significantly higher test-retest reliability than HCA (smaller inter-run Euclidean distances). Further, RSA-derived target-sgACC connectivity correlated positively with depression severity, which was absent in HCA-derived targets. In addition, GSR improved spatial reliability for RSA but not HCA. Our results indicate that RSA exhibits superior test-retest reliability and symptom association compared to HCA, yet large-scale clinical trials are warranted to determine which approach yields superior therapeutic efficacy, and open-sourced implementation may accelerate clinical adoption.},
}

@article {pmid41822235,
year = {2026},
author = {Li, Y and Wang, R and Yan, C and Xu, X and Wang, Y and Pan, X and Song, Y and Zhang, B and Liu, Z},
title = {Application of neurodynamics theory in the study of neural circuits in major depressive disorder: a review on neural energy approaches.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {60},
pmid = {41822235},
issn = {1871-4080},
abstract = {Major depressive disorder (MDD) is accompanied by abnormal reward processing, altered dopamine transmission in the ventral tegmental area-nucleus accumbens-medial prefrontal cortex (VTA-NAc-mPFC) dopaminergic pathway, and disruptions in both neural dynamics and brain energy metabolism. Yet, how these abnormalities converge within a unified framework of neural dynamics and neural energy coding remains unclear. The purpose of this review is to integrate and critically assess computational models of neural dynamics and neural energy coding in MDD, with a particular emphasis on the multiscale modeling approaches developed in our recent work, and to organize these advances into a coherent conceptual framework linking dopamine-related circuit dysfunction to alterations in neural energy consumption. First, we constructed Hodgkin-Huxley (H-H) models for the NAc medium spiny neuron (MSN) to simulate its neurodynamics. Then, using the neural energy model, we explored the energy consumption characteristics of MSNs and found that, in the MDD condition, MSN energy consumption during spiking was lower than in controls, demonstrating the feasibility and sensitivity of this energy-based methodology. To further examine how these mechanisms scale to functional circuits, we constructed a neural network dynamical model for the VTA-NAc-mPFC dopaminergic pathway and applied an augmented neural-energy computation framework to characterize its energy consumption features. Simulations demonstrated that neural energy consumption was substantially lower in the MDD condition, primarily due to decreased mPFC energy expenditure. Distinct energy-coding patterns emerged across neuronal types, and the energy required to encode a single action potential in both MSNs and pyramidal neurons increased under MDD low dopamine situation, indicating reduced energy efficiency. Moreover, the correlation between membrane potential and instantaneous power was moderate (0.6-0.9) rather than tight, and it changed substantially with dopamine levels. This shows that neural energy consumption carries additional neural information that is not reflected directly in membrane potential signals. Together, these findings establish a unified computational framework that links dopamine deficiency, ion-channel-level dysfunction, microcircuit dynamics impairment, and large-scale reductions in neural energy consumption. Our work highlights neural energy coding as a promising mechanistic indicator and potential biomarker for MDD, and provides a generalizable methodology for investigating other neuropsychiatric disorders.},
}

@article {pmid41822138,
year = {2026},
author = {Zhang, X and Wang, B and Zhang, L and Pu, Y and Kong, XZ},
title = {Successful Public Speaking Enhances Neural Alignment in Audience Language Networks.},
journal = {Neurobiology of language (Cambridge, Mass.)},
volume = {7},
number = {},
pages = {},
pmid = {41822138},
issn = {2641-4368},
abstract = {Public speaking is a fundamental form of communication across a wide range of domains; however, the neural mechanisms underlying audience engagement during different speeches remain poorly understood. In particular, it is unclear which functional brain networks support the dynamic fluctuations of audience engagement and what neurobiological processes underlie these effects. In this study, we used naturalistic fMRI combined with intersubject correlation (ISC) analysis to examine how carefully selected and matched speeches, with varying levels of audience engagement, influence neural activity. Our results revealed that the more engaging speech elicited significantly greater interbrain neural synchronization, as indexed by ISC, across a broad range of brain regions. Notably, these engagement-related effects were most prominent in networks associated with language processing and theory of mind, highlighting their critical roles in facilitating shared audience experiences during compelling public communication. A sliding-window analysis further revealed substantial temporal fluctuations in interbrain synchronization throughout the speech. Additionally, neurobiological annotation analyses identified strong associations between engagement-related ISC effects and molecular pathways involved in trans-synaptic signaling, suggesting that intrabrain neuronal communication may contribute to modulating interbrain synchronization. By integrating naturalistic fMRI with ISC analyses, this study offers a promising framework for investigating dynamic neural synchronization among audience members. These findings have broad implications for fields such as education and leadership development, where a deeper understanding of the neural basis of audience engagement could inform strategies to enhance public speaking and communication effectiveness.},
}

@article {pmid41821657,
year = {2026},
author = {Prasanna, HS and Prasad, BNM and Ugalat, J and Vishnuvardhana, and Shankarappa, TH and Shivanna, M and Manjunathagowda, DC and Narayanappa, MG and Lakshmana, VG},
title = {Differential responses of dark and white chia (Salvia hispanica L.) to elicitation: effects on seed quality and biochemical composition.},
journal = {3 Biotech},
volume = {16},
number = {4},
pages = {140},
pmid = {41821657},
issn = {2190-572X},
abstract = {UNLABELLED: The present study investigated the impact of exogenous elicitor application on enhancing chia seed quality. The application of chitosan (200 ppm) and PGPR consortia (5000 ppm) to black chia resulted in the most notable improvements. Application of chitosan improved swelling factor (12.03 cc g[-][1]), fiber content (44.35 g 100 g[-][1]), and oil content (36.08%). The PGPR consortia maximized α-linolenic acid (ALA) accumulation (66.74%), while methyl jasmonic acid increased protein content (33.17 g 100 g[-][1]). In contrast, elicitor application to white chia exhibited a distinct response pattern. Kinetin (100 ppm) recorded the highest swelling factor (11.98 cc g[-][1]), PGPR elevated protein content (34.03 g 100 g[-][1]), and chitosan increased fiber (49.09 g 100 g[-][1]) and oil content (35.78%). The study demonstrated a significant enhancement in the accumulation of secondary metabolites, specifically total phenols and flavonoids. In summary, the application of chitosan, PGPR consortia, and kinetin significantly improved the functional and nutraceutical qualities of both seed types.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13205-026-04746-7.},
}

@article {pmid41821632,
year = {2026},
author = {Liu, C and Han, J and Wang, Y and Liang, X and Meng, X},
title = {Effects of brain-computer interface-based rehabilitation on lower limb function and activities of daily living after stroke: a systematic review and meta-analysis.},
journal = {Frontiers in neurology},
volume = {17},
number = {},
pages = {1746958},
pmid = {41821632},
issn = {1664-2295},
abstract = {BACKGROUND: Lower limb motor dysfunction is a common sequela of stroke that significantly impacts patients' walking safety and independence in daily living. Although brain-computer interface (BCI) technology has demonstrated efficacy in upper limb rehabilitation, its effects on lower limb recovery have not yet been systematically evaluated.

METHODS: A systematic literature search was conducted across seven databases (PubMed, Web of Science, Embase, China National Knowledge Infrastructure, SinoMed, VIP Database, and Wanfang Data.) to identify studies investigating BCI for post-stroke lower limb dysfunction, encompassing records published up to September 2025. All statistical analyses were performed using Review Manager software (version 5.4.1).

RESULTS: Thirteen studies involving 582 participants were included. BCI training significantly improved the scores of Fugl-Meyer Assessment for Lower Extremity (FMA-LE, MD = 2.67, 95%CI: 2.31-3.03, P < 0.00001, I [2] = 0%), Berg Balance Scale (BBS, MD = 7.04, 95%CI: 3.14-10.94, P = 0.0004), and Modified Barthel Index (MBI, MD = 6.72, 95%CI: 1.74-11.69, P = 0.008). Furthermore, a single study reported significant improvement in functional mobility measured by the Timed Up and Go Test (TUGT). Subgroup analysis for activities of daily living MBI showed that a cumulative training time of ≥ 500 min was associated with greater improvement.

CONCLUSION: BCI-based training is an effective approach for improving lower limb recovery after stroke, demonstrating benefits in motor function, balance, and functional mobility. While evidence for certain outcomes remains limited, the dose-dependent effect on daily living activities underscores the importance of sufficient training duration. Future research should validate these findings and clarify effects across a broader range of functional measures.

https://www.crd.york.ac.uk/PROSPERO/view/CRD420251150558, identifier: CRD420251150558.},
}

@article {pmid41821240,
year = {2026},
author = {Lee, HY and Fahad, MAA and Park, M and Kang, HJ and Jahan, N and Shanto, PC and Kim, H and Lee, BT and Bae, SH},
title = {In Vitro and In Vivo Evaluation of Decellularized Porcine Femoral Aorta Reinforced With Electrospun Coarse Polycaprolactone Fibers for Vascular Graft Application.},
journal = {Artificial organs},
volume = {},
number = {},
pages = {},
doi = {10.1111/aor.70106},
pmid = {41821240},
issn = {1525-1594},
support = {2021R1G1A1094894//Ministry of Science and ICT, South Korea/ ; 2015R1A6A1A03032522//National Research Foundation of Korea/ ; //Soonchunhyang University, Republic of Korea/ ; },
abstract = {BACKGROUND: The clinical translation of small-diameter vascular grafts (SDVGs) is still limited due to severe complications, including thrombosis, intimal hyperplasia, and arteriosclerosis, commonly associated with synthetic polymer-based grafts. To address these challenges, combining synthetic polymers with naturally derived extracellular matrices (ECMs) offers a promising strategy to enhance biofunctionality and remodeling potential.

METHOD: This study developed a composite vascular graft by electrospinning a polycaprolactone (PCL) fibrous outer layer onto decellularized porcine femoral aorta extracellular matrix (PECM), generating a hybrid PCL-PECM graft. Decellularization was validated using H&E staining and DNA quantification, ensuring effective cellular removal without compromising protein content. Scanning electron microscopy (SEM) was used to evaluate the interface between PCL and PECM. Mechanical properties were assessed via tensile testing. Hemocompatibility was evaluated by hemolysis testing and blood clotting index (%BCI). In vitro biocompatibility was assessed using cell culture assays, and in vivo remodeling was evaluated through subcutaneous implantation in a rat model, followed by histological analysis.

RESULTS: H&E staining and DNA analysis confirmed complete decellularization. SEM images revealed no delamination between layers, and the PCL layer significantly enhanced the mechanical strength of the graft. Hemolysis ratio remained below 5%, and %BCI exceeded 80%, indicating excellent hemocompatibility. In vitro studies confirmed cytocompatibility, while histological staining of explanted grafts showed robust cell infiltration and ECM remodeling.

CONCLUSION: The PCL-PECM vascular graft demonstrates excellent structural integrity, mechanical performance, hemocompatibility, and remodeling potential, indicating its promise as a next-generation SDVG.},
}

@article {pmid41821070,
year = {2026},
author = {Xie, W and Lei, H and Ning, C and Dong, D and Zhang, X and Rao, H},
title = {Network analysis of childhood trauma and meaning in life in adolescents with and without depression.},
journal = {BMC psychology},
volume = {},
number = {},
pages = {},
doi = {10.1186/s40359-026-04218-w},
pmid = {41821070},
issn = {2050-7283},
support = {82371537//National Natural Science Foundation of China/ ; 2024JJ5496//Natural Science Foundation of Hunan Province/ ; kq2202408//Natural Science Foundation of Changsha City/ ; },
}

@article {pmid41820589,
year = {2026},
author = {Kong, L and Zhuang, Y and Zhu, B and Wang, H and Chen, Y and Shen, Y and Feng, X and Hu, S and Lai, J},
title = {A multi-omics analysis of gut bacteriome, virome, and serum metabolome in bipolar depression.},
journal = {Npj mental health research},
volume = {5},
number = {1},
pages = {},
pmid = {41820589},
issn = {2731-4251},
support = {2023YFC2506200, 2023YFC2506203//National Key Research and Development Program of China/ ; 82571735, 82471542//National Natural Science Foundation of China/ ; 2024C03098, 2025C02109//Key Research & Development Program of Zhejiang Province/ ; JNL-2023001B//Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; },
abstract = {The involvement of microbiota-gut-brain axis in bipolar disorder (BD) has been uncovered, yet the specific tripartite interplay between the gut bacteriome, virome, and serum metabolome remains to be elucidated. We conducted a cross-sectional multi-omics analysis on 90 drug-free patients with bipolar depression and 30 healthy controls. A significant between-group difference in gut bacterial α-diversity was observed. Non-parametric test revealed 1929 bacterial and 134 viral species with significant inter-group difference, among which 249 bacterial and 7 viral species remained significant after FDR correction (Padjusted < 0.05). Metabolomic analysis identified 261 significantly differential serum metabolites, which were enriched in 70 biological pathways and 40 pathways remained significant after correction. Integration of the datasets revealed strong cross-omic correlations, while only eight significant viral-metabolic correlations were detected. Post-FDR significant correlations with clinical features were exclusively observed between differential metabolites and scores of disease severity, with a predominance of negative correlations. Clinically, a random forest model integrating bacteriome, virome, and metabolome features achieved superior discriminative power (AUC = 0.986) compared to single-omics models (metabolites: 0.970; bacteria: 0.823; viruses: 0.732). This work demonstrated a dysregulated bacteriome-virome-metabolome network of patients with bipolar depression, providing a robust panel of candidate biomarkers for the precise diagnosis of BD.},
}

@article {pmid41820551,
year = {2026},
author = {Li, Y and Li, S and Li, Y and Pang, X and Yi, C and Jiang, L and Yao, D and Wu, W and Li, F and Xu, P},
title = {Temporal synchrony and spatial similarity of interbrain subnetworks predict dyadic social interaction.},
journal = {Communications biology},
volume = {},
number = {},
pages = {},
doi = {10.1038/s42003-026-09854-x},
pmid = {41820551},
issn = {2399-3642},
support = {W2411084//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82372084//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Human social behaviors involve complex interactions between individuals, and understanding how interbrain neural activity reflects and predicts these interactions is critical for advancing social cognitive neuroscience. While electroencephalography (EEG) hyperscanning has been widely used to explore interpersonal neural dynamics, most studies focus on pairwise regional coupling, overlooking the brain's intrinsic network-level organization. Here, we propose a spatiotemporal network analysis framework that combines Bayesian non-negative matrix factorization with EEG source imaging to identify interpretable subnetworks with spatiotemporal information. Applying this framework to dyadic EEG datasets from interactive decision-making tasks identifies eight task-relevant subnetworks, including the default mode network (DMN), somatosensory-motor network (SMN), and visual network (VN). Effective interpersonal coordination was associated with enhanced network-level time-domain interbrain synchrony and spatial-domain inter-subject similarity, and the fusion of these metrics reliably predicted interactive behaviors. Notably, synchrony and similarity involving DMN, VN, and SMN emerge as robust predictors of interactive behaviors, with spatiotemporal coupling most prominent within these subnetworks. These findings reveal spatiotemporal network signatures underlying interpersonal neural synchronization and demonstrate the importance of distributed subnetworks and their temporal and spatial alignment in achieving effective social interactions. This framework provides a useful computational tool for probing the neurobiological basis of social behaviors.},
}

@article {pmid41818827,
year = {2026},
author = {Hore, A and Chakrabarti, S and Bandyopadhyay, S},
title = {Incorporating a variety of synaptic dynamics in neuromorphic hardware: Different types of inhibition and plasticity.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae512d},
pmid = {41818827},
issn = {1741-2552},
abstract = {OBJECTIVE: This study aims to design a CMOS-based circuit that mimics the behavior of real brain synapses, focusing on both plasticity and inhibi- tion. The goal is to improve the biological realism and learning ability of neuromorphic hardware.

APPROACH: A unified CMOS-based synaptic architecture is proposed that integrates short-term plasticity (STP) and long-term plasticity (LTP) with two forms of synaptic inhibition: divisive and subtractive. The STP circuit models short- term depression (STD) and facilitation (STF), while the LTP mechanism employs spike-timing-dependent plastic- ity (STDP) to capture temporally driven synaptic mod- ifications. Furthermore, a spiking neuronal network is designed to demonstrate biologically accurate inhibitory effects and to perform max pooling via divisive inhibition. All circuits are implemented and simulated in TSMC 180 nm CMOS using Cadence Virtuoso.

MAIN RESULTS: The proposed circuits successfully reproduce key biological features of synaptic behavior. The STP and LTP blocks enable time-dependent modulation of synaptic weights, while the inhibitory networks exhibit both divisive and subtractive control over postsynaptic firing frequency. The maxpooling operation, achieved via divisive inhibi- tion, allows the target neuron to respond to the input with the highest spiking activity selectively. Simulation results confirm the correct functional behavior of all the designed circuits.

