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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@article {pmid41759187,
year = {2026},
author = {Koseki, S and Hayashibe, M and Owaki, D},
title = {Human-inspired bipedal locomotion: from neuromechanics to mathematical modelling and robotic applications.},
journal = {Journal of the Royal Society, Interface},
volume = {23},
number = {235},
pages = {},
doi = {10.1098/rsif.2025.0662},
pmid = {41759187},
issn = {1742-5662},
support = {//NSK Foundation for the Advancement of Mechatronics/ ; //Japan Society for the Promotion of Science/ ; },
abstract = {Human bipedal locomotion arises from continuous, closed-loop interactions between neural control and biomechanical structure-collectively referred to as neuromechanics. The relationship between human locomotion and robotic locomotion is deeply interconnected through shared principles of neuromechanics, thereby providing a comprehensive framework for understanding human movement and informing robotic system design. In this review, we synthesize insights from neuroscience, biomechanics, computational modelling and robotics to establish a cohesive perspective on human-inspired bipedal locomotion. We begin by outlining essential anatomical and physiological principles, such as spinal circuits, supraspinal coordination and musculoskeletal structure. Next, we analyse mathematical models-ranging from simplified neural oscillators to complex musculoskeletal simulations-that formalize these mechanisms. Finally, we discuss the embodiment of these models in bipedal robots, which promotes reciprocal advancements in both biological understanding and engineering innovation. Rather than offering a comprehensive literature survey, we focus on pivotal developments, emerging trends and unresolved questions that shape this interdisciplinary domain. By integrating diverse fields, this review aims to enhance the design of agile, energy-efficient robots and deepen our understanding of human locomotion.},
}

@article {pmid41758857,
year = {2026},
author = {Qiu, L and Hu, Y and Wu, M and Long, B and Chen, T and Pan, J},
title = {A Multi-Scale Attention-based Reconstruction Fusion Network for Motor Imagery Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3668760},
pmid = {41758857},
issn = {2168-2208},
abstract = {Motor imagery (MI) is a widely used cognitive paradigm in brain-computer interface (BCI) systems, where accurate and efficient MI decoding is essential for real-time human-machine interaction. However, the non-stationary nature and pronounced inter-subject variability of electroencephalography (EEG) signals pose significant challenges to reliable decoding. To address these issues, we propose a multi-scale attention-based reconstruction fusion network (MSARFNet) for MI-EEG decoding. The proposed framework employs parallel multi-scale convolutional branches to extract discriminative spatio-temporal features at different temporal resolutions. An attention-based reconstruction fusion module is then introduced to selectively diminish non-dominant information while promoting effective interaction among multi-scale features. Furthermore, a local-global temporal encoding strategy is designed to enhance transient MI-related responses through local temporal context aggregation and subsequently capture long-range temporal dependencies via global temporal modeling. Subject-dependent experiments conducted on the BCI Competition IV 2a and 2b datasets demonstrate that MSARFNet achieves average classification accuracies of 84.64% and 87.96%, respectively, outperforming several state-of-the-art methods. These results indicate that MSARFNet provides an effective and robust solution for EEG-based MI decoding.},
}

@article {pmid41758662,
year = {2026},
author = {Senneka, SJ and Dadarlat, MC},
title = {Integration of learned artificial sensation with vision during freely moving navigation.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {123},
number = {9},
pages = {e2521769123},
doi = {10.1073/pnas.2521769123},
pmid = {41758662},
issn = {1091-6490},
abstract = {Humans rely on both proprioceptive and visual feedback during reaching, integrating these two sensory streams to improve movement accuracy and precision. Patients using a brain-computer interface will similarly require artificial proprioceptive feedback in addition to vision to finely control a prosthesis. Intracortical microstimulation (ICMS) elicits sensory perceptions that could replace the lost proprioceptive signal. However, some learning may be required for encoding artificial sensation, as current technology does not give access to neurons with all of the desired encoding properties. We developed a freely moving mouse behavioral task in which to test learning and integration of artificial sensory information with natural vision. Mice implanted with a 16-channel microwire array in the primary somatosensory cortex were trained to navigate to randomly selected targets upon the floor of a custom behavioral training chamber. Target location was encoded with visual and/or patterned multichannel ICMS feedback. Mice received multimodal feedback from the beginning of training of the behavioral task, achieving 75% on multimodal trials after approximately 1,000 training trials. Mice also quickly learned to use the ICMS signal to locate invisible targets, achieving 75% proficiency on ICMS-only trials when tested. Critically, we found that performance with ICMS was as good or better than performance with natural vision, and that performance on multimodal trials significantly exceeded unimodal performance (vision or ICMS), demonstrating that animals rapidly learned to integrate natural vision with artificial sensation.},
}

@article {pmid41757508,
year = {2026},
author = {Guo, B and Yan, K and Deng, Y and Zhao, W and Chen, X and Xue, C and Chai, Y and Quan, P and Goel, N and Basner, M and Mao, T and Rao, H},
title = {Domain-Specific Circadian Rescue following Sleep Deprivation.},
journal = {Sleep},
volume = {},
number = {},
pages = {},
doi = {10.1093/sleep/zsag054},
pmid = {41757508},
issn = {1550-9109},
abstract = {STUDY OBJECTIVES: Circadian rhythms regulate sleep-wake cycles and modulate cognitive functions over a 24-hour period. Following sleep loss, certain cognitive performance partially rebounds in the early evening, a phenomenon known as circadian rescue. Yet, the magnitude and domain specificity of circadian rescue remain poorly understood. Here, we integrate experimental and meta-analytic approaches to differential contributions of circadian and homeostatic processes to cognitive rescue following sleep deprivation.

METHODS: In Study 1, 54 healthy adults remained awake for 35 consecutive hours while repeatedly completing the Psychomotor Vigilance Task (PVT), the Digit Symbol Substitution Test (DSST), and the Karolinska Sleepiness Scale (KSS). Performance dynamics were modeled using the two-process framework of sleep regulation. In Study 2, a meta-analysis of published data contextualized these findings across protocols.

RESULTS: Results reveal domain-specific circadian recovery rates of 33.0%-52.1% for PVT, 45.7% for DSST, and 23.5% for KSS, indicating that subjective sleepiness is predominantly driven by homeostatic load, whereas objective cognitive performance retains significant circadian modulation under conditions of acute homeostatic pressure.

CONCLUSIONS: These findings clarify how circadian and homeostatic drives interact to shape cognitive task performance and subjective sleepiness outcomes under sleep loss, with practical implications for optimizing performance in fatigue-prone environments.},
}

@article {pmid41757349,
year = {2026},
author = {Wang, Z and Han, Y and Yang, P and Jia, C and Li, C and Yuan, S and Wei, P and Hu, R},
title = {Liquid-liquid phase separation couples MKRN2-mediated ubiquitination of CSDE1 with neurodevelopmental disorders.},
journal = {Frontiers in cellular neuroscience},
volume = {20},
number = {},
pages = {1757304},
pmid = {41757349},
issn = {1662-5102},
abstract = {BACKGROUND: Makorin-2 (MKRN2) is an E3 ubiquitin ligase involved in multiple biological processes, yet its role in neurological disorders remains poorly understood. This study aims to elucidate how MKRN2 regulates the RNA-binding protein CSDE1-a molecule linked to autism-related genes-and to explore the functional implications of this interaction in neurodevelopment.

METHODS: Using mass-spectrometry screening, we identified CSDE1 as a direct substrate of MKRN2. Ubiquitination sites were validated through mutagenesis of conserved lysine residues. Liquid-liquid phase separation (LLPS) assays were performed in HEK293 and SH-SY5Y cells, and behavioral phenotypes were assessed in Mkrn2-knockout mice. Statistical analyses included appropriate tests for comparing ubiquitination levels, condensate formation, and social behavior outcomes.

RESULTS: MKRN2 mediates CSDE1 ubiquitination at four lysine residues (K81, K91, K208, K727). Deletion of MKRN2 or mutation of these sites abolished ubiquitination. MKRN2 and CSDE1 formed co-localized condensates via LLPS, which was disrupted by functional impairment of either protein. Mkrn2-knockout mice exhibited sex-specific social abnormalities-increased sociability in males and social withdrawal in females-recapitulating autism-spectrum disorder (ASD) heterogeneity. We further identified MARK1 and HNRNPUL2, ASD-associated mRNAs, as ubiquitination-dependent targets of CSDE1, linking aberrant condensate dynamics to synaptic plasticity deficits.

CONCLUSION: Our study reveals an LLPS-coupled ubiquitination mechanism by which MKRN2 regulates CSDE1, providing a novel molecular pathway underlying neurodevelopmental disorders. These findings offer new insights for understanding and treating neurological diseases such as ASD.},
}

@article {pmid41756006,
year = {2026},
author = {Zhang, H and Deng, H and Zhai, Y and Zhang, J and Zhao, Z and Gong, L},
title = {Subtyping insomnia disorder with a population graph attention autoencoder: revealing two distinct biotypes.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1766155},
pmid = {41756006},
issn = {1662-4548},
abstract = {Insomnia disorder (ID) is neurobiologically heterogeneous and often eludes characterization by traditional group-level neuroimaging. Subtyping based on neuroimaging and clinical data offers a promising strategy for identifying biologically and clinically meaningful ID subgroups. To address this need, we developed a Gray Matter Population Graph Attention Autoencoder (GM-PGAAE) to subtype insomnia disorder in a cohort comprising 140 patients diagnosed with ID and 57 matched healthy controls. Each subject was represented as a node defined by atlas-based gray matter (GM) volumes. Population edges combined imaging-derived intersubject correlations with clinical similarity via a Hadamard product, generating an adjacency matrix that jointly encodes structural and phenotypic relationships. A Graph Attention Autoencoder learned low-dimensional embeddings that adaptively weighted informative intersubject connections, and clustering these embeddings identified distinct subtypes. Regional and network-level differences were further assessed using Voxel-Based Morphometry (VBM) and individualized differential structural covariance networks (IDSCNs). Through this framework, two ID subtypes were identified. Compared with Subtype 2, Subtype 1 showed higher symptom severity and greater GM reductions-particularly in the cerebellar vermis, thalamus, middle occipital cortex, fusiform gyrus, and paracentral lobule-alongside negative associations between GM volume and clinical scores. IDSCNs further revealed reduced thalamocortical and subcortical Z-scores in Subtype 1, indicating subtype-specific network alterations. Overall, GM-PGAAE integrates structural MRI and clinical measures to derive individualized embeddings and delineate biologically distinct ID subtypes.},
}

@article {pmid41755857,
year = {2026},
author = {Yu, X and Yin, C and Liu, X and Liu, J and Zhu, Y and Li, D and Zhang, D and Lee, HJ and Ji, B and Tian, L},
title = {Competitive Mg[2+] Regulation of Biomolecular Condensate Microenvironments Enables Diverse Macrophage Response.},
journal = {JACS Au},
volume = {6},
number = {2},
pages = {1308-1318},
pmid = {41755857},
issn = {2691-3704},
abstract = {The intrinsic microenvironments of biomolecular condensates play decisive roles in applications spanning synthetic cell construction, targeted drug delivery systems, cell engineering, bioreactor development, and precision disease interventions. Recent studies highlight that divalent cations play a central role in modulating the internal condensate microenvironments. However, the complex multivalent interaction networks within condensates create significant challenges in unraveling the molecular mechanisms. This study employs model systems of cationic peptides (arginine decamer (R10), lysine decamer (K10)) and polyanionic polymers (polyadenylic acid (PolyA), polyinosinic acid (PolyI), polyglutamic acid (PolyE), polyaspartic acid (PolyD)) to systematically investigate Mg[2+]-mediated modulation of condensate properties. Mg[2+] enrichment dynamically controls ionic microenvironments through competitive interactions with polyelectrolytes. When interpolyelectrolyte affinity dominates (e.g., R10/PolyA), weakly bound Mg[2+] enhances the surface potential, promoting small-molecule enrichment and ribozyme catalytic efficiency. Conversely, when Mg[2+]-polyelectrolyte binding prevails (e.g., R10/PolyE), stable ion-polyelectrolyte complexes reduce the system polarity and amplify dye accumulation but compromise phase stability. Macrophage coculture experiments demonstrate that R10/PolyA@Mg condensates enable targeted magnesium delivery, significantly boosting TNF-α secretion and immune regulation. These findings establish a mechanistic framework for ion-mediated control of condensate microenvironments, offering theoretical insights into the intracellular ionic regulation of phase separation. This work suggests a Mg[2+]-responsive condensate design strategy for modulating macrophage responses, providing a foundation for the design of biomaterials with a tunable immunostimulatory potential.},
}

@article {pmid41755195,
year = {2026},
author = {Sztyler, B and Królak, A and Strumiłło, P},
title = {Influence of EEG Signal Augmentation Methods on Classification Accuracy of Motor Imagery Events.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {4},
pages = {},
doi = {10.3390/s26041258},
pmid = {41755195},
issn = {1424-8220},
abstract = {This study investigates the impact of various data-augmentation techniques on the performance of neural networks in EEG-based motor imagery three-class event classification. EEG data were obtained from a publicly available open-source database, and a subset of 25 patients was selected for analysis. The classification task focused on detecting two types of motor events: imagined movements of the left hand and imagined movements of the right hand. EEGNet, a convolutional neural network architecture optimized for EEG signal processing, was employed for classification. A comprehensive set of augmentation techniques was evaluated, including five time-domain transformations, three frequency-domain transformations, two spatial-domain transformations and two generative approaches. Each method was tested individually, as well as in selected two- and three-method cascade combinations. The augmentation strategies were tested using three data-splitting methodologies and applying four ratios of original-to-generated data: 1:0.25, 1:0.5, 1:0.75 and 1:1. Our results demonstrate that the augmentation strategies we used significantly influence classification accuracy, particularly when used in combination. These findings underscore the importance of selecting appropriate augmentation techniques to enhance generalization in EEG-based brain-computer interface applications.},
}

@article {pmid41755176,
year = {2026},
author = {Fraternali, M and Magosso, E and Borra, D},
title = {Inferring Arm Movement Direction from EEG Signals Using Explainable Deep Learning.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {4},
pages = {},
doi = {10.3390/s26041235},
pmid = {41755176},
issn = {1424-8220},
support = {Project MNESYS (PE0000006, DN. 1553 11.10.2022)//Ministero dell'Università e della Ricerca/ ; Project DARE (PNC0000002, CUP: B53C22006450001, D.D. 931 of 06/06/2022)//Ministero dell'Università e della Ricerca/ ; },
abstract = {Decoding reaching movements from non-invasive brain signals is a key challenge for the development of naturalistic brain-computer interfaces (BCIs). While this decoding problem has been addressed via traditional machine learning, the exploitation of deep learning is still limited. Here, we evaluate a convolutional neural network (CNN) for decoding movement direction during a delayed center-out reaching task from the EEG. Signals were collected from twenty healthy participants and analyzed using EEGNet to discriminate reaching endpoints in three scenarios: fine-direction (five endpoints), coarse-direction (three endpoints), and proximity (two endpoints) classifications. To interpret the decoding process, the CNN was coupled with explanation techniques, including DeepLIFT and occlusion tests, enabling a data-driven analysis of spatio-temporal EEG features. The proposed approach achieved accuracies well above chance, with accuracies of 0.45 (five endpoints), 0.64 (three endpoints) and 0.70 (two endpoints) on average across subjects. Explainability analyses revealed that directional information is predominantly encoded during movement preparation, particularly in parietal and parietal-occipital regions, consistent with known visuomotor planning mechanisms and with EEG analysis based on event-related spectral perturbations. These results demonstrate the feasibility and interpretability of CNN-based EEG decoding for reaching movements, providing insights relevant for both neuroscience and the prospective development of non-invasive BCIs.},
}

