@article {pmid42230569,
year = {2026},
author = {Hao, ZJ and Wu, QH and Li, YL and Guo, ZM and Li, ZW and Wang, G and Meng, M and Yuan, SL and Wufuer, Y and Zhang, MH and Chen, J and Yang, T and Chen, MX and Zhu, J and Qi-Hang, W and Li, Q and Yu, SH and Lu, M and Xiong, HY and Feng, YR and Dong, MQ and Xu, JH and Xu, JL and Chen, L and Yang, HT and Miao, JK and Zhu, H and Yang, B and Zhao, HY and Shi, XM and Bian, S and Li, TY and Hu, RG},
title = {Anti-asthma drug montelukast induces autistic behaviors via disrupting neuronal retinoic acid signaling.},
journal = {Signal transduction and targeted therapy},
volume = {11},
number = {1},
pages = {},
pmid = {42230569},
issn = {2059-3635},
mesh = {Cyclopropanes/adverse effects ; Humans ; Animals ; *Quinolines/adverse effects/administration & dosage ; *Tretinoin/metabolism ; Signal Transduction/drug effects ; Rats ; Sulfides/adverse effects ; *Acetates/adverse effects/administration & dosage ; Neurons/pathology/metabolism/drug effects ; *Anti-Asthmatic Agents/adverse effects ; *Retinoic Acid Receptor alpha/genetics/metabolism ; Female ; *Autism Spectrum Disorder/chemically induced/genetics/pathology/metabolism ; *Autistic Disorder/chemically induced/genetics/pathology ; },
abstract = {Autism spectrum disorders (ASD) affect approximately 1.0% of children worldwide with still increasing global prevalence. The fact that genetic factors contribute to less than 50% of ASD suggests some critical yet enigmatic roles of non-genetic factors in ASD etiology. Here, we reported that montelukast (MTK), a cysteinyl leukotriene receptor antagonist and one of the most commonly prescribed anti-asthma drugs, potently disrupted neuronal retinoic acid (RA) signaling and altered synaptic plasticity of the primary neurons from rat pre-frontal cortex (PFC). Prenatal or early postnatal exposure to MTK induced autistic-like behaviors in wild-type rats, which could be significantly alleviated by supplementing all-trans retinoic acid (atRA). MTK altered neuronal RA signaling and forebrain patterning in brain organoids derived from human embryonic stem cells through antagonizing RA in RA signaling. Meanwhile, molecular docking followed by biochemical validation strongly indicated that MTK could physically interact with RA receptors (RARs), e.g. RA receptor α (RARA). Furthermore, multi-center survey with a large Chinese ASD cohort suggested that MTK administration during early childhood might indeed increase the risk of ASD in children. Altogether, our findings have not only established MTK use as a yet unrecognized risk factor for human ASD, but highlighted the key importance of safer use of medicines to prevent ASD.},
}

Cyclopropanes/adverse effects
Humans
Animals
*Quinolines/adverse effects/administration & dosage
*Tretinoin/metabolism
Signal Transduction/drug effects
Rats
Sulfides/adverse effects
*Acetates/adverse effects/administration & dosage
Neurons/pathology/metabolism/drug effects
*Anti-Asthmatic Agents/adverse effects
*Retinoic Acid Receptor alpha/genetics/metabolism
Female
*Autism Spectrum Disorder/chemically induced/genetics/pathology/metabolism
*Autistic Disorder/chemically induced/genetics/pathology

@article {pmid42230793,
year = {2026},
author = {Sun, S and Li, J and Wang, S and Li, J and Ren, J and Bao, Z and Sun, L and Ma, X and Zheng, F and Ma, S and Sun, L and Wang, M and Yu, Y and Ma, M and Wang, Q and Chen, Z and Ma, H and Wang, X and Wu, Z and Zhang, H and Yan, K and Yang, Y and Zhang, Y and Zhang, S and Lei, J and Teng, ZQ and Liu, CM and Bai, G and Wang, YJ and Li, J and Wang, X and Zhao, G and Jiang, T and Belmonte, JCI and Qu, J and Zhang, W and Liu, GH},
title = {Author Correction: CHIT1-positive microglia drive motor neuron ageing in the primate spinal cord.},
journal = {Nature},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41586-026-10728-9},
pmid = {42230793},
issn = {1476-4687},
}

@article {pmid42231717,
year = {2026},
author = {Saeed, S and Sang, R and Zhixin, L and Wang, H and Xu, L and Zhang, X and Hu, S},
title = {Circadian rhythms in major depressive disorder: mechanistic insights and therapeutic frontiers.},
journal = {Annals of medicine},
volume = {58},
number = {1},
pages = {2671594},
doi = {10.1080/07853890.2026.2671594},
pmid = {42231717},
issn = {1365-2060},
mesh = {Humans ; *Major Depressive Disorder/therapy/physiopathology/genetics ; *Circadian Rhythm/physiology/genetics ; Hypothalamo-Hypophyseal System/physiopathology/metabolism ; Pituitary-Adrenal System/physiopathology ; Melatonin/metabolism ; Phototherapy/methods ; Chronotherapy/methods ; Sleep/physiology ; CLOCK Proteins/genetics ; },
abstract = {BACKGROUND: Major Depressive Disorder (MDD) has emerged as a leading cause of disability worldwide, affecting over 264 million people. Recent evidence reveals that disruption of circadian rhythms may be fundamental to MDD pathophysiology, opening new avenues for therapeutic intervention.

METHODS: This review synthesizes current understanding of the intricate relationship between circadian system disruption and MDD, highlighting molecular mechanisms and clinical implications. We examine evidence from genetic studies, clinical observations, and therapeutic trials.

RESULTS: Patients with MDD exhibit profound alterations in circadian-regulated processes, including sleep-wake cycles, diurnal mood patterns, and metabolic functions. Genetic studies have identified variants in core clock genes, particularly CLOCK, TIMELESS, and CRY1, that correlate with both circadian disruption and MDD susceptibility. These genetic insights, combined with evidence of dysregulated hypothalamus-pituitary-adrenal axis function and abnormal melatonin signaling, suggest that circadian dysfunction may be causal in MDD pathogenesis rather than merely symptomatic.

CONCLUSIONS: Emerging chronotherapeutic approaches, such as light therapy, sleep interventions, and targeted pharmacology, show significant potential for improving depressive symptoms. Personalized circadian-based treatments, guided by genetic and molecular markers, could transform MDD care. Advancing our understanding of the circadian-depression connection offers a promising path to revolutionizing treatment strategies.},
}

Humans
*Major Depressive Disorder/therapy/physiopathology/genetics
*Circadian Rhythm/physiology/genetics
Hypothalamo-Hypophyseal System/physiopathology/metabolism
Pituitary-Adrenal System/physiopathology
Melatonin/metabolism
Phototherapy/methods
Chronotherapy/methods
Sleep/physiology
CLOCK Proteins/genetics

@article {pmid42232026,
year = {2026},
author = {Zhu, Y and Yu, X and Yin, C and Liu, X and Wu, Z and Zhang, D and Lee, HJ and Tian, L},
title = {Mg[2+]-Dependent Remodeling of Biomolecular Condensates' Microenvironments for Tunable Molecular Uptake and Altered Biochemical Dynamics.},
journal = {Chem & bio engineering},
volume = {3},
number = {5},
pages = {535-545},
pmid = {42232026},
issn = {2836-967X},
abstract = {Biomolecular condensates have emerged as a transformative paradigm in biomedical and materials sciences due to their unique capacity for molecular sequestration and dynamic adaptability. Precise modulation of their microenvironmental properties enables versatile applications including protocell engineering, targeted therapeutics, and smart bioreactor systems. Here, we demonstrate that multivalent ions, exemplified by magnesium ions (Mg[2+]), exert concentration-dependent regulation of condensate physicochemical properties and biological functions. Using a model system composed of cationic arginine decamer (R10) and anionic polyglutamate (PolyE), we systematically show that Mg[2+] concentration gradients influence the size distribution, surface charge, viscosity, and internal polarity. Critically, we establish links between ion-induced microenvironmental changes and functional outcomes: (i) dsDNA structural stability and ssDNA hybridization kinetics are altered in an ion-dependent manner; (ii) guest molecule enrichment capacity shows selective tuning; and (iii) alkaline phosphatase (ALP) catalytic efficiency exhibits nonlinear dose-response relationships. These findings offer mechanistic insights into cellular ion homeostasis and provide design principles for ion-responsive synthetic condensates with programmable functionality. Our work bridges fundamental biophysical principles with translational applications in smart biomaterials and precision medicine.},
}

@article {pmid42232076,
year = {2026},
author = {Mannino, C and Sorrentino, P and Chavez, M and Corsi, MC},
title = {Neuronal avalanches as a predictive biomarker for guiding tailored BCI training programs.},
journal = {Imaging neuroscience (Cambridge, Mass.)},
volume = {4},
number = {},
pages = {},
pmid = {42232076},
issn = {2837-6056},
abstract = {Motor imagery-based Brain-Computer Interfaces (BCIs) restore control in persons with motor impairments, but up to 30% of users struggle, a phenomenon known as "BCI inefficiency". This study tackles a key limitation of current protocol: the use of fixed-length sessions training paradigms that ignore individual learning variability. We propose a novel approach based on neuronal avalanches, spatiotemporal cascades of brain activities, as biomarkers to characterize and predict user-specific learning. From electroencephalography data across four sessions in 20 subjects, we characterized avalanches by their length and their spatiotemporal size. These features showed significant training and task effects and were found to correlate to BCI performance across sessions. We further assessed their ability to predict BCI success through longitudinal models, achieving up to 91% accuracy, improved by spatial filtering on selected brain regions. These findings demonstrate the utility of neuronal avalanche dynamics as robust biomarkers for BCI training, supporting the development of personalized protocols aimed at mitigating BCI illiteracy.},
}

@article {pmid42232464,
year = {2026},
author = {Hess, RM and Lavadi, RS and Agarwal, N and Levy, EI},
title = {The role of ambulatory surgical centers in current neurosurgical practice.},
journal = {Surgical neurology international},
volume = {17},
number = {},
pages = {275},
pmid = {42232464},
issn = {2229-5097},
abstract = {BACKGROUND: The recent focus on improving quality and reducing cost within the US healthcare system has increased care being performed in the outpatient setting. The impact on neurosurgeons' practice patterns has not yet been fully elucidated. In addition, how this transition may affect neurosurgery resident training is unclear. To better understand these issues, we surveyed neurosurgeons.

METHODS: A 13-question survey was sent to Council of State Neurosurgical Societies email subscribers. The survey focused on training or practice level, location, practice setting, ambulatory surgical center (ASC) utilization, and types of procedures performed at ASCs. Responses were tabulated. Statistical analysis was performed.

RESULTS: Among 11,091 subscribers, 101 responses (0.9%) were recorded. Most of the respondents (57.4%) utilized an ASC in their practice. The commonly performed procedures were microdiscectomy (98.1%), hemilaminectomy (94.2%), battery changes (87.5%), single-level anterior cervical discectomy and fusion (84.6%), single-level lumbar or thoracic laminectomy (80.8%), and peripheral nerve decompression (66.7%). Cranial procedures were seldom performed. Other device-related procedures were common and included vagal nerve stimulation (32.5%), spinal cord stimulation (67.5%), baclofen pump placement (25%), and baclofen pump replacement (27.5%). Only 17.1% of respondents who worked in academia taught residents in an ASC.

CONCLUSION: According to our survey results, most neurosurgeons have incorporated ASCs into their practices in some capacity and most frequently for simple spine procedures, device-related procedures, and peripheral nerve decompression. The limited resident involvement in procedures in the ASC setting, even among attending academic neurosurgeons, suggests an increased need for ASC incorporation in residency training.},
}

@article {pmid42232652,
year = {2026},
author = {Gao, Z and Zhu, Z and Wang, S and Wu, Y and Song, Z and Guo, Y and Wang, H and Mao, YJ},
title = {Effects of motor imagery brain-computer interface task on quantitative EEG features in patients with prolonged disorders of consciousness.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1815881},
pmid = {42232652},
issn = {1662-4548},
abstract = {OBJECTIVE: To analyze quantitative electroencephalographic (EEG) characteristics during Motor Imagery Brain-Computer Interface (MI-BCI) task in patients with prolonged disorders of consciousness (pDoC).

METHODS: Forty-three patients with pDoC due to various brain injuries were enrolled. Based on modified Coma Recovery Scale-Revised (CRS-R) assessments, the patients were divided into 19 in the unresponsive wakefulness syndrome (UWS) group and 24 in the minimally conscious state (MCS) group. All patients underwent 5 min of resting-state (RS) EEG followed by 5 min of MI-BCI task. Relative power, DTABR, and average brain engagement (BE) during MI-BCI were analyzed across resting and MI-BCI states using Fast Fourier Transform (FFT) spectra.

RESULTS: Mixed-design ANOVA showed significant main effects of condition and group across all EEG frequency bands, indicating clear differences between the RS and MI-BCI conditions and between UWS and MCS patients. Significant group × condition interactions were found in the delta, beta, and gamma bands, as well as in DTABR. Simple effects analysis showed that delta power was higher in RS than in MI-BCI in both groups, with UWS consistently exhibiting higher delta power than MCS under both conditions. In contrast, beta and gamma power were higher in MI-BCI than in RS in both groups. For beta power, UWS was higher than MCS under RS, whereas MCS was higher than UWS under MI-BCI, showing a reversal of the interaction pattern. For gamma power, MCS showed higher values than UWS under both conditions, with a larger between-group difference during MI-BCI. DTABR was significantly higher in RS than in MI-BCI in both groups; however, MCS exhibited higher DTABR than UWS under RS, whereas the opposite pattern was observed under MI-BCI. In addition, during MI-BCI tasks, the MCS group showed greater average BE than the UWS group.

CONCLUSION: MI-BCI shows potential as a diagnostic or assessment tool for evaluating the level of consciousness in patients with pDoC.},
}

@article {pmid42232896,
year = {2026},
author = {Alsolai, H and Khan, S and Mahendran, RK and Panwar, A and Alabduallah, BI and Alhayan, F},
title = {Rehab-DRLX: explainable neurorehabilitation prognosis using deep reinforcement learning and transformer-based models.},
journal = {Frontiers in computational neuroscience},
volume = {20},
number = {},
pages = {1808274},
pmid = {42232896},
issn = {1662-5188},
abstract = {Neurorehabilitation poses a crucial problem in clinical recovery tasks, particularly for individuals with poor motor functions and neurological impairments, and problems in activities of daily living (ADL). To resolve this, we design a novel model, Rehab-DRLX, with a hybrid deep learning (HDL) framework that combines deep reinforcement learning (DRL) with an explainable transformer model to provide interpretable, accurate prognostic results. The propounded model is designed to effectively process the multimodal data inputs, which include clinical records, sensor-entrenched motion data, and neuroimaging, along with time-dependent recovery patterns from its reinforced representation learning (RRL) module. The RRL module employs a convolutional neural network (CNN) within the DRL agent, which performs spatiotemporal feature encoding and dynamically recovers a policy from its reward-guided learning method. To ensure interpretability, the explainable prognosis transformer (XPT) is utilized, which contains clinical contextual positional encoding and a hierarchical attention mechanism to enable transparent and reliable decision-making. This duality in the Rehab-DRLX architecture enables effective forecasting of the recovery outcomes, including functional independence probability, with both interpretability and accuracy, addressing the drawbacks of conventional black box prognosis tools. The experimental results of Rehab-DRLX show the noteworthy improvements in metrics such as accuracy (94.6%), F1-score (0.93), root mean square (RMSE) (0.082), and mean absolute error (MAE) (0.061) compared to existing studies. The ablation studies reveal the significant contribution of every architectural component and its overall performance. The results show the practical viability of Rehab-DRLX, which not only improves decision-making but also builds clinical trust through explainable insights.},
}

@article {pmid42233289,
year = {2026},
author = {Aksen, DE and Potenza, MN and Meda, SA and Pearlson, GD and Lichenstein, SDD},
title = {Behavioral Inhibition Network Predicts Alcohol Use in Men and Stress in Women.},
journal = {Journal of studies on alcohol and drugs},
volume = {},
number = {},
pages = {},
doi = {10.15288/jsad.25-00235},
pmid = {42233289},
issn = {1938-4114},
abstract = {OBJECTIVE: Impulsivity, a complex construct linked to addictions, is often inconsistently assessed and conceptualized, making it difficult to effectively target in addiction treatment. The current study aimed to identify neural substrates underlying distinct impulsivity domains and explore their relationships with alcohol use and stress in both women and men.

METHOD: We utilized a whole-brain machine learning strategy, connectome-based predictive modeling (CPM), to investigate brain networks linked to four composite impulsivity-related domains previously identified in the NIAAA-funded Brain and Alcohol Research in College Students dataset: impulsive action, approach/appetitive motivation, impulsivity/compulsivity, and behavioral inhibition/punishment sensitivity (BIPS). CPM (5-fold cross-validation, 100 repeats, and permutation testing) was applied using Monetary Incentive Delay Task fMRI data from 287 undergraduates. Identified networks were examined in relation to alcohol use and stress across sexes.

RESULTS: The CPM model predicting BIPS was significant (r = 0.24, p = .001). Higher BIPS was associated with increased connectivity between default mode, motor/sensory, and cerebellar networks, and decreased connectivity among medial frontal, frontoparietal, default mode, and motor/sensory networks. BIPS network strength differed by sex (t(285) = 8.26, p < .001), with negative associations with alcohol use (p < .05) in men and positive associations with stressful life events (p < .05) in women.