SIGNIFICANCE: This work provides a simple and effective hardware solution for modeling fundamental synaptic functions. It supports adaptive learning and efficient processing in neuromorphic sys- tems. The results can help build better brain-like systems for AI, robotics, and brain-computer interfaces.},
}

@article {pmid41818825,
year = {2026},
author = {Tidare, J and Johansson-Alvarez, M and Plantin, J and Palmcrantz, S and Astrand, E},
title = {Exploration of using "distance-to-bound" to manipulate the difficulty during motor imagery BCI training after stroke - A clinical two-cases study.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae512c},
pmid = {41818825},
issn = {1741-2552},
abstract = {Motor Imagery-based Brain-Computer Interfaces (MI-BCIs) is a promising technology for neurorehabilitation after stroke. However, many face challenges in using a BCI because they fail to produce discriminable patterns in their brain activity. Personalizing the BCI task difficulty could help the learning process of these users but there is currently very limited knowledge on which methods can be used online. Our aim was to explore a distance-to-bound (DTB) approach for adapting MI BCI task difficulty in real time. Approach: Two chronic stroke patients performed 12 BCI training sessions over 4 weeks during which they performed MI of open- and close hand movements and received continual visual feedback based on multivariate decoding of ongoing electroencephalogram (EEG) activity. We increased the difficulty and maintained it by adapting it in real time based on DTB decoding metrics and, by using a multiple-session design, we investigated the stability of this approach and how it related to MI-related EEG activity of each patient. Main results: We show that patients had to produce stronger alpha and beta event-related desynchronisation/synchronisation (ERDS) pattern across the sensorimotor cortical areas of the brain to receive positive feedback. In addition, we show that the online adaptation converged within sessions as well as accommodating for drift in the data both within and between sessions. We suggest that the DTB approach can effectively be used to control BCI task difficulty which could, in future BCIs, serve as a potential tool to guide patients to produce functionally relevant activity patterns. However stronger sensorimotor ERDS did not correlate to improved motor function in one of our two patients. As this result is observational and cannot support causal claims, it exemplifies the need to individually tailor the translation of DTB outputs to feedback considering the stroke lesion and EEG activity profile of the specific patient. Significance: This study provides valuable insights and considerations for BCI difficulty adaptation in the aim of developing more effective training protocols in BCI-based stroke rehabilitation. .},
}

@article {pmid41815306,
year = {2026},
author = {Ezzeldin, M and Hassan, AE and Ezzeldin, R and Adachi, K and Soliman, Y and Alshekhlee, A and Hussain, MS and Niazi, M and Sheriff, F and Bushnaq, S and Asif, K and Tanweer, O and Alaraj, A and Grandhi, R and Janjua, N and Vela-Duarte, D and Chaubal, V and Al Matairi, A and Mir, O and Mealer, L and Ezepue, C and AlMajali, M and Chaudhari, A and Martucci, M and Abdulrazzak, MA and Maud, A and Rodriguez, G and Miller, S and Quispe-Orozco, D and Suppakitjanusant, P and Froukh, M and Bains, N and Bhatti, I and Xu, J and Abou-Mrad, T and Salah, W and Shoraka, O and Ali, Z and Zaidat, O and Siddiq, F},
title = {Carotid Artery Stenting Outcomes in Comprehensive Stroke Hospitals (CASSH): A Prospective Multicenter Study.},
journal = {Stroke (Hoboken, N.J.)},
volume = {6},
number = {2},
pages = {e002201},
pmid = {41815306},
issn = {2694-5746},
abstract = {BACKGROUND: The CASONI study (Carotid Artery Stenting Outcomes by Neurointerventional Surgeons) showed that proceduralist experience significantly reduces complications in carotid artery stenting. The CASSH study (Carotid Artery Stenting Outcomes in Comprehensive Stroke Hospitals) prospectively evaluates real-world carotid artery stenting outcomes by fellowship-trained neurointerventionalists at comprehensive stroke centers across the United States to validate and expand on CASONI's findings.

METHODS: CASSH is a multicenter, prospective observational study conducted across 15 US comprehensive stroke centers from January 2023 to December 2024. Adults with symptomatic ≥50% or asymptomatic ≥70% carotid stenosis undergoing carotid artery stenting by fellowship-trained neurointerventionalists were included. The primary outcome was a 30-day composite of procedure-related death, stroke, or myocardial infarction. Secondary outcomes included nonprocedural mortality, access site complications, stent thrombosis, and other adverse events. Logistic regression identified predictors of adverse outcomes.

RESULTS: Among 889 patients (mean age 70.3±9.9 years; 61.4% male), 87.1% had hypertension and 63.1% were symptomatic. The 30-day composite primary outcome occurred in 1.2% (mortality 0.8%, ischemic stroke 0.3%, hemorrhagic stroke 0.2%, myocardial infarction 0.2%). Composite secondary outcome occurred in 5.4%, most commonly access site complications (1.7%) and nonprocedural mortality (1.5%). Higher preprocedural modified Rankin Scale (odds ratio [OR], 1.42), National Institutes of Health Stroke Scale score (OR, 1.09), and longer fluoroscopy times (OR, 1.02) were associated with increased complication risk. Mortality was independently predicted by elevated modified Rankin Scale (OR, 1.72), higher National Institutes of Health Stroke Scale score (OR, 1.15), older age (OR, 1.05 per year), and lower ejection fraction (OR, 0.96). Postprocedural antiplatelet therapy was protective, reducing both complications (OR, 0.03) and mortality (OR, 0.07).

CONCLUSIONS: Carotid artery stenting performed by fellowship-trained neurointerventionalists at comprehensive stroke centers is associated with low rates of periprocedural stroke, myocardial infarction, and death. These outcomes align with the landmark CREST-2 trial (Carotid Revascularization and Medical Management for Asymptomatic Carotid Stenosis Trial), particularly in asymptomatic patients, and are strongly influenced by preprocedural disability, stroke severity, age, and cardiac function, underscoring the importance of patient selection and optimized perioperative care.},
}

@article {pmid41812365,
year = {2026},
author = {Khalili, MD and Abootalebi, V and Saeedi-Sourck, H and Santoro, A and Behjat, HH},
title = {Small-world scale-free brain networks from EEG with application to motor imagery decoding and brain fingerprinting.},
journal = {Computers in biology and medicine},
volume = {206},
number = {},
pages = {111606},
doi = {10.1016/j.compbiomed.2026.111606},
pmid = {41812365},
issn = {1879-0534},
abstract = {Developing individualized spatial models that capture the complex dynamics of multi-electrode EEG data is essential for accurately decoding global neural activity. A widely used approach is network modeling, where electrodes are represented as nodes. A key challenge lies in defining the network edges and weights, as precise connectivity estimation is critical for enhancing neural characterization and extracting discriminative features, such as those needed for task decoding. Traditional EEG-derived brain graphs often fail to capture biologically grounded organizational principles such as small-world structure and heavy-tailed (scale-free) connectivity patterns. To address this gap, we introduce a framework for inferring subject-specific EEG-based brain graphs that are explicitly designed to exhibit small-world and scale-free properties. Our approach begins by computing phase-locking values (PLV) between EEG channel pairs to build a backbone graph, which is then refined into an individualized small-world and scale-free network. To reduce computational complexity while preserving subject-specific characteristics, we apply Kron reduction to the resulting graph. Using two public EEG datasets, we evaluate the proposed method on motor imagery (MI) decoding and brain fingerprinting tasks. Our approach improves MI classification accuracy by 4-7% compared to conventional PLV, small-world, and scale-free graph models, and enhances differential identifiability in fingerprinting by 8-20% across six canonical frequency bands. These gains were statistically significant in both applications. Moreover, integrating graph signal processing features derived from our constructed graphs with classical EEG features further boosts performance. Overall, our findings highlight the potential of the proposed graph construction framework to enhance EEG analysis. By jointly capturing local segregation, global integration, and hub-driven hierarchical organization, the method strengthens downstream decoding and identification tasks, with promising implications for a wide range of applications in cognitive neuroscience and brain-computer interface research.},
}

@article {pmid41811559,
year = {2026},
author = {Chen, Q and Jing, Y and Bu, W and Zhang, J and Liu, W and Shi, C and Liu, C and Su, D},
title = {RELA as a Diagnostic Biomarker for Parkinson's Disease by Integrating Ferroptosis, Lipid Metabolism, and Neuroinflammation.},
journal = {Inflammation},
volume = {},
number = {},
pages = {},
doi = {10.1007/s10753-026-02478-7},
pmid = {41811559},
issn = {1573-2576},
support = {2024202003//Jinan Municipal Health Commission Science and Technology Plan Project/ ; 202204040490//Shandong Provincial Medical and Health Science and Technology Development Plan Projec/ ; },
}

@article {pmid41810068,
year = {2026},
author = {Hao, H and Jiao, X and Zhou, G and Chen, L and Wang, M and He, J and Lang, X and Zhang, J and Shi, L and An, M and Yan, L and Zhu, Y and Yang, Y},
title = {An Analogue Memristor Based on Conjugated Porous Polymer Composite for Artificial Synapse.},
journal = {Exploration (Beijing, China)},
volume = {6},
number = {1},
pages = {20250234},
pmid = {41810068},
issn = {2766-2098},
abstract = {Artificial synapses have emerged as a pivotal technological advancement in mimicking brain functions. Organic memristors are desirable for hardware implementation of artificial synapses, owing to their remarkable mechanical flexibility, high biocompatibility at cell-device interfaces, and adjustable material structure. Developing appropriate organic polymers with carbon dots modification will enable the memristor to possess analog-type resistive switching behavior, crucial for realizing brain-like associative learning and adapting dynamic variations of neuron connection strength. In this work, an artificial synapse based on the analogue organic memristor integrating neuromorphic computing and neural interface functions is proposed, utilizing synthetic conjugated porous polymers to construct composites with boron-doped carbon dots. The structure-property relationship of alkynyl and alkyl chains in polymers is elucidated, alongside the synergistic effect of local photoinduced redox and hole templating in composites that endows the device with analog-type resistive switching behavior. Moreover, the memristor presents impressive synaptic plasticity and associative memory learning potential for neuromorphic computing, and further serves as a core unit in flexible artificial neural interface chips, demonstrating dynamic information transmission with neural systems. This study will promote the further development of organic artificial synapses for neuromorphic computing and brain-machine interfaces.},
}

@article {pmid41809911,
year = {2026},
author = {Reyes-Jiménez, F and Rosas-Agraz, F and Macias-Naranjo, E and Alvarado-Rodríguez, FJ and Vélez-Pérez, H and Romo-Vázquez, R and Guzmán-Quezada, E},
title = {EEG and EMG dataset for analyzing movement-related cortical potentials in hand gesture tasks.},
journal = {Data in brief},
volume = {65},
number = {},
pages = {112596},
pmid = {41809911},
issn = {2352-3409},
abstract = {This dataset contains electroencephalography (EEG) and electromyography (EMG) recordings acquired during the execution of specific motor tasks aimed at eliciting movement-related cortical potentials (MRCP). The goal is to provide an accessible resource for research in brain-computer interfaces (BCI), neurorehabilitation, and EEG-based prosthetic control. Data were collected from 40 healthy participants aged 18-30 years across five sessions, each comprising ten right-hand fist closure movements, guided by a custom Python-based visual interface. EEG signals were recorded using a 32-channel EMOTIV Flex 2 wireless system following the international 10-10 system, with a sampling rate of 128 Hz and electrode placement focused on the central cortical areas. All recordings, including raw EEG, raw EMG, and event triggers synchronized with the visual interface, were stored in .CSV format. To demonstrate that the EEG recordings in the dataset contain sufficient low-frequency information for MRCP analysis, we applied a standard preprocessing pipeline consisting of a common average reference (CAR), a Anti-Laplacian spatial filter, and a 0.1-1 Hz Butterworth band-pass filter. This procedure was used only for internal validation, allowing us to visualize the expected MRCP components from the nine motor-related electrodes. It is important to emphasize that these processed signals are not included in the database. The public dataset contains only the raw EEG and EMG recordings, so that users may apply their preferred preprocessing and analysis methods. The dataset was collected under controlled laboratory conditions at the Medical Devices Laboratory, Universidad Autónoma de Guadalajara, and represents a valuable contribution to the understanding and application of MRCP in BCI research.},
}

@article {pmid41809488,
year = {2026},
author = {Al-Sheikh, U and Cheng, H and Bakrbaldawi, AAA and He, L and Chen, D and Zhan, R and Kang, L and Zhang, Y},
title = {A transcriptomic resource for glial GABA-associated ASH neuronal aging and candidate pathways.},
journal = {Frontiers in aging neuroscience},
volume = {18},
number = {},
pages = {1677754},
pmid = {41809488},
issn = {1663-4365},
abstract = {INTRODUCTION: Neuronal aging is tightly linked to neurodegeneration with dysregulation of GABA (gamma-aminobutyric acid), the primary inhibitory neurotransmitter, contributing to age-associated neuronal impairment. Our prior work demonstrated that restoring the key GABA-synthesizing enzyme UNC-25 (glutamic acid decarboxylase, GAD) in Caenorhabditis elegans AMsh glia mitigates age-related neurodegeneration. This study aims to provide a transcriptomic resource and identify potential pathways associated with glial GABA modulation during neuronal aging.

METHODS: ASH neurons from day 1 and day 7 nematodes were isolated and FACS-purified (Psra-6::RFP+/Pgpa-4::GFP-) from three distinct groups: Wild-type, unc-25 mutants, unc-25 mutants with AMsh glia-specific UNC-25 rescue. RNA-seq used Illumina NovaSeq (150 bp PE reads, aligned to WormBase WS293). DESeq2 identified DEGs (FDR < 0.05, fold-change ≥ 1); clusterProfiler performed GSEA and pathway enrichment. Comparisons also included AMsh glia vs. ASH neurons in wild young adults.

RESULTS: Here, we present transcriptomic data of glutamatergic ASH sensory neurons (a critical target of aging-related neurodegeneration) from three aging groups: wild-type worms, unc-25 (GABA-deficient) mutants, and unc-25 mutants with AMsh glia-specific UNC-25 rescue. Transcriptomic analyses revealed distinct transcriptional profiles across groups. Notably, the Hedgehog signaling pathway and its transcriptional effector TRA-1/GLI, the C. elegans GLI ortholog, were specifically upregulated in the glial rescue group, while the neuroprotective transcription factor HSF-1 was downregulated, suggesting these pathways as potential mediators of glial GABA-associated neuroprotection. We also provide transcriptomic comparisons between AMsh glia and ASH neurons in young worms, laying a foundation for understanding glia-neuron crosstalk.

CONCLUSIONS: This work establishes a valuable transcriptomic resource for glial GABA-associated ASH neuronal aging and identifies candidate pathways, offering critical molecular insights to dissect age-related neurodegeneration mechanisms and inform potential therapeutic targets.},
}

@article {pmid41809440,
year = {2026},
author = {Yan, Y and Zhao, X and Zhang, Y and Li, W and Lin, Z and Zhou, Y and Fang, S and Huang, J and Chen, CL and Lin, Z and Xu, X},
title = {Cerebral oxygen extraction and blood flow in community-based older adults: associations with white matter hyperintensity and neurocognitive function.},
journal = {Brain communications},
volume = {8},
number = {2},
pages = {fcag056},
pmid = {41809440},
issn = {2632-1297},
abstract = {Cerebral oxygen extraction fraction (OEF) and cerebral blood flow (CBF) are key haemodynamic markers. Emerging evidence suggests that they may exert compensatory effects on small vessel disease and cognitive outcomes, with potentially nonlinear relationships, particularly in community-dwelling seniors. Therefore, we conducted a cross-sectional study of 296 participants from the Heritage Study in China. OEF was assessed using T2-relaxation-under-spin-tagging (TRUST) MRI, while CBF was measured using phase contrast MRI. White matter hyperintensity (WMH) volumes were segmented through T2 fluid-attenuated inversion recovery (FLAIR) imaging and log-transformed. Neurocognitive function was evaluated across multiple domains and summarized as a global composite Z-score. Based on the median values of CBF and OEF, participants were categorized into four quadrants and generalized linear models were used to examine associations between OEF CBF patterns and WMH and cognition. Participants with high OEF and low CBF had highest WMH volume (4.48 ± 8.02 cm3) and worse cognitive performance (-0.13 ± 1.04). Overall, higher OEF was significantly related to lower global cognition (P = 0.012), whereas lower CBF was significantly associated with greater WMH burden (P = 0.001). Compared with those in high OEF and low CBF, individuals in low OEF and high CBF exhibited significantly lower WMH volume (β = -0.55, 95% confidence interval (CI) = [-1.05, -0.05]) and better cognition (β = 0.28, 95% CI = [0.02, 0.54]). In contrast, low OEF and low CBF were associated with relative cognitive reserve (β = 0.32, 95% CI = [0.02, 0.61]) but higher WMH volume. Domain-based analyses for attention, visuospatial and memory functions showed similar results. To further explore potential nonlinear effects, response surface analysis was performed to investigate relationships among OEF, CBF, WMH, and global cognition, revealing a significant association between CBF and WMH (β = -1.42, 95% CI = [-2.85, -0.01]). For global cognitive performance, OEF was negatively associated with cognitive outcomes (OEF: β = -0.49, 95% CI = [-0.87, -0.11], OEF[2]: β = 0.01, 95% CI = [0.00, 0.01]), indicating a U-shaped association between OEF and cognition. Notably, when CBF was high, cognition was relatively preserved even under higher OEF. In summary, OEF emerged as a sensitive marker of cognitive vulnerability in community-based seniors, particularly in attention, executive function, visuospatial ability, and memory, while CBF was the primary determinant of WMH burden. Combined OEF CBF patterns enabled classification of at-risk community-dwelling individuals, with the 'misery perfusion' pattern (high OEF, low CBF) showing the most adverse profile and representing a promising target for early risk stratification.},
}