@article {pmid41755073,
year = {2026},
author = {Ji, Y and Kim, DH and Hong, J},
title = {Enhanced EEG Emotion Recognition Using MIMO-Based Denoising and Band-Wise Attention Graph Neural Network.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {4},
pages = {},
doi = {10.3390/s26041133},
pmid = {41755073},
issn = {1424-8220},
support = {none//Changwon National University Samsung Changwon Hospital Joint Collaboration Research Support Project；the Academic Award from the Lee Won Yong Brain Research Fund/ ; },
abstract = {Electroencephalogram (EEG) signals serve as a primary input for brain-computer interface (BCI) systems, and extensive research has been conducted on EEG-based emotion recognition. However, because EEG signals are inherently contaminated with various types of noise, the performance of emotion recognition is often degraded. Furthermore, the use of a Band Feature Extraction Neural Network (BFE-Net), a state-of-the-art (SOTA) method in this field, has limitations with respect to independent band-wise feature extraction and a simplistic band aggregation process to obtain final classification results. To address these problems, this study proposes the noise-robust band-attention BFE-Net framework, aiming to improve the conventional BFE-Net from two perspectives. First, we implement multiple-input, multiple-output (MIMO)-based preprocessing. Specifically, we utilize multichannel minima-controlled recursive averaging for precise non-stationary noise covariance estimation and generalized eigenvalue decomposition for subspace filtering to enhance the signal-to-noise ratio. Second, we propose an attention-based band aggregation mechanism. By integrating a band-wise self-attention mechanism, the model learns dynamic inter-band dependencies for more sophisticated feature fusion for classification. Experimental results on the SEED and SEED-IV datasets under a subject-independent protocol show that our model outperforms the SOTA BFE-Net by 3.27% and 3.34%, respectively. This confirms that rigorous MIMO noise reduction, combined with frequency-centric attention, significantly enhances the reliability and generalization of BCI systems.},
}

@article {pmid41755066,
year = {2026},
author = {Li, H and Xu, G and Feng, S and Du, C and Han, C and Kuang, J and Zhang, S},
title = {Improving Individual-Specific SSVEP-BCI with Adaptive Channel and Subspace Selection in TRCA.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {4},
pages = {},
doi = {10.3390/s26041123},
pmid = {41755066},
issn = {1424-8220},
support = {2025PT-ZCK-06//the Key Research and Development Project in Shaanxi Province/ ; },
abstract = {The individual-specific steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) is characterized by individual calibration data, resulting in satisfactory performance. However, existing individual-specific SSVEP-BCIs employ generalized channels and task-related subspaces, which seriously limit their potential advantages and lead to suboptimal solutions. In this study, AS-TRCA was proposed to develop a purely individual-specific SSVEP-BCI by fully exploiting individual-specific knowledge. AS-TRCA involves optimal channel learning and selection (OCLS) as well as optimal subspace selection (OSS). OCLS aims to pick the optimal subject-specific channels by employing sparse learning with spatial distance constraints. Meanwhile, OSS adaptively determines the appropriate number of optimal subject-specific task-related subspaces by maximizing profile likelihood. The extensive experimental results demonstrate that AS-TRCA can acquire meaningful channels and determine the proper number of task-related subspaces for each subject compared to traditional methods. Furthermore, combining AS-TRCA with existing advanced calibration-based SSVEP decoding methods, including deep learning methods, to establish a purely individual-specific SSVEP-BCI can further enhance the decoding performance of these methods. Specifically, AS-TRCA improved the average accuracy as follows: TRCA 7.21%, SSCOR 7.61%, TRCA-R 6.58%, msTRCA 7.70%, scTRCA 4.47%, TDCA 2.91%, and bi-SiamCA 3.23%. AS-TRCA is promising for further advancing the performance of SSVEP-BCI and promoting its practical applications.},
}

@article {pmid41751235,
year = {2026},
author = {Fu, B and Lin, K and Chen, Y and Zhang, J and Jin, Z and Li, L},
title = {The Right PPC Plays an Important Role in the Interaction of Temporal Attention and Expectation: Evidence from a tACS-EEG Study.},
journal = {Biomedicines},
volume = {14},
number = {2},
pages = {},
doi = {10.3390/biomedicines14020336},
pmid = {41751235},
issn = {2227-9059},
support = {62571096//National Natural Science Foundation of China/ ; 62176045//National Natural Science Foundation of China/ ; 2025ZNSFSC0453//Sichuan Science and Technology Program/ ; BX202402//Sichuan Province Innovative Talent Funding Project for Postdoctoral Fellows/ ; },
abstract = {Background/Objectives: Temporal attention and temporal expectation are two key mechanisms that facilitate perception by prioritizing information at specific moments and by leveraging temporal predictability, respectively. While their behavioral interaction is established, the underlying neural mechanisms remain poorly understood. Building on functional magnetic resonance imaging (fMRI) evidence linking temporal attention to parietal cortex activity and the role of alpha oscillations in temporal prediction, we investigated whether the right posterior parietal cortex (rPPC) may be involved in integrating these two processes. Methods: Experiment 1 used a behavioral paradigm to dissociate temporal expectation from attention across 600 ms and 1400 ms intervals. Experiment 2 retained only the 600 ms interval, combining behavioral assessments with electroencephalography (EEG), recording following transcranial alternating current stimulation (tACS) applied to the rPPC to probe neural mechanisms. Results: Experiment 1 showed an attention/expectation interaction exclusively at 600 ms: enhanced expectation improved response times under attended, not unattended, conditions. Experiment 2 replicated these behavioral and event-related potential (ERP) findings. Temporal attention modulated N1 amplitude: in attended conditions, the N1 was significantly more negative under high versus low expectation, while no difference was observed in unattended contexts. Anodal tACS over the rPPC reduced this N1 amplitude difference between high and low attentional expectation conditions to non-significance. Restricting analyses to attended conditions, paired-samples t-tests revealed that alpha-band power differed between high and low expectation under sham tACS, but this difference was absent under anodal tACS, which also attenuated the corresponding behavioral attention/expectation interaction effects. Conclusions: These findings provide suggestive evidence that the rPPC may be key to integrating temporal attention and expectation, occurring in early processing stages and specific to brief intervals.},
}

@article {pmid41750613,
year = {2026},
author = {Li, N and Wang, J and Chen, S and Li, T},
title = {Integrative Multi-Omics Mendelian Randomization Reveals Oxidative Stress Mechanisms in Major Depressive Disorder, Bipolar Disorder, and Schizophrenia.},
journal = {Antioxidants (Basel, Switzerland)},
volume = {15},
number = {2},
pages = {},
doi = {10.3390/antiox15020233},
pmid = {41750613},
issn = {2076-3921},
support = {2021ZD0200404//China Brain Project (STI 2030)/ ; 2021ZD0200800//China Brain Project (STI 2030)/ ; 82230046//National Natural Science Foundation of China/ ; 20241203A14//Key R&D by Hangzhou Science and Technology Bureau/ ; CXTD202501053//Zhejiang Clinovation Pride/ ; },
abstract = {BACKGROUND: Oxidative stress (OS) has been widely implicated in pathophysiology of major psychiatric disorder. However, establishing robust causal links and delineating the specific molecular mechanisms involved continue to pose significant research challenges.

METHODS: We performed a multi-omics analysis focusing on 817 oxidative stress-related genes (OSGs) in major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ). We applied summary data-based Mendelian randomization (SMR), integrating large-scale genome-wide association studies for MDD, BD, and SCZ with quantitative trait loci datasets from both blood and brain tissues, including measures of DNA methylation, gene expression, and protein abundance.

RESULTS: Multi-omics integration yielded supportive evidence across blood and brain tissues implicating ACE and ACADVL in SCZ, where genetically predicted increases in their methylation, expression, and protein abundance were associated with reduced disease risk. IGF1R was associated with bipolar disorder (BD) risk in blood-specific analyses. Brain-specific analyses further nominated ENDOG as a candidate gene for SCZ. Single-cell SMR indicated that increased ENDOG expression was associated with higher SCZ risk in astrocytes, CD4[+] naïve T cells, CD8[+] effector T cells, and natural killer cells, suggesting a potential immune-brain interaction.

CONCLUSIONS: This study provides multi-level genetic evidence supportive of a potential causal role for specific OSGs in major psychiatric disorders. We identify ACE, ACADVL, IGF1R, and ENDOG as candidate genes for further investigation, offering insights into epigenetic and transcriptional mechanisms that could inform future research on therapeutic targets.},
}

@article {pmid41750230,
year = {2026},
author = {Xu, Z and Yu, Z},
title = {Maximizing Single-Feature Separability for Improving Transfer Learning in Motor Imagery EEG Decoding.},
journal = {Brain sciences},
volume = {16},
number = {2},
pages = {},
doi = {10.3390/brainsci16020230},
pmid = {41750230},
issn = {2076-3425},
support = {2022ZD0211700//The Technology Innovation 2030/ ; },
abstract = {BACKGROUND/OBJECTIVES: Motor imagery (MI) EEG-based brain-computer interfaces (BCIs) are promising for neurorehabilitation, but practical use is often hindered by time-consuming per-user calibration and performance instability across sessions/users.

METHODS: To mitigate this issue, we aim to improve subject-dependent MI classification by leveraging labeled training data from other subjects within the same dataset via transfer learning. We propose Maximizing Single-Feature Separability (MSFS), a lightweight plug-in regularization applied during target-subject fine-tuning. MSFS operates on the network feature layer and constructs batch-wise target positions by maximizing a silhouette-based separability criterion for each feature dimension. The target position computation is implemented in a fully vectorized GPU-friendly manner.

RESULTS: We evaluate MSFS on BCI Competition IV-2a and IV-2b datasets using three representative backbone networks (EEGNet, ShallowConvNet, ATCNet). MSFS consistently improves standard transfer learning across both datasets and all backbones. When compared against representative transfer learning algorithms from the literature, MSFS remains competitive against the literature baselines. Ablation analysis confirms the effectiveness of each algorithm component. Few-shot experiments further indicate that MSFS is still beneficial when the target subject provides limited labeled data.

CONCLUSIONS: MSFS provides a within-dataset transfer learning enhancement for MI EEG decoding, improving target-subject accuracy under limited calibration data without relying on external datasets, and can be readily integrated into common deep MI classification pipelines.},
}

@article {pmid41750203,
year = {2026},
author = {Duan, X and Xie, S and Cui, Y and Ji, T and Yan, H},
title = {A Neurophysiological Stratification Framework for Intermediate Motor Imagery-BCI Users Based on Independent Event-Related Brain Dynamics.},
journal = {Brain sciences},
volume = {16},
number = {2},
pages = {},
doi = {10.3390/brainsci16020202},
pmid = {41750203},
issn = {2076-3425},
support = {62220106007//Key International (Regional) Cooperation Research Project of National Natural Science Foundation of China/ ; 62407034//National Natural Science Foundation of China/ ; 24YJCZH046//The Ministry of education of Humanities and Social Science project/ ; 2024JC-YBQN-0704//Natural Science Basic Research Program of Shaanxi/ ; 2024K005//Social Science Fund Project of Shaanxi/ ; 24JS042//Scientific Research Program Funded by Shaanxi Provincial Education Department/ ; },
abstract = {Background: Motor imagery-based brain-computer interfaces (MI-BCIs) enable individuals who are unable to perform physical movements to interact with the external world by imagining movements. Users are typically classified as good performers or BCI-illiterate based on the classification accuracy of distinct EEG patterns (e.g., 60% or 70%). Yet, studies show that approximately 70% of users fall within intermediate accuracies between 60% and 80%, and although exceed the chance level, they often fail to achieve reliable MI-BCI control. Intermediate users often exhibit asymmetric motor imagery abilities between left and right hands, highlighting the need for refined early assessment and stratified training approaches. Methods: We employed ICA to decompose each participant's EEG data and extract independent ERD/ERS components as indicators using a rule-based automated framework. This framework integrated dipole localization, ERD/ERS characteristics, and frequency-band power features of ICs. Importantly, we applied a power spectral parameterization approach to remove the 1/f-like background activity in power estimation and used statistical methods to precisely estimate the latency and duration of ERD. The extracted indicators were subsequently subjected to clustering analysis to categorize participants into four groups. Results: In addition to good performers (24.8%) and poor performers (35.8%), two groups were identified: LgoodRpoor (27.5%), who performed well in left-hand MI but poorly in right-hand MI, and LpoorRgood (11.9%), who showed the opposite pattern. Notably, these unilateral performers did not show significant differences in contralateral ERD but exhibited substantial differences in ipsilateral ERS. Conclusions: The proposed independent event-related brain dynamics model enables more refined stratification of MI-BCI users. Findings from this characterization study may inform the design of graded training protocols, especially for users demonstrating unilateral motor imagery proficiency.},
}

@article {pmid41750196,
year = {2026},
author = {Öztürk, MK and Göksel Duru, D},
title = {Leveraging Cross-Subject Transfer Learning and Signal Augmentation for Enhanced RGB Color Decoding from EEG Data.},
journal = {Brain sciences},
volume = {16},
number = {2},
pages = {},
doi = {10.3390/brainsci16020195},
pmid = {41750196},
issn = {2076-3425},
abstract = {OBJECTIVES: Decoding neural patterns for RGB colors from electroencephalography (EEG) signals is an important step towards advancing the use of visual features as input for brain-computer interfaces (BCIs). This study aims to overcome challenges such as inter-subject variability and limited data availability by investigating whether transfer learning and signal augmentation can improve decoding performance.

METHODS: This research introduces an approach that combines transfer learning for cross-subject information transfer and data augmentation to increase representational diversity in order to improve RGB color classification from EEG data. Deep learning models, including CNN-based DeepConvNet (DCN) and Adaptive Temporal Convolutional Network (ATCNet) using the attention mechanism, were pre-trained on subjects with representative brain responses and fine-tuned on target subjects to parse individual differences. Signal augmentation techniques such as frequency slice recombination and Gaussian noise addition improved model generalization by enriching the training dataset.

RESULTS: The combined methodology yielded a classification accuracy of 83.5% for all subjects on the EEG dataset of 31 previously studied subjects.

CONCLUSIONS: The improved accuracy and reduced variability underscore the effectiveness of transfer learning and signal augmentation in addressing data sparsity and variability, offering promising implications for EEG-based classification and BCI applications.},
}

@article {pmid41750174,
year = {2026},
author = {Sorkin, GC and Caffes, NM and Shank, JP and Hershey, JL and Knaub, DE and Krebs, JC and Niazi, MH},
title = {Current State of the Clinical Applications of Artificial Intelligence in Stroke: A Literature Review.},
journal = {Brain sciences},
volume = {16},
number = {2},
pages = {},
doi = {10.3390/brainsci16020173},
pmid = {41750174},
issn = {2076-3425},
abstract = {BACKGROUND: Artificial intelligence (AI) has emerged as a transformative tool in medicine, leveraging rapid analysis of large datasets to accelerate diagnosis, enhance clinical decision-making, and improve clinical workflows. This is highly relevant in stroke care given the time-sensitive nature of the disease process. This review evaluates the current landscape of evidence-based medicine utilizing AI in stroke, with emphasis on its use in phases of clinical care across the stroke continuum, including pre-hospital, acute, and recovery phases. This offers a comprehensive understanding of the current state of AI in both practice and literature.

METHODS: A review of major databases was conducted, identifying peer-reviewed literature evaluating the use of AI and its level of evidence across the stroke continuum. Given the heterogeneity of study designs, interventions, and outcome metrics spanning multiple disciplines, findings were synthesized narratively.

RESULTS: Across all phases of care, there remain no randomized controlled trials (RCTs) evaluating patient-level outcome data using AI (Level A). In the pre-hospital phase of care, AI has been used to identify stroke symptoms and assist EMS routing/training but presently remains limited to research. AI is most studied in the acute phase of care, representing the only phase to achieve commercial application in imaging detection and telestroke assistance, supported by non-randomized evidence (Level B-NR). In the recovery phase, AI may enhance wearable technologies, tele-rehabilitation, and robotics/brain-computer interfaces, with early RCTs (Level B-R) supporting the latter two, representing the strongest evidence for AI in stroke care to date.