CONCLUSIONS: Identifying a neuromarker of BIPS in young adults may inform targeted interventions for impulsive behaviors, considering sex differences. Future research should explore whether neuromodulation or other interventions targeting this network could mitigate problem drinking in men and stress-related concerns in women.},
}

@article {pmid42234314,
year = {2026},
author = {Kumar, A and Kuo, SH},
title = {Investigating Cerebello-Cortical Networks With EEG: Advances and Future Challenges.},
journal = {Cerebellum (London, England)},
volume = {25},
number = {4},
pages = {},
pmid = {42234314},
issn = {1473-4230},
support = {#R01 NS136686/NS/NINDS NIH HHS/United States ; },
abstract = {The cerebellum is widely recognized for its contributions to motor, cognitive, and affective processes through dynamic cerebello-cortical networks. Recent studies using cerebellar-cortical electroencephalography (EEG), a technique that enables noninvasive, millisecond-resolution recordings of cerebellar and cortical activity, have revealed disease-specific spectral and network alterations in patients with movement and neurodegenerative disorders, including ataxia, essential tremor (ET), Parkinson's disease (PD), dystonia, as well as in healthy individuals. Synchronous cerebellar-cortical EEG reliably detects these signals and captures network dynamics, providing mechanistic insights into cerebellar-specific functions and interactions that may inform the development of brain-computer interfaces, targeted neuromodulation, and future applications in neurological disorders.},
}

@article {pmid42235550,
year = {2026},
author = {Quattrociocchi, I and Caracci, V and Rotondo, E and Colamarino, E and Pichiorri, F and Riccio, A and Cincotti, F and Toppi, J and Astolfi, L},
title = {Improving P300 morphology through single-trial latency realignment: a comparative study of template-matching approaches.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae7766},
pmid = {42235550},
issn = {1741-2552},
abstract = {Trial-to-trial latency variability - well known as latency jitter - is a major source of distortion in event-related potential (ERP) analysis, particularly for late cognitive components such as the P300. Several template-matching algorithms have been proposed to estimate single-trial latency and improve ERP reconstruction, but direct comparisons across different methodological approaches remain limited. This study provides a structured evaluation of three representative algorithms: the Woody Filter (WF), operating in the time domain; the Adaptive Wavelet Filter (CWT-AWF), extending template matching to the time-frequency domain; and ReSync, a decomposition-based method that combines signal decomposition with time-restricted realignment. Approach.The algorithms were evaluated using surrogate EEG-like data with controlled amplitude ratios (reported as SNR) and known latency jitter, and real EEG recordings from healthy participants performing an auditory oddball task. Performance was assessed in terms of latency-estimation accuracy, latency variability, ERP morphology, and waveform quality. Results. Across simulated conditions, ReSync achieved significantly lower latency-estimation errors and reduced variability compared to WF and CWT-AWF, demonstrating robustness even at low SNR levels. Importantly, this advantage persisted when all methods were constrained within the same temporal window, indicating that performance gains are not solely attributable to time restriction. In real EEG data, all algorithms enhanced P300 morphology relative to non-aligned averages, but ReSync yielded the most consistent improvements, including the lowest latency jitter and stable latency distributions within a range consistent with previous findings. Complementary SNR analysis further indicated improved waveform quality when interpreted jointly with latency-based metrics. ReSync also remained stable across both single-channel and multi-channel realignment strategies. Significance. These findings highlight the advantage of combining decomposition and targeted realignment for mitigating ERP latency jitter. ReSync provides a reliable and morphology-preserving framework for single-trial ERP analysis, with potential applications in cognitive neuroscience, brain-computer interfaces, and clinical contexts. .},
}

@article {pmid42094556,
year = {2026},
author = {Abramovich Krasa, B and Kunz, EM and Kamdar, F and Avansino, D and Hahn, N and Singh, A and Card, NS and Wairagkar, M and Iacobacci, C and Hochberg, LR and Brandman, DM and Stavisky, SD and Henderson, JM and Willett, FR and Druckmann, S},
title = {Premotor cortex uses a compositional neural geometry to plan words.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {42094556},
issn = {2692-8205},
abstract = {Speech requires precise serial ordering of words and phonemes into novel combinations. To accomplish this, the brain is believed to flexibly prepare utterances before producing them, even allowing pronunciation of never-before spoken words. To discover how neural populations achieve this, intracortical activity from premotor cortex was recorded while two speech neuroprosthesis pilot clinical trial participants attempted to speak factorially-balanced phoneme sequences. During preparation, activity encoded not only the next-phoneme, but multiple upcoming phoneme positions spanning whole words. We found that word-level plans were formed by compositionally combining phoneme representations, a mechanism that may enable efficient planning of novel sequences. When utterances contained more than one word, premotor cortex activity was largely limited to the first word, suggesting that articulatory planning is segmented by higher-order features. Together, these results reveal a compositional, hierarchically-segemented planning geometry, potentially a universal neural strategy for sequence organization across higher levels of language.},
}

@article {pmid42223595,
year = {2026},
author = {Klei, DS and Benders, KEM and Leenen, LPH and van Wessem, KJP},
title = {Epidemiology and outcomes of traumatic sternal fractures and associated blunt cardiac injury: a nationwide cohort study in the Netherlands.},
journal = {European journal of trauma and emergency surgery : official publication of the European Trauma Society},
volume = {52},
number = {1},
pages = {},
pmid = {42223595},
issn = {1863-9941},
mesh = {Humans ; Male ; Netherlands/epidemiology ; *Sternum/injuries ; Middle Aged ; *Wounds, Nonpenetrating/epidemiology ; Retrospective Studies ; *Fractures, Bone/epidemiology ; *Heart Injuries/epidemiology ; Female ; Aged ; Registries ; Hospital Mortality ; Adult ; Incidence ; Injury Severity Score ; Accidents, Traffic/statistics & numerical data ; },
abstract = {PURPOSE: Comprehensive data on epidemiology, trauma mechanisms, associated injuries, and outcomes of traumatic sternal fractures are scarce. This study analysed nationwide data to improve diagnosis and management within the Dutch healthcare system.

METHODS: This nationwide retrospective cohort study using the Dutch National Trauma Registry included adult patients admitted with traumatic sternal fractures between 2015 and 2023. Patients with prehospital cardiopulmonary resuscitation or penetrating trauma were excluded. Incidence, patient characteristics, trauma mechanisms, associated injuries, and in-hospital outcomes were analysed. Subgroup analyses evaluated patients with concomitant blunt cardiac injury (BCI).

RESULTS: Of 568,399 adult trauma admissions, 4,765 patients (0.84%) sustained traumatic sternal fractures. Median age was 62 years; 60% were male. Motor vehicle accidents (48%) and falls (28%) were the leading mechanisms. 35% were severely injured (ISS ≥ 16). Associated injuries included rib fractures (51%), spinal fractures (36%), and lung contusions (18%). Critical care unit admission was 40%, with median mechanical ventilation duration of 4 days; median hospital stay was 5 days. In-hospital mortality was 5.7%, and 30-day mortality 6.0%. BCI occurred in 9.5% of patients and was associated with a higher number of injuries and increased injury severity, emergency interventions, and critical care admission, but not higher mortality.

CONCLUSION: Traumatic sternal fractures are uncommon, but the incidence in The Netherlands is gradually rising. Sternal fractures frequently occur with severe multisystem injuries. Patients with BCI showed greater injury severity and resource needs. Future research should focus on criteria and clinical significance of BCI, and sternal fracture-specific outcomes and treatment strategies in large patient cohorts.},
}

Humans
Male
Netherlands/epidemiology
*Sternum/injuries
Middle Aged
*Wounds, Nonpenetrating/epidemiology
Retrospective Studies
*Fractures, Bone/epidemiology
*Heart Injuries/epidemiology
Female
Aged
Registries
Hospital Mortality
Adult
Incidence
Injury Severity Score
Accidents, Traffic/statistics & numerical data

@article {pmid42225660,
year = {2026},
author = {Sarkar, S and Nathan, K and Kilicarslan, A and Eguren, D and Grossman, R and Contreras-Vidal, JL},
title = {EEG-Controlled Exoskeleton for Walking and Standing: A Longitudinal Multimodal Dataset of Healthy Individuals.},
journal = {Scientific data},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41597-026-07476-w},
pmid = {42225660},
issn = {2052-4463},
abstract = {Brain-machine interfaces (BMIs) translate brain signals into motor commands for assistive devices. Despite significant advances, the long-term effects of BMI training on neural adaptation, classifier stability, and individual variability remain poorly understood. We present a multimodal, longitudinal dataset collected from seven healthy participants over nine sessions spanning 15 to 81 days. The dataset includes high-density electroencephalography (EEG), electrooculography (EOG), inertial measurement unit (IMU) data, and exoskeleton state information during BMI control. During the open-loop training phase, participants performed kinesthetic motor imagery (KI) while a remotely controlled exoskeleton executed walking and stopping commands. After the open loop training phase, the system transitioned to closed loop BMI control. For closed-loop control, lower delta band EEG signals were classified using Local Fisher Discriminant Analysis and a Gaussian Mixture Model. The classifier was continuously updated using open-loop data from Sessions#1-5, after which its parameters were fixed. The dataset also includes post-experiment MRI scans from five participants performing KI while viewing themselves walking in the exoskeleton. This report outlines the experimental setup, data collection, and preliminary validation, providing a resource for future BMI research.},
}

@article {pmid42226205,
year = {2026},
author = {Gosden, J and Ascione, G and Wolf, S and Turek, JW and George, I},
title = {Alpha-gal xenoantigens in bioprosthetic valve recipients: clinical implications for bioprosthesis longevity.},
journal = {Journal of cardiothoracic surgery},
volume = {},
number = {},
pages = {},
doi = {10.1186/s13019-026-04270-y},
pmid = {42226205},
issn = {1749-8090},
abstract = {BACKGROUND: Structural valve degeneration (SVD) is a key limitation of bioprosthetic heart valves (BHVs). The underlying mechanisms for this degeneration and pathophysiology remains only partially defined. Emerging evidence implicates a xenogeneic carbohydrate epitope, galactose-α-1,3-galactose (Alpha-gal), as a potential driver of immune-mediated valve deterioration. This review explores the current knowledge on alpha-gal (AG) sensitization and evidence linking it to SVD and the potential clinical implications.

METHODS: A literature search was conducted using Embase, PubMed and Scopus, using variants of the following keywords, such as "alpha-gal", "bioprosthetic valve", and "degeneration". Studies included reported human subject findings and focused on BHVs. Only original works were permitted, published between January 2014 and December 2025.

RESULTS: Six studies met the inclusion criteria. Case reports demonstrated heterogenous clinical outcomes with, rapid SVD observed in some alpha-gal sensitized patients, while other patients showed tolerance to bioprosthetic implantation in the perioperative and short-term period. The only study with longitudinal follow-up demonstrated that anti-AG IgG responses were associated with increased SVD and calcification. Another study found no perioperative adverse valvular outcomes, although follow-up was limited to in-hospital assessment. Overall, his manuscript identifies that AG sensitization may contribute to SVD in certain patients, however, its broader significance remains uncertain.

CONCLUSIONS: Immune recognition of AG may contribute to SVD based on the limited available evidence. Larger prospective investigations are required to clarify a causal relationship and to assist in guiding potential preventative strategies. Recognition of this mechanism may ultimately inform management of valve replacement and bioprosthesis selection plans.},
}

@article {pmid42226947,
year = {2026},
author = {Delavari, F and Santaniello, S},
title = {Lateralization in scalp EEG brain connectivity during hand motor imagery can improve task classification for brain-computer interfaces.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {103},
pmid = {42226947},
issn = {1871-4080},
abstract = {This study evaluates brain connectivity reorganization during motor imagery (MI) tasks and assesses the predictive value of EEG-based functional connectivity measures for MI classification compared to µ-band (8-13 Hz) power spectrum of selected EEG channels, which are commonly used in MI decoders. We analyzed left- and right-hand MI EEG data from the BCI Competition IV 2a (BCI-IV-2a) and PhysioNet Motor Imagery (PHYS-MI) datasets. Phase Locking Value (PLV), cross-correlation (CC), weighted Phase Lag Index (wPLI), and Granger causality (GC) were evaluated as connectivity measures, and their decoding performance was compared against µ-band power features using Random Forest classifiers. Feature importance and graph-theoretical metrics were also used to examine node relevance, edge contributions, and global network topology across MI conditions. We found that PLV yields the most reliable MI decoding performance across both datasets, with accuracy comparable to power (65.3 ± 11.0% vs. 61.3 ± 11.0% and 58.4 ± 9.9% vs. 58.6 ± 15.7%, mean ± std. dev. across subjects for BCI-IV-2a and PHYS-MI, respectively). Moderate correlation (R [2] = 0.62 and 0.40 for BCI-IV-2a and PHYS-MI, respectively) was found between the mean difference in PageRank centrality of the nodes of the PLV-based network in left- vs. right-hand MI and the Gini importance score of the single-channel power values. Also, while the PLV-based network topology remained stable over time, a small set of connections (7.8 ± 4.5% and 3.1 ± 2.5% of edges) lateralized to the hemisphere contralateral to the movement altered considerably and enhanced classification accuracy by 6.7 ± 5.6% and 16.3 ± 7.5% across subjects. These findings suggest that MI primarily modulates a limited number of task-specific functional connections. Rather than replacing established power-based approaches, connectivity measures provide complementary, network-level insight into how MI-related information is organized, which may inform interpretable feature selection and the design of future brain-computer interface models.},
}

@article {pmid42227949,
year = {2026},
author = {Zhang, T and Ngetich, RK and Zhang, J and Jin, Z and Li, L},
title = {Erratum to: The role of emotion in economic decision making: behavioral and neurophysiological evidence from the Wheel of Fortune Gambling Task.},
journal = {Reviews in the neurosciences},
volume = {},
number = {},
pages = {},
doi = {10.1515/revneuro-2026-0101},
pmid = {42227949},
issn = {2191-0200},
}

@article {pmid42227984,
year = {2026},
author = {Shen, Y and You, C and Zhang, Y and Ji, N and Zhao, X and Zhang, P and Huang, S and Kang, H and Liu, X and Peng, Y and Sun, C and Yan, B and Zhang, Y and Zhu, S and Zhu, W and Lei, T and Tang, Z and Ding, M and Hu, F and Shu, K},
title = {Assessment of the utility of optically pumped magnetometer magnetoencephalography in preoperative localization of refractory epilepsy: A prospective study.},
journal = {Epilepsia},
volume = {},
number = {},
pages = {},
doi = {10.1002/epi.70273},
pmid = {42227984},
issn = {1528-1167},
support = {SCZ2024008//Jian Dao (JD) Major Program of Hubei Province/ ; 2022YFC2403905//National Key Research and Development Program of China/ ; 2023YFC2510001//National Key Research and Development Program of China/ ; },
abstract = {OBJECTIVE: Precise localization of the epileptogenic zone (EZ) is crucial for epilepsy surgery success. Optically pumped magnetometer magnetoencephalography (OPM-MEG) is a promising noninvasive technique requiring rigorous clinical validation.

METHODS: In this prospective diagnostic study, 68 patients with refractory epilepsy underwent 90-min interictal OPM-MEG. Dipoles were fitted to interictal epileptiform discharges for localization. The primary objective was to evaluate the spatial concordance between OPM-MEG and the EZ defined by intracranial electroencephalography (iEEG; stereo-EEG or electrocorticography), assessed at the sublobar level using Gwet AC1. The secondary objective was to evaluate the diagnostic value of OPM-MEG for surgical outcome. This analysis included 51 patients who underwent curative intervention (resection or thermocoagulation). The reference standard was a composite of the treated brain region and seizure freedom (International League Against Epilepsy [ILAE] class 1 or Engel class I) at ≥12-month follow-up, from which sensitivity, specificity, and diagnostic odds ratio (OR) were calculated.

RESULTS: OPM-MEG showed almost perfect agreement with iEEG-based EZ localization overall (AC1 = .885, concordance rate = 90.0%), with substantial agreement in temporal (80.1%, AC1 = .723) and almost perfect agreement in extratemporal regions (92.0%, AC1 = .926). The Euclidean centroid distance between OPM-MEG and iEEG localizations was significantly shorter in concordant versus discordant cases. In the assessment of diagnostic value, OPM-MEG demonstrated a sensitivity of 85.7% and specificity of 65.2% (OR = 11.25) under ILAE criteria, and a sensitivity of 73.0% and specificity of 64.3% (OR = 4.86) under Engel criteria.

SIGNIFICANCE: OPM-MEG demonstrates high concordance with iEEG for EZ localization and provides robust diagnostic value for predicting postoperative seizure freedom, supporting its utility in the presurgical evaluation of refractory epilepsy.},
}

@article {pmid42229420,
year = {2026},
author = {Zhou, Q and Dong, B and Gao, P and Wei, J and Xiao, J and Wang, W and Liang, P and Lin, D and Lu, J and Zuo, XN and He, H},
title = {AmygdalaGo-BOLT for boundary-aware segmentation of the human amygdala.},
journal = {Cell reports methods},
volume = {},
number = {},
pages = {101473},
doi = {10.1016/j.crmeth.2026.101473},
pmid = {42229420},
issn = {2667-2375},
abstract = {Tracing the boundaries of the amygdala from brain images remains a major challenge in human neuroscience. Although large-scale neuroimaging studies increasingly collect thousands of scans to investigate structural development in children and adolescents, reliable segmentation of the amygdala is difficult due to its small size and complex morphology-particularly in pediatric populations. To address this, we developed AmygdalaGo-BOLT, a boundary-aware deep learning model specifically designed for amygdala segmentation. The model was trained and validated on 1,086 manually labeled pediatric MRI scans, with independent datasets used to assess generalizability. It integrates multiscale feature extraction, spatial priors, and self-attention mechanisms within a compact encoder-decoder architecture to enhance boundary detection. Across imaging centers and age groups, AmygdalaGo-BOLT demonstrates strong agreement with expert manual annotations, while substantially improving efficiency and accuracy relative to existing tools. This enables robust and scalable analysis of amygdala morphology in population neuroscience studies where manual tracing is impractical.},
}

@article {pmid42229507,
year = {2026},
author = {Swanson, E and Dohle, E and Bashford, L and Horsfall, HL and Jovanovic, L and Muirhead, W and Brannigan, JFM},
title = {Recalibration of implantable brain-computer interfaces to enable long-term independent use - a systematic review.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae7694},
pmid = {42229507},
issn = {1741-2552},
abstract = {Implantable brain-computer interfaces (iBCIs) decode neural signals to generate command signals for effector devices to restore lost functions, such as movement or speech. However, maintaining device performance over time requires recalibration of decoding algorithms due to inherent instability in neural signals. Objective: To systematically review recalibration procedures in iBCIs for patients with motor impairments, focusing on the clinical implications of recalibration requirements and strategies which can enable long-term, independent use. Approach: A systematic search was conducted across EMBASE, MEDLINE, and CINAHL databases to identify studies involving recalibration of iBCIs. Data on recalibration frequency, duration, staff requirements, and location were extracted and analysed. Main Results: Recalibration practices varied widely amongst studies and were typically performed according to predetermined study protocols, rather than practical need following deteriorating device performance. Common practices include manual recalibration requiring a specialist research team, semi-automatic recalibration which could be performed by a non-specialist caregiver, and automatic recalibration methods whereby patients did not require assistance. Devices utilising electrocorticography (ECoG) recording arrays generally required less frequent recalibration compared to those using microelectrode arrays (MEAs). Extended independent use was more frequently reported with ECoG-based iBCIs. Significance: Reducing recalibration frequency or complexity can improve patient autonomy, which is crucial for enhancing long-term independent iBCI use in home and clinical settings. ECoG iBCIs typically have a low recalibration burden due to inherent signal stability. Conversely, MEA iBCIs typically involve a higher recalibration burden, though recent studies have reduced this by incorporating spectral data and continuously updating models. Despite this progress, recalibration procedures are often not fully defined in iBCI studies, and where they are, they usually relate to the study protocol rather than the clinically meaningful recalibration requirement due to worsening device performance. Future studies should continue to develop user-friendly recalibration procedures and outline the clinically relevant recalibration requirements where possible.},
}

@article {pmid42217986,
year = {2026},
author = {Alcala, I and Desailly, E and Arcizet, F and Marazova, K and Gauvain, G and Picaud, S},
title = {Vision restoration: From prostheses to genetic-based brain-machine interfaces.},
journal = {Handbook of clinical neurology},
volume = {218},
number = {},
pages = {387-400},
doi = {10.1016/B978-0-443-22212-2.00017-3},
pmid = {42217986},
issn = {0072-9752},
mesh = {Humans ; *Brain-Computer Interfaces ; Animals ; *Visual Prosthesis ; *Optogenetics/methods ; *Genetic Therapy/methods ; *Vision Disorders/therapy ; },
abstract = {Visual restoration is the major challenge for brain-machine interfaces because vision requires perception of images containing a high number of pixels that have to be presented at a high refresh rate. Classically, visual prostheses were made of electrode arrays with electrode numbers varying from very few to more than thousands. They demonstrated the feasibility of restoring useful vision either at the retinal level in diseases with photoreceptor degeneration or at the cortical level following optic nerve atrophy. Patients can find contrasted objects on a table and read letters or even words. However, they cannot recognize faces. The revolution of biotechnology and gene therapy is offering novel strategies to stimulate neuronal circuits without direct contact to the tissue as with electrodes. Optogenetic therapy is rendering neurons sensitive to light, thanks to a microbial opsin while sonogenetic therapy generates neurons sensitive to ultrasound waves. While optogenetic therapy has already been validated in patients recovering some vision following photoreceptor degeneration, sonogenetic therapy has only been evaluated in rodents at the cortical level. These novel brain-machine interfaces offer novel perspectives for restoring vision in blind patients, but their applications may easily extend to other handicaps or neurologic diseases.},
}

Humans
*Brain-Computer Interfaces
Animals
*Visual Prosthesis
*Optogenetics/methods
*Genetic Therapy/methods
*Vision Disorders/therapy

@article {pmid42218423,
year = {2026},
author = {Qiao, MX and Wei, W and Zhou, M and Li, ML and Zhang, YM and Li, XJ and Deng, W and Guo, WJ and Wang, Q and Yu, H and Li, T},
title = {Habenular structural-functional dysconnectivity in bipolar disorder: evidence from multimodal imaging and transcriptomic integration.},
journal = {BMC psychiatry},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12888-026-08216-5},
pmid = {42218423},
issn = {1471-244X},
support = {82101598//National Natural Science Foundation of China/ ; 82230046//National Natural Science Foundation of China/ ; 2025HZGF10//Construction Fund of Key Medical Disciplines of Hangzhou/ ; CXTD202501053//Zhejiang Clinovation Pride/ ; STI2030-2021ZD0200404//China Brain Project/ ; },
abstract = {BACKGROUND: Bipolar disorder (BD) is a highly heritable condition characterized by recurrent shifts between manic and depressive states. Here we investigated the potential involvement of the habenula because it plays a central role in negative affect and behavioral regulation.