@article {pmid41809003,
year = {2026},
author = {Mishler, J and Salimi, M and Koloski, M and Rembado, I and Shilyansky, C and Mishra, J and Ramanathan, D},
title = {Cortical oscillations reflect opponent ensemble dynamics through coordinated multifrequency activity.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.02.20.707132},
pmid = {41809003},
issn = {2692-8205},
abstract = {Neural oscillations are widely used as proxies for neuronal activity, where power in individual frequency bands is commonly interpreted as functionally indexing neural circuit engagement. However, power in individual frequency bands shows heterogeneous and sometimes opposing relationships with neuronal activity across regions and behavioral contexts, challenging the assumption of a stable frequency-to-circuit mapping. Here we show that glutamatergic population activity in rat medial prefrontal cortex is not stably linked with power in isolated frequency bands, but rather with dynamically recurring multi-frequency amplitude co-fluctuations. These multi-frequency patterns, termed spectral motifs, occurred in opponent pairs with nearly identical frequency composition but inverted relationships to population calcium activity. This opponent motif structure, observed across cortical regions and species, provides a key component for understanding how oscillations are linked to neuronal activity. We found that shifts in motif opponency balance explained changes in glutamatergic activity that occur during brain-computer interface learning better than models based on frequency band power alone. Furthermore, opponent motifs map selectively onto opponent cell ensembles and enable bidirectional mapping between local field potentials and ensemble activity. These findings identify multi-frequency opponent motifs as a conserved organizational principle linking oscillatory dynamics to population-level circuit states and challenge the notion that individual frequency bands can serve as interpretable functional units mapping onto neural circuit activity.},
}

@article {pmid41808199,
year = {2026},
author = {Chen, X and Zeng, GQ and Ma, R and Li, N and Zhang, M and Zhang, X},
title = {The Distinct Roles of the Dorsolateral Prefrontal Cortex and Dorsal Anterior Cingulate Cortex in Cognitive Control: Evidence From Transcranial Temporal Interference Stimulation.},
journal = {Psychophysiology},
volume = {63},
number = {3},
pages = {e70269},
doi = {10.1111/psyp.70269},
pmid = {41808199},
issn = {1469-8986},
support = {2024YFF0507600//National Key R&D Program of China/ ; 2021ZD0202101//The Chinese National Programs for Brain Science and Brain-like Intelligence Technology/ ; 32571266//The National Natural Science Foundation of China/ ; 32171080//The National Natural Science Foundation of China/ ; 32400919//The National Natural Science Foundation of China/ ; 32200914//The National Natural Science Foundation of China/ ; ZSYS[2024]001//the Project of Guizhou Key Laboratory of Artificial Intelligence and Brain-inspired Computing QianKeHe Platform/ ; 2408085QC081//Natural Science Foundation of Anhui Province/ ; 24YJCZH014//the Humanities and Social Science Fund of the Ministry of Education of China/ ; AHWJ2024Aa10016//Anhui Provincial Health Scientific Research Project/ ; //Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; },
mesh = {Humans ; *Gyrus Cinguli/physiology ; Male ; Adult ; Female ; *Dorsolateral Prefrontal Cortex/physiology ; *Memory, Short-Term/physiology ; Young Adult ; *Transcranial Direct Current Stimulation ; *Executive Function/physiology ; Stroop Test ; *Prefrontal Cortex/physiology ; Conflict, Psychological ; },
abstract = {Correlational evidence has accumulated on the distinct roles of dorsolateral prefrontal cortex (dlPFC) and dorsal anterior cingulate cortex (dACC) in cognitive control. However, causal evidence, especially regarding the dACC, is lacking. One of the main reasons is the limited focality and penetration depth of the conventional transcranial stimulation methods in targeting deep brain regions. This study aims to provide evidence for the dlPFC and dACC's roles in cognitive control using a novel transcranial stimulation method, i.e., temporal interference (TI) stimulation. By comparing pre- and post-stimulation effects on the conflict effect (CE) across individuals with different levels of working memory capacity (WMC), we seek to elucidate the differential impact of stimulating these brain regions and their interaction with WMC in enhancing cognitive control abilities. Cognitive control was assessed using the CE in a Stroop task. The study compared the pre- and post-stimulation effects of TI stimulation (dlPFC, dACC, and sham) on CE among individuals with varying levels of WMC. The results showed that dACC stimulation enhanced cognitive control regardless of WMC, while dlPFC stimulation improved control only in low WMC individuals. Distinct effects of dlPFC and dACC stimulation on cognitive control in varying WMC levels support the hypothesis that they play differing roles. TI stimulation shows promise for enhancing cognitive control.},
}

Humans
*Gyrus Cinguli/physiology
Male
Adult
Female
*Dorsolateral Prefrontal Cortex/physiology
*Memory, Short-Term/physiology
Young Adult
*Transcranial Direct Current Stimulation
*Executive Function/physiology
Stroop Test
*Prefrontal Cortex/physiology
Conflict, Psychological

@article {pmid41806473,
year = {2026},
author = {Cimarosto, P and Velut, S and Cabrera Castillos, K and Torre-Tresols, JJ and Roy, RN and Dehais, F},
title = {Near-invisible c-VEP-based passive BCI for mental workload monitoring.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae4ff6},
pmid = {41806473},
issn = {1741-2552},
abstract = {Flickering visual stimuli, either periodic (SSVEP) or aperiodic (c-VEP), have shown strong potential for implementing reactive Brain-Computer Interfaces (BCIs) and enabling hands-free interaction. Yet, their adaptation to passive BCIs remains limited, largely due to the distracting nature of flickers and its impact on visual comfort. Approach: In this study, we introduce an unobtrusive approach that embeds near--invisible, texture-based flickers over regions of interest in the user interface, combined with a c-VEP passive BCI pipeline to assess mental workload. We validated the approach in two experiments: (i) within an ecologically valid multitasking microworld of flying, and (ii) in a flight Simulator, where cognitive workload was systematically varied across three levels. Main results: Results at the group level disclosed that the amplitude of visual ERPs was significantly reduced under higher workload, providing an insightful neural marker for workload assessment. Moreover, results demonstrated that the proposed pipeline successfully enabled the derivation of indexes sensitive to workload-related modulation. Significance: These findings highlight the potential of textured flicker and c-VEP-based passive BCIs for monitoring cognitive workload in complex operational environments.},
}

@article {pmid41806395,
year = {2026},
author = {Liu, C and Liu, K},
title = {A Lightweight Transformer Model for Robust EEG Emotion Recognition Using Channel-Wise Differential Entropy.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ae4fc2},
pmid = {41806395},
issn = {2057-1976},
abstract = {With the increasing demand for emotion recognition technology in fields such as healthcare, humancomputer interaction, and education, efficiently and accurately decoding emotional information from EEG signals has become a research hotspot. This paper proposes a brain EEG emotion recognition model, Channel-wise Differential Entropy Transformer (CWDET), based on the combination of differential entropy (DE) features and Transformer encoder. In this method, DE features of EEG signals are first extracted in five frequency bands: δ, θ, α, β, and γ. Each channel is treated as an independent input token, and through simple but efficient embedding and positional encoding, low-dimensional information is mapped into highdimensional space. The multi-head self-attention mechanism is then employed to achieve global feature fusion across channels, effectively reducing data redundancy and computational cost. The experiments conducted on the SEED and SEED-IV datasets achieved high classification accuracies of 98.63% and 99.16%, respectively, with the model performing excellently in terms of standard deviation and stability. Further analysis of the attention weights reveals that the model automatically focuses on key brain regions such as the prefrontal area, central, and centralparietal junction. Even when selecting only a subset of channels, the model still maintained 93.44% recognition performance on the SEED-IV dataset. Comparative experiments with various existing advanced methods show that CWDET offers a simple structure and computational efficiency while maintaining high performance, providing a feasible low-resource solution for practical EEG emotion recognition applications. This work not only provides new theoretical and practical support for the development of EEG emotion recognition technology but also lays a solid foundation for future generalization research across subjects and sessions.},
}

@article {pmid41806126,
year = {2026},
author = {Bai, S and Cao, X and Xie, M and Sun, G and Wang, X and Zheng, L and Li, X and Lin, Z and Gao, L},
title = {Acoustic Flutter Processing in the Inferior Colliculus of Awake Marmosets: Complementary Rate Coding Modulated by Acoustic Parameters.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {41806126},
issn = {1995-8218},
abstract = {The acoustic flutter is processed through complementary monotonic rate coding and cannot be modulated by other acoustic parameters in the auditory cortex (AC). However, it remains unclear how the inferior colliculus (IC) encodes acoustic flutter, especially when changing other acoustic parameters. Here, we recorded IC neural activity in response to acoustic flutter and determined the existence of conjunctive processing between repetition rate and other acoustic parameters. We found that most IC neurons also encode the repetition rate at the flutter range through complementary monotonic rate coding. In addition, although the acoustic parameters did not change their monotonicity, most IC neurons encode both repetition rate and other acoustic parameters, different from the flutter processing in AC. Thus, complementary monotonic rate coding for acoustic flutter was widespread in the auditory system; however, coding specificity for repetition rate increased from IC to AC, and the capacity for conjunctive coding with other acoustic parameters decreased.},
}

@article {pmid41805787,
year = {2026},
author = {Wang, Y and Yu, X and Lu, S},
title = {Strategies for updating rules driven by reinforcement learning to solve social dilemmas.},
journal = {PloS one},
volume = {21},
number = {3},
pages = {e0341925},
pmid = {41805787},
issn = {1932-6203},
mesh = {Humans ; *Cooperative Behavior ; *Learning ; *Reinforcement, Psychology ; *Game Theory ; Algorithms ; },
abstract = {This study incorporates historical performance into traditional imitation rules and proposes a moderated strategy update rule. In this framework, an individual's temporal historical performance is calculated using the BM model. By adjusting the parameter δ, the influence of historical performance on strategy learning is determined, and the evolution of cooperation is subsequently observed. Results show that the proposed strategy update rule promotes cooperation more effectively than the traditional version, and systemic cooperation is further enhanced as δ increases. The reason why the proposed rule enhances cooperation is that it amplifies the evaluation of cooperative behavior while compressing the evaluation of defective behavior. Although establishing system objectives may hinder the diffusion of cooperative behavior, appropriate performance evaluation mechanisms can mitigate this adverse effect. Our results indicate that multidimensional evaluation can provide a theoretical basis for explaining cooperative behavior in complex environments.},
}

Humans
*Cooperative Behavior
*Learning
*Reinforcement, Psychology
*Game Theory
Algorithms

@article {pmid41804589,
year = {2026},
author = {Koc, G and Yousif, MAA and Ozturk, M},
title = {Motor and Cognitive Imagery Detection from MEG Signals Using Wavelet-Based Common Spatial Pattern Analysis.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2650022},
doi = {10.1142/S012906572650022X},
pmid = {41804589},
issn = {1793-6462},
abstract = {Brain-computer interface (BCI) technology supports the interactions of individuals with severe neuromuscular limitations with their environment. This work presents a classification approach for distinguishing motor imagery (MI) from speech-related cognitive imagery (CI) such as word generation and arithmetic subtraction, using magnetoencephalography (MEG) signals. Differentiating MI and CI/SI processes is relevant for expanding command diversity in hybrid BCI systems and for clarifying the distinct neural mechanisms underlying motor versus verbal-semantic processing. Although a large proportion of noninvasive BCI studies focus on MI, this distinction has received relatively limited attention, particularly in MEG-based approaches. Making this distinction is important for increasing command diversity in hybrid BCI systems and for improving the understanding of neural mechanisms associated with motor and verbal-semantic processing. Tasks from an open-access MEG dataset were analyzed across six binary pairs (H-F, H-W, H-S, F-W, F-S, W-S). MEG signals were processed using two frequency-separation strategies: a broad-band configuration (FSB-1: 8-14[Formula: see text]Hz and 14-30[Formula: see text]Hz) and a narrow-band configuration (FSB-2: six sub-bands between 8 and 32[Formula: see text]Hz). Time-frequency features were extracted using continuous wavelet transform (CWT), and spatial features via the common spatial pattern (CSP) method. Feature selection followed a two-stage procedure: (i) t-test ranking to obtain a shared feature set for all task pairs; and (ii) subject- and task-specific optimization of feature number. The initial evaluation based on the shared feature set showed that the FSB-2/CWT approach yielded better classification accuracies compared to FSB-1/CWT (H-F: 56%, H-W: 71%, H-S: 66% versus 54%, 68%, 64%). With subject- and task-adaptive optimization, additional improvements were observed. Accuracies increased to 60%, 72%, and 69% for FSB-1, and to 63%, 75%, and 71% for FSB-2, for H-F, H-W, and H-S, respectively. Overall, the findings indicate that the proposed CWT[Formula: see text]CSP framework, particularly when combined with adaptive feature optimization, offers an interpretable analysis approach that can contribute to MI-CI discrimination in MEG-based BCI systems under limited data conditions.},
}

@article {pmid41804086,
year = {2026},
author = {Yang, T and Cao, M and Qian, Z and Chen, J},
title = {Flavor-Oriented Brain-Computer Interface (Flavor-BCI): Neural Decoding of Eating and Sensory Perception With Emerging Applications in Food Evaluation.},
journal = {Comprehensive reviews in food science and food safety},
volume = {25},
number = {2},
pages = {e70442},
doi = {10.1111/1541-4337.70442},
pmid = {41804086},
issn = {1541-4337},
support = {82151311//National Natural Science Foundation of China/ ; 81827803//National Major Scientific Instruments and Equipment Development Project/ ; 81727804//National Major Scientific Instruments and Equipment Development Project/ ; BE2020705//Jiangsu Province Key Research and Development Program/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Taste Perception/physiology ; *Taste/physiology ; *Brain/physiology ; Eating ; },
abstract = {Flavor-induced sensory satisfaction is critical for food acceptance and market success. However, traditional sensory evaluation methods, relying heavily on subjective assessments, often fail to accurately reflect real-time, objective neural processing underlying complex multisensory flavor experiences. This limitation highlights the need for innovative methods that objectively quantify how flavors are perceived and integrated within the brain. In this review, we first examine the neural pathways underlying flavor perception, focusing on how gustatory, olfactory, and oral somatosensory inputs interact with reward and hedonic networks to form integrated flavor experience. Building on this foundation, we then outline the latest strategies for developing flavor-oriented brain-computer interface (flavor-BCI), summarizing key features of various neuroimaging techniques and associated technical implementation workflows. Finally, we assess emerging applications of flavor-BCI in sensory assessment and consumer decision-making and identify opportunities and challenges for future food design and product development. Flavor perception begins with parallel encoding of chemical stimuli in the primary gustatory and olfactory cortices and in trigeminal pathways. These signals are subsequently integrated in higher order regions, forming a distributed neural network across cortical, limbic, and subcortical structures that support flavor recognition, hedonic appraisal, and motivated eating. Flavor-BCI systems record neural activity from these regions using electrophysiology or neuroimaging and apply advanced algorithms to decode neural representations, translating them into objective sensory outputs. Relative to traditional evaluations, this approach enables real-time, precise quantification of flavor experience. Flavor-BCI thus offers promising avenues for intelligent sensory evaluation and novel human-machine interactions.},
}

Humans
*Brain-Computer Interfaces
*Taste Perception/physiology
*Taste/physiology
*Brain/physiology
Eating

@article {pmid41802861,
year = {2026},
author = {Yang, J and Jiang, H and Bai, Y and Ni, G and Teng, X},
title = {Short-Term Perceptual Training Modulates Neural Responses to Deepfake Speech but Does Not Improve Behavioral Discrimination.},
journal = {eNeuro},
volume = {},
number = {},
pages = {},
doi = {10.1523/ENEURO.0300-25.2026},
pmid = {41802861},
issn = {2373-2822},
abstract = {Rapid advancements in artificial intelligence (AI) have enabled text-to-speech (TTS) systems to produce voices increasingly indistinguishable from humans, posing significant societal risks, particularly through potential misuse in fraud and deception. To address this concern, this study combined behavioral assessments and neural measures using electroencephalography (EEG) to examine whether short-term perceptual training enhances people's ability to distinguish AI-generated from human speech. Thirty participants (of either sex) listened to sentences produced by human speakers and corresponding AI-generated clones, judging each sentence as either human or AI-generated before and after a brief (∼12-minute) training session, during which voices were explicitly labeled as "human" or "AI". Behaviorally, participants showed consistently poor discrimination before and after training, with only minimal improvement. However, neural analyses revealed substantial training-induced changes. Specifically, temporal response function (TRF) analysis identified significant neural differentiation between speech types at early (∼55 ms, ∼210 ms) and later (∼455 ms) auditory processing stages following training. Additional EEG analyses, including spectral power and decoding, were conducted to further investigate training effects, but these measures revealed limited differentiation. The findings here highlight a dissociation between behavioral and neural sensitivity: while listeners struggle to behaviorally discriminate sophisticated AI-generated voices, their auditory systems rapidly adapt to subtle acoustic differences following short-term exposure. Understanding this neural-behavioral dissociation is crucial for developing effective perceptual training protocols and informing policies to mitigate societal threats posed by increasingly realistic synthetic voices.Significance Statement Artificial intelligence (AI)-generated voices are becoming increasingly indistinguishable from real human speech, raising serious concerns about fraud as scammers can convincingly impersonate trusted individuals. Our study shows that even when listeners cannot behaviorally distinguish AI-generated voices from real human voices, brief perceptual training enables their brains to detect subtle acoustic differences. Our findings thus reveal a dissociation between neural sensitivity and behavioral performance in recognizing AI-generated speech. By identifying this gap, we highlight an important opportunity: developing specialized training programs that guide listeners to recognize and utilize these subtle differences. Such targeted training could significantly enhance people's ability to identify synthetic voices, offering potential protection against the growing risks of scams and misinformation enabled by increasingly realistic AI speech technologies.},
}

@article {pmid41799891,
year = {2026},
author = {Xiao, Y and Kellis, S and Reiche, CF and Solzbacher, F},
title = {Classifying motion states from neural activity of non-human primates for brain-computer interfaces.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1714738},
pmid = {41799891},
issn = {1662-4548},
abstract = {INTRODUCTION: Brain-computer interface (BCI) systems commonly decode neural activity from sensorimotor areas to generate continuous control signals for cursors, robotic limbs, or other effectors. Although these decoders perform well during intended movement, neural activity persists during periods of intended non-movement, which can lead to unintended effector activation and reduced control stability. Accurately identifying intended stationary states therefore represents a key component for achieving stable and reliable BCI control.

