@article {pmid41929000,
year = {2026},
author = {Replogle, JM and Marks, JD and Fernandez, MG and Yuan, H and Yu, D and Winters, E and Jawahar, VM and Deshmukh, R and Sutanto, R and Kowal, I and Frankenfield, A and Shi, R and Carlomagno, Y and Jansen-West, K and Todd, TW and Kopach, A and Sandra Ndayambaje, I and Qi, YA and Shantaraman, A and Pozo-Cabanell, I and Sheth, U and Yue, M and Duong, D and Ferguson, SM and Bennett, DA and Damme, M and Boeve, BF and Day, GS and Kellman, B and Skarnes, WC and Petersen, RC and Josephs, KA and Graff-Radford, NR and McDonough, JA and Prudencio, M and Barmada, SJ and Zhang, Y and Hao, L and DeTure, M and Rawlinson, B and Engelberg-Cook, E and Casey, MC and Perez, N and Dickson, DW and Wingo, A and Liu, Y and Seyfried, NT and Wingo, TS and Mosalaganti, S and Petrucelli, L and Ward, ME},
title = {Neurodegeneration risk variants promote lysosomal TMEM106B fibril accumulation.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {41929000},
issn = {2692-8205},
abstract = {Variants in TMEM106B and GRN, which encode lysosomal proteins, interact through unknown mechanisms to increase the risk of age-related cognitive decline and neurodegeneration. Here, we show that these variants converge on a single molecular intermediate: the cleaved intra-lysosomal fibril core of TMEM106B, a precursor to amyloid fibrils that accumulate in the aging brain. A protein-coding TMEM106B risk variant (p.T185) drives fibril core accumulation by impairing its degradation and GRN risk variants amplify this effect. Mice over-expressing the fibril core develop hallmarks of neurodegeneration, and cryo-electron tomography reveals intra-lysosomal fibrils in cultured neurons, mice, and diseased human brain. In GRN-mutation carriers, in whom fibril burden is greatest, fibrils extrude through ruptured lysosomal membranes. These findings identify intra-lysosomal TMEM106B fibrillization as a convergent neurodegeneration mechanism and potential therapeutic target.},
}

@article {pmid41964479,
year = {2026},
author = {Majumder, S and Halder, A and Bisht, P and Roy, M and Biswas, U},
title = {Wheelchair movement signal classification from EEG for motor-impaired individuals using novel deep learning architecture.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {},
number = {},
pages = {1-20},
doi = {10.1080/17483107.2026.2651889},
pmid = {41964479},
issn = {1748-3115},
abstract = {Purpose: Traditional wheelchair controls often limit independence and pose safety risks for motor-impaired users. To address these challenges, this study explores the potential of EEG-based control systems that allow users to operate powered wheelchairs through brain signals rather than physical movements. Materials and Methods: We developed a hybrid deep learning model that integrates Long Short-Term Memory (LSTM) and 1D-Convolutional Neural Networks (1D-CNN) with skip connections to capture both temporal and spatial EEG signal features. The model was trained and evaluated on a public EEG dataset to classify intended wheelchair movements. Performance metrics, including accuracy, precision, recall, and F1 score, were computed. Confidence interval tests and ablation studies were conducted to assess statistical reliability and component contribution. Results: The proposed model achieved an accuracy of 98.08% with 0.98 precision, recall, and F1 score, outperforming ten state-of-the-art methods. Confidence interval analysis confirmed the model's statistical superiority, while ablation results demonstrated the importance of the LSTM-CNN fusion and skip connections in enhancing prediction performance. Conclusions: The LSTM-CNN architecture with skip connections offers a reliable and accurate EEG-based control approach for powered wheelchairs, improving safety and independence for users with severe motor impairments. In future, proposed model may lead to EEG-responsive wheelchair systems to aid mobility and self-directed activity, contributing to improved quality of life and rehabilitation outcomes.},
}

@article {pmid41964718,
year = {2026},
author = {Mathon, B and Jacquens, A and Gourvennec, E and Carpentier, A},
title = {Outpatient supratentorial craniotomy for brain lesions: a pilot feasibility and safety study.},
journal = {Neurosurgical review},
volume = {49},
number = {1},
pages = {},
pmid = {41964718},
issn = {1437-2320},
}

@article {pmid41965314,
year = {2026},
author = {Hosoo, H and Araki, K and Masuda, Y and Ishida, H and Matsumaru, Y},
title = {Endovascular neural interfaces: current platforms and clinical readiness.},
journal = {Journal of neurointerventional surgery},
volume = {},
number = {},
pages = {},
doi = {10.1136/jnis-2025-024870},
pmid = {41965314},
issn = {1759-8486},
abstract = {Neurointerventional techniques are facilitating a new class of neural interfaces that record and stimulate brain activity from within the cerebral vasculature. Conventional scalp electroencephalography (EEG) is safe and widely scalable but is limited by skull attenuation and volume conduction, whereas electrocorticography and stereoelectroencephalography provide higher-amplitude signals at the cost of craniotomy or stereotactic depth implantation and procedure-related morbidity. Endovascular approaches offer a distinct access paradigm by leveraging familiar catheter-based workflows to reach cortical veins and dural sinuses. They occupy a practical middle ground that enhances signal quality relative to scalp EEG while mitigating some of the procedural risks associated with open or multi-trajectory intracranial implants. This narrative review summarizes the historical evolution and major device classes, including catheter-based electrodes, stent-electrode arrays, and emerging leadless or wireless systems, with emphasis on leading clinical platforms such as Stentrode (a stent-electrode recording array from Synchron, New York, USA), and EP-01 (an EEG device from Epsilon Medical, Japan). We synthesize evidence on implantation targets, deliverability, signal characteristics relevant to epilepsy evaluation and brain-computer interface applications, stimulation feasibility, and translational constraints governing clinical adoption, including antithrombotic management, vascular patency, imaging surveillance, complications, and device failure modes. We highlight decision-linked endpoints, particularly concordance with conventional intracranial EEG for seizure lateralization, and outline essential reporting elements needed to compare studies across anatomical locations, referencing strategies, and artifact environments. Finally, we provide pragmatic recommendations for neurointerventional adoption and identify priorities for next-generation device development, registries, and multicenter prospective trials.},
}

@article {pmid41965644,
year = {2026},
author = {Jiangyi, L and Shuping, L and Yulei, S and Congdon, N and Lei, C and Yuanbo, L},
title = {Retinal nerve fiber layer thickness in epilepsy: a meta-analysis comparing affected patients with healthy controls.},
journal = {BMC ophthalmology},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12886-026-04789-7},
pmid = {41965644},
issn = {1471-2415},
}

@article {pmid41969217,
year = {2026},
author = {Alsubaie, M and Alshammari, S and Ahmed, Y and Esmail, S and Adams, K and Rouhani, H and Ríos Rincón, A},
title = {Brain-Computer Interface Games for Cognitive Assessment: A Scoping Review.},
journal = {The Canadian journal of neurological sciences. Le journal canadien des sciences neurologiques},
volume = {},
number = {},
pages = {1-44},
doi = {10.1017/cjn.2026.10606},
pmid = {41969217},
issn = {0317-1671},
}

@article {pmid41970658,
year = {2026},
author = {Liu, M and Wang, S and Cui, W and Zhang, S and Zhao, L and Beyette, FR},
title = {Render EEG-Based Brain-Computer Interfaces Calibration-Free: Trade Space for Time in EEG Decoding.},
journal = {IEEE open journal of engineering in medicine and biology},
volume = {7},
number = {},
pages = {94-105},
pmid = {41970658},
issn = {2644-1276},
abstract = {Goal: Electroencephalogram-based brain-computer interfaces (EEG BCIs) have broad applications in neurorehabilitation, clinical assessment, and assistive technologies. However, their practical deployment is severely limited by subject-specific calibration, which requires time-consuming data collection and model retraining for each user, significantly reducing usability. This reliance on calibration arises from the conventional "one-model-fits-all" strategy: "relying on a single general model to handle all data complexity like subject variability. When its limited generalization falls short, time must be spent on calibration to adapt the model." Methods: To address this limitation, we propose a trade-space-for-time strategy for calibration-free EEG decoding: "Instead of adapting one model to every user, we maintain a pool of compact models, including a general model and multiple biased models, where each biased model specializes in decoding a specific type of subject pattern. For a new input, the system automatically selects the most suitable model based on data characteristics, enabling instant adaptation without retraining." Compact deep learning models make this design feasible by allowing fast switching and low storage cost, which would be impractical with large-scale architectures. Results: Experiments on multiple public EEG datasets show that the proposed strategy achieves performance comparable to within-subject decoding: slightly higher in one dataset (0.7672 vs. 0.7601), nearly identical in another (0.7568 vs. 0.7572), and marginally lower in a third (0.8804 vs. 0.8888). Conclusions: These results demonstrate that our approach effectively eliminates calibration while preserving accuracy, providing a practical and scalable alternative for EEG BCIs. The framework also has potential applications in other neuroimaging modalities such as fMRI and fNIRS.},
}

@article {pmid41970718,
year = {2026},
author = {Han, F and Yu, Q and Zheng, H and Li, G and Li, Q and Tian, Q and Zhang, Y and Liu, Z},
title = {A new paradigm for brain-machine interface electrodes: From static to dynamic, advancing toward embodied intelligence.},
journal = {Innovation (Cambridge (Mass.))},
volume = {7},
number = {4},
pages = {101258},
pmid = {41970718},
issn = {2666-6758},
}

@article {pmid41971043,
year = {2026},
author = {Kiser, A and Cantürk, A and Volosyak, I},
title = {SSVEP-driven BCI authentication with reduced number of EEG electrodes across high and low frequency ranges.},
journal = {Frontiers in neuroergonomics},
volume = {7},
number = {},
pages = {1741655},
pmid = {41971043},
issn = {2673-6195},
abstract = {Growing concerns over data privacy, credential theft, and spoofing attacks have highlighted the limitations of traditional authentication methods in high-security settings. To address these challenges, we propose a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) authentication system that verifies short-lived, session-specific identity prompts using neural activity. The proposed system uses a single flickering visual stimulus to encode a unique, system-generated random code that remains unknown to the user. Instead of relying on conscious input, the system directly extracts the user's brain responses to the stimulus. Authentication is achieved by matching the frequency components of the recorded electroencephalography (EEG) signals to those embedded in the visual stimulus, enabling implicit verification without prior training or manual interaction. In an online BCI study involving 21 healthy participants, we evaluated four configurations differing in stimulation frequencies and EEG electrode count. Mean symbol-level accuracy reached 99% (95% CI: 98.3 -99.6) for high-frequency stimulation with three electrodes, 95% (95% CI: 91.1 -98.2) for high-frequency stimulation with a single electrode, 97% (95% CI: 95.1 -98.4) for low-frequency stimulation with three electrodes, and 96% (95% CI: 94.5 -97.8) for low-frequency stimulation with a single electrode. The corresponding mean trial durations were 38.6 s, 76.6 s, 17.2 s, and 27.1 s, respectively. Participants generally rated high-frequency flickering stimuli as more comfortable, whereas setup time and EEG wearability were identified as the main barriers to usability. These findings demonstrate that SSVEP-based authentication can provide accurate and training-free implicit authentication, while also offering potential resistance to spoofing attacks. The results suggest that this passive BCI approach is a promising direction for secure authentication, although practical deployment will require further improvements in speed, comfort, and wearability.},
}

@article {pmid41971081,
year = {2026},
author = {Feng, X and Jin, J and Ye, X and Wu, J and Yang, B and He, Q and Luo, P and Lu, J and Yang, X},
title = {Exploring the anti-diabetic potential of peimisine through bioinformatics analysis and in vitro studies.},
journal = {Frontiers in pharmacology},
volume = {17},
number = {},
pages = {1766852},
pmid = {41971081},
issn = {1663-9812},
abstract = {Fritillariae Cirrhosae Bulbus is a traditional herb with diverse activities, yet its active metabolites against type 2 diabetes (T2D) remain unclear.

OBJECTIVE: This study aimed to identify key bioactive metabolites from Fritillariae Cirrhosae Bulbus through database mining, and to evaluate the therapeutic potential of the selected metabolite peimisine against T2D through bioinformatics and experimental validation.

METHODS: Metabolites were retrieved from TCMSP. Following ADME screening and literature validation, six metabolites were identified, from which peimisine was selected based on AlogP. Its targets were predicted using multiple databases, followed by GO and KEGG enrichment analyses and disease association analyses. Glucose uptake and gluconeogenesis assays were conducted in HepG2 cells, and key targets were further analyzed via PPI network and molecular docking.

RESULTS: Six metabolites were identified, with peimisine selected as the most promising candidate. Bioinformatics analysis predicted 48 potential targets, with enrichment in metabolic pathways and a strong association with T2D. Experimentally, peimisine at 20 μM increased glucose uptake by up to 36.30% and reduced medium glucose by 57.65% under normal conditions; in an insulin-resistance model, it restored uptake by 42.82% and lowered glucose by 15.32%. It also significantly suppressed gluconeogenic enzymes, reducing PEPCK mRNA by 80% and G6PD by 31% relative to control. HSP90AA1 was identified as a central target, with a docking score of -7.9 kJ/mol.

CONCLUSION: Peimisine, a metabolite of Fritillariae Cirrhosae Bulbus, demonstrates anti-T2D potential by enhancing glucose uptake and suppressing gluconeogenesis, likely through targeting HSP90AA1, supporting its development as a phytotherapeutic candidate for T2D.},
}

@article {pmid41971349,
year = {2026},
author = {Zhong, Y and Wang, Z and Zhao, X and Xu, T and Zhou, T and Hu, H},
title = {Electroencephalogram-based multimodal attention level classification using deep learning techniques.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1791677},
pmid = {41971349},
issn = {1662-5161},
abstract = {This study aims to develop a novel attention level prediction method using a multimodal brain-computer interface system that integrates electroencephalogram (EEG), electrocardiogram (ECG), and electrooculogram (EOG) signals to enhance prediction accuracy and robustness. We propose the Multi-Feature Enhanced Attention Network (MEAN), which leverages the complementary strengths of these signals: EEG provides insights into brain electrical activity, ECG captures heart rate variability to reflect emotional and cognitive states, and EOG records eye movements for contextual attention level information. The model is designed to address the limitations of single-modality signals, such as noise susceptibility and limited information range. Experimental results demonstrate that MEAN achieves an average accuracy of 0.9524, outperforming traditional models. The model exhibits superior adaptability, particularly in handling EEG and multimodal data, and shows enhanced predictive performance compared to existing approaches. In conclusion, the proposed MEAN model effectively integrates multimodal physiological signals to improve attention level prediction, offering a robust and accurate solution for applications requiring attention level monitoring. This research provides a foundation for advancing applications in education, work efficiency assessment, and cognitive enhancement technologies, highlighting the potential of multimodal approaches for understanding and predicting attention states.},
}

@article {pmid41971353,
year = {2026},
author = {Tou, SLJ and Warschausky, SA and Karlsson, P and Huggins, JE},
title = {Individualized electrode subset improves the calibration accuracy of an EEG P300-design brain-computer interface for people with severe cerebral palsy.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1720969},
pmid = {41971353},
issn = {1662-5161},
abstract = {INTRODUCTION: This study examined the effect of individualized electroencephalogram (EEG) electrode location selection for non-invasive P300-design brain-computer interfaces (BCIs) in people with varying severity of cerebral palsy (CP) in a post-hoc offline analysis.

METHODS: A forward selection algorithm was used to select the best performing eight electrodes (of an available 32) to construct an individualized electrode subset for each participant. Custom electrode subset size was chosen to be 8 because BCI accuracy of the individualized subset was compared to accuracy of a widely used default subset.

RESULTS: Across 51 participants, individualized subsets improved calibration accuracy only for the severe CP cohort (mean +28.6% absolute; 95% CI [13.4%, 46.1%]; p < 0.0001). No group-level benefit was detected for mild CP or typically developing controls, although several individuals in these groups improved (2/17 mild CP; 1/10 controls). In the subset with held-out testing data (mild CP and controls), calibration gains did not translate to higher testing accuracy; among controls, the subset effect was reduced on testing (-9.6%, 95% CI [-13.3%, -5.8%], p < 0.0001), with no evidence of change for mild CP. Participants with severe CP typically required larger subsets to approach asymptotic accuracy, whereas ≤ 8 electrodes were sufficient for most others.

DISCUSSION: The findings suggested that electrode selection can accommodate atypical neuroanatomy in people with severe CP, while the default electrode locations are sufficient for people with milder impairments from CP and typically developing individuals.},
}

@article {pmid41971720,
year = {2026},
author = {Luo, TJ},
title = {Domain generalized feature embedded learning for calibration-free event-related potentials recognition.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {77},
pmid = {41971720},
issn = {1871-4080},
abstract = {Event-related potentials (ERPs) play an important role for building EEG-based brain computer interfaces (BCIs). However, due to the complex and varied spatio-temporal characteristics of ERPs across subjects, data distribution across subjects a very important issue to be solved for constructing calibration-free BCIs. To achieve calibration-free ERPs recognition, we propose a Domain Generalized Feature Embedded Learning (DGFEL) method. First, we align the ERPs of each existed subject based on covariance centroids. Then, we enhanced the aligned samples based on xDAWN filter and extract spatio-temporal features. Finally, the spatio-temporal features are further generalized by the decomposed adversarial loss, and we construct a neural network embedding backbone to implement features generalization across subjects. The proposed method has been systematically validated on two benchmark EEG-based ERP datasets, and its classification performance surpasses several state-of-the-art methods as well as deep learning models. Moreover, it effectively captures robust features from existed source subjects, and can be generalized to new subjects without accessing target ERP samples. Our method therefore provides a novel selection to construct calibration-free ERP-BCIs.},
}

@article {pmid41973557,
year = {2026},
author = {Kroflic, N and Kunavar, T and Pauw, K and Babic, J},
title = {Feedback-Related Dynamics of Hierarchical Error Processing in Goal-Directed Action.},
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.2026.3683510},
pmid = {41973557},
issn = {1558-0210},
abstract = {The performance monitoring system is essential for adaptive behavior and the development of brain-machine interfaces that utilize neural feedback signals. The posterior medial frontal cortex generates different error-related potentials (ErrP), including error-related negativity (ERN), N2, and feedback-related negativity (FRN), which encode specific aspects of performance evaluation. In this study, we reexamine the hierarchical framework of error processing by investigating how low-level execution error detection and correction influence high-level outcome evaluation as reflected in FRN dynamics. Furthermore, we examine whether neural signals associated with outcome errors maintain consistent or distinct feature representations under different experimental conditions of altered feedback availability. Using a visuomotor rotation task, we manipulated the availability of visual feedback in three blocks to examine how immediate sensory error detection and corrective actions interact with outcome processing. Participants (n = 16) performed reaching movements while experiencing unexpected cursor rotations (±20° and ±40°; 20% probability) that challenged their sensorimotor control and task success. EEG recordings revealed that the FRN showed valence sensitivity in Blocks 1 and 3, while Block 2 exhibited a surprise-driven response without outcome differentiation. In contrast, posterior negativity appeared only in Blocks 1 and 3, where participants could detect and correct movement errors. This posterior response emerged on trials requiring corrective movements, regardless of final outcome, and appears to be driven by the availability of sensory feedback and error correction rather than by outcome valence. Furthermore, we demonstrate robust classification between low-level and high-level error signals and their conditional outcome-related variations, providing a foundation for more informative feedback in adaptive neural interfaces.},
}

@article {pmid41961852,
year = {2026},
author = {Iwama, S and Matsuoka, A and Ushiba, J},
title = {Brain-computer interface-based neurofeedback training enables transferable control of cortical state switching in humans.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {123},
number = {15},
pages = {e2525769123},
doi = {10.1073/pnas.2525769123},
pmid = {41961852},
issn = {1091-6490},
support = {JPMJPR23I1//MEXT | Japan Science and Technology Agency (JST)/ ; JPMJMS2012//MEXT | Japan Science and Technology Agency (JST)/ ; 20H05923//MEXT | Japan Society for the Promotion of Science (JSPS)/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Neurofeedback/methods/physiology ; Male ; Female ; Adult ; Young Adult ; Learning/physiology ; Electroencephalography ; Double-Blind Method ; },
abstract = {Behavioral flexibility relies on transient neural dynamics that govern cortical state transitions. However, whether humans can deliberately learn to control such state transitions and generalize trained neural dynamics beyond contexts remains unclear. Here, we demonstrate that operation of a brain-computer interface (BCI) which links time evolution of sensorimotor activity with real-time feedback enables volitional control over the targeted neural population. Compared with a double-blind sham control group, trained participants modulated sensorimotor oscillations in the absence of BCI. Data-driven latent-state analysis further revealed stronger interregional phase coupling and steeper broadband spectral slope in the medial frontal cortex during transitions. The training-induced reorganization of sensorimotor dynamics was found during movement execution and associated with performance improvement, indexed by reduced reaction times for both muscle contraction and relaxation. These findings provide evidence that learned control over cortical state transitions enhances behavioral flexibility beyond the training context.},
}

Humans
*Brain-Computer Interfaces
*Neurofeedback/methods/physiology
Male
Female
Adult
Young Adult
Learning/physiology
Electroencephalography
Double-Blind Method

@article {pmid41962159,
year = {2026},
author = {Chen, SW and Gallo, P and Afshari, FT and Herbert, K and Lo, WB and Rodrigues, D and Solanki, G and Pepper, J},
title = {Ventricular indices in infants with enlargement of the subarachnoid space.},
journal = {Journal of neurosurgery. Pediatrics},
volume = {},
number = {},
pages = {1-9},
doi = {10.3171/2025.11.PEDS25305},
pmid = {41962159},
issn = {1933-0715},
abstract = {OBJECTIVE: The aim of this study was to characterize ventricular measurements in children diagnosed with enlargement of the subarachnoid spaces (ESS) to determine ventricular morphology.

METHODS: Children diagnosed with ESS were retrospectively identified between 2015 and 2023. Inclusion required a craniocortical width > 5 mm on neuroimaging. Demographic data and developmental outcomes were collected. Referrals to therapy services, including speech and language therapy, occupational therapy, or physiotherapy, were recorded. Ventricular size was quantified using the Evans Index (EI), bicaudate index (BCI), and cella media index (CMI) measured on axial T2-weighted MR images.

RESULTS: Of 101 children, 98 presented with macrocephaly; 3 were diagnosed incidentally through imaging. The mean age at referral was 9.0 ± 5.8 months, with a mean follow-up of 26.3 months. The median initial and final occipitofrontal circumference percentiles were 99.2 (IQR 5.6) and 99.6 (IQR 1.9), respectively. The mean craniocortical width was 9.92 mm. Ventricular indices were near or slightly above normal limits. In males, the mean EI, BCI, and CMI were 0.30 (range 0.22-0.38), 0.15 (range 0.09-0.21), and 4.43 (range 2.29-6.57), respectively. In females, the mean EI, BCI, and CMI were 0.29 (range 0.23-0.35), 0.15 (range 0.11-0.19), and 4.18 (range 2.38-5.98), respectively. No child required neurosurgical intervention. Developmental concerns prompted referrals to speech and language therapy in 56.4% of patients, physiotherapy in 16.8%, and occupational therapy in 13.9%; 4% had referrals across multiple domains.

