@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.},
}

@article {pmid41891008,
year = {2026},
author = {Johnson, SN and Rybář, M and Greenspon, CM and Moore, DD and Downey, JE and Dekleva, BM and Hatsopoulos, NG},
title = {Limb state accounts for differences between motor imagery and action in motor cortex.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.03.13.26348353},
pmid = {41891008},
abstract = {The motor cortex is involved not only in movement execution but also in motor imagery, a process leveraged by decoding algorithms for brain-computer interface (BCI) applications in individuals with severe motor impairments. Previous work has shown that population activity during execution and imagery occupies partially overlapping regions of neural state space while also engaging distinct subspaces unique to each motor state, suggesting that decoders trained in one condition may not generalize to the other. Moreover, movement execution likely includes neural representations of both motor output and proprioceptive feedback, which themselves may occupy distinct or overlapping regions of neural state space. To explore these distinctions, we studied two individuals with incomplete spinal-cord injuries and partial residual proximal arm function performing a center-out reaching task in three conditions: motor imagery, active execution, and passive movement. We found that decoders trained on neural activity from motor imagery failed to generalize to either active or passive movements. In contrast, decoders trained on active or passive movement activity generalized reciprocally. Population analysis revealed distinct dynamics depending on limb state and proprioceptive feedback, which could explain this lack of generalization. These results suggest that motor imagery engages motor cortical representations distinct from those recruited during actual movements, either actively or passively generated, with important implications for the design of BCI decoders.},
}

@article {pmid41891367,
year = {2026},
author = {Cho, W and Jung, M and Chung, TD},
title = {Janus Synapses as Modular Neurointerfaces.},
journal = {ACS applied materials & interfaces},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsami.6c01266},
pmid = {41891367},
issn = {1944-8252},
abstract = {The nervous system processes information by translating chemical signals into electrical and biochemical responses, ultimately driving biological adaptation and computation. Chemical synapses are the primary communication channels between neurons, operating with remarkable speed and precision to enable complex neural information processing. In this perspective, we focus on these native signaling principles and explore the potential of synaptic structures as neurointerface modules. Building on this view, we argue that electrodes can be engineered to function as complementary synaptic terminals, enabling neuron-device communication that directly leverages the chemical, electrical, and biological logic of neural systems. In particular, we discuss whether synaptic cell adhesion molecules can be harnessed as synaptogenic cues to redefine electrode surfaces as functional synaptic counterparts of neuronal terminals, and we examine the distinctive properties and emerging applications of such interfaces.},
}

@article {pmid41892049,
year = {2026},
author = {Ghosh, S and Bhuvanakantham, R and Sindhujaa, P and Harishita, PB and Mohan, A and Gulyás, B and Máthé, D and Padmanabhan, P},
title = {A Cloud-Aware Scalable Architecture for Distributed Edge-Enabled BCI Biosensor System.},
journal = {Biosensors},
volume = {16},
number = {3},
pages = {},
doi = {10.3390/bios16030157},
pmid = {41892049},
issn = {2079-6374},
mesh = {*Biosensing Techniques ; *Brain-Computer Interfaces ; *Cloud Computing ; Humans ; Electroencephalography ; Signal Processing, Computer-Assisted ; },
abstract = {BCI biosensors enable continuous monitoring of neural activity, but existing systems face challenges in scalability, latency, and reliable integration with cloud infrastructure. This work presents a cloud-aware, real-time cognitive grid architecture for multimodal BCI biosensors, validated at the system level through a full physical prototype. The system integrates the BioAmp EXG Pill for signal acquisition with an RP2040 microcontroller for local preprocessing using edge-resident TinyML deployment for on-device feature/inference feasibility coupled with environmental context sensors to augment signal context for downstream analytics talking to the external world via Wi-Fi/4G connectivity. A tiered data pipeline was implemented: SD card buffering for raw signals, Redis for near-real-time streaming, PostgreSQL for structured analytics, and AWS S3 with Glacier for long-term archival. End-to-end validation demonstrated consistent edge-level inference with bounded latency, while cloud-assisted telemetry and analytics exhibited variable transmission and processing delays consistent with cellular connectivity and serverless execution characteristics; packet loss remained below 5%. Visualization was achieved through Python 3.10 using Matplotlib GUI, Grafana 10.2.3 dashboards, and on-device LCD displays. Hybrid deployment strategies-local development, simulated cloud testing, and limited cloud usage for benchmark capture-enabled cost-efficient validation while preserving architectural fidelity and latency observability. The results establish a scalable, modular, and energy-efficient biosensor framework, providing a foundation for advanced analytics and translational BCI applications to be explored in subsequent work, with explicit consideration of both edge-resident TinyML inference and cloud-based machine learning workflows.},
}

*Biosensing Techniques
*Brain-Computer Interfaces
*Cloud Computing
Humans
Electroencephalography
Signal Processing, Computer-Assisted

