@article {pmid41876829,
year = {2026},
author = {Zou, Z and Wang, B and Chen, T and Fan, S and Ye, B},
title = {A brain-edge co-evolution framework for zero-trust real-time hot patching in power equipment.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-45643-6},
pmid = {41876829},
issn = {2045-2322},
support = {SGXJDK00DWJS2500136//the Science and Technology Project of State Grid Xinjiang Electric Power Co., Ltd/ ; },
}

@article {pmid41877259,
year = {2026},
author = {Toppi, J and Pichiorri, F and Ciaramidaro, A and Mohebban, S and Patarini, F and Tagliamonte, NL and Di Tommaso, F and Ferrara, M and Scorza, M and Bigioni, A and Serratore, G and Guredda, G and Scivoletto, G and Mattia, D and Tamburella, F},
title = {Investigating the role of therapist-patient interaction during robot-assisted gait training after incomplete spinal cord injury: the INTER-RO-GAIT randomized controlled trial.},
journal = {Trials},
volume = {},
number = {},
pages = {},
doi = {10.1186/s13063-026-09644-0},
pmid = {41877259},
issn = {1745-6215},
support = {GR-2019-12369207//Ministero della Salute/ ; },
abstract = {BACKGROUND: In the neurorehabilitation framework of treadmill-based robot-assisted gait training (t-RAGT), a threefold relationship among physiotherapist (Pht), patient (Pt), and the selected robotic device should be considered. Furthermore, the type of visual FeedBack (FB) selected for the training and how the Pht guides and supports the Pt have an important impact on Pt's engagement. Pht-Pt interaction is mostly effective when FB with high technical content is employed, and it affects Pt's visual attention and emotional experience during training. The INTER-RO-GAIT project proposes an experimental modulation of Pht-Pt interaction during the training with the Lokomat device, to primarily investigate its role in the effectiveness of t-RAGT for individuals with subacute and chronic incomplete spinal cord injury (i-SCI) through a longitudinal randomized controlled trial (RCT), by means of clinical scales and biomechanical data. Timed walking tests for gait speed evaluation (10-Meter Walking Test and 6-Minute Walking Test) are considered as primary outcome measures, while clinical scales for the assessment of lower limbs' force, spasticity, pain, clonus, spasms, and independence in activities of daily living are selected as secondary outcome measures. The biomechanical assessment includes overground gait analysis to assess recovery of motor functions, and human-Lokomat interaction analysis to measure the active Pt participation in the exercise and evaluate its evolution along training. Secondary aims are as follows: (i) to identify neurophysiological indices derived from electroencephalography (EEG) hyperscanning data monitoring the Pht-Pt relationship along t-RAGT; (ii) to evaluate the Pt's engagement in terms of Visual Attention during the RAGT; (iii) to investigate the correlation between the rehabilitation outcome and the neurophysiological indices or the psychological metrics referring to Pht-Pt relationship.

METHODS: Fifty participants from I.R.C.C.S. Fondazione Santa Lucia (Rome, Italy) will be enrolled and randomized into a single-blind RCT to investigate the effects of 12 Lokomat t-RAGT sessions administered with two different levels of Pht-Pt interaction (high level of interaction for the experimental (EXP) group and low level of interaction for the control (CTRL) group), as an add-on training to conventional rehabilitation. Before and after the whole t-RAGT, as well as at the first, the mid, and the last training session, a battery of clinical, biomechanical, psychological, and neurophysiological assessments will be conducted.

DISCUSSION: Given that incomplete subacute or chronic SCI may lead to long-term disability for which cost-effective rehabilitation options are critically needed, INTER-RO-GAIT aims at providing evidence for an optimal Pht-Pt interaction to potentially boost the t-RAGT effects on Pts' performance, improving robotic rehabilitation protocols and devices development even beyond the specific gait application.

TRIAL REGISTRATION: Patient-therapist INTERaction During RObotic GAIT Rehabilitation After Spinal Cord Injury (INTER-RO-GAIT); ClinicalTrial.gov platform registration number: GR-2019-12369207 on 31st July 2024.},
}

@article {pmid41877429,
year = {2026},
author = {Ke, S and Li, Y and Qu, Y and Huang, H and Hao, M and Yang, L and Wu, Q and Ye, C and Chu, PK and Yu, XF and Wang, J},
title = {Spectrally Defined Bipolar Black Phosphorus Memristor Enables All-Optical Boolean Logic and Multispectral Computing.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e22710},
doi = {10.1002/adma.202522710},
pmid = {41877429},
issn = {1521-4095},
support = {2024YFB3614200//National Key R&D Program of China/ ; 62274058//National Natural Science Foundation of China/ ; 62404237//National Natural Science Foundation of China/ ; 32471459//National Natural Science Foundation of China/ ; 2024A1515030176//Guangdong Basic and Applied Basic Research Foundation/ ; 2025B1515020088//Guangdong Basic and Applied Basic Research Foundation/ ; 2023A1515110590//Guangdong Basic and Applied Basic Research Foundation/ ; 2024B1212010010//Guangdong Provincial Key Laboratory of Multimodality Non-Invasive Brain-Computer Interfaces/ ; JQ0209-2025//Original Innovation Project in SIAT/ ; RCJC20200714114435061//Shenzhen Science and Technology Program Grants/ ; DON-RMG 9229021//City University of Hong Kong Donation Research Grants/ ; 9220061//City University of Hong Kong Donation Research Grants/ ; GZC20241837//China Postdoctoral Science Foundation/ ; 2025WK2013//Natural Science Foundation of Hunan Province/ ; B2302028//Shenzhen Medical Research Fund/ ; },
abstract = {Although optoelectronic memristors with nonvolatile bipolar photoconductivity enable in-sensor vision-centric neuromorphic hardware, achieving wavelength-defined polarity inversion across a broad spectrum remains a challenging task. Herein, a stable optoelectronic memristor composed of nonstoichiometric lead oxide (PbOx) coated black phosphorus (BP) nanosheets is demonstrated. The optoelectronic processes in the PbOx-BP heterostructure result in programmable polar photoresponses across the 365 nm - 1,550 nm wavelength range. Visible light causes positive photoconductance via photoelectrochemical Ag[+] reduction and conductive filament reconstruction. Conversely, ultraviolet light drives the reverse photogenerated electron transfer to chemically oxidize the Ag CFs, while infrared light induces their localized melting via the photothermal effect. This bipolar optoelectronic tunability enables all-optical Boolean logic operations, allowing for the realization of 14 binary functions through optical reconfiguration. Furthermore, multispectral computing tasks, including edge extraction and spectral noise suppression, are performed, yielding a classification accuracy of up to 98.6% for 16 crop species using an all-optical convolutional neural network. The ultra-thin oxide coating presents an effective surface modification approach to improve two-dimensional devices, while the optoelectronic bipolarity establishes a framework for all-optical modulation in neuromorphic machine vision.},
}

@article {pmid41878268,
year = {2026},
author = {Velut, S and Thielen, J and Chevallier, S and Corsi, MC and Dehais, F},
title = {Neurophysiological screening of individual variability for robust decoding in c-VEP-based BCI.},
journal = {Imaging neuroscience (Cambridge, Mass.)},
volume = {4},
number = {},
pages = {},
pmid = {41878268},
issn = {2837-6056},
abstract = {Code-modulated visual evoked-potential (c-VEP)-based reactive brain-computer interfaces (BCIs) deliver high information-transfer rates with minimal calibration, yet performance often collapses when models are transferred between users. We, therefore, pursue a two-fold aim: first, to pinpoint neurophysiological predictors that explain this inter-participant variability; second, to identify a decoding pipeline that sustains accuracy across users in a burst-c-VEP paradigm (brief, aperiodic flashes at 3 Hz). From 24 participants, we find that stronger inter-epoch correlation (R ≈ 0.80), larger peak-to-peak amplitude of the flash-VEP, larger α bandpower, larger θ bandpower, and lower δ bandpower are five neurophysiological predictors that correlate between high performers (> 90% accuracy) and low performers (< 70%), enabling a 22 s "go/no-go" calibration. We then compare three preprocessing schemes (small, combined, participant-specific) paired with three decoders-a convolutional neural network, a Riemannian xDAWN-LDA baseline, and GREEN, a wavelet-based symmetric positive definite neural network. Subject-specific alignment plus GREEN achieves 93% trial-level accuracy in both intra- and cross-participant settings, eliminating the 15-20% transfer loss obtained with the other tested decoding models while keeping the total calibration under 1 min. In conclusion, rapid user screening with these neurophysiological predictors, followed by this lightweight, user-specific pipeline, yields burst-c-VEP control that is fast to deploy and robust across individuals.},
}

@article {pmid41880939,
year = {2026},
author = {Zhang, HG and Jialin, A and Chen, ZR and Zhang, JQ and Wang, C and Cao, MN and Li, XJ and Yin, XW and Ye, JX and Xue, C and Zhong, BL and Deng, W},
title = {Left cortical activation and combined diagnostic utility during three verbal fluency tasks in major depressive disorder: A multi-channel fNIRS study.},
journal = {Psychiatry research},
volume = {360},
number = {},
pages = {117101},
doi = {10.1016/j.psychres.2026.117101},
pmid = {41880939},
issn = {1872-7123},
abstract = {BACKGROUND: Recent functional near-infrared spectroscopy (fNIRS) studies have shown reduced left cortical hemodynamic responses in major depressive disorder (MDD), suggesting a promising neuroimaging biomarker for diagnosis. However, given MDD's pronounced clinical heterogeneity and widespread cognitive impairments, reliance on a single task-based activation index may be insufficiently sensitive. Therefore, this study aims to combine three Chinese verbal fluency tasks (VFTs) with distinct cognitive demands to delineate MDD-related aberrant neural response patterns and to derive more comprehensive, robust fNIRS biomarkers for objective diagnostic classification.

METHODS: This study recruited 60 patients with MDD and 60 demographically matched healthy controls (HCs). Hemodynamic changes in the left cortex were measured using a 48-channel fNIRS during the three VFTs. Demographics information, clinical characteristics and VFT performance were also collected.

FINDINGS: Each Chinese VFT variant elicited a different pattern of left cortical activation. Relative to HCs, patients with MDD exhibited significantly reduced activation in both the left dorsolateral and medial prefrontal cortices. Moreover, integrating neural activation indices across all three VFTs substantially enhanced the discrimination between MDD patients and HCs compared with any single task.

CONCLUSIONS: In light of the heterogeneous nature of depression and its broad impact on multiple cognitive domains, combining multiple cognitive paradigms may develop richer and more reliable fNIRS-based biomarkers for the identification of MDD.},
}

@article {pmid41881762,
year = {2026},
author = {Zhao, W and Rao, J and Wang, R and Chai, Y and Mao, T and Quan, P and Deng, Y and Chen, W and Wang, S and Guo, B and Zhang, Q and Rao, H},
title = {Retraction notice to "Test-retest reliability of coupling between cerebrospinal fluid flow and global brain activity after normal sleep and sleep deprivation" [NeuroImage 309 (2025) 121097].},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121851},
doi = {10.1016/j.neuroimage.2026.121851},
pmid = {41881762},
issn = {1095-9572},
}

@article {pmid41882308,
year = {2026},
author = {Machhi, V and Shah, A},
title = {Emotion detection unveiled: A cognitive-computational synthesis of physiological models, machine learning, and datasets.},
journal = {Cognitive, affective & behavioral neuroscience},
volume = {},
number = {},
pages = {},
pmid = {41882308},
issn = {1531-135X},
abstract = {This comprehensive survey synthesizes state-of-the-art advancements in emotion recognition based on physiological signals, specifically focusing on the paradigm shift occurring between 2021 and 2025. Crucially, we move beyond a technical review by establishing a novel Cognitive-Computational Synthesis Framework (CCSF). This framework explicitly maps multimodal physiological manifestations (e.g., electroencephalogram (EEG), electrocardiogram (ECG), and galvanic skin response (GSR)) to underlying cognitive processes, such as attentional allocation, arousal regulation, and perceptual bias, providing a theoretical foundation for explainable AI (XAI) in affective computing. We meticulously examine the transition from traditional machine learning to advanced deep learning architectures, highlighting how recent innovations in Transformers, self-supervised learning, and diffusion models have shattered previous performance plateaus. While earlier dimensional models were often limited to 70-75% accuracy, this survey details how modern architectures now achieve benchmarks exceeding 95% on seminal datasets like SEED and DREAMER. Furthermore, the survey provides a rigorous analysis of 40 key studies (identified via PRISMA protocols), evaluating them based on their validation strategies, cross-subject generalizability, and adversarial robustness. By bridging the gap between raw physiological data and cognitive theory, this work offers a strategic roadmap for the next generation of robust, interpretable, and real-time emotion recognition systems.},
}