CONCLUSIONS: Despite the potential for AI to transform all phases of care across the stroke continuum, major challenges remain, including transparency, generalizability, equity, and the need for externally validated clinical studies.},
}

@article {pmid41750145,
year = {2026},
author = {He, B and Liu, C and Qi, Z and Xue, N and Yao, L},
title = {NeuroGator: A Low-Power Gating System for Asynchronous BCI Based on LFP Brain State Estimation.},
journal = {Brain sciences},
volume = {16},
number = {2},
pages = {},
doi = {10.3390/brainsci16020141},
pmid = {41750145},
issn = {2076-3425},
support = {LG-GG202402-05//Lingang Laboratory/ ; },
abstract = {The continuous handling of the large amount of raw data generated by implantable brain-computer interface (BCI) devices requires a large amount of hardware resources and is becoming a bottleneck for implantable BCI systems, particularly for power-constrained wireless systems. To overcome this bottleneck, we present NeuroGator, an asynchronous gating system using Local Field Potential (LFP) for the implantable BCI system. Unlike a conventional continuous data decoding approach, NeuroGator uses hierarchical state classification to efficiently allocate hardware resources to reduce the data size before handling or transmission. The proposed NeuroGator operates in two stages: Firstly, a low-power hardware silence detector filters out background noise and non-active signals, effectively reducing the data size by approximately 69.4%. Secondly, a Dual-Resolution Gate Recurrent Unit (GRU) model controls the main data processing procedure on the edge side, using a first-level model to scan low-precision LFP data for potential activity and a second-level model to analyze high-precision LFP data for confirmation of an active state. The experiment shows that NeuroGator reduces overall data throughput by 82% while maintaining an F1-Score of 0.95. This architecture allows the Implantable BCI system to stay in an ultra-low-power state for over 85% of its entire operation period. The proposed NeuroGator has been implemented in an Application-Specific Integrated Circuit (ASIC) with a standard 180 nm Complementary Metal Oxide Semiconductor (CMOS) process, occupying a silicon area of 0.006mm2 and consuming 51 nW power. NeuroGator effectively resolves the resource efficiency dilemma for implantable BCI devices, offering a robust paradigm for next-generation asynchronous implantable BCI systems.},
}

@article {pmid41750143,
year = {2026},
author = {Nan, J and Bai, Y and Jiang, H and Zhao, Y and Xiao, Y and Ni, G},
title = {Delta-Band EEG Microstate Dynamics as Promising Candidate Markers of Central Vertigo Severity.},
journal = {Brain sciences},
volume = {16},
number = {2},
pages = {},
doi = {10.3390/brainsci16020143},
pmid = {41750143},
issn = {2076-3425},
support = {2023YFF1203503//Yanru Bai/ ; 2024XPD-0028//Yanru Bai/ ; TJYXZDXK-3-021C//Yanru Bai/ ; },
abstract = {Background/Objectives: Central vertigo (CV) lacks objective biomarkers for severity assessment. This study examined whether resting-state EEG microstate dynamics across frequency bands can distinguish CV severity. Methods: Resting-state EEG was recorded from 50 patients with stroke-related CV and 31 healthy controls. Patients were classified as moderate (MD, n = 31) or severe (SV, n = 19) based on Dizziness Handicap Inventory scores. Microstate analysis was performed in the delta, theta, alpha, and beta bands to assess microstate topographies, temporal parameters, and transition probabilities. Correlations with clinical measures and receiver operating characteristic analyses were conducted. Results: CV patients showed severity-dependent alterations in EEG microstate dynamics, most pronounced in the delta band. Delta-band microstate transition probabilities correlated significantly with symptom severity and balance confidence. The delta-band transition from microstate C to microstate B accurately differentiated MD from SV patients (AUC = 0.983). Conclusions: Delta-band EEG microstate transition dynamics reflect network dysfunction in CV and may serve as promising candidate biomarkers for CV severity stratification.},
}

@article {pmid41750125,
year = {2026},
author = {Costa, A and Schmalzried, E and Tong, J and Khanyan, B and Wang, W and Jin, Z and Bergese, SD},
title = {Stroke Rehabilitation, Novel Technology and the Internet of Medical Things.},
journal = {Brain sciences},
volume = {16},
number = {2},
pages = {},
doi = {10.3390/brainsci16020124},
pmid = {41750125},
issn = {2076-3425},
abstract = {Stroke continues to impose an enormous morbidity and mortality burden worldwide. Stroke survivors often incur debilitating consequences that impair motor function, independence in activities of daily living and quality of life. Rehabilitation is a pivotal intervention to minimize disability and promote functional recovery following a stroke. The Internet of Medical Things, a network of connected medical devices, software and health systems that collect, store and analyze health data over the internet, is an emerging resource in neurorehabilitation for stroke survivors. Technologies such as asynchronous transmission to handle intermittent connectivity, edge computing to conserve bandwidth and lengthen device life, functional interoperability across platforms, security mechanisms scalable to resource constraints, and hybrid architectures that combine local processing with cloud synchronization help bridge the digital divide and infrastructure limitations in low-resource environments. This manuscript reviews emerging rehabilitation technologies such as robotic devices, virtual reality, brain-computer interfaces and telerehabilitation in the setting of neurorehabilitation for stroke patients.},
}

@article {pmid41748535,
year = {2026},
author = {Liu, R and Wang, Z and Zhong, C and Chen, Y and Sun, B and Jian, J and Ma, H and Gao, D and Yang, J and Li, L and Liu, K and Hu, X and Lin, H},
title = {Femto-joule threshold reconfigurable all-optical nonlinear activators for picosecond pulsed optical neural networks.},
journal = {Light, science & applications},
volume = {15},
number = {1},
pages = {},
pmid = {41748535},
issn = {2047-7538},
support = {61975179//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12104375//National Natural Science Foundation of China (National Science Foundation of China)/ ; 52025023//National Natural Science Foundation of China (National Science Foundation of China)/ ; 91950204//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Achieving optical computing with thousands of tera-operations per second per watt per square millimeter (TOPs/W/mm[2]) is the key to surpassing electrical computing. This realization requires a breakthrough in the design of a new optical computing architecture and nonlinear activation functions. By leveraging the Kerr effect of silicon and the saturable absorption of graphene, we designed an all-optical nonlinear activator based on a graphene-silicon integrated photonic crystal cavity. The ultralow-threshold, high-speed, compact, and reconfigurable all-optical nonlinear activator could achieve a saturable absorption energy threshold of 4 fJ and a response time of 1.05 ps, a reconfigurable nonlinear activation threshold of 30 fJ and a response time of 4 ps, and an ultrasmall size of 15 μm × 10 μm. This device provides foundation blocks for the picosecond pulsed optical neural network chip to achieve 10[6] TOPs/W/mm[2] level optical computing.},
}

@article {pmid41748031,
year = {2026},
author = {Zhan, X and Chen, X and Zhu, L and Kong, W},
title = {Exploration of the mental attention mechanisms in motor imagery-based EEG decoding.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110721},
doi = {10.1016/j.jneumeth.2026.110721},
pmid = {41748031},
issn = {1872-678X},
abstract = {BACKGROUND: Brain-Computer Interface (BCI) systems enable direct communication between the brain and external devices, with motor imagery (MI)-based BCIs as a key paradigm. Although decoding neural signals has advanced via machine learning and deep learning, the influence of human factors,especially mental attention on performance remains underexplored.

NEW METHOD: This study quantitatively investigates how mental attention modulates MI decoding. Specifically, it examines the enhancement of Common Spatial Pattern (CSP) features under high attention and evaluates attention-based data selection as a decoding criterion.

RESULTS: Experimental results demonstrate that applying mental attention as a trial selection strategy (Strategy 2) markedly improves MI decoding performance, yielding an 11.6% increase relative to the baseline accuracy of 61.3% observed without attention. These findings highlight that integrating real-time mental attention monitoring into BCI systems can enhance decoding robustness and stability, paving the way for personalized and context-aware brain-computer interactions in neurorehabilitation, cognitive training, and intelligent assistive technologies.

Prior studies focused largely on algorithmic innovations. In contrast, this work adopts a user-centric perspective, showing that attention-informed trial selection significantly improves performance even within standard CSP-based pipelines.

CONCLUSIONS: Incorporating mental attention into decoding frameworks enhances MI-BCI performance. This approach may improve the robustness and user-adaptability of online BCI systems, contributing to more effective and user-friendly neurotechnology.},
}

@article {pmid41747352,
year = {2026},
author = {Xu, P and Zhang, X and Fang, Y and Zhou, Y and Li, Z and Pei, C},
title = {Effect of bacterial cellulose crystal form on its oil-water separation.},
journal = {Carbohydrate research},
volume = {563},
number = {},
pages = {109866},
doi = {10.1016/j.carres.2026.109866},
pmid = {41747352},
issn = {1873-426X},
abstract = {Cellulose hydrogels have demonstrated outstanding performance in separating oil-in-water emulsions, particularly notable for efficient "water-removing" behavior. However, the strong intrinsic hydration ability of cellulose often limits separation flux, and the influence of cellulose crystalline forms on separation performance remains largely unexplored. In this study, bacterial cellulose (BC) hydrogel was used as the starting material. The crystal structure was converted to cellulose II via alkali treatment and to cellulose III through ethylenediamine treatment. The structure, wettability, and separation performance of the three crystalline cellulose hydrogels (BC-I, BC-II, and BC-III) were systematically investigated for various oil-in-water emulsions. The results showed that all three hydrogels exhibit superhydrophilicity and underwater superoleophobicity, achieving separation efficiencies exceeding 98.1% for all emulsions. However, a significant difference in separation flux was observed, in the order: BC-III > BC-I > BC-II. Notably, the BC-III hydrogel attained a maximum flux of 2806.5 L m[-2] h[-1] MPa[-1] for a cyclohexane-in-water emulsion. The performance differences are mainly attributed to the microstructural and hydration state changes induced by crystalline transformation: BC-II exhibited the lowest flux due to its dense fibrous network and high bound water content, whereas BC-III, while retaining a porous network, optimized water transport channels through its specific crystalline arrangement, resulting in the highest separation flux. This work reveals that the crystalline form of cellulose is a critical factor in regulating its oil-water separation performance, providing a novel strategy for designing high-flux cellulose-based separation membranes.},
}

@article {pmid41744701,
year = {2026},
author = {Gracia, DI and Iáñez, E and Ortiz, M and Azorín, JM},
title = {Denoising Non-Invasive Electroespinography Signals by Different Cardiac Artifact Removal Algorithms.},
journal = {Biosensors},
volume = {16},
number = {2},
pages = {},
pmid = {41744701},
issn = {2079-6374},
support = {PID2021-124111OB-C31//MICIU/AEI/10.13039/501100011033 and by ERDF, EU/ ; PID2024-156759OB-C31//MICIU/AEI/10.13039/501100011033 and by ERDF, EU/ ; CIACIF/2022/108//"Consellería de Educación, Universidades y Empleo (Generalitat Valenciana)" and the European Social Fund, and grant PRE2022-103336 funded by MICIU/AEI/10.13039/501100011033 and by ESF+/ ; },
abstract = {The non-invasive recording of spinal cord neuronal activity, also known as electrospinography (ESG), using high-density surface electromyography (HD-sEMG) is a promising emerging biosensing modality. However, these recordings often contain electrocardiographic (ECG) artifacts that must be removed for accurate analysis. Given the emerging nature of ESG and the lack of dedicated signal processing methods, this study assesses the performance of seven established EMG denoising algorithms for their ability to preserve the broad spectral bandwidth needed for future ESG characterization: Template Subtraction (TS), Adaptive Template Subtraction (ATS), High-Pass Filtering at 200 Hz (HP200), ATS combined with HP200, Second-Order Extended Kalman Smoother (EKS2), Stationary Wavelet Transform (SWT), and Empirical Mode Decomposition (EMD). Performance was quantified using six metrics: Relative Error (RE), Signal-to-Noise Ratio (SNR), Cross-Correlation (CC), Spectral Distortion (SD), and Kurtosis Ratio (KR2) and its variation (ΔKR2). ESG data were recorded from nine healthy participants at brachial and lumbar plexus sites with various electrode configurations. ATS consistently outperformed all other methods in suppressing cardiac artifacts of varying shapes. Although it did not fully preserve low-frequency content, ATS achieved the best balance between artifact removal and signal integrity. Algorithm performance improved when ECG contamination was lower, especially in brachial plexus recordings with closer reference electrodes.},
}

@article {pmid41744603,
year = {2026},
author = {Wang, Y and Ge, M and Xu, S},
title = {Advances in Brain-Computer Interfaces (BCI): Challenges and Opportunities.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {11},
number = {2},
pages = {},
doi = {10.3390/biomimetics11020157},
pmid = {41744603},
issn = {2313-7673},
abstract = {It appears that the frontier of neural engineering is rapidly advancing towards seamless integration between biological neural networks and digital systems [...].},
}

@article {pmid41743846,
year = {2026},
author = {Jin, J and Li, J and Pan, X and Xu, R and Cichocki, A and Du, W and Qian, F},
title = {A Domain Generalization Method for EEG Based on Domain-Invariant Feature and Data Augmentation.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {7},
number = {},
pages = {0508},
pmid = {41743846},
issn = {2692-7632},
abstract = {Brain-computer interface (BCI) technology, which controls external devices by directly decoding brain activities, has made important progress and practical applications in recent years in many fields. However, the domain bias issue in cross-domain applications remains a significant challenge in the practical implementation of BCI technology. This is particularly acute in scenarios where target data are unavailable, largely because of the noise sensitivity and acquisition limitations inherent in electroencephalography (EEG) signal data. When processing nonstationary EEG signals, existing domain generalization methods face limitations: Adversarial training may compromise model stability, while global feature alignment approaches struggle to sufficiently decouple category-dependent and category-independent features, thereby constraining generalization performance. Therefore, in this paper, we propose a hybrid approach based on domain-invariant feature learning and data enhancement. We introduce a "fixed" structure enhancement method that combines domain-invariant feature learning with data enhancement strategies, decouples domain-invariant features from other features, optimizes cross-domain feature extraction, and reduces the effect of noise in data. Through extensive experimental validation on multiple publicly available datasets, the model proposed in this paper outperforms the existing state-of-the-art methods, providing a novel and effective solution to the domain bias problem in BCI.},
}

@article {pmid41743816,
year = {2026},
author = {Hons, M and Kober, SE and Wriessnegger, SC and Wood, G},
title = {Hybrid EEG-fNIRS phoneme classification based on imagined and perceived speech.},
journal = {Frontiers in neuroergonomics},
volume = {7},
number = {},
pages = {1696865},
pmid = {41743816},
issn = {2673-6195},
abstract = {INTRODUCTION: Individuals affected by severe motor impairments often have no means of communicating with others. To build an intuitive speech prosthesis, imagined speech brain-computer interface research began to prosper with numerous studies attempting to classify imagined speech from brain signals. While unimodal neuroimaging techniques, such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have been widely used, multimodal approaches combining two or more of them remain scarce.

METHODS: In this study offline phoneme decoding based on hybrid EEG-fNIRS data was performed. Twenty-two right-handed participants performed imagined and perceived speech trials encompassing four phonemes /a/,/i/,/b/ and /k/. Features in the form of power spectral densities and mean hemoglobin concentration changes were extracted from EEG and fNIRS data, respectively. Features were ranked according to the mutual information criterion relative to the target vector, and the optimal number of features to include was determined through optimization via 10-fold cross-validation.

RESULTS: Hybrid classification yielded accuracy scores of 77.29% and 76.05% regarding imagined and perceived speech, respectively. In both conditions, hybrid and EEG-based classification performances did not differ significantly, while fNIRS based phoneme discrimination produced lower accuracies.