METHODS: We propose a neural-state classification framework (cpSVM) that distinguishes stationary and movement states directly from intracortical neural activity. This model combines principal component analysis, correlation-based feature selection, and a linear support vector machine classifier. Offline evaluations were performed using multi-unit recordings from the premotor and primary motor cortices of two non-human primates during a center-out cursor task. Performance was compared against a conventional kinematics-based threshold-crossing method.

RESULTS: Correlation-informed dimensionality reduction revealed a clear low-dimensional separation between stationary and movement states, supporting the selection of task-relevant neural features. The cpSVM achieved high classification performance, with mean accuracies of 0.936 and 0.930 across the two subjects. Compared with the threshold-crossing method, the cpSVM consistently improved accuracy, sensitivity, specificity, and F-score, while substantially reducing spurious state transitions and improving output continuity.

DISCUSSION: These findings demonstrate that stationary and movement states can be reliably distinguished from intracortical neural signals using a low-dimensional, correlation-informed classification approach. The proposed framework provides a promising strategy to suppress unintended effector activation and improve continuity and stability in BCI control systems.},
}

@article {pmid41798213,
year = {2026},
author = {Al-Bander, B},
title = {Editorial: Brain-Computer Interfaces (BCIs) for daily activities: innovations in EEG signal analysis and machine learning approaches.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1795349},
doi = {10.3389/fnhum.2026.1795349},
pmid = {41798213},
issn = {1662-5161},
}

@article {pmid41799923,
year = {2024},
author = {Chen, X and Wang, R and Khalilian-Gourtani, A and Yu, L and Dugan, P and Friedman, D and Doyle, W and Devinsky, O and Wang, Y and Flinker, A},
title = {A neural speech decoding framework leveraging deep learning and speech synthesis.},
journal = {Nature machine intelligence},
volume = {6},
number = {4},
pages = {467-480},
pmid = {41799923},
issn = {2522-5839},
abstract = {Decoding human speech from neural signals is essential for brain-computer interface (BCI) technologies that aim to restore speech in populations with neurological deficits. However, it remains a highly challenging task, compounded by the scarce availability of neural signals with corresponding speech, data complexity and high dimensionality. Here we present a novel deep learning-based neural speech decoding framework that includes an ECoG decoder that translates electrocorticographic (ECoG) signals from the cortex into interpretable speech parameters and a novel differentiable speech synthesizer that maps speech parameters to spectrograms. We have developed a companion speech-to-speech auto-encoder consisting of a speech encoder and the same speech synthesizer to generate reference speech parameters to facilitate the ECoG decoder training. This framework generates natural-sounding speech and is highly reproducible across a cohort of 48 participants. Our experimental results show that our models can decode speech with high correlation, even when limited to only causal operations, which is necessary for adoption by real-time neural prostheses. Finally, we successfully decode speech in participants with either left or right hemisphere coverage, which could lead to speech prostheses in patients with deficits resulting from left hemisphere damage.},
}

@article {pmid41798016,
year = {2026},
author = {Benachour, A and Medvedev, V and Zinchenko, O},
title = {Correction: Mouse-tracking as a tool for investigating strategic behavior in Public Goods Game: an experimental pilot study.},
journal = {Frontiers in psychology},
volume = {17},
number = {},
pages = {1807328},
doi = {10.3389/fpsyg.2026.1807328},
pmid = {41798016},
issn = {1664-1078},
abstract = {[This corrects the article DOI: 10.3389/fpsyg.2025.1635677.].},
}

@article {pmid41795730,
year = {2026},
author = {Li, C and Zhong, W and Li, H and Tao, Y and Huang, J and Lu, J and Zhang, X and Wu, J},
title = {Three-dimensional microsurgical anatomy of the cerebral hemisphere from medial to lateral: a fiber-dissection study.},
journal = {Acta neurochirurgica},
volume = {168},
number = {1},
pages = {},
pmid = {41795730},
issn = {0942-0940},
support = {2024AH051901//Scientific Research Project of Higher Education Institutions in Anhui Province/ ; H202429//Research Project of Wannan Medical College/ ; KF2024016//Research Project of Wannan Medical College/ ; 24dz2261500//Shanghai Science and Technology Committee，Shanghai Key Laboratory of Clinical and Translational Brain-Computer Interface Research/ ; 2023ZKZD13//Innovation Program of Shanghai Municipal Education Commission/ ; },
mesh = {Humans ; *Imaging, Three-Dimensional/methods ; Dissection/methods ; *Microsurgery/methods ; *White Matter/anatomy & histology/surgery ; *Cerebrum/anatomy & histology/surgery ; Male ; Adult ; Female ; Middle Aged ; *Gray Matter/anatomy & histology/surgery ; Aged ; Cadaver ; },
abstract = {BACKGROUND: Accurate exposure of lesions on the medial cerebral hemisphere remains technically challenging, and current imaging cannot fully depict the subcortical intricate architecture extending from the medial surface outward. Although portions of this anatomy have been described, a comprehensive topographic characterization from medial to lateral is still lacking.

OBJECTIVES: To provide a systematic, layer-by-layer topographic analysis of the white-matter fiber tracts and deep gray-matter nuclei from the medial surface to the lateral convexity of the cerebral hemisphere by combining stepwise fiber dissection with three-dimensional (3D) photography.

METHODS: Twelve adult human cerebral hemispheres, fixed in 10% formalin and prepared with the Klingler fiber-dissection technique, were examined under 6× - 40 × magnification. Dissection commenced at the medial surface and proceeded outward, exposing commissural, association, and projection fibers as well as adjacent subcortical nuclei. High-resolution stereoscopic images were captured after each stage to document 3D spatial relationships.

RESULTS: From medial to lateral, the hemisphere comprised orderly layers of commissural, association, and projection systems interwoven with deep nuclei, forming a complex but reproducible arrangement in all specimens. The study provides complete medial exposure of these structures and demonstrates consistent positional relationships among different specimens.

CONCLUSIONS: This 3D fiber-dissection study offers the layer-by-layer depiction of the cerebral hemisphere from medial to lateral, clarifying spatial relationships among key white-matter bundles and deep nuclei. The anatomic insights gained may facilitate safer, more precise neurosurgical approaches and refine understanding of hemispheric connectivity.},
}

Humans
*Imaging, Three-Dimensional/methods
Dissection/methods
*Microsurgery/methods
*White Matter/anatomy & histology/surgery
*Cerebrum/anatomy & histology/surgery
Male
Adult
Female
Middle Aged
*Gray Matter/anatomy & histology/surgery
Aged
Cadaver

@article {pmid41795668,
year = {2026},
author = {Ling, Y and Sun, P and Wang, C and Peng, G and Wang, Y and Zhou, X and He, Z and Liu, B and Zhang, J and Yu, J and Su, Y and Li, K and Guo, T and Luo, B},
title = {A novel eye-tracking digital marker outperforms plasma biomarkers in detecting cognitive impairment.},
journal = {Alzheimer's & dementia : the journal of the Alzheimer's Association},
volume = {22},
number = {3},
pages = {e71253},
pmid = {41795668},
issn = {1552-5279},
support = {2022C03064//the Key Research and Development Program of Zhejiang/ ; 2025ZFJH01//the Fundamental Research for the Central Universities/ ; 2022KY067//Medical and Health Science and Technology Project of Zhejiang Province/ ; 82422027//the National Natural Science Foundation of China/ ; U24A20340//the National Natural Science Foundation of China/ ; 82171197//the National Natural Science Foundation of China/ ; 82371484//the National Natural Science Foundation of China/ ; 2023B1515020113//Guangdong Basic and Applied Basic Science Foundation for Distinguished Young Scholars/ ; },
mesh = {Humans ; *Biomarkers/blood ; *Cognitive Dysfunction/diagnosis/blood ; Male ; Female ; Amyloid beta-Peptides/blood ; Aged ; tau Proteins/blood ; *Eye-Tracking Technology ; Positron-Emission Tomography ; Magnetic Resonance Imaging ; Neuropsychological Tests ; Aged, 80 and over ; },
abstract = {INTRODUCTION: Detecting and monitoring cognitive performance in older adults is critical. In this study, we evaluated the validity of an eye-tracking tool in diagnosing cognitive impairment.

METHODS: We recruited 119 cognitively unimpaired (CU) individuals and 157 cognitively impaired (CI) patients who completed digital eye-tracking tests and cognitive scales. Of them, 154, 120, 53, and 146 underwent plasma biomarker tests, amyloid-β positron emission tomography (Aβ-PET) scans, tau-PET scans, and magnetic resonance imaging (MRI) scans. The diagnostic performance of eye-tracking markers and their relationships to Alzheimer's disease biomarkers and cognition were examined.

RESULTS: The eye-tracking panel exhibited better performance (area under the curve [AUC] = 0.865) in classifying CI from CU compared to plasma Aβ42/40 (AUC = 0.699), p-Tau217 (AUC = 0.769), p-Tau217/Aβ42 (AUC = 0.801), glial fibrillary acidic protein (GFAP; AUC = 0.804), and neurofilament light chain (NfL) (AUC = 0.826).

DISCUSSION: These findings demonstrate the validity of digital eye-tracking markers for screening patients with cognitive impairment, providing a novel digital marker for detecting cognitive decline in older adults.},
}

Humans
*Biomarkers/blood
*Cognitive Dysfunction/diagnosis/blood
Male
Female
Amyloid beta-Peptides/blood
Aged
tau Proteins/blood
*Eye-Tracking Technology
Positron-Emission Tomography
Magnetic Resonance Imaging
Neuropsychological Tests
Aged, 80 and over

@article {pmid41793476,
year = {2026},
author = {Wang, X and Wang, D and Zhang, C and Zhang, H and Wang, W and Qian, W and Zhou, J and Zhao, Y and Gao, J and Hu, Z and Qin, J and Wang, Z and Zheng, Y and Yin, G and Dong, H},
title = {miR-214-3p exacerbates mitochondrial dysfunction in parkinson's disease: a multi-omics and mechanistic study.},
journal = {Experimental brain research},
volume = {244},
number = {4},
pages = {},
pmid = {41793476},
issn = {1432-1106},
support = {YKK23132//Nanjing Health Science and Technology Development Project/ ; SLJ0216//Leading Talent Project of Jiangsu Province Traditional Chinese Medicine/ ; YKK20102//Nanjing Health Science and Technology Development Special Fund Project/ ; M2021088//General Program of the Jiangsu Commission of Health/ ; YKK21121//Nanjing Health Science and Technology Development General Project/ ; NA2021062071//Project of Nanjing Infectious Disease Clinical Medical Center Construction/ ; RCMS23010//Talent Lift Project of Nanjing Second Hospital/ ; XZR2024043//the Natural Science Foundation Project of Nanjing University of Chinese Medicine/ ; },
abstract = {UNLABELLED: Parkinson’s disease (PD) involves the loss of dopaminergic neurons, and prodromal PD exhibits elevated miR-214-3p, suggesting its role as a biomarker and pathogenic factor. This study investigated miR-214-3p’s effects on mitochondrial function in dopaminergic SH-SY5Y cells and mouse primary cortical neurons. In SH-SY5Y cells, proteomic/transcriptomic analyses and target prediction confirmed GFM1 as a direct target of miR-214-3p. miR-214-3p upregulation downregulated GFM1, causing severe mitochondrial bioenergetic impairment: increased reactive oxygen species (ROS), reduced oxygen consumption, diminished ATP production, and decreased respiratory chain complexes (RCC) I/IV expression. Critically, restoring GFM1 reversed these mitochondrial deficits and neuronal dysfunction. In mouse primary cortical neurons, miR-214-3p overexpression also impaired RCC I/IV but did not affect GFM1, revealing a cell type-dependent regulatory mechanism. These findings demonstrate that elevated miR-214-3p impairs mitochondrial function in a cell-specific manner. In dopaminergic cells, this damage is mediated by GFM1 downregulation, highlighting the miR-214-3p/GFM1 axis as a potential cell-type specific therapeutic target for PD and related dopaminergic neuronopathies.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00221-026-07267-0.},
}

@article {pmid41792767,
year = {2026},
author = {Zhao, X and Zhou, H and Zhang, X and Tang, R and Gan, Y and Zhuang, T and Zhu, Y and Qin, Z and Chen, Y and Fu, Y and Zhang, D and Xu, L and Wang, S and Shen, Z and Hu, S and Wang, M},
title = {fMRI-guided V1-targeted rTMS improves depressive symptoms in adolescents and young adults with bipolar disorder: a double-blind randomized controlled trial.},
journal = {BMC medicine},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12916-026-04766-3},
pmid = {41792767},
issn = {1741-7015},
support = {2025KY1557//Zhejiang Province Medical and Health Science and Technology Plan Project/ ; 2024GZ86//Public Welfare Applied Research Project on Population Health (Medical & Health Focus) of Huzhou Municipal Science and Technology Bureau/ ; 226-2022-00193, 226-2022-00002, 2023ZFJH01-01, 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; LTGY23H090013//Zhejiang Provincial Basic Public Research Program/ ; 2025C01137//Zhejiang Provincial Key Research and Development Program/ ; 52407261, 82201675//National Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: Bipolar depression (BD-D) in adolescents and young adults is associated with disrupted neural circuits underlying affective regulation, particularly those involving the orbitofrontal cortex (OFC). Despite the promise of repetitive transcranial magnetic stimulation (rTMS) as a non-invasive intervention, effective targeting strategies that engage these dysfunctional circuits remain insufficiently explored. This study investigates the clinical efficacy of a novel rTMS protocol targeting the primary visual cortex (V1) node of the V1-OFC functional circuit in adolescents and young adults with BD-D.

METHODS: We conducted a double-blind randomized controlled trial. Fifty-two adolescents and young adults BD-D participants were randomized to active rTMS group (10 Hz, 100% RMT) or sham rTMS group (20% RMT) targeting the V1 region that exhibited the strongest functional connectivity with the OFC (MNI: - 12, - 81, 6). rTMS was administered over 3 weeks (5 sessions/week, 15 sessions in total), with all participants receiving adjunctive lurasidone (40-80 mg/day). The primary outcome was the change in depressive symptoms measured by the Montgomery-Åsberg Depression Rating Scale (MADRS) at baseline, week 3, and week 8. Secondary outcomes included HAMD-24, QIDS-SR, and HAMA. Resting-state fMRI was performed at baseline and after the 3-week intervention to examine changes in functional connectivity related to rTMS.

RESULTS: A total of 43 participants completed a 3-week intervention, and 37 completed the 8-week follow-up. Compared with the sham group, the active rTMS group showed significantly greater reductions in depressive symptoms. Between-group differences were significant on the primary outcome MADRS at week 8 (t(35) = - 3.595, pFDR < 0.01), with a parallel effect detected for the secondary outcome on the QIDS-SR (t(35) = - 3.653, pFDR < 0.01). HAMD-24 scores also differed significantly at week 3 (t(35) = - 3.921, pFDR < 0.01). No significant changes were found in anxiety symptoms. Resting-state fMRI indicated altered connectivity in the anterior cingulate cortex and right superior occipital gyrus, suggesting modulation of mood-related visual circuits. No severe adverse effects were reported in all participants.

CONCLUSIONS: The study preliminarily demonstrated that the navigated rTMS precisely targeting the V1-OFC circuit may be a safe and potentially effective intervention for adolescents and young adults with BD-D.

TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT05929183.},
}

@article {pmid41788543,
year = {2026},
author = {Wang, Z and Liu, X and Xie, J and Lin, Y},
title = {Evoked potentials in stroke rehabilitation: current applications, emerging technologies, and future directions.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1758767},
pmid = {41788543},
issn = {1662-4548},
abstract = {Evoked potentials (EPs) are increasingly explored as objective neurophysiological biomarkers to complement scale-based assessment in stroke rehabilitation. This narrative review summarizes current evidence on the use of somatosensory evoked potentials (SEPs), motor evoked potentials (MEPs), and event-related potentials (ERPs) for monitoring recovery and guiding therapy. We first outline the physiological basis and stroke-relevant features of each modality, then synthesize data on how EP measures relate to motor, sensory, balance, cognitive and language outcomes, with particular emphasis on longitudinal changes during rehabilitation and responses to specific interventions, including neuromuscular electrical stimulation, robot-assisted training and non-invasive brain stimulation. Emerging applications such as perturbation-evoked cortical responses for postural control, EP-based brain-computer interfaces and EP-guided or closed-loop neuromodulation are discussed, together with advances in high-density recordings, connectivity analysis, and machine-learning-based multimodal prediction models. Finally, we highlight key methodological and practical challenges-protocol heterogeneity, small single-center studies, limited trial evidence, feasibility constraints and gaps in clinical integration-and propose priorities for standardization and translational research. Overall, EPs hold substantial promise as pathway-specific, temporally precise biomarkers to enable more mechanism-informed and individualized stroke rehabilitation monitoring.},
}

@article {pmid41788540,
year = {2026},
author = {Lin, D and Zhang, Q and Chen, H and Lu, Y and Chen, H and Li, L and Mayet, AM and Zhang, G and Miao, X and Qiu, X},
title = {High accuracy EEG signal classification for brain computer interfaces using advanced neural architectures.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1752176},
pmid = {41788540},
issn = {1662-4548},
abstract = {INTRODUCTION: This study proposes advanced neural network architectures for classifying specific motor-related electroencephalography (EEG) tasks, employing deep feature extraction techniques. We analyzed EEG data from the MILimbEEG dataset, consisting of recordings from 60 individuals as they performed eight distinct motor movements: baseline with eyes open, left-hand closing, right-hand closing, dorsiflexion and plantarflexion of both the left and right feet, as well as rest periods between tasks. The high precision achieved in this study underscores the efficacy of sophisticated computational models like the GMDH network in enhancing the interpretation of EEG signals for the development of brain-computer interfaces (BCIs). This research significantly advances the potential of EEG as a reliable modality for BCIs, effectively translating brain activity into actionable commands suitable for neurorehabilitation and assistive technologies.