@article {pmid41882345,
year = {2026},
author = {Haggerty, J and Qureshi, Q and Gabriel, ED and Borges, PG and Davis, P and Wingel, K and Cai, J and Sargur, K and Kim, MJ and Dubey, A and Garwood, I and Vaz, A and Richardson, AG and Chen, HI and Hammer, LH and Gold, J and Litt, B and Yoshor, D and Beauchamp, M and Halpern, C and Pesaran, B and Cajigas, I},
title = {Thalamus: a real-time system for synchronized, closed-loop multimodal behavioral and electrophysiological data capture.},
journal = {Communications engineering},
volume = {},
number = {},
pages = {},
doi = {10.1038/s44172-026-00646-z},
pmid = {41882345},
issn = {2731-3395},
support = {5K12NS129164-02//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; },
abstract = {Precise and synchronized multimodal data capture in neurosurgical environments is essential for further understanding brain function and will be crucial to advancing the development of brain-computer interface technology. We have developed an open-source software platform named Thalamus, for multimodal data capture integrated with existing sensors and hardware commonly utilized in the operating room and other clinical environments such as pulse oximeters, inertial sensors, electromyography and neural electrophysiology. Thalamus facilitates synchronous recording of neural and behavioral data, enabling real-time computation for closed-loop experiments and detailed analysis of complex motor functions and neural activity. Thalamus uses a modular, configurable node-based pipeline with a tiered Python and C + + architecture. These design elements allow Thalamus to support a wide range of high-resolution sensors for diverse behavioral data types and enable robust closed-loop synchronization of various data streams. Validation experiments demonstrate that Thalamus is capable of data integration and concurrent analysis with up to sub-millisecond precision, offering great potential for enhancing neurosurgical research and clinical applications. By leveraging conventional sensors and hardware already in use, Thalamus supports adoption into the clinical environment, paving the way for more comprehensive, data-driven approaches to neurological care and improving the personalization and rigor of treatment strategies.},
}

@article {pmid41870922,
year = {2026},
author = {Wei, Y and Mai, X and Li, Y and Luo, R and Cheng, R and Meng, J},
title = {High-Performance Cross-Subject Decoding of Multiclass Rhythmic Motor Imagery Using EEG Data from 100 Subjects.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2026.3676837},
pmid = {41870922},
issn = {1558-0210},
abstract = {OBJECTIVE: Effective cross-subject decoding is essential for reducing calibration time and enhancing the practical usability of brain-computer interfaces (BCIs). However, large inter-subject variability in EEG features poses a major challenge, particularly for motor imagery (MI) paradigms. Recent studies have shown that rhythmic MI can induce steady-state movement-related rhythms (SSMRR), which provide more structured electrophysiological features than conventional sensorimotor rhythms (SMR) and may offer a promising basis for efficient cross-subject decoding.

METHODS: In this study, we comprehensively explored ways to achieve high-performance cross-subject decoding based on the rhythmic MI paradigm from both model and data perspectives.

RESULTS: We achieved an encouraging cross-subject four-class decoding accuracy of 72.94%±13.80% using a streamlined multilayer perceptron (MLP)-based network on a self-collected dataset comprising 100 BCI-naïve participants. From a model perspective, networks composed of simple MLP-based functional modules can achieve results comparable to, or even superior to, those of several state-of-the-art (SOTA) models. From a data perspective, increasing the training set size substantially improves cross-subject decoding performance (from 61.78% to 72.94%). Moreover, we revealed a strong positive correlation between EEG feature consistency and cross-subject decoding accuracy, providing a physiological explanation for why enlarging the training data scale enhances cross-subject generalization. Finally, we explored strategies for selecting high-quality training data. We found that feature-consistency-based selection serves as a more reliable criterion than within-subject decoding accuracy.

SIGNIFICANCE: Overall, our study provides novel insights into cross-subject EEG decoding from the perspectives of model design, data scale and quality. The code is available in https://github.com/SJTUwyxuan/RhythmicMI-CrossSubject.},
}

@article {pmid41871461,
year = {2026},
author = {Mei, T and Wang, Y and Gou, H and Chang, C and Hu, S and Zhang, X},
title = {EEG-CMT: Spatial-Temporal Representation of EEG for Emotion Recognition Using Convolutional Neural Networks and Vision Transformers.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ae55aa},
pmid = {41871461},
issn = {2057-1976},
abstract = {Background Recent researches on electroencephalogram (EEG) based emotion recognition face challenges in effectively mapping the spatial positional relationships of EEG acquisition electrodes. Additionally, conventional models struggled to simultaneously capture both fine-grained temporal-spatial features and long-range dependencies in EEG signals. New method To address these limitations, we propose a novel EEG data processing method that incorporates spatial relative position encoding and a hybrid neural architecture integrating convolutional neural networks (CNNs) with self-attention mechanisms. This approach systematically encodes the spatial topology of electrodes to enhance the representation of temporal-spatial information. CNNs are employed to extract localized temporal-spatial micro-patterns, while self-attention modules model global contextual dependencies across extended sequences, thereby enhancing model's representational capacity. Results The experimental results and feature visualizations demonstrate that our method achieves state-of-the-art performance on two benchmark emotion recognition datasets, reaching an average accuracy of 97.51% on the SEED dataset and 96.13% on the SEED-IV dataset. Moreover, the learned spatial features align well with known neuroscientific patterns of emotional processing. Comprehensive ablation studies further validate the necessity and effectiveness of both the spatial-encoded data processing strategy and the hybrid architecture design. Comparison with Existing Methods Compared to other hybrid neural network models, our proposed method (EEG-CMT) achieves the highest classification accuracy. Specifically, it outperforms baseline algorithms by margins ranging from 0.86% to 11.43% on the SEED dataset, and from 9.49% to 39.52% on the SEED-IV dataset. Conclusions The proposed method effectively addresses key limitations in existing EEG-based emotion recognition models by jointly leveraging spatial topology and hybrid modeling techniques. These innovations significantly improve the model's ability to recognize emotions from EEG data and provide neural interpretable insights, offering a promising direction for future research in affective brain-computer interfaces.},
}

@article {pmid41872323,
year = {2026},
author = {Qiu, S and Liu, L and Xiang, B and Jin, Z and Li, Y and Li, D and Hou, H and Li, K and Wei, G and Xie, J and Li, S and Liu, S and Chen, C and Liang, X and Sun, Q and Xiong, W},
title = {Template-independent genome editing and restoration for correcting frameshift disorders.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {41872323},
issn = {2157-846X},
support = {2021ZD0203304//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; U23A20442//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Frameshift mutations, responsible for >20% of Mendelian inherited diseases, pose substantial therapeutic challenges. Here we developed Template-Independent Genome Editing for Restoration (TIGER), a platform for the efficient and precise correction of frameshift mutations across various models. By identifying reproducible nucleotide-level factors that influence therapeutic efficacy across cells and tissues, we developed a scoring system for guide RNA (gRNA)-Cas9 outcomes. Approximately 75% of deletion and 50% of insertion mutations produced ≥30% in-frame products, sufficient for phenotypic restoration, with 38% and 65% achieving wild-type correction, respectively. To expand the applicability of TIGER across species and genome wide, we retrained the inDelphi algorithm to predict therapeutic gRNAs for single-nucleotide frameshifts. In a mouse model of deafness, delivery of SpCas9 and optimal gRNA via dual adeno-associated virus restored hearing thresholds to wild-type levels, with ~90% of in-frame edits being wild type. TIGER provides a robust and broadly applicable strategy for in vivo correction of inherited frameshift diseases.},
}

@article {pmid41874079,
year = {2026},
author = {Lorente-Piera, J and Manrique-Huarte, R and Picciafuoco, S and Lima, JP and Serra, V and Manrique, M},
title = {Beyond the Air-Bone Gap: The Role of Bone Conduction Thresholds in Predicting Functional Outcomes and Guiding Surgical Decision-Making in Active Middle Ear and Bone Conduction Implants.},
journal = {Audiology research},
volume = {16},
number = {2},
pages = {},
pmid = {41874079},
issn = {2039-4330},
abstract = {Introduction: In patients with conductive and mixed hearing loss, implantable hearing devices such as active middle ear implants (AMEIs) and bone conduction implants (BCIs) are established alternatives when conventional hearing aids fail. Although bone conduction (BC) thresholds are routinely used as eligibility criteria, their role as frequency-specific predictors of postoperative functional outcomes remains poorly defined. This study aimed to evaluate the influence of preoperative BC thresholds across the audiometric spectrum on postoperative speech recognition outcomes after implantation with AMEIs and BCIs. Methods: A retrospective observational study was conducted at a tertiary referral center including patients implanted with BCIs or AMEIs. Pre- and postoperative audiological data were analyzed, including air and bone conduction thresholds, frequency-segmented BC measures (low, mid, and high frequencies), cochlear frequency gradient (ΔBC Slope), and speech recognition scores (SRSs) at 65 dB HL one year after implantation. Results: 102 patients were included (50 BCI, 52 AMEI). Both implant types achieved significant postoperative improvements in tonal thresholds and SRS compared with pre-implantation values (all p < 0.001). High-frequency BC thresholds (BC-High, 4-6 kHz) showed a significant inverse correlation with postoperative SRS in both BCI (r = -0.382, p = 0.001) and AMEI users (r = -0.398, p < 0.001), and emerged as the only independent predictor in multivariable models (BCI: β = -0.533, p = 0.022; AMEI: β = -0.491, p = 0.020). Low- and mid-frequency BC measures were not associated with postoperative speech outcomes (all p > 0.05). ROC analyses demonstrated excellent discriminative performance of BC-High for identifying suboptimal outcomes, with area under the curve values of 0.92 for BCI (p = 0.001) and 0.94 for AMEI (p = 0.002), and implant-specific cutoff values of >47 dB HL and >61 dB HL, respectively. Conclusions: High-frequency BC thresholds showed the strongest association with postoperative speech recognition after implantable hearing rehabilitation. BC-High could function as a prognostic marker of functional outcome rather than an eligibility criterion, providing clinically meaningful information to refine preoperative counseling and individualized decision-making within current indication frameworks.},
}

@article {pmid41875491,
year = {2026},
author = {Haxel, L and Kapoor, J and Ziemann, U and Macke, JH},
title = {EDAPT: Towards calibration-free BCIs with continual online adaptation.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae5689},
pmid = {41875491},
issn = {1741-2552},
abstract = {Brain-computer interfaces (BCIs) suffer from accuracy degradation as neural signals drift over time and vary across users, requiring frequent recalibration that limits practical deployment. Our goal is to develop a framework that eliminates the need for separate calibration phases by enabling continual, real-time model adaptation to new users and changing signal characteristics. Approach. We propose EDAPT, a task- and model-agnostic framework for continual online learning. EDAPT first establishes a robust baseline decoder through population-level pretraining on data from multiple users. It then personalizes this model during deployment using supervised continual finetuning on a trial-by-trial basis. Due to its modular design, EDAPT can be composed with unsupervised domain adaptation techniques to further address distribution shifts. Main results.We validate EDAPT across nine datasets, three BCI paradigms, and four deep learning architectures. EDAPT consistently improves decoding accuracy over static models for nearly all subjects and datasets, raising mean balanced accuracy from 0.80 to 0.87 on representative datasets (Table 3). Ablation studies confirm that the combination of population-level pretraining and online finetuning is the primary driver of this performance gain, with further improvements on some datasets when using unsupervised domain adaptation techniques. We demonstrate real-time feasibility of the framework, with adaptation latencies under 200 milliseconds on consumer-grade hardware. Our scaling analysis further reveals that decoding accuracy is primarily determined by the total pretraining data budget, rather than its specific allocation between subjects and trials. Significance. These findings demonstrate that continual online learning is a practical and effective strategy for creating high-performance, user-adaptive BCIs. By systematically addressing the bottleneck of model recalibration, EDAPT reduces a major barrier to the widespread adoption of BCI technology and helps advance neurotechnology toward robust, user-friendly, real-world applications.},
}

@article {pmid41875494,
year = {2026},
author = {Kim, D and Song, CY and Hsieh, HL and Shanechi, MM},
title = {Unsupervised learning of multiscale switching dynamical system models from multimodal neural data.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae5688},
pmid = {41875494},
issn = {1741-2552},
abstract = {OBJECTIVE: Neural population activity often exhibits regime-dependent non-stationarity in the form of switching dynamics. Learning accurate switching dynamical system models can reveal how behavior is encoded in neural activity. Existing switching approaches have primarily focused on learning models from a single neural modality, either continuous Gaussian signals such as local field potentials (LFPs) or discrete Poisson signals such as spiking activity. However, multiple neural modalities are often recorded simultaneously to measure different spatiotemporal scales of brain activity, and all these modalities can encode behavior. Moreover, regime labels are typically unavailable in training data, posing a significant challenge for learning models of regime-dependent switching dynamics. These gaps highlight the need for a new unsupervised method that can learn switching dynamical system models for multiscale data and do so without requiring regime labels.