METHODS: We investigated bilateral habenular volume and seed-based resting-state functional connectivity in a discovery cohort (78 BD, 102 controls) and an independent replication cohort (72 BD, 85 controls). Associations among habenular features, clinical symptoms, and molecular correlates were examined by integrating pathway-specific polygenic risk scores and brain-wide gene expression data from the Allen Human Brain Atlas.

RESULTS: Across both cohorts, BD was associated with reduced bilateral habenular volume and increased rs-FC between the habenula and right precentral gyrus. Habenular volume correlated positively with severity of mania symptoms and negatively with severity of symptoms of anxiety and somatization. Polygenic risk scores linked the altered volume to dopaminergic pathways and altered connectivity to serotonergic pathways, while transcriptomic data linked the altered connectivity to changes in expression of synaptic membrane structures, transporter complexes, and other proteins involved in synaptic transmission.

CONCLUSIONS: Structural, functional and transcriptomic data identify the habenula as a critical neural hub in BD and therefore important for understanding pathogenesis and clinical manifestations.

CLINICAL TRIAL NUMBER: Not applicable.},
}

@article {pmid42219941,
year = {2026},
author = {Wang, D and Huang, K and Zhou, X and Ye, X and Chen, J and Xu, H and Dong, G and Yuan, T and Chen, X and Zhou, H and Potenza, MN and Fu, J},
title = {Food Addiction Risk Accelerates Fat Accumulation in Youth: Potential Protective Roles of Left Insula and Mindful Eating.},
journal = {Obesity (Silver Spring, Md.)},
volume = {},
number = {},
pages = {},
doi = {10.1002/oby.70225},
pmid = {42219941},
issn = {1930-739X},
support = {2023C03047//Key Research and Development Program of Zhejiang Province/ ; 2021YFC2701900//National Key Research and Development Program of China/ ; 82370863；82501790//National Natural Science Foundation of China/ ; BMI2400013//Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence, Zhejiang University/ ; },
abstract = {OBJECTIVE: Food addiction (FA) is implicated in obesity, yet the potential moderating role of mindful eating and the underlying neural mechanisms in youth remain unclear.

METHODS: This study integrated a multicenter cross-sectional survey, a longitudinal study with 6- and 12-month follow-ups, and an independent magnetic resonance imaging (MRI) sample. FA, eating motives, mindful eating, BMI z-score, fat content, and visceral fat level were assessed. Analyses utilized structural equation modeling, latent growth modeling, and voxel-based morphometry.

RESULTS: Among 2071 screened, 1601 youth (55.5% boys; mean age = 12.69 ± 3.04 years) completed the baseline survey, with 880 and 564 completing the 6- and 12-month follow-ups, respectively. FA mediated the relationship between eating motives and weight status, and mindful eating moderated this pathway (p < 0.05). Longitudinally, baseline FA predicted accelerated accumulation of fat content and visceral fat level, but not BMI z-score (p > 0.05). The independent 75-MRI sample revealed that left insula gray-matter volume was negatively associated with FA but positively associated with mindful eating.

CONCLUSIONS: FA may link eating motives to fat accumulation in youth, particularly abdominal fat; mindful eating may be protective, with left insula structure and left insula-striatum connectivity as possible neural correlates.},
}

@article {pmid42220611,
year = {2026},
author = {Xiao, X and Ma, C and Wang, Y and Zhu, J and Chen, J and Li, S and Wang, Y and Gong, W and Si, K},
title = {SynClear: A one-step synchronous clearing and labeling strategy for multiscale 3D brain mapping.},
journal = {Materials today. Bio},
volume = {38},
number = {},
pages = {103261},
pmid = {42220611},
issn = {2590-0064},
abstract = {High-resolution three-dimensional imaging is essential for resolving the multiscale organization of biological tissues. However, conventional workflows treat tissue clearing and molecular labeling as separate steps, leading to a kinetic mismatch between reagent transport and probe binding that limits imaging depth, labeling uniformity, and throughput. Here, we introduce SynClear, a one-step strategy that synchronizes nuclear labeling with tissue clearing by embedding fluorescent probes within a chemically engineered clearing medium. This integrated formulation enables rapid and uniform labeling across millimeter-scale samples while preserving endogenous fluorescence and remaining compatible with multiplexed immunostaining. We demonstrate the general applicability of SynClear across diverse tissue types, including mouse brain, peripheral organs, and post-mortem human cortex. In mouse brain sections, SynClear supports accurate 3D atlas registration and quantitative mapping of cytoarchitecture. In glioblastoma models, it resolves pathological features across scales, from tumor boundaries to immune microenvironments. In human cortex, it enables laminar-resolved structural analysis and neuronal subtype mapping. By coupling labeling and clearing within a single chemical framework, SynClear provides a robust and scalable platform for volumetric tissue imaging, with potential applications in both basic neuroscience and translational pathology.},
}

@article {pmid42222354,
year = {2026},
author = {Zheng, ZW and Liu, H and Guo, LY and Fan, LN and Liu, C and Li, FR and Zhou, R and Qu, L and Wang, RM and Xu, WQ and Yang, GM and Dong, B and Dong, Y and Wu, ZY},
title = {Prevalence of pre-existing neutralizing antibodies to AAV5 and AAV8 in patients with Wilson's disease.},
journal = {Molecular therapy. Advances},
volume = {34},
number = {2},
pages = {201755},
pmid = {42222354},
issn = {3117-387X},
abstract = {Wilson's disease (WD) is an autosomal recessive copper metabolism disorder. AAV-based gene therapy is promising but hindered by pre-existing neutralizing antibodies (NAbs), with no region-specific data on AAV5 and AAV8 NAbs in WD patients. This study aimed to address this gap. We investigated AAV5 and AAV8 NAb seroprevalence and dynamics in a cohort of Chinese WD patients via a cell-based transduction inhibition assay. Results showed that seroprevalence of AAV8 (58.52%) was higher than that of AAV5 (44.89%), with AAV8 NT50 titers 4.6-fold higher (p < 0.001). Seroprevalence increased with age, and AAV5 and AAV8 NAbs were strongly correlated (r = 0.848, p < 0.001) with no AAV5-only positivity. Longitudinal data revealed stable serostatus (3.8% seroconversion, no seroreversion) and no significant associations with other clinical parameters. The p.I1148T variant of ATP7B correlated with higher NAb titers. These findings provide epidemiological insights into pre-existing immunity to AAV vectors in WD patients and may help inform vector selection considerations for future gene therapy studies. Early intervention and personalized strategies may improve therapeutic accessibility. This study provides critical data for AAV-ATP7B trial design in Chinese WD patients.},
}

@article {pmid42223364,
year = {2026},
author = {Bose, A and Gupta, P and Vemuri, K and Das, D and Ghosh, D and Kumar Dana, S and Saha, S and Hens, C},
title = {Ordinal pattern of brain electrical activity as a marker of stroke-induced alterations in motor imagery task.},
journal = {Chaos (Woodbury, N.Y.)},
volume = {36},
number = {6},
pages = {},
doi = {10.1063/5.0314904},
pmid = {42223364},
issn = {1089-7682},
mesh = {Humans ; *Stroke/physiopathology ; *Electroencephalography/methods ; Female ; Male ; *Brain/physiopathology ; *Imagination/physiology ; Middle Aged ; Entropy ; Aged ; Adult ; },
abstract = {While the multidimensional features of electroencephalographic (EEG) signals have proven to be a valuable source of information, the development of a comprehensive diagnostic tool remains elusive due to variability of responses as observed within the subjects and epochs. We investigate whether ordinal-pattern-based complexity measures of EEG signals can capture stroke-related alterations in motor imagery (MI) tasks. EEG recordings from 36 stroke patients (acute and minor) and 36 healthy controls were analyzed using permutation entropy (PE), a robust symbolic measure of temporal irregularity. Stroke patients perform left- and right-hand MI tasks, while controls are recorded only under eye-open MI and eye-closed resting conditions. Results show that resting-state EEG from healthy participants exhibits low PE values, reflecting structured and regular dynamics, whereas eye-open MI EEG from the same cohort produces high PE values consistent with near-maximally complex, information-rich neural dynamics. Stroke patients demonstrate intermediate PE values during MI tasks, suggesting altered but partially preserved physiological complexity. These findings indicate that entropy-based measures can distinguish between healthy and stroke-related neural dynamics, providing potential biomarkers for tailoring brain-computer interface (BCI) driven rehabilitation strategies.},
}

Humans
*Stroke/physiopathology
*Electroencephalography/methods
Female
Male
*Brain/physiopathology
*Imagination/physiology
Middle Aged
Entropy
Aged
Adult

@article {pmid42223450,
year = {2026},
author = {Meyer, LM and Zamani, M},
title = {But do we need high bandwidth? Applications and scaling challenges of invasive brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {23},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ae6dfd},
pmid = {42223450},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces/trends/standards ; Humans ; *Electroencephalography/methods ; },
abstract = {Invasive brain-computer interfaces (iBCIs) have expanded from single to thousands of channels, primarily driven by the goal to restore autonomy and social participation for people with severe neurological impairment. This article evaluates whether this increase in bandwidth (here, the aggregate neural data stream) aligns with clinical benefit or yields diminishing returns against rising challenges. The application landscape reveals that performance typically improves with rising channel count. However, the performance curve also depends on other factors such as task complexity, the evaluation metric, spatial redundancy, and decoder capacity. For today's clinical goals (reliable communication and functional motor restoration), moderate bandwidth already suffices when coupled with model-based priors, structured output spaces, and shared-control architectures; next-horizon goals, e.g. unconstrained natural speech, embodied dexterity, and cognitive restoration, however, require abundant sampling but remain constrained by biological, technical, and ethical hurdles, with the engineering trilemma of bandwidth, power, and latency as the primary bottleneck for fully implantable systems. Solving this requires a shift towards low-power on-implant processing to handle increasing neural datastreams. Looking forward, the field is increasingly orienting toward solutions that balance risk and resolution. Large-scale micro-electrocorticography (µECoG) arrays represent such an approach and complement intracortical strategies, aiming to resolve the long-standing trade-off between invasiveness and bandwidth in clinically viable iBCIs.},
}

*Brain-Computer Interfaces/trends/standards
Humans
*Electroencephalography/methods

@article {pmid42214321,
year = {2026},
author = {Uengsawapak, B and Kongwudhikunakorn, S and Kiatthaveephong, S and Polpakdee, W and Chaisaen, R and Chuenchit, C and Manoonpong, P and Bhakdisongkhram, G and Wilaiprasitporn, T},
title = {EEG-based dataset explicitly targets the transitions between sitting and standing for exploring neural activation patterns in Motor Imagery and execution.},
journal = {GigaScience},
volume = {},
number = {},
pages = {},
doi = {10.1093/gigascience/giag065},
pmid = {42214321},
issn = {2047-217X},
abstract = {This study presents the first publicly accessible electroencephalography (EEG) dataset explicitly targeting sit-to-stand and stand-to-sit transitions during both motor execution (ME) and motor imagery (MI) tasks. Twenty-two healthy participants performed sitting and standing transitions under well-controlled experimental conditions while 60-channel EEG, electrooculography (EOG), and electromyography (EMG) signals were synchronously recorded. The dataset enables the exploration of neural activation patterns associated with lower-limb movements and supports the development of EEG-based brain-computer interface (BCI) algorithms for mobility assistance and rehabilitation. To validate the dataset, benchmark classification was conducted on three baseline deep learning methods-CTNet, EEGNet, and TCANet. Given the high inter-subject variability inherent to EEG, leave-one-subject-out cross-validation (LOSOCV) is used to ensure no subject bias during evaluation. Results demonstrated consistent decoding performance with mean accuracies of approximately 81% for ME and 73% for MI, indicating the reliability and usability of the dataset. Additionally, analyses of movement-related cortical potentials (MRCPs) and event-related desynchronization/synchronization (ERD/ERS) patterns revealed distinct neural signatures across the transition phases. This dataset provides a comprehensive foundation for studying lower-limb motor control, neural dynamics, and the advancement of MI-based BCIs for rehabilitation and assistive technologies.},
}

@article {pmid42215489,
year = {2026},
author = {Liu, L and Ferrante, O and Ghafari, T and Hetenyi, D and Yang, S and Hirschhorn, R and Gorska-Klimowska, U and Sripad, P and Taheriyan, F and Brown, T and Das, D and Kahraman, K and Bonacchi, N and Pitts, M and Mudrik, L and Jensen, O and Luo, H and Melloni, L},
title = {An open multi-center MEG-EEG dataset for studying conscious visual perception.},
journal = {Scientific data},
volume = {13},
number = {1},
pages = {},
pmid = {42215489},
issn = {2052-4463},
support = {TWCF0389,TWCF0486//Templeton World Charity Foundation (Templeton World Charity Foundation, Inc.)/ ; grant number 227420//Wellcome Trust (Wellcome)/ ; NIHR203316//DH | National Institute for Health Research (NIHR)/ ; },
mesh = {Humans ; Female ; *Magnetoencephalography ; *Visual Perception ; *Electroencephalography ; *Consciousness ; Young Adult ; Male ; Adult ; Magnetic Resonance Imaging ; },
abstract = {Here, we present a large-scale, multi-center dataset of combined magnetoencephalographic (MEG) and electroencephalographic (EEG) recordings, along with eye-tracking data and high-resolution structural MRI (T1); complementing with iEEG and fMRI datasets that are shared in accompanying data papers. The data was obtained through an adversarial collaboration between advocates of two neuroscientific theories of consciousness: the Global Neuronal Workspace Theory and the Integrated Information Theory. The dataset includes recordings from 100 individuals (mean age 22.79 ± 3.59 years, 54 female, all right-handed) across two research centers (UK and China), using a standardized data collection protocol. During the experiment, participants were asked to perform a non-speeded Go/No-Go target detection task, during which they were exposed to visual stimuli from four distinct categories (faces, objects, letters, false fonts) presented at different orientations (front, left, right view), and for varying durations (0.5, 1.0, 1.5 s), under different task conditions. The quality of the data was assessed and organized according to the Brain Imaging Data Structure (BIDS). It is accompanied by extensive metadata to enhance reusability.},
}

Humans
Female
*Magnetoencephalography
*Visual Perception
*Electroencephalography
*Consciousness
Young Adult
Male
Adult
Magnetic Resonance Imaging

@article {pmid42150595,
year = {2026},
author = {Li, X and Zheng, C and Tian, Y},
title = {Distinct electrophysiological profiles of bacterial mechanosensitive channels for sonogenetic actuator selection.},
journal = {Journal of neural engineering},
volume = {23},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ae6f83},
pmid = {42150595},
issn = {1741-2552},
mesh = {Animals ; Rats ; *Ion Channels/physiology/genetics ; *Mechanotransduction, Cellular/physiology ; Local Field Potential Measurement ; *Primary Visual Cortex/physiology ; *Escherichia coli Proteins/genetics/physiology ; Male ; *Ultrasonic Waves ; },
abstract = {Objective.Sonogenetics combines ultrasound stimulation with genetically encoded mechanosensitive (MS) ion channels for cell-targeted neuromodulation. Actuator choice, however, remains largely empirical becausein vivoelectrophysiological response signatures are rarely compared under matched conditions. Here, we conducted an exploratoryin vivobenchmarking of three bacterial MS channels (MscL-G22S, MscL-G22N, and MscS) during transcranial ultrasound stimulation in anesthetized rat primary visual cortex (V1).Approach.local field potentials (LFPs) were recorded via a microelectrode array from V1 expressing AAV-delivered channels during graded ultrasound stimulation (1 MHz;Ispta100-400 mW cm[-2]). We quantified baseline activity, ultrasound-evoked potentials (UEPs), trial-to-trial response distributions, and frequency-band power dynamics.Main results.Channel identity shaped both baseline and ultrasound-evoked cortical activity. MscS increased baseline LFP total power (∼2.5 dB vs control,P= 0.0035), whereas MscL-G22S shifted baseline band composition (reduced theta, enhanced gamma). MscL-G22S showed the lowest detectable UEP threshold, producing a detectable N1 at 100 mW cm[-2]and an intensity-dependent N1 increase up to ∼2-fold at 400 mW cm[-2](P< 0.0001). Latency depended on both channel and intensity: MscL-G22N responded faster at low intensity, while MscL-G22S accelerated at higher intensities. MscL expression narrowed trial-to-trial response distributions (bimodal to unimodal). Spectrally, MscL-G22N enhanced theta power, whereas MscL-G22S recruited beta-gamma oscillations at high intensity.Significance.Under matched stimulation and expression conditions, bacterial MscL produced distinct network-level response profiles spanning UEP threshold, response timing, trial-to-trial consistency, and oscillatory modulation. These exploratory benchmarks provide quantitative reference data for comparing sonogenetic actuators and may inform actuator selection for closed-loop neuromodulation.},
}

Animals
Rats
*Ion Channels/physiology/genetics
*Mechanotransduction, Cellular/physiology
Local Field Potential Measurement
*Primary Visual Cortex/physiology
*Escherichia coli Proteins/genetics/physiology
Male
*Ultrasonic Waves

@article {pmid42205683,
year = {2026},
author = {Jha, N and Liu, C and Rogers, A and Lozano, A},
title = {Payers, Proof, and Public Trust: Lessons From Deep Brain Stimulation for Scaling Brain-Computer Interfaces.},
journal = {Mayo Clinic proceedings. Digital health},
volume = {4},
number = {2},
pages = {100366},
pmid = {42205683},
issn = {2949-7612},
}

@article {pmid42206960,
year = {2026},
author = {Lee, HK and Kim, HB and Park, SU and Joo, J and Min, J and Lee, G and Kang, J and Jeong, H and Yoo, JY and Won, SM},
title = {Full-Stack Architectures for Intelligent Brain-Computer Interfaces.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e75838},
doi = {10.1002/advs.75838},
pmid = {42206960},
issn = {2198-3844},
support = {RS-2024-00427006//Korea Planning & Evaluation Institute of Industrial Technology/ ; IITP-2025-RS-2020-II201821//National Research Foundation of Korea (NRF) grant funded by the Korea government/ ; RS-2025-02303342//National Research Foundation of Korea (NRF) grant funded by the Korea government/ ; RS-2024-00406152//National Research Foundation of Korea (NRF) grant funded by the Korea government/ ; RS-2024-00406674//Basic Research Laboratory Project from the National Research Foundation/ ; RS-2024-00418086//Korea Institute for Advancement of Technology/ ; RS-2024-00435693//Korea Institute for Advancement of Technology/ ; },
abstract = {Brain-computer interfaces (BCIs) have made consistent advances in supporting motor and communication functions; nevertheless, their adoption in everyday environments remains constrained by enduring challenges, including chronic instability at the electrode-tissue interface, motion-induced artifacts, inter-user variability, and strict power and bandwidth limitations. To address these issues, recent work has increasingly focused on system-level innovations encompassing electrode design, wireless communication strategies, and neural decoding algorithms. At the interface level, enhancements in electrochemical performance and mechanical compliance improve long-term electrode-tissue coupling and help maintain signal integrity during naturalistic movement. For signal acquisition and transmission, miniaturized front-end electronics and energy-efficient telemetry architectures enable higher channel counts while minimizing power consumption and optimizing bandwidth utilization. In parallel, decoding approaches have evolved from static, feature-based pipelines toward adaptive machine-learning and deep-learning methods that are more resilient to nonstationary neural signals and capable of supporting low-latency, closed-loop operation. This review consolidates findings from contemporary preclinical and human studies to provide a comprehensive perspective on system-level engineering strategies for practical BCI technologies, emphasizing neural interface architecture and system-design approaches that enhance signal stability and real-world usability, while also identifying emerging design paradigms that may facilitate next-generation BCIs with improved scalability and broader practical impact.},
}