DISCUSSION: This study represents an innovative phoneme decoding attempt based on multimodal EEG-fNIRS data, both in terms of imagined speech and perception. Four-class imagined speech classification was primarily driven by EEG features yet outperformed comparable previous studies.},
}

@article {pmid41743674,
year = {2026},
author = {Hu, X and Wei, Z and Liu, M and Geng, H and Zhang, H},
title = {Digital therapeutics into geriatric cardiovascular emergency care.},
journal = {Frontiers in digital health},
volume = {8},
number = {},
pages = {1673080},
pmid = {41743674},
issn = {2673-253X},
abstract = {This mini review investigates the applications of digital therapeutics (DTx) and artificial intelligence (AI) in geriatric cardiovascular emergency care. Key elements include AI-driven biosensing for real-time risk stratification, blockchain-based secure data interoperability, tele-rehabilitation frameworks, and emerging technologies such as digital twins and brain-computer interfaces. Clinical validations shows that AI-enhanced portable ultrasound systems integrated with virtual reality (VR) optimizes diagnostic protocols and resuscitation workflows, while machine learning models achieve superior accuracy in predicting readmission risks and improving medication adherence. Notable research advances included: (1) Compared with conventional monitoring, AI biosensing improved arrhythmia detection sensitivity; (2) Deep learning models were superior to traditional methods in predicting cardiovascular events; (3) VR-assisted cardiac rehabilitation reduced anxiety scores; (4) The predictive readmission algorithm achieved high accuracy through frailty-comorbidity integration; (5) chatbot guided intervention improved medication adherence. However, limitations remain in this field, particularly in addressing age-related data biases and ethical challenges surrounding algorithmic transparency. Future researches should prioritize developing adaptive interfaces for elderly users, and advancing biocybernetic human-machine interfaces capable of stabilizing autonomic dysregulation. Importantly, these innovations must be validated in conjunction with geriatrics to ensure equitable implementation across diverse older populations.},
}

@article {pmid41743427,
year = {2026},
author = {Luo, Y and Liu, X and Yang, M},
title = {Current status and future prospects of brain-computer interfaces in the field of neurological disease rehabilitation.},
journal = {Frontiers in rehabilitation sciences},
volume = {7},
number = {},
pages = {1666530},
pmid = {41743427},
issn = {2673-6861},
abstract = {Neurological disorders represent a significant category of diseases that profoundly affect human health, accounting for the second leading cause of global mortality. This group of conditions includes stroke, multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), spinal cord injury, Parkinson's disease, and cerebral palsy, among others. These disorders are highly susceptible to sequelae and profoundly impact individuals' daily lives. In this context, Brain-Computer Interface (BCI) technology has demonstrated considerable potential in the domain of neurorehabilitation, although numerous challenges remain. The manuscript provides a comprehensive review of recent advancements in research and clinical applications, highlighting current limitations and outlining future directions. It elucidates the applicability and constraints of Brain-Computer Interface (BCI) technology across various diseases and patient populations. To facilitate insights across different conditions, comparative tables are presented, aligning BCI strategies with therapeutic targets, outcomes, advantages, limitations, and existing evidence gaps. The scope extends beyond motor restoration to include under-explored domains, such as neuropathic pain, with a focus on real-world translation, including home and community feasibility and the distinction between assistive and rehabilitative applications. The review distills overarching limitations within the field, such as small sample sizes, protocol heterogeneity, and limited longitudinal evidence, while synthesizing the most recent studies. An actionable research and development roadmap is proposed to guide next-generation BCI rehabilitation, incorporating individualized cortical-network simulators, self-architecting decoders, adaptive therapy approaches akin to game seasons, and proprioceptive "write-back" mechanisms via peripheral interfaces. Moreover, the review reveals significant research focal points and critical issues that warrant further investigation in the context of neurological rehabilitation utilizing BCI technology.},
}

@article {pmid41742930,
year = {2026},
author = {Saha, S and Karlsson, P and Anderson, C and Kavehei, O and McEwan, A},
title = {Individualized brain-computer interface for people with disabilities: a review.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1738876},
pmid = {41742930},
issn = {1662-5161},
abstract = {Brain-computer interfaces (BCIs) facilitate functional interaction between the brain and external devices, enabling users to bypass their typical peripheral motor actions to control assistive and rehabilitative technologies (ARTs). This review critically evaluates the state-of-the-art BCI-based ARTs by integrating the psychosocial and health-related factors impacting user needs, highlighting the influence of brain changes during development and aging on the design and ethical use of BCI technologies. As direct human-computer interfaces, BCI-based ARTs offer extended degrees of freedom via augmented mobility, cognition and communication, especially to people with disabilities. However, the innovation in BCI-based ARTs is guided by the complexity of disability types and levels of function across users that define individual needs. Therefore, an adaptable design is essential for tailoring a BCI-based ART that can fulfill user-specific requirements, which may hinder the scalability of BCIs for their widespread adoption across users with disabilities. The trade-offs between implantable and non-implantable BCIs are explored along with complex decisions around informed consent for people with communication or cognitive disabilities and pediatric settings. Non-implantable BCIs offer broader accessibility and transferability across users due to wider standardized signal acquisition and algorithm generalization, making them suited for a more comprehensive user group. This review contributes to the field by providing individualized user needs-informed discussion of BCI-based ARTs, emphasizing the need for adaptable designs that align the evolving functional and developmental needs of users with disabilities.},
}

@article {pmid41742408,
year = {2026},
author = {Chen, S and Zhang, B and Qin, T and Zhu, M and Chen, Q and Xia, L and Pan, H and Yang, Q and Guo, S and Gong, R and Jiang, Q and Li, H and Zhang, X and Cheng, P and Qi, X and Chen, W and Mo, W},
title = {Endogenous retrovirus-derived RNA-DNA hybrids induce microglial synaptic pruning in autism models.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2026.01.011},
pmid = {41742408},
issn = {1097-4199},
abstract = {Microglia-mediated neuroinflammation is increasingly recognized as a key pathological component in autism spectrum disorders (ASDs), though the mechanisms driving microglial activation remain largely elusive. Our study reveals that deficiency in the high-risk ASD gene SETDB1, as well as maternal immune activation (MIA), elevates complement protein C4b expression specifically in prefrontal cortex (PFC) neurons. This upregulation triggers excessive microglial synaptic pruning, leading to autistic-like behaviors. Furthermore, we found that microglia elimination improved synaptic density, while complete C4b knockout rescued all observed autistic-like phenotypes in mice. C4b expression is driven by RNA-DNA hybrids formed through the reactivation of endogenous retroviruses (ERVs). Notably, we identify that existing FDA-approved HIV medications, which inhibit retrotranscriptional activity, substantially reduce C4b levels and alleviate ASD symptoms. These findings underscore the crucial role of C4b in microglia-mediated synaptic pruning in ASD and highlight the therapeutic potential of targeting ERV reactivation with existing HIV medications.},
}

@article {pmid41742172,
year = {2026},
author = {Marcos-Martínez, D and Santamaría-Vázquez, E and Pérez-Velasco, S and Ruiz-Gálvez, CR and Martín-Fernández, A and Pascual-Roa, B and Martínez-Velasco, R and Martínez-Cagigal, V and Hornero, R},
title = {Motor imagery-based neurofeedback in older adults: neural signatures and feasibility in a randomized controlled trial targeting age-related cognitive decline.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-026-01912-z},
pmid = {41742172},
issn = {1743-0003},
support = {0124 EUROAGE MAS 4 E//European Union/ ; },
}

@article {pmid41741724,
year = {2026},
author = {Chen, H and Zhang, Y and Cui, L and Fan, J and Zhu, H and Wu, S and Zhou, H and Zhang, Y and Song, G and Jiang, N and Zhu, M and Lou, C and Chen, W and Lou, J},
title = {Mechanical force regulates the inhibitory function of PD-1.},
journal = {EMBO reports},
volume = {},
number = {},
pages = {},
pmid = {41741724},
issn = {1469-3178},
support = {T2394512//MOST | National Natural Science Foundation of China (NSFC)/ ; 32200549//MOST | National Natural Science Foundation of China (NSFC)/ ; 32090044//MOST | National Natural Science Foundation of China (NSFC)/ ; 11672317//MOST | National Natural Science Foundation of China (NSFC)/ ; T2394511//MOST | National Natural Science Foundation of China (NSFC)/ ; 12172371//MOST | National Natural Science Foundation of China (NSFC)/ ; 32301035//MOST | National Natural Science Foundation of China (NSFC)/ ; XDB37020102//Strategic Priority Research Program of the Chinese Academy of Sciences/ ; YZTZ-2022-0080-0015//Beijing Medical Award Foundation ()/ ; 242102310348//| Henan Provincial Science and Technology Research Project ()/ ; },
abstract = {The immune checkpoint molecule, programmed cell death 1 (PD-1), critically regulates T-cell activation upon binding PD-L1 or PD-L2, making it a key target in cancer immunotherapy. Although extensively studied, the molecular mechanism of the inhibitory function of PD-1 remains incompletely understood. Using the biomembrane force probe (BFP), we measure catch-slip bond behavior between PD-1 and PD-L1/PD-L2 under force. Steered molecular dynamics (SMD) simulation reveals a force-induced bound state distinct from the force-free state observed in solved complex structures. Disrupting interactions that stabilize either state weakens the catch bond, and diminishes the inhibitory function of PD-1. Interestingly, soluble forms of PD-L1/PD-L2 compete with their surface-bound counterparts and attenuate PD-1-mediated T-cell inhibition, suggesting that soluble PD-1 ligands could potentially serve as anti-PD-1 drugs. Tumor growth studies using a gain of function mutant based on the catch-bond mechanism confirm the anti-cancer activity of soluble PD-L1. Our findings highlight that mechanical force governs the inhibitory function of PD-1 and suggest that PD-1 acts as a mechanical sensor in T-cell suppression. Thus, mechanical regulation should be considered when designing PD-1 blocking therapies.},
}

@article {pmid41741650,
year = {2026},
author = {Francioni, V and Tang, VD and Toloza, EHS and Ding, Z and Brown, NJ and Harnett, MT},
title = {Vectorized instructive signals in cortical dendrites.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {41741650},
issn = {1476-4687},
abstract = {Vectorization of teaching signals is a key element of almost all modern machine learning algorithms, including backpropagation, target propagation and reinforcement learning. Vectorization allows a scalable and computationally efficient solution to the credit assignment problem by tailoring instructive signals to individual neurons. Recent theoretical models have suggested that neural circuits could implement single-phase vectorized learning at the cellular level by processing feedforward and feedback information streams in separate dendritic compartments[1-5]. This presents a compelling, but untested, hypothesis for how cortical circuits could solve credit assignment in the brain. Here we used a neurofeedback brain-computer interface task with an experimenter-defined reward function to test for vectorized instructive signals in dendrites. We trained mice to modulate the activity of two spatially intermingled populations (four or five neurons each) of layer 5 pyramidal neurons in the retrosplenial cortex to rotate a visual grating towards a target orientation while we recorded GCaMP activity from somas and corresponding distal apical dendrites. We observed that the relative magnitudes of somatic and dendritic signals could be predicted using the activity of the surrounding network and contained information about task-related variables that could serve as instructive signals, including reward and error. The signs of these putative teaching signals depended on the causal role of individual neurons in the task and predicted changes in overall activity over the course of learning. Furthermore, targeted optogenetic perturbation of these signals disrupted learning. These results demonstrate a vectorized instructive signal in the brain, implemented via semi-independent computation in cortical dendrites, unveiling a potential mechanism for solving credit assignment in the brain.},
}

@article {pmid41741014,
year = {2026},
author = {Du, M and Shi, P and Liu, Z and Lu, X and Cao, L and Liu, B and Liu, X and Liu, W and Liu, S and Ming, D},
title = {Multidimensional Acoustic-Prosodic Quantification Framework Using Unscripted Speech for Autism Spectrum Disorder Identification.},
journal = {Autism research : official journal of the International Society for Autism Research},
volume = {},
number = {},
pages = {e70206},
doi = {10.1002/aur.70206},
pmid = {41741014},
issn = {1939-3806},
support = {23JCZDJC01030//the Natural Science Foundation of Tianjin/ ; 24ZXZSSS00330//the Natural Science Foundation of Tianjin/ ; 24HHNJSS00012//Autonomous Project of Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration/ ; 25HHNJSS00015//Autonomous Project of Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration/ ; },
abstract = {Although clinical observations have noted early speech abnormalities in children with autism spectrum disorder (ASD), automatic speech-based detection remains challenging. This is primarily due to the reliance on scripted tasks, which younger children often struggle to complete and which are not generalizable to large-scale, non-clinical screening. To address this, we developed an unscripted speech-based framework to quantify atypical acoustic-prosodic patterns for automatic ASD identification in naturalistic interactions. It processes free-flowing conversations, extracts multidimensional acoustic features from the time and frequency domains, and models ASD-related prosodic patterns for classification. For evaluation, we collected spontaneous speech from 88 children with ASD (3-10 years) and 82 typically developing (TD) children (3-9 years) during naturalistic interactions on daily topics (e.g., toys, animated movies, storybook reading). Group comparisons revealed atypical prosodic patterns in ASD, including reduced speech continuity, speech rate, and Formant 3, alongside increased zero-crossing rate, pitch, pitch variability, and Formant 1 (all p < 0.01). Using these features, a linear discriminant analysis classifier achieved robust performance (accuracy = 0.85 ± 0.07, F1 = 0.86 ± 0.07). Further analyses indicated no significant gender interaction (p > 0.05), but a pronounced effect of speech context (p < 0.01), with atypical patterns being more evident in open-ended dialogues than in text-guided settings. Moreover, these patterns correlated with clinical scores (p < 0.05), particularly language ability, demonstrating the framework's utility for assessing ASD severity. These findings underscore the importance of analyzing unscripted speech to capture atypical prosodic patterns and provide a basis for large-scale ASD screening outside clinical settings.},
}

@article {pmid41737820,
year = {2026},
author = {Khalili, MD and Abootalebi, V and Saeedi-Sourck, H},
title = {A Dimensionality Reduction Approach for Motor Imagery Brain-Computer Interface Using Functional Clustering and Graph Signal Processing.},
journal = {Journal of medical signals and sensors},
volume = {16},
number = {},
pages = {6},
pmid = {41737820},
issn = {2228-7477},
abstract = {BACKGROUND: This paper introduces an approach for dimensionality reduction and classification of electroencephalogram signals in motor imagery brain-computer interface (MI-BCI) systems.

MATERIALS AND METHODS: The proposed Kron-reduced generic learning regularization with differential evolution (K-GLR-DE) framework leverages graph signal processing (GSP) with a meta-heuristic optimizer, integrating functional clustering, Kron reduction, regularized common spatial patterns with generic learning (GLRCSP), and differential evolution (DE). Brain graphs are constructed within a structural-functional framework, where edge weights are defined based on geometric distances and correlations. Graph's dimensionality reduction is achieved by applying physiological regions of interest (ROIs) and Kron reduction to preserve essential topological-spectral features. Feature extraction is performed using graph total variation and GLRCSP, followed by DE-based feature selection.

RESULTS: The approach was evaluated on BCI Competition III Dataset IVa and the PhysioNet eegmmidb dataset. The support vector machine with a radial basis function (SVM-RBF) classifier achieved superior performance, yielding a mean accuracy of 96.46% ± 0.81% on BCIC III-IVa.