@article {pmid41892610,
year = {2026},
author = {Yang, S and Zhang, L and Cheng, Y and Zheng, Y and Zheng, S and Guo, J and Zheng, L},
title = {STHMA: Decoupling Spatio-Temporal Dynamics in EEG via Hybrid State Space Modeling.},
journal = {Brain sciences},
volume = {16},
number = {3},
pages = {},
doi = {10.3390/brainsci16030267},
pmid = {41892610},
issn = {2076-3425},
support = {2023B0303040002//Guangdong S&T Program/ ; },
abstract = {Background/Objectives: Decoding affective states from Electroencephalography (EEG) signals is fundamental to non-invasive Brain-Computer Interfaces. Despite recent advances, accurate recognition is impeded by the inherently non-stationary nature of physiological signals and the entanglement of spatio-temporal dynamics within high-dimensional recordings. While Transformers excel at global modeling, they often neglect the continuous dynamical properties of neural signals and suffer from quadratic complexity. Methods: In this paper, we propose the Spatio-Temporal Hybrid Mamba-Attention (STHMA), a framework designed to explicitly disentangle and model EEG dynamics via linear-complexity State Space Models. First, to incorporate domain knowledge, we introduce a Dual-Domain Physics-Aware Embedding module. This module fuses learnable temporal convolutions with explicit frequency-domain spectral features, ensuring fidelity to neurophysiological principles. Second, we propose a novel Decoupled Spatial-Temporal Scanning strategy. By dynamically reconfiguring the serialization of the data tensor, our model strictly separates the learning of instantaneous functional connectivity from the tracking of emotional state evolution, thereby preventing the structural collapse common in 1D sequence models. Results: Extensive experiments on the FACED and SEED-V datasets demonstrate that the STHMA achieves state-of-the-art performance, significantly exceeding the random chance baselines (11.11% for 9-class FACED and 20.00% for 5-class SEED-V). Conclusions: The results validate that combining Physics-Aware Embeddings with decoupled state-space modeling offers a scalable and effective paradigm for EEG emotion recognition.},
}

@article {pmid41893390,
year = {2026},
author = {Hu, C and Wang, X and Pan, T and Dong, Y and Zhang, X},
title = {Application of Brain-Computer Interactive Rehabilitation Training Combined With a Gait Robot: A Randomized Controlled Trial.},
journal = {Archives of physical medicine and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.apmr.2026.02.008},
pmid = {41893390},
issn = {1532-821X},
abstract = {OBJECTIVE: To explore the effects of brain-computer interactive (BCI) rehabilitation training combined with gait robot (GR) on gait recovery in patients with hemiplegia after stroke.

DESIGN: Randomized controlled trial.

SETTING: Hospital settings across the Shandong Provincial Third Hospital.

PARTICIPANTS: A total of 120 eligible subjects were enrolled and randomly allocated, via random-number table, into 4 equal groups (n=30 each): control (routine training), BCI, GR, and BCI-GR (BCI combined with GR training).

INTERVENTIONS: Each group received its designated training once daily, 6 d/wk, for 8 consecutive weeks.

MAIN OUTCOME MEASURES: Fugl-Meyer Assessment-Lower Extremity (FMA-LE), Berg Balance Scale (BBS), Functional Ambulation Category (FAC), integrated electromyography (IEMG), and stride parameters. Assessments were conducted at baseline, 4 wk, and 8 wk.

RESULTS: After 8 wk, all interventions-BCI, GR, and BCI-GR-significantly improved lower-limb function, muscle activity, and gait compared with control (all P<.01). FMA-LE increased by +12.93 (BCI-GR), +12.16 (BCI), and +12.07 (GR) versus +8.20 in control; BBS improved by +14.53, +13.63, and +14.30 versus +9.53; FAC improved by +1.73, +1.73, and +1.77 versus +1.37. IEMG of tibialis anterior and gastrocnemius increased most in BCI-GR (+0.156 and +0.063), significantly higher than BCI and GR (P<.05), whereas co-contraction ratio decreased most in BCI-GR (-18.59%; P<.05). Stride parameters (step frequency, step length, step width, and walking speed) improved in all intervention groups, with BCI-GR showing greater gains in step frequency (+25.90) and step width reduction (-12.87) versus single interventions (P<.05). No other significant differences among intervention groups were observed at week 8.