CONCLUSIONS: This study presents one of the largest studies evaluating ventricular indices in children diagnosed with ESS. Despite mild ventriculomegaly and macrocephaly, no children underwent neurosurgical intervention. However, the association with therapy input supports a shift of focus to one of facilitating the children to achieve their developmental potential, best delivered by the pediatric and/or community service. Continued neurosurgical monitoring should be reserved for children in whom the diagnosis of ESS is not secure and concerns of raised intracranial pressure or hydrocephalus persist.},
}

@article {pmid41962830,
year = {2026},
author = {Zhang, X and Wang, X and Zhu, H and Yu, H and Fu, L and Shu, W and Ye, X and Lim, Z and Saeed, S and Nguyen, HL and Pistoor, J and Rong, D and Lai, J and Wang, Z and He, Y and Wang, Y and Shen, Y and Zhou, Y and Hu, S and Tong, J},
title = {Eye-brain axis: Ocular and visual pathophysiology as driver and therapeutic target across the mood disorder trajectory.},
journal = {Progress in retinal and eye research},
volume = {112},
number = {},
pages = {101467},
doi = {10.1016/j.preteyeres.2026.101467},
pmid = {41962830},
issn = {1873-1635},
abstract = {In recent years, the promotion of multidisciplinary care and the heightened focus on patients' physical and mental well-being have sparked increased research interest in the mental health burden associated with ophthalmic diseases. In response, we assembled a multidisciplinary team of ophthalmologists, psychiatrists, neurobiologists, and computer scientists to create a systematic and forward-looking overview aimed at guiding future research in both fundamentals of life sciences and brain-computer interface as well as clinical practice. This overview centers on mood disorders, the most prevalent psychiatric conditions among this population. We integrate evidence on the neural, humoral, and inflammatory mechanisms that connect eye disease to mood dysregulation, while also detailing the ocular manifestations typical of mood-disordered patients, including their unique features and underlying mechanisms. Furthermore, we catalog current and emerging ophthalmic and psychiatric diagnostic tools and therapeutic strategies. Finally, we propose a comprehensive multidisciplinary framework for screening, treatment, patient education, and long-term follow-up, providing researchers and clinicians with an evidence-based resource for integrated care.},
}

@article {pmid41963318,
year = {2026},
author = {Liu, S and Li, YE and Zhu, T and Zhang, L and Yang, C and Bi, Y and Bao, C and Hu, R and Ge, J and Zhang, Y},
title = {MARCH2 prevents doxorubicin-induced cardiomyopathy by stabilizing NR1H2 and promoting clearance of apoptotic cardiomyocytes.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-71580-z},
pmid = {41963318},
issn = {2041-1723},
support = {82425005//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Doxorubicin-induced cardiomyopathy (DiCM) involves impaired clearance of apoptotic cardiomyocytes (efferocytosis) by cardiac macrophages. This study reveals a central role for the MARCH2-NR1H2 axis in this process. We find that MARCH2 expression is significantly reduced in cardiac macrophages from DiCM mice and human dilated cardiomyopathy patients. Genetic ablation of MARCH2, either globally (MARCH2[-/-]) or specifically in resident cardiac macrophages (MARCH2[f/f]; CX3CR1[Cre]), exacerbates DiCM, impairs efferocytosis, and increases inflammation. Mechanistically, MARCH2 enhances the protein stability of the nuclear receptor NR1H2 via K27-linked polyubiquitination, leading to upregulation of the efferocytosis receptor MERTK. Conversely, macrophage-specific NR1H2 deficiency (NR1H2[f/f]; CX3CR1[Cre]) suppresses efferocytosis and worsens cardiac dysfunction. Importantly, pharmacological activation of NR1H2 attenuates DiCM progression. These findings identify the MARCH2-NR1H2 axis as a key regulator of macrophage efferocytosis and a potential therapeutic target for DiCM.},
}

@article {pmid41952690,
year = {2026},
author = {McDaid, J and Bailes, JE and Jha, NK and Bobustuc, G and Berlet, R and Kessinger, C and Whitcomb, E and Lee, JM and Aksenov, DP and Walker, M and Azapagic, A and Bookwalter, J and Ullah, A and Sant, H and Shea, J and Gale, BK},
title = {Correction: Efficacy of local convection enhanced delivery of chemotherapy using an intracerebral osmotic pump in a rat model of glioblastoma.},
journal = {Frontiers in oncology},
volume = {16},
number = {},
pages = {1828465},
doi = {10.3389/fonc.2026.1828465},
pmid = {41952690},
issn = {2234-943X},
abstract = {[This corrects the article DOI: 10.3389/fonc.2026.1775053.].},
}

@article {pmid41952871,
year = {2026},
author = {Wei, JX and Zhang, YM and Liang, SS and Li, QF and Huang, L and Dai, YH and Bi, ZT and Xiao, JH and Xu, JW and Zhang, YS},
title = {Effects of brain-computer interface-based rehabilitation on upper limb function, activities of daily living, and adverse events in patients with early stroke: a systematic review and meta-analysis.},
journal = {Frontiers in aging neuroscience},
volume = {18},
number = {},
pages = {1737740},
pmid = {41952871},
issn = {1663-4365},
abstract = {BACKGROUND: Brain-computer interface-based rehabilitation represents an emerging neurorehabilitation approach for post-stroke motor recovery, yet its comprehensive effects on patients in the early phase after stroke, typically defined as within 3 months of onset, remain to be fully established. This systematic review and meta-analysis evaluated effects of this intervention on upper limb function, activities of daily living, and adverse events in individuals with early stroke.

METHODS: This study was conducted following PRISMA guidelines. Eligibility criteria were established for randomized controlled trials that encompassed: (1) participants were adults (≥18 years) within 3 months of stroke onset with upper limb motor impairment; (2) interventions included brain-computer interface-based rehabilitation, and (3) outcomes that measured upper limb function, activities of daily living, and adverse events. A systematic search was performed across PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, Cochrane Library, and China National Knowledge Infrastructure databases from their inception to August 23, 2025. Two independent reviewers assessed eligibility, compiled data, and appraised methodological rigor, potential bias, and reliability of the evidence. Meta-analysis was performed using RevMan 5.4 (Cochrane Collaboration, UK) and Stata 18 (StataCorp., USA), applying random-effects models to calculate mean differences (MD) or risk ratios (RR) with 95% confidence intervals (CI). Subgroup analyses, meta-regression, sensitivity analyses, and publication bias assessments were conducted where appropriate.

RESULTS: Nine studies involving 642 participants (212 females and 430 males) with a mean age of 59.77 years were included. For primary outcomes, brain-computer interface-based rehabilitation significantly improved upper limb function in patients with early stroke (MD = 5.02, 95% CI: 3.20, 6.84). Subgroup analyses revealed that no statistically significant differences were observed in the improvement of upper limb functionality among various patient demographics and intervention characteristics (all p > 0.05). For secondary outcomes, the pooled analysis suggested a potential improvement in activities of daily living with BCI-based rehabilitation (MD = 7.68, 95% CI: 0.32, 15.03), although this finding was accompanied by very high heterogeneity (I [2] = 88%) and was not robust in sensitivity analyses, indicating low certainty of evidence. Subgroup analyses indicated that greater benefits might be observed in patients within 30 days after stroke onset and with intervention durations not exceeding 3 weeks. Regarding safety, preliminary data from a single study suggested no significant difference in adverse events between groups (p = 0.87), but the evidence base is currently insufficient to draw firm conclusions.

CONCLUSIONS: Brain-computer interface-based rehabilitation is effective in improving upper limb motor function in patients with early stroke. Current evidence suggests a potential benefit for activities of daily living, but the evidence is of low certainty due to substantial heterogeneity and limited robustness. Subgroup analyses identified time from onset and intervention duration as potential effect modifiers for activities of daily living. Preliminary safety data from a single study are encouraging but insufficient to establish a safety profile. Further well-designed randomized controlled trials are needed to establish optimal brain-computer interface-based rehabilitation protocols, to confirm the potential benefit on activities of daily living with more robust evidence, and to evaluate long-term efficacy and safety.

PROSPERO [Register number: CRD420251144151].},
}

@article {pmid41956899,
year = {2026},
author = {Park, YJ and Kwon, J and Lee, G and Meng, K and Chung, CK},
title = {Spatiotemporal Dynamics in Pre-speech Semantic Category Decoding: An intracranial EEG Study.},
journal = {eNeuro},
volume = {},
number = {},
pages = {},
doi = {10.1523/ENEURO.0254-25.2026},
pmid = {41956899},
issn = {2373-2822},
abstract = {Despite major advances in brain-computer interfaces (BCIs), decoding high-level language representations prior to speech remains challenging. While prior efforts have primarily focused on acoustic or articulatory features, how semantic categories are decoded in time and space remains unclear. Here, we investigated how semantic representations unfold over time by analyzing high-gamma (HG, 70-170 Hz) electrocorticography (ECoG) signals from twenty subjects (7 females and 13 males) performing a word-reading task involving body- and non-body-related words. HG activity was examined from word presentation to 500 ms, capturing the pre-speech window. Group-level time-resolved decoding, pooling features across subjects within each Brodmann area (BA), revealed significant classification accuracy above chance in both hemispheres (p<0.05, FDR-corrected). In the left hemisphere, peak-performing BAs followed a frontal-temporal-occipital-parietal cascade: dorsolateral prefrontal cortex (dlPFC) (50 ms), inferior temporal and fusiform gyri (350-400 ms), and supramarginal gyrus (SMG) (500 ms). In contrast, the right hemisphere exhibited an occipital-temporal-frontal-temporal-parietal sequence: visual and temporal pole (TP) regions (50-100 ms), dlPFC (200 ms), fusiform gyrus (FG) (400 ms), and angular gyrus (450 ms). This progression contrasts with the frontal-initiated cascade of the left hemisphere, underscoring hemispheric differences in the timing of peak decoding loci. Cross-temporal regression revealed predictive interregional engagement. In the left hemisphere, early dlPFC activity (0-150 ms) predicted later SMG responses (300-350 ms). In the right, a strong but brief predictive link emerged from the TP to the angular gyrus (200-300 ms; peak R[2] ≈ 0.70). These findings demonstrate that semantic category decoding relies on temporally structured interregional interactions, revealing distinct hemispheric patterns.Significance statement This study investigates spatiotemporal dynamics in decoding semantic categories during the pre-speech interval using high-resolution intracranial EEG. We reveal a left-hemisphere cascade beginning in frontal areas and extending to temporal, occipital, and parietal regions, and a distinct right-hemisphere cascade involving early occipital and temporal pole activity. Cross-temporal regression reveals sustained left-lateral predictive temporal pattern and a brief but high-precision right-hemisphere link. These findings advance our understanding of how semantic categories are constructed in the brain over time and may inform future efforts to develop neural decoding frameworks that operate before speech output.},
}

@article {pmid41957013,
year = {2026},
author = {Zhou, T and Yu, R and Bai, X and Yang, Y and Fan, Z and Du, X and Chen, Y and Wang, Y and Yang, Z and Sun, X and Zhu, M and Pan, S},
title = {Tissue-adaptive bioelectronic fibers with temperature-induced self-tightening enable ultrastable neural interface.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-71689-1},
pmid = {41957013},
issn = {2041-1723},
support = {52373201,52103252//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Neural interfaces are essential for brain-machine communication and closed-loop neuromodulation. However, achieving durable interfaces between neural tissue and bioelectronics remains a key challenge, as conventional electronics do not actively conform to the soft, tortuous 3D architecture of neural tissue. We report a tissue-adaptive bioelectronic fiber that actively contracts to wrap around neural tissues, forming ultrastable neural-electronic interfaces, and enabling highly reliable neural stimulation and recording. This fiber is fabricated via wet spinning from a precursor integrating a thermoresponsive polymer and electroactive materials, and exhibits an ultralow modulus of 0.16 MPa and a phase transition temperature of 26.7 °C. Upon contact with rat tissue, the polymer chains undergo a hydrophilic-to-hydrophobic transition, expelling water and contracting the fiber to conform tightly to the sciatic nerve. This ultrastable biointerface demonstrates reliable neural stimulation, producing stable hindlimb bending responses, while sciatic nerve action potential recordings show 99.5% signal retention under successive stimulations.},
}

@article {pmid41957822,
year = {2026},
author = {Aljanahi, A and Dalrymple, KV and Dimidi, E and Sullivan, ES},
title = {Methodologies for establishing and validating cut-points and comparative standards in medical imaging-based body composition analysis: a scoping review protocol.},
journal = {Systematic reviews},
volume = {},
number = {},
pages = {},
doi = {10.1186/s13643-026-03096-y},
pmid = {41957822},
issn = {2046-4053},
abstract = {BACKGROUND: Medical imaging-based body composition analysis (BCA) has shown promise in offering detailed, noninvasive assessments of fat, muscle, and bone, but challenges persist in establishing consistent comparative standards. Current studies reveal significant variability in methodologies, which limits comparability and clinical application. This highlights the need for a comprehensive review to explore these methodologies and address the gap in standardisation. The aim of the study is to identify and map the methodologies used in body composition imaging to establish and validate comparative standards (such as cut-points, thresholds, or normative values) and to catalogue the proposed comparative standards.

METHODS: This scoping review will be conducted following JBI methodology. The following eligibility criteria will be applied: Population: healthy subjects with no major comorbidities or individuals with cancer assessed using body composition imaging (BCI) and concept: methodologies for establishing BCI comparative standards and/or formally validating them against any outcome or other BCA reference standard. This scoping review will consider studies across all clinical settings. There will be no restrictions on the setting or purpose of the original study. Validation studies using BCI as the reference standard will not be included unless the comparative standard being validated is another BCI feature. The electronic databases to be searched are Ovid MEDLINE, Ovid Embase, Scopus, EBSCOhost CINAHL, Web of Science, Cochrane Library, and IEEE Xplore. Grey literature sources will not be included. Studies published in English will be considered, with no date restrictions applied. Two independent reviewers will screen all titles and abstracts, followed by full-text articles, and will undertake data extraction. Data extracted will be presented in tabular and/or diagrammatic form for comprehensive narrative synthesis.

DISCUSSION: The scoping review will summarise existing evidence on BCI. It will identify potential methodological gaps, describe current proposed thresholds or normative values, and highlight areas for further research to establish validated cut-points.

OSF https://doi.org/10.17605/OSF.IO/QZMN2.},
}

@article {pmid41958205,
year = {2026},
author = {Zhang, Z and Zheng, Y and Guo, K and Liang, J and Dong, M},
title = {A Few-Layer Multilayer Perceptron is Worth Attention for EEG Classification in Rapid Serial Visual Presentation Task.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2650030},
doi = {10.1142/S0129065726500309},
pmid = {41958205},
issn = {1793-6462},
abstract = {Rapid serial visual presentation (RSVP) enables efficient electroencephalography (EEG)-based brain-computer interfaces, yet single-trial decoding remains difficult due to signal overlap and multicomponent entanglement. This work developed DisCo-Former, a Transformer-based framework incorporating three priors-guided components, including trend-periodicity disentanglement, channel-level embeddings that preserve global temporal pattern, and contrastive learning that exploits target-adjacent nontargets. Although DisCo-Former surpassed existing approaches, analysis revealed a consistent attention collapse: attention maps became nearly uniform, and value projection weights shrank toward zero. Removing the Transformer encoder yields DisCo-MLP, a purely multilayer perceptron (MLP) variant that preserves all remaining modules. Across two datasets and three evaluation regimes, DisCo-MLP matched or outperformed its Transformer-based counterpart. In within-subject decoding, mean AUCs ranged from approximately 0.94 to 0.98 across two datasets, consistently exceeding strong baselines. These results indicate that, for RSVP-EEG decoding, effectiveness stems less from architectural complexity and more from modeling the signal's structure. Simplicity motivated by paradigm-specific neurophysiological priors offers a practical path to state-of-the-art performance in EEG-based interfaces.},
}

@article {pmid41958714,
year = {2025},
author = {Liang, C and Silva, RF and TulayAdali, and Jiang, R and Zhang, D and Qi, S and Calhoun, VD},
title = {Multimodal data fusion in neuroscience: promises, challenges and future directions.},
journal = {IEEE signal processing magazine},
volume = {42},
number = {5},
pages = {8-21},
pmid = {41958714},
issn = {1053-5888},
abstract = {Multimodal fusion provides significant benefits over single modality analysis by leveraging both shared and complementary information across diverse data sources. In this article, we systematically review methods for fusion of heterogonous multimodal biomedical data of varying dimensionality (including neuroimaging, biomics, clinical phenotypes and text), with a focus on neuroscience. We discuss the strengths and limitations of these strategies based on a survey of 302 research articles. Next, we examine the applications of these methods to a variety of scenarios spanning a continuum from scientific research to clinical practice. Finally, an in-depth discussion of common challenges and promising directions for future development of multimodal biomedical data fusion are provided. Overall, multimodal fusion shows substantial benefits and transformative potential in the field of neuroscience. Future research should prioritize improving model generalization, enhancing interpretability, addressing inherent data limitations, and developing unified platforms alongside multimodal foundational models to bridge the gap between fusion techniques, research, and application to various domains.},
}

@article {pmid41960599,
year = {2026},
author = {Ronca, V and Longo, L and Capotorto, R and Aricò, P},
title = {Editorial: Passive brain-computer interfaces: moving from lab to real-world application.},
journal = {Frontiers in computational neuroscience},
volume = {20},
number = {},
pages = {1826791},
pmid = {41960599},
issn = {1662-5188},
}

@article {pmid41961787,
year = {2026},
author = {İşcan, Z},
title = {Evaluation of long-range temporal correlations during overt and covert attention in a steady state visual evoked potential based brain-computer interface.},
journal = {PloS one},
volume = {21},
number = {4},
pages = {e0345793},
doi = {10.1371/journal.pone.0345793},
pmid = {41961787},
issn = {1932-6203},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Attention/physiology ; Male ; Female ; Adult ; Electroencephalography ; Young Adult ; Photic Stimulation ; Signal-To-Noise Ratio ; *Brain/physiology ; },
abstract = {Gaze control is required for successful brain-computer interface (BCI) operation in different paradigms. It has been shown that the performance of a steady-state visual evoked potential-based BCI is lower in covert attention when the participants attend to the stimuli covertly, without the need to move their eyes. Some studies in the literature have tried to find the brain regions that are affected by covert attention. Moreover, it has been shown that the signal-to-noise (SNR) ratio is smaller in covert attention than in overt attention. Based on the fact that brain oscillations exhibit long-range temporal correlations (LRTCs), which can be measured by the Hurst exponent, and estimated using the detrended fluctuation analysis (DFA), this is the first study focusing on the DFA differences in overt and covert attention in an SSVEP-based BCI experiment. The main hypothesis is that there should be differences between DFA exponents of EEG in overt and covert attention, as there are differences in SNR between these attentional states. Gender differences between overt and covert attention were also evaluated using DFA. The results revealed significant differences in LRTCs depending on the gender and the attentional state. These results could be taken into account for an efficient SSVEP-based BCI design.},
}

Humans
*Brain-Computer Interfaces
*Evoked Potentials, Visual/physiology
*Attention/physiology
Male
Female
Adult
Electroencephalography
Young Adult
Photic Stimulation
Signal-To-Noise Ratio
*Brain/physiology

@article {pmid41950858,
year = {2026},
author = {Alam, W and Song, KD and Ali, S and Ahmad, W and Koziol, MJ},
title = {Fusion-m6A: A lightweight hybrid deep learning framework for RNA m6A site prediction.},
journal = {Computers in biology and medicine},
volume = {208},
number = {},
pages = {111669},
doi = {10.1016/j.compbiomed.2026.111669},
pmid = {41950858},
issn = {1879-0534},
abstract = {N6-methyladenosine (m6A) is the most common mRNA modification and plays key role in RNA metabolism, gene regulation, and disease. Accurate identification of m6A sites is critical for understanding their functional and biological significance. Although experimental techniques such as Nanopore direct RNA sequencing (DRS) have advanced m6A profiling, they remain costly and laborious. Computational approaches provide scalable alternatives, but many depend on handcrafted features or computationally expensive transformer-based models. We present Fusion-m6A, a hybrid deep learning framework that integrates Word2Vec-based sequence embeddings, convolutional layers for local motif detection, bidirectional gated recurrent unit with attention for capturing long-range dependencies, and auxiliary k-mer features. The fused representations are passed through fully connected layers to predict m6A sites with high accuracy. Benchmarking across multiple human tissues and cell lines shows that Fusion-m6A consistently outperforms state-of-the-art predictors in accuracy and Matthews correlation coefficient. Crucially, the model achieves faster inference and requires substantially less memory, offering a practical and robust solution for large-scale and tissue-specific m6A site prediction. The implementation of Fusion-m6A is publicly available for reproducibility at: https://github.com/waleed551/Fusion_m6A.},
}

@article {pmid41951385,
year = {2026},
author = {Sun, SH and Ibbotson, MR},
title = {Extracellular Spike Waveforms: Morphology, Biophysics, and Classification Strategies.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {46},
number = {14},
pages = {},
doi = {10.1523/JNEUROSCI.1741-25.2025},
pmid = {41951385},
issn = {1529-2401},
mesh = {Animals ; Humans ; *Neurons/physiology/classification ; *Action Potentials/physiology ; Biophysics ; Biophysical Phenomena/physiology ; },
abstract = {Extracellular spike waveforms provide critical insights into neuronal activity, morphology, and function. Their shape can reveal cell-type identity, excitatory versus inhibitory function, and afferent projections from distal regions. The development of dense, high-channel-count probes now permits recordings from thousands of sites simultaneously, revealing a wider diversity of waveform types than previously appreciated. These advances provide an unprecedented opportunity to link waveform shape to the underlying biophysical processes of neurons and their spatial arrangement relative to the recording electrode. This review examines and catalogs the diversity of extracellular waveforms (including negative, triphasic, and positive spike waveforms), focusing on their biophysical origins and roles in neural compartments. We also discuss classification strategies, ranging from traditional feature-based approaches that use specific waveform features (such as spike duration and peak-to-trough ratios) to emerging machine learning and multimodal methods that integrate waveform shape with firing dynamics and anatomical localization. These new approaches reveal novel neuronal populations but also highlight a pressing need for standardized classification frameworks to ensure reproducibility and facilitate cross-study comparisons. Finally, we review how experimental factors such as filtering, sampling biases, and spike-sorting algorithms shape the observed diversity of extracellular waveforms. By consolidating recent progress in both experimental and computational approaches, this review provides a comprehensive resource for interpreting extracellular recordings. A deeper understanding of waveform diversity will advance basic neuroscience and accelerate applications in brain-machine interfaces, diagnostics, and neural prosthetics.},
}

Animals
Humans
*Neurons/physiology/classification
*Action Potentials/physiology
Biophysics
Biophysical Phenomena/physiology