METHODS: For each of the 16 electrodes used in the recordings, 10 critical features were extracted, resulting in a comprehensive set of 160 features per sample that encapsulate the intricate brain activities associated with each task. A Group Method of Data Handling (GMDH) neural network, structured with eight hidden layers and a decremental arrangement of neurons from 40 in the first to 5 in the last, was utilized to classify these tasks.

RESULTS: This network configuration achieved an impressive classification accuracy of approximately 96%, demonstrating a robust capability to accurately decode EEG signals tied to specific motor actions.

DISCUSSION: The high precision achieved in this study underscores the efficacy of sophisticated computational models like the GMDH network in enhancing the interpretation of EEG signals for the development of brain-computer interfaces (BCIs). This research significantly advances the potential of EEG as a reliable modality for BCIs, effectively translating brain activity into actionable commands suitable for neurorehabilitation and assistive technologies. Our findings contribute substantially to the BCI field, promising to improve clinical outcomes by enabling more precise and effective interaction with neurorehabilitation devices.},
}

@article {pmid40329470,
year = {2025},
author = {Ge, Y and Yang, Z and Su, H and Miu, J and Xie, J and Zhao, R and Liu, S and Han, C and Zhang, S and Xu, G},
title = {The critical role of melody and harmony in sleep induction: Direct evidence from electroencephalogram-based analysis.},
journal = {Annals of the New York Academy of Sciences},
volume = {1548},
number = {1},
pages = {233-247},
doi = {10.1111/nyas.15334},
pmid = {40329470},
issn = {1749-6632},
support = {2024QCY-KXJ-189//Qinchuangyuan Project/ ; 2022KXJ-129//Qinchuangyuan Project/ ; xzd012023015//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Music ; Male ; *Sleep/physiology ; Adult ; Female ; *Sleep Initiation and Maintenance Disorders/physiopathology/therapy ; Sleep Quality ; Young Adult ; Music Therapy ; },
abstract = {Insomnia, prevalent in contemporary society, is characterized by difficulties in sleep initiation and maintenance, leading to fatigue, depression, and impaired cognitive function. Although research has demonstrated the sleep induction effects of certain musical genres, the neurophysiological mechanisms through which musical constituents, namely, melody, harmony, and rhythm, induce sleep remain inconclusive. To elucidate how musical constituents influence sleep onset, we used both subjective and objective measures, including the Pittsburgh Sleep Quality Index and Karolinska Sleepiness Scale as the former and electroencephalography (EEG) analysis as the latter. The EEG data showed that melody and harmony significantly enhance sleep quality, particularly impacting the theta band and the dispersion entropy in the temporal lobes. Conversely, the effect of rhythm in sleep induction appeared less significant, with minimal activity observed in the frontal and parietal lobes. We believe the data provide a foundation for innovative approaches to music composition that emphasize melody and harmony to enhance the therapeutic potential of music in sleep management, while de-emphasizing the role of rhythm. The findings also support adapting traditional musical forms, such as classical music, for sleep induction purposes and the development of sleep aid music composition rooted in Western music theory.},
}

Humans
*Electroencephalography/methods
*Music
Male
*Sleep/physiology
Adult
Female
*Sleep Initiation and Maintenance Disorders/physiopathology/therapy
Sleep Quality
Young Adult
Music Therapy

@article {pmid38591141,
year = {2024},
author = {Xiang, Y and Zhao, Y and Cheng, T and Sun, S and Wang, J and Pei, R},
title = {Implantable Neural Microelectrodes: How to Reduce Immune Response.},
journal = {ACS biomaterials science & engineering},
volume = {10},
number = {5},
pages = {2762-2783},
doi = {10.1021/acsbiomaterials.4c00238},
pmid = {38591141},
issn = {2373-9878},
mesh = {*Microelectrodes ; *Electrodes, Implanted ; Humans ; Animals ; Neurons/immunology/physiology ; Brain/immunology/physiology ; },
abstract = {Implantable neural microelectrodes exhibit the great ability to accurately capture the electrophysiological signals from individual neurons with exceptional submillisecond precision, holding tremendous potential for advancing brain science research, as well as offering promising avenues for neurological disease therapy. Although significant advancements have been made in the channel and density of implantable neural microelectrodes, challenges persist in extending the stable recording duration of these microelectrodes. The enduring stability of implanted electrode signals is primarily influenced by the chronic immune response triggered by the slight movement of the electrode within the neural tissue. The intensity of this immune response increases with a higher bending stiffness of the electrode. This Review thoroughly analyzes the sequential reactions evoked by implanted electrodes in the brain and highlights strategies aimed at mitigating chronic immune responses. Minimizing immune response mainly includes designing the microelectrode structure, selecting flexible materials, surface modification, and controlling drug release. The purpose of this paper is to provide valuable references and ideas for reducing the immune response of implantable neural microelectrodes and stimulate their further exploration in the field of brain science.},
}

*Microelectrodes
*Electrodes, Implanted
Humans
Animals
Neurons/immunology/physiology
Brain/immunology/physiology

@article {pmid30798604,
year = {2019},
author = {Lu, L and Fu, X and Liew, Y and Zhang, Y and Zhao, S and Xu, Z and Zhao, J and Li, D and Li, Q and Stanley, GB and Duan, X},
title = {Soft and MRI Compatible Neural Electrodes from Carbon Nanotube Fibers.},
journal = {Nano letters},
volume = {19},
number = {3},
pages = {1577-1586},
doi = {10.1021/acs.nanolett.8b04456},
pmid = {30798604},
issn = {1530-6992},
abstract = {Soft and magnetic resonance imaging (MRI) compatible neural electrodes enable stable chronic electrophysiological measurements and anatomical or functional MRI studies of the entire brain without electrode interference with MRI images. These properties are important for many studies, ranging from a fundamental neurophysiological study of functional MRI signals to a chronic neuromodulatory effect investigation of therapeutic deep brain stimulation. Here we develop soft and MRI compatible neural electrodes using carbon nanotube (CNT) fibers with a diameter from 20 μm down to 5 μm. The CNT fiber electrodes demonstrate excellent interfacial electrochemical properties and greatly reduced MRI artifacts than PtIr electrodes under a 7.0 T MRI scanner. With a shuttle-assisted implantation strategy, we show that the soft CNT fiber electrodes can precisely target specific brain regions and record high-quality single-unit neural signals. Significantly, they are capable of continuously detecting and isolating single neuronal units from rats for up to 4-5 months without electrode repositioning, with greatly reduced brain inflammatory responses as compared to their stiff metal counterparts. In addition, we show that due to their high tensile strength, the CNT fiber electrodes can be retracted controllably postinsertion, which provides an effective and convenient way to do multidepth recording or potentially selecting cells with particular response properties. The chronic recording stability and MRI compatibility, together with their small size, provide the CNT fiber electrodes unique research capabilities for both basic and applied neuroscience studies.},
}

@article {pmid24276486,
year = {1981},
author = {Singh, S},
title = {Single tester triple test cross analysis in spring wheat.},
journal = {TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik},
volume = {59},
number = {4},
pages = {247-249},
pmid = {24276486},
issn = {0040-5752},
abstract = {Two experiments, each including the same 30 homozygous varieties of spring wheat plus one separate tester variety, were conducted in order to detect epistasis and to test and estimate the additive and dominance components of genetic variation for five quantitative traits: final plant height, spike length, number of spikelets per spike, 100-kernel weight and grain yield per plant. Epistasis played a significant role in the control of 100-kernel weight and yield per plant. There was a gratifyingly good agreement between the two independent methods (2¯B1i - ¯f1i - ¯Pi and 2¯Bci - ¯F1i) used to test the presence of epistasis. In both experiments, there was a remarkably uniform high dominance ratio for most of the traits studied indicating that this test cross design is equally sensitive to both additive and dominance genetic variation.},
}

@article {pmid41788397,
year = {2026},
author = {Zhang, X and Zhang, Y and Peng, H and Deng, T},
title = {Advancing individual finger classification through a sandwich enhanced CBAM network with ultra-high-density EEG data.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1751058},
pmid = {41788397},
issn = {1662-5161},
abstract = {INTRODUCTION: Ultra-High-Density Electroencephalography (uHD EEG) has gained increasing attention for its potential in individual finger decoding. However, accurately classifying these movements remains challenging due to the subtle spatial overlaps in cortical activity, which standard architectures often fail to isolate.

METHODS: To address this, we propose the Sandwich enhanced Convolutional Block Attention Module (SCBAM). The unique sandwich structure integrates dual attention mechanisms between convolutional layers, enabling the network to more effectively refine high-dimensional spatial features.

RESULTS AND DISCUSSION: The proposed network achieves an average accuracy of 78.63 (1.56)% in binary classification across ten finger pairs in five subjects, with the highest accuracy of 85% obtained at Thumb vs. Ring. The proposed network achieves an average accuracy of 61.12 (0.95)% in five-class classification across five subjects, with a highest accuracy of 62.36% on subject S2. The five-class classification is performed using 10 binary classifiers under a one-vs.-one strategy. Notably, five-class classification of individual fingers has not been extensively explored in the current literature, particularly with high-density EEG (HDEEG) data. This study addresses this gap, offering a valuable reference for future discussions. We conduct ablation studies to investigate the individual and synergistic effects of the modules in the proposed model. The results highlight the effects of two sequential attention mechanisms in this task. We conduct comparative experiments of our proposed model against six benchmark networks. The results from SCBAM significantly outperform these established models with FBCSP features. The proposed SCBAM significantly improves accuracy in binary finger classification compared to SVM and MLP using the same uHD EEG dataset. In summary, this study presents a high-performance hybrid network for individual finger classification and highlights the potential of uHD EEG for dexterous task decoding in Brain-Computer Interfaces (BCI).},
}

@article {pmid41788395,
year = {2026},
author = {Li, J and Liu, X and Wu, X and Wang, Y and Huang, X},
title = {DSP-MCF: dual stream pre-training and multi-view consistency fine-tuning for cross-subject EEG emotion recognition.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1723907},
pmid = {41788395},
issn = {1662-5161},
abstract = {INTRODUCTION: Electroencephalogram (EEG) emotion recognition is attracting increasing attention in the field of brain-computer interface due to its strong objectivity and non-forgery. However, cross-subject emotion recognition is complicated by individual variability, limited availability of EEG data, and interference in certain channels during EEG acquisition.

METHODS: We propose a novel synergistic Dual Stream Pre-training and Multi-view Consistency Fine-tuning (DSP-MCF) framework. The DSP-MCF is based on a domain generalization architecture. The framework includes a dual stream pre-training stage, wherein the spatiotemporal encoder-decoder network extracts generalized spatiotemporal representations from masked channels and reconstructs EEG features from incomplete data. Then, a multi-view consistency loss function is proposed during the multi-view consistency fine-tuning. This loss function is essential for aligning the distribution of emotion predictions derived from various perspectives, specifically from actual and masked EEG data.

RESULTS: Experimental results demonstrate that the proposed DSP-MCF framework outperforms state-of-the-art methods in cross-subject EEG emotion recognition tasks. The model achieved an accuracy of 89.76% on the SEED dataset and 77.02% on the SEED-IV dataset.

DISCUSSION: The findings indicate that the DSP-MCF framework effectively addresses individual variability and maintains robust performance even under channel loss. By integrating spatiotemporal reconstruction with multi-view consistency, the model provides a reliable solution for handling incomplete or degraded EEG signals in practical BCI applications.},
}

@article {pmid41788156,
year = {2026},
author = {Hira, R and Isomura, Y},
title = {Technical development of two-photon optogenetic stimulation and its potential application to brain-machine interfaces.},
journal = {Neurophotonics},
volume = {13},
number = {1},
pages = {010601},
pmid = {41788156},
issn = {2329-423X},
abstract = {Over the past decade, techniques enabling bidirectional modulation of neuronal activity with single-cell precision have rapidly advanced in the form of two-photon optogenetic stimulation. Unlike conventional electrophysiological approaches or one-photon optogenetics, which inevitably activate many neurons surrounding the target, two-photon optogenetics can drive hundreds of specifically targeted neurons simultaneously, with stimulation patterns that can be flexibly and rapidly reconfigured. In this review, we trace the development of two-photon optogenetic stimulation, focusing on its progression toward implementations in large field of view two-photon microscopes capable of targeted multi-neuron control. We highlight three principal strategies: spiral scanning, temporal focusing, and three-dimensional computer-generated holography, along with their combinations, which together provide powerful tools for causal interrogation of neural circuits and behavior. Finally, we discuss the integration of these optical technologies into brain-machine interfaces, emphasizing both their transformative potential and the technical challenges that must be addressed to realize their broader impact.},
}

@article {pmid41787678,
year = {2026},
author = {Lei, J and Zhong, S and Fan, R and Shu, X and Wang, G and Guo, J and Xue, S and Zheng, L and Ren, A and Ji, J and Yang, B and Duan, S and Wang, Z and Guo, X},
title = {The Ubiquitin Ligase Zinc Finger SWIM Domain-Containing Protein 8 Regulates Oligodendrocyte Development Through the Argonaute2/MicroRNA-7 Axis.},
journal = {Glia},
volume = {74},
number = {5},
pages = {e70142},
doi = {10.1002/glia.70142},
pmid = {41787678},
issn = {1098-1136},
support = {31671039//National Natural Science Foundation of China/ ; 32071257//National Natural Science Foundation of China/ ; 2016YFA0501000//National Key Research and Development Program of China/ ; 2023YFF1204400//National Key Research and Development Program of China/ ; },
abstract = {Proteostasis of proteins with intrinsically disordered regions (IDRs) is of particular importance to the development and function of the central nervous system (CNS). The conserved ZSWIM8 ubiquitin ligase, an essential regulator of mammalian brain development, is known to target IDR proteins involved in neuronal cell migration. Here we show that ZSWIM8 is also indispensable for oligodendrocyte maturation and myelination in the CNS. Loss of ZSWIM8 in the brain causes gross accumulation of IDR-rich proteins including many RNA-binding proteins (RBPs). Substrate recognition by ZSWIM8 requires its own IDRs, while ZSWIM8-mediated ubiquitination of AGO2 also depends on microRNA binding. AGO2 stabilization in ZSWIM8-null tissues disrupts target-directed microRNA degradation (TDMD) of MiR7, leading to altered gene expressions and myelination defects in vivo. Together, these results not only establish ZSWIM8 as a versatile regulator of IDR proteins but also highlight the crucial roles of RBP/miRNA homeostasis in oligodendrocyte development.},
}

@article {pmid41787487,
year = {2026},
author = {Chen, X and Qi, R and Zou, H and Jiang, L and Yang, B},
title = {Effect of Tai Chi Yunshou motor imagery training on upper limb motor dysfunction with stroke patients.},
journal = {BMC complementary medicine and therapies},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12906-026-05327-0},
pmid = {41787487},
issn = {2662-7671},
support = {2024YFF1206500//National Key Research and Development Program of China/ ; No. 32571279//National Natural Science Foundation of China/ ; No. 25YL1900100//Science and Technology Commission of Shanghai Municipality/ ; },
}

@article {pmid41787482,
year = {2026},
author = {Wang, Z and Nan, J and Zhou, Y and Liu, J and Liu, S and Xu, M and He, F and Chen, L and Ming, D},
title = {EEG based multifunctional connectivity fusion across frequency bands and parameters promote motor function assessment in stroke: a pilot study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-026-01914-x},
pmid = {41787482},
issn = {1743-0003},
support = {62376190//National Natural Science Foundation of China/ ; 62476193//National Natural Science Foundation of China/ ; 25ZXZSSS00020//Tianjin Science and Technology Program - State Key Laboratory Major Special Project/ ; },
}

@article {pmid41787382,
year = {2026},
author = {Yan, C and Liu, Y and Zhao, J and Bao, M and Zhou, Q and Feng, S and Li, H and Pan, G and Yao, L and Wang, Y},
title = {Integrating single-channel EEG neurofeedback into video game-based digital therapeutics for ADHD.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-026-01918-7},
pmid = {41787382},
issn = {1743-0003},
support = {32500952//National Natural Science Foundation of China/ ; 62336007//National Natural Science Foundation of China/ ; 2023C03003//Key Research and Development Program of Zhejiang/ ; ZJU-GENSCI2024YB003//Zju-GenSci Children's Health Research and Development Center/ ; 2021ZD0200400//STI 2030-Major Projects/ ; SN-ZJU-SIAS-002//Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study/ ; },
abstract = {BACKGROUND: Digital therapeutics have emerged as a promising non-pharmacological intervention for children with attention-deficit/hyperactivity disorder (ADHD). Personalized adaptation is key to the success of digital therapeutics. However, most existing systems depend solely on observable performance rather than real-time internal attentional state, which lead to misinterpretation or delayed adaptation.