CONCLUSIONS: BCI and GR interventions significantly improve lower-limb motor function, muscle activation, and gait in poststroke patients. Combined BCI-GR training accelerates early functional recovery and more effectively enhances muscle activation patterns compared with single interventions, providing a promising strategy for poststroke rehabilitation.},
}

@article {pmid41895045,
year = {2026},
author = {Sabourin, CJ and Lomber, SG and Negandhi, J and Cushing, SL and Papsin, BC and Gordon, KA},
title = {Assessment of neural and MAP level asymmetries in a large cohort of children with bilateral cochlear implants.},
journal = {Hearing research},
volume = {475},
number = {},
pages = {109620},
doi = {10.1016/j.heares.2026.109620},
pmid = {41895045},
issn = {1878-5891},
abstract = {Bilateral cochlear implants (BCIs) are provided to children who are deaf in both ears to restore binaural/spatial hearing, but interaural stimulation mismatches can limit potential benefits. This study aimed to: 1) investigate how the programming of stimulation parameters in BCI users differs between bilateral pairs of electrodes, and (2) evaluate the impact of sequential implantation and mismatched array types on asymmetries. A mixed effects modeling analysis assessed cochlear implants (CI) stimulation parameters and peripheral neural responses retrospectively collected (September 2003-July 2022) in 542 children with BCIs (n = 157 sequentially implanted, n = 385 simultaneously implanted, n = 465 with matched perimodiolar arrays, and n = 77 with one perimodiolar and one straight array (mismatched)). Peripheral neural measures were similar between BCIs although asymmetries in auditory nerve thresholds were measured in children implanted sequentially with mismatched arrays (mean(SE) = -2.02(0.90) dB, p < 0.05). Children with sequential BCIs had greater maximum stimulation levels (C-levels) in the first implanted ear than the second (mean(SE) = 1.64(0.03) dB, p < 0.0001) whereas C-levels were similar between ears in children with simultaneous BCIs (mean(SE) = 0.07(0.01) dB, p < 0.0001). Programming asymmetries were comparable between matched and mismatched arrays in sequential BCIs (F(1, 149.9) = 0.01, p = 0.92) and simultaneous BCIs (F(1, 377) = 0.001, p = 0.98). Overall, programming asymmetries reflect implantation sequence more than array type differences. Similar neural responses bilaterally suggest programming asymmetries arise from central effects of prior unilateral hearing, consistent with the aural preference syndrome.},
}

@article {pmid41898344,
year = {2026},
author = {Zhao, X and Li, M and Wang, Q and Deng, L and Zhao, L and Yu, H and Li, X and Deng, W and Guo, W and Li, T and Wei, W},
title = {Individualized DTI-ALPS Identifies Phase-Specific Glymphatic Dysfunction in Early-Stage Bipolar Disorder.},
journal = {Biomedicines},
volume = {14},
number = {3},
pages = {},
doi = {10.3390/biomedicines14030699},
pmid = {41898344},
issn = {2227-9059},
support = {82230046//Key Project of National Natural Science Foundation of China/ ; 20241203A14//Agriculture and Social Development of Hangzhou Science and Technology Bureauand/ ; 2024ZY01010//the Zhejiang Central Guiding Local Technology Development/ ; 2025HZGF10//the Construction Fund of Key Medical Disciplines of Hangzhou/ ; 2025HZZD14//the Construction Fund of Key Medical Disciplines of Hangzhou/ ; 2024E10107//the Zhejiang Provincial Key Laboratory of Clinical and Basic Research on Mental Disorders/ ; 82501795//the National Natural Science Foundation of China/ ; },
abstract = {Background: The glymphatic system, essential for brain waste clearance and neuroimmune regulation, remains underexplored in the context of bipolar disorder (BD) among young populations. Methods: Using diffusion tensor image analysis along the perivascular space (DTI-ALPS), we compared ALPS indices derived from the conventional FSL-based (cFSL) pipeline with those from the individualized ALPS (iALPS) pipeline. A cohort of young adults comprising 77 individuals with BD and 289 healthy controls was analyzed to evaluate methodological consistency and to identify disorder-specific alterations in glymphatic function. Results: The two pipelines showed only moderate agreement (Lin's concordance correlation coefficient = 0.52-0.60), suggesting that differences in ROI placement strategies significantly affect ALPS estimation. While the cFSL pipeline detected no group differences, the iALPS pipeline identified a trend-level reduction in ALPS index in patients with BD during depressive episodes, particularly in the right hemisphere (p = 0.036, uncorrected, FDR-adjusted p = 0.071). No significant glymphatic alterations were observed in individuals with early-stage BD. Conclusions: These findings suggest that glymphatic dysfunction in psychiatric disorders may be phase-specific on illness. The use of individualized and automated analytical strategies, such as the iALPS pipeline, appears to enhance sensitivity to subtle, state-related brain changes that conventional methods may overlook. This methodological advancement provides a more biologically informed framework for future large-scale and longitudinal studies aimed at elucidating the role of glymphatic function in the pathophysiology of psychiatric disorders.},
}