APPROACH: We develop a novel unsupervised learning algorithm that learns the parameters of switching multiscale dynamical system models using only multiscale neural observations. Doing so, the algorithm can not only fuse multiscale neural information but also account for regime-dependent switches in multiscale neural dynamics.

MAIN RESULTS: We demonstrate our method using both simulations and two distinct experimental datasets with multimodal spike-LFP observations during different motor tasks. We find that our switching multiscale dynamical system models more accurately decode behavior than switching single-scale dynamical models, showing the success of multiscale neural fusion. Further, our models outperform stationary multiscale models, illustrating the importance of tracking regime-dependent nonstationarity in multimodal neural data.

SIGNIFICANCE: The developed unsupervised learning framework enables more accurate modeling of complex multiscale neural dynamics by leveraging information in multimodal recordings while incorporating regime switches. This approach holds promise for improving the performance and robustness of brain-computer interfaces over time and for advancing our understanding of the neural basis of behavior.},
}

@article {pmid41778805,
year = {2026},
author = {Bai, K and Ge, T and Wang, C-X and Dou, Y-Y and Zhang, J-X and Li, P and Feng, X-L and Han, Y and Zhao, S-S and Su, K-M and Shang, Y-X and Yu, X and Li, S-R and Su, D and Song, J-J and Qin, X and Yu, J and Yang, C-B and Zhang, J-P and Wang, W},
title = {EEG and gut microbiota response patterns in high-altitude indigenous populations.},
journal = {mSystems},
volume = {11},
number = {3},
pages = {e0169225},
doi = {10.1128/msystems.01692-25},
pmid = {41778805},
issn = {2379-5077},
support = {2020QZDY002//Tangdu Hospital, Fourth Military Medical University/ ; axjhww//Hovering Program of Fourth Military Medical University/ ; 2018BJ003//Talent Foundation of Tangdu Hospital/ ; 2025PT-08//7T MRI Precision Neurology Platform of Shaanxi Province/ ; //Innovative Team for Early Warning and Rehabilitation of Mental Fatigue Using BCI and Virtual Reality/ ; 2024SF2-GJHX-71//Key Core Technique Program of Shaanxi Province/ ; 61240302//Science and Technology Research Project of Shaani Nuclear Industry Group Co., Ltd/ ; },
mesh = {Humans ; *Gastrointestinal Microbiome/physiology ; *Altitude ; *Electroencephalography ; Male ; Adult ; *Brain/physiology ; Female ; Young Adult ; RNA, Ribosomal, 16S/genetics ; Cognition/physiology ; },
abstract = {Indigenous high-altitude populations maintain relatively normal brain function despite chronic hypoxia, yet the underlying neurophysiological mechanisms and the potential role of gut-brain interaction remain unclear. This study combined 16S rRNA gut microbiota profiling in 211 high-altitude indigenous populations at 2, 3, and 4 km altitudes with resting-state and task-based electroencephalography recordings in 135 of them. Residents at 4 km showed enhanced delta (1-4 Hz) power across most brain regions along with increased frontal-occipital functional connectivity (FC) during resting state. During a cognitive oddball task, the 4 km group exhibited elevated P3 amplitude in response to oddball stimuli, together with larger parietal delta power. In parallel, the 4 km group displayed higher species richness and an elevated abundance of short-chain fatty acid-producing genera such as Roseburia, Blautia, and Coprococcus. Furthermore, the abundance of Blautia was positively associated with resting-state FC, a relationship that may further influence anxiety and sleep quality. Our findings demonstrate a coordinated gut-brain interaction adaptation to high altitude, highlighting the homeostatic role of microbial pathways.IMPORTANCEIndigenous high-altitude populations maintain normal cognitive function under chronic hypoxia, a process potentially involving the gut microbiota. Our study added evidence that the neural activity patterns and gut microbiota structure may work in coordination to assist the host in adapting to extreme environments.},
}

Humans
*Gastrointestinal Microbiome/physiology
*Altitude
*Electroencephalography
Male
Adult
*Brain/physiology
Female
Young Adult
RNA, Ribosomal, 16S/genetics
Cognition/physiology

@article {pmid41867018,
year = {2026},
author = {Yu, R and Shen, R and Chen, L and Li, P},
title = {Insights Into the Inhibitory Effect of Ofloxacin on Pepsin Through Peptidomics and Bioinformatics Approaches.},
journal = {Journal of biochemical and molecular toxicology},
volume = {40},
number = {4},
pages = {e70788},
doi = {10.1002/jbt.70788},
pmid = {41867018},
issn = {1099-0461},
support = {2024X007-KXZ//Beijing Polytechnic University/ ; 2023R008-JFQB//Youth Top Talent Cultivation Plan/ ; },
mesh = {*Pepsin A/chemistry/antagonists & inhibitors ; Animals ; *Ofloxacin/pharmacology/chemistry ; Cattle ; Molecular Docking Simulation ; *Proteomics/methods ; *Computational Biology/methods ; Hydrolysis ; *Peptides/chemistry ; Serum Albumin, Bovine/chemistry ; Tandem Mass Spectrometry ; },
abstract = {The hydrolysis of proteins by pepsin is of great significance for the biological utilization of proteins and the discovery of functional peptide molecules. Bovine serum albumin (BSA) and bovine collagen I (BCI) are both commonly used natural source proteins for studying the hydrolysis characteristics of pepsin. UHPLC - MS/MS, peptidomics, and molecular docking technologies were employed to investigate the underlying mechanism responsible for the inhibitory effect of ofloxacin on pepsin. The molecular weight distribution of peptides produced by pepsin in this study was mostly in the range of 600 Da to 1800 Da, and peptide segments were mostly composed of 9-11 amino acids. The predominant terminal amino acids were proline, glycine, leucine, valine, serine, and threonine. Ofloxacin led to conformational changes of the hydrolysis active sites of pepsin by forming hydrogen bonds with aspartic acids. When the key aspartic acid residues in the active center of pepsin were inhibited, the numbers of peptides TPAQD, VSVDAA, TVLFD, and TVIFD were upregulated. The hydrolysis characteristics of pepsin were changed, shown as an increase in the proportion of low molecular weight peptides and a decrease in the hydrophobicity of peptide segments in the hydrolysates. The study contributed to the evaluation of the activity of peptides from homologous protein hydrolysis by pepsin and the elucidation of the inhibitory mechanism of ofloxacin on pepsin.},
}

*Pepsin A/chemistry/antagonists & inhibitors
Animals
*Ofloxacin/pharmacology/chemistry
Cattle
Molecular Docking Simulation
*Proteomics/methods
*Computational Biology/methods
Hydrolysis
*Peptides/chemistry
Serum Albumin, Bovine/chemistry
Tandem Mass Spectrometry

@article {pmid41867762,
year = {2026},
author = {Barzon, G and De, A and Moran, I and Carnahan, C and Mazzucato, L and Kiani, R},
title = {Control of cortical population activity with patterned microstimulation.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.03.02.709018},
pmid = {41867762},
issn = {2692-8205},
abstract = {Closed-loop control of cortical activity is a central goal in systems neuroscience and clinical neuromodulation, but most approaches either rely on detailed circuit models that are unattainable in vivo or on open-loop stimulation tuned by trial and error. Here we introduce REACHable manifold Control (REACH-Ctrl), a data-driven brain-computer interface that achieves real-time control of population spiking activity using patterned microstimulation and multi-electrode recordings. REACH-Ctrl learns a finite-horizon controllability map directly from short training epochs in which random multi-electrode pulse sequences are delivered through a subset of electrodes while recording evoked responses. From these input-output data, it identifies the "reachable manifold" of population states and computes low-current microstimulation sequences that steer activity toward designated targets, without explicit knowledge of the underlying connectivity or dynamics. We test REACH-Ctrl in macaque prefrontal cortex, demonstrating high control accuracy, robust across sessions and stimulation parameters. Geometric analyses showed that multi-pulse sequences traverse a well-defined reachable manifold with substantial, but incomplete, overlap with the intrinsic neural activity manifold, revealing both on- and off-manifold components of control. Encoding models further revealed that, in our weak-stimulation regime, population responses to multi-electrode sequences are well approximated by the linear sum of localized "stimulation fields" with modest history dependence, explaining the success of our linear control approach. These results demonstrate precise, sample-efficient control of cortical population activity with clinically relevant microstimulation hardware, and provide a general blueprint for designing perturbations for sparsely observed neural circuits.},
}

@article {pmid41868420,
year = {2026},
author = {Rodino, F and Briki, M and Buclin, T and Guidi, M and Carrara, S},
title = {Dual-Biosensor for Five Drugs Detection in Precision Oncology.},
journal = {BioNanoScience},
volume = {16},
number = {4},
pages = {258},
pmid = {41868420},
issn = {2191-1630},
abstract = {ABSTRACT: The increasing demand for precision medicine, particularly in oncology, requires innovative solutions to address patient-specific inter-individual variability in drug response. Therapeutic drug monitoring (TDM) is crucial for optimizing treatment efficacy and minimizing toxic side effects, enabling precise dosage adjustments tailored to the patient's individual metabolic profile. Electrochemical biosensors offer a cost-effective, simple, and portable solution with rapid response times, making them ideal for point-of-care applications. In this work, we propose a novel dual-biosensor platform for TDM, designed to simultaneously detect multiple chemotherapeutic agents-cyclophosphamide, ifosfamide, etoposide, methotrexate, and 5-fluorouracil-for precision oncology. Following real clinical treatment scenarios, the system uses only two working electrodes integrated into a single electrochemical sensing platform, significantly reducing complexity and cost. By integrating MWCNTs with cytochrome P450 enzymes (CYP3A4 and CYP2B6), our platform achieves enhanced electron transfer and substrate specificity, enabling sensitive and selective detection of the five chemotherapeutic drugs, individually and in combination, within clinically relevant ranges. Designed for portability and rapid analysis, this dual-biosensor platform enables real-time, cost-effective drug monitoring at the point-of-care, advancing personalized cancer treatment with greater precision and accessibility.},
}

@article {pmid41869230,
year = {2026},
author = {Abazovic Bihorac, A and Kovacevic, M},
title = {Acute Ischemic Stroke: A Retrospective Study Comparing Clinical Characteristics and Outcomes in Patients With and Without Complications.},
journal = {Cureus},
volume = {18},
number = {2},
pages = {e103902},
pmid = {41869230},
issn = {2168-8184},
abstract = {BACKGROUND: Acute ischemic stroke (AIS) is a leading cause of morbidity and mortality. Post-stroke complications, both neurological and systemic, negatively affect patient outcomes, prolong hospitalization, and increase healthcare costs. Identifying high-risk patients is essential for early intervention.

AIM: To compare clinical, radiological, laboratory characteristics, and in-hospital outcomes between patients with AIS who developed complications and those who did not.

METHODS: This retrospective cohort study included 150 patients with confirmed first AIS admitted between October 2023 and October 2024. Patients were divided into two groups: Group 1 (n = 73) with in-hospital complications and Group 2 (n = 77) without complications. Demographic data, comorbidities, National Institutes of Health Stroke Scale (NIHSS) scores, brain computer tomography (CT) findings, laboratory parameters, blood pressure, complications, and outcomes were analysed. Continuous variables are presented as median (interquartile range) and categorical variables as number (%). A P-value < 0.05 was considered statistically significant.