CONCLUSIONS: The proposed K-GLR-DE method demonstrates significant performance in MI-BCI classification across various training conditions, including scenarios with small and limited training sets.},
}

@article {pmid41735747,
year = {2026},
author = {Zhang, SX and Yang, J and Lou, Y and Xu, SC and Guo, R and Xu, ZZ},
title = {Distinct Role of Specialized Cutaneous Schwann Cell Network in Acute and Chronic Pain Sensation.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {41735747},
issn = {1995-8218},
abstract = {Specialized cutaneous Schwann cells (scSCs) are a recently identified glial class implicated in cutaneous pain modulation, yet their three-dimensional architecture and role in chronic pain remain unclear. Using tissue optical clearing, we reconstructed the 3D morphology of scSCs, revealing an intricate mesh-like network, with extensive branching penetrating the epidermal layer and establishing close associations with A- and C-fiber primary sensory nerve terminals. Optogenetic activation of scSCs elicited nociceptive reflex behaviors, dependent on concurrent A- and C-fiber activation, but not affective-motivational responses. We further investigated the morphological and functional alterations of scSCs in chronic inflammatory pain and neuropathic pain models. Interestingly, scSCs were found to play a partial role in modulating nociceptive behaviors but not aversions in chronic pain. Together, these findings provide new insights into the functional dynamics of scSCs in nociceptive signal processing and their limited contribution to chronic pain states.},
}

@article {pmid41734829,
year = {2026},
author = {Zhang, J and Zeng, S and Wang, B and He, J and Jin, Z and Li, L},
title = {The Erlangen Program in Lateral Occipital Cortex: Hierarchical Encoding of Emergent Features.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121827},
doi = {10.1016/j.neuroimage.2026.121827},
pmid = {41734829},
issn = {1095-9572},
abstract = {Emergent features are fundamental concepts in Gestalt psychology, yet the neural encoding of these features, particularly a quantitative understanding of their relative superiority, remains elusive. This study bridges this gap by conceptualizing emergent features through geometric transformations within the Erlangen Program, which provides a principled framework to quantify their hierarchical relationships. We propose that the lateral occipital cortex (LOC) encodes these emergent features in accordance with the geometric hierarchies defined by this program. Using fMRI and multivariate pattern analysis, we demonstrate that LOC reliably discriminates between distinct geometric transformations (Euclidean, affine, projective, and topology). Critically, representational similarity analysis reveals that neural dissimilarities in LOC align with the relative stability of geometries predicted by the Erlangen Program. However, the LOC exhibits similar representational structures for lower-order transformations like Euclidean and affine geometries, suggesting a potential collapse of these distinctions in the region's global geometric hierarchy. Furthermore, transfer learning confirms hierarchical nesting relationships among the geometries: classifiers trained on specific geometric distinctions generalize to others in a manner consistent with the Erlangen hierarchy. These findings establish LOC as the neural substrate where emergent features are organized hierarchically by geometric stability, revealing how the visual system prioritizes invariant global structures to optimize perceptual efficiency.},
}

@article {pmid41734759,
year = {2026},
author = {Cao, K and Cheng, W and Qiu, L and Wang, Z and Zhao, Y and Yuan, Y and Wu, W and Xue, J and Zeng, L and Wu, ZY and Ma, H and Hou, T and Hume, DA and Ye, C and Duan, S and Gao, Z},
title = {More than microglial depletion: PLX5622 activates the hepatic constitutive androstane receptor to alter anesthesia and addiction.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2025.12.044},
pmid = {41734759},
issn = {1097-4199},
abstract = {The colony-stimulating factor 1 receptor (CSF1R) inhibitor PLX5622 has been widely used to deplete microglia for functional characterization and therapeutic support. Although diverse outcomes have been described after PLX5622 treatment, whether these phenotypes solely reflect microglial functions remains to be determined. Here, we show that transgenic microglial depletion did not mimic the accelerated anesthetic arousal or the alleviated nicotine addiction withdrawal symptoms observed after PLX5622 treatment in mice. We further identify that PLX5622 potently activates the mouse constitutive androstane receptor (CAR), leading to prominent induction of hepatic enzymes. The induced enzymatic activity enhances the metabolism and clearance of anesthetics and nicotine, thereby contributing to anesthetic insensitivity and addiction relief. Inactivation of CAR abolished these effects of PLX5622, indicating that the impact of PLX5622 treatment cannot be attributed exclusively to microglial depletion. Our findings raise awareness in evaluating consequences of PLX5622 treatment and provide insights into the design of specific CSF1R inhibitors.},
}

@article {pmid41733115,
year = {2026},
author = {Shetty, KS and Ravichandran, H and Rafiq, S and Achar, S},
title = {Brain Entropy and Complexity as Biomarkers of Neuroplasticity in Neurorehabilitation-A Scoping Review.},
journal = {Physiotherapy research international : the journal for researchers and clinicians in physical therapy},
volume = {31},
number = {2},
pages = {e70174},
doi = {10.1002/pri.70174},
pmid = {41733115},
issn = {1471-2865},
mesh = {Humans ; *Neuronal Plasticity/physiology ; Biomarkers ; Electroencephalography ; Entropy ; *Neurological Rehabilitation/methods ; *Brain/physiopathology ; Neuroimaging ; Stroke Rehabilitation ; Physical Therapy Modalities ; },
abstract = {BACKGROUND: Neurorehabilitation in physiotherapy depends on experience-dependent neuroplasticity; however, conventional clinical outcomes may lack sensitivity to capture dynamic neural adaptations underlying recovery. Brain entropy and complexity measures derived from EEG and neuroimaging have emerged as potential biomarkers of neural adaptability.

OBJECTIVE: To map and synthesize evidence on brain entropy and complexity as biomarkers of neuroplasticity in neurorehabilitation, with relevance to physiotherapy practice.

METHODS: A scoping review was conducted following PRISMA-ScR guidelines. PubMed, Scopus, and Web of Science were searched up to August 2025 for studies reporting quantitative entropy or complexity measures in neurological populations undergoing rehabilitation or task-based assessment.

RESULTS: Eight studies were included. Interventional studies in stroke and brain injury populations reported moderate to large within-group neural effects, with increases in entropy or complexity accompanying functional improvement following task-oriented, robotic, or brain-computer interface-based rehabilitation. Studies of higher methodological quality demonstrated more consistent entropy-outcome associations, whereas lower-quality observational studies showed greater variability. Degenerative neurological conditions are characterized by reduced neural complexity.

DISCUSSION: Brain entropy and complexity measures are sensitive indicators of neuroplastic change and may complement clinical outcomes in physiotherapy. Although not yet ready for routine clinical decision-making, these biomarkers show promise for monitoring intervention response and guiding personalized rehabilitation, pending methodological standardization and longitudinal validation.},
}

Humans
*Neuronal Plasticity/physiology
Biomarkers
Electroencephalography
Entropy
*Neurological Rehabilitation/methods
*Brain/physiopathology
Neuroimaging
Stroke Rehabilitation
Physical Therapy Modalities

@article {pmid41723170,
year = {2026},
author = {Günaydın, G and Moran, JK and Rohe, T and Senkowski, D},
title = {Causal inference shapes crossmodal postdiction in multisensory integration.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {},
pmid = {41723170},
issn = {2045-2322},
abstract = {UNLABELLED: In our environment, stimuli from different sensory modalities are initially processed within a temporal window of multisensory integration spanning several hundred milliseconds. During this window, stimulus processing is influenced not only by preceding and current information, but also by input that follows the stimulus. The computational mechanisms underlying crossmodal backward processing, which we refer to as crossmodal postdiction, are not well understood. We examined crossmodal postdiction in the Illusory Audiovisual (AV) Rabbit and Invisible AV Rabbit Illusions, in which postdiction occurs when flash-beep pairs are presented shortly before and shortly after a single flash or a single beep. We collected behavioral data from 32 participants and fitted four competing models: Bayesian Causal Inference (BCI), forced-fusion, forced-segregation, and non-postdictive BCI. The BCI model fit the data well and outperformed all other models. Building on previous findings that demonstrate causal inference during non-postdictive multisensory integration, our results show that the BCI framework can also explain crossmodal postdiction phenomena. Our findings suggest that the brain performs causal inference not only across concurrent sensory inputs but also across temporal windows, integrating information from past, present, and subsequent events across modalities to construct a unified percept.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1038/s41598-026-36884-6.},
}

@article {pmid41731569,
year = {2026},
author = {Tan, F and Qing, W and Ip, WC and Guo, Z and Li, Z and Zhang, S and Hu, X},
title = {Guided corticomuscular neuroplasticity for restoration of wrist-hand function post-stroke.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-026-01915-w},
pmid = {41731569},
issn = {1743-0003},
support = {15218324//University Grants Committee/ ; ITS/011/23 & ITT/012/23GP//Innovation and Technology Commission/ ; },
}

@article {pmid41730036,
year = {2026},
author = {Su, X and Pang, H and Zhang, H and Ma, Y},
title = {Stent-Based Electrode for Long-Term Intracranial EEG Recording in Sheep: A Preliminary Study.},
journal = {Stroke},
volume = {57},
number = {3},
pages = {e78-e80},
doi = {10.1161/STROKEAHA.125.054298},
pmid = {41730036},
issn = {1524-4628},
}

@article {pmid41728710,
year = {2025},
author = {Amrani, H and Micucci, D and Napoletano, P},
title = {Decoding EEG Signals for Brain-Computer Interfaces.},
journal = {Studies in health technology and informatics},
volume = {330},
number = {},
pages = {551-567},
doi = {10.3233/SHTI251450},
pmid = {41728710},
issn = {1879-8365},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Machine Learning ; *Signal Processing, Computer-Assisted ; },
abstract = {Electroencephalography (EEG) is a non-invasive technique that records brain electrical activity, providing critical insights into neural processes. In recent years, EEG has become integral to brain-computer interface (BCI) research. BCIs enhance human-computer interaction, support assistive solutions for people with disabilities, and enable novel clinical applications. Research in EEG-based BCIs involves several key components: signal acquisition, preprocessing, feature extraction, and classification. Advanced machine learning models, especially those that emphasize personalized and incremental learning approaches, are used to effectively decode EEG signals. This personalization accounts for individual variability and significantly improves model accuracy and robustness. Applications of EEG-based BCIs include emotion recognition, motor imagery for robot control, and EEG-to-text decoding. These applications use EEG signals to make significant advances in their respective fields. Emotion recognition improves human-computer interaction and mental health monitoring; motor imagery enables intuitive robotic control that assists individuals with motor impairments; and EEG-to-text decoding provides new communication pathways for people with severe disabilities. Despite promising advances, challenges such as signal variability, noise, and the need for sophisticated preprocessing techniques remain. Future research should prioritize interdisciplinary collaboration and technological advancements to overcome these challenges, thereby enabling EEG-based BCIs to achieve broader applicability and significantly impact various aspects of human life.},
}

*Brain-Computer Interfaces
*Electroencephalography/methods
Humans
Machine Learning
*Signal Processing, Computer-Assisted

@article {pmid41726112,
year = {2026},
author = {Nazeer, H and Noori, FM and Khan, RA},
title = {Editorial: Integrative approaches with BCI and robotics for improved human interaction.},
journal = {Frontiers in robotics and AI},
volume = {13},
number = {},
pages = {1785247},
pmid = {41726112},
issn = {2296-9144},
}

@article {pmid41723634,
year = {2026},
author = {Comaduran Marquez, D and Vaandering, K and Babwani, A and Redquest, B and Nikitovic, D and Kelly, D and Kinney-Lang, E and Kirton, A},
title = {BCI sports: exploring the potential of BCI-leveraged sport participation for children with quadriplegic cerebral palsy.},
journal = {Disability and rehabilitation},
volume = {},
number = {},
pages = {1-15},
doi = {10.1080/09638288.2026.2630787},
pmid = {41723634},
issn = {1464-5165},
abstract = {PURPOSE: Children with severe disabilities often face barriers to sport participation, limiting their fundamental human rights. Boccia is a Paralympic sport that offers inclusion for individuals with limited mobility, it does not fully accommodate those with severe motor disabilities and communication difficulties. Our group designed an assistive Boccia ramp controlled via brain-computer interface (BCI), potentially allowing individuals with severe motor disability who are non-speaking to participate. This study aimed to gain insight from caregivers and children with quadriplegic cerebral palsy (QCP) toward how BCI-leveraged Boccia might impact their opportunities for sport participation.

MATERIALS AND METHODS: We used a mixed-methods approach to gather insights from children and their families. We conducted semi-structured interviews to explore caregiver insights and experiences of their child using BCI (n = 6). Additionally, we developed a new 21-item survey to get the feedback of the children (n = 6).

RESULTS: Current participation challenges and facilitators to sport were identified, along with future possibilities and the foreseen benefits of implementing BCI technology. Children expressed keen interest in using a BCI system to access Boccia.

CONCLUSIONS: BCI-leveraged sport shows promise for caregivers and children with QCP. Successful implementation requires addressing barriers and facilitators to enable access to previously unattainable activities.},
}

@article {pmid41722497,
year = {2026},
author = {Iacomi, F and Moroni, M and Mainardi, L and Barbieri, R},
title = {Novel EEG-based signatures of brain connectivity for imagined speech.},
journal = {Computers in biology and medicine},
volume = {205},
number = {},
pages = {111555},
doi = {10.1016/j.compbiomed.2026.111555},
pmid = {41722497},
issn = {1879-0534},
abstract = {Developing effective Brain-Computer Interfaces (BCIs) based on Imagined Speech (IS) is a significant challenge, largely due to high inter-subject variability in neural patterns. This study introduces a novel analytical framework to address this issue by integrating functional, effective, and complex network analyses with a more naturalistic sentence-level experimental protocol. Our findings confirm that while IS connectivity networks are characterized by considerable variability across individuals, our methodology successfully identifies a core set of stable pathways that persist across subjects. Specifically, we identified three principal pathways: a motor-language network in the left hemisphere driven by delta-band activity (CL→FR,CR consistent in 60% of subjects), a right-hemisphere network relayed to motor planning areas via gamma-band activity (TR→CL in 40% of subjects), and a top-down visual-spatial network involving parietal regions (POL→CR in 60% of subjects). In parallel, complex network analysis reveals the gamma frequency band to be the most functionally integrated and robust spectral signature, exhibiting significantly higher mean connectivity strength compared to all other bands (e.g., p=0.0015 vs. beta) and appearing consistently in 6/10 subjects. By distinguishing these stable neural markers from subject-specific activity, this work provides more reliable EEG-based signatures for the future development of advanced speech BCIs.},
}

@article {pmid41719718,
year = {2026},
author = {Hwaidi, J and Ghanem, MC},
title = {Motor imagery EEG signal classification using minimally random convolutional kernel transform and hybrid deep learning.},
journal = {NeuroImage},
volume = {328},
number = {},
pages = {121816},
doi = {10.1016/j.neuroimage.2026.121816},
pmid = {41719718},
issn = {1095-9572},
abstract = {The brain-computer interface (BCI) establishes a non-muscle channel that enables direct communication between the human body and an external device. Electroencephalography (EEG) is a popular non-invasive technique for recording brain signals. It is critical to process and comprehend the hidden patterns linked to a specific cognitive or motor task, for instance, measured through the motor imagery brain-computer interface (MI-BCI). A significant challenge is presented by classifying motor imagery-based electroencephalogram (MI-EEG) tasks, given that EEG signals exhibit nonstationarity, time-variance, and individual diversity. Achieving good classification accuracy is also challenging due to the increasing number of classes and the inherent variability among individuals. To overcome these issues, this paper proposes a novel method for classifying EEG motor imagery signals that efficiently extracts features using the Minimally Random Convolutional Kernel Transform (MiniRocket). A linear classifier then utilises the extracted features for activity recognition. Furthermore, a novel deep learning model based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture was proposed and demonstrated to serve as a baseline. The classification via MiniRocket's features achieved higher performance than the best deep learning models at a lower computational cost. PhysioNet and BCI Comp IV 2a datasets were used to evaluate the performance of the proposed approaches. Using PhysioNet, the proposed models achieved mean accuracy values of 98.63% and 98.06%, respectively, for the MiniRocket and CNN-LSTM. With the BCI-CompIV-2a dataset, proposed models achieved mean accuracy values of 92.57% and 92.32%, respectively. The findings demonstrate that the proposed approach can significantly enhance motor imagery EEG accuracy and provide new insights into the feature extraction and classification of MI-EEG. An additional future direction is non-additive electrode-source fusion (Choquet-integral/coalition formulations) to improve robustness under low-SNR EEG and inter-subject variability.},
}