METHODS: In this study, we evaluated the effects of a tablet-based attention training game with and without EEG-informed real-time neurofeedback in children with ADHD. Participants were assigned to one of two groups: a neurofeedback group (NFb) in which the game adapted in real time based on single-channel frontal EEG signals and a standard game intervention group without neurofeedback (n-NFb). Attention and cognitive control were assessed before and after a one-month intervention.

RESULTS: All children showed improvements in attention in both parent report and children's performance in attentional tasks. The NFb group showed greater improvements in hitting accuracy (go trials) and less reductions in inhibition accuracy (no-go trials) than the n-NFb group. Both groups had significantly shorter reaction times after training. EEG analyses revealed greater improvement in attention index during training for NFb group.

CONCLUSION: Our findings suggest that video game-based digital therapeutics with EEG-informed real-time neurofeedback can effectively enhance attention in children with ADHD. The results support the potential of using adaptive neurofeedback with portable devices to enhance intervention effects.},
}

@article {pmid41785933,
year = {2026},
author = {Scalabrini, A and Palladini, M and Poletti, S and Vai, B and Calesella, F and Paolini, M and Gulino, G and Masoumi, S and Zanardi, R and Colombo, C and Northoff, G and Benedetti, F},
title = {Shift to the core: Abnormal core-periphery global topography in unipolar and bipolar depression.},
journal = {Journal of affective disorders},
volume = {405},
number = {},
pages = {121550},
doi = {10.1016/j.jad.2026.121550},
pmid = {41785933},
issn = {1573-2517},
abstract = {This study explores the global signal topography of core and periphery brain networks in Major Depressive Disorder (MDD), Bipolar disorder (BD-Dep) and healthy controls (HC) using resting-state fMRI. In a sample of 140 depressed MDD and BD patients, and 70 HC, we observed a significant shift toward increased activity in the transmodal-core regions (e.g., default mode network, frontoparietal network) at the expense of unimodal-periphery regions (e.g., visual, sensory-motor cortices) in both depressed MDD and BD patients compared to HC. Whole brain machine learning analyses further demonstrated that altered global signal dynamics can effectively distinguish MDD and BD from HC (ACC = 79% and 77% respectively). Notably, we identified a significant negative correlation between global signal correlation in unimodal-periphery networks and depressive symptom severity. Additionally, in a smaller sample of BD during mania (N = 22) a distinct topographic pattern was observed, with increased global representation in the unimodal-periphery compared to depressive states, suggesting mood state-dependent shifts in network organization. To assess multivariate discriminability across diagnostic groups, a Partial Least Squares (PLS) analysis revealed that higher Core and related network activity (DMN, FPN) predicted diagnostic assignment to MDD and BD-Dep, whereas higher Periphery and related network (e.g., visual and sensory-motor networks) predicted assignment to BD-Man and HC. The Core-Periphery (C-P) ratio emerged as the strongest predictor (VIP = 1.65). These results underscore the critical role of global signal topography in mood disorders, particularly the imbalance between core and peripheral brain networks, as a potential neurobiological marker for depressive states.},
}

@article {pmid41785382,
year = {2026},
author = {Kilwein, TM and Curry, KA and Sutcliffe, J and Manzler, CA},
title = {Beyond Averages: Uncovering Within-Person Links Between Sleep and Performance in Division I Collegiate Football Players.},
journal = {Research quarterly for exercise and sport},
volume = {},
number = {},
pages = {1-8},
doi = {10.1080/02701367.2025.2608371},
pmid = {41785382},
issn = {2168-3824},
abstract = {A robust body of research links greater sleep duration and quality to improved athletic performance and competitive outcomes. However, many athletes, particularly collegiate football players, struggle to achieve optimal sleep and accurately assess its quality. Despite the known sleep-performance relationship, little is known about how these variables manifest in real time among student-athletes. This study examined daily associations between objectively measured sleep and athletic performance in National Collegiate Athletic Association Division I football players. Sixty-five athletes aged 17 to 23 years (M = 19.88, SD = 1.41), representing a range of academic years and position groups, wore sensor-based devices over a three-week period to capture sleep metrics (sleep efficiency, latency, and total sleep time) and performance indicators (maximum acceleration, maximum velocity, and explosive movement). Multilevel modeling revealed no significant between-person effects, suggesting that athletes who slept better on average did not necessarily perform better on average. However, within-person analyses indicated that nights with longer sleep latency (estimate, -.007; 95% BCI, -.013, -.003) or lower sleep efficiency (estimate, .005; 95% BCI, .001, .010) predicted reduced maximum acceleration the next day. Conversely, days with lower maximum acceleration predicted shorter sleep latency (estimate, 6.869; 95% BCI, 3.998, 9.269) and higher sleep efficiency (estimate, -5.289; 95% BCI, -10.170, -1.027) that night. These findings underscore a dynamic, bidirectional relationship between sleep and performance at the daily level and highlight the need for individualized, athlete-centered sleep interventions that extend beyond sleep duration to include routine assessment, comprehensive education, and strategies to mitigate sleep disruptors.},
}

@article {pmid41784218,
year = {2026},
author = {Li, J and Guo, C and Zhang, C and Chang, EF and Li, Y},
title = {High-fidelity neural speech reconstruction through an efficient acoustic-linguistic dual-pathway framework.},
journal = {eLife},
volume = {14},
number = {},
pages = {},
pmid = {41784218},
issn = {2050-084X},
support = {32371154//National Natural Science Foundation of China/ ; 2025ZD0217000//National Science and Technology Major Project/ ; 24QA2705500//Science and Technology Commission of Shanghai Municipality/ ; LG-GG-202402-06//Lin Gang Laboratory/ ; LGL-1987-18//Lin Gang Laboratory/ ; },
mesh = {Humans ; Electrocorticography ; *Speech/physiology ; *Brain-Computer Interfaces ; *Linguistics/methods ; Speech Intelligibility ; },
abstract = {Reconstructing speech from neural recordings is crucial for understanding human speech coding and developing brain-computer interfaces (BCIs). However, existing methods trade off acoustic richness (pitch, prosody) for linguistic intelligibility (words, phonemes). To overcome this limitation, we propose a dual-path framework to concurrently decode acoustic and linguistic representations. The acoustic pathway uses a long-short term memory (LSTM) decoder and a high-fidelity generative adversarial network (HiFi-GAN) to reconstruct spectrotemporal features. The linguistic pathway employs a transformer adaptor and text-to-speech (TTS) generator for word tokens. These two pathways merge via voice cloning to combine both acoustic and linguistic validity. Using only 20 min of electrocorticography (ECoG) data per human subject, our approach achieves highly intelligible synthesized speech (mean opinion score = 4.0/5.0, word error rate = 18.9%). Our dual-path framework reconstructs natural and intelligible speech from ECoG, resolving the acoustic-linguistic trade-off.},
}

Humans
Electrocorticography
*Speech/physiology
*Brain-Computer Interfaces
*Linguistics/methods
Speech Intelligibility

@article {pmid41781649,
year = {2026},
author = {Tang, X and Wu, H and Li, S and Bezerianos, A and Hu, R and Chen, Z and Wang, H},
title = {Time-frequency-spatial channel attention network for semantic decoding: an exploratory EEG study.},
journal = {Medical & biological engineering & computing},
volume = {},
number = {},
pages = {},
pmid = {41781649},
issn = {1741-0444},
support = {2024ZDJS033//Key Discipline Research Capability Enhancement Project in Guangdong Province/ ; 2025KCXTD048//Guangdong University Innovation Team Program/ ; },
abstract = {Semantic decoding is a crucial approach for investigating the neural mechanisms underlying language processing and representation. Informed by brain-computer interface (BCI) technology, this study investigated methods for decoding semantic information, with an emphasis on the neural representations of semantics in language perception. Due to the limited availability of electroencephalography (EEG) datasets containing Chinese linguistic stimuli, we have specifically designed a semantic task paradigm as a promising attempt to decode language comprehension and expression in patients with aphasia using scalp EEG. This paradigm fully incorporates the processes underlying both speech perception and speech imagery by adopting tasks such as overt speech perception and silent speech imagery. Firstly, Seventeen participants of aphasia patients and healthy subjects were recruited for EEG data collection. Secondly, we constructed a deep learning model termed Time-Frequency-Spatial Channel Attention Network (TFSANet), which processes both time-domain and frequency-domain features to extract key neural signatures associated with semantics. By optimizing the model and employing multidimensional feature extraction mechanisms, we significantly improved the model's ability to decode semantically relevant EEG features. Finally, the experimental results demonstrate the proposed TFSANet could decode semantic information from EEG for ten categories of four-word phrases under an "auditory-guided" paradigm with an accuracy of 60.73% and 75.09% for aphasia patients and healthy subjects respectively.},
}

@article {pmid41781440,
year = {2026},
author = {Priori, S and Ricci, P and Consoli, D and Micheli, A and Merlini, A and Andriulli, FP},
title = {A visual imagery paradigm for BCI strategies using imagined flickering patterns.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-41324-6},
pmid = {41781440},
issn = {2045-2322},
support = {101046748//HORIZON EUROPE European Innovation Council/ ; ANR-10-LABX-07-01//Agence Nationale de la Recherche/ ; },
abstract = {Steady state visually evoked potentials (SSVEPs) are a popular type of control signals in brain-computer interfaces (BCIs), in which they are typically elicited by observing a visual stimulus flashing at a specific frequency. For some patients, using SSVEP as control signal for a BCI can be difficult, for instance if they are unable to focus their gaze over the visual stimuli. To address this issue, some approaches were presented to design a gaze-independent SSVEP-controlled BCI but some difficulties have been reported, for instance for patients suffering from locked-in syndrome. In this work we employ a visual imagery (VI) signal, in which the visual stimulus is imagined instead of observed, to drive a BCI system and offer an alternative for patients that encounter issues with standard SSVEP approaches. We tested the proposed approach with 20 untrained subjects within a 3-classes BCI resulting in an offline classification accuracy of 60.93%. These results demonstrate how this gaze-independent BCI can be used by inexperienced BCI users.},
}

@article {pmid41780622,
year = {2026},
author = {Kromm, M and Branco, MP and Raemaekers, M and Ramsey, NF},
title = {Optimal Location for Gesture Decoding in the Sensorimotor Cortex and Implications for Brain-Computer Interface Research.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121837},
doi = {10.1016/j.neuroimage.2026.121837},
pmid = {41780622},
issn = {1095-9572},
abstract = {Implantable brain-computer interfaces (iBCIs) aim to restore communication in individuals with severe motor impairments. For good iBCI performance, it is important to target an optimal location. In this study, we used high-resolution 7-Tesla functional magnetic resonance imaging (fMRI) to map the spatial distribution of brain activity that can discriminate between a large number of hand gestures. Ten able-bodied participants performed 20 different unimanual hand gestures. Using support vector machines, we measured decodability across the cortex. The highest decoding performance was achieved in the hand region of the sensorimotor cortex. Moreover, we found that a subset of six well-distinguishable gestures could predict the optimal decoding location for the full set, suggesting that a carefully chosen subset can effectively guide pre-implantation mapping. Furthermore, while significant decoding was possible from sulcal as well as gyral regions of the precentral cortex, our analyses revealed that the sulcal area did not contribute unique information beyond that found in adjacent gyral regions. Similarly, decoding in the postcentral cortex was primarily driven by the gyrus. This indicates that surface recordings may suffice for iBCIs. Together, these findings offer practical guidance for future iBCI electrode placement, with the potential to improve communication and autonomy for individuals with severe motor impairments.},
}

@article {pmid41780166,
year = {2026},
author = {Wellman, SM and Guzman, K and Suematsu, N and Thai, T and Tung, TH and Padilla, CG and Sridhar, S and Chen, K and Cambi, F and Kozai, TDY},
title = {Oligodendrocyte-specific Fus depletion preserves CA1 single-unit fidelity and stabilizes network dynamics during chronic recording.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae4d8b},
pmid = {41780166},
issn = {1741-2552},
abstract = {OBJECTIVE: Loss of oligodendrocytes (OLs) and myelin impairs neuronal firing and network stability, whereas enhancing oligodendrogenesis with clemastine improves electrophysiological stability in cortex and, to a lesser extent, hippocampus. Conditional depletion of Fus in OLs (FusOLcKO) drives developmentally regulated increases in myelin thickness via enhanced cholesterol biosynthesis. Here, we investigated whether Fus-depleted OLs differentially affect long-term extracellular recordings across cortical layers and hippocampal CA1.

APPROACH: We performed chronic electrophysiological recordings in visual cortex and CA1 of FusOLcKO mice and littermate controls over 16 weeks, combined with endpoint histology.

MAIN RESULTS: In FusOLcKO mice, visually-evoked single-unit detectability and firing rate in CA1 increased relative to wild-type littermates, whereas cortical recordings showed no improvement. At the population level, FusOLcKO cortex exhibited reduced firing rates and lower functional connectivity, indicating altered network dynamics. Post-mortem analysis revealed higher neuron density in recorded cortical regions acutely and greater excitatory synapse density in CA1 of FusOLcKO mice without significant changes in myelin profiles.

SIGNIFICANCE: Fus depletion in OLs enhances chronic hippocampal recordings but disrupts cortical network communication. These region-dependent effects highlight a differential role of OLs in supporting single-cell reliability versus population-level dynamics, offering novel insights into the interplay between oligodendrocytes, neural networks, and recording stability.},
}

@article {pmid41780162,
year = {2026},
author = {Daly, I and Withanage, R and Oliveira, J and Barbera, T and Tallent, J},
title = {Brain state dependent repetitive transcranial magnetic stimulation improves motor learning outcomes.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae4dbe},
pmid = {41780162},
issn = {1741-2552},
abstract = {Objective Motor learning is key to successful neuro-rehabilitation. Combinations of Brain-Computer Interfaces (BCIs) and repetitive transcranial magnetic stimulation (rTMS) have been proposed for neurorehabilitation following conditions such as stroke. However, rTMS is typically delivered via a fixed protocol without taking into consideration the current brain states of participants. We propose a new BCI-based rTMS delivery protocol for supporting motor learning. Specifically, we propose BCI-based brain state dependent delivery of rTMS, in which a BCI system measures the event-related desynchronisation (\ERD; a neural marker of motor learning in the alpha band, selected because it is a robust, well-established real-time EEG correlate of motor activity and cortical excitability) in order to determine when to deliver rTMS. Approach We compare our proposed rTMS delivery protocol with two state of the art comparable protocols: delivery of rTMS prior to the BCI-based motor learning and delivery of rTMS at fixed times throughout the experiment, as well as a control condition in which no rTMS was used. Each protocol is tested with a different group (n=8) of participants (n=32 total participants). Main Results Our results reveal a significant effect of changing the rTMS delivery protocol ($p=0.005$) and that our proposed rTMS delivery protocol delivers better motor learning outcomes than other state of the art rTMS delivery protocols (e.g. BCI group vs. fixed times group: p=0.003, BCI group vs. no rTMS group: p=0.03). Inspection of ERD dynamics from each of our participant groups demonstrates that our BCI-based rTMS paradigm keeps corticospinal excitability relatively stable throughout the learning period, keeping the brain in a more optimal learning state for longer. Significance These findings suggest potential applications for adaptive rTMS-BCI systems in clinical neurorehabilitation, sports skill learning, and neuroprosthetic control.},
}

@article {pmid41779656,
year = {2026},
author = {Zhang, R and Guo, X and Pan, Y and Gao, S},
title = {STAND-Net: A Spiking Temporal Attention autoeNcoDer Network for Efficient EEG Artifact Removal.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3670141},
pmid = {41779656},
issn = {2168-2208},
abstract = {Electroencephalography (EEG)-based brain computer interface (BCI) systems hold significant promise across diverse applications; however, their performance is compromised by pervasive physiological artifacts that degrade signal fidelity. While current deep neural networks (DNNs) improve artifact rejection, their high computational cost precludes deployment in wearable BCIs systems. Here, we introduce STAND-Net (Spiking Temporal Attention autoeNcoDer Network), a neuromorphic architecture that leverages event-driven spiking neurons to achieve ultra-efficient, high-fidelity EEG artifact removal. STAND-Net combines a spike-convolution encoder-decoder with leaky integrate-and-fire neurons to model spatiotemporal EEG dynamics, a dilation-enhanced residual backbone capturing long-range dependencies, and a spike-rate attention mechanism dynamically localizing artifacts via neuronal firing patterns. The system demonstrates >3.7 dB improvement in signal-to-distortion ratio over state-of-the-art methods across diverse artifacts while consuming 97.98% less power than comparable DNNs. Crucially, downstream BCI classification accuracy increased by 6.64% using STAND-Net-processed signals. This work establishes a neuromorphic framework for low-power and high quality EEG artifact removal in wearable BCI systems.},
}