RESULTS: Group 1 patients were older (73.0 (interquartile range (IQR) 66.5-79.0) vs. 69.0 (IQR 62.0-73.0) years; P < 0.001) and had higher NIHSS scores at admission (10.0 (IQR 5.0-16.0) vs. 5.0 (IQR 4.0-7.0); P < 0.001) and follow-up (6.0 (IQR 4.0-11.0) vs. 3.0 (IQR 2.0-5.0); P < 0.001). Large infarctions were more frequent in Group 1 (57.5% vs. 27.3%; P < 0.001), and glucose levels were higher (14.0 (IQR 10.1-16.3) vs. 6.8 (IQR 5.95-9.65) mmol/L; p = 0.027). Length of hospital stay and in-hospital mortality were greater in Group 1 (14.0 (IQR 10.0-17.0) vs. 7.0 (IQR 6.0-10.0) days; P < 0.001; 17.8% vs. 3.9%, respectively).

CONCLUSIONS: Patients with AIS who develop complications have distinct clinical and laboratory profiles, more severe neurological deficits, and worse in-hospital outcomes. Early risk identification may improve management and patient care.},
}

@article {pmid41862465,
year = {2026},
author = {Shen, C and Ding, H and Zhang, S and Xu, C and Zou, B and Ji, S and Liu, YR and Li, Y and Zhou, R and Liang, J and Shen, DD and Liu, Y and Chen, X and Rondard, P and He, J and Zhang, Y and Pin, JP and Liu, J},
title = {Functional and structural basis of a negative allostery within GABAB hetero-tetramers.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-70640-8},
pmid = {41862465},
issn = {2041-1723},
abstract = {G protein coupled receptors (GPCRs) oligomerization may allow signal integration from different GPCR units. The GABAB receptor, activated by the main inhibitory transmitter, GABA, is an obligatory heterodimer. It is the target of two therapeutic drugs, baclofen and GHB, and can form stable oligomers. The existence, roles, and possible allosteric interaction of GABAB oligomers remain elusive. Here, we show that GABAB oligomers exist in neurons. Their function can be specifically affected by human disease-associated mutations, demonstrating their essential role for normal brain function. The cryo-EM structure of a hetero-tetramer in the apo state reveals the heterodimers interacting in an asymmetrical way to prevent one unit from being activated. This represents a nice example of a negative allosteric interaction between GPCRs related to human diseases.},
}

@article {pmid41862569,
year = {2026},
author = {Liang, S and Tan, ZL and Ding, J and Dai, Y and Xu, Y and Ma, J and Song, XM and Yeo, BTT and Li, T},
title = {Peripheral immune-redox signatures associate with cortical network alterations in anhedonic depression.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {41862569},
issn = {1476-5578},
support = {82230046//National Natural Science Foundation of China (National Science Foundation of China)/ ; LTGY24H090012//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; LTGY23C090002//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
abstract = {Anhedonia is a core feature of major depressive disorder (MDD), yet links between peripheral molecular signatures and cortical network architecture remain poorly defined. We enrolled 210 participants, including 56 unmedicated MDD patients with high-anhedonia (HA), 61 with low-anhedonia (LA), and 93 healthy controls (HC). Morphometric similarity networks (MSNs) from structural MRI were compared between HA and LA. MSNs index individual-level network organization by quantifying inter-regional morphometric similarity. Regional MSN patterns were linked to Allen Human Brain Atlas using Spearman correlations with spin tests and a multi-K stability screen. Whole-blood RNA-seq (n = 199) was integrated with MSN features via sparse partial least squares-canonical correlation (sPLS-C), with key blood analyses repeated after leukocyte-composition adjustment. Gene Ontology over-representation and MAGMA gene-level analyses provided pathway context. HA showed greater MSN integration than LA, particularly within default-mode and somatomotor networks. MSN maps were negatively correlated with dopamine-transporter and kappa-opioid-receptor densities. Imaging-derived gene associations were enriched for regulation of Toll-like-receptor-3 signaling. In blood, sPLS-C revealed coupling between MSN features and a transcriptomic signature enriched for T-cell activation/differentiation and lymphocyte-apoptosis pathways. After composition adjustment, the pre-specified blood signature did not differ between HA and LA, indicating that between-group differences were largely composition-driven. As supportive genetic context, over-representation on MAGMA FDR-significant genes suggested protocadherin-mediated homophilic adhesion. Peripheral immune-redox pathway enrichment aligns with anhedonia-related cortical network alterations, whereas between-group blood differences are chiefly composition-driven. Adjusting for blood-cell composition is essential, this multimodal framework nominates immune-modulatory/redox targets and synaptic-adhesion biology for precision stratification and intervention.},
}

@article {pmid41863372,
year = {2026},
author = {Yin, Y and Wei, W and Deng, L and Li, X and Ma, X and Zhao, L and Deng, W and Guo, W and Sham, PC and Wang, Q and Li, T},
title = {Disrupted Structural Covariance in Schizophrenia, Bipolar Disorder, and Major Depressive Disorder.},
journal = {Schizophrenia bulletin},
volume = {52},
number = {2},
pages = {},
pmid = {41863372},
issn = {1745-1701},
support = {82230046//National Natural Science Foundation of China/ ; U25A2079//National Natural Science Foundation of China/ ; 82171499//National Natural Science Foundation of China/ ; 82571712//National Natural Science Foundation of China/ ; 82001410//National Natural Science Foundation of China/ ; 2021ZD0200404//STI 2030-Major Projects/ ; 2021ZD0200800//STI 2030-Major Projects/ ; 20241203A14//Key Research and Development by Hangzhou Science and Technology Bureau/ ; CXTD202501053//Zhejiang Clinovation Pride/ ; 2022WJC265//Hangzhou Biomedical and Health Industry Development Support Science and Technology Project/ ; 2025HZGF10//Construction Fund of Key Medical Disciplines of Hangzhou/ ; },
mesh = {Humans ; *Bipolar Disorder/physiopathology/diagnostic imaging/pathology ; *Major Depressive Disorder/physiopathology/diagnostic imaging/pathology ; *Schizophrenia/physiopathology/diagnostic imaging/pathology ; Male ; Female ; Adult ; Magnetic Resonance Imaging ; *Nerve Net/diagnostic imaging/physiopathology/pathology ; Young Adult ; Middle Aged ; *Default Mode Network/diagnostic imaging/physiopathology ; },
abstract = {BACKGROUND AND HYPOTHESIS: Shared clinical features and genetic factors in schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) have led to the hypothesis of common pathophysiological mechanisms. This study aims to elucidate aberrant transdiagnostic structural covariance patterns across these disorders employing a multivariate analytical approach.

STUDY DESIGN: Structural magnetic resonance imaging data were acquired from a sample of 704 subjects, comprising 244 healthy controls, 119 first-episode treatment-naïve SCZ individuals, 159 BD individuals, and 182 treatment-naïve MDD individuals. Seed-based partial least squares correlation analysis was applied to construct structural covariance networks (SCNs) across 6 predefined functional networks: the default mode network (DMN), dorsal attention network (DAN), frontoparietal control network (FPCN), somatomotor network (SMN), ventral attention network (VAN), and visual network. Network seeds were selected based on functional network definitions. Spatial distributions of SCNs were calculated, and individual network integrity indices were derived as measures of SCN strength. Group comparisons of network integrity were performed using multiple t-tests to identify network-specific alterations across the diagnostic groups.

STUDY RESULTS: Structural covariance patterns exhibited spatial distributions akin to those of functional networks. Network integrity showed common reductions across all 3 disorders in DMN, DAN, and FPCN, while BD showed specific reductions in the SMN, and both BD and MDD showed reductions in the VAN. Furthermore, there was a significant correlation between individualized network integrity and clinical and cognitive manifestations.

CONCLUSIONS: Our results highlight the potential of the integrity of SCNs as transdiagnostic biomarkers.},
}

Humans
*Bipolar Disorder/physiopathology/diagnostic imaging/pathology
*Major Depressive Disorder/physiopathology/diagnostic imaging/pathology
*Schizophrenia/physiopathology/diagnostic imaging/pathology
Male
Female
Adult
Magnetic Resonance Imaging
*Nerve Net/diagnostic imaging/physiopathology/pathology
Young Adult
Middle Aged
*Default Mode Network/diagnostic imaging/physiopathology

@article {pmid41864054,
year = {2026},
author = {Kaur, A and Garg, R and Prasad, S},
title = {A comprehensive review of EMG/EEG based wheelchair control systems for individuals with disabilities: HMI and BCI perspectives.},
journal = {Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology},
volume = {88},
number = {},
pages = {103134},
doi = {10.1016/j.jelekin.2026.103134},
pmid = {41864054},
issn = {1873-5711},
abstract = {Human-machine interface (HMI) and brain-computer interface (BCI) are proving to help make technologies better and helpful for people with disabilities. These systems give individuals the ability to easily control wheelchair, and enhance their quality of life. This review focuses on the use of EMG (muscle activity) and EEG (brain activity) signals, considered primarily as individual modalities, for wheelchair control. EMG signals facilitate muscle control, which is particularly useful for individuals with motor impairments or impaired limb function. On the other hand, EEG-based BCIs enable independent navigation for individuals with severe motor disorders by systematically analyzing brainwave patterns. This review covers the literature from 2014 to 2024 and focuses on signal acquisition, filtering, feature extraction, and classification techniques. It also highlights the challenges of signal processing, inter-subject interaction, and real-time optimization. Based on the analyzed studies, research gaps are identified, and future directions are outlined, including the potential integration of multimodal EEG-EMG approaches as an emerging research trend for developing more user-centric and adaptive wheelchair systems.},
}

@article {pmid41864786,
year = {2026},
author = {Hu, X and He, J and Li, N and Mo, J and Yao, S and Lu, Y and Huang, M and Jiang, P and Pang, M and He, L and Gong, J and Liu, Z and Xie, X and Xu, J and Hu, X and Krassioukov, AV and Zhang, L and Liu, B and Rong, L},
title = {Bridging cortical intentions: brain-computer interfaces for spinal cord injury recovery.},
journal = {Science bulletin},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.scib.2026.03.016},
pmid = {41864786},
issn = {2095-9281},
}

@article {pmid41865023,
year = {2026},
author = {Khalikov, R and Soghoyan, G and Sintsov, M and Lebedev, M},
title = {Wearable optomyography enables continuous neuroprosthetic control.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-025-32646-y},
pmid = {41865023},
issn = {2045-2322},
support = {21-75-30024//Russian Science Foundation/ ; 21-75-30024//Russian Science Foundation/ ; 21-75-30024//Russian Science Foundation/ ; },
abstract = {Wearable devices are increasingly used to enable human-machine interfaces, such as typing or cursor control, through wristbands that translate surface electromyographic (sEMG) signals into computer commands. However, traditional sEMG techniques face several limitations, including challenges with sensor fixation, signal cross-talk, instability over time, and susceptibility to electrical and mechanical artifacts. In this study, we propose an alternative approach to capturing and interpreting muscle activity using optomyography (OMG). Our OMG system - a wristband with 50 data channels, facilitates various computer mouse-like controls. Decoding is achieved through an efficient, compact, fully connected neural network trained on data from a center-out task performed with hand gestures. Eight able-bodied participants and one individual with limb loss successfully mastered OMG-based controls in tasks such as acquiring targets across various screen positions and playing Tetris. Performance improvements with training were assessed using metrics such as deviations from a straight trajectory, temporal deviation from an optimal path, and dwell time near the target prior to successful selection. These results highlight the potential of next-generation wearable devices to exceed conventional approaches in performance, accuracy, stability, and versatility.},
}