@article {pmid41951594,
year = {2026},
author = {Li, K and Zhang, C and Li, R and Yuan, X and Zhou, C and Qian, L and Zhang, K and Deng, W},
title = {Distinct neural substrates of obsessions and compulsions in adolescent obsessive compulsive disorder.},
journal = {Translational psychiatry},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41398-026-04024-3},
pmid = {41951594},
issn = {2158-3188},
abstract = {Adolescent obsessive-compulsive disorder (OCD) is characterized by notable clinical heterogeneity, which may limit treatment precision. Although traditional content-based models have improved symptom characterization, they may overlook key pathophysiological features. A complementary process-based framework that distinguishes between obsessions and compulsions offers a promising alternative, but the neurobiological correlates of these dimensions remain poorly understood. To address this gap, we recruited 40 adolescents with OCD and 40 matched healthy controls and conducted connectome-wide association studies (CWAS) using multivariate distance matrix regression (MDMR) to identify brain regions whose whole-brain connectivity patterns were associated with obsessive or compulsive symptom severity. Follow-up seed-based analyses were then performed to delineate the relevant circuits, and cross-modal comparisons were further used to examine the alignment of symptom-connectivity association maps with neurotransmitter receptor distributions and gene expression profiles. We found that obsessive symptoms were associated with altered connectivity patterns centered on the Dorsolateral Prefrontal Cortex and Cerebellum Posterior Lobe, whereas compulsive symptoms were linked to the Ventrolateral Prefrontal Cortex. In both cases, connectivity between each symptom-specific target and the default mode network (DMN) was negatively correlated with the severity of its corresponding symptom dimension. Moreover, the symptom-connectivity association maps for obsessions and compulsions showed distinct associations with neurotransmitter systems and transcriptomic signatures. Together, these findings provide novel evidence for distinct neurobiological substrates underlying obsession and compulsion dimensions in adolescent OCD, support the utility of process-based symptom modeling, and suggest potential targets for dimension-specific intervention.},
}

@article {pmid41951666,
year = {2026},
author = {Li, Y and Yang, Y and Wang, C and Dai, Y and Yan, X and Kong, D and Liao, Z and Wang, S and Ruan, GJ and Wang, P and Cheng, B and Liang, SJ and Miao, F},
title = {Massively parallel in-sensor skinomorphic computing.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-71697-1},
pmid = {41951666},
issn = {2041-1723},
support = {62034004//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62304104//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62305155//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62304105//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Real-time sensing and processing of a large amount of tactile information is essential for intelligent robotics and wearable technology. However, physical separation between sensors and processors in the traditional tactile sensing scheme makes these functionalities inaccessible, posing a major roadblock to the rapid advance of skinomorphic electronics. Here, we propose a massively parallel in-sensor skinomorphic computing scheme and demonstrate its promising applications in intelligent tactile perception. This scheme allows for achieving parallel sensing and processing of tactile information directly within sensor. We implement this proposed scheme by fabricating a 32×32 flexible capacitive pressure sensors array with excellent uniformity and endurance, and by cascading the sensors array with a memristive crossbar array. We experimentally demonstrate that the broken pressure patterns of the letter 'NJU' loaded on the sensors array can be sensed and restored in parallel, which is inaccessible with previously reported tactile technologies. Moreover, by networking the pressure sensors array with two memristive crossbar arrays, we show that textural features of the loaded complex pressure patterns can be directly extracted in a parallel manner and the tactile information can thus be compressed. Our work opens up an avenue for developing intelligent skins capable of real-time and high-throughput tactile perception.},
}

@article {pmid41952678,
year = {2026},
author = {McDaid, J and Bailes, JE and Jha, NK and Bobustuc, G and Berlet, R and Kessinger, C and Whitcomb, E and Lee, JM and Aksenov, DP and Walker, M and Azapagic, A and Bookwalter, J and Ullah, A and Sant, H and Shea, J and Gale, BK},
title = {Efficacy of local convection enhanced delivery of chemotherapy using an intracerebral osmotic pump in a rat model of glioblastoma.},
journal = {Frontiers in oncology},
volume = {16},
number = {},
pages = {1775053},
pmid = {41952678},
issn = {2234-943X},
abstract = {BACKGROUND: Modern protocols for the treatment of glioblastoma multiforme (GBM) involve resection surgery, followed by chemotherapy and radiation therapy and subsequently adjuvant chemotherapy. While modestly successful in prolonging overall survival, peripherally administered chemotherapy drugs have limited ability to cross the blood brain barrier (BBB), limiting their bioavailability, and thus efficacy, at the tumor site. One way of circumventing the BBB is direct delivery of chemotherapy to the tumor site. Direct application of chemotherapy into the resection cavity during surgery in the form of carmustine/bis-chloroethylnitrosourea (BCNU) wafers has had limited success, in part due to the need for wafer solubilization, which restricts drug distribution and efficacy. The primary limitation, however, is that the drug is only distributed over short distances, for a short time.

METHODS: In this study, we evaluated the efficacy of drug perfusion into the tumor resection cavity in a rat glioma model through convection enhanced delivery (CED), using an implanted microfluidic osmotic pump. We compared the effects of two alkylating agents, BCNU and temozolomide (TMZ), on tumor recurrence and survival.

RESULTS: Using pumps containing a high concentration of ferumoxytol - a superparamagnetic iron oxide nanoparticle (SPION) - tissue perfusion was demonstrated in vivo by MRI and by post-mortem histology, confirming the effectiveness of the microfluidic pump as a drug delivery device. When delivered by implanted pumps, BCNU (4mg/ml) showed significantly greater efficacy against tumor recurrence than either TMZ; 2-4mg/ml or control (a low concentration of SPION).

CONCLUSION: BCNU may be an effective choice for CED-driven, locally delivered chemotherapy in GBM.},
}

@article {pmid41942685,
year = {2026},
author = {Lim, Z and Nguyen, HL and Zeng, Y and Qu, Q and Le, Y and Sun, M and Wang, X and Zhu, H and Qian, Y and Saeed, S and Wang, H and Rong, D and Wang, Y and Zhang, X and Hu, S},
title = {Life Cycle and Circadian Rhythms in Central Resident Immunity and Neuropsychiatric Pathology.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {41942685},
issn = {1995-8218},
abstract = {The central resident immune system, commonly known as the glial system, comprises various glial cells that play a critical role in neuropsychiatric disorders. However, a systematic review exploring the relationships between the life cycles and daily rhythms of these immune cells and the pathological features of neuropsychiatric disorders is lacking. These immune cells exhibit unique developmental origins and circadian characteristics, resulting in rhythmic variations in functions such as phagocytosis, immune clearance, neurogenesis, and neurotransmitter recycling. These properties are crucial for understanding the pathological mechanisms underlying developmental disorders like major depressive disorder, autism spectrum disorder, and schizophrenia, as well as age-related conditions such as Alzheimer's and Parkinson's diseases. The daily rhythms of these immune cells correlate with diurnal variations in emotion, cognition, and motor function, involving shared processes like oxidative stress and neuroinflammation. This article systematically reviews the composition, life cycle changes, and circadian characteristics of central immune cells, highlighting their roles in neuropsychiatric diseases.},
}

@article {pmid41943963,
year = {2026},
author = {Kotov, SV and Isakova, EV and Ponomareva, ES},
title = {[Bimanual interaction as an illustration of an integrative approach in post-stroke neurorehabilitation].},
journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova},
volume = {126},
number = {3. Vyp. 2},
pages = {62-68},
doi = {10.17116/jnevro202612603262},
pmid = {41943963},
issn = {1997-7298},
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Stroke/physiopathology ; Transcranial Magnetic Stimulation ; Neuronal Plasticity ; Transcranial Direct Current Stimulation ; Brain-Computer Interfaces ; *Neurological Rehabilitation/methods ; Upper Extremity/physiopathology ; Functional Laterality ; Corpus Callosum/physiopathology ; },
abstract = {The review addresses the roles of interhemispheric asymmetry and interhemispheric interaction in the pathogenesis and medical rehabilitation after stroke, with a focus on bimanual training to restore upper-extremity motor function. After a stroke, 50-80% of patients persist with movement disorders affecting not only the paretic but also the «healthy» ipsilateral limb, leading to the avoidance of bilateral patterns in daily activities. Interhemispheric asymmetry is a fundamental property of the brain that is disrupted during a stroke, leading to an imbalance with increased excitability in the intact hemisphere and suppression of the affected hemisphere's functions, which aggravates motor and cognitive deficits after a stroke. Interhemispheric interaction, mainly through the corpus callosum, provides coordination of the brain hemispheres; however, in stroke it leads to an abnormal imbalance, reducing plasticity. Methods for restoring interhemispheric communication include noninvasive brain stimulation (transcranial magnetic stimulation and transcranial direct current stimulation), brain-computer interfaces, and physical activity modulating cerebral hemispheric connectivity. Particular attention is paid to bimanual motor training, which stimulates neuroplasticity, improves bimanual coordination, and reduces the severity of interhemispheric asymmetry. Neuromotorics, a set of bimanual exercises for training fine motor skills, is described. These approaches, especially when combined, are effective for motor recovery but depend on the stroke period and individual factors. The practical significance of integrative rehabilitation in overcoming interhemispheric imbalance and improving patients' functionality and quality of life is emphasized. Further research is needed to optimize bimanual therapy.},
}

Humans
*Stroke Rehabilitation/methods
*Stroke/physiopathology
Transcranial Magnetic Stimulation
Neuronal Plasticity
Transcranial Direct Current Stimulation
Brain-Computer Interfaces
*Neurological Rehabilitation/methods
Upper Extremity/physiopathology
Functional Laterality
Corpus Callosum/physiopathology

@article {pmid41946420,
year = {2026},
author = {Guo, X and Zhang, L and Zhang, Q and Kendrick, KM and Yao, S},
title = {A systematic review and meta-analysis of oxytocin modulation of amygdala responses to emotional stimuli and implications for anxiolytic effects.},
journal = {Neuroscience and biobehavioral reviews},
volume = {},
number = {},
pages = {106679},
doi = {10.1016/j.neubiorev.2026.106679},
pmid = {41946420},
issn = {1873-7528},
abstract = {Oxytocin (OT), a neuropeptide essential for social and emotional functions, has been proposed to be anxiolytic as indicated by its effects on inhibiting amygdala activity. However, findings are inconsistent which may be contributed to by variabilities across studies. To obtain a comprehensive overview and a more reliable assessment of the extent to which OT's anxiolytic effects are convergent, we conducted a systematic review and meta-analyses in 55 neuroimaging studies (3337 participants) examining OT's effects on brain responses to negative or stressful emotional stimuli. Results showed a gender-dependent effect of OT on modulating amygdala activity. While OT showed a significant effect on inhibiting amygdala activity in males, an enhancement effect was found in females. An activation likelihood estimation analysis further revealed that OT reduced amygdala activity in the centromedial subregion and could either decrease or increase activity in the basolateral subregion. Our study provides evidence for a gender-dependent anxiolytic effect of OT and its targeting substrates. These findings provide preliminary support for taking individual differences into consideration when developing OT-based therapeutic strategies.},
}

@article {pmid41946820,
year = {2026},
author = {Khanal, R and van Schooten, KS and Piovezan, R and Adams, R and Sansom, K and Vakulin, A},
title = {Obstructive sleep apnea risk is associated with poor physical performance: a cross-sectional analysis of the U.S. health and retirement study.},
journal = {Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine},
volume = {22},
number = {1},
pages = {},
pmid = {41946820},
issn = {1550-9397},
mesh = {Humans ; Cross-Sectional Studies ; Female ; Male ; *Sleep Apnea, Obstructive/complications/physiopathology/epidemiology ; Aged ; Middle Aged ; United States/epidemiology ; Hand Strength/physiology ; Postural Balance/physiology ; Risk Factors ; *Physical Functional Performance ; Surveys and Questionnaires ; Accidental Falls/statistics & numerical data ; },
abstract = {STUDY OBJECTIVES: Obstructive sleep apnoea (OSA) may be linked to poor physical performance and fall risk, yet this association remains underexplored. This study examined associations between OSA risk, balance, gait speed and handgrip strength (HGS) in community living adults across age-groups and sexes.

METHODS: Cross-sectional data from the 2016 Health and Retirement Study were analysed. Probable OSA was estimated with an adapted STOP-Bang questionnaire. Poor balance was defined as the inability to hold a semi-tandem stance for 10 s; slow gait speed as walking < 0.8 m/s over 2.5 m; and weak HGS as HGS-to-body mass index ratio < 1.00 m[2] for males and < 0.56m[2] for females.

RESULTS: 6,918 participants (mean age 66 ± 11 years; 57% female) were included. Probable OSA was associated with higher odds of: (i) poor balance in the overall sample (OR:1.23, 95% bootstrapped confidence interval (BCI):1.07-1.39, p = 0.002), 50-64 years (OR: 1.41, BCI: 1.15- 1.72, p < 0.001) and females (OR: 1.30, BCI: 1.10-1.56, p = 0.004); (ii) slow gait speed in the overall sample (OR:1.29, BCI:1.07-1.57, p = 0.007), 80 + years (OR:1.61, BCI:1.07-2.42, p = 0.028) and females (OR:1.39, BCI:1.03-1.91, p = 0.024); and (iii) weak HGS in the overall sample (OR:2.22, BCI:1.90-2.63, p = 0.001), 50-64 years (OR:3.40, BCI: 2.58-4.61, p < 0.001), 65-79 years (OR: 1.93, BCI:1.52- 2.47, p < 0.001), males (OR = 1.87, BCI:1.49-2.35, p < 0.001) and females (OR = 2.67, BCI 2.15-3.33, p < 0.001).

CONCLUSIONS: Poor balance, slow gait speed and weak HGS are common among older adults at high risk of OSA. Further research should evaluate causality and assess co-screening to potentially enable early detection of fall risk in older adults.

STUDY RATIONALE: OSA is a common but often undiagnosed condition that may contribute to accelerated age-related physical decline and increased fall risk. Despite known links between diagnosed OSA and motor deficits, little is known about how undiagnosed OSA relates to fall-related physical performance measures in large, community-based populations. Study Impact: This study suggests that individuals at high risk of OSA are more likely to have poor balance, slow gait speed, and weak handgrip strength, which are key predictors of fall risk. The observation of these associations in adults as young as 50 years of age warrants future research to evaluate causality and determine if co-screening of OSA and fall risk can help identify those most vulnerable.},
}

Humans
Cross-Sectional Studies
Female
Male
*Sleep Apnea, Obstructive/complications/physiopathology/epidemiology
Aged
Middle Aged
United States/epidemiology
Hand Strength/physiology
Postural Balance/physiology
Risk Factors
*Physical Functional Performance
Surveys and Questionnaires
Accidental Falls/statistics & numerical data

@article {pmid41947853,
year = {2026},
author = {Huang, J and Zou, J and Li, X and Cai, Y and Lin, B and Li, Y and Xia, X},
title = {APCformer: an aggregation-perception enhanced convolutional transformer network for MI-EEG decoding.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1766883},
pmid = {41947853},
issn = {1662-4548},
abstract = {Electroencephalogram (EEG) decoding is essential for Brain-computer interfaces (BCI) systems to predict brain activity. However, existing methods usually suffer from two core problems: (1) existing networks lack effective interaction mechanisms and insufficiently capture spatial-temporal dynamic features, leading to the loss of critical fine-grained information; (2) the modeling of long-range dependencies and local features is unbalanced, making it difficult to adapt to the temporal characteristics of EEG signals. To address these issues, this paper proposes an Aggregation-Perception Enhanced Convolutional Transformer (APCformer) network. The network adopts a branch-interactive structure as its main body and jointly extracts shallow features via multi-scale spatial-temporal convolution; an Adaptive Feature Recalibration (AFR) module is embedded to realize cross-scale feature interaction and enhancement of critical fine-grained features. The Position-aware Enhancement (PAE) module is utilized to integrate learnable positional encoding, improving the ability of deep networks to characterize the temporal positional relationships of EEG sequences and enhancing adaptability to temporal dynamic features. We further propose a Sparse Information Aggregation Transformer (SAT), which combines the attention mechanism with the maximum attention mechanism to achieve a balanced modeling of global long-term dependencies and local fine-grained features. Experimental results on the public BCI-IV 2a and BCI-IV 2b datasets show that APCformer achieves superior performance in EEG decoding tasks, with average decoding accuracies of 85.53% and 89.15%, respectively. These results highlight APCformer's strong capability in handling complex EEG features and dynamic patterns, effectively improving the efficiency and accuracy of EEG decoding.},
}

@article {pmid41838514,
year = {2026},
author = {Malcolm, K and Uribe, CA and Yamagami, M},
title = {Federated Learning in Offline and Online EMG Decoding: A Privacy and Performance Perspective.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {34},
number = {},
pages = {1814-1825},
doi = {10.1109/TNSRE.2026.3674102},
pmid = {41838514},
issn = {1558-0210},
mesh = {*Electromyography/methods ; Humans ; Algorithms ; Male ; *Privacy ; Adult ; Female ; Computer Simulation ; Young Adult ; *Machine Learning ; Brain-Computer Interfaces ; Federated Learning ; },
abstract = {Neural interfaces offer a pathway to intuitive, high-bandwidth interaction, but the sensitive nature of neural data creates significant privacy hurdles for large-scale model training. Federated learning (FL) has emerged as a promising privacy-preserving solution, yet its efficacy in real-time, online neural interfaces remains unexplored. In this study, we 1) propose a conceptual framework for applying FL to the distinct constraints of neural interface applications and 2) provide a systematic evaluation of FL-based neural decoding using high-dimensional surface electromyography across both an offline simulation and a real-time, online user study. While offline results suggest that FL can simultaneously enhance performance and privacy, our online experiments reveal a more complex landscape. We found that standard FL assumptions struggle to translate to real-time, sequential interactions with user-decoder co-adaptation. Our results show that while FL retains privacy advantages, it introduces performance tensions not predicted by offline simulations. These findings identify a critical gap in current FL methodologies and highlight the need for specialized algorithms designed to navigate the unique co-adaptive dynamics of online neural decoding.},
}

*Electromyography/methods
Humans
Algorithms
Male
*Privacy
Adult
Female
Computer Simulation
Young Adult
*Machine Learning
Brain-Computer Interfaces
Federated Learning

@article {pmid41855656,
year = {2026},
author = {Inoue, M and Hatakeyama, E and Kita, Y and Sasai, S},
title = {Large-scale training data enhances silent speech decoding with around-ear EEG.},
journal = {Journal of neural engineering},
volume = {23},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ae54d0},
pmid = {41855656},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods/instrumentation ; Male ; Female ; Adult ; *Speech/physiology ; Young Adult ; *Brain-Computer Interfaces ; Middle Aged ; Wearable Electronic Devices ; Electromyography/methods ; },
abstract = {Objective. Silent speech decoding (SSD) offers a potential communication alternative for individuals with impaired vocalization. However, conventional multi-electrode electroencephalography (EEG) or facial electromyography (EMG) systems require cumbersome preparation and are unsuitable for daily use. This study evaluates the practicality of SSD using a wearable around-ear EEG device, focusing on data scaling, cross-subject transfer, vocabulary extensibility, and online decoding performance.Approach. We collected 72 h of around-ear EEG from 24 healthy participants and one individual with incomplete locked-in syndrome (LIS) during silent, vocalized, and attempted speech, and integrated these around-ear EEG recordings with prior EMG + high-density EEG datasets, yielding 282.4 total h of training data. Using a 64-word classification task as the evaluation metric, we assessed: (1) whether larger datasets improve around-ear EEG-based SSD, (2) whether healthy-participant data supplement limited LIS-participant data despite articulatory differences, (3) transferability to unseen vocabulary, and (4) online user-interface performance.Main results. Large-scale EEG/EMG data improved SSD accuracy in both healthy participants and the LIS participant. Training on the heterogeneous dataset achieved 56.6% accuracy for healthy users and 47.3% for the LIS participant. Fine-tuning this decoder for new vocabulary increased the accuracy by 22 percentage points relative to training from scratch. Regression analysis showed that, for decoding in the LIS participant, data from the LIS participant contributed approximately four times the weight of healthy-participant data, quantifying data strategies for SSD. Online experiments achieved top-1/top-5 accuracies of 47.2%/76.0% for healthy users and 26.5%/49.1% for the LIS participant.Significance. The results indicate that lightweight, commercially feasible around-ear EEG can enable practical SSD when combined with large-scale healthy-participant data, supporting online operation. Moreover, models trained on a 64-word vocabulary facilitate decoding of a new vocabulary, providing a path toward SSD systems requiring minimal LIS-participant data. This study advances non-invasive SSD systems suitable for everyday communication.},
}

Humans
*Electroencephalography/methods/instrumentation
Male
Female
Adult
*Speech/physiology
Young Adult
*Brain-Computer Interfaces
Middle Aged
Wearable Electronic Devices
Electromyography/methods

@article {pmid41936725,
year = {2026},
author = {Zhuang, JR and Guo, PC},
title = {Attention-Enhanced U-Net for Sensor-Efficient High-Density EEG Reconstruction in Wearable Brain Monitoring Systems.},
journal = {Journal of medical systems},
volume = {50},
number = {1},
pages = {},
pmid = {41936725},
issn = {1573-689X},
abstract = {UNLABELLED: High-channel-density (HCD) electroencephalography (EEG) enables fine-grained neural sensing but is constrained by high hardware costs, spatial complexity, and limited portability. This study developed a deep learning-based method to reconstruct high-density EEG signals from low-channel-density (LCD) inputs, enabling more practical and affordable brain-monitoring systems. This study introduces VEEG-A-U-Net, a lightweight U-Net architecture enhanced with attention gates and residual learning. The model combined spherical spline interpolation with a learnable correction signal to adaptively model spatial-temporal features. The framework was trained and evaluated on the SEED dataset, using normalized mean square error (NMSE), signal-to-noise ratio (SNR), and Pearson correlation coefficient (PCC) to assess reconstruction performance. Validation was conducted through leave-one-subject-out cross-validation (LOSO-CV) and cross-dataset experiments to examine generalizability. Under the same reconstruction setting (scale factor = 2), VEEG-A-U-Net achieved competitive reconstruction performance compared with state-of-the-art methods, while requiring substantially fewer parameters and computational operations. Cross-dataset evaluations confirmed stable performance across different EEG paradigms. Inference-time analysis showed low computational latency, indicating practical feasibility for deployment in resource-constrained and edge computing environments. A preliminary clinical EEG evaluation was also conducted to explore feasibility in clinical settings.The proposed framework offers an effective and lightweight solution for reconstructing high-density EEG from sparse measurements. These findings may support the development of sensor-efficient and portable EEG systems for practical neuroengineering and brain–computer interface applications.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10916-026-02374-5.},
}

@article {pmid41940909,
year = {2026},
author = {Mukhtiar, A and Mubarak, NM and Aly Saad Aly, M},
title = {MXene Nanomaterial Interfaces: Pioneering Neural Signal Recording for Brain-Computer Interfaces and Cognitive Therapy.},
journal = {Topics in current chemistry (Cham)},
volume = {384},
number = {2},
pages = {},
pmid = {41940909},
issn = {2364-8961},
support = {IMSIU-DDRSP2601//Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU)/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Nanostructures/chemistry ; Animals ; Brain/physiology ; Nitrites ; Transition Elements ; },
abstract = {The development of cost-effective, high-accuracy MXene-based electrode devices is a promising approach for monitoring brain activity. The high conductivity and controllable surface chemistry make MXenes viable for neural stimulation and recording applications. In this review article of MXene integration into neural devices, we analyze the role of MXenes in advancing next-generation brain-computer interfaces (BCIs). High-resolution neural interfaces can be studied through cognitive rehabilitation investigations that examine real-time signal decoding capabilities and feedback systems in these devices. In addition to a summary of recent experimental findings from in vitro and in vivo models, the article also discusses engineering strategies for optimizing MXene-based systems for neural applications. The clinical implementation of future technologies must address challenges related to material stability and compatibility with biological tissues, as well as device miniaturization requirements. This investigation aims to evaluate MXenes as transformative materials that could drive breakthroughs in neural interface technology while advancing brain-machine interface functionality.},
}

*Brain-Computer Interfaces
Humans
*Nanostructures/chemistry
Animals
Brain/physiology
Nitrites
Transition Elements

@article {pmid41941779,
year = {2026},
author = {Chen, W and Daly, I and Chen, Y and Wu, X and Liang, W and He, X and Wang, X and Cichocki, A and Jin, J},
title = {Enhancing the Capability and Accuracy of Motor Imagery Classification: A Deep Neural Network-Powered Multifaceted Strategy Model.},
journal = {IEEE transactions on cybernetics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TCYB.2026.3678659},
pmid = {41941779},
issn = {2168-2275},
abstract = {Motor imagery (MI) is a popular noninvasive brain computer interface (BCI) paradigm, yet its decoding accuracy remains hindered by the inherent nonstationarity and low signal-to-noise ratio of electroencephalogram (EEG) signals. Current decoding frameworks often fail to fully exploit the intricate spatial-temporal dependencies, leading to suboptimal feature representation and the omission of latent discriminative cues. To address these challenges, we introduce a deep neural network-powered multifaceted strategy (DPMS-Net) model, a novel approach that employs dynamic convolution to unearth effective discriminative cues across multiple dimensions, including the temporal, spatial, and frequency domains. This model synergizes channel and temporal attention mechanisms to adeptly capture the salient features of EEG signals across diverse spatial-temporal dimensions, thereby mitigating the risk of omitting critical information. Furthermore, we introduce a spectral-domain analysis component that unearths subtle oscillatory signatures hidden within the EEG spectrum, providing enriched evidence for classification. We evaluated the performance of DPMS-Net on two publicly available datasets and a self-collected dataset from stroke patients. On the BCI Competition IV 2a and BCI Competition IV 2b datasets, DPMS-Net achieved subject-dependent classification accuracies of 83.93% and 88.38%, respectively, alongside subject-independent classification accuracies of 65.88% and 76.01%. In the stroke patient dataset, DPMS-Net attained a subject-dependent classification accuracy of 67.67% and a subject-independent classification accuracy of 57.58%. Experimental results indicate that DPMS-Net possesses efficient decoding capabilities and robust stability, reflecting its potential for deployment in neurorehabilitation BCI systems.},
}

@article {pmid41941946,
year = {2026},
author = {Zhou, X and Wang, L and Zhang, L and Yao, W and Li, G},
title = {Dynamic source domain selection: An adaptive EEG transfer learning framework for mitigating negative transfer.},
journal = {Journal of neuroscience methods},
volume = {432},
number = {},
pages = {110768},
doi = {10.1016/j.jneumeth.2026.110768},
pmid = {41941946},
issn = {1872-678X},
abstract = {BACKGROUND: Electroencephalography (EEG) is widely used in brain-computer interfaces (BCIs). Current transfer learning (TL) methods often merge multiple source domains, underutilizing diverse information and risking negative transfer when source-target similarity is low. Moreover, inter-subject variability further reduces TL effectiveness in motor imagery BCIs (MI-BCIs).