@article {pmid41719578,
year = {2026},
author = {Crell, MR and Kostoglou, K and Suwandjieff, P and Da Cruz, JR and Muller-Putz, GR},
title = {A non-invasive, MRCP-based BCI for online communication.},
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.2026.3666564},
pmid = {41719578},
issn = {1558-0210},
abstract = {Patients with severely impaired motor functions require a stable form of communication for their daily life. Restoring this ability can be achieved with spelling applications controlled by brain-computer interfaces (BCIs). To achieve intuitive control of the application, we propose a BCI system to asynchronously detect single movement intent from EEG. By emulating a button press, we develop a task-agnostic framework applicable to a wide range of interfaces. The system utilizes a model based on movement-related cortical potentials (MRCPs) to detect self-initiated movements without the need for external cues. Twenty participants utilized the developed system to control a spelling interface implemented as a row-column scanner (3-by-3 and 5-by-5 size layouts) to type five-letter words. Participants achieved an overall true positive rate (TPR) of 54.4±27.9% (up to 98.6% in single participants) with an average of 2.0 ± 1.9 false positives per minute (FP/min). 60.9 ± 28.5% of the target characters were correctly selected and participants were able to successfully spell a five-letter word in 41.7 ± 42.7% of all attempts. The analysis of the EEG showed that the MRCP-based classifier maintained consistent detection performance across interface configurations, underscoring its robustness and adaptability to changing applications. These findings demonstrate the potential of the approach as a non-invasive communication aid and establish a foundation for future development of home-use BCIs that offer intuitive, voluntary control with minimal calibration requirements.},
}

@article {pmid41719216,
year = {2026},
author = {Jiang, M and Qu, D and Luo, Q and Wang, X},
title = {The aging effect in the processing of Chinese interoceptive- and exteroceptive- reaction affective verbs.},
journal = {Applied neuropsychology. Adult},
volume = {},
number = {},
pages = {1-9},
doi = {10.1080/23279095.2026.2628131},
pmid = {41719216},
issn = {2327-9109},
abstract = {Great uncertainty exists with whether or not old adulthood experiences an age-related decline in affective words' processing capacity. By recruiting two age groups, taking advantage of two types of affective verbs, namely, interoceptive-reaction affective verbs and the exteroceptive-reaction affective verbs, and by manipulating the factor of affective valence, based on a valence judgment task, the present study made a meticulous scrutiny of this issue. It was found that older adults did undergo an age-related decline in processing affective words. The factor affective valence did have a role in modulating the aging effect.},
}

@article {pmid41717813,
year = {2026},
author = {Zhu, J and Bao, X and Huang, Q and Wang, T and Huang, L and Han, Y and Huang, H and Zhu, J and Qu, J and Li, K and Chen, D and Jiang, Y and Xu, K and Wang, Z and Wu, W and Li, Y},
title = {A Wearable Brain-Computer Interface for Mitigating Car Sickness via Attention Shifting.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e13040},
doi = {10.1002/advs.202513040},
pmid = {41717813},
issn = {2198-3844},
support = {2022ZD0208900//Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project/ ; 2018B030339001//the Key Research and Development Program of Guangdong Province/ ; 2024D02J0008//Guangzhou Talent Plan/ ; 2024A1515011690//Guangdong Natural Science Foundation General Program/ ; 2023QN100110//Guangdong Talent Program/ ; 62306120//National Natural Science Foundation of China/ ; },
abstract = {Car sickness, an enormous vehicular travel challenge, affects a significant proportion of the population. Pharmacological interventions are limited by adverse side effects, and effective nonpharmacological alternatives remain to be identified. Here, we introduce a novel attention-shifting method based on a closed-loop, artificial intelligence (AI)-driven, wearable mindfulness brain-computer interface (BCI) to alleviate car sickness. As the user performs an attentional task, i.e., focusing on breathing as in mindfulness, with a wearable headband, the BCI collects and analyzes electroencephalography (EEG) data via a convolutional neural network to assess the user's mindfulness state and provide real-time audiovisual feedback. This approach might sustainedly shift the user's attention from physiological discomfort toward the BCI-based mindfulness practices, thereby mitigating car sickness symptoms. The efficacy of the proposed method was rigorously evaluated in two real-world experiments, namely, short and long car rides, with a large cohort of more than 100 participants susceptible to car sickness. Remarkably, over 83% of the participants rated the BCI-based attention shifting as effective, with significant reductions in car sickness severity, particularly in individuals with severe symptoms. Furthermore, EEG data analysis revealed a neurobiological signature of car sickness, which provided mechanistic insights into the efficacy of the BCI-based attention shifting for alleviating car sickness. This study proposes a wearable, nonpharmacological intervention for car sickness, validated in a relatively large-scale study involving over 100 participants in real-world car riding. These findings, derived from a between-cohort comparison, support the potential of this approach to improve the travel experience for car sickness sufferers and represent a novel practical application of BCI technology.},
}

@article {pmid41717404,
year = {2026},
author = {Do, M and Tyler, WJ},
title = {Transcutaneous vagus nerve stimulation in breast cancer: a neuroimmune model to improve quality of life.},
journal = {Frontiers in oncology},
volume = {16},
number = {},
pages = {1731999},
pmid = {41717404},
issn = {2234-943X},
abstract = {Breast cancer care has shifted beyond remission toward optimizing long-term physiological, emotional, and functional recovery. Many survivors continue, however, to experience persistent symptom clusters, such as insomnia, fatigue, anxiety, pain, depression, and cognitive impairment. These poor quality of life outcomes reflect underlying dysregulation of autonomic, neuroendocrine, and immune systems. Autonomic imbalance characterized by vagal withdrawal and sympathetic hyperactivation is linked to increased inflammatory load, impaired stress regulation, disrupted sleep, and poorer survival outcomes. Given the role of the vagus nerve in coordinating brain-body signaling and immune modulation, transcutaneous vagus nerve stimulation (tVNS) has emerged as a promising intervention to restore autonomic balance and attenuate psychophysiological burdens. Evidence suggests that tVNS modulates locus coeruleus-norepinephrine activity, regulates arousal and sleep, reduces fatigue and anxiety, enhances cognitive function, and activates the cholinergic anti-inflammatory pathways. Supported by mechanistic and clinical evidence, we propose tVNS as a precision-guided bioelectronic strategy for improving survivorship outcomes in breast cancer.},
}

@article {pmid41716657,
year = {2026},
author = {Hong, B and Xu, Z and Zhang, T and Zhang, T},
title = {Bidirectional cross-day alignment of neural spikes and behavior using a hybrid SNN-ANN algorithm.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1772958},
pmid = {41716657},
issn = {1662-4548},
abstract = {Recent advances in deep learning have enabled effective interpretation of neural activity patterns from electroencephalogram signals; however, challenges persist in invasive brain signals for cross-day neural decoding and simulation tasks. The inherent non-stationarity of neural dynamics and representational drift across recording sessions fundamentally limit the generalization capabilities of existing approaches. We present AlignNet, a novel framework that establishes cross-modal alignment between spiking patterns and behavioral semantics through U-based representation learning. Our architecture employs hybrid SNN-ANN autoencoders to encode neural spikes and behavior into a shared latent space, where the neural spike autoencoder incorporates multiple neuron nodes following convolution layers, and the behavior autoencoder comprises standard convolution layers. These two representations are optimized through contrastive objectives to achieve session-invariant feature learning. To address cross-day adaptation challenges, we introduce a pretraining strategy leveraging multi-session single monkey experiment data, followed by task-specific fine-tuning for neural decoding and simulation. Comprehensive evaluations demonstrate that AlignNet achieves superior performance under both single-day and cross-day conditions; meanwhile, our pretrained model effectively executes decoding and simulation tasks after fine-tuning. The hybrid SNN-ANN representations exhibit temporal consistency across multi-day recording spikes while retaining behavioral semantics, thereby advancing cross-day neural interface applications.},
}

@article {pmid41716573,
year = {2026},
author = {Wang, B and Zhang, H and Kong, XZ},
title = {Growing up with siblings in the age of one child: the potentially confounding role of socioeconomic background.},
journal = {Psychoradiology},
volume = {6},
number = {},
pages = {kkaf035},
pmid = {41716573},
issn = {2634-4416},
}

@article {pmid41716118,
year = {2026},
author = {Mattioli, F and Porcaro, C and Baldassarre, G},
title = {RETRACTION: a 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface (2021J. Neural Eng. 18 066053).},
journal = {Journal of neural engineering},
volume = {23},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ae41ab},
pmid = {41716118},
issn = {1741-2552},
}

@article {pmid41714142,
year = {2026},
author = {Shang, Z and Zhang, J and Li, M and Li, S and Wang, Y and Yang, L},
title = {Dynamic encoding of reward prediction error signals in the pigeon ventral tegmental area during reinforcement learning.},
journal = {eNeuro},
volume = {},
number = {},
pages = {},
doi = {10.1523/ENEURO.0355-25.2026},
pmid = {41714142},
issn = {2373-2822},
abstract = {Reward prediction errors (RPEs) guide learning by comparing expected and obtained outcomes. In mammals, ventral tegmental area (VTA) activity is closely linked to RPE-like signaling, yet how avian VTA dynamics evolve during reinforcement learning remains less well characterized. Here we recorded VTA spiking in pigeons (2 female and 1 male) performing a cue-guided operant task in which a green cue (Cue+) predicted reward contingent on a key peck, whereas a red cue (Cue-) was unrewarded. Using a 16-channel microwire array, we analyzed pooled channel-level multi-unit activity (MUA) aligned to task events. Across sessions, Cue+ trials showed a learning-related redistribution of event-locked modulation: outcome-locked activity was more prominent early in training, while cue-locked modulation became stronger as performance stabilized, consistent with a temporal-difference-like shift of prediction-related signals. Cue- trials were sparse after early learning and showed limited cue-locked modulation in the available dataset. Together, these results provide initial evidence that pigeon VTA pooled MUA exhibits learning-related dynamics consistent with RPE-like processing and support cross-species comparisons of dopaminergic learning signals.Significance Statement This study provides initial evidence that neurons in the pigeon ventral tegmental area (VTA) may encode reward prediction error (RPE) signals during reinforcement learning. The results show that neural activity related to reward gradually shifts toward the predictive cue as learning progresses, consistent with established models in mammals. These findings suggest that the basic neural processes underlying reward-based learning may be shared across vertebrate species and contribute to a broader understanding of comparative learning mechanisms.},
}

@article {pmid41710300,
year = {2026},
author = {Asgher, U},
title = {Editorial: The convergence of AI, LLMs, and industry 4.0: enhancing BCI, HMI, and neuroscience research.},
journal = {Frontiers in computational neuroscience},
volume = {20},
number = {},
pages = {1780276},
doi = {10.3389/fncom.2026.1780276},
pmid = {41710300},
issn = {1662-5188},
}

@article {pmid41709919,
year = {2026},
author = {Zhao, Y and Sun, C and Bi, Y and Zhang, Y},
title = {Effects of visually induced motor imagery-based brain-computer interface training on motor function in patients with incomplete spinal cord injury: a small-sample exploratory trial.},
journal = {Frontiers in neurology},
volume = {17},
number = {},
pages = {1700249},
pmid = {41709919},
issn = {1664-2295},
abstract = {OBJECTIVE: This study aimed to investigate the effects of visually induced motor imagery (MI)-based brain-computer interface (BCI) training on the neurological recovery of patients with incomplete spinal cord injury (iSCI), and to preliminarily explore the underlying neural mechanisms.

METHODS: A single-center, single-blind, small-sample exploratory trial was conducted, enrolling 11 patients with iSCI who were randomly assigned to either the experimental or control group. The experimental group received visually induced BCI training based on a MI paradigm, while the control group received visually guided MI training combined with passive lower limb movements. Both groups underwent interventions five times per week for 4 weeks. Clinical assessments, including the American Spinal Injury Association (ASIA) motor/sensory scores, Berg Balance Scale (BBS), and Functional Ambulation Category (FAC), were conducted before and after the intervention. Simultaneously, electroencephalography (EEG) data were collected to analyze brain engagement, functional connectivity, and time-frequency characteristics, aiming to elucidate the neuromodulatory effects of BCI training.

RESULTS: After the intervention, both groups showed significant improvements in brain engagement, with the experimental group demonstrating greater enhancement. Compared with before rehabilitation training, the levels of θ waves in both groups significantly increased after rehabilitation training, while the levels of β waves significantly decreased (p < 0.05), especially in areas related to exercise planning and sensory integration. The connections between brain regions in the delta and theta frequency bands were significantly enhanced, and the density of brain network connections was significantly increased (p < 0.05) particularly in regions associated with motor planning and sensory integration. Clinically, all functional scores improved significantly in both groups (p < 0.05), and the experimental group showed superior improvement in ASIA motor and sensory scores, BBS, and FAC levels compared to the control group (p < 0.05).

CONCLUSION: Visually induced MI-based BCI training effectively promotes neurological recovery in patients with iSCI, as evidenced by enhanced brain network reorganization, modulation of cortical excitability, and activation of motor-related neural rhythms. This study confirms the feasibility and safety of this intervention strategy and offers a novel direction for iSCI rehabilitation.

CLINICAL TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR), identifier: ChiCTR2400095010.},
}

@article {pmid41708167,
year = {2026},
author = {Catherine Chan, KL and Yan, C and Wang, X and Huang, S and Dai, W and Luo, Y and Cheng, Y and Xu, B and Zhang, W and Shen, Y},
title = {Efficacy and neural mechanisms of a vibrotactile-enhanced, brain-controlled soft robotic glove for upper limb rehabilitation after stroke: a multicentre randomised controlled trial protocol.},
journal = {BMJ open},
volume = {16},
number = {2},
pages = {e110321},
pmid = {41708167},
issn = {2044-6055},
mesh = {Humans ; *Stroke Rehabilitation/methods/instrumentation ; Single-Blind Method ; *Robotics ; *Brain-Computer Interfaces ; *Upper Extremity/physiopathology ; Vibration ; Multicenter Studies as Topic ; Randomized Controlled Trials as Topic ; Female ; *Stroke/physiopathology ; Male ; Adult ; Middle Aged ; Feedback, Sensory ; Recovery of Function ; },
abstract = {INTRODUCTION: Soft robotic gloves (SRGs) integrated with brain-computer interfaces (BCIs) have demonstrated potential in facilitating motor recovery after stroke by enabling active, intention-driven rehabilitation. Emerging evidence suggests that incorporating vibrotactile stimulation (VTS) into SRG-BCI systems may further enhance sensorimotor feedback. The objective of this study is to evaluate the therapeutic efficacy and underlying neural mechanisms of BCI-driven, intention-based glove activation compared with automated glove-assisted training, with VTS applied identically in both groups.

METHODS AND ANALYSIS: This multicentre, single-blind, randomised controlled trial will involve 48 post-stroke patients within 1 week to 3 months after stroke onset, with stratification by time since stroke during randomisation. Participants will be randomly assigned to either the BCI-SRG group (n=24) or SRG group (n=24). Both groups will receive identical VTS. Patients in the BCI-SRG group will actively initiate movements of the SRG through motor imagery, while those in the SRG group will receive automated glove-assisted training without BCI control. The intervention will be administered 5 days per week for 4 weeks. The primary outcome measure is the Fugl-Meyer Assessment of Upper Extremity. Secondary outcome measures include Wolf Motor Function Test, International Classification of Functioning, Disability and Health Generic Set, Barthel Index, Modified Ashworth Scale, Semmes-Weinstein Monofilament Test, as well as event-related spectral perturbation and event-related desynchronisation. All assessments will be conducted at both baseline and post-intervention.

ETHICS AND DISSEMINATION: Ethics approval of this study protocol has been obtained from the Ethics Committee of the First Affiliated Hospital with Nanjing Medical University (2025-SR-508). The findings will be disseminated through peer-reviewed journals, conference presentations and communication with scientific, professional and general public audiences.