@article {pmid41860566,
year = {2026},
author = {Lee, H and Lee, S and Hwang, KS and Kim, G and Hong, Y and Kim, M and Eun, J and Kim, HN and Chou, N and Shin, H},
title = {Biocompatible Lubricant-Coated Flexible Neural Probes with Enhanced Long-Term Recording Stability.},
journal = {ACS applied bio materials},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsabm.5c02232},
pmid = {41860566},
issn = {2576-6422},
abstract = {Implantable neural probes enable high-resolution, multi-unit recordings and are essential tools for studying neurological disorders and developing brain-machine interface (BMI) technologies. However, conventional metal- or silicon-based probes exhibit significant mechanical mismatch with brain tissue, both of which elicit inflammatory responses and compromise long-term recording stability. Here, we introduce a flexible neural probe fabricated through a commercial flexible printed circuit board (FPCB) process and functionalized with a biocompatible lubricant coating to overcome these challenges. The inherent flexibility of the FPCB minimizes mechanical mismatch with brain tissue, while the coating enhances surface hydrophobicity and reduces insertion friction, thereby minimizing tissue damage during implantation. Its resistance to water ingress contributes to maintaining the probe's electrical insulation stability, supporting stable long-term performance. In chronic mouse hippocampal implants, lubricant-coated probes maintained consistent neural signal quality for several weeks, while immunohistochemical analysis revealed markedly reduced astrocytic and microglial activation (GFAP/Iba1) compared with uncoated controls, indicating effective mitigation of neuroinflammation. In vitro cell viability assays further confirmed the high biocompatibility of the coated devices. Importantly, because this approach leverages scalable and cost-effective FPCB manufacturing, it enables the production of flexible neural interfaces that combine long-term electrical and biological stability with manufacturing practicality. This work establishes a broadly applicable strategy for next-generation neural probes, offering durable, minimally invasive, and scalable solutions for chronic recordings in BMI systems, deep brain stimulation, and neurological disease models.},
}

@article {pmid41861408,
year = {2026},
author = {Ukaegbu, UFF and Houshmand, S and Hammond, L and Adams, K and Andersen, J and Rouhani, H},
title = {Navigation Paradigms for Non-invasive BCI-controlled Wheelchairs: A Systematic Review.},
journal = {Progress in biomedical engineering (Bristol, England)},
volume = {},
number = {},
pages = {},
doi = {10.1088/2516-1091/ae5563},
pmid = {41861408},
issn = {2516-1091},
abstract = {Brain-controlled powered wheelchairs represent a promising advancement for individuals with neurological conditions that significantly impair motor function. Despite substantial progress, brain-controlled wheelchairs have not been adapted for real-world settings. This article systematically reviews recent trends in brain-computer interface (BCI) technology for wheelchair navigation and control, highlighting the contributions and limitations of various navigation paradigms. This review was conducted in accordance with the PRISMA guidelines, sourcing studies from four databases (PubMed, Scopus, IEEE Xplore, Google Scholar) published between 2000 and April 2025. This review focused on non-invasive BCI paradigms and real-world navigation experiments. The results were narratively synthesized and classified into two primary categories: BCI-based navigation paradigms and wheelchair-based navigation paradigms, along with intersecting concepts such as single-variant BCI, hybrid BCI, control switches, and proportional control. Of the 149 full-text articles reviewed, 47 were included and categorized by navigation paradigm, comprising 20 BCI-based and 27 wheelchair-based studies, with 6 involving participants with motor disabilities. Quality assessment scores ranged from 40% to 95%, with approximately 40% of the studies demonstrating a low risk of bias. The findings indicate that low-level navigation control was predominant in BCI wheelchair studies, with 31 studies employing minimal or no obstacle avoidance. Most studies (57%) integrated sensors for obstacle avoidance, localization, mapping, and autonomous navigation. Twenty-two studies utilized control switches, and five incorporated proportional control for wheelchair navigation. Additionally, motor imagery and steady-state visually evoked potential (SSVEP) paradigms have emerged as the most common approaches for generating control commands, highlighting their potential for effective navigation. Given the potential societal impact on a large number of individuals, future research should prioritize enhancing the reliability and adaptability of BCI wheelchair systems in real-world environments. .},
}

@article {pmid41861827,
year = {2026},
author = {Liu, V and Kong, Z and Fu, J and Zheng, L and Wang, I and Wang, M and Du, Y and Zuo, L and Qiu, B and Zhong, C and Zhu, L and Yuan, Z and Zhang, X and Hongwen Song, },
title = {Moral inconsistency is based on the vmPFC's insufficient representation across tasks and connectedness.},
journal = {Cell reports},
volume = {},
number = {},
pages = {117058},
doi = {10.1016/j.celrep.2026.117058},
pmid = {41861827},
issn = {2211-1247},
abstract = {Moral inconsistency-misaligning one's behavior with the same moral principle of judging others-undermines personal reputations and social relationships. This study explores the neural underpinnings of moral inconsistency in a profit-honesty trade-off setting with functional magnetic resonance imaging and transcranial temporal interference stimulation (tTIS). Experiment 1 demonstrated that participants showed inconsistent sensitivity to profit and honesty between moral behavior and moral judgment tasks. Furthermore, multivariate pattern analyses showed that participants with higher moral inconsistency exhibited reduced judge score representation across tasks and weaker connectedness during the moral behavior task in the ventromedial prefrontal cortex (vmPFC). Experiment 2 showed that tTIS modulation of the vmPFC increased moral inconsistency. These findings indicate the vmPFC's involvement in the neural basis of moral inconsistency. While individuals with higher moral inconsistency may be aware of moral principles when making decisions, they consider moral principles less and do not integrate them into their own behaviors.},
}

@article {pmid41862057,
year = {2026},
author = {Canal-Rivero, M and Baca-García, E and Barrigón, ML and Ruiz-Veguilla, M and Crespo-Facorro, B},
title = {Shifting vulnerabilities in suicide mortality from the COVID-19 crisis to the socioeconomic aftermath in Spain (2016-2024): A Bayesian triple-interaction analysis.},
journal = {Journal of affective disorders},
volume = {405},
number = {},
pages = {121650},
doi = {10.1016/j.jad.2026.121650},
pmid = {41862057},
issn = {1573-2517},
abstract = {BACKGROUND: The transition from the acute Coronavirus Disease 2019 (COVID-19) crisis to the subsequent socioeconomic aftermath introduced complex stressors. We aimed to determine the differential impacts of pandemic onset (March 2020) and the socioeconomic aftermath (July 2021) on suicide mortality in Spain, examining heterogeneous effects by sex and age.

METHODS: We analysed 108 months (2016-2024) of national registry data. Using a Bayesian Interrupted Time-Series (ITS) design with a Triple Interaction framework (Sex×Age×Event), we isolated immediate (level) and long-term (trend) risk trajectories, adjusting for Gross Domestic Product (GDP), Public Health Expenditure (PHE), and (COVID-19) mortality. Leave-One-Out Cross-Validation (LOO-CV) was used to validate the complex specification against simpler models.

RESULTS: Impacts differed fundamentally across demographics. Pandemic onset was associated with an immediate increase in men aged 80+ (Rate Ratio [RR] = 1.46; 95% BCI 1.13-1.90), while other male groups remained stable. Conversely, the socioeconomic aftermath triggered a delayed acute shock in women, specifically aged 15-29 (RR = 1.66; 95% BCI 1.05-2.68). Bayesian comparison confirmed simpler models failing to account for triple interactions obscured these effects.

LIMITATIONS: The ecological design precludes causal inference at the individual level.

CONCLUSIONS: Suicide risk pathways were highly heterogeneous: male vulnerability was concentrated in the elderly during the initial viral threat, whereas female vulnerability emerged later as a delayed response to the socioeconomic aftermath. Prevention requires adapting strategies to the distinct nature of immediate isolation in older men versus delayed socioeconomic strain in women.},
}

@article {pmid41853193,
year = {2026},
author = {Khanam, H and Hoque, A and Jafar Mazumder, MA and Arafat, MT},
title = {Catechol functionalized polyguluronate enriched sodium alginate wetspun fibers with immobilized platelet lysate for diabetic wound healing.},
journal = {RSC advances},
volume = {16},
number = {16},
pages = {14328-14349},
pmid = {41853193},
issn = {2046-2069},
abstract = {The development of advanced wound dressings with multifunctional properties is crucial for accelerating healing in diabetic wounds. Platelet lysate contains many biologically active substances, which have tremendous clinical benefits in treating diabetic wounds. However, its clinical use and therapeutic efficacy are severely limited by its poor mechanical qualities and the sudden release of active chemicals. To address these challenges and minimize the risk of wound infection, sodium alginate-polyethylene glycol wetspun fibers were developed and immobilized with platelet lysate. Furthermore, surface modification with dopamine introduced catechol groups, enhancing interfacial adhesion and bioactivity to promote effective healing in diabetic wounds. Morphological and physicochemical analyses confirmed improved thermal stability and crystalline behavior in the dopamine modified fibers (SA-PEG-D-PL). The modified fibers achieved sustained PL release over 18 days with 90% cumulative release, a 30% improvement over free PL and a 20% improvement over unmodified fibers. The whole blood clotting index demonstrated a notably lower BCI of 15% for dopamine functionalized fibers, indicating enhanced coagulation potential due to increased surface striation and water absorption. Moreover, in a diabetic mice wound model, the functionalized fibers drove >85% wound closure by day 7 and complete reepithelialization by day 14, while reducing scar formation to a scar index of 7.3, significantly lower than controls (22-42.6). These outcomes suggest that the synergistic effects of dopamine functionalization and PL immobilization on alginate based fibrous matrices not only improve mechanical and biological responses but also accelerate wound closure and minimize scarring. Overall, the developed dopamine modified fibers demonstrate high potential as an advanced wound care material for diabetic patients.},
}

@article {pmid41855051,
year = {2026},
author = {He, X and Daly, I and Gu, W and Chen, Y and Wu, X and Chen, W and Wang, X and Cichocki, A and Jin, J},
title = {TBMSCCN: Two-Branch Multi-Scale Convolutional Correlation Network for Steady-State Visual Evoked Potential Classification.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3676014},
pmid = {41855051},
issn = {1558-2531},
abstract = {In recent years, artificial neural networks have been effectively used to improve the target recognition performance of steady-state visual evoked potential (SSVEP) based Brain-Computer interfaces (BCIs). However, these models require the collection of a large number of calibration trials from users, which typically results in a poor user experience. When fewer calibration trials are acquired this leads to insufficient training of model parameters and weak recognition performance. To tackle these issues, this study proposes a two-branch multi-scale convolutional correlation network (TBMSCCN) in which a correlation network framework is introduced to reduce the model training parameters and prior knowledge of the SSVEP is used to enhance the model representation ability and convergence. First, a multi-scale temporal convolution module is designed to learn local temporal dependencies in a parallel two-branch feature extraction module. Next, a contrastive loss function is constructed in the latent feature space, which can guide the model to learn the intra-class consistent features while speeding up model convergence. Finally, a group convolution module is used as a decision layer to reduce the network parameters, while learning distinguishability features between targets and non-targets. Our offline tests on two public datasets show that proposed TBMSCCN method outperforms TRCA, eTRCA, DNN, Conv-CA and Bi-SiamCA in individual calibration scenarios, which can achieve an average information transform rates (ITRs) of 378.03 ± 139.18 bit/min and 198.92 ± 111.27 bit/min on the "Benchmark" dataset and the "Beta" dataset respectively. Additionally, proposed TBMSCCN method outperform FBCCA, ttCCA, EEGNet, and TST-CFSR in calibration-free scenarios. Furthermore, an online Chinese spelling experiment confirmed the real-world effectiveness of the proposed method. The proposed model has the characteristics of low parameter and strong robustness, which can facilitate the practical engineering application of SSVEP-Based-BCI system. The code is available at https://github.com/xinjieHe123/TBMSCCN.},
}

@article {pmid41855458,
year = {2026},
author = {Song, S and Li, X and Pan, P},
title = {Application and prospects of brain-computer interface technology for motor function reconstruction after brachial plexus injury.},
journal = {Annals of medicine},
volume = {58},
number = {1},
pages = {2646355},
doi = {10.1080/07853890.2026.2646355},
pmid = {41855458},
issn = {1365-2060},
mesh = {Humans ; *Brain-Computer Interfaces/trends ; *Brachial Plexus/injuries/physiopathology ; Recovery of Function/physiology ; *Brachial Plexus Neuropathies/rehabilitation/physiopathology ; },
abstract = {BACKGROUND: Brachial plexus injury (BPI) is a severe peripheral nerve disorder leading to significant upper limb motor dysfunction. While traditional surgeries like nerve grafting and tendon transfer exist, functional outcomes are often suboptimal due to biomechanical limitations and slow neural recovery. Brain-computer interface (BCI) technology has emerged as a promising innovative pathway for motor function reconstruction.

OBJECTIVE: This review systematically evaluates the current applications, physiological mechanisms, and technical challenges of BCI technology specifically within the clinical framework of BPI rehabilitation.

METHODS: We analysed recent research breakthroughs focusing on neural repair mechanisms, clinical translational applications of BCI-controlled neuroprosthetics, and the integration of novel biomaterials.