@article {pmid41776620,
year = {2026},
author = {Kim, KT and Jeong, JH and Sung, DJ and Lee, JY and Kim, L and Kim, DJ and Kim, SJ and Kim, H and Lee, SJ},
title = {Motor imagery BCI enables more practical and user-friendly exoskeleton control than smartwatch for users with spinal cord injury: a preliminary study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-026-01924-9},
pmid = {41776620},
issn = {1743-0003},
support = {2017-0-0043//Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean Government (Development of Non-Invasive Integrated BCI SW Platform to Control Home Appliances and External Devices by User's Thought via AR/VR Interface)/ ; 2017-0-0043//Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean Government (Development of Non-Invasive Integrated BCI SW Platform to Control Home Appliances and External Devices by User's Thought via AR/VR Interface)/ ; 2017-0-0043//Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean Government (Development of Non-Invasive Integrated BCI SW Platform to Control Home Appliances and External Devices by User's Thought via AR/VR Interface)/ ; 2017-0-0043//Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean Government (Development of Non-Invasive Integrated BCI SW Platform to Control Home Appliances and External Devices by User's Thought via AR/VR Interface)/ ; 2017-0-0043//Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean Government (Development of Non-Invasive Integrated BCI SW Platform to Control Home Appliances and External Devices by User's Thought via AR/VR Interface)/ ; 2017-0-0043//Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean Government (Development of Non-Invasive Integrated BCI SW Platform to Control Home Appliances and External Devices by User's Thought via AR/VR Interface)/ ; 2017-0-0043//Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean Government (Development of Non-Invasive Integrated BCI SW Platform to Control Home Appliances and External Devices by User's Thought via AR/VR Interface)/ ; RS-2024-00417959//National Research Foundation funded by the Korean government (Ministry of Science and ICT)/ ; RS-2024-00417959//National Research Foundation funded by the Korean government (Ministry of Science and ICT)/ ; RS-2024-00417959//National Research Foundation funded by the Korean government (Ministry of Science and ICT)/ ; RS-2024-00417959//National Research Foundation funded by the Korean government (Ministry of Science and ICT)/ ; RS-2024-00417959//National Research Foundation funded by the Korean government (Ministry of Science and ICT)/ ; RS-2024-00417959//National Research Foundation funded by the Korean government (Ministry of Science and ICT)/ ; RS-2024-00417959//National Research Foundation funded by the Korean government (Ministry of Science and ICT)/ ; },
}

@article {pmid41776592,
year = {2026},
author = {Zhong, S and Tang, X and Cheng, X and Pan, Y},
title = {Bodily maps of subject-specific feelings and academic emotions among high school students.},
journal = {BMC psychology},
volume = {},
number = {},
pages = {},
doi = {10.1186/s40359-026-04283-1},
pmid = {41776592},
issn = {2050-7283},
support = {24YJC190006//Humanities and Social Sciences Research Project of the Ministry of Education of China/ ; 226-2025-00127//Fundamental Research Funds for the Central Universities/ ; 62577047//National Natural Science Foundation of China/ ; LMS25C090002//Zhejiang Provincial Natural Science Foundation of China/ ; },
}

@article {pmid41775059,
year = {2026},
author = {Wu, R and Berezutskaya, J and Freudenburg, ZV and Ramsey, NF},
title = {Across-speaker articulatory reconstruction from sensorimotor cortex for generalizable brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae4cca},
pmid = {41775059},
issn = {1741-2552},
abstract = {OBJECTIVE: Speech brain-computer interfaces (BCIs) can restore speech features like articulatory movements from brain activity. However, for individuals with vocal tract paralysis, lack of articulatory movements can pose a challenge for speech BCI development. To address this challenge, our study aims at extracting generalizable articulatory features from a group of native Dutch speakers and reconstructing these features from brain data of a separate group of able-bodied individuals.

APPROACH: We applied a tensor component analysis (TCA) model to extract generalisable articulatory features from a publicly available articulatory movement dataset. To reconstruct articulatory features from the brain, we analyzed data of three able-bodied participants P1, P2 and P3 with high-density electrocorticography (HD-ECoG) electrode arrays implanted over the sensorimotor cortex. For each participant, a separate TCA model was applied to extract neural features. A gradient boosting regression model was used to reconstruct articulatory features from neural features. Reconstruction performance was measured as Pearson's correlation coefficient (PCC) between the reconstructed and the generalizable articulatory features.

RESULTS: The extracted articulatory features showed even contributions across participants, indicating that these features captured generalizable articulatory kinematic patterns. By using these features, we were able to reconstruct articulatory features from brain data. PCC between the reconstructed and original articulatory features were significantly above chance for all three participants, with mean PCCs of 0.80, 0.75 and 0.76 for P1, P2 and P3 respectively.

SIGNIFICANCE: With the rapid development of speech BCI, our research demonstrates that speech-related articulatory features can be restored from HD-ECoG signal using generalizable articulatory features derived from able-bodied individuals. With the potential to reconstruct audio or speech-related facial movements from the reconstructed articulatory features, our framework may provide a new way for developing speech BCIs for people unable to produce mouth movements.},
}

@article {pmid41774933,
year = {2026},
author = {Zhu, X and Yin, G and Shi, D and Wang, L and Yan, J and Feng, D and Wei, Z and Wang, Y and Wang, B and Tan, S and Zhao, Y},
title = {MAGCANet: A multiscale adaptive graph-convolutional attention network for MI-EEG decoding.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ae4c94},
pmid = {41774933},
issn = {2057-1976},
abstract = {Motor imagery EEG (MI-EEG) decoding remains challenging due to low signal-to-noise ratios and pronounced inter-subject variability. Although end-to-end deep models reduce reliance on manual feature engineering, many existing architectures may introduce temporal leakage through non-causal operations and often rely on fixed spatial topologies that cannot accommodate subject- and trial-specific connectivity patterns. Approach. We propose MAGCANet, which integrates five core components: (i) a Multiscale Causal Convolution Module (MCCM) for hierarchical temporal encoding under explicit causal constraints, (ii) a Temporal Convolution Module (TCM) to capture complex temporal dynamics, (iii) an Adaptive Graph Convolution Module (AGCM) for sample-specific topology learning in latent space, (iv) a Multi-Head Self-Attention Module (MHSAM) for global feature aggregation, and (v) a Classification Block for final decision making. Together, these components enforce temporal causality, adapt spatial interactions to individual dynamics, and produce discriminative representations robust to inter-subject variability. Results. On the BCI Competition IV-2a and IV-2b datasets, MAGCANet achieves strong single-subject accuracies of 88.58\% and 91.13\%, respectively. Under Leave-One-Subject-Out (LOSO) evaluation, the model maintains accuracies of 70.49\% and 79.49\%, demonstrating competitive and stable cross-subject generalization. MAGCANet is highly lightweight, with only 0.0194M parameters, and achieves low inference latency (2.23 ms). Qualitative analyses, including feature clustering and channel occlusion, further highlight the model's interpretability and its ability to capture relevant EEG patterns. Significance. MAGCANet provides a robust and interpretable solution for MI-EEG decoding, balancing high precision with computational efficiency, and offering a reliable method for real-time BCI applications.},
}

@article {pmid41772895,
year = {2026},
author = {Sun, WB and Xu, JJ and Chen, YL and Feng, ZC and Li, HF and Chen, DF and Wu, ZY},
title = {Heterozygous Loss-of-Function Variants of KCNJ10 Cause Paroxysmal Kinesigenic Dyskinesia.},
journal = {Movement disorders : official journal of the Movement Disorder Society},
volume = {},
number = {},
pages = {},
doi = {10.1002/mds.70247},
pmid = {41772895},
issn = {1531-8257},
support = {81330025//National Natural Science Foundation of China/ ; 82101526//National Natural Science Foundation of China/ ; 82301421//National Natural Science Foundation of China/ ; 188020-193810101/089//Research Foundation for Distinguished Scholar of Zhejiang University/ ; },
abstract = {BACKGROUND: Heterozygous variants of potassium inwardly rectifying channel subfamily J member 10 (KCNJ10) were previously reported to be enriched in several patients with paroxysmal kinesigenic dyskinesia (PKD).

OBJECTIVES: The aim was to confirm the pathogenesis of KCNJ10 variants and the relationship between KCNJ10 variants and PKD phenotypes.

METHODS: The whole-exome sequencing followed by Sanger sequencing were used to screen the potential pathogenic KCNJ10 variants in a cohort of PKD patients. Functional studies were performed to check the pathogenicity of the variants. The clinical characteristics of KCNJ10-related PKD patients reported to date were reviewed.

RESULTS: Five heterozygous KCNJ10 variants including c.76C>T (p.R26*), c.436C>T (p.L146F), c.484A>G (p.T162A), c.524G>A (p.R175Q), and c.923del (p.G308Afs*17), were detected in five pedigrees and three sporadic patients. All variants had extremely low frequency in normal populations and were highly conserved between species. They influenced the location or expression of potassium inwardly rectifying channel (Kir) 4.1 and resulted in the Kir currents of cell decreased to varied degrees. Up to date, 31 KCNJ10 variants had been reported to manifest as PKD, and a significant majority (22/31, 71%) were in the cytoplasmic domain near the C-terminus. Notably, the KCNJ10-related PKD patients showed a pronounced male predominance.

CONCLUSIONS: The study confirmed the correlation between PKD and the loss-of-function of Kir4.1 resulted from heterozygous KCNJ10 variants. The distribution bias of PKD-related KCNJ10 variants as well as the male predominance in affected individuals shed light on the mechanism investigation of this subtype of PKD. © 2026 International Parkinson and Movement Disorder Society.},
}

@article {pmid41771420,
year = {2026},
author = {Lim, J and Wang, PT and Sohn, WJ and Lin, D and Thaploo, S and Bashford, L and Bjanes, DA and Nguyen, A and Gong, H and Armacost, M and Shaw, SJ and Kellis, S and Lee, B and Lee, D and Heydari, P and Andersen, RA and Nenadic, Z and Liu, CY and Do, AH},
title = {Real-Time Brain-Computer Interface Control of Walking Exoskeleton with Bilateral Sensory Feedback.},
journal = {Brain stimulation},
volume = {},
number = {},
pages = {103065},
doi = {10.1016/j.brs.2026.103065},
pmid = {41771420},
issn = {1876-4754},
abstract = {PURPOSE: Brain-computer interfaces (BCIs) offer a pathway to restore ambulation in indi-viduals with spinal cord injury (SCI). However, existing BCI systems for gait are unidirectional and lack sensory feedback. This study aimed to demonstrate that a bidirectional brain-computer interface (BDBCI) can simultaneously enable real-time brain-controlled walking and artificial leg sensation via electrical stimulation of the sensory cortex.

METHODS: Epilepsy patients undergoing bilateral interhemispheric subdural electrocorticog-raphy (ECoG) implantation were recruited for this proof-of-concept study. Motor mapping identified electrodes in the leg motor cortex for decoding stepping intent, while sensory stimu-lation mapping determined stimulation sites in the somatosensory cortex to elicit artificial leg percepts. A custom embedded BDBCI decoded motor intent in real time to actuate a robotic gait exoskeleton (RGE) from ECoG signals and delivered leg swing sensory feedback via direct cortical stimulation. Performance was assessed through correlations between cued and decoded states, sensory reliability tasks, and control experiments.

RESULTS: One subject was recruited and achieved a high decoding performance (ρ = 0.92 ± 0.04, lag of 3.5 ± 0.5 s) across 10 runs of operating the BDBCI-controlled RGE. Bilateral leg percepts were validated through a blind step-counting task (92.8% accuracy, p < 10[-6]). Control experiments verified that decoding was not affected by stimulation artifacts. No adverse events were reported.

DISCUSSION: This study establishes the feasibility of an embedded system BDBCI for restor-ing both motor control and artificial sensation of walking. Leveraging interhemispheric leg sen-sorimotor cortices is safe and yields superior decoding compared to prior lateral brain convexity approaches. These findings provide a foundation for translating BDBCI technology into fully implantable systems for SCI patients with paraplegia.},
}

@article {pmid41770462,
year = {2026},
author = {Chen, J and Li, YW and Yao, ST and Huang, YH and Ma, WM and Li, ZH and Lan, Y and Xu, GQ and Ding, Q},
title = {The Influence of M1 and DLPFC iTBS on BCI Performance: A TMS and fNIRS Study.},
journal = {Translational stroke research},
volume = {17},
number = {2},
pages = {},
pmid = {41770462},
issn = {1868-601X},
support = {82472619 (YL)//National Natural Science Foundation of China/ ; 82072548 (GX), 82272588 (GX)//National Natural Science Foundation of China/ ; 82102678 (QD)//National Natural Science Foundation of China/ ; 202206010197 (YL) and 202201020378 (YL)//Guangzhou Municipal Science and Technology Program/ ; 2024A04J3082 (QD)//Guangzhou Municipal Science and Technology Program/ ; 2022YFC2009700 (YL)//Natural Key Research and Development Program of China/ ; A2024500(QD)//Guangdong Medical Research Foundation/ ; },
abstract = {Brain-computer interface (BCI) control inefficiency often occurs in stroke survivors due to insufficient sensorimotor activity generated during motor imagery. Previous studies focused on upregulating excitability of primary motor cortex (M1) alone. Dorsolateral prefrontal cortex (DLPFC), an important region for motor imagery, may be effective for improving BCI performance. This study is aimed at investigating how intermittent theta burst stimulation (iTBS) targeted on M1 and DLPFC influences BCI performance and its neural mechanisms.25 healthy subjects (9 males) received four types of iTBS (i.e., M1 iTBS, DLPFC iTBS, combination of M1 and DLPFC iTBS and sham iTBS) on separate days. BCI control testing, functional near-infrared spectroscopy assessment and single-pulse transcranial magnetic stimulation were performed before and immediately after iTBS in each session. Corticospinal excitability, brain activation, and functional connectivity were calculated. Our results revealed that corticospinal excitability was significantly increased after M1 iTBS (P = 0.016), with the magnitude of increase positively correlated with BCI performance (P = 0.013). Frontoparietal network functional connectivity was significantly increased after DLPFC iTBS (P's<0.05), with the magnitude of increase positively correlated with changes in BCI performance (P's<0.05). In conclusion, M1 iTBS and DLPFC iTBS alone influences BCI performance through specific neural mechanisms, and the combination of M1 and DLPFC iTBS did not induce any significant results. M1 iTBS could influence BCI performance by enhancing corticospinal excitability, while DLPFC iTBS could influence BCI performance by increasing frontoparietal network connectivity. These findings could contribute to the advancement of novel therapeutic strategies aimed at enhancing BCI effectiveness for neurological populations. Trial registration: The study was retrospectively registered in the Chinese Clinical Trial Registry (ChiCTR2500097678). Registration Date: 2025-02-24.},
}

@article {pmid41768366,
year = {2026},
author = {Feng, Y and Guo, X and Huang, P and Ji, X and Jia, N and Yang, S and Hu, S},
title = {Cerebrospinal Fluid Genetics Enhance Risk Stratification in Bipolar Disorder.},
journal = {MedComm},
volume = {7},
number = {3},
pages = {e70629},
pmid = {41768366},
issn = {2688-2663},
abstract = {Bipolar disorder (BD) research confronts challenges: blood-based biomarkers offer limited insights into neurobiology, while cerebrospinal fluid (CSF) collection is clinically unusual. Linking genetic susceptibility to pathophysiology remains crucial for biologically informed risk stratification. We integrated cohort data and genome-wide association study (GWAS) summary statistics: the largest BD meta-analysis, CSF multi-omics profiles including 3107 proteomic and 2602 metabolomic participants, and a validation cohort of 247,834 UK Biobank participants. Unsupervised clustering revealed four single-nucleotide variant (SNV) clusters: metabolic-imbalance, metabolic-active, human leukocyte antigen (HLA)+immune, and HLA-immune. These clusters exhibited distinct clinical features, with the metabolic-imbalance cluster showing multi-directional associations with 21 psychiatric traits, while the HLA-immune cluster was associated with emotional instability in BD patients (odds ratio [OR] = 1.14, p = 0.027). The optimized multimodal cluster-specific polygenic risk scores (PRS) model significantly outperformed clinical-only prediction factors (C-index = 0.77), with the metabolic-imbalance PRS contributing a 22.6% incremental predictive value (hazard ratio [HR] = 1.23, 95% CI: 1.04-1.45, p = 0.016). Risk reclassification showed an 84% reduction in false-negative rates in the low-risk subgroup, identifying a high-risk layer with a 17.6-fold increased BD incidence. Altogether, genetically informed substitutes for CSF biomarkers emerged as a scalable tool for risk prediction, overcoming the barriers of CSF collection while capturing neurobiological heterogeneity.},
}

@article {pmid41767407,
year = {2026},
author = {Gao, Y and Ma, Y and Liu, Y and Yin, G and Qin, Y},
title = {Multi-branch Domain Adversarial Neural Network with dynamic weight allocation for multi-source EEG classification.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {58},
pmid = {41767407},
issn = {1871-4080},
abstract = {To address challenges such as the strong non-stationarity and inter-subject distribution shifts of EEG data, as well as the limitations of conventional DANN-based methods in feature representation and multi-source domain adaptation, a Multi-branch Domain Adversarial Neural Network with Multi-scale Channel Attention (MBCA-DANN) is proposed. To enhance feature richness, a multi-scale channel attention Module (MSCA) is designed, which provides multi-scale features and adaptively adjusts the feature channel weights, improving the feature capture ability of the network. A multi-branch architecture is constructed by combining auxiliary Maximum Mean Discrepancy (MMD), domain discriminators, and label discriminators, ensuring optimal matching between the source and target domains. Furthermore, a multi-source domain method with dynamic weight allocation is introduced, enhancing classification performance and robustness. Experimental results demonstrate that the classification accuracy for single-source domain transfer on the MII and MIII datasets is 71.89% and 71.82%, respectively, while the multi-source domain transfer classification accuracy improves to 79.83% and 82.87%. The model achieves a classification accuracy of 98.69% on the fatigue detection dataset, outperforming all currently known state-of-the-art algorithms, validating its strong generalization ability and providing an effective solution for multi-source cross-subject EEG classification.},
}

@article {pmid41766383,
year = {2026},
author = {Lecomte, A and Mazenq, L and Blatché, MC and Lecestre, A and Larrieu, G},
title = {Monolithic 3D Nanoelectrode Arrays on CMOS Circuitry for Scalable, High-Resolution Neural Recording.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {},
number = {},
pages = {e12016},
doi = {10.1002/smll.202512016},
pmid = {41766383},
issn = {1613-6829},
support = {863245//European Commission/ ; },
abstract = {Understanding brain function and neurodegenerative disorders, and accelerating preclinical drug development, demand neural interfaces that combine nanoscale sensitivity with high-resolution, large-scale recording capability. Here, we present a monolithically integrated high-density nanoelectrode array (HD-NEA) featuring vertical high-aspect ratio nanowire electrodes embedded within the back-end-of-line of commercial CMOS circuitry. Using a low-temperature (<400 °C), wafer-scale post-fabrication strategy, we decouple nanostructure formation from circuit integration while preserving CMOS functionality. The resulting 3D array, comprising 26,400 electrodes, achieves high yield and uniformity across 4-in. wafers. When interfaced with in vitro cortical neurons, the HD-NEA yields significantly higher spike amplitudes and signal-to-noise ratios than planar microelectrodes, without requiring electroporation. High-resolution spike mapping revealed steeper spatial signal decay, consistent with closer neuron-nanowires coupling, and enabled the detection of distinct waveform morphologies including putative dendritic signals. These results position HD-NEA as a scalable and CMOS-compatible nanobiointerface, enabling high-fidelity neural recording for neuroscience research, brain-machine interfacing, and bioelectronic diagnostics.},
}

@article {pmid41763275,
year = {2026},
author = {Shi, B and Liu, M and Wang, Y},
title = {ATCRN: Attention-guided Temporal Convolutional Remix Network for P300 speller.},
journal = {Journal of neuroscience methods},
volume = {430},
number = {},
pages = {110727},
doi = {10.1016/j.jneumeth.2026.110727},
pmid = {41763275},
issn = {1872-678X},
abstract = {BACKGROUND: The P300 speller is a prominent brain-computer interface (BCI) that facilitates communication by detecting P300 event-related potentials. However, its performance is substantially constrained by the low signal-to-noise ratio of EEG signals and the inherent temporal variability of the P300 response.