@article {pmid42212280,
year = {2026},
author = {Yu, H and Wang, J and Li, Q and Xu, P and Xu, S and Chen, C and Lu, J and Li, F and Yao, D and Xu, P and Hou, J and Ma, X and Yi, C},
title = {Dynamic central-peripheral balance in brain-muscle interactions reveals motor impairment in post-stroke hemiplegia: an exploratory study.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {102},
pmid = {42212280},
issn = {1871-4080},
abstract = {Hemiplegia following stroke is characterized by disrupted neuromuscular interactions, yet the central-peripheral dynamics remain unclear. This study investigated dynamic causal interactions between electroencephalography (EEG) and electromyography (EMG) using the adaptive directed transfer function (ADTF) during a thumb-pressing task in hemiplegic patients and explored the central-peripheral balance between central motor commands and peripheral sensory feedback. Results suggested that patients with better motor functions may exhibit a dynamic transition from relatively balanced bidirectional interactions to centrally dominated descending control and back to balance. Patients with more severe hemiplegia exhibited pronounced descending control impairment and ascending feedback enhancement, particularly on the affected side. The difference between the out-degrees of central-peripheral pathways during the motor preparatory phase served as a potential predictor of motor function, as assessed by the Barthel Index. This finding provides exploratory evidence for the imbalance between peripheral-to-central and central-to-peripheral coupling as a potential neural biomarker for functional recovery, tentatively supporting the development of more targeted and personalized rehabilitation strategies.},
}

@article {pmid42213304,
year = {2026},
author = {Shi, B and Li, J and Shao, B and Song, C and Bai, G and Lan, T and Gao, T and Li, Y and Song, T and Sun, B},
title = {Stiffness-Switchable Conductive Nanocomposites with Temperature-Invariant Conductivity for Long-Term Brain-Computer Interfaces on Hair-Covered Scalp.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {},
number = {},
pages = {e73966},
doi = {10.1002/smll.73966},
pmid = {42213304},
issn = {1613-6829},
support = {22408248//National Natural Science Foundation of China/ ; 62274116//National Natural Science Foundation of China/ ; 62474120//National Natural Science Foundation of China/ ; ZZ2506//Jiangsu Provincial Key Laboratory of Carbon-Based Functional Materials and Devices for High-Tech Research/ ; //111 Program/ ; //Collaborative Innovation Center of Suzhou Nano Science and Technology/ ; 0004/2025/RDP//Macau SAR/ ; },
abstract = {Reliable neural recording on densely hair-covered scalp remains challenging due to the incompatibility between efficient hair penetration, conformal skin contact, and low-impedance electrical interfacing. Here, we report a claw-shaped dry electrode that integrates thermoresponsive phase-transition networks for reversible stiffness switching, with temperature-invariant conductivity enabled by crystallization-induced confinement, achieving comfortable, low-impedance neural interfacing on densely hair-covered scalps. The electrode comprises a bottlebrush polymer/multi-walled carbon nanotubes (MWCNTs) composite, in which crystallizable alkyl side chains act as switching units, enabling rigid hair penetration at ambient conditions and compliant, adhesive scalp interfacing at skin temperature. Importantly, side-chain crystallization imposes spatial confinement on MWCNTs, enabling efficient percolated networks with an ultralow percolation threshold (0.47 wt.%) and high electrical conductivity (1.8 S m[-1]). Meanwhile, strong MWCNTs-polymer interfacial interactions provide multipoint anchoring that helps preserve conductive pathway continuity across phase transitions, achieving low electrode-scalp impedance (∼38 kΩ) upon softening and conformal contact. Validated by steady-state visual evoked potential measurements, this electrode enables high-fidelity and frequency-resolved neural signal acquisition and maintains stable operation for over 100 days, supporting a fully wearable brain-computer interface with real-time drone control.},
}

@article {pmid42202831,
year = {2026},
author = {Gorenshtein, A and Omar, M and Barash, Y and Nadkarni, G and Klang, E},
title = {Large Language Models Integrated into Brain-Computer Interfaces for Communication and Control: A Systematic Review.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ae737b},
pmid = {42202831},
issn = {2057-1976},
abstract = {Large language models (LLMs) are starting to be coupled with brain-computer interfaces (BCIs) for assistive communication, but the resulting systems differ widely in where the model sits in the pipeline and in what they actually measure. We performed a systematic review, prepared according to PRISMA, of eleven studies that combine an LLM with a BCI for communication or control. The included work covers P300, SSVEP, cVEP, passive affective and auditory paradigms, and five integration patterns: autocomplete, post-edit correction, intent expansion, dynamic interface generation and affective support. For each study we extracted the hardware and decoding pipeline, the LLM and prompting strategy, latency reporting and outcomes; we used scenario-appropriate metrics rather than a single common metric. Risk of bias was judged with an adapted ROBINS-I framework that stratified studies into online, offline-simulation and system-proposal categories. In the copy-spelling scenario, two studies that measured keystroke savings directly reported values above 50%, with one study exceeding 60% in a multi-turn condition; on an intent-based ALS message-bank task, one online study reached 42 characters per minute with a semantic accuracy of 88%. None of the eleven studies enrolled motor-impaired patients, seven of eleven relied on remote OpenAI endpoints, and reporting of end-to-end latency and failure modes was sparse. We propose a five-category taxonomy of BCI/LLM integration, separate findings that are supported from those that are still speculative, and give a checklist of metrics that should be reported by future studies. The taxonomy and the reporting checklist are the main contributions; clinical benefit for the target population remains to be shown.},
}

@article {pmid42204016,
year = {2026},
author = {Chen, X and Qiu, Y and Fu, Y and Shen, M and Chen, H},
title = {Item-specific source misattribution drives short-term source amnesia.},
journal = {Psychonomic bulletin & review},
volume = {33},
number = {5},
pages = {},
pmid = {42204016},
issn = {1531-5320},
support = {32171046//National Natural Science Foundation of China/ ; 32200844//National Natural Science Foundation of China/ ; 32441105//National Natural Science Foundation of China/ ; 32471093//National Natural Science Foundation of China/ ; 2022ZD0210800//Science and Technology Innovation 2030-"Brain Science and Brain-like Research" Major Project/ ; 226-2024-00118//Fundamental Research Funds for the Central Universities/ ; 226-2024-00207//Fundamental Research Funds for the Central Universities/ ; 226-2025-00127//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; *Memory, Short-Term/physiology ; *Amnesia/physiopathology ; *Mental Recall/physiology ; Adult ; Young Adult ; Female ; Male ; },
abstract = {Source amnesia refers to the failure to remember the source format of information despite remembering the content itself. While well-documented in long-term memory, recent studies have revealed that source amnesia can also occur in short-term or working memory. Across four experiments, the present study aimed to investigate why short-term source amnesia arises, focusing on whether it results from source misattribution between items or item-specific interference caused by repeated exposure to the same content in different formats. We found that source misattribution persisted even for a single item presented per trial, suggesting that item-source misbinding between simultaneously presented items is not necessary for source-amnesia effect. Source misattribution was significantly reduced when the test item was novel or had consistently appeared in a single format across trials, but reliably emerged when the same item had been presented in different formats. These findings suggest that short-term source amnesia reflects item-specific source misattribution, driven by the coexistence of conflicting source traces for the same content. We propose that the task-irrelevant source information for target stimuli is stored in an intermediate representational state-activated long-term memory-which maintains weak bindings to its content but lacks robust contextual indexing.},
}

Humans
*Memory, Short-Term/physiology
*Amnesia/physiopathology
*Mental Recall/physiology
Adult
Young Adult
Female
Male

@article {pmid42190717,
year = {2026},
author = {Yu, C and Dong, X and Zhang, Y and Wan, X and Zhou, Y and Zheng, Y and Liu, H and Li, P and Cui, Z and Wan, C and Li, Y},
title = {Development and feasibility of a motor imagery-based brain-computer interface-controlled closed-loop functional electrical stimulation system for swallowing rehabilitation.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae730b},
pmid = {42190717},
issn = {1741-2552},
abstract = {OBJECTIVE: Conventional swallowing functional electrical stimulation (FES) is usually delivered in open loop or triggered by peripheral signals, which may not align precisely with voluntary swallowing intention. We developed a motor imagery-based brain-computer interface (MI-BCI)-controlled closed-loop swallowing FES system for post-stroke dysphagia (PSD) and investigated its neurophysiological basis, decoding performance, and short-term feasibility.

APPROACH: Two experiments were conducted. In Experiment 1, swallowing motor imagery (SMI)-related electroencephalography (EEG) features were identified in healthy controls (HC, n = 15), patients with PSD (n = 15), and post-stroke patients without dysphagia (PSND, n = 15). A threshold-based decoder based on Fp1 spectral power ratios was then validated in an independent HC cohort (n = 10). In Experiment 2, 10 patients with PSD received 10 sessions of MI-BCI-controlled closed-loop swallowing FES over 2 weeks, and feasibility, usability, and safety were assessed.

MAIN RESULTS: During SMI, Fp1 spectral power ratios decreased relative to rest. The δ/α ratio decreased significantly in all three groups, whereas the δ/(α + β) ratio and the (δ + θ)/(α + β) ratio decreased significantly in HC and PSND and showed the same downward trend in PSD. Patients with PSD also showed higher θ-band power at T3 than HC and PSND (P = 0.0382). The decoder achieved a mean classification accuracy of 71.5% in the independent validation cohort. In Experiment 2, adherence was 100%, with 29.8 ± 6.2 successful closed-loop triggers per session, a mean System Usability Scale score of 72.8 ± 4.2, and no serious adverse events.

SIGNIFICANCE: These findings support the technical feasibility of the proposed system, indicate acceptable short-term usability, and show no major safety concerns during the intervention period. Trial registration: ChiCTR2400079388.},
}

@article {pmid42193456,
year = {2026},
author = {Aktepe, OH and Ulasli, T and Butun, O and Yalcin, S},
title = {Elevated B12/CRP Index as a Simple Prognostic Indicator in Patients with Metastatic Renal Cell Carcinoma Treated with First-Line Targeted Therapy.},
journal = {Biomedicines},
volume = {14},
number = {5},
pages = {},
doi = {10.3390/biomedicines14051131},
pmid = {42193456},
issn = {2227-9059},
abstract = {Background/Objectives: The vitamin B12 (VB12)/C-reactive protein (CRP) index (BCI), a clinically derived index calculated as serum VB12 multiplied by CRP, has shown prognostic value in several cancers. However, its association with survival outcomes in metastatic renal cell carcinoma (mRCC) remains unclear. Therefore, the aim of the present study was to evaluate the prognostic significance of BCI in patients with mRCC treated with targeted therapy. Methods: The BCI was calculated as serum VB12 concentration (pg/mL) × serum CRP concentration (mg/L). The patients were categorized into two BCI prognostic subgroups, high BCI (BCI > 40,000) and low BCI (≤40,000). Survival differences between prognostic subgroups were measured using the Kaplan-Meier method with a log-rank test. Univariate and multivariable analyses were used to determine the association between the selected variables and survival outcomes. Results: We included 213 patients with mRCC, with a median follow-up time of 76 months. The median progression-free survival (PFS) and overall survival (OS) were 10.9 months and 47.7 months, respectively. Patients with high BCI had poorer PFS and OS times than those with low BCI (7.8 months vs. 12.6 months, p = 0.002 for PFS; 22.6 months vs. 68 months, p < 0.001 for OS, respectively). After adjusting for potential confounders, high BCI remained independently associated with poorer PFS and OS (hazard ratio [HR]: 2.40, 95% confidence interval [CI] 1.35-4.26, p = 0.003 for PFS; HR 2.01, 95% CI 1.40-2.88, p < 0.001 for OS). Conclusions: BCI appears to be a promising prognostic biomarker in patients with mRCC treated with first-line targeted therapy. However, its applicability to immune checkpoint inhibitor-based or combination regimens requires prospective validation.},
}

@article {pmid42193552,
year = {2026},
author = {Wang, L and Huang, Y and Liu, Y and Zhou, J},
title = {When Scarcity Meets Sustainability: Consumer Preferences for Recycled Products.},
journal = {Behavioral sciences (Basel, Switzerland)},
volume = {16},
number = {5},
pages = {},
doi = {10.3390/bs16050673},
pmid = {42193552},
issn = {2076-328X},
support = {STI 2030 Major Projects 2021ZD0200409//Ministry of Science and Technology/ ; Grant No. 72371226//National Natural Science Foundation of China/ ; },
abstract = {The widespread disposal of waste has led to severe environmental challenges, making the reuse of materials critical for sustainable development. Recycled products, which transform waste into valuable items, are gaining increasing attention from consumers. This research examines how perceived resource scarcity shapes consumer preferences for recycled products and the psychological mechanisms underlying this effect. Across four studies, we induced perceptions of scarcity using two distinct approaches and found that consumers experiencing resource scarcity exhibit higher purchase intentions for recycled products compared with those who do not. This effect is mediated by holistic thinking, which allows consumers to integrate information about a product's past and present identities, enhancing appreciation for transformation and reuse. Moreover, perceived product contamination moderates this relationship. When contamination concerns are low, scarcity strengthens preference for recycled products, whereas high contamination perceptions weaken or eliminate this effect. These findings extend understanding of how resource scarcity influences sustainable consumption, highlight the cognitive processes driving recycled product demand, and provide practical guidance for policymakers and businesses promoting environmentally responsible consumption.},
}

@article {pmid42194293,
year = {2026},
author = {Huang, CJ and Cao, CF and Shyu, KK and Lee, TM and Lee, PL},
title = {Continual-Learning-Enhanced CNN-Transformer Framework for Real-Time Motor-Imagery BCI in Virtual Environments.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {13},
number = {5},
pages = {},
doi = {10.3390/bioengineering13050536},
pmid = {42194293},
issn = {2306-5354},
abstract = {Motor imagery (MI)-based brain-computer interfaces (BCIs) provide an intuitive pathway for neural interaction and rehabilitation, yet their practical deployment remains constrained by long calibration requirements, substantial inter-subject variability, and the non-stationary nature of EEG signals. These challenges are amplified when using dry-electrode EEG, which offers superior convenience for real-world systems but produces noisier and less stable recordings than traditional wet electrodes. As a result, online or real-time four-class MI detection-especially with dry electrodes-has been explored only in a limited number of studies, underscoring an important gap in the field and the need for adaptive, intelligent models capable of coping with continuous signal drift. In this study, we propose a real-time MI-BCI framework that integrates immersive action observation (AO) in virtual reality with a continual learning strategy to manage the evolving nature of dry-EEG features. A CNN-Transformer hybrid model is first initialized through AO-enhanced pre-training and subsequently refined via online continual adaptation during user interaction. This continual learning mechanism enables the classifier to incrementally assimilate new MI patterns while preserving previously acquired knowledge, thereby mitigating the performance degradation that typically arises in extended MI-BCI sessions. Experimental results across four motor classes demonstrate improved decoding accuracy and strengthened sensorimotor activation over time, confirming the system's capacity for user-specific and session-to-session adaptation. By addressing the rarely studied combination of dry electrodes, online four-class MI decoding, and continual learning, the proposed approach enhances MI-BCI robustness, reduces calibration burden, and supports sustainable long-term deployment in intelligent neurotechnology applications.},
}

@article {pmid42194318,
year = {2026},
author = {Gravunder, A and Studnicki, A and Kline, J and Behboodi, A and Bulea, TC and Damiano, DL},
title = {Novel Time-Series Forecasting Method to Enhance Accuracy of Real-Time EEG Detection for BCI-Based Neurofeedback Motor Training in Individuals with Cerebral Palsy and Other Neurological Disorders.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {13},
number = {5},
pages = {},
doi = {10.3390/bioengineering13050561},
pmid = {42194318},
issn = {2306-5354},
support = {13-CC-0110//National Institutes of Health Clinical Center/ ; },
abstract = {Real-time detection of motor intent using electroencephalography (EEG) with high accuracy remains a technical challenge for neurorehabilitation. Brain-computer interface-based neurofeedback training (BCI-NFT) paradigms need to detect pre-movement EEG to activate robotics or electrical stimulation nearly simultaneously with movement to promote neuroplasticity. We present a novel detection method commonly used in time-series forecasting (e.g., stock market trends), identifying crosses in fast (short) and slow (long) moving average windows to identify negative deflections in slow movement-related cortical potentials (MRCPs) or event-related desynchronization (ERD) within -400-+100 ms of movement onset. We recorded EEG data from the Cz electrode during our cued ankle dorsiflexion BCI-NFT paradigm in four adult participants, two neurotypical and two with cerebral palsy. Simulated real-time offline analyses demonstrated an 85.9% mean true positive rate and 14.1% false positive rate of detecting motor intent at a mean -182 ms from movement onset. We further evaluated whether the detection indicated a MRCP and/or ERD, with MRCP detected in 70-80% of trials in three participants, but high ERD detection (87%) instead in the other. Preliminary results indicate that this approach offers a straightforward, accurate, and well-timed method for real-time EEG detection during neurofeedback training and as a control signal for brain-computer interfaces.},
}

@article {pmid42194335,
year = {2026},
author = {Fan, K and Gu, Q and Ruan, Y},
title = {EEG-ShuffleFormer: A Multi-View Hybrid Network Integrating Time-Frequency and Raw Signal Representations for Few-Channel Motor Imagery EEG Classification.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {13},
number = {5},
pages = {},
doi = {10.3390/bioengineering13050578},
pmid = {42194335},
issn = {2306-5354},
abstract = {Electroencephalogram (EEG) signals hold significant research value in brain function decoding, disease diagnosis, and brain-computer interfaces (BCIs). Few-channel EEG recording devices feature superior portability, simple operation, and facilitated real-time monitoring implementation. However, few-channel motor imagery (MI) EEG signals inherently suffer from data scarcity and limited spatial discriminative information, which pose critical challenges, including insufficient feature extraction and poor robustness in classification tasks. To address these issues, this paper presents EEG-ShuffleFormer, a hybrid network that integrates two complementary views of EEG signals: time-frequency representations obtained via continuous wavelet transform and the original raw signal representations. A lightweight ShuffleNet backbone extracts local features, followed by a Transformer encoder that models long-range temporal dependencies. Evaluated on the BCI Competition IV Dataset 2b, the proposed method achieves an average classification accuracy of 82.23%, with a substantial improvement on challenging subjects compared to the closest baseline method. Compared with existing methods, the proposed multi-view fusion strategy raises the performance floor while maintaining high accuracy on typical subjects, demonstrating its potential to enhance robustness for different subjects in few-channel scenarios.},
}

@article {pmid42195385,
year = {2026},
author = {Bastidas-Benalcazar, N and Calero-Apunte, JA and Almeida-Galarraga, D and Navas-Boada, P and Alvarado-Cando, O and Tirado-Espín, A and Villalba-Meneses, F and Carvajal Mora, H and Orozco Garzón, N},
title = {The Neuro-Cardiac Symbiotic Engine: A Multimodal Fusion Architecture for Cognitive State Decoding via High-Performance Computing.},
journal = {Life (Basel, Switzerland)},
volume = {16},
number = {5},
pages = {},
doi = {10.3390/life16050830},
pmid = {42195385},
issn = {2075-1729},
support = {577.A.XVII.25//Universidad de Las Américas/ ; 41991//Universidad San Francisco de Quito/ ; },
abstract = {Robust decoding of latent cognitive states from non-stationary physiological time series is a challenging high-dimensional signal processing problem. Traditional unimodal frameworks based only on electroencephalography often show covariate shift and weak cross-task generalization. This study presents the Neuro-Cardiac Symbiotic Engine, a multimodal fusion architecture that combines high-frequency cortical EEG dynamics with low-frequency autonomic regulation derived from heart rate variability within a unified discriminative feature space. The pipeline integrates spectral decomposition and autonomic quadratic descriptors through a memory-optimized high-performance computing workflow on the CEDIA supercomputer. To reduce domain discrepancy between memory and piloting tasks, we design a few-shot calibration strategy based on affine manifold alignment and probabilistic ensemble inference. Validation on 29 subjects reaches a mean classification accuracy of 99.13 percent, far above the zero-shot baseline near 38 percent. Topological analysis also indicates phase-space contraction under high workload, where fused vagal and frontal-parietal biomarkers concentrate system dynamics into a low-entropy attractor. The results establish a mathematically grounded framework for passive brain-computer interfaces and show that orthogonal neuro-visceral integration is critical for reliable cognitive state estimation.},
}