@article {pmid41899754,
year = {2026},
author = {Daube, A and Lima-Carmona, YE and Hernández Solís, DG and Contreras-Vidal, JL},
title = {A Systematic Review and Meta-Analysis of EEG, fMRI, and fNIRS Studies on the Psychological Impact of Nature on Well-Being.},
journal = {International journal of environmental research and public health},
volume = {23},
number = {3},
pages = {},
doi = {10.3390/ijerph23030377},
pmid = {41899754},
issn = {1660-4601},
support = {1757949//U.S. National Science Foundation/ ; 2137255//U.S. National Science Foundation/ ; },
mesh = {Humans ; *Electroencephalography ; Spectroscopy, Near-Infrared ; *Magnetic Resonance Imaging ; *Nature ; *Brain/physiology/diagnostic imaging ; },
abstract = {Exposure to nature has been associated with benefits to human well-being, commonly evaluated using standardized psychological assessments and, more recently, neuroimaging modalities such as Electroencephalography (EEG), functional Magnetic Resonance Imaging (fMRI), and functional Near-Infrared Spectroscopy (fNIRS). This systematic review and meta-analysis addresses the following questions. (1) How is the impact of nature on well-being studied using psychological and neuroimaging modalities and what does it reveal? (2) What are the challenges and opportunities for the deployment of wearable neuroimaging modalities to understand the impact of nature on the brain's health and well-being? A search on PubMed, IEEE Xplore, and ClinicalTrials.gov (March 2024) identified 33 studies combining neuroimaging and psychological assessments during exposure to real, virtual or imagined natural environments. Studies were analyzed by tasks, populations, neuroimaging modality, psychological assessment, and methodological quality. Most studies were conducted in Asia (n = 23 or 70%). Healthy participants were the dominant target population (70%). In total, 61% of the studies were conducted in natural settings, while 39% used visual imagery. EEG was the most common modality (82%). STAI (n = 8) and POMS (n = 8) were the most common psychological assessments. Only seven studies included clinical populations. Two separate meta-analyses of nine studies with explicit experimental and control groups revealed a significant positive effect of nature exposure on psychological outcomes (Hedges' g = 0.30; p = 0.0021), and a larger effect on neurophysiological outcomes (Hedges' g = 0.43; p = 0.0004), both with moderate-to-high heterogeneity. Overall, exposure to nature was associated with reductions in negative emotions in clinical populations. In contrast, healthy populations showed a more balanced psychological response, with nature exposure being associated with both increases in positive emotions and reductions in negative emotions. Notably, 88% of the studies presented methodological weaknesses, highlighting key opportunities for future neuroengineering research on the neural and psychological effects of nature exposure.},
}

Humans
*Electroencephalography
Spectroscopy, Near-Infrared
*Magnetic Resonance Imaging
*Nature
*Brain/physiology/diagnostic imaging

@article {pmid41899838,
year = {2026},
author = {Tibermacine, A and Naidji, I and Tibermacine, IE and Mamen, L and Rabehi, A and Habib, M},
title = {EEG-TriNet++: A Transformer-Guided Meta-Learning Framework for Robust and Generalizable Motor Imagery Classification.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {13},
number = {3},
pages = {},
doi = {10.3390/bioengineering13030307},
pmid = {41899838},
issn = {2306-5354},
abstract = {Motor imagery (MI) classification using EEG signals is central to brain-computer interfaces but remains challenging due to low signal-to-noise ratio, non-stationarity, and high inter-subject variability. We introduce EEG-TriNet++, a multi-branch deep learning architecture that enhances both classification accuracy and cross-subject generalization. The model integrates three complementary components: convolutional spatial-spectral encoders for channel-wise and frequency-specific patterns, bidirectional LSTMs to model temporal dynamics, and a Transformer head for global relational reasoning. A patchwise tokenization strategy and neural architecture search optimize the trade-off between efficiency and representational capacity. To address individual differences, a model-agnostic meta-learning (MAML) module enables rapid adaptation to new users with limited data. Evaluated on two public MI datasets under within-subject and leave-one-subject-out (LOSO) protocols, EEG-TriNet++ achieves 79.1% and 78.6% accuracy in within-subject tasks, and 72.4% and 71.3% in LOSO settings. Ablation studies validate the contribution of each module, and comparisons with state-of-the-art methods demonstrate consistent performance gains under identical conditions.},
}