NEW METHOD: To address the above issues, we propose an adaptive EEG dynamic transfer learning framework. The framework first performs time-frequency decomposition on EEG signals using wavelet transform convolution. It then realizes dynamic adaptive matching of features between the source domain and the target domain, thereby reducing negative transfer. Specifically, a feature extractor maps EEG signals to a latent space with discriminative representations. Next, the dynamic migration-based attention module matches source and target domain samples within this latent space, ensuring a high degree of alignment. Finally, a novel combined loss function is co-optimized to reduce both marginal and class-conditional discrepancies arising from the multimodal structure of EEG signals.

RESULTS: The model is validated on the BNCI2014001, BNCI2014002, and BNCI2015001 datasets to assess its classification performance. The accuracy rates of the three datasets are 78.78%, 82.11%, and 78.19%, respectively.

The results indicate that the method is robust to subject variability. The average accuracy of the proposed method outperforms the baseline algorithms, with improvements ranging from 0.13% to 27.7%.

CONCLUSIONS: for research articles: Our approach addresses the domain shift challenge in MI-BCIs by enabling effective cross-domain knowledge transfer. This capability to bridge distribution disparities significantly enhances the real-world applicability of such systems.},
}

@article {pmid41942448,
year = {2026},
author = {Niu, J and Xia, J and Liu, Q and He, Y and Li, W and Chen, K and Zhang, X and Qiu, J and Chen, H and Li, J and Liao, W},
title = {Brain energetic landscapes shape state dysregulation in major depressive disorder: a morphological network controllability perspective.},
journal = {Translational psychiatry},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41398-026-04025-2},
pmid = {41942448},
issn = {2158-3188},
abstract = {Aberrant dynamic shifts in brain states are a hallmark of cognitive and behavioral dysfunctions in major depressive disorder (MDD), yet the underlying mechanisms of these disturbances remain elusive. Leveraging network control theory of morphological networks, we characterized aberrant brain dynamics and energy deficits of MDD patients in two independent cohorts. MDD patients exhibited reduced dynamic stability, characterized by elevated intra-state transitions and diminished inter-state transitions, which were associated with impaired control energy. Region-specific deficits of energy regulation capacity were observed in key nodes of the default mode and limbic networks, including the posterior cingulate cortex and temporal pole, which correlated with cognition and clinical symptoms in MDD patients. MDD-related energy inefficiency was related to multiscale energy architectures at cellular, molecular, and biological levels, including mitochondrial morphologies and functions, energy metabolism pathways, and brain metabolic patterns. Additionally, we demonstrated an association between energy demands and cortical dynamics, indicating a disrupted energy-dependent neurophysiological activity in MDD patients. Together, these results identified the energetic fundamentals underlying pathological brain-state transitions in MDD patients. Identifying energy-vulnerable nodes from a controllability perspective may therefore provide valuable targets for restoring normative neural dynamics in MDD.},
}

@article {pmid41929704,
year = {2026},
author = {Lin, D and Tran, T and Thaploo, S and Matias, JGE and Pixley, JE and Nenadic, Z and Do, AH},
title = {Perception of brain-computer interface implantation surgery for motor, sensory, and autonomic restoration in spinal cord injury and stroke.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1678175},
pmid = {41929704},
issn = {1662-4548},
abstract = {INTRODUCTION: Stroke and spinal cord injury (SCI) can profoundly diminish quality of life across physical and psychosocial domains, with motor and sensory deficits often persisting despite current therapies. Invasive brain-computer interface (BCI) systems, particularly electrocorticography (ECoG)-based approaches, offer a potential means to bypass neural injury and restore function. To inform development and deployment, it is critical to understand candidate users' willingness to adopt such technology and how that willingness relates to their functional goals and rehabilitation priorities.

METHODS: We conducted a survey assessing receptiveness to surgical implantation of ECoG grids for BCI use and eliciting participants' rehabilitative goals and perceived priorities across motor and sensory domains. We examined associations between willingness to undergo implantation and (1) the level of functional recovery hypothetically offered, (2) stated rehabilitative priorities, and (3) self-reported disability.

RESULTS: We surveyed 71 participants: stroke (n = 33), SCI (n = 37), and both stroke and SCI (n = 1). Across this cohort, respondents reported a high willingness to undergo surgery for ECoG-based BCI if it could restore basic functions, including upper-extremity control, gait, bowel/bladder function, and sensation. Willingness to pursue implantation showed no correlation with the degree of functional recovery promised by the hypothetical BCI. Likewise, willingness did not correlate with participants' rehabilitative priorities or their level of disability.

DISCUSSION: These findings indicate a strong interest in invasive BCIs even when only basic functions may be restored, independent of disability severity or stated priorities. This suggests that first-generation commercial invasive BCIs with limited functionality may still find receptive users. However, stated interest may not translate to informed surgical consent in real-world contexts, thereby highlighting the risk of overly optimistic expectations. Hence, robust, transparent consent frameworks and balanced communication are essential as invasive BCIs move toward clinical deployment.},
}

@article {pmid41932127,
year = {2026},
author = {Cheng, C and Shang, R and Wang, Z and Li, H and Jia, Z},
title = {MBDA: A modality-balanced framework with data augmentation and alignment for multimodal emotion recognition.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {201},
number = {},
pages = {108852},
doi = {10.1016/j.neunet.2026.108852},
pmid = {41932127},
issn = {1879-2782},
abstract = {Multimodal Emotion Recognition (MER) aims to infer human emotional states by integrating complementary information from heterogeneous modalities. However, existing MER methods often suffer from modality imbalance, cross-modal misalignment, and limited data diversity, which hinder their robustness and generalization. To address these issues, we propose a Modality-Balanced framework with Data Augmentation and Alignment (MBDA), which integrates modality-aware augmentation, feature alignment, and counterfactual knowledge distillation into a unified framework in a progressive learning manner. MBDA boosts data diversity while preserving semantic consistency through modality-aware augmentation, enforces robust multi-level alignment across modalities, and adaptively rebalances modality contributions through counterfactual knowledge distillation. Experiments on the DEAP and SEED-IV datasets demonstrate that MBDA consistently outperforms state-of-the-art methods, achieving accuracies of 93.86%, 95.11%, 91.02%, and 92.66% on DEAP-A, DEAP-V, DEAP-AV, and SEED-IV, respectively.},
}

@article {pmid41932888,
year = {2026},
author = {Ji, SY and Wang, WW and Yang, Y and Xu, P and Zhang, J and Zhao, X and Xi, K and Zang, SK and Shen, DD and Mao, C and Shen, Q and Zhang, Y},
title = {Dynamic monomer-dimer transition in ligand-induced apelin receptor activation.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-71325-y},
pmid = {41932888},
issn = {2041-1723},
support = {92353303//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32141004//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81922071//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {G-protein-coupled receptors (GPCRs) are significant signal transducers that exist as monomers and in multiple oligomeric forms. However, molecular mechanism driving their dynamic interconversion to regulate intricate signaling in class A GPCRs remains elusive, compounding our understanding of their related pathophysiological functions. Here, we present a set of 12 assemblies of the apelin receptor (APLNR), including dimeric apo state, agonistic small molecule- or nanobody-bound state of monomeric and dimeric APLNR with and without G-proteins, providing a detailed dynamic view of the monomer-dimer transition. High-resolution cryo-EM structures reveal that different ligands induce varying degrees of pre-dissociation of dimers in the absence of G-protein, with G-protein coupling facilitating the transition from dimeric to monomeric receptor. These insights enhance our understanding of the dynamic regulation of class A GPCRs between monomeric and dimeric forms and advance the rational drug design strategies aimed at selectively modulating of APLNR signaling.},
}

@article {pmid41933048,
year = {2026},
author = {Wang, T and Gong, H and Ye, G and Chen, R and Sun, S and Huang, X and Zhang, B and Jiang, L and Zhang, Y and Chen, T and Pan, Y and Xu, J and Jin, M and Chen, K and Mao, W and Xu, Q},
title = {Multiple pathways of CD34[+] cell differentiation during embryogenesis.},
journal = {Cell death and differentiation},
volume = {},
number = {},
pages = {},
pmid = {41933048},
issn = {1476-5403},
support = {82030008//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31830039//National Natural Science Foundation of China (National Science Foundation of China)/ ; W2541023//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82400490//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82200479//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82400575//National Natural Science Foundation of China (National Science Foundation of China)/ ; U24A20799//National Natural Science Foundation of China (National Science Foundation of China)/ ; LMS25H020009//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
abstract = {CD34 has long been defined as a canonical marker for endothelial progenitors as well as hematopoietic stem cells, implicating its role in vascular development and hematopoiesis. However, the precise developmental hierarchy and lineage potential of CD34[+] cells remain controversial. In this study, we integrated inducible genetic lineage tracing techniques, proteomics and single-cell RNA-seq (scRNA-seq) analyses to elucidate the dynamic developmental trajectory of CD34[+] cells during various embryonic periods in both humans and mice. Remarkably, our analyses indicated that the progeny of CD34[+] cells marked distinct, spatiotemporally restricted progenitor waves with divergent fates, at which point cells adopted endothelial, hematopoietic and fibroblastic fates, respectively. During gastrulation (E6.5-E8.5), an initial wave of CD34[+] progenitors predominantly orchestrates vasculogenesis via a Kdr-dependent mechanism. Subsequently, from E9.5 to E14.5, cell cycle activation serves as a molecular switch, facilitating the endothelial-to-hematopoietic transition (EHT) of CD34[+] progenitors. Unexpectedly, we identify a wave of CD34[+] progenitors in late embryogenesis that gives rise to fibroblasts, distinct from earlier endothelial or hematopoietic lineages. Furthermore, because umbilical cord blood is a valuable source of different circulating stem/progenitor cells, we distinguish circulating endothelial progenitors from fibroblast progenitors in human cord blood by unique molecular signatures, with GFPT2 specifically marking the fibroblast progenitors. Collectively, our study provides a high-resolution spatiotemporal atlas of CD34[+] cells during embryogenesis, redefining the temporal shifts of CD34[+] cells in cell states and offering a precise framework for manipulating CD34[+] cells in regenerative medicine.},
}

@article {pmid41933504,
year = {2026},
author = {Torbahn, G and Schoene, D and Ernst, IG and Schwingshackl, L and Rücker, G and Knüttel, H and Kemmler, W and Sieber, CC and Batsis, JA and Villareal, DT and Stroebele-Benschop, N and Volkert, D and Kiesswetter, E},
title = {EffectS of Lifestyle Interventions in Older PEople With Obesity (Effective SLOPE): a Systematic Review With Network Meta-Analyses.},
journal = {Obesity reviews : an official journal of the International Association for the Study of Obesity},
volume = {},
number = {},
pages = {e70123},
doi = {10.1111/obr.70123},
pmid = {41933504},
issn = {1467-789X},
support = {01KG1903//German Federal Ministry of Education and Research (BMBF)/ ; },
abstract = {BACKGROUND/AIM: We conducted a systematic review with network meta-analyses (NMA) summarizing the effects and safety of lifestyle interventions containing nutrition (NUT; e.g., calorie restriction), exercise (EX; e.g., aerobic/resistance exercise) and behavior change interventions (BCI; e.g., behavioral therapy) on physical function, body composition, quality of life, psychosocial outcomes, health and adverse events in community-dwelling older adults with obesity.

METHODS: We used the methodology proposed by Cochrane and searched six databases and one trial registry for eligible randomized controlled trials (RCTs; intervention duration ≥ 12 weeks) up to May 2022 with a full new search in MEDLINE and a re-assessment of previously identified eligible trial registry entries in October 2025. Random-effects NMA ((standardized) mean difference ((S)MD), 95% confidence intervals) were conducted if possible.

RESULTS: We included 72 RCTs (n = 6716) for descriptive summaries and 54 RCTs (n = 4249) for NMA. NUT+EX+BCI improved physical function (performance batteries) compared to control (SMD 3.37 [1.76;4.97]; high certainty of evidence). NUT+EX+BCI may reduce body (MD -8.69 [-13.14;-4.25]) and fat mass (MD -6.58 [-10.44;-2.73]) while not negatively affecting fat-free mass (MD -1.38 [-3.52;0.76]) or bone mineral density (MD -0.01 [-0.05;0.02]) (evidence very uncertain). Other interventions (single/combined) may also be effective; however, effects were often imprecise. For psychosocial outcomes, quality of life, and health events, data were insufficient or too heterogeneous to derive clear results.

CONCLUSION: The evidence suggests that NUT+EX+BCI interventions are most suitable for the management of obesity in older adults. Nevertheless, further RCTs-especially in frail populations and on patient-relevant outcomes-are needed.},
}

@article {pmid41937954,
year = {2026},
author = {Esteves, D and Vourvopoulos, A},
title = {EEG biomarkers of the sense of embodiment: methodological gaps and evidence-based recommendations from a systematic review.},
journal = {Frontiers in systems neuroscience},
volume = {20},
number = {},
pages = {1756407},
pmid = {41937954},
issn = {1662-5137},
abstract = {INTRODUCTION: The sense of embodiment (SoE), describing the experience of owning, controlling, and being located within a body, underpins virtual reality (VR) interaction, brain-computer interfaces (BCIs), and multisensory body-illusion research. Although SoE is typically assessed through subjective questionnaires, their variability and limited validity have motivated the search for objective neural markers. Electroencephalography (EEG) has become the most widely used technique given its portability and high temporal resolution; however, the existence of a consistent EEG correlate of embodiment remains unclear.

METHODS: This systematic review summarizes 35 EEG studies (2010-June 2025) identified through structured database searches, examining SoE across immersive and non-immersive VR, augmented reality, and non-VR paradigms. We analyze EEG features including spectral power, event-related desynchronization/synchronization (ERD/ERS), connectivity, and temporal dynamics, and examine methodological variability in illusion induction and SoE assessment.

RESULTS: Across studies, the reduction of the alpha-band over central-parietal regions emerges as the most recurrent correlate of embodiment. Beta-band decreases and gamma-band increases appear in several studies but lack consistent replication, while findings in Delta and Theta bands remain sparse and contradictory. Considerable heterogeneity is found in VR paradigms, EEG setups, preprocessing, and psychometric tools, contributing to inconsistent results and limiting cross-study comparability.

DISCUSSION: Critically, no EEG feature demonstrates sufficient reproducibility to qualify as a universal biomarker of SoE, and no standardized protocol for EEG-based embodiment assessment currently exists. Overall, this review highlights both the promise and current limitations of EEG-based approaches to measuring embodiment. We conclude by identifying methodological gaps and outlining recommendations to support the development of reliable EEG markers for future applications in VR rehabilitation, MI-BCIs, cognitive neuroscience, and clinical interventions.},
}

@article {pmid41938598,
year = {2026},
author = {Paneru, B},
title = {A multi-dimensional CNN-Bi-GRU for IoT-based brain-computer interface in early epileptic seizure detection.},
journal = {Biology methods & protocols},
volume = {11},
number = {1},
pages = {bpag010},
pmid = {41938598},
issn = {2396-8923},
abstract = {The study focuses on seizure detection using EEG data from Mendeley. An early-alert IoT-BCI system is designed to simulate real-time support for patients during seizures. The proposed Multi-Dimensional CNN-Bi-GRU (MDCBG) outperforms hybrid deep learning models, achieving 97.43% accuracy, surpassing baseline EEGNet (92.17%) and CTNET (85.11%), along with models evaluated through ablation studies on seizure vs. non-seizure prediction. The proposed model, along with other models like Bi-GRU with attention, Bi-LSTM-GRU, and XGBoost, also performs well on classifying various types of seizures. SHAP analysis shows Channel 5 contributes most to predictions. An IoT-based automation system is simulated on seizure detection for triggering micro devices near the patient's environment. This approach supports early seizure warning and guides home-automation strategies to assist patients.},
}

@article {pmid41939130,
year = {2024},
author = {Xu, S and Scott, K and Manshaii, F and Chen, J},
title = {Heart-brain connection: How can heartbeats shape our minds?.},
journal = {Matter},
volume = {7},
number = {5},
pages = {1684-1687},
pmid = {41939130},
issn = {2590-2385},
abstract = {Recent neuroscience reveals the heart's impact on brain activity through blood pulsations, affecting mitral cells in the olfactory bulb. This connection, involving mechanosensitive ion channels like Piezo2, links cardiovascular dynamics to neuronal function, offering new treatments for neurological disorders, advancing closed-loop brain-computer interfaces, and emphasizing the body-mind interconnectivity.},
}

@article {pmid41940264,
year = {2026},
author = {Li, P and Qi, G and Zhao, S and Huang, A and Guan, W},
title = {EEG-based brain functional connectivity dynamics in manual and video-based car-following observation among young drivers.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {72},
pmid = {41940264},
issn = {1871-4080},
abstract = {UNLABELLED: Understanding the neurophysiological mechanisms underlying driving behavior in young drivers is essential for improving cognitive-aware driver assistance and vehicle-human interaction systems. This study systematically examines EEG dynamics and functional brain network reconfigurations across both manual and video-based car-following observation, providing a neurophysiological framework for differentiating driving modes among young adult drivers. EEG characteristics were analyzed under three car-following strategies-aggressive, conservative, and personalized-implemented within a simulated driving environment, to capture the variability of cognitive engagement during distinct control demands. Key findings reveal that power spectral density (PSD) in the θ, β, and γ bands, combined with brain functional connectivity (BFN) measures, effectively characterizes workload-related modulation and attentional resources across driving conditions. A novel computational framework integrating Time-Frequency Common Mutual Information (TFCMI) features with a Parallel Compact Convolutional Neural Network (PCNet) achieved an average classification accuracy of 85.26%, surpassing traditional single-modality approaches. Neurotopographic results further indicate context-dependent functional specialization: frontal regions showed stronger activation and connectivity during manual control, while occipital regions exhibited enhanced synchronization during video-based car-following observation tasks. Collectively, these findings advance the understanding of driving-related cognitive processes in young drivers and provide neuroergonomic insights for designing adaptive human-machine interfaces in future intelligent transportation systems.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-026-10442-2.},
}

@article {pmid41940265,
year = {2026},
author = {Huang, K and Yang, H and Zhu, S and Chen, Y and Li, T and Zhao, L and Gong, A and Nan, W and Xu, J and Fu, Y},
title = {Ethical risks and considerations of brain-controlled and neuromodulation technologies.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {74},
pmid = {41940265},
issn = {1871-4080},
abstract = {Brain-controlled technology (BCT), centered on brain-computer interfaces (BCI), acquires and decodes neural signals to convert subjective intentions into control commands for external devices, establishing an intention output loop. In contrast, neuromodulation technology applies external physical stimuli to the central nervous system to regulate neuronal excitability and brain network states, achieving energy input for functional modulation and therapeutic purposes. The inherent differences in mechanisms and application goals determine that the ethical risk profiles and governance priorities of these two technologies cannot be conflated. Current public communication is characterized by terminology misuse and concept generalization, notably the misinterpretation of neuromodulation as controlling the brain. In response to the resulting ethical anxiety caused by capability extrapolation, this paper first clarifies the functional positioning of both technologies. Subsequently, a three-dimensional assessment model based on reality, reversibility, and technological dependence is constructed to map a stratified ethical risk landscape. The analysis reveals a significant asymmetry in risk distribution: risks of BCT are primarily concentrated on neural privacy leakage and responsibility attribution dilemmas within the intention decoding process, whereas risks of neuromodulation are deeply embedded in the potential erosion of personal identity and subject autonomy induced by external stimuli. To address institutional gaps in the current regulatory system regarding consumer-grade devices and long-term effects, this paper proposes a differentiated tiered governance strategy. It advocates establishing terminology demystification and conceptual rectification as the frontline defense for risk governance. On this basis, the strategy enforces physical defense mechanisms such as hardware fusing and parameter safety windows on the technical side, and strengthens data desensitization and algorithmic accountability on the data side. Ultimately, a multi-subject synergistic governance mechanism covering the full lifecycle from research and development and clinical trials to social application is constructed to provide institutional support for responsible innovation in neurotechnology.},
}

@article {pmid41940343,
year = {2026},
author = {Otake, H and Senta, N and Ushiba, J and Takemi, M},
title = {Neural correlates of individual differences in motor learning under reinforcement contexts.},
journal = {iScience},
volume = {29},
number = {4},
pages = {115336},
pmid = {41940343},
issn = {2589-0042},
abstract = {Rewards and punishments shape motor learning, yet individuals vary in their adaptation speed and skill retention. Previous studies have linked these processes to two electroencephalographic signatures: feedback-related negativity (FRN) and sensorimotor event-related desynchronization (ERD). However, their roles in individual learning differences remain unclear. We recorded electroencephalography while 64 adults performed a visuomotor rotation task where gains or losses scaled with movement error. Using Lasso regression, we examined whether these neural markers accounted for individual variability in learning and retention. Results demonstrated that the interaction between sensorimotor alpha-ERD during movement preparation in late adaptation and feedback condition explained retention. Stronger alpha-ERD predicted better retention only in the reward condition, whereas neither ERD nor FRN explained adaptation rates. These findings indicate that late-phase alpha-ERD reflects neural mechanisms supporting motor memory stabilization, which becomes behaviorally relevant specifically under positive reinforcement. Thus, pairing reward with interventions enhancing sensorimotor cortical excitability may facilitate skill maintenance.},
}

@article {pmid41929610,
year = {2026},
author = {Abdalla, N and El Arab, RA and Abdrbo, A and Almari, M and Ayoub, MY and Alsaaideh, B and Dagamseh, MS and Almagharbeh, WT and Abuadas, F and Abu Mahfouz, MS and Gaballah, MK},
title = {Artificial intelligence in rehabilitation: a review of clinical effectiveness, real-world performance, safety, and equity across modalities and settings.},
journal = {Frontiers in digital health},
volume = {8},
number = {},
pages = {1737957},
pmid = {41929610},
issn = {2673-253X},
abstract = {BACKGROUND: Rehabilitation faces a scale problem: millions who could benefit lack timely, effective services. Artificial intelligence (AI) and device-based modalities (e.g., robotics and VR) can extend reach and personalise care when validated, yet decision-makers lack a consolidated view of clinical usefulness, translation to practice, safety, equity, and cost.