NEW METHOD: We propose the Attention-guided Temporal Convolutional Remix Network (ATCRN), an end-to-end model that synergistically integrates a novel Temporal Convolutional Remix Network (TCRN) with a dual-attention framework. The TCRN employs multi-level skip connections to enable dynamic, cross-hierarchical fusion of local and global temporal features, addressing the variable latency of P300. Externally, the Convolutional Block Attention Module (CBAM) suppresses noise in spatial and channel dimensions. Internally, Efficient Channel Attention (ECA) modules within TCRN block perform dynamic channel recalibration.

RESULTS: On BCI Competition III Dataset II, ATCRN achieved character recognition rates of 99% and 98% for two subjects at the 15th repetition, and yielded superior information transfer rates. Evaluation across eight ALS patients showed robust P300 detection (average AUC-ROC 0.882).

ATCRN outperforms both established CNN/TCN benchmarks and recent Transformer-based models across two public datasets, achieving state-of-the-art results in P300 detection and character spelling.

CONCLUSION: The proposed ATCRN provides a novel, robust, and effective decoding framework for the P300 speller. The integration of TCRN for temporal feature fusion and the dual-attention mechanism for feature refinement offers a practical solution for advancing BCI applications.},
}

@article {pmid41762696,
year = {2026},
author = {Song, D},
title = {Decoding Naturalistic Episodic Memory with Artificial Intelligence and Brain-Machine Interface.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e20125},
doi = {10.1002/advs.202520125},
pmid = {41762696},
issn = {2198-3844},
support = {N66001-14-C-4016//Defense Advanced Research Projects Agency (DARPA) Restoring Active Memory (RAM) program/ ; RF1DA055665/1R01EB031680//NIH/NIDA BRAIN Initiative - Theories, Models and Methods (TMM) program/ ; HR0011-25-3-0142//DARPA Investigating how Neurological Systems Process Information in REality (INSPIRE) program/ ; },
abstract = {Episodic memory integrates what, where, and when of experience into a coherent autobiographical narrative. Decades of research have identified hippocampal place, time, and concept cells as neural correlates of these components. Yet a major challenge remains: real-life memory encoding occurs in high-dimensional, naturalistic settings, where multimodal sensory, emotional, and cognitive processes intertwine across time and context. Traditional paradigms and analytical tools are insufficient to decode the neural activity underlying such complex experiences. Recent advances in artificial intelligence (AI) offer new means to address this challenge. AI models, such as variational autoencoders and multimodal alignment frameworks, can extract latent representations from neural and behavioral data, capturing the naturalistic structure of memory encoding. Large language models further provide powerful frameworks for interpreting subjective memory reports, linking verbal narratives to memory encoding. When integrated with closed-loop brain-machine interfaces (BMIs) capable of recording from and manipulating large populations of neurons in relevant brain regions, these tools make it possible to address the long-standing questions: how to decode memory codes during naturalistic behaviors and whether these memory codes causally generate memories rather than merely correlate with them. This integrated AI-BMI framework outlines a roadmap from mapping to engineering memory, with implications for Alzheimer's disease, traumatic brain injury, and PTSD.},
}

@article {pmid41761229,
year = {2026},
author = {Song, J and Wang, N and Li, Z and Zhang, X and Lv, Z and Shan, X and Yang, Y and Liu, J and Chai, X},
title = {Decoding multi-class motor attempt from the affected unilateral limbs in chronic stroke patients.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-026-01920-z},
pmid = {41761229},
issn = {1743-0003},
support = {HX202409200057//Collaborative Research and Development Project "Deep Brain Stimulation System Development and Technical Research"/ ; 2025ZD0215200//National Key R&D Program on Brain Science and Brain-Like Research: Precision Regulation Technology for Spinal Cord-Peripheral Nerve Integration Aimed at Motor Function Restoration/ ; },
}

@article {pmid41760680,
year = {2026},
author = {Zhang, S and Liu, Z and Jiang, T and Wang, C and Wang, J and Wang, H and Fan, M and Yang, L and Li, Y and Ding, L and Yu, Y and Hao, X and Ma, S and Xu, B and Chen, X and Ye, C and Chen, X and Chu, PK and Jin, S and Ding, F and Yu, XF and Sun, Z and Wang, J},
title = {Strong optical anisotropy in one-dimensional phosphorus wavy tubes.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-70129-4},
pmid = {41760680},
issn = {2041-1723},
abstract = {Anisotropic materials with intrinsic one-dimensional architectures, where chains or tubes align along a crystallographic axis, exhibit direction-dependent optical responses and serve as ideal building blocks for polarization-sensitive optoelectronics. While progress exists in engineered compounds, discovering elemental crystals with naturally ordered one-dimensional building blocks exhibiting giant optical anisotropy remains challenging. Here, we report the synthesis of a direct-bandgap semiconducting one-dimensional phosphorus single crystal composed of unique wavy polygonal tubes. The monoclinic lattice structure is revealed by single-crystal X-ray diffraction and advanced transmission electron microscopy. The crystal exhibits giant birefringence in the visible and near-infrared regions, stemming from electron localization and anisotropic transitions of the phosphorus 3p orbital along the tube axis. The low-symmetry structure endows remarkable linear and nonlinear optical anisotropies, including orientation-dependent photoluminescence, Raman scattering, and second-harmonic generation. This study establishes a paradigm for designing giant optical anisotropies, opening avenues for on-chip polarization devices and nonlinear photonic circuits.},
}

@article {pmid41760219,
year = {2026},
author = {Lai, Z and Feng, D and Liang, M and Liang, W and Xu, Y and Ke, J},
title = {[Research progress on flexible electrode technology in brain computer interface applications].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {43},
number = {1},
pages = {186-192},
doi = {10.7507/1001-5515.202508066},
pmid = {41760219},
issn = {1001-5515},
abstract = {Flexible electrode as a revolutionary brain computer interface (BCI) technology in the field of neural engineering, has achieved high-fidelity acquisition and long-term stable transmission of electroencephalographic signals through their exceptional bio-compatibility. This review systematically elucidates the design paradigms and material innovation systems of flexible electrodes, focusing on their transitional medical value from aspects such as electrode materials, signal acquisition and processing. It identifies the current technical bottlenecks that urgently need to be broken through and outlines the future development directions, hoping to provide a systematic technical road-map and evaluation framework for the technical development of next-generation BCI.},
}

@article {pmid41760218,
year = {2026},
author = {Wang, Y and Li, W and Chen, X},
title = {[A review of noninvasive brain-computer interfaces combined with transcranial electrical stimulation for neural rehabilitation].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {43},
number = {1},
pages = {178-185},
doi = {10.7507/1001-5515.202509061},
pmid = {41760218},
issn = {1001-5515},
abstract = {The rehabilitation of motor dysfunction following stroke remains a major clinical challenge, underscoring the urgent need to develop novel therapeutic strategies to improve functional recovery in patients. Brain-computer interface (BCI) technology has emerged as a cutting-edge approach in neurorehabilitation, demonstrating significant potential for motor function restoration. Transcranial electrical stimulation (tES), a non-invasive neuromodulation technique, can promote neuroplasticity by regulating cortical excitability. In recent years, studies have begun to explore the combination of BCI with tES to synergistically enhance neural remodeling within the central nervous system. This integrated multi-technology strategy is increasingly becoming a key focus in the field of neurorehabilitation. This review systematically summarized recent advances in tES-BCI integrated systems for neurorehabilitation, with a particular emphasis on widely adopted BCI paradigms and tES parameter configurations and stimulation modalities. Based on a comprehensive synthesis of existing evidence, this review summarizes the efficacy of this combined intervention strategy in rehabilitating upper and lower limb motor functions following stroke, highlights the methodological limitations and clinical translation challenges present in current research, and aims to provide insights for mechanistic exploration, system optimization, and clinical translation of integrated BCI-tES technology.},
}

@article {pmid41760207,
year = {2026},
author = {Qi, Q and Li, M},
title = {[A time-frequency transform and Riemannian manifold-based domain adaptation method for motor imagery in brain source space].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {43},
number = {1},
pages = {87-96},
doi = {10.7507/1001-5515.202507056},
pmid = {41760207},
issn = {1001-5515},
abstract = {To accurately capture and address the multi-dimensional feature variations in cross-subject motor imagery electroencephalogram (MI-EEG), this paper proposes a time-frequency transform and Riemannian manifold based domain adaptation network (TFRMDANet) in a high-dimensional brain source space. Source imaging technology was employed to reconstruct neural electrical activity and compute regional cortical dipoles, and the multi-subband time-frequency feature data were constructed via wavelet transform. The two-stage multi-branch time-frequency-spatial feature extractor with squeeze-and-excitation (SE) modules was designed to extract local features and cross-scale global features from each subband, and the channel attention and multi-scale feature fusion were introduced simultaneously for feature enhancement. A Riemannian manifold embedding-based structural feature extractor was used to capture high-order discriminative features, while adversarial training promoted domain-invariant feature learning. Experiments on public BCI Competition IV dataset 2a and High-Gamma dataset showed that TFRMDANet achieved classification accuracies of 77.82% and 90.47%, with Kappa values of 0.704 and 0.826, and F1-scores of 0.780 and 0.905, respectively. The results demonstrate that cortical dipoles provide accurate time-frequency representations of MI features, and the unique multi-branch architecture along with its strong time-frequency-spatial-structural feature extraction capability enables effective domain adaptation enhancement in brain source space.},
}

@article {pmid41760200,
year = {2026},
author = {Song, L and Zhang, Y and Wei, Y and Liu, Y and Wang, C and Xu, G},
title = {[Microstate dynamics in motor imagery of stroke patients with transcranial alternating current stimulation modulation].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {43},
number = {1},
pages = {26-33},
doi = {10.7507/1001-5515.202508021},
pmid = {41760200},
issn = {1001-5515},
abstract = {Transcranial alternating current stimulation (tACS) holds significant potential for improving motor function in stroke patients, but its underlying mechanisms remain unclear. In this study, 20 Hz tACS was applied to 15 stroke patients, and their motor imagery (MI) signals were collected before and after stimulation, which were for assessment by combining with the Fugl-Meyer Assessment for Upper Extremity (FMA-UE). Additionally, 11 subjects were recruited as a healthy control group. The study demonstrated that FMA-UE scores of stroke patients significantly increased after tACS intervention. The duration of EEG microstate C and F decreased significantly, while microstate D (coverage, duration, and occurrence probability) increased markedly, and microstate E decreased. The transition probabilities of C→D and D→B were positively correlated with FMA-UE scores. Based on these findings, this study concludes that 20 Hz tACS can enhance neuroplasticity and motor function in patients, and the transition probabilities (C→D/D→B) may serve as potential indicators for assessing motor function, providing experimental evidence for the clinical application of tACS and the development of rehabilitation brain-computer interfaces.},
}

@article {pmid41760197,
year = {2026},
author = {Fu, Y and Cheng, T and Luo, R and Zhao, L and Li, T and Su, L and Xu, J},
title = {[A scientific definition of brain-computer interfaces (BCIs): Essential components, fundamental characteristics, capability boundaries, and scope delimitation].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {43},
number = {1},
pages = {1-7},
doi = {10.7507/1001-5515.202511002},
pmid = {41760197},
issn = {1001-5515},
abstract = {Brain-computer interfaces (BCIs) are communication and control systems centered on neural signals that incorporate both the user and the brain into a closed-loop interaction framework, and are widely regarded as a transformative paradigm in human-computer interaction. However, despite the existence of broadly accepted definitions within the research community, the rapid acceleration of BCI translation and commercialization has led to increasing ambiguity in scientific definitions, expansion of conceptual scope, and overstatement of technical capabilities. To address these issues, this paper proposed a scientifically grounded definition of BCIs and systematically analyzed their essential system components and fundamental characteristics. On this basis, the major and specific factors that constrain the capability boundaries of current and foreseeable BCI systems were examined. Furthermore, the scope of BCI was explicitly delineated by distinguishing BCIs from adjacent neurotechnologies based on their functional roles and system characteristics. This work aims to promote a more rigorous and coherent understanding of BCI definitions, scope, and capability limits within the academic community, and to provide essential theoretical foundations for responsible translation and long-term development. By clarifying conceptual boundaries and realistic expectations, it seeks to mitigate risks associated with conceptual generalization and distorted projections in both research and industrial practice, thereby fostering a more rational, robust, and sustainable ecosystem for the BCI field.},
}

@article {pmid41759685,
year = {2026},
author = {Pang, Z and Li, Z and Zhang, R and Dong, Q and Cheng, Z and Cui, H and Chen, X},
title = {A High-Performance SSVEP-BCI System Based on High-Frequency Flickers in the Peripheral Visual Field.},
journal = {Brain research bulletin},
volume = {},
number = {},
pages = {111795},
doi = {10.1016/j.brainresbull.2026.111795},
pmid = {41759685},
issn = {1873-2747},
abstract = {BACKGROUND: The existing steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) primarily use central visual field flickers with a stimulus frequency of 8-20Hz, which is prone to exhibit strong flicker perception in users. Considering that, this study aims to develop an SSVEP-based BCI system which is both high-performance and low-flicker-perception by employing high-density electrodes and high-frequency flickers in the peripheral visual field.

METHODS: A custom-made electroencephalogram (EEG) cap with high-density electrodes was used to acquire more EEG data. To alleviate flicker perception, this study combined high-frequency visual stimulation with peripheral visual field stimulation. The proposed system encoded 40 targets using annuli with an angular range in 2.1°-4.1° and high-frequency flickers in the range of 32.00-36.68Hz. For signal decoding, the task-discriminant component analysis (TDCA) was first applied to the peripheral visual field SSVEP-based BCI system with weak response.

RESULTS: Through online experiments, the feasibility of this system was verified. It achieved an average classification accuracy of 83.22 ± 11.95% and an information transfer rate (ITR) of 178.21 ± 43.84 bits/min. Moreover, the role of high-density electrodes to obtain more useful EEG information and thus improving the classification accuracy has been proved.

The online ITR of this system was the highest for current peripheral visual field SSVEP-based BCIs.

CONCLUSION: The proposed system not only provides novel ideas for the design of BCI systems with weak flicker, but also provides reference value for the future application of high-density electrodes in SSVEP-based BCIs.},
}

@article {pmid41759684,
year = {2026},
author = {Ye, J and Xu, M and Hu, J and Yu, H and Zhang, S and Jiang, L and Li, F and Xu, P and Dai, A},
title = {Predicting Long-Term Prognosis in Comatose Patients through Brain Network Analysis under Name-Evoked Stimulation.},
journal = {Brain research bulletin},
volume = {},
number = {},
pages = {111794},
doi = {10.1016/j.brainresbull.2026.111794},
pmid = {41759684},
issn = {1873-2747},
abstract = {Accurate prognosis assessment of comatose patients remains a significant challenge in neurocritical care. Growing evidence indicates that brain connectivity is integral to the maintenance of consciousness and may be linked to its recovery. In this study, we recorded bedside electroencephalography (EEG) from comatose patients during an auditory oddball name-calling task to investigate task-related dynamic causal modeling (DCM) connectivity and to examine whether connectivity strengths correlated with patients' functional recovery. Our findings reveal that a bidirectional model, incorporating reciprocal connectivity among the superior frontal gyri, superior parietal lobules, and primary auditory cortices, was significantly associated with the neural processing of name-calling stimuli in comatose patients. Furthermore, the strength of these DCM connections demonstrated a capacity to predict long-term prognostic outcomes, as evaluated via the Glasgow Outcome Scale-Extended scale. Together, these results provide evidence supporting the potential of DCM-derived biomarkers in evaluating functional prognosis in comatose patients. (ChiCTR2000033586).},
}