@article {pmid41899962,
year = {2026},
author = {Xu, Z and Yu, Z},
title = {Entropy-Based Dual-Teacher Distillation for Efficient Motor Imagery EEG Classification.},
journal = {Entropy (Basel, Switzerland)},
volume = {28},
number = {3},
pages = {},
doi = {10.3390/e28030310},
pmid = {41899962},
issn = {1099-4300},
support = {2022ZD0211700//The Technology Innovation 2030/ ; },
abstract = {Motor imagery (MI) EEG classification is a key component of noninvasive brain-computer interfaces (BCIs) and often must satisfy strict latency constraints in online or edge deployments. Although ensembling can reliably improve MI decoding accuracy, its inference cost grows linearly with the number of ensemble members, making it impractical for low-latency applications. To address these issues, we propose an entropy-based dual-teacher distillation framework that transfers ensemble teacher knowledge to a single deployable backbone. From an information theoretic perspective, two failure modes are common in small and noisy MI datasets: elevated predictive entropy (noisy decisions) and large fluctuation across late training epochs (unstable convergence and unreliable checkpoint selection). Thus, we introduce an exponential moving average (EMA) teacher with entropy-gated activation as a low-pass filter in parameter space to reduce the student's prediction noise. In addition, a two-stage cosine annealing schedule is employed to suppress late-stage oscillations and improve the robustness of final checkpoint selection. Experiments on two public MI benchmarks (BCI Competition IV-2a and IV-2b) with three representative backbones (EEGNet, ShallowConvNet, and ATCNet) under the subject dependent protocol show consistent accuracy gains over the ensemble teacher and strong distillation baselines. On IV-2a, our method achieves an average accuracy of 0.7713 across the backbones, surpassing both the original models (0.7222) and the corresponding ensembles (0.7482); on IV-2b, it achieves 0.8583 versus 0.8432 (original) and 0.8529 (ensemble).},
}

@article {pmid41900188,
year = {2026},
author = {Zhang, S and Shan, J and Lv, S and Liu, Y and Miao, J and Liu, Z and Ning, E and Xu, Z and Liu, J and Wang, M and Jin, H and Cai, X and Song, Y},
title = {A Comb-Shaped Flexible Microelectrode Array for Simultaneous Multi-Scale Cortical Recording.},
journal = {Micromachines},
volume = {17},
number = {3},
pages = {},
doi = {10.3390/mi17030301},
pmid = {41900188},
issn = {2072-666X},
support = {62333020, 62121003, T2293730, T2293731, 62471291, 62171434, 62501572, 62374004//National Natural Science Foundation of China/ ; 2021ZD02016030//Major Program of Scientific and Technical Innovation 2030/ ; 8091A170201//Joint Foundation gram of the Chinese Academy of Sciences/ ; F252069//Natural Science Foundation of Beijing/ ; },
abstract = {High-resolution, multi-modal neural interfaces are essential for advancing systems neuroscience and brain-computer interface technologies. This study designed and fabricated a 128-channel comb-shaped flexible micro-electrode array. The device integrates a biocompatible Parylene substrate with a flexible thin-film microprobe array, enabling simultaneous recording of electrocorticography (ECoG), intracortical local field potentials (LFP), and neuronal action potentials (spikes) from the cortical surface and superficial layers. Microelectrode sites were modified with platinum black nanoparticles, significantly reducing impedance. In vivo experiments in rats demonstrated the array's ability to capture high-fidelity signals across different recording depths. Key findings included the acquisition of opposing LFP trends and polarity reversals between adjacent channels, reflecting local microcircuit dynamics. The array also reliably recorded neural activity during audiovisual cross-modal sensory stimulation. These results validate the device as an effective tool for multi-scale electrophysiology, successfully balancing high spatial resolution and signal quality with minimal tissue invasiveness, thereby offering significant potential for fundamental research and neural engineering applications.},
}

@article {pmid41900229,
year = {2026},
author = {Shang, L and Liu, J and Lv, S and Jiang, L and Liu, Y and Hua, S and Luo, J and Cai, X},
title = {From Physical Replacement to Biological Symbiosis: Evolutionary Paradigms and Future Prospects of Auditory Reconstruction Brain-Computer Interfaces.},
journal = {Micromachines},
volume = {17},
number = {3},
pages = {},
doi = {10.3390/mi17030343},
pmid = {41900229},
issn = {2072-666X},
support = {62121003, T2293730, T2293731, 62333020, 62171434, and 62471291//National Natural Science Foundation of China/ ; F252069//Natural Science Foundation of Beijing/ ; 2022YFC2402501, 2022YFB3205602//National Key Research and Development Program of China/ ; No.PTYQ2024BJ0009//Scientific Instrument Developing Project of the Chinese Academy of Sciences/ ; 2021ZD0201600//Major Program of Scientific and Technical Innovation 2030/ ; },
abstract = {Auditory Brain-Computer Interfaces (BCIs) constitute the vital intervention for profound sensorineural hearing loss where the auditory nerve is compromised, yet their clinical efficacy remains restricted by substantial biological bottlenecks and limited spectral resolution. This review critically examines the evolutionary paradigm of auditory restoration, tracing the transition from static physical replacement to dynamic biological symbiosis. We systematically analyze physiological barriers across cochlear, brainstem, and cortical levels, elucidating how rigid interfaces provoke chronic tissue responses and why linear encoding protocols fail in distorted central tonotopy. The article synthesizes emerging methodologies in material science, demonstrating how soft, bio-integrated electronics and biomimetic topologies effectively address mechanical impedance mismatches. Furthermore, the trajectory of neural encoding is evaluated, highlighting the paradigm shift from traditional envelope extraction to deep learning-driven non-linear mapping and adaptive closed-loop neuromodulation. Finally, the potential of high-resolution modulation techniques, including optogenetics and sonogenetics, alongside AI-facilitated intent perception for active listening, is assessed. It is concluded that future neuroprostheses must evolve into symbiotic systems capable of seamlessly integrating with neural plasticity to enable high-fidelity cognitive reconstruction.},
}