RESULTS: BCI technology facilitates cortical remapping after standard BPI procedures like nerve transfers by providing synchronised closed-loop feedback. Unlike applications for amputees that drive external prosthetics, BCI in BPI focuses on in-situ muscle activation via a "neural bypass" to prevent disuse atrophy and restore a sense of agency. Furthermore, BCI-mediated neuromodulation shows unique potential in alleviating chronic deafferentation pain by down-regulating pathological cortical hyperexcitability. Emerging technologies like conductive hydrogels and hybrid BCI systems are addressing current bottlenecks in signal stability and control accuracy.

CONCLUSION: BCI technology represents a transformative approach for BPI rehabilitation, moving from mechanical substitution to biological reactivation. Overcoming technical barriers in signal reliability and establishing personalised rehabilitation systems are essential for their broad clinical translation.},
}

Humans
*Brain-Computer Interfaces/trends
*Brachial Plexus/injuries/physiopathology
Recovery of Function/physiology
*Brachial Plexus Neuropathies/rehabilitation/physiopathology

@article {pmid41856938,
year = {2026},
author = {Wang, G and Song, X and Jiang, L and Zhang, Y and Yao, D and Lu, J and Xu, P and Li, F},
title = {A Lightweight Dual-Attention Neural Network for Robust and Efficient EEG Motor Imagery Decoding.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2650026},
doi = {10.1142/S0129065726500267},
pmid = {41856938},
issn = {1793-6462},
abstract = {Motor imagery-based brain-computer interface (MI-BCI) faces a critical challenge in achieving effective spatial-temporal feature modeling while maintaining a compact model parameterization. Herein, a lightweight model was proposed, termed as Dual-Attention-EEGNet (DA-EEGNet), which extends the EEGNet backbone by integrating a channel attention module and a depth attention module to selectively emphasize informative electrodes and temporally discriminative features. Two widely used MI benchmark datasets and three evaluation strategies, i.e. subject-dependent scenario, subject-independent scenario, and dataset-independent classification scenario, were utilized to verify the model's performance. Despite its compact design, DA-EEGNet contains merely 3.97[Formula: see text]k trainable parameters and achieves average classification accuracies of [Formula: see text] and [Formula: see text], outperforming or matching existing deep learning approaches that rely on substantially larger parameter counts. Ablation studies further confirm the complementary contributions of the channel and depth attention modules. In addition, visualization analyses, including temporal attention heatmaps and motor-area topographies, demonstrate that DA-EEGNet captures neurophysiologically meaningful spatial-temporal patterns consistent with MI-related brain activity. These results indicate that DA-EEGNet provides a favorable parameter-accuracy trade-off and serves as an efficient and interpretable baseline for MI-BCI applications.},
}

@article {pmid41857029,
year = {2026},
author = {Liu, W and Chen, Y and Wang, X and Fang, T and Wang, R and Cheng, Y and Zhao, X and Fan, Q and Gao, W and Ming, D},
title = {Dual-axis myelination covariance drives the functional connectivity emergence during infancy.},
journal = {Nature communications},
volume = {17},
number = {1},
pages = {},
pmid = {41857029},
issn = {2041-1723},
support = {82202249//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Myelin Sheath/physiology/metabolism ; White Matter/physiology/growth & development/diagnostic imaging ; Gray Matter/physiology/growth & development/diagnostic imaging ; Connectome/methods ; Infant, Newborn ; Male ; Female ; Infant ; Magnetic Resonance Imaging ; *Brain/physiology/growth & development ; *Nerve Net/physiology ; },
abstract = {The mechanisms linking structural maturation to the emergence of functional networks in the perinatal brain remain unresolved. While prevailing models attribute functional connectivity to white matter myelination, neonates paradoxically exhibit adult-like resting-state networks despite profoundly immature white matter tracts. Here, we proposed gray matter myelination covariance as a critical basis of early functional connectivity emergence. We introduced a dual-axis myelination covariance framework and derived a myelination-function coupling (MFC) index specific to the newborn brain. Results revealed that the MFC exhibited distinct spatial patterns dominated by primary sensory and motor cortices, increased with age, and showed a distance-dependent strength. Crucially, neonatal MFC patterns showed a strong spatial correlation with gene expression profiles implicated in neurovascular coupling and specifically predicted later behaviors. These findings suggest that during infancy, the integration of brain function is not initially dominated by only the white matter connections but is also shaped by the synchrony of intracortical microstructure that reflects shared developmental trajectories, which offers a framework for understanding the formation of the developmental connectome.},
}

Humans
*Myelin Sheath/physiology/metabolism
White Matter/physiology/growth & development/diagnostic imaging
Gray Matter/physiology/growth & development/diagnostic imaging
Connectome/methods
Infant, Newborn
Male
Female
Infant
Magnetic Resonance Imaging
*Brain/physiology/growth & development
*Nerve Net/physiology

@article {pmid41857304,
year = {2026},
author = {Wang, Z and Xu, M and Yao, J and Yu, Y and Hu, B and Wang, Y and Wang, Y and Zhang, X},
title = {Review of electroencephalography and electromyography research in robotics: opportunities and challenges.},
journal = {Visual computing for industry, biomedicine, and art},
volume = {9},
number = {1},
pages = {},
pmid = {41857304},
issn = {2524-4442},
support = {62072388//National Natural Science Foundation of China/ ; 2024HZ01040037//Fujian Provincial Science and Technology Major Project/ ; 20244BAB28039//Jiangxi Provincial Natural Science Foundation Key Project/ ; 3502Z20231043//Xiamen Public Technology Service Platform/ ; },
abstract = {In the evolving nexus of neuroscience and robotics, the symbiotic fusion of electroencephalography (EEG) and electromyography (EMG) is emerging as a paradigm-shifting avenue for enhancing human-machine interfaces. While EEG, which captures the subtle electrical nuances of the brain, offers a potent channel for nuanced brain-machine communication, EMG serves as a bridge, converting neuromuscular intentions into actionable directives for robotic apparatuses. This review highlights the current methodologies in which EEG and EMG not only function in silos but also converge harmoniously to dictate robotic control. By delving deeper into this, the intricate synergy between cognitive processes, muscular responses, and machine actions can be unraveled. Subsequently, the discourse also navigates through the myriad challenges encountered in realizing real-time, seamless integration of these bio-signals with robotics and the innovative solutions poised to address them. The aim is to provide a comprehensive understanding of the interplay between neuroscience and robotics. This insight will help drive breakthroughs in adaptive human-machine collaboration.},
}

@article {pmid41857397,
year = {2026},
author = {Liu, J and Peng, F and Li, P and Yao, C and Jin, S and Wu, G and Zhang, T and Liang, Q and Wang, X and Du, X},
title = {Mechanistic insights into cannabidiol-mediated TrkB activation via FRS2 interaction in attenuating Alzheimer's disease pathology and cognitive impairment.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {41857397},
issn = {1476-5578},
support = {82550005//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Alzheimer's disease (AD) is characterized by progressive synaptic failure, neuroinflammation, amyloid and tau pathology, yet effective disease-modifying therapies remain limited. Cannabidiol (CBD) has shown neuroprotective potential in AD, but its direct molecular targets and signaling mechanisms remain unclear. Here, we demonstrate that CBD ameliorates cognitive and emotional deficits in 3×Tg-AD mice by restoring synaptic integrity and plasticity. At the mechanistic level, CBD activated TrkB signaling independently of BDNF, leading to suppression of tau hyperphosphorylation via the PI3K/AKT/GSK3β pathway and attenuation of neuroinflammation and amyloid pathology through inhibition of the JAK2/STAT3/SOCS1 axis. Using isothermal shift assays combined with biophysical binding analyses, we identified FRS2, a core adaptor protein of TrkB, as a direct molecular target of CBD. Molecular dynamics simulations further revealed that CBD stabilizes the FRS2-TrkB interface, thereby facilitating TrkB activation. Importantly, genetic knockdown of FRS2 abolished CBD-induced TrkB signaling and its downstream neuroprotective effects in both cellular and in vivo AD models. Together, these findings identify FRS2 as a critical signaling node mediating BDNF-independent TrkB activation by CBD and establish a mechanistic framework linking CBD to disease-modifying pathways in AD.},
}

@article {pmid41858309,
year = {2026},
author = {Xiao, Y and Yang, L and Qu, Y and Zhang, S and Ke, S and Ke, C and Li, Y and Hao, M and Wang, C and Xue, P and Zhang, Z and Huang, H and Liu, Y and Cheng, Z and Ye, C and Chu, PK and Yu, XF and Wang, J},
title = {Natural Superlattice 2D Materials-based Volatile Memristor Promotes Artificial Nociceptor.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {},
number = {},
pages = {e14931},
doi = {10.1002/smll.202514931},
pmid = {41858309},
issn = {1613-6829},
support = {2024YFB3614200//National Key R&D Program of China/ ; 62365010//National Natural Science Foundation of China/ ; 62274058//National Natural Science Foundation of China/ ; 2023A1515110590//Guangdong Basic and Applied Basic Research Foundation/ ; 2024A1515030176//Guangdong Basic and Applied Basic Research Foundation/ ; 2025B1515020088//Guangdong Basic and Applied Basic Research Foundation/ ; 20232BCJ23011//Jiangxi Provincial Cultivation Program for Academic and Technical Leaders of Major Disciplines/ ; JCYJ20220818100806014//Shenzhen Science and Technology Program/ ; 2024B1212010010//Guangdong Provincial Key Laboratory of Multimodality Non-Invasive Brain-Computer Interfaces/ ; DON-RMG 9229021//City University of Hong Kong Donation Research Grants/ ; 9220061//City University of Hong Kong Donation Research Grants/ ; },
abstract = {Memristors show promise in neuromorphic computing because of their resistive switching properties and memory functions. The integration of high-performance memristor devices with sensors offers an effective pathway toward energy-efficient edge-computing systems. Herein, using the natural superlattice 2D material of BiTiS3 composed of alternating BiS and TiS2 sublayers, a volatile memristor with a low operating voltage is designed and demonstrated. The lattice distortion and sulfur vacancies in BiTiS3 enhance ion migration and filament formation, as verified by conductive atomic force microscopy and X-ray photoelectron spectroscopy. This defect-induced enhancement of ion transport promotes the rapid formation and dissolution of conductive filaments, thereby implementing the memristors' volatile switching behavior. The nociceptive functions, such as pain hypersensitivity and allodynia, are mimicked. This biomimetic nociceptor system effectively emulates the biological pain response pathways, converts physical stimuli into electrical signals, and generates the appropriate neural-like outputs. Our results highlight the potential of memristors in bioinspired electronics and reveal a new strategy for intelligent bionic devices and artificial sensing systems.},
}

@article {pmid41859480,
year = {2026},
author = {Shao, Z and Gu, Z and Che, L and Yu, Z and Li, Y},
title = {Dynamic graph based attention spectral network for motor imagery-brain computer interface.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1755549},
pmid = {41859480},
issn = {1662-5161},
abstract = {Motor imagery-based brain computer interface (MI-BCI) have been increasingly adopted in neurorehabilitation and related fields. The performance of MI-electroencephalogram (MI-EEG) decoding algorithms is central to the advancement of MI-BCI. However, current studies often lack rigorous investigation into the brain's complex network organization. Moreover, most existing methods do not incorporate the cross-frequency coupling (CFC) phenomena that occur during MI into their algorithmic designs, nor do they adequately account for how temporal dynamics across different MI stages influence decoding outcomes. To address these limitations, we propose the Dynamic Spectral-Spatial Interaction Convolution Neural Network (DSSICNN), a parameter-efficient MI-EEG decoding framework that jointly extracts temporal-spectral-spatial features. DSSICNN adopts a dual-branch parallel architecture to concurrently learn spatial representations in both Euclidean and non-Euclidean domains. It further integrates a CFC-inspired attention module to model cross-spectral interactions, followed by an additional attention mechanism that quantifies the contributions of distinct MI stages to decoding performance. DSSICNN achieves decoding performance on two public datasets that surpasses the current state-of-the-art (SOTA) under both session-dependent and session-independent settings. Beyond its empirical advantages, DSSICNN offers design insights for developing Graph Neural Network (GNN)-based MI-EEG decoding algorithms and provides a network neuroscience-inspired perspective for understanding the neurophysiological mechanisms underlying MI.},
}