@article {pmid42195527,
year = {2026},
author = {Wu, Q and Gong, Y and Liu, X},
title = {Bridging the Gap: Integrated High-Density Microelectrode Arrays for Cellular, Organoid, and Clinical Electrophysiology.},
journal = {Micromachines},
volume = {17},
number = {5},
pages = {},
doi = {10.3390/mi17050611},
pmid = {42195527},
issn = {2072-666X},
abstract = {High-density microelectrode arrays (HDMEAs) have become increasingly important tools in neuroscience and biomedical engineering because of their high spatial and temporal resolution for recording and modulating electrical activity across diverse biological systems. Initially developed for in vitro studies of cultured cells, HDMEAs are now being applied to increasingly complex models, including organoids, animal systems, and even human neural systems. These advancements enable a deeper investigation of cellular interactions, network dynamics, and disease mechanisms, as well as providing novel therapeutic and diagnostic tools for neurological disorders. This review explores the evolution of HDMEAs, emphasizing recent innovations in their design, fabrication, and functionalization. We discuss their applications across cellular models, organoid systems, animal studies, and human electrophysiology, and highlight current challenges such as biocompatibility, long-term stability, scalability, and translational deployment. Finally, we outline future directions for advancing HDMEA technologies in both research and clinical settings.},
}

@article {pmid42196223,
year = {2026},
author = {Ma, S and Li, Y and Fei, T},
title = {CRISPR Screening in Hepatocellular Carcinoma: From Tumor Progression to Immune Evasion and Therapeutic Resistance.},
journal = {International journal of molecular sciences},
volume = {27},
number = {10},
pages = {},
doi = {10.3390/ijms27104241},
pmid = {42196223},
issn = {1422-0067},
support = {2023A1515140084//Guangdong Basic and Applied Basic Research Foundation/ ; 32470673//National Natural Science Foundation of China/ ; B16009//the 111 Project/ ; 2022JH13/10200026//the Construction Project of Liaoning Provincial Key Laboratory, China/ ; },
mesh = {Humans ; *Carcinoma, Hepatocellular/genetics/therapy/immunology/pathology ; *Liver Neoplasms/genetics/immunology/therapy/pathology ; *Drug Resistance, Neoplasm/genetics ; *CRISPR-Cas Systems ; Animals ; Disease Progression ; *Tumor Escape/genetics ; *Clustered Regularly Interspaced Short Palindromic Repeats ; *Immune Evasion/genetics ; },
abstract = {Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and a leading cause of cancer-related mortality worldwide. Despite advances in targeted therapies and immunotherapies, clinical outcomes remain poor owing to profound molecular heterogeneity, intrinsic therapeutic resistance, and complex immune evasion mechanisms. Although genomic profiling has identified recurrent alterations in HCC, large-scale functional validation of candidate drivers and vulnerabilities remains challenging. CRISPR (clustered regularly interspaced short palindromic repeats)-based screening technologies have transformed this landscape by enabling systematic interrogation of gene function in physiologically relevant contexts. In this review, we summarize recent studies that have applied CRISPR screening approaches in HCC research. These efforts have uncovered multilayered dependency programs that govern ferroptosis resistance, metabolic reprogramming, epigenetic regulation, tumor suppressor networks, immune evasion, and resistance to targeted therapies. We also discuss the major limitations of current studies, including model bias, incomplete representation of HCC heterogeneity, and technical constraints intrinsic to pooled screening. Overall, integration of CRISPR screening with patient-derived models, single-cell readouts, and precision editing technologies is expected to accelerate mechanistic discovery and biomarker-guided therapeutic prioritization for HCC.},
}

Humans
*Carcinoma, Hepatocellular/genetics/therapy/immunology/pathology
*Liver Neoplasms/genetics/immunology/therapy/pathology
*Drug Resistance, Neoplasm/genetics
*CRISPR-Cas Systems
Animals
Disease Progression
*Tumor Escape/genetics
*Clustered Regularly Interspaced Short Palindromic Repeats
*Immune Evasion/genetics

@article {pmid42197993,
year = {2026},
author = {Hu, X and Kang, M and Liu, Y and Shi, T and Shi, X and Fu, Y and Gong, A},
title = {Design and Experimental Investigation of a Multi-Level Heartbeat Sound Feedback-Based Neurofeedback System: Neural Mechanisms.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {10},
pages = {},
doi = {10.3390/s26103187},
pmid = {42197993},
issn = {1424-8220},
support = {62006246, 82172058, 62376112, 81771926, 61763022//National Natural Science Foundation of China/ ; 2023M734315//China Postdoctoral Science Foundation/ ; 959202413100//the Xi'an Science and Technology Association Youth Talent Support Program/ ; },
mesh = {*Neurofeedback/physiology/methods ; Humans ; Brain-Computer Interfaces ; Electroencephalography/methods ; *Heart Rate/physiology ; Male ; Brain/physiology ; },
abstract = {Auditory neurofeedback training (NFT) based on brain-computer interfaces (BCIs) has recently entered the precision motor domain as a task-embedded neural state regulation paradigm. Compared to traditional standalone NFT approaches (e.g., relaxation or attention training designed to enhance general cognitive abilities), task-embedded paradigms integrate feedback directly into the motor task execution process. However, this design inevitably creates a dual-task scenario, and the effects of such a scenario on neural activity and behavioral performance have received limited systematic investigation in the existing literature. This study designed and implemented a closed-loop BCI system employing five-level heartbeat sound feedback and used this system as a research platform to examine the immediate neural mechanism changes and potential dual-task interference effects induced by single-session auditory NFT in moderately skilled shooters. The system maps real-time EEG features onto graded auditory signals varying in playback rate and volume intensity, incorporating a dynamic threshold adjustment mechanism. Twenty-two moderately skilled shooters completed three within-subject conditions (no-sound baseline, SMR enhancement, and theta suppression) in a single session with 32-channel EEG and behavioral data recorded simultaneously. Analyses employed whole-brain cluster-based permutation tests, cross-frequency coupling analysis, and functional connectivity analysis. Cluster-based permutation tests revealed that theta feedback induced a significant frontal 4-7 Hz suppression cluster (cluster p = 0.004), whereas SMR feedback did not produce significant 12-15 Hz enhancement at the group level. Theta feedback elicited cross-frequency spillover as follows: sensorimotor SMR power decreased significantly in theta responders (d = -0.69), with frontal theta and sensorimotor SMR changes positively correlated (r = 0.67, p < 0.001). Functional connectivity analysis using debiased weighted phase lag index (dwPLI) further demonstrated significant theta-band network reorganization (cluster p = 0.034). At the neural level, clear modulation effects were observed, but shooting ring values did not improve significantly under feedback conditions, and aiming time was significantly prolonged-a behavioral pattern consistent with potential dual-task interference from task-embedded auditory feedback. Single-session auditory NFT can act on the prefrontal cognitive control network and induce cross-frequency network reorganization, but the feedback channel itself constitutes a parallel task that may limit the short-term transfer of induced neural states to behavioral performance. This study examined the neural mechanisms of task-embedded auditory NFT and reported the dual-task costs that have been less characterized in prior "task + feedback" research, providing design considerations and preliminary mechanistic evidence for future development of auditory NFT in precision motor skill training.},
}

*Neurofeedback/physiology/methods
Humans
Brain-Computer Interfaces
Electroencephalography/methods
*Heart Rate/physiology
Male
Brain/physiology

@article {pmid42198020,
year = {2026},
author = {Zhang, H and Siok, WT and Wang, N},
title = {Imagined Speech Brain-Computer Interface: A Task-Oriented Review of Neural Decoding.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {10},
pages = {},
doi = {10.3390/s26103212},
pmid = {42198020},
issn = {1424-8220},
support = {P0053210, P0053738, P0048377, P0056428, P0058097//Hong Kong Polytechnic University/ ; C5033-24G//Research Grants Council Collaborative Research Fund/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Speech/physiology ; *Brain/physiology ; Electroencephalography ; *Imagination/physiology ; },
abstract = {Imagined speech decoding has attracted growing interest in brain-computer interface (BCI) research, as it may enable language-related information to be recovered from non-overt neural activity. Current studies in this area are often treated as a single, unified research problem, despite substantial differences in decoding target, output constraints, and system output forms. This review examines recent imagined speech decoding research from a task-oriented perspective, with a focus on how different neural decoding tasks are defined, constrained by their output spaces, and expressed through different output pathways. The included studies are organized into four main task levels: semantic/intent, phoneme/syllable, word, and sentence/language decoding. They are further compared along two auxiliary dimensions: output-space property and output pathway, with particular attention to closed-set and open-vocabulary settings. The review shows that current studies span markedly different linguistic granularities and communication objectives, from low-bandwidth intent recognition to text or speech reconstruction. Finally, it concludes that imagined speech should not be treated as a single homogeneous decoding problem, and that a task-oriented framework provides a clearer basis for comparing heterogeneous studies and guiding future communication-oriented BCI research.},
}

*Brain-Computer Interfaces
Humans
*Speech/physiology
*Brain/physiology
Electroencephalography
*Imagination/physiology

@article {pmid42198082,
year = {2026},
author = {Suffian, M and Ieracitano, C and Mammone, N and Pascarella, A and Ferlazzo, E and Morabito, FC},
title = {An EEG-Based Edge-AI Framework for Alzheimer's and Creutzfeldt-Jakob Disease Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {10},
pages = {},
doi = {10.3390/s26103274},
pmid = {42198082},
issn = {1424-8220},
mesh = {Humans ; *Creutzfeldt-Jakob Syndrome/diagnosis/classification/physiopathology ; *Alzheimer Disease/diagnosis/classification/physiopathology ; *Electroencephalography/methods ; *Artificial Intelligence ; Convolutional Neural Networks ; Signal Processing, Computer-Assisted ; Algorithms ; Deep Learning ; Neural Networks, Computer ; Female ; },
abstract = {Electroencephalography (EEG) has emerged as a promising non-invasive tool for the diagnosis of neurodegenerative disorders, and artificial intelligence (AI) has shown significant potential in this domain, as demonstrated by recent studies. However, strong inter-subject variability remains a major challenge, limiting the ability of AI-based models to learn disease-specific features that generalize across individuals, thereby hindering the development of clinically deployable subject-independent systems. In this work, we propose a cross-subject, AI-based EEG classification framework to distinguish between Alzheimer's disease (AD), Creutzfeldt-Jakob disease (CJD), and healthy control subjects using clinical EEG data collected from a local hospital. A lightweight hybrid deep learning model is developed, combining a two-layer one-dimensional convolutional neural network with a two-layer Transformer encoder to capture both local temporal patterns and long-range dependencies in EEG signals. The proposed model achieves an average classification accuracy of 97%, representing a 3% improvement over a baseline model evaluated on a cohort of 36 subjects. To assess deployment feasibility in real-time clinical settings, the trained model is implemented and evaluated on an edge-AI platform (NVIDIA Jetson AGX Orin), demonstrating energy efficiency for the inference with a compact model footprint. These results indicate that the proposed approach provides an accurate, efficient, and practically deployable solution for subject-independent EEG-based classification of neurological disorders.},
}

Humans
*Creutzfeldt-Jakob Syndrome/diagnosis/classification/physiopathology
*Alzheimer Disease/diagnosis/classification/physiopathology
*Electroencephalography/methods
*Artificial Intelligence
Convolutional Neural Networks
Signal Processing, Computer-Assisted
Algorithms
Deep Learning
Neural Networks, Computer
Female

@article {pmid42202025,
year = {2026},
author = {Won, C and Cho, YU and Kweon, S and Cho, S and Kwon, C and Kim, HW and Lee, JY and Park, SH and Han, S and Kim, YT and Jang, J and Jekal, J and Kim, JG and Jang, KI and Xu, S and Gao, W and Cho, IJ and Yu, KJ and Lee, T},
title = {Structurally engineered ultrasoft PEDOT:PSS fiber microelectrodes with enhanced electrochemical performance for neural interfaces.},
journal = {Science advances},
volume = {12},
number = {22},
pages = {eaee2754},
doi = {10.1126/sciadv.aee2754},
pmid = {42202025},
issn = {2375-2548},
mesh = {Animals ; Microelectrodes ; *Polystyrenes/chemistry ; Mice ; *Neurons/physiology ; *Polymers/chemistry ; *Bridged Bicyclo Compounds, Heterocyclic/chemistry ; Hippocampus/physiology ; *Electrochemical Techniques ; Thiophenes ; },
abstract = {Stable and reliable neural interfacing is essential for the diagnosis and treatment of chronic neurological disorders. Flexible neural probes are particularly important for this purpose, as they minimize tissue damage and inflammatory responses while maintaining stable electrode-tissue coupling; however, achieving both high electrical performance and tissue-like mechanics remains challenging. Here, we present a poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) fiber microelectrode (PFME), an all-organic neural probe capable of recording single-neuron activities with potential for long-term interfacing. The PFME is entirely composed of organic components and fabricated without thermal processing. In addition, the posttreatment process enables to selectively remove PSS binder networks while promoting PEDOT chain alignment to optimize mechanical compliance and electrochemical performance. In vivo, the PFME enables stable single-unit recordings from the mouse hippocampus. Histological analysis after 1 week of implantation reveals minimal glial activation comparable to that elicited by a conventional probe. This structurally engineered PFME establishes a pathway to achieve minimally invasive neural interfacing platforms for chronic applications.},
}

Animals
Microelectrodes
*Polystyrenes/chemistry
Mice
*Neurons/physiology
*Polymers/chemistry
*Bridged Bicyclo Compounds, Heterocyclic/chemistry
Hippocampus/physiology
*Electrochemical Techniques
Thiophenes

@article {pmid42202176,
year = {2026},
author = {Zhao, R and Daly, I and He, X and Xu, R and Wang, C and Wang, X and Cichocki, A and Jin, J},
title = {Breaking the Depth Barrier in Motor Imagery Classification via a Residual Depthwise-Separable Network.},
journal = {IEEE transactions on cybernetics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TCYB.2026.3690707},
pmid = {42202176},
issn = {2168-2275},
abstract = {Lightweight networks that include depthwise-separable convolution are widely used in motor imagery (MI) electroencephalogram (EEG) decoding of brain-computer interface (BCI). Many established MI classification networks are relatively shallow, preventing them from benefiting from the hierarchical feature extraction capabilities of deeper structures. Due to suboptimal residual connection structures, the mismatched residual baseline layer design, and the poor compatibility between data preprocessing and residual modules, the deepening of networks cannot be effectively combined with residual structures. This creates a depth barrier that hinders further performance improvements. To address these challenges, we propose a novel method, residual depthwise-separable deep neural network (ResDSNet), built upon an unraveled view-path analysis of residual connection structures. The analysis reveals that the residual mechanism achieves optimal performance when the layer distribution across different paths approximates a binomial distribution. Furthermore, we design a residual depthwise-separable convolution module and a tailored data-preprocessing module that effectively integrate with the residual structure, filtering noise and retaining MI task features. We evaluate ResDSNet on three publicly available datasets, including the BCI Competition IV Dataset IIa, the BCI Competition IV Dataset IIb, and the PhysioNet dataset, which collectively contain EEG signals recorded from 127 human subjects. ResDSNet achieves accuracies of 79.36%, 84.95%, and 64.13%, outperforming state-of-the-art methods by 3.16%, 1.59%, and 8.40% with statistical significance. Experimental results indicate that ResDSNet fully unlocks the hierarchical representation capabilities of deep networks for MI-EEG decoding, achieving robust performance and demonstrating substantial potential to overcome the inherent challenges in BCIs.},
}

@article {pmid42185714,
year = {2026},
author = {Lim, Z and Nguyen, HL and Zeng, Y and Qu, Q and Le, Y and Sun, M and Wang, X and Zhu, H and Qian, Y and Saeed, S and Wang, H and Rong, D and Wang, Y and Zhang, X and Hu, S},
title = {Correction to: Life Cycle and Circadian Rhythms in Central Resident Immunity and Neuropsychiatric Pathology.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
doi = {10.1007/s12264-026-01638-x},
pmid = {42185714},
issn = {1995-8218},
}

@article {pmid42186045,
year = {2026},
author = {Belfrouh, S and Salmam, FZ and Errattahi, R and Hanine, M and Obidallah, WJ and Alhulayyil, H},
title = {Artificial intelligence for brain-to-speech decoding in paralysis: a systematic review.},
journal = {BMC medical informatics and decision making},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12911-026-03552-8},
pmid = {42186045},
issn = {1472-6947},
abstract = {The loss of communication constitutes a critical challenge for people living with paralysis. Brain-computer interfaces (BCIs) paired with artificial intelligence (AI) provide an opportunity to restore this ability. This systematic review examined the use of AI to decode speech from brain signals through both invasive and non-invasive neural interfaces. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we reviewed 115 studies published between 2019 and 2025 to extract data on acquisition protocols, signal preprocessing, and AI architectures. The quality of each study was evaluated using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). The results indicated that the invasive approach achieved a higher median multiclass classification accuracy than the non-invasive method (77.7%, interquartile range (IQR): 61.4-93.2% vs. 73.0%, IQR: 49.3-89.2%; N = 82 studies with comparable multiclass metrics), computed from the best-performing model per study across heterogeneous task types, vocabulary sizes, and predominantly subject-dependent evaluation paradigms (84.3% of studies). However, this narrow gap in raw accuracy (4.7% points) should not be interpreted as a direct cross-modality performance ranking, as it obscures substantial differences in task complexity (invasive studies typically decoded larger vocabularies and continuous speech), evaluation paradigm, and participant population. Additionally, the hybrid convolutional neural network/recurrent neural network (CNN/RNN) architecture and transformers outperformed traditional classifier models. Nevertheless, the quality assessments showed significant limitations; notably, 62.6% of the studies evaluated had a high risk of selection bias due to patient characteristics, and only six studies (5.2%) validated results in paralyzed individuals-all relying on invasive modalities. Among these, classification accuracy ranged from 47.1% to 90.0%, while word error rates for continuous speech decoding ranged from 25.6% to 58.8%, demonstrating feasibility but with substantial variability across paralyzed cohorts. No non-invasive study has demonstrated functional speech decoding in paralyzed populations. This validation gap represents the most urgent translational priority in this field. We proposed a decision framework to address accuracy, cost constraints, and clinical applicability.},
}

@article {pmid42188271,
year = {2026},
author = {Zhang, Y and Liu, Y},
title = {User Needs and Preferences for Multimodal Interaction in Social Robots for Later-Life Support: An Exploratory Survey and Conceptual Five-Layer Architecture.},
journal = {Journal of Intelligence},
volume = {14},
number = {5},
pages = {},
doi = {10.3390/jintelligence14050085},
pmid = {42188271},
issn = {2079-3200},
abstract = {Social robots hold promise for enhancing later-life support, but user needs and preferences for multimodal interaction modalities remain underexplored. This study explores awareness, willingness, perceived barriers, and modality-function associations across multiple interaction modalities among middle-aged and older adults, and proposes a conceptual five-layer architecture for design guidance. A questionnaire survey with 199 Chinese respondents (aged 45-64: 89.4%, 65+: 10.6%) examined perceptions of voice, visual, gestural, affective, sEMG, and brain-computer interface interactions. Voice and visual modalities were the most preferred; gesture and affective interactions were moderately accepted; awareness of sEMG was high but may reflect confusion with other sensor technologies; and BCI awareness and willingness were low. Based on survey findings and the literature, a conceptual five-layer architecture is presented to inform future social-robot design. The sample predominantly comprised middle-aged participants, so findings reflect prospective later-life users rather than the broader older-adult population. This study offers user-centered insights into multimodal social-robot interaction and provides design implications for future development rather than evaluating emotional-health interventions.},
}

@article {pmid42189404,
year = {2026},
author = {He, E and Chen, K and Liu, S and Chen, H and Xiao, Y and Chen, R and Tu, P and Pan, G and Lin, P},
title = {Advances in neuroprostheses: interfaces, materials, and applications.},
journal = {Nano convergence},
volume = {13},
number = {1},
pages = {},
pmid = {42189404},
issn = {2196-5404},
support = {62301483//Natural Science Foundation of China/ ; 62574180//Natural Science Foundation of China/ ; LZ26F010001//Zhejiang Provincial Natural Science Foundation of China/ ; 2026C01008//Pioneer R&D Program of Zhejiang/ ; },
abstract = {Neuroprostheses have become a pivotal technology for restoring sensory, motor, and cognitive functions, offering transformative therapeutic strategies for neurological disorders by bridging or bypassing damaged neural pathways through electronic systems. However, achieving long-term stability and high-fidelity interaction between biological and electronic systems remains a significant challenge due to the mismatch at the neural interface. This review examines the critical role of nanotechnology in building high performance neuroprostheses across six key classes: motor, visual, tactile, language, memory and olfactory. A system architecture of the neuroprostheses is proposed that highlights two critical interfaces, namely, "neural-electronic" and "environment-electronic" interfaces. We survey recent advances in materials and devices that shape better neural electrodes and novel sensors, and discuss the potential utilization of neuromorphic computing for efficient edge processing in neuroprostheses. This review aims to outline future trajectories toward high-throughput bidirectional interaction, biomimetic encoding, and adaptive closed-loop systems, aspiring to achieve seamless integration between electronic systems and biological neural circuitry.},
}

@article {pmid42190715,
year = {2026},
author = {Ju, S and Ming, G and Dong, G and Gao, X and Wang, Y},
title = {A color-coded SSVEP-based brain-computer interface.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae7309},
pmid = {42190715},
issn = {1741-2552},
abstract = {OBJECTIVE: Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) predominantly employ frequency, phase, or spatial coding. This study proposes a color-dimension SSVEP encoding scheme and evaluates its feasibility and elicited response characteristics.