TRIAL REGISTRATION NUMBER: ChiCTR2500106951.},
}

Humans
*Stroke Rehabilitation/methods/instrumentation
Single-Blind Method
*Robotics
*Brain-Computer Interfaces
*Upper Extremity/physiopathology
Vibration
Multicenter Studies as Topic
Randomized Controlled Trials as Topic
Female
*Stroke/physiopathology
Male
Adult
Middle Aged
Feedback, Sensory
Recovery of Function

@article {pmid41707762,
year = {2026},
author = {Li, T and Wang, L and Zhao, Y and Su, C and Eickhoff, SB and Olsson, A and Feng, C and Pan, Y},
title = {Neural commonalities and dissociations of human social and experiential learning.},
journal = {Neuroscience and biobehavioral reviews},
volume = {},
number = {},
pages = {106611},
doi = {10.1016/j.neubiorev.2026.106611},
pmid = {41707762},
issn = {1873-7528},
abstract = {Humans navigate the world by learning from both social interactions and direct experiences. Although these two learning strategies are essential for adaptive survival, a systematic neural comparison between them has been lacking. Here, we combined quantitative meta‑analysis with large‑scale network mapping to identify the shared and distinct brain systems underlying social and experiential learning (as represented by Pavlovian conditioning) in healthy humans. Both learning modes engaged common regions involved in value computation, such as the ventral striatum and anterior insula. However, they showed largely dissociable network patterns across the brain: social learning was primarily linked to networks involved in social cognition, whereas experiential learning was predominantly associated with reward and cognitive control. These distinct connectivity profiles reliably differentiated the two learning modes at both aggregate and individual levels. Additionally, we found that appetitive and aversive forms of social learning were supported by separate brain networks. Taken together, our findings provide convergent evidence for how the human brain flexibly reuses core value-processing circuits while engaging specialized networks tailored to distinct learning demands.},
}

@article {pmid41706793,
year = {2026},
author = {Fu, B and Li, F and Li, J and Ji, Y and Li, Y and Quan, Y and Zhang, L and Shi, G},
title = {Improved Spontaneous EEG Signal Decoding Efficiency by Function Predefined Convolutional Neural Network.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2026.3652882},
pmid = {41706793},
issn = {2162-2388},
abstract = {A spontaneous electroencephalogram (EEG)-based brain-computer interface (BCI) is an ideal form of brain-computer interaction. The classical decoding methods can achieve classification by using meaningful manual features, but their performance is poor. The neural network (NN) methods have significantly improved the performance, but their interpretability and computational efficiency are much lower than those of the classical methods. This is because NN abandons the strong a priori knowledge of neuroscience and completely relies on training to extract EEG features. How to integrate the characteristics of neural signals into the design of the basic operator of the NNs while retaining its learning ability is the focus of this work. In this work, we proposed a function predefined convolutional NN (FPCNN) to search for the best frequency points and channel weights to decode spontaneous EEG signals. Among the FPCNN, a novel function predefined convolutional (FPC) layer adopts a learnable way to search for the key spatial-frequency parameters of spontaneous EEG, making its parameters have clear physical meanings. Furthermore, a trainable quadrature detector (TQD) based on FPC was constructed, and the quadrature characteristic was utilized to ensure the capture of complex phase change signals. The core contribution of our method lies in the proposal of a novel NN operator for decoding spontaneous EEG, and a quadrature scheme for handling the phase changes of signals. The experimental results show that the proposed FPCNN significantly improves the performance by 2.09% (${}^
{\ast }
$), 3.08% (${}^
{\ast }
$), and 3.41% (${}^
{\ast \ast }$
), respectively, compared with the state-of-the-art (SOTA) methods on three spontaneous EEG datasets. Moreover, the training and testing time cost of FPCNN in a non-GPU environment only takes 67.96 and 19.36 s per epoch. Its savings in computing resources and time are very beneficial for EEG processing in diverse environments. In addition, visualization experiments demonstrated the interpretability and stability of the proposed FPCNN. The experimental results show that our method is efficient, stable, and interpretable. This work has effectively improved the decoding efficiency of spontaneous EEG signals and demonstrated the power of combining traditional signal processing methods with NNs.},
}

@article {pmid41703350,
year = {2026},
author = {Zhu, H and Zhang, Y and Beierholm, U and Shams, L},
title = {Crossmodal interaction of flashes and beeps across time and number follows Bayesian causal inference.},
journal = {Psychonomic bulletin & review},
volume = {33},
number = {3},
pages = {58},
pmid = {41703350},
issn = {1531-5320},
mesh = {Humans ; Bayes Theorem ; *Auditory Perception/physiology ; *Visual Perception/physiology ; *Illusions/physiology ; Adult ; Young Adult ; Female ; Male ; },
abstract = {Multisensory perception requires the brain to dynamically infer causal relationships between sensory inputs across various dimensions, such as temporal and spatial attributes. Traditionally, Bayesian Causal Inference (BCI) models have generally provided a robust framework for understanding sensory processing in unidimensional settings where stimuli across sensory modalities vary along one dimension such as spatial location, or numerosity (Samad et al., PloS one, 10 (2), e0117178, 2015). However, real-world sensory processing involves multidimensional cues, where the alignment of information across multiple dimensions influences whether the brain perceives a unified or segregated source. In an effort to investigate sensory processing in more realistic conditions, this study introduces an expanded BCI model that incorporates multidimensional information, specifically numerosity and temporal discrepancies. Using a modified sound-induced flash illusion (SiFI) paradigm with manipulated audiovisual disparities, we tested the performance of the enhanced BCI model. Results showed that integration probability decreased with increasing temporal discrepancies, and our proposed multidimensional BCI model accurately predicts multisensory perception outcomes under the entire range of stimulus conditions. This multidimensional framework extends the BCI model's applicability, providing deeper insights into the computational mechanisms underlying multisensory processing and offering a foundation for future quantitative studies on naturalistic sensory processing.},
}

Humans
Bayes Theorem
*Auditory Perception/physiology
*Visual Perception/physiology
*Illusions/physiology
Adult
Young Adult
Female
Male

@article {pmid41336488,
year = {2025},
author = {Ivucic, G and Pahuja, S and Li, H and Schultz, T},
title = {Geo-GCN: Geometric-Graphical Convolutional Network for EEG-based Auditory Attention Detection.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2025},
number = {},
pages = {1-5},
doi = {10.1109/EMBC58623.2025.11251825},
pmid = {41336488},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; *Convolutional Neural Networks ; *Graph Neural Networks ; *Attention ; Speech ; Hearing ; *Brain-Computer Interfaces ; *Auditory Perception ; Humans ; Datasets as Topic ; Male ; Female ; Hearing Loss, Sensorineural ; },
abstract = {Auditory attention detection (AAD) reveals listeners' attention to a speech stimulus based on their elicited electroencephalography (EEG) signals. We propose a geometric graph convolutional network (Geo-GCN) that uses the physical layout of EEG sensors to construct a distance-based adjacency matrix. This enables Geo-GCN to perform more biologically informed feature learning than standard GCNs. Using data from participants with normal hearing (NH) and hearing-impaired (HI), our method outperforms traditional GCNs. Geo-GCN also demonstrates lower performance variability among participants. Analysis of separate NH and HI groups shows consistent gains over standard GCN, underlining the benefit of explicit modeling of scalp geometry. These findings highlight the potential of geometry-aware graph neural networks to improve EEG-based auditory attention detection, particularly in heterogeneous populations with varied hearing capabilities.},
}

*Electroencephalography/methods
*Convolutional Neural Networks
*Graph Neural Networks
*Attention
Speech
Hearing
*Brain-Computer Interfaces
*Auditory Perception
Humans
Datasets as Topic
Male
Female
Hearing Loss, Sensorineural

@article {pmid41703299,
year = {2026},
author = {Xu, Z and Wang, H and Yu, J and Deng, Y and Tian, X and Ni, R and Xia, F and Yang, L and Xu, C and Zhang, L and Luo, R and Chen, P and Zhang, X and Liu, Y and Hou, J and Zhang, M and Chen, S and Su, L and Sun, H and He, Y and Chen, D and Chen, X and Miao, Z and Xie, J and Liu, X and Zhao, J and Ke, B and Tian, X and Zeng, L and Zhang, L and Tang, X and Yang, S and Liu, J and Wang, X and Yan, W and Shao, Z},
title = {Publisher Correction: Psychedelics elicit their effects by 5-HT2A receptor-mediated Gi signalling.},
journal = {Nature},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41586-026-10249-5},
pmid = {41703299},
issn = {1476-4687},
}

@article {pmid41702101,
year = {2026},
author = {Pan, CX and Sokol-Hessner, P},
title = {Trajectories of learning about others: Liking and affiliation follow similar but distinct paths.},
journal = {Acta psychologica},
volume = {264},
number = {},
pages = {106477},
doi = {10.1016/j.actpsy.2026.106477},
pmid = {41702101},
issn = {1873-6297},
abstract = {People quickly form stable impressions of others, but impressions are just the beginning of social interaction. Surprisingly little is known about how impressions may relate to the desire to connect with others, or how they update over time in the presence of complex and changing information. In an online task, participants learned about 12 targets' actions and the contexts of those actions through a series of ten two-sentence vignettes, and rated targets on likeability and desire to connect after each vignette. Actions were positive or negative, and contexts provided dispositional or situational explanations for actions. For some targets, information type in the first five vignettes (e.g., positive dispositional) differed from the last five vignettes (e.g., negative situational). Participants updated impressions and affiliative desires quickly, and for some trajectories, the order of information learned mattered. Most importantly, liking and the desire to connect followed similar but different paths through these trajectories of information, establishing that impressions and affiliative desires are related but distinct constructs.},
}

@article {pmid41701987,
year = {2026},
author = {Li, X and Yang, H and Hu, K and Wu, R and Chen, R and Ni, G and Liu, L and Su, R},
title = {CDI-DTI: A Strong Cross-Domain Interpretable Drug-Target Interaction Prediction Framework Based on Multi-Strategy Fusion.},
journal = {Journal of chemical information and modeling},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.jcim.5c02908},
pmid = {41701987},
issn = {1549-960X},
abstract = {Accurate prediction of drug-target interaction (DTI) is pivotal for drug discovery, yet existing methods often fail to address challenges like cross-domain generalization, cold-start prediction, and interpretability. In this work, we propose CDI-DTI, a novel cross-domain interpretable framework for DTI prediction, designed to overcome these limitations. By integrating multimodal features-textual, structural, and functional-through a multistrategy fusion approach, CDI-DTI ensures robust performance across different domains and in cold-start scenarios. A multisource cross-attention mechanism is introduced to align and fuse features early, while a bidirectional cross-attention layer captures fine-grained intramodal drug-target interaction. At the late fusion stage, we incorporate Gram Loss for feature alignment and a deep orthogonal fusion module to eliminate redundancy. Experimental results on several benchmark data sets demonstrate that CDI-DTI significantly outperforms existing methods, particularly in cross-domain and cold-start tasks, while maintaining high interpretability for practical applications in drug-target interaction prediction.},
}

@article {pmid41480673,
year = {2026},
author = {Singh, N and Cohen, DJ and Chen, S and Shah, MA and Stebbins, A and Kosinski, AS and Brothers, L and Vemulapalli, S and Kirtane, AJ and Dizon, JM and George, I and Leon, MB and Nazif, TM},
title = {Outcomes of Patients With New Left Bundle Branch Block After TAVR: TVT Registry Insights.},
journal = {Circulation. Cardiovascular interventions},
volume = {19},
number = {2},
pages = {e015441},
doi = {10.1161/CIRCINTERVENTIONS.125.015441},
pmid = {41480673},
issn = {1941-7632},
mesh = {Humans ; *Transcatheter Aortic Valve Replacement/adverse effects/mortality ; *Bundle-Branch Block/mortality/physiopathology/therapy/diagnosis/epidemiology ; Male ; Female ; Registries ; Aged, 80 and over ; Treatment Outcome ; Aged ; *Aortic Valve Stenosis/surgery/mortality/diagnostic imaging/physiopathology ; Risk Factors ; Time Factors ; Risk Assessment ; Incidence ; United States/epidemiology ; Patient Readmission ; Action Potentials ; },
abstract = {BACKGROUND: Cardiac conduction disturbances remain the most frequent complication of transcatheter aortic valve replacement (TAVR), but the clinical implications of new left bundle branch block (LBBB) after TAVR remain controversial. Here, we aim to assess the impact of new LBBB after TAVR on patient outcomes in a large, real-world registry.

METHODS: The study population consisted of patients in the TVT registry (Society of Thoracic Surgery and American College of Cardiology Transcatheter Valve Therapy Registry) who underwent TAVR for aortic stenosis between 2016 and 2022 and were discharged alive from the index hospitalization. Key exclusion criteria included preexisting conduction defects and a permanent pacemaker before TAVR or during the index hospitalization. Clinical outcomes were compared between patients with and without new LBBB using Cox proportional hazards models adjusted for baseline demographic, clinical, and echocardiographic variables.

RESULTS: Among 202 533 TAVR recipients, 32 933 (16.3%) developed new LBBB after TAVR. Over the study period, there was a significant decrease in the incidence of new LBBB from 19.9% in the first quarter of 2016 to 14.4% in the third quarter of 2022. Patients with new LBBB after TAVR, compared with those without LBBB, had significantly greater 1-year all-cause mortality (adjusted hazard ratio, 1.19 [95% CI, 1.13-1.25]; P<0.001), hospital readmission (adjusted hazard ratio, 1.23 [95% CI, 1.19-1.28]; P<0.001), and new pacemaker requirement (adjusted hazard ratio, 3.50 [95% CI, 3.26-3.76]; P<0.001). Patients with new LBBB also had lower Kansas City Cardiomyopathy Questionnaire Overall Summary scores (adjusted difference, -1.7 points [95% CI, -2.1 to -1.3]; P<0.001) and left ventricular ejection fraction (adjusted difference, -2.8% [95% CI, -3.4% to -2.2%]; P<0.001).