@article {pmid41846942,
year = {2026},
author = {Daie, K and Aitken, K and Rózsa, M and Bull, MS and Humphreys, PC and Wang, ZC and Kinsey, L and Kulkarni, M and Stachenfeld, KL and Eckstein, MK and Kurth-Nelson, Z and Clopath, C and Lillicrap, TP and Botvinick, M and Golub, M and Mihalas, S and Svoboda, K},
title = {Functional reorganization of motor cortex connectivity during learning.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.03.03.709199},
pmid = {41846942},
issn = {2692-8205},
abstract = {Learning new tasks requires the brain to reshape the flow of neural activity, but how these changes arise from dynamic neural connectivity remains unclear. Here, we used two-photon photostimulation and calcium imaging to map learning-related changes in connectivity in layer 2/3 of mouse motor cortex, induced by learning of an optical brain-computer interface (BCI) task. Mice rapidly (within minutes) learned to change activity in a conditioned neuron to earn rewards. Activity changes were sparse; the conditioned neuron increased activity more than surrounding neurons. Mapping connectivity before and after learning revealed changes in motor cortex connectivity, enriched in neurons that were active before trial initiation, analogous to motor cortex populations that are active preceding movement. Motor cortex plasticity reroutes preparatory activity to neurons that are active later and control the conditioned neuron. Our findings show how rapid learning can be achieved through structured changes in motor cortex connectivity.},
}

@article {pmid41849802,
year = {2026},
author = {Shaikh, UQ and Kalra, A and Lowe, A and Niazi, IK},
title = {Multi-head noise regression for single-channel EEG: estimating ocular and muscle contamination to guide artifact removal.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae541d},
pmid = {41849802},
issn = {1741-2552},
abstract = {EEG is often contaminated by ocular (EOG) and muscle (EMG) artifacts, yet many pipelines apply uniform denoising, risking distortion of clean neural activity. We propose a two-head, single-channel regressor that estimates EOG and EMG noise-to-signal ratio (NSR, dB) from short segments and test whether it can guide selective artifact reduction, including downstream BCI decoding. Approach. Using EEGdenoiseNet clean EEG and artifact exemplars, we synthesised 2-s single-channel mixtures with known EOG/EMG NSR spanning -10 to +10 dB and trained several model families to jointly regress both NSRs. Generalisation was evaluated on an independent eyeblink dataset via agreement with regression-based ocular-reference topographies, and in two applications: (i) gating stationary wavelet blink removal on a P3 ERP dataset and (ii) gating the same denoiser on a 55-subject RSVP P300 speller dataset (FP1/FP2). Main results. A dilated temporal convolutional network (TCN) performed best (EOG: MAE ≈ 1.8 dB, R[2] ≈ 0.82; EMG: MAE ≈ 1.0 dB, R[2] ≈ 0.94) with low bias across NSR. The EOG head recovered blink topographies (median spatial correlation ≈ 0.91). On the P3 dataset, indiscriminate wavelet denoising reduced significant ERP channels, whereas TCN-guided gating preserved 22-23 of 24 while processing ~9-20% of segments. On the speller dataset, denoising all epochs reduced decoding, while selective denoising improved AUC (θ = 9 dB: ΔAUC = 0.327, p = 0.0040) while denoising 12.45 ± 9.29% of test segments. Significance. Multi-head noise regression provides interpretable, continuous ocular and muscle contamination estimates that can act as control signals for conservative, noise-aware artifact handling under constrained-lead conditions. .},
}

@article {pmid41850148,
year = {2026},
author = {Oullier, O and Roser, F and Barbaste, P and Vasques, X},
title = {Improving consciousness assessment through neuroadaptive artificial intelligence and quantum-enhanced brain-computer interfaces.},
journal = {Clinical neurology and neurosurgery},
volume = {266},
number = {},
pages = {109396},
doi = {10.1016/j.clineuro.2026.109396},
pmid = {41850148},
issn = {1872-6968},
abstract = {Accurate assessment of consciousness in patients with disorders of consciousness (DoC) remains a major clinical challenge, particularly when motor impairment masks evidence of preserved awareness. Recent advances in neuroadaptive artificial intelligence (NA-AI) may help transform brain-computer interfaces (BCIs) from experimental systems into more clinically scalable tools tailored to each patient, continuously adjusting their models in real time to changes in an individual's (neuro)physiological signals. Generative and self-adapting AI models can account for inter-individual variability and temporal instability in neural signals, enabling faster calibration, improved robustness and personalized decoding of conscious intent. AI world-model approaches further enable realistic and dynamic representations of a patient's neurophysiology, allowing BCIs to interpret neural activity in the context of evolving brain states rather than static classifications of consciousness levels. Emerging work in quantum-enhanced machine and deep learning suggests that some current computational bottlenecks in BCIs, including high-dimensional optimization and complex pattern discovery, may be further alleviated. We argue that the convergence of neuroadaptive AI and quantum-enabled computation could improve the sensitivity, speed and reliability of consciousness assessments. Given the exploratory stage of quantum-AI research, rigorous clinical validation and governance frameworks will be required to ensure safe deployment and improved patient outcomes. If validated, quantum-AI BCIs could reduce diagnostic uncertainty, improve prognostication and support ethically grounded decision-making for patients unable to communicate.},
}

@article {pmid41850543,
year = {2026},
author = {Li, Y and Li, S and Xie, J and Yao, D and Li, F and Xu, P and Wu, J and Jiang, L},
title = {Unlocking Interbrain Neural Signatures Differences During Triadic Cooperation and Competition: Evidence from EEG Hyperscanning.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121865},
doi = {10.1016/j.neuroimage.2026.121865},
pmid = {41850543},
issn = {1095-9572},
abstract = {Cooperation and competition are fundamental to human social interaction. While recent hyperscanning studies have linked stronger interbrain synchrony (IBS) to successful cooperation, most have focused on dyadic interactions, leaving the underlying neural mechanisms of group-level social behavior largely unknown. Here, we employed EEG hyperscanning to investigate interbrain neural dynamics of triadic cooperative and competitive interactions. Distinct interbrain network patterns emerged in the delta and beta bands, with cooperation showing enhanced frontal-parietal IBS and more efficient network properties. Non-parametric cluster-based permutation tests further identified significant regional differences in a left-lateralized frontal-temporal-parietal cluster in both bands. Crucially, increased delta-band frontal-parietal IBS was closely associated with better group-level cooperative performance. Moreover, classification and prediction models based on delta-band interbrain metrics successfully distinguished interaction types and predicted cooperative outcomes. These findings uncover interbrain neurocognitive traits that reflect specific social behavioral contexts, highlighting the pivotal role of frontal-parietal synchrony and delta-band modulations in supporting group cooperation. Together, our results advance the understanding of the neural basis of triadic social interaction and underscore the potential of interbrain network signatures as biomarkers for decoding and predicting complex social behaviors.},
}

@article {pmid41851249,
year = {2026},
author = {Leinders, S and Aarnoutse, EJ and Branco, MP and Freudenburg, ZV and Geukes, SH and Schippers, A and Verberne, MSW and van den Boom, MA and van der Vijgh, BH and Crone, NE and Denison, T and Ramsey, NF and Vansteensel, MJ},
title = {Implanted brain-computer interface functionality during nighttime in late-stage amyotrophic lateral sclerosis.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-44228-7},
pmid = {41851249},
issn = {2045-2322},
support = {UH3NS114439/NS/NINDS NIH HHS/United States ; ADV 320708/ERC_/European Research Council/International ; UGT7685//Nederlandse Organisatie voor Wetenschappelijk Onderzoek/ ; U01DC016686/DC/NIDCD NIH HHS/United States ; },
abstract = {Brain-computer interfaces (BCIs) hold promise as assistive communication technology for people with severe paralysis. Although such BCIs should be available 24/7, feasibility of nocturnal BCI use has not been investigated. Here, we addressed this question using data from an electrocorticography-BCI user with amyotrophic lateral sclerosis. We investigated nocturnal dynamics of neural signal features used for BCI control. Additionally, we assessed nocturnal performance of a decoder trained on daytime data, by quantifying the number of unintentional BCI activations at night. Finally, we developed a nightmode functionality and assessed its performance. Mean and variance of low and high frequency band power were significantly higher at night than during the day. When applied to night data, daytime decoders caused unintentional BCI activations in 100% of nights (245 unintended click-commands and 13 unintended caregiver-calls per hour). The specifically developed nightmode functionality, however, functioned error-free in 79% of nights over a period of ± 1.5 years, allowing the user to reliably call the caregiver. Reliable nighttime use of a BCI requires strategies to adjust to circadian and sleep-related signal changes. This demonstration of a reliable nightmode and its long-term use by an individual with amyotrophic lateral sclerosis underscores the importance of 24/7 BCI reliability.},
}

@article {pmid41851364,
year = {2026},
author = {Yang, P and Duan, Y and Wang, L and Gao, Y and Zhang, Y and Liang, Z and Zhou, X and Wang, D and Yang, J},
title = {An early detection framework for young Chinese learners at risk of reading difficulty using fNIRS and deep learning.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-44379-7},
pmid = {41851364},
issn = {2045-2322},
support = {JWC20240116//teaching reform and research projects of Sichuan Normal University/ ; 23YJC880062//Research project of Ministry of Education of China/ ; BG2024025//Major Science and Technology Special Program of Jiangsu Province/ ; },
abstract = {Reading difficulty (RD), a neurodevelopmental disorder affecting language acquisition in children, necessitates early screening for effective educational interventions. This study proposes the RD-risk Classifier (RDr-C), a novel framework integrating functional near-infrared spectroscopy (fNIRS) with deep learning, specifically combining a dual-layer graph convolutional network (GCN), a bidirectional long short-term memory network (BiLSTM), and multi-head self-attention mechanisms (MSA) for 7-8-year-old children's literacy assessment. The model was validated using fNIRS signals from 30 participants (16 experimental group, 14 control group) during the visual sign recognition test and phonetic discrimination test, with performance evaluated through 5 runs × 5-fold cross-validation experiments. Results show that RDr-C achieved a mean classification accuracy of 99.60% and 99.66% in visual and auditory tests, respectively, significantly outperforming traditional convolutional neural networks (CNN), long short-term memory networks (LSTM), and existing fNIRS classification models (e.g., fNIRS-T, fNIRSNet). Furthermore, leave-one-subject-out cross-validation demonstrates that RDr-C achieves global accuracies of 89.33% and 87.93% on visual and auditory tasks, respectively, with corresponding Kappa coefficients of 0.78 and 0.76, confirming its robustness across individuals. Feature shuffling and wavelet transformation visualizations further confirm robust feature separation, highlighting the model's ability to capture distinct hemodynamic patterns associated with RD. By integrating the spatial feature extraction of GCN, the temporal modeling of BiLSTM, and the global dependency capture of MSA, this work establishes a non-invasive neuroimaging paradigm for educational neuroscience. The high-precision classification lays a technical foundation for early screening tools, with future applications extending to multimodal brain-computer interfaces and longitudinal intervention monitoring.},
}

@article {pmid41851425,
year = {2026},
author = {Springer, J and Steinbrink, GM and Tetmeyer, L and Mellen, K and Marcussen, B and Bond, DS and Wu, Y and Carr, LJ},
title = {Feasibility and preliminary efficacy of a 12-week primary care-based behavioral counseling intervention among adults with cardiovascular disease risk factors.},
journal = {Journal of behavioral medicine},
volume = {},
number = {},
pages = {},
pmid = {41851425},
issn = {1573-3521},
support = {Google//Google/ ; },
abstract = {Physical activity (PA) and dietary counseling are recommended for adults with cardiovascular disease (CVD) risk factors. However, these programs are seldom implemented in primary care. This study evaluated the feasibility and preliminary efficacy of a 12-week primary care-based behavioral counseling intervention (BCI) for adults with CVD risk factors. Participants were primarily recruited through a novel clinical screening and referral workflow implemented in six local Family Medicine clinics to participate in a single-arm, pre-post study. Participants received a 12-week, theory-based (Multi-Process Action Control), remotely-delivered BCI that included health education, health coaching, and a wearable activity and sleep monitor. Changes in psychosocial mechanisms of action (e.g., habits, identity), behavioral outcomes (PA, diet, sleep), and health outcomes (cardiometabolic and self-reported) were assessed with paired t-tests, and Cohen's d effect sizes were calculated. The relationships between baseline behaviors and observed changes in behaviors from pre-post intervention were tested with simple linear regression. Ninety-seven participants (mean age = 50.6 years, 64% women) completed the BCI. Moderate-large improvements were observed for behavioral regulation skills, health habits, and health identity psychosocial mechanisms of action (d = 0.75-1.03). Muscle-strengthening exercises, daily kilocalories, whole fruit and total protein intake, and several sleep parameters improved to a small-moderate degree (d = 0.23-0.64). Small-moderate improvements in diastolic blood pressure, body weight, total fat mass, depressive symptoms, fatigue, general health, and quality of life were also observed (d = 0.25-0.53). While no significant overall changes in device-based PA were observed, participants not meeting aerobic PA guidelines at baseline showed small-moderate improvements in daily steps and moderate-vigorous PA (d = 0.25-0.53). Participants with lower baseline steps and dietary quality showed greater improvements in these behaviors (r = - 0.54 and - 0.49, respectively), though regression to the mean may also explain these findings. Retention (85%) and adherence (e.g., 98% coaching attendance) were high. Results support the feasibility and preliminary efficacy of a 12-week, remotely-delivered BCI-mediated through primary care-to change targeted psychosocial mechanisms of action, and specific health behaviors and outcomes. Importantly, participants with less favorable behaviors at baseline benefited most. A randomized controlled trial is warranted to confirm these findings.},
}