METHODS: We conducted an umbrella review of reviews using a Population-Exposure-Outcome framework. Searches span biomedical, allied health, and engineering databases from inception to September 1, 2025. We distinguished AI-enabled (ML/DL) interventions from technology-assisted (no ML demonstrated) modalities and synthesised outcomes across impairment, activity, independence, usability/safety, equity, and economics.

FINDINGS: The most reproducible clinical signal is activity improvement for post-stroke upper limb with technology-assisted training (robotics with or without VR) that increases task-specific practice; effects on impairment and independence are inconsistent once dose is matched and assessors are blinded. Claims of non-inferiority are not established when prespecified margins and confidence-interval testing are absent, so parity is interpreted as no between-group advantage under those conditions. Across AI-enabled domains, a development-to-deployment performance drop is evident most notably for brain-computer-interface classifiers and computer-vision movement evaluation limiting immediate clinical impact. Imaging-based decision support (radiomics/CNN) is closer to practice but varies by software and site, requiring local calibration and impact evaluation before pathway change. Reported adverse events are generally mild, yet usability, adherence, equity, and cost are under-measured, particularly in home and hybrid delivery. Prediction-model and trial reporting frequently fall short of contemporary AI standards; representation skews toward high-income settings, and subgroup performance is seldom reported.

CONCLUSION: An adjunct-first posture is warranted. Adoption should be gated by minimum clinically important difference-anchored benefit under dose symmetry and blinded assessment; external, multi-site validation with declared lab-to-clinic performance loss; subgroup fairness with mitigation; decision-grade economic value; interoperability; and readiness for regulation, change control, and cybersecurity. Priorities include pragmatic, multi-site, assessor-blinded, dose-matched trials; standardised safety/usability capture for home use; and a public, living evidence atlas. AI can expand rehabilitation when held to clinical standards that matter to patients and services. With clear adoption gates and continuous post-market monitoring, systems can extend access and independence without sacrificing rigour, safety, equity, or fairness.},
}

@article {pmid41926164,
year = {2026},
author = {Traoré, N and Zabré, P and Millogo, O and Sié, A and Vounatsou, P},
title = {Assessing the role of interventions and climate on malaria mortality among children under five years of age: insights from two decades of data from the Health Demographic Surveillance System of Nouna, Burkina Faso.},
journal = {Journal of global health},
volume = {16},
number = {},
pages = {04080},
pmid = {41926164},
issn = {2047-2986},
mesh = {Humans ; Burkina Faso/epidemiology ; Child, Preschool ; *Malaria/mortality/prevention & control ; Infant ; Seasons ; *Climate ; Insecticide-Treated Bednets/statistics & numerical data ; Female ; Male ; Infant, Newborn ; Population Surveillance ; },
abstract = {BACKGROUND: Malaria is a preventable disease that causes serious illness and death. In 2022, it remained the leading cause of death among children under five years of age in Burkina Faso, despite significant intervention efforts over the past two decades. Research on the effects of interventions and climatic factors on malaria morbidity has expanded, but their effects on malaria mortality remain unclear. We aimed to estimate the effects of interventions and lagged climatic factors on malaria mortality among children under five years of age in northwest Burkina Faso. We further evaluated the role of climatic seasonality in patterns of malaria mortality.

METHODS: We investigated the seasonal patterns of malaria mortality among children under five years of age and their association with climatic factors, such as rainfall and land surface temperature (LST), using wavelet analysis on mortality data from the Nouna Health Demographic Surveillance System spanning 2002-2021. Furthermore, we assessed the effects of interventions, including coverage of insecticide-treated nets (ITNs) and artemisinin-based combination therapies (ACTs), on malaria mortality alongside climate effects using Bayesian negative binomial temporal models for the period 2013-2021.

RESULTS: The lag time in the effects of climatic factors varied over time. Malaria mortality, rainfall, and LST showed a 12-month seasonal cycle throughout the years, while LST also had a six-month cycle in specific years. Rainfall lagged by 1.5 to 2 months and LST by 1 to 1.5 months, depending on the seasonal cycle and year. Rainfall was positively associated with malaria mortality (mortality rate ratio (MRR) = 1.59; 95% Bayesian credible interval (BCI) = 1.18, 1.95), LST showed a decrease in mortality (MRR = 0.68; 95% BCI = 0.52, 0.86), and ITN was associated with a reduction in mortality (MRR = 0.59; 95% BCI = 0.42, 0.79); however, ACT was not statistically important.

CONCLUSIONS: We found that ITN was more effective in reducing malaria mortality than temperature, but rainfall had a greater opposing impact on increasing malaria mortality. The seasonal mortality pattern was more influenced by rainfall than by temperature. Varying climatic lag times highlight the need for adaptive strategies. Policymakers should focus on climate-informed planning, sustained ITN coverage, and reassessment of ACT strategies to further reduce malaria mortality.},
}

Humans
Burkina Faso/epidemiology
Child, Preschool
*Malaria/mortality/prevention & control
Infant
Seasons
*Climate
Insecticide-Treated Bednets/statistics & numerical data
Female
Male
Infant, Newborn
Population Surveillance

@article {pmid41926597,
year = {2026},
author = {Samal, S and Xiao, S and Nelson, S and Kolhe, O and Khan, HF and Matin, MH and Lee, WJ and Ahmed, M and Wang, D and Wang, T and Pikes, T and Scott, AN and Rodriguez, JA and Olson, MR and Deng, Q and Parkinson, EI and Rochet, JC and Jayant, K and Mei, J},
title = {Blood-catalyzed n-doped polymers for reversible optical neural control.},
journal = {Science (New York, N.Y.)},
volume = {392},
number = {6793},
pages = {eadu5500},
doi = {10.1126/science.adu5500},
pmid = {41926597},
issn = {1095-9203},
mesh = {Animals ; Mice ; Zebrafish ; *Polymers/chemistry ; Polymerization ; Catalysis ; Infrared Rays ; *Biocompatible Materials/chemistry ; Sodium Channels ; Neurons/physiology ; },
abstract = {Biocompatible integration of synthetic materials with living tissue remains a major challenge for bioelectronics. In this case, substrate-free conducting polymer (CP) interfaces could help bridge this gap. We report in vivo assembly of n-doped poly(benzodifurandione) (n-PBDF) using whole blood-catalyzed polymerization in awake zebrafish and mice. This approach leverages endogenous catalysts, specifically hemoproteins, to form stable, thermally and ionically sensitive CP networks, ensuring long-term compatibility throughout the lifespan. We showcase the impact of this interface through reversible, cellular, and subcellular neuromodulation using near-infrared (NIR) light, including in vivo polymerized n-PBDF. Electrophysiological studies confirmed that n-PBDF alters intrinsic sodium ion channel excitability, and NIR light stimulation amplifies this modulation through thermoionic-induced shunting, providing on-demand, millisecond-scale reversible inhibitory control of excitability, a feature recapitulated in actively behaving mice.},
}

Animals
Mice
Zebrafish
*Polymers/chemistry
Polymerization
Catalysis
Infrared Rays
*Biocompatible Materials/chemistry
Sodium Channels
Neurons/physiology

@article {pmid41927190,
year = {2026},
author = {Rustamzadeh, O and Hosseini, SA and Tanha, RR and Akbarfahimi, N},
title = {Robotic rehabilitation and intelligent algorithms improving the performance skills of stroke patients: a scoping review.},
journal = {Journal of bodywork and movement therapies},
volume = {46},
number = {},
pages = {308-331},
doi = {10.1016/j.jbmt.2025.09.037},
pmid = {41927190},
issn = {1532-9283},
mesh = {Humans ; *Stroke Rehabilitation/methods/instrumentation ; *Robotics/methods ; Hand Strength/physiology ; Algorithms ; Upper Extremity/physiopathology ; Range of Motion, Articular/physiology ; Exoskeleton Device ; },
abstract = {BACKGROUND: This scoping review highlights major advances and persisting gaps in robotic and AI-driven rehabilitation for stroke, evaluating their impact on hand strength, dexterity, and ROM, and offering clinicians practical, updated guidance.

METHODS: Studies that focused on robotic-assisted technologies (RATs) in upper limb rehabilitation for stroke survivors (2014-2024) were included. Study designs unrelated to stroke, animal studies, and conference abstracts were excluded. Systematic searching in PubMed, Web of Science, Scopus, and Google Scholar employed robotic rehabilitation, AI, hand function, and stroke recovery-related terms. Data extraction encompassed intervention type, duration of treatment, dosage of therapy, outcome measures, cost-effectiveness, and patient satisfaction. Types of robotic rehabilitation: end-effector robots, exoskeletons, soft robotic gloves (SRGs), brain-computer interfaces (BCIs), and AI-enhanced virtual reality (AIVR).

RESULTS: These devices can augment motion, grip strength, and functional independence, especially in chronic and subacute stroke patients. Therapies are made fine-grained by algorithms to balance challenge and engagement, thus lightning therapists' burdens. Conventional energy sources may offer a more attractive option at shorter timelines and with reasonably predictable availability. Models that can be done at home enhance adherence at that higher level, though usability appears high for most models. Still, challenges with setup and independence for participants remain.

CONCLUSION: Robotic rehabilitation has a significant impact on motor function (MF) among stroke patients. Despite this, obstacles such as cost, accessibility, and long-term efficacy need even more research. Therapy dose optimization, adaptive AI integration, and cognitive-emotional outcome assessment are all areas of gaps in robotic rehabilitation that still need to be addressed.},
}

Humans
*Stroke Rehabilitation/methods/instrumentation
*Robotics/methods
Hand Strength/physiology
Algorithms
Upper Extremity/physiopathology
Range of Motion, Articular/physiology
Exoskeleton Device

@article {pmid41927340,
year = {2026},
author = {Amlie-Lefond, C and Cooper, A and Barry, D and Shaw, DWW},
title = {Fluid-Attenuated Inversion Recovery Correlates with Stroke Onset in Childhood Arterial Ischemic Stroke.},
journal = {AJNR. American journal of neuroradiology},
volume = {47},
number = {4},
pages = {1089-1092},
pmid = {41927340},
issn = {1936-959X},
mesh = {Humans ; Child ; Female ; Male ; Adolescent ; Child, Preschool ; *Ischemic Stroke/pathology/diagnostic imaging ; *Magnetic Resonance Imaging/methods ; Infant ; Bayes Theorem ; },
abstract = {BACKGROUND AND PURPOSE: In adults, time since stroke onset correlates with safety and efficacy of recanalization therapies, and the appearance of FLAIR hyperintense signal is considered a proxy for time. The time to FLAIR hyperintensity in childhood stroke is unknown but is of interest because time of stroke onset in childhood stroke is often unknown.

MATERIALS AND METHODS: Time to FLAIR hyperintensity on brain MRI performed on children within 24 hours of stroke onset was studied with Bayesian accelerated failure time models.

RESULTS: A total of 82 MRIs with FLAIR imaging were available from 72 children (37 girls), median age of 10.9 years (range: 0.8-18.0 years). Seventy-two percent (52/72) of children had anterior circulation stroke. Median time between stroke onset and MRI was 7 hours (range: 0.5-23.5 hours). The median estimated time to FLAIR presence was 5.4 hours (50% Bayesian credible interval [BCI], 2.9-8.8; 90% BCI, 0.7-16.7) for all patients, and 6.0 hours (50% BCI, 4.4-7.3; 90% BCI, 1.9-8.8) for anterior circulation only strokes. For all patients, when no signal hyperintensity on FLAIR is observed, there is 50% chance the stroke occurred more than 5.4 hours ago and a 25% chance the stroke occurred more than 8.8 hours ago. For anterior circulation only strokes without FLAIR hyperintense signal, there is a 50% chance the stroke occurred more than 6.0 hours ago, and a 25% chance the stroke occurred more than 7.3 hours ago.

CONCLUSIONS: FLAIR signal hyperintensity can be used to estimate time since stroke ictus in childhood stroke. Children may have a similar FLAIR signal change timing compared with adults, suggesting that they may have a similar window for effective recanalization therapies.},
}

Humans
Child
Female
Male
Adolescent
Child, Preschool
*Ischemic Stroke/pathology/diagnostic imaging
*Magnetic Resonance Imaging/methods
Infant
Bayes Theorem

@article {pmid41927547,
year = {2026},
author = {Malpas, SC and Wright, BE and Guild, SJ and Heppner, P and Gallichan, RJ and Leung, DP and Kim, SH and Boesley, Q and Tan, S and Kondo, M and McAllister, DJ and Windsor, JA and Campbell, D and Alan Barber, P and McCormick, D},
title = {Long-term brain pressure monitoring via a discrete microimplant; a first-in-human safety and initial efficacy trial in adults and children with hydrocephalus.},
journal = {Nature communications},
volume = {17},
number = {1},
pages = {},
pmid = {41927547},
issn = {2041-1723},
mesh = {Humans ; *Hydrocephalus/physiopathology/diagnosis/surgery ; *Intracranial Pressure/physiology ; Child ; Adult ; Male ; Female ; Monitoring, Physiologic/instrumentation/methods ; Child, Preschool ; Adolescent ; Middle Aged ; Young Adult ; Aged ; Prostheses and Implants ; Infant ; Brain ; },
abstract = {Emerging neurotechnologies such as brain-computer interfaces and implantable sensors offer considerable promise in the treatment of a broad range of neurological conditions. The key challenges are reducing the implant size, powering it, and confirming long-term accuracy and safety. Here we report the development of a novel type of implantable medical device that measures intracranial pressure long term and which weighs only 0.28 g. Currently the management of hydrocephalus patients relies heavily on non-specific symptoms e.g. headache and there is a lack of actionable data to drive decisions that are not solely hospital based such as imaging. The implant is designed to sit within the cerebral cortex. In a group of 10 adults and 10 children with hydrocephalus we demonstrated that the device was safe and capable of remotely monitoring intracranial pressure in patients at home for up to 18 months (ClinicalTrial.gov NCT06402786). In several children shunt failures occurred and these were associated with raised ICP. Instead of relying on non-specific symptoms such as headache, physicians were able to obtain real-time intracranial pressure readings that can lead to changes in the management of these complex patients.},
}

Humans
*Hydrocephalus/physiopathology/diagnosis/surgery
*Intracranial Pressure/physiology
Child
Adult
Male
Female
Monitoring, Physiologic/instrumentation/methods
Child, Preschool
Adolescent
Middle Aged
Young Adult
Aged
Prostheses and Implants
Infant
Brain

@article {pmid41928763,
year = {2026},
author = {Tsubaki, T and Kashihara, S and Asai, T and Imamizu, H and Nambu, I},
title = {Polarity-considered EEG microstates improve classification accuracy of oddball stimulus.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1712380},
pmid = {41928763},
issn = {1662-5161},
abstract = {Brain-computer interfaces (BCIs) require efficient feature extraction and dimensionality reduction from high-dimensional neural signals. Electroencephalogram (EEG) microstate analysis is a rapid and noise-resistant approach that classifies instantaneous EEG states into several spatial distribution patterns (templates). Previous BCI studies using the EEG microstate approach have typically used aggregated metrics, such as duration, frequency of occurrence, or time coverage, and have rarely applied pointwise microstate labeling as temporally ordered, one-dimensional sequences for robust classification. Moreover, the physiological relevance of EEG topographic polarity has often been overlooked, despite its potential to reveal smoother state transitions and align with event-related potential components. In this study, we applied polarity-considered microstate labeling to stimulus-driven classification in an oddball paradigm. EEG data from 40 healthy participants (20 per response type) were analyzed across three factors: stimulus modality (auditory or visual), modality condition (unimodal or cross-modal), and response type (key-response task or mental counting task). Preprocessed 32-channel EEG data were labeled with microstate templates (A-E ± topographical polarity) using a winner-take-all approach, and the resulting sequences were classified using multiple machine-learning models. The results showed that tree-based ensemble models (Random Forest, XGBoost, and CatBoost) achieved the most stable and accurate performance in the key-response task with cross-modal visual targets. These models reached an area under the receiver operating characteristic curve above 0.8 and a mean F1 score of 0.83. Preserving polarity improved classification by approximately 20% across tasks, doubling the label-space granularity and revealing temporal patterns aligned with the N200 and P300 components. Visual stimuli generally outperformed auditory stimuli, and cross-modal benefits emerged primarily in key-response tasks. These findings demonstrate that polarity-considered microstate labeling enhances classification accuracy and interpretability in BCIs. This method highlights the potential for real-time applications, such as P300 spellers and multimodal attention monitoring.},
}

@article {pmid41928799,
year = {2026},
author = {Ouyang, Z and Walmsley, K and Luo, S and Tippett, D and Wyse-Sookoo, K and Fifer, M and Vansteensel, MJ and Angrick, M and Ramsey, N and Crone, NE},
title = {Stable speech BCI performance during slow progression of ALS: A longitudinal ECoG study.},
journal = {Research square},
volume = {},
number = {},
pages = {},
doi = {10.21203/rs.3.rs-9156039/v1},
pmid = {41928799},
issn = {2693-5015},
abstract = {Background Electrocorticographic (ECoG) speech brain-computer interfaces (BCIs) show promise for restoring communication in amyotrophic lateral sclerosis (ALS), but the long-term stability of speech-related neural signals and decoding performance during disease progression remains unclear. We tracked signal characteristics and decoding over 25 months in a participant with ALS to determine how high-gamma (HG, 70-170 Hz) activity changes over time and whether these changes affect offline speech decoding. Methods We implanted two 8×8 subdural ECoG grids over left sensorimotor cortex (SMC) in a participant with slowly progressive bulbar variant ALS. Across 25 months, the participant performed an overt syllable-repetition task (12 consonant-vowel tokens) during simultaneous ECoG and audio recording. We quantified HG activation ratio (ActR), spectral signal-to-noise ratio (SNR; HG/HF, where HF = 300-499 Hz), and peak z-scored HG responses. Speech acoustics were evaluated using first/second formants (F1/F2) and the triangular vowel space area (tVSA). Offline EEGNet-based decoders were assessed in two stages: models trained on post-implant months 1-6 were tested on months 7-25, while models trained on stabilized data (months 7-11) were tested on the remaining period (months 12-25). Electrode-level saliency assessed spatial contributions to decoding. Results Acoustic analyses showed a significant reduction in tVSA over two years (-44.6 Hz[2]/day; P < 10 [-] [7]), consistent with mild intelligibility decline. Neural metrics (ActR and SNR) followed a biphasic trajectory: increasing during the first 6 months, after which ActR stabilized (0.041%/day; P = 0.13), and SNR declined gradually (-0.46%/day, P < 10 [- 4]). The model trained on months 1-6 achieved 55.7% accuracy (chance: 8.33%), but performance declined over time (-0.019%/day; P = 2.1×10 [-] [4]). Conversely, the model trained on months 7-11 achieved higher accuracy (65.9%) on subsequent data with no significant temporal decline (P = 0.23). Conclusions Speech-related HG features exhibited an initial unstable period followed by a long-term gradual SNR reduction, potentially reflecting disease progression. Models trained after signal stabilization generalized robustly to data recorded over a year later. These findings confirm that despite reduced absolute HG power and mild acoustic degradation of speech, cortical features remain stable enough to support durable ECoG speech BCIs without frequent recalibration. These findings will motivate future adaptive calibration algorithms that account for slow signal changes while leveraging stable spatial representations in ventral SMC. ClinicalTrials.gov Identifier NCT03567213.},
}

@article {pmid41928997,
year = {2026},
author = {Emonds, AMX and Okorokova, EV and Blumenthal, GH and Collinger, JL and Bensmaia, SJ and Miller, LE and Downey, JE and Sobinov, AR},
title = {Overlap in neural representations of coordinated wrist and finger movements in human motor cortex.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.03.19.712976},
pmid = {41928997},
issn = {2692-8205},
abstract = {Dexterous hand function underlies many essential human activities, from tool use to expression through gestures. Coordinated digit movements are enabled by the intricate musculature of the hand and forearm, which also imposes mechanical coupling between the digits and wrist, constraining their independent control. It remains unclear whether motor cortex inherits these constraints in its activity or encodes digit and wrist independently. To address this problem, we asked individuals with intracortical microelectrode arrays implanted in motor cortex to attempt flexion and extension of individual digits, either in isolation or in combination with attempted wrist movements. We could accurately decode which digit was moving based on cortical recordings, and channels selective for digit identity were arranged somatotopically across the recording arrays. Nevertheless, the activity during flexion or extension overlapped between digits, and movement direction of a given digit could be reliably inferred by a decoder trained on movements of other digits. This directional signal was largely invariant to the digit's initial posture. The population axis describing digit movement direction was aligned with the axes associated with wrist flexion-extension or pronation-supination. This alignment persisted during simultaneous wrist and digit movements, which complicated efforts to control them individually. However, by decoding wrist and digit motion from activity orthogonal to the shared direction axis, a participant was able to achieve continuous control of virtual hand movements with improved speed and reduced unintended movements. Together, the results identify both a code for digit identity and a low-dimensional flexion-extension signal which is shared across the digits and wrist. This arrangement is consistent with muscle-like biomechanical constraints on motor cortical activity, which must be accounted for to improve coordinated BCI control.},
}