CONCLUSIONS: New LBBB after TAVR is associated with worse 1-year outcomes, including death, rehospitalization, and permanent pacemaker, as well as worse health status and lower left ventricular ejection fraction. These findings suggest that continued efforts to limit the development of conduction disturbance after TAVR are warranted.},
}

Humans
*Transcatheter Aortic Valve Replacement/adverse effects/mortality
*Bundle-Branch Block/mortality/physiopathology/therapy/diagnosis/epidemiology
Male
Female
Registries
Aged, 80 and over
Treatment Outcome
Aged
*Aortic Valve Stenosis/surgery/mortality/diagnostic imaging/physiopathology
Risk Factors
Time Factors
Risk Assessment
Incidence
United States/epidemiology
Patient Readmission
Action Potentials

@article {pmid41701745,
year = {2026},
author = {Heiney, K and Józsa, M and Rule, ME and Sprekeler, H and Nichele, S and O'Leary, T},
title = {Information theoretic measures of neural and behavioural coupling predict representational drift.},
journal = {PLoS computational biology},
volume = {22},
number = {2},
pages = {e1013130},
doi = {10.1371/journal.pcbi.1013130},
pmid = {41701745},
issn = {1553-7358},
abstract = {In many parts of the brain, population tuning to stimuli and behaviour gradually changes over the course of days to weeks in a phenomenon known as representational drift. The tuning stability of individual cells varies over the population, and it remains unclear what drives this heterogeneity. We investigate how a neuron's tuning stability relates to its shared variability with other neurons in the population using two published datasets from posterior parietal cortex and visual cortex. We quantified the contribution of pairwise interactions to behaviour or stimulus encoding by partial information decomposition, which breaks down the mutual information between the pairwise neural activity and the external variable into components uniquely provided by each neuron and by their interactions. Information shared by the two neurons is termed 'redundant', and information requiring knowledge of the state of both neurons is termed 'synergistic'. We found that a neuron's tuning stability is positively correlated with the strength of its average pairwise redundancy with the population. We hypothesize that subpopulations of neurons show greater stability because they are tuned to salient features common across multiple tasks. Regardless of the mechanistic implications of our work, the stability-redundancy relationship may support improved longitudinal neural decoding in technology that has to track population dynamics over time, such as brain-machine interfaces.},
}

@article {pmid41700523,
year = {2026},
author = {Xu, D and Hong, J and Park, K and Ahn, JH},
title = {Flexible Surface Electrodes for Electrocorticography in Neurological Diseases and Brain-Computer Interface Applications.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {},
number = {},
pages = {e14286},
doi = {10.1002/smll.202514286},
pmid = {41700523},
issn = {1613-6829},
support = {20012355//Ministry of Trade, Industry and Energy (MOTIE)/ ; },
abstract = {Flexible electrocorticography (ECoG) surface electrode arrays have broadened their application scope from clinical neural recording tools to integral components of brain-computer interface (BCI) systems. Currently used ECoG arrays are typically fabricated with metal contacts embedded in silicone carriers, offering limited mechanical flexibility. This restricts their ability to achieve optimal conformal contact with the brain cortex. Moreover, their channel count is constrained by bulky and cumbersome cabling systems. The recent integration of flexible nanomaterials and advanced patterning techniques into surface electrodes has enabled the development of ultrathin, high-density arrays that conform intimately to the cortical surface. These arrays incorporate on-site amplification and multiplexing capabilities while maintaining stable impedance over extended implantation periods. This review article highlights recent technological advancements in ECoG surface electrode arrays, as well as emerging strategies for their application in the diagnosis and treatment of neurological disorders. In addition, it presents current efforts to incorporate surface electrodes into BCI systems through the utilization of neural signals.},
}

@article {pmid41699180,
year = {2026},
author = {Cheng, S and Guo, J and Zhou, YL and Luo, X and Zhang, G and Zhang, YZ and Yang, Y and Xie, J and Xu, P and Shen, DD and Zang, S and Yang, H and Zhen, X and Zhang, M and Zhang, Y},
title = {De novo design of GPCR exoframe modulators.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {41699180},
issn = {1476-4687},
abstract = {G-protein-coupled receptors (GPCRs) are important therapeutic targets and have been targeted mainly through their orthosteric site, where the endogenous agonist binds[1]. However, allosteric modulation has emerged as a promising and innovative strategy in the realm of GPCR drug discovery[1]. Here, drawing inspiration from the natural regulation of GPCRs by transmembrane proteins, we have developed GPCR exoframe modulators (GEMs), de novo designed proteins that specifically target the transmembrane domain of GPCRs. Utilizing a hallucination-like design approach, we crafted GEMs with three strategic structural prompts to achieve the desired binding modes. We selected the dopamine D1 receptor as a prototypical model and systematically investigated four GEMs. Structural studies and functional assays revealed that these GEMs bind to the transmembrane domains and function as diverse allosteric modulators, including agonist-positive allosteric modulator, negative allosteric modulator and biased allosteric modulator. The ago-PAM GEM restores the activity of various D1 receptor loss-of-function mutants, suggesting a promising therapeutic target for GPCR-related disorders. Our work introduces GEMs that target the transmembrane domain as potent agents for allosteric GPCR modulation and highlights the potential of deep learning-based approaches in the design of function-oriented membrane proteins.},
}

@article {pmid41698950,
year = {2026},
author = {Hong, J and Wang, W and Najafizadeh, L},
title = {ChatBCI, a P300 speller BCI with context-driven word prediction leveraging large language models, from concept to evaluation.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {6379},
pmid = {41698950},
issn = {2045-2322},
mesh = {*Brain-Computer Interfaces ; Humans ; *Event-Related Potentials, P300/physiology ; Electroencephalography ; *Language ; Male ; Female ; Adult ; User-Computer Interface ; Large Language Models ; },
abstract = {P300 speller brain computer interfaces (BCIs) allow users to compose sentences by selecting target keys on a graphical user interface (GUI) through the detection of P300 component in their electroencephalogram (EEG) signals following visual stimuli. Most existing P300 speller BCIs require users to spell all or the first few initial letters of the intended word, letter by letter. Consequently, a large number of keystrokes could be required to write an intended sentence, thereby, increasing user's time and cognitive load. There is a need for more efficient and user-friendly methods for faster, and practical sentence composition. In this work, we introduce ChatBCI, a P300 speller BCI that leverages the zero-shot learning capabilities of large language models (LLMs) to suggest words from user-spelled initial letters or predict the subsequent word(s), reducing keystrokes and accelerating sentence composition. ChatBCI retrieves word suggestions through remote queries to the GPT-3.5 API. A modified GUI, displaying GPT-3.5 word suggestions as extra keys is designed. Stepwise linear discriminant analysis (SWLDA) is used for the P300 classification. Seven subjects completed two online spelling tasks: 1) copy-spelling a self-composed sentence using ChatBCI, and 2) improvising a sentence using ChatBCI's word suggestions. Results demonstrate that for the copy-spelling task, on average, ChatBCI outperforms letter-by-letter BCI spellers, reducing time and keystrokes by [Formula: see text] and [Formula: see text], respectively, and increasing information transfer rate by [Formula: see text]. For the improvised sessions, ChatBCI achieves [Formula: see text] keystroke savings across subjects. Overall, ChatBCI, by employing remote LLM queries outperforms traditional spellers without requiring local model training or storage. ChatBCI's (multi-)word prediction capability paves the way for developing next-generation speller BCIs that are efficient and effective for real-time communication, specially for users with communication and motor disabilities.},
}

*Brain-Computer Interfaces
Humans
*Event-Related Potentials, P300/physiology
Electroencephalography
*Language
Male
Female
Adult
User-Computer Interface
Large Language Models

@article {pmid41698889,
year = {2026},
author = {Du, W and Zhang, J and Wang, Y and Li, M and Cao, J and Yang, B and He, Q and Shao, X and Ying, M},
title = {Palmitic acid activates c-Myc via dual palmitoylation-dependent pathways to promote colon cancer.},
journal = {Cell discovery},
volume = {12},
number = {1},
pages = {12},
pmid = {41698889},
issn = {2056-5968},
support = {U23A20534//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {c-Myc is broadly hyperactivated in colon cancer, yet the mechanisms sustaining its transcriptional activation remain elusive. Here we identify palmitic acid (PA) as a metabolite cue that activates c-Myc via dual palmitoylation-dependent pathways operating across tumor initiation and progression. In colitis models, PA-rich diets exacerbate inflammation and enrich MYC target programs without increasing Myc mRNA. Mechanistically, the palmitoyltransferase ZDHHC9, upregulated by IL-1β, directly palmitoylates c-Myc at C171, enhancing c-Myc/MAX dimerization and transcriptional activity; genetic or pharmacologic inhibition diminishes c-Myc palmitoylation and target gene expression. During tumor progression, c-Myc transactivates FATP2, increasing PA uptake and reinforcing c-Myc palmitoylation, thereby establishing a feedforward loop and metabolic addiction to PA. Functionally, PA accelerates xenograft growth, whereas targeting ZDHHC9 and FATP2 inhibits c-Myc function to suppress tumor burden. These findings uncover metabolite-driven control of c-Myc through palmitoylation and highlight ZDHHC9/FATP2 as actionable vulnerabilities for colon cancer treatment.},
}

@article {pmid41698423,
year = {2026},
author = {Wu, W and Daly, I and Chen, W and Liu, L and Liang, W and Chen, Y and Wang, X and Cichocki, A and Jin, J},
title = {HCFNet: A Heterogeneous Frequency Bands Coupling CNN for Enhanced Short-Time Fast Response in Motor Imagery Decoding.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110717},
doi = {10.1016/j.jneumeth.2026.110717},
pmid = {41698423},
issn = {1872-678X},
abstract = {BACKGROUND: Motor imagery signals encompass a broad range of frequency components, and frequency band decomposition can improve the precision of frequency-domain features, helping the model focus on task-relevant information. However, existing methods often treat signals from different frequency bands uniformly, overlooking their heterogeneity and coupling, which leads to redundant features and loss of cooperative information.

NEW METHOD: We propose a HCFNet that explores heterogeneous feature extraction and coupling across frequency bands. HCFNet first separates the raw signal into high and low-frequency bands, extracting spatiotemporal features through specialized modules. A cross-frequency coupling module then fuses these features, using data augmentation for regularization to capture robust spectral-spatiotemporal features and high-low frequency coupling.

RESULTS: We evaluated our model on the BCIC-IV-2a and OpenBMI benchmark datasets, and our model achieves average accuracies of 82.41% and 76.52%. Notably, HCFNet maintains excellent performance even with shorter time windows.

HCFNet outperforms all the state-of-the-art methods we benchmark against. Compared with traditional multi-band isomorphic methods, frequency-band heterogeneous coupling performs better in capturing task-related features and significantly reduces redundancy during feature fusion.

CONCLUSIONS: This study significantly advances the decoding technology of motor imagery signals through an innovative frequency-band heterogeneous coupling method. Its substantial potential for rapid responses brings tangible improvements to brain-computer interface systems and is expected to be further applied in domain adaptation, cross-domain alignment, and cross-subject contexts in the future.},
}

@article {pmid41643315,
year = {2026},
author = {Xie, X and Fan, Z and Mou, H and Lan, Y and Wang, Y and Wang, M and Pan, Y and Chen, G and Chen, W and Zhang, S},
title = {AutoSimTTF: a fully automatic pipeline for personalized electric field simulation and treatment planning of tumor treating fields.},
journal = {Physics in medicine and biology},
volume = {71},
number = {4},
pages = {},
doi = {10.1088/1361-6560/ae4288},
pmid = {41643315},
issn = {1361-6560},
mesh = {Humans ; *Radiotherapy Planning, Computer-Assisted/methods ; *Precision Medicine/methods ; Automation ; *Neoplasms/radiotherapy/diagnostic imaging/therapy ; *Electricity ; },
abstract = {Objective. Tumor treating fields (TTFields) is an emerging cancer therapy whose efficacy is closely linked to the electric field (EF) intensity delivered to the tumor. However, current computational workflows for simulating the EF and planning treatment rely on time-consuming manual segmentation and proprietary software, hindering efficiency, reproducibility, and accessibility.Approach. We introduce AutoSimTTF, a fully automatic pipeline for personalized EF simulation and optimized treatment planning for TTFields. The end-to-end workflow utilizes advanced deep learning model for automated tumor segmentation, conducts finite element method-based EF simulation, and determines a computationally optimized treatment plan via a novel, physics-based parameter optimization method.Main results. The automated segmentation module achieved high precision, yielding a Dice similarity coefficient of 0.91 for the whole tumor. In terms of efficiency, the active planning workflow was completed in approximately 12 min, significantly outperforming conventional multi-day manual processes. The pipeline's simulation accuracy was validated against a conventional semi-automated workflow, demonstrating deviations of less than 14.1% for most tissues. Critically, the parameter optimization generated personalized transducer montages that produced a significantly higher EF intensity at the tumor site (up to 111.9% higher) and substantially improved field focality (19.4% improvement) compared to traditional fixed-array configurations.Significance. AutoSimTTF addresses major challenges in efficiency and reproducibility, paving the way for data-driven personalized TTFields therapy and large-scale computational research.},
}

Humans
*Radiotherapy Planning, Computer-Assisted/methods
*Precision Medicine/methods
Automation
*Neoplasms/radiotherapy/diagnostic imaging/therapy
*Electricity

@article {pmid41697859,
year = {2026},
author = {Liu, H and Li, M and Yang, Y and Li, Z},
title = {A simple deep transfer learning model with feature alignment block for motor imagery decoding.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-17},
doi = {10.1080/10255842.2026.2627492},
pmid = {41697859},
issn = {1476-8259},
abstract = {To address data scarcity and distribution shifts in motor imagery electroencephalogram (MI-EEG) based brain computer interface, we propose a 1-dimensional convolution-based deep transfer learning model with embedded Feature Alignment block (1DC-DTL-FA) in this article. It integrates multi-stage feature extraction, classification, and FA block. Unlike complex models, it utilizes Neural Architecture Search (NAS) to automatically locate the optimal FA position in Euclidean space Evaluated on BCI 2000 and BCI IV2a datasets, 1DC-DTL-FA achieved superior accuracies of 89.80% and 82.96%. The results demonstrate that this simple architecture effectively handles complex feature extraction and online alignment, outperforming state-of-the-art models in MI-EEG decoding.},
}

@article {pmid41697833,
year = {2026},
author = {Li, L and Chen, W},
title = {EEG-Based Emotion Recognition Using Spatial-Temporal Graph-Aware Network with Channel Selection.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3665517},
pmid = {41697833},
issn = {2168-2208},
abstract = {Electroencephalogram (EEG)-based emotion recognition holds great potential in intelligent human computer interaction and brain-computer interface systems, as the brain generates distinct electrical activity patterns under different emotional states. However, EEG information often contains data from numerous channels, leading to high computational cost and potential redundancy. Existing channel selection methods often rely on uniform rules, lacking frequency-specific adaptability and inter-channel modeling, which can cause information loss and reduced performance during dimensionality reduction. To address this issue, we propose a novel framework that combines discriminative channel selection with hierarchical spatial-temporal modeling to enhance both per formance and efficiency. In preprocessing, wavelet coherence and mutual information are used to adaptively select informative channels across multiple frequency bands. The selected signals are then processed by a Spatial Temporal Graph-aware Network (STG-Net), which models spatial relationships between channels through graph convolution, extracting spatial features from each time frame. Coupled with a temporal modeling module, the network further captures the evolving temporal patterns of emotional states across consecutive frames. Finally, frequency spatial-temporal features are fused for emotion classification. Compared to the state-of-the-art methods, our approach achieves superior performance in both recognition accuracy and model efficiency.},
}

@article {pmid41694587,
year = {2026},
author = {Wu, W and Du, J and Li, J and Zhang, S and Kang, X and Cao, Y and Chen, J and Pan, Z and Huang, X and Xu, Z and Yang, B and He, Q and Yang, X and Yan, H and Luo, P},
title = {Inhibition of Cathepsin B protects against vandetanib-induced hepato-cardiotoxicity by restoring lysosomal damage.},
journal = {International journal of biological sciences},
volume = {22},
number = {4},
pages = {1752-1774},
pmid = {41694587},
issn = {1449-2288},
mesh = {*Piperidines/adverse effects/toxicity ; *Quinazolines/adverse effects/toxicity ; Animals ; *Cathepsin B/antagonists & inhibitors/metabolism ; *Lysosomes/metabolism/drug effects ; Humans ; Mice ; *Cardiotoxicity/prevention & control/metabolism ; Apoptosis/drug effects ; },
abstract = {Vandetanib, a critical therapy for advanced thyroid and RET-driven cancers, is limited by life-threatening hepato-cardiotoxicity. This study identifies lysosomal protease cathepsin B (CTSB) as the central mediator of vandetanib-induced organ damage through STAT3-driven transcriptional activation. CTSB triggers mitochondrial apoptosis by cleaving the lysosomal calcium channel mucolipin TRP cation channel 1 (MCOLN1), disrupting calcium/AMP-activated protein kinase (AMPK) signaling and autophagy flux. Crucially, the natural compound tannic acid directly binds and inhibits CTSB, completely protecting against hepato-cardiotoxicity without compromising vandetanib's antitumor efficacy in preclinical models. Overall, our findings establish CTSB-mediated lysosomal dysfunction and MCOLN1-calcium-AMPK axis disruption as the core mechanism of vandetanib-induced hepato-cardiotoxicity, and identify tannic acid as a readily translatable adjuvant strategy to prevent this toxicity. These findings redefine CTSB as a druggable target for kinase inhibitor toxicities and position tannic acid as a clinically translatable adjuvant to enhance vandetanib's safety profile. By preserving lysosomal function and calcium homeostasis, this strategy addresses a critical unmet need in precision oncology, enabling prolonged, safer use of vandetanib and related tyrosine kinase inhibitors. The discovery of shared lysosomal injury mechanisms across organs also opens avenues for preventing multi-organ toxicities in broader cancer therapies.},
}

*Piperidines/adverse effects/toxicity
*Quinazolines/adverse effects/toxicity
Animals
*Cathepsin B/antagonists & inhibitors/metabolism
*Lysosomes/metabolism/drug effects
Humans
Mice
*Cardiotoxicity/prevention & control/metabolism
Apoptosis/drug effects