APPROACH: Seven isoluminant colors were paired to form 21 combinations, and four stimulation paradigms (sliding checkerboard, reversing checkerboard, flickering checkerboard, and solid-color flicker) were used to investigate the modulatory effects of color on SSVEP. Offline simulations and an online four-target SSVEP-BCI were conducted for validation purposes.

MAIN RESULTS: Under identical frequencies and initial phases, different color combinations produced separable SSVEP patterns in amplitude, topography, and phase, enabling reliable classification. At 10 Hz, the online four-target system with solid-color flicker achieved an average information transfer rate (ITR) of 80 ± 0 bits per minute (bits/min).

SIGNIFICANCE: The proposed approach introduces an additional encoding dimension for SSVEP-BCI, expanding stimulus design options and supporting broader applications.},
}

@article {pmid42174338,
year = {2026},
author = {Pan, Y and Porteous, F and Rosenbaum, D and Fallgatter, A and Ehlis, AC and Koole, SL and Dikker, S},
title = {Inter-brain coupling tracks emotional co-regulation.},
journal = {Cognitive, affective & behavioral neuroscience},
volume = {},
number = {},
pages = {},
pmid = {42174338},
issn = {1531-135X},
abstract = {When we have a negative emotional experience, we often recount this experience to others. Such emotional sharing plays a key role in building and maintaining interpersonal relationships, and is a vital component of most psychotherapies. Yet, while research shows the importance of social relationships for brain health, the neural underpinnings of emotional sharing remain largely unknown. Here, we asked whether successful processing and regulation of negative emotions can be linked to shared brain responses between dyads during and after emotional sharing. Participants watched videos eliciting either negative or neutral emotions, after which they shared their feelings about these videos with a friend. We simultaneously recorded the brain activity of both friends using functional near-infrared spectroscopy (fNIRS) and compared inter-brain coupling within sharer-listener dyads before, during, and after sharing sessions. We found that shifts in inter-brain coupling were associated with changes in mood. Specifically, an increase in inter-brain coupling after recounting a video that elicited negative emotions was associated with reduced anger and dejection in listeners and increased vigor in sharers. These findings suggest that inter-brain coupling facilitates the co-regulation of negative emotions, and thereby maintaining a healthy homeostatic balance. This knowledge holds potential relevance for informing psychotherapeutic interventions.},
}

@article {pmid42179096,
year = {2026},
author = {Hari, K and Anand, A and Naveed, A and P, V},
title = {Genetic algorithm-optimized machine learning approaches for EEG-based silent speech decoding.},
journal = {Journal of medical engineering & technology},
volume = {},
number = {},
pages = {1-13},
doi = {10.1080/03091902.2026.2676658},
pmid = {42179096},
issn = {1464-522X},
abstract = {The phases of human communication consist of speech perception, production, and imagination. The objective of this work is to understand and analyse the changes that occur in the neural signals during the hearing phase by examining electroencephalogram (EEG) patterns of the subject for different sentences. We propose optimising the decoding process using Genetic Algorithms (GA). Six different experiments are performed on Dataset 3 of coSpeech EEG Database. Both handcrafted features and CNN-based features are used for classification. GA is used for two purposes - channel selection as well as feature selection. Two classifiers - decision trees and SVMs are used for sentence classification. A benchmark accuracy of 41.92% is obtained using the proposed methods. Accuracy improves in the alpha, beta and gamma frequency sub-bands (41.79%, 40.92%, 40.27% respectively). Channel selection using GA reduces the computational load significantly (∼ 90%) while producing comparable results (34.37%, 33.20%, 32.93% in the alpha, beta and gamma sub-bands). This work highlights that EEG is a viable, non-invasive way to decode speech from subjects, which would help people with speech disorders communicate in a better way without exertion. Silent speech decoding has applications in assisting speech-impaired individuals, ensuring private communication, and enhancing human-computer interaction.},
}

@article {pmid42179723,
year = {2026},
author = {Choudhary, PK and Choudhary, S and Saha, S and Rajpoot, YS and Ciocan, VC and Nicolae-Lucian, V and Gorgan, CM and Șufaru, C},
title = {Brain-computer interfaces and neural synchronization in esports: a systematic review of effects on reaction time, decision-making, and cognitive performance.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1774230},
pmid = {42179723},
issn = {1662-5161},
abstract = {BACKGROUND: The rapid expansion of esports has intensified interest in the cognitive and neurophysiological mechanisms underlying elite performance, particularly reaction time (RT), decision-making (DM), and neural efficiency. Advances in brain-computer interfaces (BCIs) offer targeted neural modulation that may enhance these abilities through improved neural synchronization. To systematically review evidence on the effects of BCI-based neural synchronization, including motor imagery (MI) BCIs, visual evoked potential (VEP/c-VEP) systems, neural entrainment, and dual-brain coupling, on RT, DM, and related cognitive outcomes in esports athletes and competitive gamers.

METHODS: Following PRISMA 2020 guidelines, comprehensive searches were conducted across PubMed, Scopus, Web of Science, IEEE Xplore, PsycINFO, ScienceDirect, and Google Scholar. Studies examining BCI-induced neural modulation and its cognitive or performance effects in esports players or experienced gamers were included. Eighteen studies met the criteria, comprising controlled trials, pre-post interventions, cross-sectional neurophysiology studies, comparative behavioural analyses, and supporting systematic reviews. Due to methodological heterogeneity, results were synthesised narratively. Although the review follows PRISMA 2020 guidelines for systematic study identification and selection, the synthesis adopts a structured integrative narrative approach due to substantial heterogeneity in study designs, BCI modalities, and outcome measures.

RESULTS: Across studies, BCI-mediated neural synchronization produced consistent improvements in RT, DM accuracy, cortical oscillatory stability, and neural connectivity. MI-BCI and gamified systems enhanced MI accuracy, user engagement, and cognitive load regulation. VEP-based BCIs accelerated perceptual processing by improving signal reliability and reducing latency. Dual-brain coupling improved coordinated decision behaviour. Additional evidence indicates that experienced gamers display superior working memory, attentional control, and visuomotor coordination compared with non-gamers. However, variability in study design, small samples, and moderate risk of bias limit the strength of causal inference.

DISCUSSION: BCI-based neural synchronization shows promise as a tool for enhancing neurocognitive performance in esports athletes. Future studies should prioritize standardized training protocols, multimodal neural-measurement methods, and longitudinal designs to determine long-term effectiveness and real-world applicability.},
}

@article {pmid42181257,
year = {2026},
author = {Xia, Y and Jin, S and Zhou, W and Huang, R and Huang, S},
title = {A comparative study of five telerehabilitation therapies for improving core symptoms in stroke patients: A network meta-analysis (2,833 patients).},
journal = {iScience},
volume = {29},
number = {6},
pages = {115774},
pmid = {42181257},
issn = {2589-0042},
abstract = {This network meta-analysis demonstrates that virtual reality therapy exhibits significant advantages in specific functional domains of remote rehabilitation: Remote virtual reality technology demonstrated the most pronounced effects in improving gait (SUCRA = 92.4%, standardized mean difference [SMD] = -1.27) and upper limb functional recovery (SUCRA = 71.3%, SMD = -0.64), while remote brain-computer interfaces showed the most significant effects in fine motor control. SMD = -1.27) and upper limb functional recovery (SUCRA = 71.3%, SMD = -0.64), while remote brain-computer interfaces showed the greatest effect in fine motor control (SUCRA = 87.6%, SMD = -1.20). Regarding quality of life improvement, exoskeleton training yielded the best results (SUCRA = 62.4%, SMD = 0.05). The findings of this study provide evidence-based support for developing personalized telerehabilitation protocols tailored to specific rehabilitation goals in clinical practice. This approach facilitates a shift in the telerehabilitation field from empirical selection to precision-targeted intervention strategies.},
}

@article {pmid42182055,
year = {2026},
author = {Feng, C and Zhang, E and Jia, Y and Zhu, Z and Zhu, J and Wu, D and Xu, K},
title = {Distributed cortico-subcortical networks enable robust speech state detection from sparse intracranial recordings.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1816455},
pmid = {42182055},
issn = {1662-4548},
abstract = {INTRODUCTION: Accurate and reliable detection of speech state transitions is a prerequisite for practical speech brain-computer interfaces (BCIs). While cortical language areas have been extensively studied, it remains unclear whether speech onset information is exclusively localized to these regions or distributed across a broader cortico-subcortical network. Here, we investigated the feasibility of decoding speech state transitions using sparse stereo-electroencephalography (SEEG) recordings that sample both cortical and subcortical structures.

METHODS: Four Mandarin-speaking epilepsy patients undergoing clinical SEEG monitoring performed a sentence-reading task. Neural signals were segmented and labeled as rest or speech based on acoustic onset. A convolutional neural network was trained to classify speech states using broadband or high-gamma features derived from different anatomical channel subsets. We further evaluated continuous decoding performance, model robustness to channel dropout, and the specific contributions of different brain regions.

RESULTS: Speech state decoding accuracy exceeded chance level (50%) in all participants, with peak single-participant accuracies surpassing 90%. Models integrating both cortical and subcortical signals generally outperformed those restricted to a single anatomical domain. Notably, broadband signals yielded higher classification accuracy than high-gamma features. In continuous decoding simulations, performance remained above chance, although reduced relative to discretized evaluation. Crucially, decoding accuracy was robust to random channel reduction (up to 50%) and remained above 70% even after excluding classical speech-related cortical regions. Contribution analyses indicated participant-specific patterns of model sensitivity, with relatively higher contributions observed in frontal regions and the thalamus in multiple participants.

DISCUSSION: These findings support the hypothesis that speech state information is represented in a distributed cortico-subcortical network rather than being confined to canonical language areas. The robustness of decoding performance despite channel reduction and regional exclusion suggests that sparsely sampled SEEG data can effectively drive speech detection modules. This study demonstrates the feasibility of utilizing deep brain recordings for speech BCIs, offering a pathway toward more stable and generalized implantable systems. Moreover, such autonomous speech state detection may also serve as an ethical safeguard, ensuring that neural language decoding is activated only during intended communicative acts.},
}

@article {pmid42182066,
year = {2026},
author = {Feng, C and Ni, H and Zhu, Z and Jiang, H and Zheng, Z and Ming, W and Wang, S and Xu, K and Zhu, J},
title = {Dataset of chronic intracranial EEG of epilepsy patients via responsive neurostimulation system.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1815732},
pmid = {42182066},
issn = {1662-4548},
}

@article {pmid42183960,
year = {2026},
author = {Chen, LW and Lian, YH and Dong, XL and Luo, KL and Zhan, LQ and Xie, LL and Sun, QK and Lin, W and Gan, SR and Cheng, XP and Ni, J and Chen, XY},
title = {Intermittent Theta-Burst Stimulation (iTBS) Improves Motor Coordination and Modulates Neuroinflammation and Autophagy in SCA3/MJD Mice.},
journal = {Cerebellum (London, England)},
volume = {25},
number = {4},
pages = {},
pmid = {42183960},
issn = {1473-4230},
support = {2023QH1088//Startup Fund for Scientific Research of Fujian Medical University/ ; 82402952//National Natural Science Foundation of China/ ; YXRQN-CXY2025//the Excellence Talent Program of The First Affiliated Hospital of Fujian Medical University/ ; },
mesh = {Animals ; *Machado-Joseph Disease/therapy/physiopathology/pathology/genetics ; *Autophagy/physiology ; Mice ; Mice, Transgenic ; Ataxin-3/genetics/metabolism ; *Transcranial Magnetic Stimulation/methods ; *Neuroinflammatory Diseases/therapy/pathology/physiopathology ; Cerebellum/pathology/metabolism ; *Psychomotor Performance/physiology ; Disease Models, Animal ; },
abstract = {Spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD) is an autosomal dominant neurodegenerative disorder characterized by misfolded ataxin-3 aggregation and neuronal intranuclear inclusions. Its primary symptom is progressive ataxia, progressively restricting daily living activities. While repetitive transcranial magnetic stimulation (rTMS) may alleviate symptoms, the effects and mechanisms of specific rTMS paradigms, particularly intermittent and continuous theta burst stimulation (iTBS/cTBS), remain unclear in SCA3. This study therefore aimed to investigate the impacts of iTBS and cTBS on motor coordination, cerebellar neuroinflammation, and autophagy in SCA3 transgenic mice. Thirty 14-week-old SCA3 transgenic mice were randomly divided into sham, cTBS, and iTBS groups. Cerebellar stimulation was delivered at 30% maximum output (600 pulses/session, once daily, 5 days/week for 2 weeks). Motor coordination was assessed via rotarod and CatWalk gait analysis. Pathological changes were evaluated by measuring ataxin-3 protein and ubiquitin-positive inclusions. Cerebellar neuroinflammation was analyzed using Iba-1, CD206, and a cytokine array, while autophagy was assessed via Beclin-1 and LC3B expression. iTBS significantly improved motor coordination in SCA3 mice, reducing rotarod falls (vs. sham P < 0.001, vs. cTBS P < 0.05) and improving gait symmetry (vs. sham P < 0.05) and regularity index (vs. sham P < 0.01, vs. cTBS P < 0.01). It also alleviated cerebellar pathology, lowering ataxin-3 expression (vs. sham P < 0.01, vs. cTBS P < 0.01) and ubiquitin-positive inclusions (vs. sham P < 0.01, vs. cTBS P < 0.05). While both iTBS and cTBS increased Iba-1-positive cells (P < 0.05 and P < 0.05, respectively, vs. sham), only iTBS raised CD206-positive cells (vs. sham P < 0.05) and downregulated pro-inflammatory cytokines. Furthermore, iTBS activated autophagy, enhancing Beclin-1 (vs. sham P < 0.05) and LC3B expression (vs. sham P < 0.0001, vs. cTBS P < 0.001). iTBS improved motor coordination and alleviated core cerebellar pathology in SCA3 mice. This effect may be mediated through the downregulation of cerebellar neuroinflammation and the activation of autophagy. Furthermore, the therapeutic efficacy of iTBS was superior to that of cTBS across multiple dimensions, demonstrating distinct paradigm specificity.},
}

Animals
*Machado-Joseph Disease/therapy/physiopathology/pathology/genetics
*Autophagy/physiology
Mice
Mice, Transgenic
Ataxin-3/genetics/metabolism
*Transcranial Magnetic Stimulation/methods
*Neuroinflammatory Diseases/therapy/pathology/physiopathology
Cerebellum/pathology/metabolism
*Psychomotor Performance/physiology
Disease Models, Animal

@article {pmid42184169,
year = {2026},
author = {Mathon, B and Mokhtari, K and Galanaud, D and Carpentier, A},
title = {Development and preclinical evaluation of a hybrid stereoelectroencephalographic-laser depth electrode for magnetic resonance imaging-guided interstitial thermal therapy in drug-resistant epilepsy.},
journal = {Epilepsia},
volume = {},
number = {},
pages = {},
doi = {10.1002/epi.70313},
pmid = {42184169},
issn = {1528-1167},
abstract = {OBJECTIVE: This study was undertaken to design and validate a hybrid depth electrode combining stereoelectroencephalographic (sEEG) recording and magnetic resonance-guided laser interstitial thermal therapy (MRgLITT) under real-time magnetic resonance thermometry, to streamline the transition from invasive localization to focal ablation in patients with drug-resistant focal epilepsy.

METHODS: We engineered a magnetic resonance imaging (MRI)-compatible depth probe that integrated intracerebral EEG contacts and a central optical fiber for laser delivery. The contact materials and geometry were optimized to reduce susceptibility artifacts and preserve the proton resonance frequency (PRF) thermometry. Preclinical testing included MRI artifact screening in phantoms, thermal performance testing, PRF thermometry validation against temperature sensors in phantoms and ovine brain, artifact quantification versus clinical depth electrodes, electrophysiologic signal quality assessment before/after thermal stress, and in vivo canine feasibility with serial MRI and histology. MRI compatibility was confirmed for a next generation contact variant.

RESULTS: The optimized contact design produced small MRI artifacts and preserved PRF thermometry outside an approximately 2-mm pericontact exclusion zone. Thermal testing showed localized heating with rapid postlaser decay, modulation by coolant flow, and performance comparable to that of clinical LITT applicators. In dipole-phantom testing, baseline electrophysiological recordings from the new hybrid electrode were comparable to clinical depth-electrode controls, whereas a previously heated hybrid electrode showed increased noise under low-amplitude conditions. In vivo, MRgLITT produced sharply demarcated lesions that scaled with the delivered energy without hemorrhage, edema, midline shift, or device damage. Histological examination revealed coagulative necrosis with a narrow perilesional zone and no carbonization at the contacts.