@article {pmid41851546,
year = {2026},
author = {Graham, F},
title = {Daily briefing: China approves world-first brain-computer interface device.},
journal = {Nature},
volume = {},
number = {},
pages = {},
doi = {10.1038/d41586-026-00888-z},
pmid = {41851546},
issn = {1476-4687},
}

@article {pmid41840016,
year = {2026},
author = {Alhudhaif, A},
title = {Distance-based temporal similarity metrics for adaptive channel selection in multi-modal EEG-fNIRS BCI frameworks.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-44052-z},
pmid = {41840016},
issn = {2045-2322},
support = {PSAU/2024/01/31819//Adi Alhudhaif/ ; },
}

@article {pmid41840138,
year = {2026},
author = {Jude, JJ and Levi-Aharoni, H and Acosta, AJ and Allcroft, SB and Nicolas, C and Lacayo, BE and Card, NS and Wairagkar, M and Levin, AD and Brandman, DM and Stavisky, SD and Willett, FR and Williams, ZM and Simeral, JD and Hochberg, LR and Rubin, DB},
title = {Restoring rapid natural bimanual typing with a neuroprosthesis after paralysis.},
journal = {Nature neuroscience},
volume = {},
number = {},
pages = {},
pmid = {41840138},
issn = {1546-1726},
support = {23SCEFIA1156586//American Heart Association (American Heart Association, Inc.)/ ; 23SCEFIA1156586//American Heart Association (American Heart Association, Inc.)/ ; A2295R, A4820R//Office of Research and Development (VHA Office of Research and Development)/ ; A2295R, A4820R, N2864C, A3803R//Office of Research and Development (VHA Office of Research and Development)/ ; A2295R, A4820R//Office of Research and Development (VHA Office of Research and Development)/ ; A4820R//Office of Research and Development (VHA Office of Research and Development)/ ; A2295R, A4820R, A3803R//Office of Research and Development (VHA Office of Research and Development)/ ; A2295R, A4820R, N2864C//Office of Research and Development (VHA Office of Research and Development)/ ; U01DC017844, R01DC014034//U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; U01DC017844//U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; U01DC017844, R01DC014034//U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; U01DC017844//U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; U01DC017844//U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; U01DC017844//U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; K23DC021297//U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; U01NS123101//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; U01NS123101//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; U01NS123101//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; U01NS123101//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; U01NS123101//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; U01NS123101//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; U01NS123101//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; U01NS123101//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; Postdoctoral Fellowship//A.P. Giannini Foundation/ ; HT94252310153//United States Department of Defense | United States Army | Army Medical Command | Congressionally Directed Medical Research Programs (CDMRP)/ ; Pilot Award from the Simons Collaboration for the Global Brain (872146SPI)//Simons Foundation/ ; },
abstract = {Here, recognizing keyboard typing as a familiar, high information rate communication paradigm, we developed an intracortical brain-computer interface (iBCI) typing neuroprosthesis providing bimanual QWERTY keyboard functionality for people with paralysis. Typing with this iBCI involves only attempted finger movements, which are decoded accurately with as few as 30 calibration sentences. Sentence decoding is improved using a 5-gram language model. This typing neuroprosthesis performed well for two iBCI clinical trial participants with tetraplegia-one with amyotrophic lateral sclerosis and one with spinal cord injury. Typing speed is user-regulated, reaching 110 characters per minute, resulting in 22 words per minute with a word error rate of 1.6%. This resembles able-bodied typing accuracy and provides higher throughput than current state-of-the-art hand motor iBCI decoding. In summary, a typing neuroprosthesis decoding finger movements, provides an intuitive, familiar and easy-to-learn paradigm for individuals with impaired communication due to paralysis.},
}

@article {pmid41840417,
year = {2026},
author = {Cheng, XP and Wu, YQ and Luo, KL and Wu, D and Lv, L and Xie, LL and Zhan, LQ and Zhou, YZ and Ni, J and Chen, XY},
title = {Differences in brain function in cognitive impairment after stroke in different hemispheres of the brain: a functional near-infrared spectroscopy study.},
journal = {BMC neurology},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12883-026-04827-3},
pmid = {41840417},
issn = {1471-2377},
support = {2023QH1112//the Startup Fund for Scientific Research of Fujian Medical University/ ; 61773124//the National Natural Science Foundation of China/ ; 82402952//the National Natural Science Foundation of China/ ; },
}

@article {pmid41840816,
year = {2026},
author = {Hu, Z and Wang, J and Zhou, K and Ma, S and Hu, J},
title = {Application and Research Progress of BCI in Post-Stroke Psychiatric Disorders: A Narrative Review.},
journal = {Medical science monitor : international medical journal of experimental and clinical research},
volume = {32},
number = {},
pages = {e951399},
doi = {10.12659/MSM.951399},
pmid = {41840816},
issn = {1643-3750},
mesh = {Humans ; *Stroke/complications/psychology/physiopathology ; *Brain-Computer Interfaces ; *Mental Disorders/etiology/therapy/physiopathology ; Stroke Rehabilitation/methods ; Quality of Life ; },
abstract = {Post-stroke psychiatric disorders (PSPD), including depression, anxiety, and cognitive impairment, significantly hinder stroke survivors' rehabilitation and quality of life, with traditional interventions often showing limited efficacy. Brain-computer interface (BCI) technology has emerged as a promising tool for neurological regulation and rehabilitation, showing substantial potential in PSPD assessment and intervention. This narrative review comprehensively synthesizes the latest research advances in BCI applications for PSPD, covering underlying mechanisms, principal applications, clinical studies, technical challenges, and prospective directions. It highlights BCI's substantial potential in objective assessment, targeted neuromodulation, and promotion of neuroplasticity, while also addressing unresolved issues such as heterogeneous patient responses, technical limitations, and integration into routine clinical practice. By integrating current evidence and clarifying both achievements and gaps, this review provides theoretical insights and practical guidance for future basic and clinical research in the field.},
}

Humans
*Stroke/complications/psychology/physiopathology
*Brain-Computer Interfaces
*Mental Disorders/etiology/therapy/physiopathology
Stroke Rehabilitation/methods
Quality of Life

@article {pmid41844800,
year = {2026},
author = {Feng, Y and Jia, N and Huang, P and Hu, S and Yang, S},
title = {Cross-ancestry genetic architecture reveals shared biological pathways of major psychiatric disorders.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {41844800},
issn = {1476-5578},
abstract = {Psychiatric disorders, including bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SCZ), share substantial genetic overlap. We conducted a cross-ancestry multivariate genome-wide association study (GWAS) integrating European and East Asian populations to uncover shared genetic underpinnings. Our analyses identified 403 loci associated with shared polygenic liability to psychiatric disorders, including 88 novel regions. Cross-ancestry fine-mapping highlighted robust shared signals, notably at VRK2 (rs7596038), consistently significant across ancestries. Gene prioritization revealed 90 high-confidence candidate genes enriched in neurodevelopmental pathways. Single-nucleus RNA sequencing implicated excitatory neurons and astrocytes as key cellular contexts, emphasizing NCAM1-FGFR1 and NEGR1-NEGR1 signaling pathways. Mendelian randomization analyses provided causal evidence linking shared genetic liability to structural brain alterations, particularly in regions crucial for emotion and cognition. Polygenic risk scores derived from shared genetic liability substantially enhanced predictive accuracy for BD and SCZ, demonstrating strong trans-ancestry validity. These results advance understanding of shared genetic architecture in psychiatric disorders, highlighting potential therapeutic targets and emphasizing the critical importance of diverse ancestry studies in precision psychiatry.},
}

@article {pmid41844810,
year = {2026},
author = {Ullah, W and Dai, Q and Zulqarnain, RM and Fiidow, MA},
title = {Explainable artificial intelligence for early Alzheimer's diagnosis using enhanced grey relational features and multimodal data.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-43707-1},
pmid = {41844810},
issn = {2045-2322},
support = {62476126//National Natural Science Foundation of China/ ; },
abstract = {Alzheimer's disease, a progressive neurodegenerative disorder, presents a growing global health challenge due to its increasing prevalence and lack of accessible early diagnostic methods. Even though it has enhanced the diagnostic accuracy of machine learning, there is a major concern about striking a balance between predictive performance and interpretability. The proposed study presents an interpretable and sustainable machine learning architecture for early diagnosis of Alzheimer's disease based on multimodal, structured clinical and behavioral data, including demographics, vascular risk factors, lifestyle, and cognitive data. We perform extensive feature engineering to derive composite features, including blood pressure ratio, MMSE age ratio, cholesterol ratio, and cognitive decline score. The class imbalance is addressed using the Synthetic Minority Oversampling Technique. We also introduce a new strengthened Grey Relational Grade index based on the theory of grey system and the policy of sigmoid normalization. This greatly enhances the feature-diagnosis correlation (0.725 to 0.891), representing complicated nonlinear associations. This paper compared seven mainstream classifiers, such as Logistic Regression, Random Forest, Extreme Gradient Boosting, Light Gradient Boosting Machine, CatBoost, Stacking Ensembles, and Deep Neural Networks, in the context of model comparison. Among them, Deep Neural Networks achieve the best performance (accuracy: 98.01%, AUC: 99.43%), followed by a CatBoost-based Stacking Ensemble (Accuracy: 97.91%, AUC: 98.10%). Shapley Additive Explanations make models easier to understand by showing important modifiable predictors like family history, smoking, and early cognitive symptoms. This study presents that combining enhanced Grey Relational Grade metrics with robust machine learning and deep learning models produces an accurate, interpretable, and potentially effective framework for early AD risk assessment, which can be used to implement effective, behavior-centric prevention strategies in ageing demographics.},
}

@article {pmid41846866,
year = {2026},
author = {Yang, CD and Guo, A and Lin, KY},
title = {Brain-Computer Interfaces for Vision Recovery in Precortical Vision Loss.},
journal = {Eye and brain},
volume = {18},
number = {},
pages = {561691},
pmid = {41846866},
issn = {1179-2744},
abstract = {INTRODUCTION: Precortical vision loss remains a major global health challenge. Advances in brain-computer interfaces (BCIs) offer a new pathway towards restoring functional vision by bypassing damaged structures in the visual pathway.

METHODS: This narrative review aims to synthesize the current evidence on BCIs for precortical vision recovery, including non-invasive and invasive techniques. Device design, testing, and outcomes are discussed, with an emphasis on developments in technology and engineering.

RESULTS: Non-invasive BCIs induce neuroplasticity and may restore vision in conditions of precortical vision loss such as glaucoma and optic neuropathy. Cortical visual prostheses demonstrate the ability to evoke visual precepts and recover functional vision. Integration of artificial intelligence and high-density electrode arrays has improved image encoding and device adaptability to enhance user experience and rehabilitation potential. Patient selection, safety, and long-term outcomes remain active areas of investigation.

DISCUSSION: BCIs present a paradigm shift in treating precortical blindness that offers hope for patients with no alternative options. Yet, challenges persist, including surgical risks, durability, and variability in response. Personalization of stimulation protocols and further technical refinement are needed to optimize efficacy and accessibility.

CONCLUSION: BCIs are a promising experimental modality for precortical vision restoration. Continued research and interdisciplinary collaboration are essential to address current limitations.},
}

@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.},
}