@article {pmid41883146,
year = {2026},
author = {Akbar, TF and Jimenez-Rodriguez, CA and Biktimirova, R and Hermes, I and Kurth, T and Pham, MD and Tsurkan, M and Friedrichs, J and Morgan, FLC and Kleemann, H and Guskova, O and Freudenberg, U and Fratzl, P and Werner, C and Tondera, C and Minev, IR},
title = {Conductive Hydrogels for Exogenous Sensing and Cell Fate Control.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e72866},
doi = {10.1002/adma.72866},
pmid = {41883146},
issn = {1521-4095},
support = {101125081/ERC_/European Research Council/International ; 518476867//German Research Foundation/ ; },
abstract = {Next generation technologies linking living systems to computers will require materials built on biology, an approach that may address persistent challenges in stable and multimodal information exchange. Here, we present a semi-synthetic hydrogel, designed to emulate key features of native extracellular matrix (ECM) while offering electrically tunable functionality. We engineer interactions between sulfated glycosaminoglycans (sGAGs) and a semiconducting organic polymer (poly(3,4-ethylenedioxythiophene), PEDOT) within a soft hydrogel network (PEDOT:sGAGh). We demonstrate control over the material's nanoarchitecture, electrochemical behavior, and biomolecular interactions. In particular, PEDOT:sGAGh exhibits affinity for bioactive proteins, including growth factors, and allows their release or retention to be modulated by low-voltage stimulation. This enables electrical control over macromolecular cues for cell differentiation, a capability not found in natural ECM or conventional conductive hydrogels. These functions are achieved with ultra-low PEDOT content (≈1 wt.%), preserving the hydrogel's tissue-like softness and high water content. The PEDOT:sGAGh material can be integrated as a bioactive coating on electrodes, or into 3D organic electrochemical transistors (OECTs). Our results position PEDOT:sGAGh as a versatile platform for realizing biohybrid circuits that bridge molecular signaling and solid-state electronics, thus paving the way for brain-machine interfaces that operate beyond purely electrical modes of interaction.},
}

@article {pmid41883491,
year = {2026},
author = {Zhang, T and Zhang, R and Zeng, X and Zeng, M and Xu, Y and Xiong, Y and Zhang, G and Guo, D and Yao, D},
title = {A new BCI paradigm based on biological brain - digital twin brain dialogue.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {70},
pmid = {41883491},
issn = {1871-4080},
abstract = {Brain-computer interface (BCI) establishes a bidirectional pathway between the brain and external devices. Its applications fall into two main categories: utilizing the brain as a controller (e.g., for prosthetics) or as a modulation target (e.g., for cognitive regulation). Progress in BCI is constrained by two core bottlenecks: in brain control, limited understanding of neural coding mechanisms restricts improvements in the accuracy and robustness of encoding/decoding algorithms; in brain regulation, one-size-fits-all regulatory strategies struggle to address significant individual variability, resulting in heterogeneous therapeutic responses. Inspired by neuroscience advances, this perspective proposes a new biological brain - digital twin brain based BCI (BDBCI) paradigm. Here, the biological brain acts as an empirical anchor and ultimate validation platform, while a high-fidelity digital twin brain (DTB) serves as a theoretical inference engine and virtual testbed. Specifically, experimental induction is applied to the biological brain to distill preliminary conclusions, such as brain-behavior mappings and brain-stimulation causal relationships, which are then used to construct and calibrate the DTB model. Subsequently, on the DTB platform, large-scale model deduction is conducted to validate and deepen these preliminary insights mechanistically, thereby optimizing control/regulation parameters or informing the parameter ranges for the next round of experimental induction and model deduction. Through this BDBCI paradigm, we aim to advance BCI research from empirical trial-and-error toward a new era of model-driven, predictable, and explainable precision science.},
}

@article {pmid41886100,
year = {2026},
author = {Zou, G and Chen, L and Tan, H and Zeng, F and Dong, A and Li, G and Fu, J and Yu, K and Du, L and Liu, Q and Chen, X and Wang, H},
title = {A GRASS-guided phased progressive brain-computer interface approach for post-infarction hand motor recovery.},
journal = {Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology},
volume = {47},
number = {4},
pages = {},
pmid = {41886100},
issn = {1590-3478},
support = {82102665//the National Natural Science Foundation of China/ ; 2024CZ010198//the 2024 Huizhou Science and Technology Bureau Scientific Research Project/ ; 20231345//the 2023 Guangdong Provincial Administration of Traditional Chinese Medicine Scientific Research Project/ ; 21YF1404600//the Shanghai Sailing Program/ ; },
}