SIGNIFICANCE: This patented hybrid sEEG-laser electrode supports a "diagnose-model-treat-verify" strategy along a single stereotactic trajectory, enabling sEEG confirmation followed by MRgLITT without a second stereotactic implantation in selected patients. These data support progression to first-in-human evaluation and integration into epilepsy surgery workflows, particularly for MRI-negative focal epilepsies, where minimally invasive strategies are favored.},
}

@article {pmid42184181,
year = {2026},
author = {An, Y and Tong, Y and Wang, W and Su, SW},
title = {Enhancing Brain Signal Generation Through A Hybrid Approach Integrating Reinforcement Learning And Diffusion Models.},
journal = {IEEE transactions on medical imaging},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TMI.2026.3696676},
pmid = {42184181},
issn = {1558-254X},
abstract = {Developing a reliable EEG-based Brain Computer Interface (BCI) system typically requires large and diverse training datasets, but collecting sufficient data remains challenging due to subject fatigue and interindividual variability. To address these limitations, this study proposes a reinforcement learning-enhanced EEG diffusion (RLED) framework for adaptive data augmentation in endogenous EEG tasks, with a focus on motor imagery and emotion recognition. The framework integrates a reinforcement learning mechanism to dynamically regulate the diffusion training process and achieve a flexible balance among temporal, spectral, and class-related features. Experiments on four datasets demonstrate that the proposed method generates high-quality synthetic EEG signals and consistently improves classification performance. These findings show that the proposed RLED framework may serve as a promising tool for EEG data augmentation and generalization in practical BCI applications.},
}

@article {pmid42184467,
year = {2026},
author = {Lu, J and Wang, D and Kong, D and Meng, K and Zhang, R and Wan, H and Chen, M},
title = {Identifying the seizure onset zone with phase-amplitude coupling.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {203},
number = {},
pages = {109151},
doi = {10.1016/j.neunet.2026.109151},
pmid = {42184467},
issn = {1879-2782},
abstract = {Accurate identification of the seizure onset zone (SOZ) is critical for the diagnosis and treatment of drug-resistant epilepsy (DRE). In recent years, although phase-amplitude coupling (PAC) has played an important role in epilepsy-related studies, few investigations have focused on applying PAC methods to SOZ identification. To this end, leveraging the capability of PAC to characterize neural interactions within the brain, this study computes the modulation index (MI) from clinical electrocorticography (ECoG) recordings of DRE patients. Subsequently, a statistical analysis of temporally evolving distributions of MI values across multiple frequency bands is conducted to analyze the differences in MI distribution features between SOZ and non-seizure onset zone (NSOZ) regions. Finally, distribution features of MI values are integrated with machine learning techniques to systematically evaluate the influence of different frequency bands and time windows on SOZ identification performance. The results demonstrate that MI distribution features can achieve accurate SOZ identification, with classification accuracy reaching 90.69%, indicating their potential as biomarkers for SOZ identification.},
}

@article {pmid42184469,
year = {2026},
author = {Zhang, Y and Li, T and Jiang, J and Li, H and Sun, R and Li, Y and Chen, W},
title = {Altered temporal organization of neural response dynamics during attention processing differentiates ADHD subtypes in children.},
journal = {NeuroImage. Clinical},
volume = {50},
number = {},
pages = {104011},
doi = {10.1016/j.nicl.2026.104011},
pmid = {42184469},
issn = {2213-1582},
abstract = {BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) shows marked heterogeneity, and conventional event-related potential (ERP) measures have limited sensitivity to subtype differences. This study examined whether alterations in the temporal organization of neural responses during attentional processing differentiate ADHD subtypes.

METHODS: Children with predominantly inattentive ADHD (ADHD-I), combined-type ADHD (ADHD-C), and typically developing (TD) controls completed an auditory oddball task during electroencephalography. Neural responses were analyzed using time-resolved scalp topographies, low-dimensional neural trajectory analysis, and data-driven neural state modeling. Associations with clinical symptoms were examined.

RESULTS: Both ADHD subtypes showed altered temporal alignment of neural responses relative to TD children, particularly during target processing. Neural trajectories exhibited reduced differentiation between standard and target stimuli, with ADHD-I showing reduced trajectory separation and ADHD-C showing exaggerated but inefficient state excursions. Data-driven analyses further revealed subtype-specific alterations in neural state stability and transitions, which showed exploratory associations with attentional and behavioral impairment.

CONCLUSIONS: ADHD is characterized by disrupted temporal organization of neural responses that is not captured by conventional ERP measures. Subtype-specific neural dynamics provide a mechanistic account of ADHD heterogeneity.},
}

@article {pmid42184818,
year = {2026},
author = {Hennesy, TB and Zander, DA and Kryzer, TJ and Honce, JM and Carlson, ML and Nassiri, AM},
title = {Magnetic Resonance Imaging Artifact Associated With the Oticon Medical Sentio Ti Transcutaneous Bone Conduction Hearing Implant.},
journal = {Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {},
number = {},
pages = {},
doi = {10.1097/MAO.0000000000004949},
pmid = {42184818},
issn = {1537-4505},
abstract = {OBJECTIVE: To evaluate magnetic resonance (MR) imaging artifact and image distortion associated with the Oticon Medical Sentio Ti bone conduction implant (BCI) and identify optimized imaging techniques.

STUDY DESIGN: Cadaveric study.

INTERVENTION: One cadaveric head specimen was unilaterally implanted with Sentio Ti BCI according to the manufacturer's instructions.

MAIN OUTCOME MEASURES: Imaging was performed with a Siemens 1.5 Tesla MR machine on XA60 software before and after implantation. Imaging was performed with both standard and metal mitigation techniques. Image scoring (diagnostic vs. nondiagnostic) and qualitative assessment of anatomic subsites were performed by 2 experienced neuroradiologists.

RESULTS: Image distortion and artifact were noted in all postimplant sequences. For all sequences, imaging of the ipsilateral middle ear, mastoid, and internal auditory canal (IAC) was nondiagnostic. The axial T1 turbo spin echo high bandwidth sequence had the best artifact reduction; however, the ipsilateral temporal bone remained nondiagnostic. Notably, nonecho planar diffusion-weighted imaging (non-EPI DWI) was nondiagnostic for both the ipsilateral temporal bone and the contralateral IAC and middle ear.

CONCLUSIONS: After implantation of the Sentio Ti BCI, imaging of the ipsilateral temporal bone is rendered nondiagnostic on all MR sequences due to artifact despite the use of metal mitigation techniques. Importantly, the non-EPI DWI HASTE sequence, which is used for cholesteatoma surveillance, is nondiagnostic for all ipsilateral and most contralateral temporal bone subsites, making cholesteatoma surveillance challenging with an implant in place. This finding is critical for clinical decision-making, as rehabilitation of conductive hearing loss in the setting of chronic otitis media is among the most common indications for use of a BCI.},
}

@article {pmid42185119,
year = {2026},
author = {Ding, Y and Kosnoff, J and He, B},
title = {A holistic perspective on noninvasive brain-computer interfaces.},
journal = {Trends in neurosciences},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.tins.2026.04.009},
pmid = {42185119},
issn = {1878-108X},
abstract = {Brain-computer interfaces (BCIs) decode neural activity to enable direct communication with external devices. This process consists of three modules: signal acquisition, signal processing, and output translation. While invasive BCIs have demonstrated sophisticated and intuitive capabilities, their reliance on surgical implantation limits widespread use. Noninvasive BCIs, in contrast, are more broadly applicable but have traditionally been constrained by low spatial resolution and suboptimal signal quality. Emerging methodological advances are beginning to overcome these limitations. In this review, we examine recent progress in noninvasive BCIs, focusing on neuromodulation-paired BCIs for signal enhancement, deep neural network-based signal processing approaches, and expanded applications through robotic integration. Together, these parallel developments are driving the emergence of more robust, intuitive, and adaptive BCI systems for human use.},
}

@article {pmid42185310,
year = {2026},
author = {Jian, Y and Jin, S and Liu, P and Zheng, X and Hong, X and Han, Y and Semyanov, A and Duan, S and Tong, X},
title = {GABA signaling in NG2 glia mediates empathy-like behavior under observational social defeat.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-73488-0},
pmid = {42185310},
issn = {2041-1723},
abstract = {Empathy, ranging from emotional contagion to consolation, is central to social cognition. While neural mechanisms of observed pain are well studied, how witnessing trauma affects empathy-related behaviors remains unclear. Using an observational social defeat (OSD) model, we find that OSD-exposed mice display enhanced allogrooming toward defeated conspecifics, indicating increased consolation behavior. Whole-brain cFos mapping and fiber photometry reveal selective activation of medial amygdala (MeA) GABAergic neurons during empathic allogrooming. NG2 glia modulate this behavior via GABA signaling; their specific ablation in the MeA reduces inhibitory synaptic transmission, disinhibiting neighboring GABAergic neurons and increasing allogrooming. Single-cell RNA analysis reveals that GABA signaling originates from Gad1-expressing NG2 glia. Genetic knockout of Gad1 in NG2 glia recapitulates the phenotype. This mechanism requires elevated corticosterone induced by social defeat. Our findings highlight the role of NG2 glia-GABA neuron interactions in promoting prosocial empathy and suggest targeting GABA signaling in NG2 glia as a potential therapeutic strategy for vicarious trauma.},
}

@article {pmid42185473,
year = {2026},
author = {Choi, D and Yip, C and Choi, A and Park, J},
title = {Trust-gated synthetic EEG augmentation reduces performance drops when generalizing to new patients.},
journal = {NPJ digital medicine},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41746-026-02778-0},
pmid = {42185473},
issn = {2398-6352},
support = {2025 NSERC Discovery Grant Bridge Fund//University of Calgary/ ; },
abstract = {Synthetic augmentation can silently harm subject-disjoint EEG generalization. We propose trust-gated augmentation (TGA), a control layer that scores synthetic windows using a teacher trained on real data to ensure label consistency and confidence; only samples above a confidence quantile q are eligible. A fail-closed selector injects synthetic data only if the validation AUROC exceeds the real-only AUROC by a margin; otherwise, it reverts to real-only. In PainMunich chronic-pain EEG (n = 189; 101 chronic pain/88 controls) at 5% subject scarcity, ungated augmentation harmed 56% of paired runs (ΔAUROC < - 0.01), whereas TGA at q = 0.99 reduced harm to 24% with comparable mean AUROC. In BCI IV-2a motor imagery (n = 9) at 25% scarcity, strict gating improved AUROC (0.679 vs. 0.627) and reduced harm (0.16 vs. 0.44). A covariance-manifold audit showed synthetic windows were strongly off-manifold (mean distance ratio 2.39 × 10[4]), motivating explicit governance.},
}

@article {pmid42185513,
year = {2026},
author = {He, M and Sha, L and Tang, G and Pang, J and Jin, L and Fu, Y and Huang, S and Wang, W and Wen, S and Yao, Y and Wei, P and Chen, L},
title = {Towards generalizable seizure monitoring: EpiVLM for cross-environment detection and classification.},
journal = {NPJ digital medicine},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41746-026-02810-3},
pmid = {42185513},
issn = {2398-6352},
support = {JCYJ20220818100213029//Shenzhen Science and Technology Innovation Committee/ ; ZYYC23011//1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University/ ; 2025NSFTD0027//the Supported by Sichuan Science and Technology Program/ ; STI2030-Major Projects+2021ZD0204300//the Ministry of Science and Technology of the People's Republic of China grant/ ; },
abstract = {The translation of automated seizure detection from controlled clinical units to real-world settings is hindered by heterogeneous recording conditions and limited expert monitoring. We introduce EpiVLM, a multimodal vision-language system that combines clinically structured prompts with video reasoning for cross-environment seizure monitoring. Evaluated on a robust and diverse dataset of 232 video recordings from 127 patients, totaling 11,666 expert-annotated segments from two tertiary centers, unconstrained home recordings, and an independent public dataset, EpiVLM recognized five major semiologies with accuracy 0.795-0.947 and sensitivity 0.842-0.957. With prompts and decision thresholds fixed a priori, performance remained consistent across diverse real-world acquisition conditions without site-specific recalibration. In external validation sets, EpiVLM sustained strong recognition while maintaining low video-level false detections (0.47-2.45%) and timely detection (mean onset-to-detection delay <6 s). Compared with standard video deep-learning baselines, EpiVLM achieved superior overall performance. These results support scalable seizure recognition from routine video and motivate prospective evaluation for remote outcome monitoring.},
}

@article {pmid42185690,
year = {2026},
author = {Zhang, J and Zhang, H and Yang, Y},
title = {Generative diffusion meets domain adaptation: a framework for EEG cross-subject motor imagery classification.},
journal = {Brain informatics},
volume = {},
number = {},
pages = {},
doi = {10.1186/s40708-026-00308-y},
pmid = {42185690},
issn = {2198-4018},
support = {21A13022003//Humanity and Social Science Foundation of the Ministry of Education of China/ ; LMS26F020033//Natural Science Foundation of Zhejiang Province/ ; },
abstract = {Cross-subject motor imagery classification remains challenging due to EEG data scarcity and inter-subject variability. This study proposes a novel framework integrating generative data augmentation with domain adaptation. First, we employ a diffusion probabilistic model to generate high-fidelity synthetic EEG samples, effectively enriching the training data. Subsequently, we propose the AMSC-DANN architecture, which synergizes an Adaptive Multi-Scale Convolution (AMSC) module for extracting multi-granular features with a Domain Adversarial Neural Network (DANN). This combination enables the model to learn discriminative temporal-spectral representations while simultaneously aligning feature distributions across different subjects. Extensive experiments on BCI Competition IV datasets 2a and 2b demonstrate that our proposed framework outperforms state-of-the-art baselines, validating its effectiveness in enhancing cross-subject generalization.},
}

@article {pmid42172365,
year = {2026},
author = {Cramer, SC and Stein, J and Richards, LG and Hayward, KS},
title = {Advances in Stroke 2026: Recovery and Rehabilitation.},
journal = {Stroke},
volume = {57},
number = {6},
pages = {1792-1795},
doi = {10.1161/STROKEAHA.125.054526},
pmid = {42172365},
issn = {1524-4628},
}

@article {pmid42173506,
year = {2026},
author = {Gong, C and Song, Z and He, Z and Liu, H and Han, R and Li, S and Gao, J and Wei, Y and Wen, D and Xue, T and Xu, Z},
title = {Interfacial Polarization Engineering in MXene-Polymer Nanofibers for High-Output Triboelectric Nanogenerators.},
journal = {Langmuir : the ACS journal of surfaces and colloids},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.langmuir.6c01705},
pmid = {42173506},
issn = {1520-5827},
abstract = {In mechanical energy harvesting and sensing, triboelectric nanogenerators (TENGs) have garnered significant attention for effectively extracting energy from low-frequency, irregular motion and directly transducing it into sensing signals. However, poly(vinylidene fluoride)-based (PVDF-based) TENGs frequently exhibit limitations, including low power density, low output current, and high matched load resistance during operation. Herein, we report a TENG based on MXene, hydroxypropyl methylcellulose (HPMC), and PVDF-HFP composite nanofibers membrane (MHPm) as the negative tribo-layer and Al foil as the counter tribo-layer/electrode for low-frequency mechanical energy harvesting and real-time, ultrasensitive respiratory monitoring. HPMC acts as a synergistic regulator that promotes interfacial interactions among MXene, HPMC, and PVDF-HFP, reduces the coherent stacking scale of MXenes, and facilitates the maintenance of a discrete conductor-polymer-conductor structure, thereby strengthening interfacial polarization and electrical output performance. Under the drive of 70 N and 6 Hz, this device achieves a peak-to-peak (p-p) open-circuit voltage of 1.02 kV, a p-p short-circuit current density of 0.133 A·m[-2], and a peak power density of 27.40 W·m[-2] at a matched load resistance of 20 MΩ, while maintaining a stable current output over 15,000 contact-separation cycles. Moreover, the electrical outputs also provide well-differentiated breathing waveforms, enabling direct self-powered signal acquisition and supporting integrated wearable functionality.},
}

@article {pmid42173881,
year = {2026},
author = {Zheng, JL and Zheng, YX and Chen, K and Wang, S and Liang, JW and Wang, SX and Yang, L and Shi, Y},
title = {Cryo-EM structures of ALECT2 filaments from human renal biopsies.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-73602-2},
pmid = {42173881},
issn = {2041-1723},
support = {2025YFC3409700//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; 82371415, 82170724//National Natural Science Foundation of China (National Science Foundation of China)/ ; 2024SSYS0018//Science and Technology Department of Zhejiang Province/ ; 2023-PT310-01//Chinese Academy of Medical Sciences (CAMS)/ ; 2025M782772//China Postdoctoral Science Foundation/ ; },
abstract = {Leukocyte chemotactic factor 2 is a recently identified amyloidogenic protein, whose abnormal aggregation defines a systemic amyloidosis termed ALECT2 amyloidosis. Due to the lack of reliable biomarkers, diagnosis relies primarily on histological demonstration and typing of amyloid deposits in renal biopsies. However, immunohistochemical detection of ALECT2 is often inconsistent, leading to diagnostic uncertainty. The underlying basis remains poorly understood, reflecting our limited knowledge of ALECT2 deposits. Here, using cryo-electron microscopy (cryo-EM), we determined the structures of ALECT2 filaments from renal biopsies of five living patients. Unlike filaments assembled from recombinant proteins in vitro, all 133 residues of mature LECT2 are incorporated into the filament cores, with native disulfide linkages preserved. The filaments consistently adopt the shared six-layered folds in all five patients, indicating a common mechanism of amyloidogenesis. Because all residues are incorporated into the fibril core, epitope accessibility is limited. This can explain variability in immunohistochemical detection and thus highlights the need for conformation-specific antibodies and antibody-independent detection strategies for improving diagnostic accuracy. This biopsy-based workflow not only expands the availability of patient-derived tissue for cryo-EM studies but also demonstrates the potential of cryo-EM as a tool for precise diagnosis of systemic amyloidosis.},
}

@article {pmid42174242,
year = {2026},
author = {Fu, TM and Liu, G and Milkie, DE and Ruan, X and Görlitz, F and Shi, Y and Ferro, V and Divekar, NS and Wang, W and York, HM and Kilic, V and Mueller, M and Liang, Y and Daugird, TA and Gacha-Garay, MJ and Larkin, KA and Adikes, RC and Harrison, N and Shirazinejad, C and Williams, S and Nourse, JL and Sheu, SH and Gao, L and Li, T and Mondal, C and Achour, K and Hercule, W and Stabley, DR and Emmerich, K and Dong, P and Drubin, DG and Liu, ZJ and Mumm, JS and Koyama, M and Killilea, AN and Bravo-Cordero, JJ and Keene, CD and Luo, L and Kirchhausen, T and Pathak, MM and Arumugam, S and Nuñez, JK and Gao, R and Matus, DQ and Martin, BL and Swinburne, IA and Betzig, E and Legant, WR and Upadhyayula, S},
title = {A multimodal adaptive optical microscope for in vivo imaging from molecules to organisms.},
journal = {Nature methods},
volume = {},
number = {},
pages = {},
pmid = {42174242},
issn = {1548-7105},
support = {LDRD 7647437 and 7721359//DOE | LDRD | Lawrence Berkeley National Laboratory (Berkeley Lab)/ ; 1DP2GM136653//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; 1DP2GM136653//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; F32GM133131//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; R35GM118149//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; DP2AT010376//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; R01NS109810//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; T32 CA078207/CA/NCI NIH HHS/United States ; T32EY7143‑22//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; R35GM118149//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; R01OD020376//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; R01CA244780//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; R01‑DC005982//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; DP2AT010376//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; R01NS109810//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; R01GM121597//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; R35GM150290//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; 1R01DC021710//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; 1DP2GM136653//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; P30‑CA196521//U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)/ ; GM130386//U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)/ ; },
abstract = {Understanding biological systems requires observing features and processes across vast spatial and temporal scales, spanning nanometers to centimeters and milliseconds to days, often using multiple imaging modalities within complex native microenvironments. Yet, achieving this comprehensive view is challenging because microscopes optimized for specific tasks typically lack versatility due to inherent optical and sample handling tradeoffs, and frequently suffer performance degradation from sample-induced optical aberrations in multicellular contexts. Here, we present Multimodal Optical Scope with Adaptive Imaging Correction (MOSAIC), a reconfigurable microscope that integrates multiple advanced imaging techniques including light-sheet, label-free, super-resolution and multiphoton, all equipped with adaptive optics. MOSAIC enables noninvasive imaging of subcellular dynamics in both cultured cells and live multicellular organisms, nanoscale mapping of molecular architectures across millimeter-scale expanded tissues and structural/functional neural imaging within live mice. MOSAIC facilitates correlative studies across biological scales within the same specimen, providing an integrated platform for broad biological investigation.},
}