@article {pmid41929051,
year = {2026},
author = {Karrenbach, MA and Wang, H and Johnson, Z and Ding, Y and He, B},
title = {EEG Foundation Model Improves Online Directional Motor Imagery Brain-computer Interface Control.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.03.24.714020},
pmid = {41929051},
issn = {2692-8205},
abstract = {Brain-Computer interfaces (BCIs) offer a link between neural signals and external computation, enabling control of devices for the purposes of restoring function to motor-affected individuals and enhancing capabilities of a wider set of populations. Electroencephalography (EEG) offers a high temporal resolution for dynamic and potential real-time feedback for non-invasive systems. However, its practical efficacy remains limited due to low spatial resolution and poor signal-to-noise ratio, leading to insufficient decoding accuracy and unintuitive control paradigms that hinder reliable user interaction. In this study, we present a framework for an online EEG foundation model by creating a custom foundation model through spectrogram reconstruction of compact temporal windows and online constraints during pretraining. We evaluate the performance of the model in a challenging control paradigm of single-arm, directional motor imagery with dynamic movements for guided and free movement cursor control tasks. Our foundation model approach achieved a final average accuracy of 51.3% during a goal-oriented guided control task. This represents a 15.8% increase over a conventional deep learning framework and a 26.3% increase above chance level, evaluated in a cohort of 11 human participants. During the free movement task, the foundation model invoked a higher rate of completion and lower completion times. Furthermore, the custom EEG foundation model demonstrated superior adaptability from same-session finetuning and indicated an enhanced capability to assist subject learning. These findings highlight the potential of EEG foundation models to support more robust and intuitive non-invasive BCI systems, providing a promising modelling framework for future BCI development.},
}

@article {pmid41920806,
year = {2026},
author = {Kostoglou, K and Müller-Putz, GR},
title = {Opposing cortical forces: Alpha slowing and sensorimotor mu acceleration during motor-related BCI training.},
journal = {PLoS computational biology},
volume = {22},
number = {4},
pages = {e1014112},
doi = {10.1371/journal.pcbi.1014112},
pmid = {41920806},
issn = {1553-7358},
abstract = {Brain-computer interfaces (BCIs) depend on the reliable decoding of brain activity, yet key rhythms like alpha and mu are not spectrally static and can shift with cognitive and motor demands. Here, we investigated within-session changes in instantaneous alpha/mu frequency and magnitude during motor-related BCI calibration using an oscillator-tracking framework based on an extended Kalman filter (EKF). We applied this method to four public EEG datasets spanning motor execution and imagery tasks. Across all datasets, we observed consistent increases in mu instantaneous frequency and magnitude over central sensorimotor regions, indicative of motor engagement and possible training-related neuroplasticity. In contrast, posterior and surrounding cortical areas often showed alpha slowing, suggestive of declining vigilance or cognitive fatigue, or alternatively, resource reallocation via inhibition of task-irrelevant regions. These opposing spatial trends underscore the functional heterogeneity of alpha-band activity across the cortex. Our results highlight the potential of real-time frequency tracking not only to improve decoding accuracy but also to monitor neurophysiological state changes and guide adaptive adjustments in BCI calibration paradigms.},
}

@article {pmid41922362,
year = {2026},
author = {Chen, J and Qi, Y and Wang, Y and Pan, G},
title = {Human-like cognitive generalization for large models via mental representation-guided supervision.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-71267-5},
pmid = {41922362},
issn = {2041-1723},
support = {LR24F020002//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; 624B2127//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Recent advancements in deep neural networks (DNNs), particularly large-scale language models, have demonstrated remarkable capabilities in image and natural language understanding. Although scaling up model parameters with increasing volume of training data has progressively improved DNN capabilities, achieving complex cognitive abilities-such as understanding abstract concepts, reasoning, and adapting to novel scenarios, which are intrinsic to human cognition-remains a major challenge. In this study, we show that mental representation-guided supervised learning, utilizing a small set of brain signals, can effectively transfer human conceptual structures to DNNs, significantly enhancing their comprehension of abstract and even unseen concepts. Experimental results further indicate that the enhanced cognitive capabilities lead to substantial performance gains in challenging tasks, including few-shot/zero-shot learning and out-of-distribution recognition, while also yielding highly interpretable concept representations. These findings highlight that mental representation-guided supervision can effectively augment the complex cognitive abilities of large models, offering a promising pathway toward developing more human-like cognitive abilities in artificial systems.},
}

@article {pmid41922634,
year = {2026},
author = {Zhi, J and Zhang, Q and Li, Y and Zhang, J and Liu, P and Nan, J and Li, Y and Li, D},
title = {Joint MVMD-based optimal feature selection and FW-LS-TWSVM for motor imagery recognition.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-46642-3},
pmid = {41922634},
issn = {2045-2322},
support = {262102211021//the Key Science and Technology Program of Henan Province/ ; 262102211056//the Key Science and Technology Program of Henan Province/ ; 25A520003//the Key Science Research Project of Colleges and Universities in Henan Province/ ; },
abstract = {The Motor Imagery-Brain Computer Interface (MI-BCI) system is an effective approach for motor neurorehabilitation training and human-machine collaborative control. However, the current MI-BCI systems' decoding accuracy and real-time performance still fall short of practical requirements. To address this issue, this study proposes a model combining MVMD-based optimal feature selection and the Fuzzy Weighted Least Squares Twin Support Vector Machine (FW-LS-TWSVM). First, raw data is decomposed into multiple Intrinsic Mode Functions (IMFs) using Multivariate Variational Mode Decomposition (MVMD). Then, Common Spatial Pattern (CSP) is employed to extract features from each IMF, and a feature selection method based on F-statistics is used to adaptively identify the optimal IMFs and their corresponding features, thereby extracting optimal frequency information. Subsequently, this paper introduces, for the first time, the application of the FW-LS-TWSVM to MI-BCI EEG decoding, aiming to enhance the identification efficiency of outliers. The proposed method was validated on two publicly available motor imagery datasets, achieving accuracies of 87.40% and 88.48%, respectively. Comparative analysis revealed that both the frequency band decomposition method and the FW-LS-TWSVM classification model contributed significantly to the decoding accuracy. Compared to traditional frequency band decomposition, SVM, and its improved variants, the proposed method not only achieved higher accuracy but also required relatively less training time. These results indicate that the proposed model can facilitate the development of MI-BCI systems, enhance the behavioral capabilities of healthy individuals, and help improve the quality of life for patients with neurological disabilities.},
}

@article {pmid41922703,
year = {2026},
author = {Aczel, B and Szaszi, B and Clelland, HT and Kovacs, M and Holzmeister, F and van Ravenzwaaij, D and Schulz-Kümpel, H and Hoffmann, S and Nilsonne, G and Kosa, L and Torma, ZA and Abdelfatah, Y and Aberson, CL and Acar, OA and Acem, E and Adamkovic, M and Adamovich, T and Adiasto, K and Ahnström, L and Akil, AM and Al-Busaidi, AS and Al-Hoorie, AH and Albers, CJ and Allen, PJ and Alsalti, T and Altman, M and Alzahawi, S and Ambrosini, E and Anafinova, S and Anand, R and Angerer, M and Angulo-Brunet, A and Antonietti, A and Arato, J and Arenas, A and Aviña, MM and Azevedo, F and Bachl, M and Bago, B and Bahník, Š and Baker, BJ and Balayan, E and Baldwin, CL and Banai, B and Banas, K and Bartoš, F and Baskin, E and Bastiaansen, JA and Bault, N and Bauman, CW and Beazer, QH and Behnke, M and Bendixen, T and Berger, S and Bernard, A and Bernardic, U and Bloom, PA and Boldt, A and Bosch-Rosa, C and Botvinik-Nezer, R and Bouyamourn, A and Bozkurt, O and Brehm, L and Breuer, J and Briggs, R and Brohmer, H and Buchanan, E and Buckenmaier, J and Buckley, J and Buczny, J and Burghart, M and Butt, BH and Byrd, N and Cafarelli, V and Callahan, P and Capitán, T and Carriere, K and Cataldo, AM and Cepaluni, G and Chan, E and Chandler, JJ and Chang, CC and Chen, X and Chen, SS and Chen, F and Chen, H and Chirkov, V and Cialfi, D and Clarke, B and Coelho, SG and Cohen, C and Collins, J and Cook, SW and Corlazzoli, G and Cummins, J and Czymara, C and D'hondt, J and Rosa, AD and Davis, AMB and Davis, CP and Day, MV and De Keyzer, F and de Leeuw, JR and de Vries, TR and Debnath, R and Dechterenko, F and Demiral, EE and Desgroseilliers, M and Dianovics, D and Diveica, V and Dochow-Sondershaus, S and Dohle, S and Dong, L and Dora, J and Dorrough, AR and Dreber, A and Du, H and Edlund, JE and Eerland, A and Efendić, E and Elder, J and Elsherif, MM and Ernst, M and Estrada, E and Eudave, L and Evans, TR and Farrera, A and Ferrouhi, EM and Fiala, L and Fialho, FM and Fiechter, JL and Fišar, M and Flores-Kanter, PE and Folwarczny, M and Fossum, JL and Franco, VR and Freichel, R and Freire, D and Frese, J and Furnas, AC and Gaebler, JD and Gajary, LC and Galang, CM and Ganschow, B and Garrison, SM and Gasiorowska, A and Ponne, BG and Gauriot, R and Geminiani, A and Geraldes, D and Gernsbacher, MA and Giani, C and Glerean, E and Gligorić, V and Gnambs, T and Godefroidt, A and González-Bustamante, B and Goreis, A and Graf-Vlachy, L and Grieder, M and Grigoryev, D and Grinschgl, S and Grüning, DJ and Guassi Moreira, JF and Guichet, C and Gurgand, L and Habibnia, H and Hafenbrack, AC and Hafenbrädl, S and Häffner, C and Hagemeister, F and Haigh, M and Hajdu, N and Hajimoladarvish, N and Hall, JD and Hamjediers, M and Hardwick, RM and Harma, M and Harp, NR and Hartvig, ÁD and Heiberger, RH and Heim, A and Hernæs, Ø and Hernaus, D and Heyman, T and Hicks, J and Hogeveen, J and Höpler, J and Houlihan, SD and Huber, C and Hughes, C and Hummler, T and Huth, K and Ingendahl, M and Ishii, T and Isler, O and Izydorczak, K and Jackson, IR and Jahn, A and Jain, M and Jakubow, A and Jang, D and Jang, J and Jekel, M and Jia, F and Jiménez-Leal, W and Johnson, R and Jones, A and Jungkunz, S and Kačmár, P and Kaiser, C and Kalaycı, Y and Kantorowicz, J and Karabulut, A and Karch, JD and Karimi-Rouzbahani, H and Karl, JA and Kažemekaitytė, A and Kazlou, A and Kekecs, Z and Kim, J and Kirchler, MH and Kiss-Dobronyi, B and Klasmeier, KN and Klein, JW and Koba, C and Kołczyńska, M and Kolias, P and Kolouch Grabovský, M and Korbmacher, M and Korda, Ž and Kowal, M and Kretzschmar, A and Krivoshchekov, V and Krypotos, AM and Kubsch, M and Kunisato, Y and Lacko, D and Landwehr, JR and Lange, M and Lee, H and Lee, D and Lee, S and Lemay, EP and Lempert, D and Leo, A and Lesage, E and Levin, JM and Li, P and Lin, J and Lindsay, L and Lisovoj, D and Liu, M and Liu, S and Liu, T and Iacono, SL and Lodder, P and López-Bueno, R and Lopez-Nicolas, R and Loter, K and Lou, NM and Lovakov, A and Lu, JG and Ludwig, J and Luebber, F and Lukavský, J and Luo, CQ and Lyu, X and Maassen, E and Máčel, M and Mack, ML and Madan, CR and Mädebach, A and Maffly-Kipp, J and Mallinson, DJ and Marchetti, I and Marghetis, T and Marini, MM and Fages, DM and Martínez, M and Martinoli, M and Masiliunas, A and Massoni, S and Mathieu, KC and Mayer, S and Mayer, DJ and Mayer, M and McCormick, EM and McDonough, IM and McGowan, AL and McIntyre, MM and McKee, P and Meier, AN and Meier, PF and Melero, H and Merkle, C and Merz, R and Michaelides, MP and Michaelsen, P and Mikolajczak, G and Mill, W and Millroth, P and Miroshnik, KG and Misiak, M and Mora, YL and Moreau, D and Moreh, C and Morvinski, C and Mushtaq, F and Nagy, T and Nater, C and Naumann, E and Navarrete, G and Nebe, S and Nedderhoff, A and Nennstiel, R and Neugebauer, M and Nicolaisen-Sobesky, E and Nielsen, YA and Niso, G and Nowak, B and Okan, M and Ong, K and Onicas, AI and Oswald, C and Otten, K and Pandey, S and Pantazi, M and Papale, P and Pärnamets, P and Pauer, S and Pavlov, YG and Pawel, S and Peelle, JE and Peetz, HK and Peez, A and Pesciarelli, F and Peterson, BD and Petruželka, B and Petter, J and Pfänder, J and Pfuhl, G and Phillips, J and Pietryka, MT and Pirrone, A and Pit, IL and Plachti, A and Plank, IS and Ploner, M and Poldrack, RA and Pollmann, MMH and Porcher, S and Präg, P and Pua, AAY and Pugel, J and Puri, R and Püski, M and Radkani, S and Raes, L and Rafaï, I and Raiber, K and Rathje, S and Rehms, R and Reshetnikov, M and Reynolds, CJ and Reynolds, JP and Rigaud, K and Rioux, C and Rivera, S and Robertson, O and Román-Caballero, R and Ropovik, I and Röseler, L and Ross, RM and Rotella, A and Rüffer, FF and Rusche, F and Rusconi, M and Russo, I and Sahm, AHJ and Salamon, J and Samahita, M and Sanaei, A and Sangchooli, A and Sarafoglou, A and Scandola, M and Schaak, H and Schaerer, M and Schares, E and Schilling, HT and Schmalz, X and Schmidt, K and Schonberg, T and Schreiner, MR and Schröder, JM and Schubert, AL and Schuetze, B and Schultz, DH and Schulze, L and Schwartz, ST and Schwitter, N and Scoggins, B and Seetahul, Y and Seri, R and Shanks, DR and Shaw, ST and Shaw, J and Shen, Q and Siemroth, C and Sladekova, M and Somo, A and Sondhi, A and Sonmez, B and Spantig, L and Speekenbrink, M and Stamos, A and Stasielowicz, L and Steckermeier, LC and Steinkamp, SR and Stoevenbelt, AH and Street, CNH and Suchow, JW and Sunde, HF and Sundquist, J and Suschevskiy, V and Swain, SD and Szecsi, P and Szekely-Copîndean, RD and Szumowska, E and Tacconelli, A and Talbert, E and Tang, JP and Tendeiro, JN and Testori, M and Toffalini, E and Tomašević, A and Topel, S and Torkkeli, L and Tozzi, L and Traczyk, J and Trinidad, A and Trübutschek, D and Turek, K and Uhlich, M and Uhlmann, EL and Urbanska, K and Van Assche, J and van Assen, MALM and van Dongen, NNN and van Lieshout, K and van Veldhuizen, R and Varga, MA and Vaughn, LA and Venczel, F and Vezzoli, M and Vierus, P and Visalli, A and Voldal, E and Votta, F and Wagenmakers, EJ and Waldendorf, A and Walker, MJ and Wall, MB and Wallen, H and Wang, K and Wang, I and Wang, YA and Weinmann, M and Weiß, M and Westheide, C and Wichman, A and Wilcke, JC and Williams, BJ and Wisniewski, D and Woiczyk, TKA and Woźniak, M and Wright, JD and Youyou, W and Wulff, JN and Yang, T and Yeung, SK and Yuen, KSL and Zawistowski, M and Zein, RA and Zhao, X and Zheng, Z and Zhou, S and Ziller, C and Zimmerman, D and Zogmaister, C and Zultan, R and Fox, N and Errington, TM and Nosek, BA},
title = {Investigating the analytical robustness of the social and behavioural sciences.},
journal = {Nature},
volume = {652},
number = {8108},
pages = {135-142},
pmid = {41922703},
issn = {1476-4687},
support = {//Amsterdam Brain and Cognition/ ; //Czech Science Foundation/ ; //Marie Skłodowska-Curie grant/ ; },
mesh = {*Social Sciences/standards/statistics & numerical data/methods ; *Behavioral Sciences/standards/statistics & numerical data ; Reproducibility of Results ; Humans ; *Research Design/standards ; *Behavioral Research/standards ; },
abstract = {The same dataset can be analysed in different justifiable ways to answer the same research question, potentially challenging the robustness of empirical science[1-3]. In this crowd initiative, we investigated the degree to which research findings in the social and behavioural sciences are contingent on analysts' choices. We examined a stratified random sample of 100 studies published between 2009 and 2018, in which, for one claim per study, at least five reanalysts independently reanalysed the original data. The statistical appropriateness of the reanalyses was assessed in peer evaluations, and the robustness indicators were inspected along a range of research characteristics and study designs. We found that 34% of the independent reanalyses yielded the same result (within a tolerance region of ±0.05 Cohen's d) as the original report; with a four times broader tolerance region, this indicator increased to 57%. Of the reanalyses conducted, 74% reached the same conclusion as the original investigation, 24% yielded no effects or inconclusive results and 2% reported the opposite effect. This exploratory study indicates that the common single-path analyses in social and behavioural research should not be simply assumed to be robust to alternative analyses[4]. Therefore, we recommend the development and use of practices to explore and communicate this neglected source of uncertainty.},
}

*Social Sciences/standards/statistics & numerical data/methods
*Behavioral Sciences/standards/statistics & numerical data
Reproducibility of Results
Humans
*Research Design/standards
*Behavioral Research/standards

@article {pmid41922776,
year = {2026},
author = {Lei, T and Scheid, MR and Flint, RD and Glaser, JI and Slutzky, MW},
title = {Active dissociation of intracortical spiking and high gamma activity.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {41922776},
issn = {1476-4687},
abstract = {Cortical high gamma-band activity (HGA) is used in many scientific investigations[1-18], yet its biophysical source is a matter of debate. Two leading hypotheses are that HGA predominantly represents summed postsynaptic potentials or-more commonly-that it predominantly represents summed local spikes. If the latter were true, the nearest neurons to an electrode should contribute most to HGA recorded on that electrode. To test these hypotheses, here we trained monkeys (Macaca mulatta) to decouple local spiking from HGA on a single electrode using a brain-machine interface. Their ability to decouple them suggested that HGA is probably not generated simply by summed local spiking. Instead, HGA correlated with co-firing of neuronal populations that were widely distributed across millimetres of cortex. The neuronal spikes that contributed more to this co-firing also contributed more to, and preceded, spike-triggered HGA. These results suggest that HGA arises mainly from summed postsynaptic potentials triggered by the synchronous co-firing of widely distributed neurons.},
}

@article {pmid41924028,
year = {2026},
author = {Zheng, Y and Wang, X and Zheng, L and Zhang, H and Wang, F and Zhuo, Y},
title = {Multidimensional dynamic characterization and decoding of finger movements using magnetoencephalography.},
journal = {Imaging neuroscience (Cambridge, Mass.)},
volume = {4},
number = {},
pages = {},
pmid = {41924028},
issn = {2837-6056},
abstract = {The similarity of neural activity in finger movements poses challenges for accurate decoding using many non-invasive imaging techniques. Magnetoencephalography (MEG), with its relatively high spatial resolution, offers the potential to capture the underlying dynamic neural differences. In this study, we recorded MEG signals during single extension movements of the right-hand fingers, examining the time-varying cortical activation patterns across different frequency bands and their contribution to decoding finger movements. Our results demonstrate that signals below 8 Hz not only enable effective movement classification but also reveal millisecond-scale neural activation patterns in the sensorimotor cortex. Furthermore, incorporating the spatiotemporal dynamics of neural activity may enhance decoding performance for fine motor control. These findings highlight the value of integrating temporal, frequency, and spatial dimensions in studying motor neural activity and underscore MEG's potential for broader applications in movement-related neurophysiology and brain-computer interface research.},
}

@article {pmid41915503,
year = {2026},
author = {Wang, H and Jia, Z and Shen, Y and Wang, Z and Li, S and Tang, Z and Shu, K and Hu, F and Wu, D},
title = {SACM: SEEG-Audio Contrastive Matching for Chinese Speech Decoding.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3678858},
pmid = {41915503},
issn = {1558-2531},
abstract = {OBJECTIVE: Speech disorders such as dysarthria and anarthria can severely impair patients' ability to communicate verbally. Speech decoding brain-computer interfaces (BCIs) offer a potential alternative by directly translating speech intentions into spoken words, serving as speech neuroprostheses.

METHODS: This paper reports an experimental protocol for Mandarin Chinese speech decoding BCIs and proposes a contrastive learning-based decoding algorithm termed SEEG and Audio Contrastive Matching (SACM). Stereo-electroencephalography (SEEG) and synchronized audio data were collected from ten patients with drug-resistant epilepsy as they performed a word-level reading task. SACM leverages the cross-modal correlation between neural activity and audio signals to decode matched speech segments.

RESULTS: The proposed framework achieved accuracies significantly exceeding random matching in both isolated-word and continuous speech decoding tasks, and outperformed SEEG-only baselines across seven backbone architectures in the isolated-word setting. Electrode- wise analysis revealed that a single ventral sensorimotor cortex electrode achieved performance comparable to that of the full electrode array. Our code is publicly available.

SIGNIFICANCE: To our knowledge, this is the first work on multimodal decoding for tonal speech BCIs.},
}

@article {pmid41916314,
year = {2026},
author = {Li, XY and Bao, YF and Hu, CC and Dong, Y and Wu, ZY},
title = {Pyramidal signs in Huntington's disease: An early clinical indicator associated with proximity to disease onset.},
journal = {Med (New York, N.Y.)},
volume = {},
number = {},
pages = {101071},
doi = {10.1016/j.medj.2026.101071},
pmid = {41916314},
issn = {2666-6340},
abstract = {BACKGROUND: Previous studies in Huntington's disease (HD) have primarily focused on striatal degeneration, while pyramidal signs remain insufficiently characterized. Understanding the clinical significance of pyramidal signs in HD is crucial for elucidating the disease's pathogenesis.

METHODS: In a cross-sectional cohort, 29 individuals with premanifest HD (pre-HD) and 196 patients with manifest HD underwent standardized neurological examination. Serum neurofilament light-chain (sNfL) levels, clinical assessments, and MRI were obtained. In addition, a prospective longitudinal cohort including 15 individuals with pre-HD and 51 patients with manifest HD was followed up.

FINDINGS: In the cross-sectional cohort, the prevalence of pyramidal signs was 44.8% in individuals with pre-HD and 63.3% in patients with manifest HD. Individuals with pre-HD with pyramidal signs exhibited significantly higher sNfL levels (17.4 vs. 9.3 pg/mL, p = 0.0010) and were closer to predicted motor onset (p = 0.0005) compared with those without pyramidal signs. In the longitudinal analysis, linear mixed-effects models revealed that the rates of disease progression did not differ significantly between pyramidal-sign-positive and -negative patients with manifest HD. Cox regression analysis further indicated that pyramidal signs emerged approximately 13.3 years before the predicted motor onset, in a manner dependent on CAG repeat length.