@article {pmid41886201,
year = {2026},
author = {Jihen, S and Karmani, S and Belwafi, K and Jemmali, M and Djemal, R},
title = {Filter bank CSP with Riemannian weighting for disability-centric motor imagery brain computer interface.},
journal = {Brain informatics},
volume = {},
number = {},
pages = {},
doi = {10.1186/s40708-026-00295-0},
pmid = {41886201},
issn = {2198-4018},
support = {2502150165//University of Sharjah/ ; },
abstract = {Brain-computer interfaces (BCIs) were initially created to help individuals with disabilities control devices and communicate without muscle movement. Today, BCIs are used for prosthetic control, cognitive enhancement, and neurological rehabilitation. The BCI system depends on analyzing electroencephalogram (EEG) signals captured from the brain. Decoding these EEG signals is a complex process that combines multiple algorithms to extract meaningful information from these intricate and noisy signals. One of the most popular techniques is the Common Spatial Patterns (CSP), which helps preserve useful and sensitive information. This paper presents an optimized extension of the CSP model for extracting EEG data features in a multiclass setting using Riemannian geometry-based weighting. The use of weighting based on Riemannian geometry enhances the robustness of covariance matrix computation, thereby decreasing the influence of noise that can significantly distort the mean of covariance matrices in the traditional CSP method. The proposed approach is also extended by the integration of a multi-band filter bank, providing a more detailed examination of EEG signals. Three classifiers, Linear Discriminant Analysis (LDA), Random Forest Classifier (RFC), and Multi-Layer Perceptron (MLP), are employed to differentiate features across four motor imagery tasks. LDA achieves an accuracy of 80.40%, while MLP and RFC reach 80.02% and 80.90%, respectively. The results obtained using a majority vote combining the decisions of the three classifiers are 81.83% for accuracy and Recall, 82.74% for precision, and 81.87% for F1-score. The proposed architecture is evaluated using the BCI Competition IV set 2a dataset, proving its effectiveness in EEG signal classification for BCI applications.},
}

@article {pmid41886379,
year = {2026},
author = {Obaid, A and Hanna, ME and Huang, SW and Hu, YT and Jáidar, O and Nix, W and Ding, JB and Melosh, NA and Wu, YW},
title = {Ultrasensitive measurement of brain penetration mechanics and blood vessel rupture with microscale probes.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {123},
number = {13},
pages = {e2529147123},
doi = {10.1073/pnas.2529147123},
pmid = {41886379},
issn = {1091-6490},
support = {NS014861//HHS | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; Seed Grant//SU | Wu Tsai Neurosciences Institute, Stanford University (Wu Tsai Neurosciences Institute)/ ; Startup Fund//Academia Sinica (AS)/ ; 114-2321-B-001 -005//National Science and Technology Council (NSTC)/ ; gift fund//GG gift fund/ ; 113-2321-B-001-012//National Science and Technology Council (NSTC)/ ; },
mesh = {Animals ; *Brain/blood supply/physiology ; Microelectrodes ; *Electrodes, Implanted/adverse effects ; Rats ; *Blood Vessels ; Biomechanical Phenomena ; Male ; Brain-Computer Interfaces ; },
abstract = {Microscale electrodes, on the order of 10 to 100 µm, are rapidly becoming critical tools for neuroscience and brain-machine interfaces for their high channel counts and spatial resolution, yet the mechanical details of how probes at this scale insert into brain tissue are largely unknown. Here, we performed quantitative measurements of the force and compression mechanics together with real-time microscopy for in vivo insertion of a systematic series of microelectrode probes as a function of diameter (7.5 to 100 µm and rectangular Neuropixels) and tip geometry (flat, angled, and electrochemically sharpened). These results elucidated the role of tip geometry, surface forces, and mechanical scaling with diameter. Surprisingly, the insertion force postpia penetration was constant with distance and did not depend on tip shape. Real-time microscopy revealed that at small enough lengthscales (<25 µm), blood vessel rupture and bleeding during implantation could be entirely avoided. This appears to occur via vessel displacement, avoiding capture on the probe surface which led to elongation and tearing for larger probes. We propose a three-zone model to account for the probe size dependence of bleeding, and provide mechanistic guidance for probe design.},
}

Animals
*Brain/blood supply/physiology
Microelectrodes
*Electrodes, Implanted/adverse effects
Rats
*Blood Vessels
Biomechanical Phenomena
Male
Brain-Computer Interfaces