CONCLUSIONS: The presence of pyramidal signs in pre-HD reflects closer proximity to disease onset, but it lacks prognostic value in manifest HD. These signs typically emerge more than a decade before motor symptom onset and serve as a simple and predictive marker for physicians.

FUNDING: This study was supported by the National Natural Science Foundation of China to Z.-Y.W. (82230062, Beijing).},
}

@article {pmid41916607,
year = {2026},
author = {Xue, Y and Cai, X and Liu, H},
title = {Passive pitch rotation enables optimal vibrational stabilization in hawkmoth forward flight.},
journal = {Journal of the Royal Society, Interface},
volume = {23},
number = {237},
pages = {},
doi = {10.1098/rsif.2025.1011},
pmid = {41916607},
issn = {1742-5662},
support = {//Japan Society for the Promotion of Science/ ; },
mesh = {Animals ; *Flight, Animal/physiology ; *Wings, Animal/physiology ; Vibration ; *Models, Biological ; *Manduca/physiology ; Biomechanical Phenomena ; Rotation ; },
abstract = {Flying insects maintain stable flight through both active control and passive mechanisms that exploit natural wing and body vibrations. One such mechanism, vibrational stabilization, uses high-frequency wing vibrations to create a virtual spring effect that helps insects like hawkmoths stay stable during hovering. In addition, the flexible musculoskeletal system contributes pitch stiffness to add a stabilizing effect that may vary with forward flight speed but has not been fully explored. This study develops a fluid-structure interaction model that integrates the dynamics of an elastic wing hinge with unsteady flapping aerodynamics. We introduce a vibrational stabilization framework to investigate the passive stability of the hawkmoth Manduca sexta across a broad range of forward flight velocities. The framework reveals that natural wing vibrations enhance flight stability at all speeds. At low speeds, vibrational stiffness generates a restorative pitching moment, while at higher speeds, damping effects from wing vibrations dominate. The model shows that biologically realistic hinge stiffness values optimize vibrational stabilization throughout the flight envelope. This flexible-vibrational mechanism significantly improves robustness against external pitch disturbances, reducing reliance on active neural control. These findings offer useful design principles for biomimetic flying robots, potentially simplifying their control architectures.},
}

Animals
*Flight, Animal/physiology
*Wings, Animal/physiology
Vibration
*Models, Biological
*Manduca/physiology
Biomechanical Phenomena
Rotation

@article {pmid41916974,
year = {2026},
author = {Lai, S and Huang, Y and Ma, S and Hao, H and Dai, A and Yan, X and Yang, J and Wang, S and Ren, Q and Zhang, Y and Hu, P and Li, J and Zheng, X and Brosius, J and Deng, C},
title = {FSHR and LHR functional compensation reveals the mechanism and treatment of Ovarian Hyperstimulation Syndrome.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-71338-7},
pmid = {41916974},
issn = {2041-1723},
support = {32270438//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32170498//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Gain-of-function mutations in the human follicle-stimulating hormone receptor (FSHR) cause spontaneous ovarian hyperstimulation syndrome (OHSS), a serious reproductive disorder. However, the molecular physiology and treatment options for OHSS remain elusive. Notably, estrildid finches naturally carry an FSHR variant (Thr449Ala) analogous to the pathogenic mutation in humans yet are resistant to OHSS. Here we show that this resistance stems from significantly reduced luteinizing hormone receptor expression in estrildid ovarian granulosa cells. Furthermore, treatment with the luteinizing hormone receptor antagonist alleviates OHSS symptoms in mouse models. Single-cell RNA transcriptomic reveals functional compensation of the two receptors to regulate estrogen production and vascular permeability, resembling the adaptive mechanisms observed in estrildid finches. Our study unravels the molecular mechanism underlying the physiological adaptation of estrildid ovaries to high FSHR constitutive activity and is a example of how the concept of Darwinian Medicine could be exploited to identify novel drug targets for ovarian hyperstimulation syndrome treatment.},
}

@article {pmid41917049,
year = {2026},
author = {Pollina, L and Struber, L and de Seta, V and Russo, E and Karakas, S and Chabardes, S and Aksenova, T and Charvet, G and Shokur, S and Micera, S},
title = {Decoupling simultaneous motor imagination and execution via orthogonal ECoG neural representations.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-71234-0},
pmid = {41917049},
issn = {2041-1723},
abstract = {The brain coordinates multiple parallel motor programs, ensuring synergy and preventing interference during movements. Yet, performance often degrades when brain-machine interfaces are used during concurrent tasks or ongoing movements. We suggest that latent neural representations may represent a strategy to solve this issue. In this study, we addressed this question using neural signals from a tetraplegic individual with partial residual motor function, implanted with a wireless epidural electrocorticography (ECoG) device. By adapting dimensionality reduction techniques, we found that motor execution and motor imagery span partially overlapping subspaces in mesoscale neural signals, shaped by specific frequency band contributions. Despite substantial shared variance, we show that identifying orthogonal, condition-specific dimensions enables successful decoding of executed and imagined movements, even when performed simultaneously. These findings show that ECoG signals can expose separable neural subspaces, allowing executed and imagined actions to be harnessed independently and in concert. This opens a promising avenue to develop brain-machine interfaces that can simultaneously control multiple external devices or operate alongside natural movements.},
}

@article {pmid41918517,
year = {2026},
author = {Guerrero, AI and Vivanco, C and Pavez, G and Sabat, P and Maldonado, K and Barilari, MF and Quiñones, RA and Carrasco, P and Toro, F and Gutiérrez, J and Sepúlveda, M},
title = {Drivers of body condition in South American sea lion pups along a latitudinal gradient.},
journal = {Conservation physiology},
volume = {14},
number = {1},
pages = {coag018},
pmid = {41918517},
issn = {2051-1434},
abstract = {Body condition is a key proxy of fitness in pinnipeds, reflecting nutritional status and maternal investment. In the South American sea lion (Otaria flavescens), pup growth and survival depend on maternal foraging success, making pup condition a sensitive indicator of local environments. We quantified spatial and interannual variation in pup body condition across five Chilean breeding colonies spanning 21-53°S during the austral summers of 2024 and 2025. We captured 157 live pups (95 males, 62 females), measured morphometrics and calculated a body condition index (BCI = mass/length). To account for seasonal effects, BCI values were standardized to allow comparisons across sites and years. We tested the effects of sex, year, locality and satellite-derived net primary productivity (NPP). Male pups consistently showed higher standardized BCI than females. Locality was the strongest predictor: Isla Marta (southern limit) exhibited significantly higher values than all other sites, followed by Isla Metalqui. Cobquecura, Isla Choros and Punta Lobos showed lower or intermediate values. Year alone had no effect, but a significant locality × year interaction indicated interannual variability in northern colonies, particularly Punta Lobos. NPP was not retained in top-ranked models, suggesting broad-scale productivity does not directly predict pup condition at this resolution. The pronounced latitudinal gradient, with larger, better-conditioned pups at higher latitudes, is consistent with expectations under Bergmann's rule, which refers to the tendency of animals to be larger in colder climates and smaller in warmer ones. These results underscore the combined influence of local ecological conditions, maternal effects and intrinsic sex differences on pup condition and reinforce the value of South American sea lion pups as sentinels of ecosystem variability along the Chilean coast.},
}

@article {pmid41920177,
year = {2026},
author = {Van Damme, S and Mumford, L and Thompson, A and Murphy, C and Ali, T and Hegazi, A and Wang, R and Chang, K and Raza, IA and Chau, T and Kingsnorth, S},
title = {Family Experiences in a Pediatric Clinical Brain-Computer Interface Program: A Qualitative Study.},
journal = {The American journal of occupational therapy : official publication of the American Occupational Therapy Association},
volume = {80},
number = {3},
pages = {},
doi = {10.5014/ajot.2026.051474},
pmid = {41920177},
issn = {0272-9490},
mesh = {Humans ; Child ; Female ; Male ; *Brain-Computer Interfaces/psychology ; Adolescent ; Qualitative Research ; *Children with Disabilities/rehabilitation ; *Occupational Therapy ; *Parents/psychology ; Interviews as Topic ; Adult ; },
abstract = {IMPORTANCE: Brain-computer interfaces (BCIs) are access technologies that can improve the occupational participation of children with disabilities. The research on the experiences of pediatric BCI users and their families is currently limited.

OBJECTIVE: To explore experiences of pediatric BCI use and future expectations through caregiver perspectives.

DESIGN: A qualitative, descriptive study using purposeful sampling and inductive thematic analysis. Investigator triangulation and reflexivity enhanced credibility.

SETTING: Zoom for Healthcare virtual platform.

PARTICIPANTS: Fifteen parents (12 mothers and 3 fathers) of children and youth with disabilities (ages 6-18 yr; 9 females and 6 males) who participated in a recreational BCI program at a pediatric rehabilitation hospital, with the option of additional at-home BCI use, were selected via purposive sampling.

OUTCOMES AND MEASURES: In-depth, semistructured interviews were used to collect data.

RESULTS: Three major themes emerged from the central topic of experiencing play using BCIs: (1) transformative experiences, (2) personalization for success, and (3) future hopes.

CONCLUSIONS AND RELEVANCE: By documenting family experiences with and expectations of BCIs, these findings can guide the development of BCI use in clinical and recreational programs. Occupational therapy practitioners can use the transformative potential of BCI technology to create new pathways for participation and empowerment in the lives of children and youth with disabilities. Plain-Language Summary: Children with complex disabilities often cannot take part in play and recreation. Many activities are not accessible to them. Brain-computer interface (BCI) technologies can help kids play without needing to move or speak. We asked families using BCIs about their experiences. They shared that use of a BCI empowered their child and allowed others to consider them in a new light. Some families enjoyed the programming, and others found the activities too simple over time. Many families shared that BCI headsets were uncomfortable. A better design for kids with disabilities is important. Families hope that BCIs will help kids control their environment in the future. Occupational therapists should understand how kids and families feel about using BCIs. This study helps occupational therapists learn about the benefits of BCIs in their practice and the challenges of using them.},
}

Humans
Child
Female
Male
*Brain-Computer Interfaces/psychology
Adolescent
Qualitative Research
*Children with Disabilities/rehabilitation
*Occupational Therapy
*Parents/psychology
Interviews as Topic
Adult

@article {pmid41920265,
year = {2026},
author = {Wang, C and Lu, J and Fu, H and Feng, X and Xu, Z and Luo, P and Yang, B and He, Q and Yang, X},
title = {Glycyrrhizic Acid Alleviates Osimertinib-Induced Cutaneous Toxicity by Inhibiting Keratinocyte Apoptosis and Inflammation.},
journal = {Phytotherapy research : PTR},
volume = {},
number = {},
pages = {},
doi = {10.1002/ptr.70310},
pmid = {41920265},
issn = {1099-1573},
support = {No. 82274018//National Natural Science Foundation of China/ ; 2020YFE0204300//National Key Research and Development Program of China/ ; },
abstract = {Osimertinib is a primary treatment for patients with EGFR-mutated non-small cell lung cancer. But a significant number of patients receiving Osimertinib treatment suffer from cutaneous toxicity, which includes symptoms such as rash, itching, and hair loss. This study aims to help clinical patients suffering from cutaneous toxicity to improve their quality of life. Mice treated with 50 mg/kg/day Osimertinib for 42 days exhibited different levels of cutaneous toxicity. PI/Annexin-V apoptosis assay and western blotting were used to assess keratinocyte apoptosis and DNA damage. Osimertinib upregulated inflammatory factors including CCL2, CCL27, and IL18. Glycyrrhizic acid (GA) is the most important active ingredient in licorice with pharmacological effects such as anti-inflammatory, antiviral, and anti-apoptotic. Due to its rich bioactivity, the research about GA has always been popular. However, the effects of it on relieving cutaneous toxicity have not been studied yet. We have explored the therapeutic effects and mechanisms of GA on keratinocytes and C57BL/6 mice. Thirty milligrams/kg/day of GA could effectively reduce the frequency and severity of cutaneous toxicity induced by Osimertinib, restore epidermal thickness in mice, reduce DNA damage, and lower the expression levels of inflammatory factors. Our results indicated that GA could potentially mitigate the cutaneous toxicity caused by Osimertinib, which could position it as a promising adjunct in clinical practice.},
}

@article {pmid41920737,
year = {2026},
author = {Jude, JJ and Haro, S and Levi-Aharoni, H and Hashimoto, H and Acosta, AJ and Card, NS and Wairagkar, M and Brandman, DM and Stavisky, SD and Williams, ZM and Cash, SS and Simeral, JD and Hochberg, LR and Rubin, DB},
title = {Decoding intended speech with an intracortical brain-computer interface in a person with long-standing anarthria and locked-in syndrome.},
journal = {Cell reports},
volume = {45},
number = {4},
pages = {117162},
doi = {10.1016/j.celrep.2026.117162},
pmid = {41920737},
issn = {2211-1247},
abstract = {Intracortical brain-computer interfaces (iBCIs) for decoding intended speech have provided individuals with ALS and severe dysarthria an intuitive method for high-throughput communication. These advances have been demonstrated in individuals who are still able to vocalize and move speech articulators. Here, we decoded intended speech from an individual with long-standing anarthria, locked-in syndrome, and ventilator dependence due to advanced symptoms of ALS. We found that phonemes, words, and higher order language units could be decoded well above chance. While sentence decoding accuracy was below that of demonstrations in participants with dysarthria, we attained an extensive characterization of neural signals underlying speech in a person with locked-in syndrome and identify directions for future improvement. These include closed-loop speech imagery training and decoding linguistic (rather than phonemic) units from neural signals in middle precentral gyrus to augment decoding at the sentence level. These results demonstrate that usable speech decoding from motor cortex may be feasible in people with anarthria and ventilator dependence.},
}

@article {pmid41909202,
year = {2026},
author = {Ming, G and Pei, W and Tian, S and Chen, X and Gao, X and Wang, Y},
title = {A High-Speed Visual BCI Based on Hybrid Frequency-Phase-Space Encoding and High-Density EEG Decoding.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {7},
number = {},
pages = {0555},
pmid = {41909202},
issn = {2692-7632},
abstract = {Brain-computer interface (BCI) technology establishes a direct communication pathway between the brain and external devices. Current visual BCI systems suffer from insufficient information transfer rates (ITRs) for practical use. Spatial information, a critical component of visual perception, remains underexploited in existing systems because the limited spatial resolution of recording methods hinders the capture of the rich spatiotemporal dynamics of brain signals. This study proposed a hybrid frequency-phase-space encoding method, integrated with high-density electroencephalogram (EEG) recordings, to develop high-speed BCI systems. EEG data were recorded using a 256-channel standard cap, and 4 electrode configurations comprising 66, 32, 21, and 9 parieto-occipital electrodes, extracted from 256-, 128-, and 64-channel caps (abbreviated as 66/256, 32/128, 21/64, and 9/64), were systematically compared. In the classical frequency-phase encoding the 40-target BCI paradigm, the 66/256, 32/128, and 21/64 electrode configurations brought theoretical ITR increases of 83.66%, 79.99%, and 55.50% over the traditional 9/64 setup. In the proposed frequency-phase-space encoding 200-target BCI paradigm, these increases climbed to 195.56%, 153.08%, and 103.07%, respectively. The online BCI system achieved an average actual ITR of (472.72 ± 15.06) bits per minute. Taken together, these findings clarify how the spatiotemporal encoding strategy and electrode density jointly determine achievable ITRs and provide quantitative design guidelines for future high-speed visual BCIs.},
}

@article {pmid41909203,
year = {2026},
author = {Ma, Y and Zhang, C and Nie, F and Qin, H and Zhang, Q and Zhang, Y and Yang, L and Liu, L},
title = {Construction, Control, and Application of Cyborg Animal Composed of Biological and Electromechanical Systems.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {7},
number = {},
pages = {0486},
pmid = {41909203},
issn = {2692-7632},
abstract = {The limitations of biohybrid and mechanical robots, including insufficient control accuracy, limited flexibility, long-term stability, and endurance, have spurred considerable research interest in cyborg animals, which leverage the innate locomotion capabilities, physiological systems, and natural intelligence of organisms to perform tasks with high adaptability, superior performance, and extended endurance. This study provides a comprehensive overview of cyborg animals within the framework of animal taxonomy, summarizing the current state of research from a zoological perspective. Subsequently, the effect of different control techniques on the locomotion performance of cyborg animals was examined, with a special emphasis on 2 prominent research areas: brain-computer interfaces and muscle-receptor electrical stimulation. In addition, the role of advances in electronic backpack design and navigation control algorithms in enabling closed-loop control and applications, including swarm robotics, environmental exploration, and human-machine interaction, is also introduced, offering valuable insights for developing cyborg animals. This study highlights 4 critical aspects essential for the future advancement of cyborg animals by synthesizing recent progress and clarifying technical distinctions: adaptation between control strategies and animals, biocompatibility and stability of electronic backpacks, construction of interactive hybrid robotic systems, and ethical and welfare considerations related to the experimental animals, with the hope of facilitating the optimization and application of cyborg animal systems.},
}

@article {pmid41911327,
year = {2026},
author = {Zhang, R and Zhou, W and Wang, Y and Liu, G},
title = {STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {229},
pages = {},
doi = {10.3791/70425},
pmid = {41911327},
issn = {1940-087X},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Imagination/physiology ; *Software ; },
abstract = {Motor imagery-brain-computer interfaces (MI-BCIs) have demonstrated significant potential for neurorehabilitation and cognitive neuroscience. However, a standardized and reproducible MI-EEG workflow for configurable spatial-temporal-frequency feature analysis remains limited, and many pipelines require complex configuration and parameter tuning with limited interpretability, hindering practical deployment and generalization. To address these challenges, an STFEEG-Tool was developed to provide a user-friendly, standardized, and interpretable workflow for EEG decoding in MI paradigms. STFEEG-Tool enables fine-grained configuration of temporal, frequency-band, and spatial segmentation, allowing the extraction of multiscale MI features. The toolbox integrates multiple feature extraction algorithms, including common spatial patterns (CSP) and divergence-based CSP (div-CSP), along with various classifiers, such as support vector machines (SVMs), Ridge Regression Classifier, and Lasso Classifier. A dynamic time-frequency scalp topographical map is provided to summarize spatial patterns across time-frequency segments and support interpretation of decoding results. Overall, STFEEG-Tool serves as a reproducible and extensible platform for fine-grained MI-EEG analysis, facilitating the translation of fine-grained decoding pipelines into practical, user-oriented applications.},
}

*Brain-Computer Interfaces
*Electroencephalography/methods
Humans
*Imagination/physiology
*Software

@article {pmid41912561,
year = {2026},
author = {Elwasify, F and Shaaban, E and Abdelmoneem, RM},
title = {EEG imagined speech neuro-signal preprocessing and deep learning classification.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {},
pmid = {41912561},
issn = {2045-2322},
}

@article {pmid41912889,
year = {2026},
author = {Kim, HR and Kang, D and Kim, DH and Jeong, B and Kim, K and Park, BS and Yang, HR and Kwon, HM and Koch, M and Young, CN and Lee, BJ and Kim, JG},
title = {TonEBP as a key regulator of hypothalamic leptin signaling and resistance.},
journal = {Cellular and molecular life sciences : CMLS},
volume = {},
number = {},
pages = {},
doi = {10.1007/s00018-026-06150-z},
pmid = {41912889},
issn = {1420-9071},
support = {Research Assistance Program (2021) in the Incheon National University//Incheon National University/ ; },
}

@article {pmid41914668,
year = {2026},
author = {Li, D and Wang, J and Xu, J and Zhang, Y},
title = {Feature alignment and enhancement network with guided tuning for non-stationary EEG classification.},
journal = {Journal of neural engineering},
volume = {23},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ae512e},
pmid = {41914668},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods/classification ; *Brain-Computer Interfaces/classification ; *Imagination/physiology ; *Neural Networks, Computer ; *Brain/physiology ; },
abstract = {Objective.Electroencephalogram (EEG) signal variability caused by external factors and subject differences limits the adaptation of motor imagery (MI) classification models in brain-computer interfaces (BCIs). Existing domain alignment methods often inadequately utilize critical source and target domains information, leading to negative transfer problems. This paper proposes a Feature Alignment and Enhancement Framework for cross-domain MI-EEG classification to address these limitations.Approach.First, by aligning the covariance matrices of the source and target domains, the spatial distributions of the two domains are preliminarily aligned, establishing a consistent foundation for feature mapping. Second, a conditional domain adversarial network optimizes cross-domain representations, reducing distribution discrepancies while enhancing discriminability. Finally, this paper introduces an EEG feature-based guided tuning method. This method extracts high-confidence features from both the source and target domains and generates centroid features to construct cross-domain feature banks. The input feature representations are dynamically optimized by attending to the relationships between centroid features, thus enhancing the model's adapt-ability to target domain tasks.Main results.Experimental data show that in the four-class MI task of the BCI Competition IV-2a dataset, the cross-session and cross-subject model classification accuracies were 76.89% and 57.91%, respectively. The model achieved accuracy rates of 84.61% and 82.78% on the BCI Competition IV-2b datasets and the High Gamma Datasets, respectively, as well as 84.09% and 70.81%.Significance.The proposed framework effectively mitigates cross-domain variations, providing a reliable solution for cross-session and cross-subject MI-EEG classification.},
}

Humans
*Electroencephalography/methods/classification
*Brain-Computer Interfaces/classification
*Imagination/physiology
*Neural Networks, Computer
*Brain/physiology

@article {pmid41772194,
year = {2026},
author = {Bagnato, S and Boccagni, C and Bonavita, J},
title = {Eye movements as indicators of awareness in prolonged disorders of consciousness: a scoping review of behavioral and neural evidence.},
journal = {Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology},
volume = {47},
number = {3},
pages = {},
pmid = {41772194},
issn = {1590-3478},
abstract = {BACKGROUND: In prolonged disorders of consciousness (DoC), visual fixation and smooth pursuit are among the earliest signs of re-emerging awareness. Yet, because their physiology relies heavily on brainstem–cerebellar circuitry, it remains debated whether these ocular behaviors invariably reflect consciousness or, after severe brain injury, also arise from reflexive/subcortical mechanisms.

METHODS: To address this question, we conducted a PRISMA-ScR scoping review of PubMed/MEDLINE and Scopus to verify the behavioral and neural correlates of fixation and pursuit. Ocular behavior evidence was classified as cognitively mediated, not cognitively mediated or indeterminate for each study.

RESULTS: We included 24 studies spanning clinical assessment, instrumented eye‑tracking, neurophysiology, neuroimaging and brain–computer interfaces. Clinically, fixation and pursuit were the most common signs accompanying transition from unresponsive wakefulness syndrome to the minimally conscious state; still, in isolation their specificity remained undetermined. Eye-tracking improved detection and, under explicit, goal‑directed tasks, demonstrated task‑contingent responses, occasionally prompting diagnostic reclassification. Neural measures showed that task‑locked ocular behaviors frequently co‑occurred with cortical responses, whereas some studies—especially for visual fixation—found no task‑linked neural correlates.