@article {pmid41887546,
year = {2026},
author = {Chen, L and Tang, C and Gao, H and Zhang, L and Cheng, S and Wang, Z and Liu, S and Ming, D},
title = {Transcutaneous auricular vagus nerve stimulation facilitates visuomotor association learning: Behavioral and electrophysiological evidence.},
journal = {NeuroImage},
volume = {331},
number = {},
pages = {121879},
doi = {10.1016/j.neuroimage.2026.121879},
pmid = {41887546},
issn = {1095-9572},
abstract = {Associating visual cues with appropriate motor responses is a fundamental adaptive skill. Transcutaneous auricular vagus nerve stimulation (taVNS) may enhance visuomotor association (VMA) learning, though its neural mechanisms remain unclear. Electroencephalogram (EEG), with its millisecond temporal resolution, offers unique advantages for elucidating the neurodynamic of VMA plasticity. This single-blind, sham-controlled, between-subjects study investigated whether taVNS facilitates VMA learning through behavioral and EEG analysis. Participants (each group N = 19) performed a VMA task (associating five oracle pictures with five keyboard keys) before and after 20-min active/sham taVNS. Behavioral results revealed that compared to the sham group, the active group exhibited shorter reaction time, higher response accuracy and larger learning curve integration, confirming the positive effect of taVNS on VMA learning. Neurophysiologically, taVNS reduced the P200 and P300 amplitudes, enhanced N170 negativity and attenuated error-related negativity. Cross-regional-frequency phase-amplitude coupling results demonstrated enhanced synchronization of frontal-parietal-occipital neural cross-frequency activity. Additionally, parietal-occipital θ, α, β band inter-trial phase coherence was enhanced in the active group. These findings demonstrate that taVNS enhances VMA acquisition through optimizing visual and error processing efficiency. This study establishes a neurophysiological basis for taVNS's cognitive enhancement potential, suggesting its utility in rehabilitative paradigms targeting associative learning deficits.},
}

@article {pmid41889816,
year = {2026},
author = {Posani, L},
title = {Decodanda: a Python toolbox for best-practice decoding and geometric analysis of neural representations.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.03.16.711920},
pmid = {41889816},
issn = {2692-8205},
abstract = {Neural decoding is a powerful approach for inferring which variables are represented in the activity of a population of neurons, with broad applications ranging from basic neuroscience to clinical settings such as brain-computer interfaces. More recently, decoding has also been used as a cross-validated tool for studying the computationally relevant properties of representational geometry, revealing not only whether a variable is encoded, but also how it is encoded and which computations the collective activity of neural populations may support. However, decoding analyses present several technical challenges and common pitfalls that can lead to misleading conclusions if not handled carefully. Here, we introduce Decodanda, a Python toolbox for decoding and geometric analysis of neural population activity. Decodanda provides functions for decoding arbitrary variables and for quantifying geometric features of neural representations, including shattering dimensionality and cross-condition generalization performance (CCGP). Importantly, the package automates several essential best-practice safeguards, including trial-based cross-validation to avoid training-testing leakage from temporally correlated neural traces (a particularly important issue for calcium imaging data), null models for statistical significance, pseudo-population pooling, and cross-variable balancing to determine which of a set of correlated variables is genuinely encoded in the activity. Decodanda is agnostic to the specific classifier used for decoding, and it is designed to be both user-friendly and highly customizable, allowing researchers to assemble flexible analysis pipelines from modular building blocks. Here, we provide an overview of the design principles of Decodanda and illustrate its use cases in neuroscience research. Documentation, example notebooks, and source code are available at github.com/lposani/decodanda .},
}

@article {pmid41889927,
year = {2026},
author = {Fan, Y and Ma, Y and Zolotavin, P and Topalli, G and Wang, W and Karlsson, M and Karlsson, M and Luan, L and Xie, C and Chi, T},
title = {High-channel-count neural recording and stimulation platform with 5,376 simultaneous recording channels.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.03.13.709972},
pmid = {41889927},
issn = {2692-8205},
abstract = {Advancing neural interfaces requires large-scale, high-density recording technologies capable of capturing full-spectrum neural activity across cortical and subcortical regions. Here, we present a scalable approach to integrate neural electrodes with advanced application-specific integrated circuits (ASICs). Specifically, we custom-designed an ASIC with 5,376 simultaneous channels, each sampling at 20 kS/s and enabling >1.3 Gb/s total data streaming throughput. The ASIC incorporates in-pixel amplification, time-division multiplexed ADCs, and on-chip stimulation capabilities, ensuring precise signal acquisition with minimal power consumption while maintaining a low noise level of 5.5 $\mu$Vrms. We further developed an interconnect strategy using gold bump bonding, which allows for high-density integration of the flexible probe and rigid chip. We demonstrate the capacity of this platform through the integration with a flexible $\mu$ECoG array. The resulting device allows for the high-resolution mapping of in vivo field potentials on the cortical surfaces of rat brains, supported by the precise localization of evoked sensory activities. These results prove an effective approach towards highly integrated neural interfaces with applications in brain-computer interfaces, neuroprosthetics, and large-scale functional brain mapping.},
}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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