CONCLUSIONS: Overall, fixation and pursuit are sensitive, although context‑dependent, indicators of awareness. Isolated visual signs—especially simple fixation—warrant cautious interpretation: absent convergent neural signatures may reflect either limited sensitivity to minimal consciousness or genuinely reflexive/subcortical control. Further studies are needed to quantify cognitive involvement in cases of isolated fixation or pursuit.},
}

@article {pmid41904270,
year = {2026},
author = {Mei, S and He, N and He, W and Yan, J and Chu, C and Zeng, Z and Yan, F and Li, D and Pu, Y and Zhang, C and Kong, XZ},
title = {Pallidal and subthalamic stimulations modulate inter-hemispheric interaction and asymmetry in Parkinson's disease.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {41904270},
issn = {1476-5578},
support = {32400882//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Substantial asymmetries of motor dysfunction are evident in patients with Parkinson's disease (PD), the mechanisms of which remain largely unexplored. This study investigated how deep brain stimulation (DBS) targeting the globus pallidus interna (GPi) and subthalamic nucleus (STN) modulates characteristics of hemispheric lateralization in PD patients, with particular emphasis on motor asymmetries and hemispheric integration (via homotopic functional connectivity) and segregation (via hemispheric asymmetry in connectivity). Resting-state functional magnetic resonance imaging (fMRI) and Unified Parkinson's Disease Rating Scale (UPDRS) III scores were analyzed from 55 PD patients who underwent either bilateral GPi- or STN-DBS. Both targets produced significant improvements in motor function. Notably, stimulation effects on motor asymmetry depend on patients' baseline asymmetry direction (DBS OFF): STN-DBS consistently reduced asymmetry in the leftward-asymmetry patients, whereas GPi-DBS has stronger effects in rightward patients. In both cases, stimulation led to a more symmetric pattern. Beyond motor outcomes, motor gains were associated with changes in homotopic connectivity in the lateral occipital region, overlapping the extrastriate body area, suggesting a compensatory role of visual networks. These findings highlight the contribution of the visual networks to motor improvement and reveal target-dependent effects of DBS on both motor asymmetry and non-motor cognitive domains.},
}

@article {pmid41905427,
year = {2026},
author = {Yan, H and Chai, B and Yu, M and Chen, J and Wang, Y and Zhang, Z and Yang, W and Lu, J and Yang, B and He, Q and Luo, P and Yang, X},
title = {Duvelisib upregulates p27 expression and leads to intestinal damage via the NEDD4L/CK1ε axis.},
journal = {Biochemical pharmacology},
volume = {},
number = {},
pages = {117939},
doi = {10.1016/j.bcp.2026.117939},
pmid = {41905427},
issn = {1873-2968},
abstract = {Duvelisib-induced enterotoxicity is one of the most noteworthy clinical concerns, yet due to its unclear mechanism, effective intervention strategies remain lacking. Here, we demonstrated that duvelisib increased the protein stability of casein kinase 1ε (CK1ε) by down-regulating the expression of NEDD4 like E3 ubiquitin protein ligase (NEDD4L), which in turn induced p27-dependent G0/G1 cell cycle arrest in small intestinal epithelial cells and led to intestinal injury. Meanwhile, we found that β, β-dimethyl-acryl-alkannin (ALCAP2), which could upregulate the protein level of NEDD4L, had protective effect against the toxicity in vivo. Collectively, our findings identified the excessive accumulation of CK1ε as a key cause of duvelisib-induced enterotoxicity, and reduction in the protein stability of CK1ε represents a potential therapeutic strategy to prevent duvelisib-induced enterotoxicity.},
}

@article {pmid41905536,
year = {2026},
author = {Guo, W and Zhao, X and Xu, G and Wang, Z and Zhou, T and Zhou, W and Liu, H and Xu, T and Hu, H},
title = {Real-time channel selection for enhanced steady-state visual evoked potentials online brain-computer interface systems.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110757},
doi = {10.1016/j.jneumeth.2026.110757},
pmid = {41905536},
issn = {1872-678X},
abstract = {BACKGROUND: In recent years, researchers have actively developed new brain-computer interface (BCI) systems while seeking optimal channel combinations. Although channel selection has advanced significantly in BCI systems, few studies have specifically addressed channel selection for steady-state visual evoked potentials-BCI (SSVEP-BCI).

NEW METHOD: This study proposes an online SSVEP-BCI system with dynamic channel selection during experiments. The proposed method constructs a multidimensional feature framework that incorporates signal energy, stability, and inter-channel correlation. The abnormality of each feature is then quantified to generate corresponding anomaly scores. These anomaly scores are integrated through a hierarchical decision mechanism to produce a comprehensive channel quality score. Based on this score, bad channels are precisely identified and removed, enabling training-free and dynamic channel selection.

RESULTS: The proposed multi-Adaptive priority-based SSVEP channel selection (MAPS-CS) method achieves the best performance compared with three existing channel selection methods. For the standard filter bank canonical correlation analysis (FBCCA), the accuracy of the proposed method was increased for four stimulus durations. Compared with the channel ensemble (CE) method, the proposed method achieved accuracy improvements of 3.5%, 4.1%, 4.4%, and 6.5% for stimulus durations of 2 s, 1.5 s, 1 s, and 0.5 s, respectively.

The proposed method provides the best performance in FBCCA compared with CE, binary harmony search (BHS) and TOP-K local optimization channel selection (TOP-K-LOCS).

CONCLUSIONS: These results confirm that the system can effectively detect bad channels in laboratory-based online experiments.},
}

@article {pmid41906020,
year = {2026},
author = {Xue, W and Lu, W and Zhang, X and Wang, H},
title = {Fixed-time formation behavior control for unmanned ground vehicle-manipulators.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-43223-2},
pmid = {41906020},
issn = {2045-2322},
support = {360302022401//the Efficient space-time coordination of swarm aircraft/ ; 82260364//the National Natural Science Foundation of China/ ; 360302042406//the Research on Motion Control Mechanism and Regulation Strategy for Brain Computer Interface/ ; },
abstract = {Unmanned ground vehicle-manipulators (UGVMs), which integrate mobility and dexterous manipulation, are increasingly deployed in complex environments. However, their formation control is challenged by nonholonomic constraints, external disturbances, and multi-task conflicts. This paper proposes a fixed-time formation behavior control (Fixed-FBC) method UGVMs operating in static obstacle environments under external disturbances and system uncertainties. The approach introduces a nonholonomic null-space behavioral control (N-NSBC) framework that integrates fixed-time stability strategy for rapid convergence, systematic incorporation of nonholonomic constraints to inherently avoid local minima by resolving yaw-position coupling, and transformation of multi-objective coordination into unified velocity commands. To address dynamic uncertainties, an adaptive fixed-time tracking controller is developed that employs adaptive laws to estimate unknown system parameters in real-time while utilizing sliding mode techniques to reject external disturbances. Simulation results demonstrate a [Formula: see text] reduction in settling time compared to conventional methods, along with effective coordination of formation maintenance, obstacle avoidance, and manipulator control.},
}

@article {pmid41907360,
year = {2026},
author = {Corti, S and Ferrucci, R and Angotzi, GN and Arighi, A and Brambilla, P and Buijs, E and Carrafiello, G and Crippa, M and De Momi, E and Del Debbio, P and Folgieri, R and Giachetti, M and Giannì, AB and Magnoni, W and Marceglia, S and Massimini, M and Stigliani, D and Stocco, M and Tanga, A and Ottoboni, L and Barbieri, S},
title = {Minds and machines: AI's transformative role in human identity and medicine.},
journal = {Digital health},
volume = {12},
number = {},
pages = {20552076251390473},
pmid = {41907360},
issn = {2055-2076},
abstract = {The application of artificial intelligence (AI) in medicine presents unprecedented potential but challenges traditional notions of human identity and medical ethics. Through a systematic literature review and thematic analysis of the interdisciplinary conference "Minds and Machines", we examine the transformative impact of AI on medical practice, consciousness, and human enhancement considering the clinical, ethical, and regulatory contexts. Successful integration of AI requires a delicate balance between innovation and safeguarding the human component in healthcare through robust ethical frameworks, enhanced medical education, and person-centered implementation.},
}

@article {pmid41907799,
year = {2026},
author = {Khan, H and Nazeer, H and Mirtaheri, P},
title = {Open access individual finger movement dataset with fNIRS.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1747655},
pmid = {41907799},
issn = {1662-5161},
}

@article {pmid41901024,
year = {2026},
author = {Mac-Auliffe, D and Surapaneni, A and Millán, JDR},
title = {Reopening Motor Learning Windows: Targeted Re-Engagement of Latent Pathways via Non-Invasive Neuromodulation.},
journal = {Life (Basel, Switzerland)},
volume = {16},
number = {3},
pages = {},
pmid = {41901024},
issn = {2075-1729},
abstract = {Motor recovery after stroke, spinal cord injury, or traumatic brain injury reflects relearning rather than simple restitution, as surviving circuits retain plastic potential that can be re-engaged through temporally precise stimulation. This review synthesizes convergent findings demonstrating that Hebbian and spike-timing-dependent mechanisms govern reorganization across cortical, striatal, and spinal levels. Leveraging these timing rules to shape excitability during receptive network states enables durable changes in connectivity and behavior. This effect depends on temporal precision, physiological state, and reinforcement-not stimulus intensity alone-within plasticity windows regulated by metaplastic mechanisms that determine whether Hebbian processes are expressed. Together, these principles define a translational framework for neurorehabilitation, emphasizing biomarker-guided, adaptive, and scalable strategies aligned with intrinsic rules of experience-dependent reorganization.},
}

@article {pmid41901531,
year = {2026},
author = {Turan, S and Çıray, RO},
title = {Comparative Effects of BCI-Based Attention Training, Methylphenidate, and Citicoline on Attention and Executive Function in School-Age Children: A Quasi-Experimental Study.},
journal = {Medicina (Kaunas, Lithuania)},
volume = {62},
number = {3},
pages = {},
pmid = {41901531},
issn = {1648-9144},
support = {THIZ-2023-1670//Bursa Uludağ Üni̇versi̇tesi̇/ ; },
mesh = {Humans ; *Methylphenidate/therapeutic use/pharmacology ; Child ; Male ; Female ; *Executive Function/drug effects ; *Attention Deficit Disorder with Hyperactivity/drug therapy/therapy/psychology ; *Attention/drug effects ; *Cytidine Diphosphate Choline/therapeutic use/pharmacology ; Central Nervous System Stimulants/therapeutic use ; },
abstract = {Background and Objectives: Attention-Deficit Hyperactivity Disorder (ADHD) is a neurological condition characterized by cognitive task difficulty, impulsivity, hyperactivity and loss of attention. This study compared four approaches for improving attention and related skills in school-age children: COGO Brain-Computer Interface (BCI)-based attention training, methylphenidate, citicoline, and their combined use. Materials and Methods: A quasi-experimental pre-post design was used with four groups: COGO + methylphenidate (n = 44), COGO + citicoline (n = 44), COGO-only (n = 44), and citicoline-only (n = 42). Children completed baseline and post-treatment assessments, including the CPT-3 and several behavioral and emotional rating scales. Analyses included paired t-tests, ANCOVA, and repeated-measures ANOVA, adjusting for age. Results: The strongest improvements appeared in the COGO + methylphenidate group, especially in measures of sustained attention and reaction time consistency. The COGO + citicoline group showed clearer gains in inhibitory control (fewer commission errors) and reductions in anxiety/emotional symptoms. The COGO-only and citicoline-only groups showed little to no measurable change. Despite these within-group patterns, there were no significant differences between groups on CPT-3 outcomes or behavioral/emotional scales. Conclusions: This trial showed that combining COGO-based attention training with medication is both feasible and well-tolerated in children with attention and executive function difficulties. Moreover, the integrated approach produced measurable improvements across attentional performance and behavioral regulation domains.},
}

Humans
*Methylphenidate/therapeutic use/pharmacology
Child
Male
Female
*Executive Function/drug effects
*Attention Deficit Disorder with Hyperactivity/drug therapy/therapy/psychology
*Attention/drug effects
*Cytidine Diphosphate Choline/therapeutic use/pharmacology
Central Nervous System Stimulants/therapeutic use

@article {pmid41901953,
year = {2026},
author = {Hu, C and Liu, Q and Xu, C and Li, G and Li, Y},
title = {Dual-Manifold Contrastive Learning for Robust and Real-Time EEG Motor Decoding.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {6},
pages = {},
pmid = {41901953},
issn = {1424-8220},
support = {2022ZD0210400//STI2030-Brain Science and Brain-Inspired Intelligence Technology/ ; 2023QN10Y209//GUANGDONG TALENTS PROGRAM/ ; 2024B1212010010//Guangdong Provincial Key Laboratory of Multimodality Non-Invasive Brain-Computer Interfaces/ ; XMHT20230115002//Shenzhen Strategic Emerging Industry Support Plans/ ; \#2023A1515011478//Guangdong Basic and Applied Basic Research Foundation/ ; ZR2021ZD40//Major Basic Research Project of Shandong Natural Science Foundation/ ; pdjh2024a135//Guangdong Climbing Program Special Funds/ ; },
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Algorithms ; *Machine Learning ; Signal Processing, Computer-Assisted ; Brain/physiology ; Male ; Adult ; },
abstract = {Brain-computer interfaces (BCIs) have great potential for consumer electronics, as they enable the decoding of brain activity to control external devices and assist human-computer interaction. However, current decoding methods for BCIs face several challenges, such as low accuracy, poor stability under electrode shift, and slow processing for real-time use. In this paper, we propose a hybrid decoding framework designed to address the challenges of current EEG decoding methods. Our method combines manifold learning with contrastive learning. The core of our method lies in a dual-manifold model that uses non-negative matrix factorization (NMF) and a contrastive manifold learning framework to extract clear and useful features from brain signals. To improve decoding stability, we introduce a joint training strategy that enhances feature learning. Furthermore, the system is optimized for real-time interaction, reducing the system latency to 100 ms. We collect EEG signals from 15 subjects performing motor execution tasks and 10 subjects performing motor imagery tasks to construct a motor EEG dataset. On this dataset, the proposed method achieves superior decoding performance, reaching F1-scores of 0.7382 for the motor imagery tasks and 0.8361 for the motor execution tasks. Furthermore, the method maintains robustness even with reduced electrode counts and altered spatial distributions, highlighting its potential as a decoding solution for reliable and portable BCI systems.},
}

*Electroencephalography/methods
Humans
*Brain-Computer Interfaces
Algorithms
*Machine Learning
Signal Processing, Computer-Assisted
Brain/physiology
Male
Adult

@article {pmid41902016,
year = {2026},
author = {Althobaiti, M},
title = {Unsupervised Dynamic Time Warping Clustering for Robust Functional Network Identification in fNIRS Motor Tasks.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {6},
pages = {},
pmid = {41902016},
issn = {1424-8220},
mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; Brain-Computer Interfaces ; Cluster Analysis ; Male ; Adult ; Female ; Foot/physiology ; Hand/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Functional near-infrared spectroscopy (fNIRS) is a valuable non-invasive modality for brain-computer interfaces (BCIs), but robust signal interpretation is challenged by the significant temporal variability of the hemodynamic response. Standard linear methods, such as Pearson correlation, often fail to capture functional connectivity when signals exhibit temporal jitter. This study validates an unsupervised Dynamic Time Warping (DTW) clustering framework to robustly identify motor networks from fNIRS data by accommodating non-linear temporal shifts. We analyzed a public fNIRS dataset (N = 30) across right-hand (RHT), left-hand (LHT), and foot tapping (FT) tasks. A robust preprocessing pipeline was implemented, including Wavelet Motion Correction and Common Average Referencing (CAR) to remove artifacts and global systemic noise. The core method involved computing Z-score normalized DTW distance matrices, followed by hierarchical clustering. To validate the framework, we benchmarked it against a standard Pearson Correlation method. Results show that the unsupervised DTW framework achieved a network identification accuracy of 53.17%, significantly outperforming the standard Pearson correlation benchmark (48.06%) with a statistically significant difference (p < 0.05). The framework successfully detected distinct, somatotopically correct modulations: superior-medial activation during foot tapping and lateralized activation during hand tapping. These findings demonstrate that unsupervised DTW clustering is a robust, data-driven approach that outperforms conventional linear methods in capturing functional networks during motor tasks, showing significant potential for next-generation asynchronous BCIs.},
}

Humans
Spectroscopy, Near-Infrared/methods
Brain-Computer Interfaces
Cluster Analysis
Male
Adult
Female
Foot/physiology
Hand/physiology
Algorithms
Signal Processing, Computer-Assisted
Young Adult

@article {pmid41902060,
year = {2026},
author = {Hristov, H and Minchev, Z and Shoshev, M and Kancheva, I and Koleva, V and Vakarelsky, T and Dimitrov, K and Prodanov, D},
title = {Sensing Cognitive Responses Through a Non-Invasive Brain-Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {6},
pages = {},
pmid = {41902060},
issn = {1424-8220},
support = {101086815//European Union's Horizon Europe program under grant agreement VIBraTE/ ; },
mesh = {Humans ; *Cognition/physiology ; Heart Rate/physiology ; Male ; Electroencephalography ; Female ; Adult ; *Brain-Computer Interfaces ; Young Adult ; Galvanic Skin Response/physiology ; },
abstract = {Cognitive stress, also known as mental workload, constitutes a central topic within the field of psychophysiology due to its role in modulating attention, autonomic regulation, and stress reactivity. Furthermore, it bears direct relevance to practical monitoring systems that employ non-invasive sensing techniques. This study investigates whether a multimodal, non-invasive measurement setup can detect systematic physiological differences between Resting periods and short episodes of cognitive load within the same individuals. Additionally, it explores the capacity of such a system to differentiate tasks characterized by varying cognitive demands. A sequential, within-subject protocol was employed, comprising five consecutive phases (rest 1, Stroop, rest 12, subtraction, rest 3), during which five modalities were recorded concurrently: EEG, heart rate (HR), galvanic skin response (GSR), facial surface temperature, and oxygen saturation (SpO2). Beyond phase-wise inspection of time-series data, an exploratory assessment of similarity across participants was conducted using correlation coefficients. The maximum cross-participant correlations observed were 0.88 (HR), 0.90 (GSR), 0.83 (facial temperature), and 0.77 (SpO2); however, these correlations were used only as exploratory descriptors of inter-individual similarity and did not imply a significant phase effect. For inferential analysis, phase-wise epoch means were evaluated through one-factor repeated-measures ANOVA. The heart rate exhibited a robust main effect of phase (F(4, 32) = 10.5862, p_GG = 0.01044, ηp[2] = 0.5696), with higher HR observed during cognitive load epochs (e.g., 77.841 ± 11.777 bpm at rest 1 versus 83.926 ± 14.532 bpm during subtraction). The relatively large standard deviation reflects variability between subjects rather than variability within epochs. Regarding processed baseline-referenced GSR, the omnibus phase effect was not statistically significant under the conservative Greenhouse-Geisser correction; therefore, GSR was interpreted as exploratory in this dataset. Facial temperature and SpO2 likewise did not show statistically significant omnibus phase effects under Greenhouse-Geisser correction (e.g., SpO2: p_GG = 0.1209). EEG-derived measures provide supplementary central evidence of task engagement; entropy variations within an approximate dynamic range of 0.2 to 0.8 were observed, and the α/θ ratios demonstrated nearly a twofold distinction between rest and cognitive load epochs across different leads.},
}

Humans
*Cognition/physiology
Heart Rate/physiology
Male
Electroencephalography
Female
Adult
*Brain-Computer Interfaces
Young Adult
Galvanic Skin Response/physiology

@article {pmid41902355,
year = {2026},
author = {Yu, Y and Liu, W and Ju, S and He, L and Chen, N and Chernov, AN and Zhang, J and Mao, J and Liu, G},
title = {Global Trends in Research of Brain-Computer Interfaces in Neuroscience From 2014 to 2023: A Bibliometric Analysis.},
journal = {CNS neuroscience & therapeutics},
volume = {32},
number = {4},
pages = {e70851},
doi = {10.1002/cns.70851},
pmid = {41902355},
issn = {1755-5949},
support = {202510631036//National Training Program of Innovation and Entrepreneurship for Undergraduates/ ; },
mesh = {*Brain-Computer Interfaces/trends ; *Bibliometrics ; *Neurosciences/trends ; Humans ; *Biomedical Research/trends ; },
abstract = {AIM: Brain-computer interfaces (BCIs) represent a promising technology for addressing neurological disorders, with growing research interest globally. This study aimed to map global research trends in BCI neuroscience from 2014 to 2023 via bibliometric analysis, identifying key contributors and hot topics to inform future research.

METHODS: A total of 2386 publications related to BCIs in neuroscience were retrieved from the Web of Science Core Collection. Bibliometric analyses, including co-authorship networks, keyword co-occurrence, and burst detection, were performed using VOSviewer, R, and CiteSpace. The study analyzed publications by country, institution, journal, author, and keyword to map the landscape of global research activity.

RESULTS: China emerged as the country with the highest number of publications, and the International Journal of Neural Engineering was the most productive journal. Co-authorship analysis revealed collaborative networks across global institutions, while keyword co-occurrence and burst detection identified electroencephalography (EEG), rehabilitation, and motor cortex as the most prominent research hotspots in recent years.

CONCLUSION: This analysis provides a reference for researchers and data support for future studies, clarifying the global landscape and priorities in BCI neuroscience research.},
}

*Brain-Computer Interfaces/trends
*Bibliometrics
*Neurosciences/trends
Humans
*Biomedical Research/trends

@article {pmid41903116,
year = {2026},
author = {Zhang, E and Shotbolt, M and Abdel-Mottaleb, M and Chen, S and Andre, V and Tian, J and Shulgach, J and Murphy, M and Noga, B and Liang, P and Griffin, D and Weber, D and Pardo, M and Pane, S and Khizroev, S},
title = {Magnetoelectric Nanoparticle-Based Wireless Brain-Computer Interface: Underlying Physics and Projected Technology Pathway.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e24329},
doi = {10.1002/advs.202524329},
pmid = {41903116},
issn = {2198-3844},
support = {N66001-19-C-4019//Defense Advanced Research Projects Agency/ ; ECCS-211082//National Science Foundation/ ; 5P30240139-02/NH/NIH HHS/United States ; },
abstract = {Magnetoelectric nanoparticles (MENPs) provide a fully wireless and minutely invasive platform for bidirectional brain-computer interfaces (BCIs) by locally transducing magnetic fields into electric fields, and vice versa. The achievable spatial and temporal resolutions are governed by the control of magnetic field energy at the nanoparticle level. Since the introduction of the MENP concept a decade and a half ago, independent studies have demonstrated MENP-mediated neural activation in vitro and in vivo, establishing a strong proof of concept for wireless neuromodulation. In contrast, MENP-based neural recording remains largely theoretical, with existing models indicating that in vivo implementation is feasible. However, progress toward scalable and reliable MENP-based BCIs is hindered by an incomplete understanding of the nonlinear physics governing MENP operation and nanoparticle-cell interactions. This study addresses this gap by developing a comprehensive theoretical framework that explicitly incorporates nonlinear effects and correlates neuromodulation predictions with available experimental data. The analysis identifies nanoparticle properties and magnetic field amplitude and frequency as key performance determinants. Properly engineered MENPs are predicted to enable deepbrain and cortical neuromodulation and recording with submillimeter spatial resolution and millisecondscale temporal precision, offering a pathway toward clinically viable BCIs without implanted electrodes or genetic modification.},
}