@article {pmid42167252,
year = {2026},
author = {Feng, X and Le, T and Liu, B and Zhao, H and Su, Y and Li, K and Shao, Y and Liu, J and Li, Z and Deng, G and Cao, K and Zhu, Z and Chen, J and Zeng, L and Han, Y and Yang, H and Yu, YQ and Duan, S and Sun, L},
title = {Slow-wave sleep engages brainstem circuitry to prevent stress-induced anxiety.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2026.04.041},
pmid = {42167252},
issn = {1097-4199},
abstract = {The beneficial effects of sleep on anxiety are established, but the mechanisms remain unclear. We identify a GABAergic circuit from the parafacial zone (PZ) to the lateral parabrachial nucleus (LPB) neurons that project to the oval bed nucleus of the stria terminalis (ovBNST) as a node for slow-wave sleep (SWS)-mediated anxiolysis. Optogenetic activation of PZ GABAergic neurons following social defeat stress induces time-locked SWS and prevents anxiety. Multi-region Ca[2+] recording reveals suppressed activity in LPB and ovBNST during natural and PZ-initiated SWS. The LPB-ovBNST pathway is required to drive wakefulness and anxiety, whereas the LPB-basal forebrain pathway promotes arousal without affecting anxiety. PZ neurons inhibit LPB calcitonin gene-related peptide (CGRP)-expressing neurons, which promote wakefulness and anxiety via ovBNST. This effect specifically requires LPB input to ovBNST corticotropin-releasing hormone (Crh) neurons. Thus, we define a PZ[Vgat]-LPB[CGRP]-ovBNST[Crh] circuit essential for sleep-related anxiolysis, providing a potential therapeutic target for anxiety disorders.},
}

@article {pmid42172342,
year = {2026},
author = {Zheng, L and Pan, L and Fu, X and Wang 王思羽, S and Wu, Y and Xiao, H and Yang, J and Wang 王思雨, S and Yang, L and Wu, X and Pan, F and Yang, H and Chen, G and Wang, H},
title = {The posteroventral part of the medial amygdala nucleus glutamatergic neurons encodes conspecifics' individual identity in rodents.},
journal = {Science advances},
volume = {12},
number = {21},
pages = {eady9830},
doi = {10.1126/sciadv.ady9830},
pmid = {42172342},
issn = {2375-2548},
mesh = {Animals ; Mice ; *Neurons/metabolism/physiology ; Olfactory Bulb/physiology ; Odorants ; Male ; Social Behavior ; *Corticomedial Nuclear Complex/physiology/metabolism/cytology ; Female ; Vesicular Glutamate Transport Protein 2/metabolism ; *Amygdala/physiology ; Smell/physiology ; Behavior, Animal ; Optogenetics ; },
abstract = {The medial amygdala (MeA) processes social olfactory cues, but its precise neural mechanisms remain unclear. We identified the posteroventral MeA (MeApv) as critical for individual conspecific odor discrimination in mice. Exposure to conspecifics or their odors markedly elevates calcium signals and c-Fos expression in MeApv VGluT2-positive neurons. Optogenetic silencing of these neurons or activating Gad2-positive neurons disrupts odor-driven social behaviors, including identity recognition, odor discrimination, and sex discrimination. Social information is directly relayed from the accessory olfactory bulb (AOB) to the MeApv, and acute AOB-MeApv pathway disruption impairs social discrimination. A distinct MeApv VGluT2-positive neuron population encodes individual-specific cues, as revealed by microendoscopic calcium imaging at a single-cell resolution. Selective silencing of these neurons induces deficits in odor-guided social interactions with related conspecifics, confirming the MeApv as a central hub for social information encoding. These findings establish the MeApv's dual necessity and sufficiency in translating olfactory signals into social behavioral responses.},
}

Animals
Mice
*Neurons/metabolism/physiology
Olfactory Bulb/physiology
Odorants
Male
Social Behavior
*Corticomedial Nuclear Complex/physiology/metabolism/cytology
Female
Vesicular Glutamate Transport Protein 2/metabolism
*Amygdala/physiology
Smell/physiology
Behavior, Animal
Optogenetics

@article {pmid42160746,
year = {2026},
author = {Chen, H and Wang, J and Lai, S and Peng, G and Zong, G and Yuan, C and Luo, B},
title = {Smoking Cessation, Weight Change, and Risk of Dementia: A Prospective Cohort Study.},
journal = {Neurology},
volume = {106},
number = {12},
pages = {e218123},
doi = {10.1212/WNL.0000000000218123},
pmid = {42160746},
issn = {1526-632X},
mesh = {Humans ; Female ; *Dementia/epidemiology ; *Smoking Cessation ; Male ; *Weight Gain/physiology ; Prospective Studies ; Aged ; Middle Aged ; Risk Factors ; Cohort Studies ; },
abstract = {BACKGROUND AND OBJECTIVES: Smoking cessation is universally prioritized for the prevention of cardiovascular disease and cancer, but its impact on dementia risk remains uncertain. We aimed to evaluate the associations of smoking cessation and postcessation weight gain with long-term risk of dementia and cognitive trajectories.

METHODS: We conducted a prospective cohort study using data from the US Health and Retirement Study (1995-2020). A total of 32,802 dementia-free adults (mean age 60.5 years [SD 10.7]; 57.1% female) were included. Smoking status and body weight were assessed biennially through structured interviews. The primary outcome was incident dementia identified using the Langa-Weir algorithm, and the secondary outcome was cognitive function measured on a 27-point scale.

RESULTS: Over 25 years of follow-up (median 9.9 years, interquartile range 4.4-16.4 years), 5,868 dementia cases were documented. Compared with current smokers, individuals who quit during follow-up had a lower dementia risk after quitting (hazard ratio 0.84, 95% CI 0.73-0.95), similar to those who had quit before baseline (0.79, 0.72-0.87) and to never smokers (0.75, 0.69-0.83). The benefits of cessation were largely limited to participants with no or modest 2-year postcessation weight gain (≤5 kg). By contrast, the association of quitting accompanied by >10-kg weight gain was not statistically significant (1.33, 0.87-1.82). Restricted cubic spline analysis showed decreasing dementia risk with longer time since quitting, and the risk approached that of never smokers and plateaued at around 7 years after cessation. Cognitive trajectory analyses showed that quitting was associated with long-term slower cognitive decline (slope difference 0.19 points per decade, 95% CI 0.00-0.38) but no transient cognitive change (0.57; 95% CI -0.69 to 1.83), especially among those with minor weight gain (slope difference 0.23 per decade, 95% CI 0.03-0.43).

DISCUSSION: Smoking cessation was associated with a sustained lower dementia risk and slower cognitive decline, comparable to never smokers and those without short-term risk increase. However, postcessation weight gain may attenuate these advantages, highlighting the need for weight management in cessation programs. These findings should be interpreted cautiously, given the potential residual confounding and measurement error.},
}

Humans
Female
*Dementia/epidemiology
*Smoking Cessation
Male
*Weight Gain/physiology
Prospective Studies
Aged
Middle Aged
Risk Factors
Cohort Studies

@article {pmid42161119,
year = {2026},
author = {Pei, X and Sun, M and Chen, R and Wu, M and Wang, X and Xu, D and Li, R and Luo, S and Jin, Y and Tang, YN and Lu, Y and Wang, Q and Ma, L and Hu, B and Mei, Y and Xiao, X and Wei, D and Liu, Y and Song, E},
title = {Dual-frequency-channel integrated bioelectronics for in-sensor decoupling high-dimension neurophysiologic signals.},
journal = {Biosensors & bioelectronics},
volume = {309},
number = {},
pages = {118784},
doi = {10.1016/j.bios.2026.118784},
pmid = {42161119},
issn = {1873-4235},
abstract = {Accurate electrophysiological mapping of biological signals with high spatial and temporal resolution has always been an important requirement to elucidate physiological functions. Herein, we develop a photolithographic organic electrochemical transistor (OECT) matrix with two frequency-dependent channels, which can spatiotemporally map electroneurographic and neurotransmitter signals. The active material can be patterned photolithographically, forming a nanoscale interpenetrating network. The porous structure facilitates fast ion transport, establishing a high-frequency channel to monitor electroneurographic signals; meanwhile enzymatic reaction of glutamate on the surface creates a low-frequency channel to detect neurotransmitter signals, due to the relatively slow diffusion and doping processes. A low detection limit down to 900 zM for glutamate is achieved. During the test, the horseshoe network structure of the OECT array gives the device the ability of conformal contact on the surface of the cerebral cortex, avoiding the motion artifact noise, and the signal-to-noise ratio (SNR) can reach ∼40 dB. The dual-frequency channels efficiently decouple electroneurographic and neurotransmitter signals to avoid signal interference. Finally, the photolithographic matrix images dual-mode neurophysiological patterns in the cerebral cortex of mice, and can dynamically colocalize epileptic focus with high resolution for precise neurosurgical intervention.},
}

@article {pmid42161915,
year = {2026},
author = {Lu, C and Jiang, L and Jia, Q and Jiao, P and Li, S and Sun, S and Luo, J and Wang, M and Cai, X and Wu, Y},
title = {Analysis and dynamic modeling of firing synchronization in electrically interconnected dual-compartment neuronal networks.},
journal = {Microsystems & nanoengineering},
volume = {12},
number = {1},
pages = {},
doi = {10.1038/s41378-026-01309-x},
pmid = {42161915},
issn = {2055-7434},
support = {62121003, T2293730, T2293731,62333020, 62171434, 62471291//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {In vitro cultured neuronal networks offer controllable experimental models for investigating neuronal information processing mechanisms and network plasticity. However, research into synchronization and functional connectivity transitions following physical electrical interconnection between isolated compartments remains elusive. This study presents a microsystem that includes a compartmentalized microchamber neuron chip (CMNC) with programmable electrical interconnection and multichannel electrophysiological recording capabilities. The microsystem is utilized to establish artificial electrical interconnection between dual-compartment neuronal networks (DCNNs). We quantitatively evaluated network functional connectivity throughout control, interconnection, and post-disconnection phases, focusing on three key dimensions: spike timing synchrony, firing activity correlation and phase coherence. The experimental data showed that the electrical interconnection had sustained effects on firing synchrony and phase coherence across the DCNNs. After disconnection, synchrony decreased but remained significantly higher than control levels, suggesting a plastic response of the neuronal networks to the electrical coupling. To bridge experimental observations with mechanistic insights, we developed an Electrical-Interconnection Wilson-Cowan Model (EI-WCM), which quantitatively links physical coupling parameters (K) to network-level integration dynamics. The integrated microsystem and dynamical model presented here provide a stable, controllable platform and approach for studying functional connectivity, synergetic interactions and plasticity of neuronal networks, demonstrating significant potential for applications in brain-computer interfaces and neuronal information processing.},
}

@article {pmid42162083,
year = {2026},
author = {Chaturvedi, S and Ahirwal, MK},
title = {SHAP analysis of an improved EEG-based mental workload classification framework: utilizing data augmentation and explainable AI.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-52330-z},
pmid = {42162083},
issn = {2045-2322},
abstract = {Mental workload (MWL) classification using electroencephalogram (EEG) signals is crucial for cognitive neuroscience and is also a challenging research area in brain-computer interface (BCI). Since the EEG signals fluctuate a lot across sessions and individuals, there is a need for a robust classification model that generalizes well for real-world applications. In this work, we used the publicly available dataset "An EEG dataset for cross-session mental workload estimation: passive BCI competition of the Neuroergonomics Conference 2021", and the standard EEGNet model to classify the MWL into three classes (Low, Med, and High). To improve the performance of the model, a synthetic minority oversampling technique (SMOTE) was used by creating synthetic EEG samples, and key hyperparameters (F1, F2, and D) of EEGNet were systematically varied to identify the optimal configuration. Furthermore, Shapley Additive Explanations (SHAP) analysis was performed to identify the most influential EEG channels for model prediction. The proposed approach achieves the highest accuracy of 80.5% and 82.7% without and with SMOTE, respectively. The comparative analysis showed that applying SMOTE resulted in an average performance improvement of approximately 3%. A Wilcoxon signed-rank test confirmed that this improvement was statistically significant (p < 0.05). Finally, the SHAP analysis revealed that the most informative EEG channels were located over the parieto-occipital and temporal regions, which is consistent with established neurophysiological evidence related to MWL processing. The proposed framework improves both performance and explainability in EEG-based MWL classification, representing a systematic integration of SMOTE and SHAP analysis.},
}

@article {pmid42162276,
year = {2026},
author = {Liu, W and Wu, SA and Zhang, BX and Guo, SH and Li, L and Sun, W and Xiong, X and Nan, J and Wu, J and Zeng, L and Li, P and Cai, ZY and Ye, HF and Zhang, S and Nie, S and Li, B and Wu, D and Cheng, P and Qi, X and Fang, D and Chen, W and Zhang, Y and Chen, Q and Yang, ZH and Han, J and Mo, W},
title = {Tau aggregates cause reactivation of transposable DNA elements, leading to Z-RNA-ZBP1-mediated neuronal death.},
journal = {Nature neuroscience},
volume = {},
number = {},
pages = {},
pmid = {42162276},
issn = {1546-1726},
abstract = {Once tau aggregates are formed, their neurotoxicity significantly contributes to neuronal death and cognitive decline in tauopathies, with Alzheimer's disease being the most well-known example. Despite its central pathogenic role, however, effective therapeutic strategies targeting the neurotoxicity of tau remain poor. Here we demonstrate the pathogenic role of neuronal cell death in tau-related neurodegeneration (PS19 mouse model). Tau-expressing neurons undergo cell death through Z-DNA-binding protein 1 (ZBP1) activation triggered by endogenous Z-RNAs. These Z-RNAs are derived from reactivated transposable elements that are typically silenced within heterochromatin. Tau aggregates show a strong affinity for H3K9me3-modified chromatin, effectively sequestering these epigenetic marks from heterochromatin protein 1 (HP1), thereby disrupting the condensation of constitutive heterochromatin. Clinically, an inverse correlation between ZBP1 expression levels in excitatory neurons and cognitive performance in individuals with Alzheimer's disease was observed. Importantly, Zbp1 haploinsufficiency significantly ameliorated cognitive deficits in aged (24-month-old) tau-transgenic mice, highlighting the therapeutic potential of ZBP1 inhibition to combat neurodegeneration in tauopathies.},
}

@article {pmid42163088,
year = {2026},
author = {Shah, HA and Khan, A},
title = {Modeling and classifying neuronal activity with a fusion of mathematical and machine learning techniques.},
journal = {BMC bioinformatics},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12859-026-06487-z},
pmid = {42163088},
issn = {1471-2105},
abstract = {Predicting neuron spike patterns is crucial because spikes are the brain's fundamental language, revealing how information is encoded and transmitted. Such prediction also supports disease diagnosis, brain-machine interfaces, and the control of robotic arms, wheelchairs, and neuromorphic AI design. Yet, simulation techniques often suffer from limited biological realism, numerical instability, and poor generalization across diverse neuronal activity types. These limitations are further compounded by the scarcity of high-quality, labeled datasets that capture the full spectrum of neuronal dynamics, restricting the training and evaluation of machine learning models. To address these challenges, we proposed SpikeNet, a hybrid framework that integrates the Izhikevich neuron model with the Runge-Kutta fourth-order (RK4) algorithm to generate synthetic voltage signals that are both biologically plausible and computationally precise. These signals are then used to train a Bidirectional Long Short-Term Memory (Bi-LSTM) network, which effectively captures long-range temporal dependencies in spike trains. SpikeNet combines accurate simulations with advanced sequence modeling to improve spike pattern classification, providing a scalable solution for reliable data generation and prediction. The proposed model was evaluated on multiple datasets, including single-spike data, multi-label spike data, and the Allen dataset, and demonstrated strong performance across all evaluation metrics.},
}

@article {pmid42164752,
year = {2026},
author = {Bushnell, BD and Boes, N and Cil, A and Farmer, K and Gilot, G and Kassam, H and Khan, A and Miniaci, A and Port, J and Sanchez-Sotelo, J and Schultzel, M and Steinmann, S and Suri, M and Weinstein, D and Wright, M},
title = {Augmentation for rotator cuff repair - clinical use patterns and limited patient access: the American Shoulder and Elbow Surgeons bio-advocacy work group survey.},
journal = {JSES reviews, reports, and techniques},
volume = {6},
number = {3},
pages = {100748},
pmid = {42164752},
issn = {2666-6391},
abstract = {BACKGROUND: Over the last decade, treatment algorithms of rotator cuff pathology have increasingly included various forms of augmentation of rotator cuff repair (RCR). This study aimed to quantify real-world clinical patterns for RCR augmentation and provide consensus statements for clinical practice and payor consideration. It was our hypothesis that augmentation would be popular amongst surgeons, especially for reduction in retear rates, and that a high percentage of respondents would also identify restrictions to access.

MATERIAL AND METHODS: The American Shoulder and Elbow Surgeons Advocacy Committee distributed a 12-question digital survey to all current members of American Shoulder and Elbow Surgeons. The survey evaluated current surgical techniques and augmentation usage, limitations on augmentation access, target patients for augmentation selection, and desired clinical outcomes. Questions were analyzed as either frequency of response or as a rank average with 95% confidence intervals.

RESULTS: The survey was sent to 1,210 surgeons, and 103 surgeons participated in the survey (8.5% response rate). The survey revealed the following: (1) use of RCR augmentation is reported by 76.2% and 85.1% of surgeons for partial-thickness tears (PTT) and full-thickness tears (FTT), respectively. However, 74.5% of surgeons indicate that they have limited or variable access to augmentation options. (2) A bioinductive collagen implant (BCI) is the most preferred form of augmentation for PTT (52.5% of respondents), while both the BCI (45.5%) and human dermal allograft augmentation (45%) are most preferred for FTT.(3) The decision to use augmentation is largely based on positive clinical outcomes (9.4/10) and a defined target patient population (8.4/10), with the most critical outcome being a lower retear rate for both PTT (7/10) and FTT (8/10). (4) For PTT, patient comorbidities (7/10) are of greatest concern and are the most impactful criteria for the decision to use augmentation (6/10). For FTT, poor tendon quality (8.6/10) and increasing tear size (2.9-9.1/10) are of greatest concern, with tear size indicated as the most impactful criteria for selecting augmentation (7.6/10).

CONCLUSION: This expert-opinion survey confirmed the growing popularity of RCR augmentation and the significant limitations in access faced by surgeons and their patients. BCI and human dermal allograft were the most popular augmentation options. Surgeons identify multiple factors as important to decision-making for implant use, including positive clinical outcomes, low retear rates, defined patient populations, patient comorbidities, poor tendon quality, and tear size. Research in this area continues to expand, but additional work on payor approval remains to ensure appropriate access to this technology.},
}

@article {pmid42166269,
year = {2026},
author = {Rao, Z and Lu, Z and Xiao, J and Li, K and Zhu, M and Guan, Z and Chen, Y and Cao, H and Li, Y},
title = {Calibration-Free Online Detection in Wearable Motor Imagery Brain-Computer Interfaces.},
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.2026.3695774},
pmid = {42166269},
issn = {1558-0210},
abstract = {Motor imagery brain-computer interfaces (MI-BCIs) remain challenging for practical use due to their reliance on multi-channel EEG devices and long calibration. To address these limitations, we proposed a wearable system for calibration-free online decoding using a lightweight, few-channel EEG headband, enabling portability, ease of use, and rapid setup. Specifically, we first built a large-scale wearable MI-EEG dataset from 100 healthy subjects to train a subject-independent model. We then developed a CNN-based temporal convolutional network (CTCNet) for online MI detection, which reduced computational complexity while maintaining high decoding performance. Furthermore, we introduced a supervised self-training (SST) strategy that leverages labeled online data and progressively fine-tunes a pre-trained subject-independent model, enabling calibration-free BCI operation without offline calibration. Four online experiments were conducted, involving 25 healthy subjects (Experiments II-IV) and 10 stroke patients (Experiment V). With the SST strategy, the accuracy of the subject-independent model improved from 69 % initially to 81 % after the first update and further increased to 86% after the second update, surpassing the subject-specific model (80%). Stroke patients exhibited a similar improvement trend. Moreover, simulated experiments confirmed the superiority of the subject-independent model compared to training from scratch. These findings demonstrate the effectiveness of the wearable MI-BCI system based on SST and CTCNet for online MI detection and highlight its substantial potential for motor recovery in stroke patients.},
}

@article {pmid42166618,
year = {2026},
author = {Wagner, A and Eisenkolb, VM and Utzschmid, A and Gempt, J and Jacob, SN and Meyer, B},
title = {Chronic Implantation of Planar Microelectrode Arrays as a Brain-Computer Interface: A Technical Note and Operational Workflow.},
journal = {Operative neurosurgery (Hagerstown, Md.)},
volume = {},
number = {},
pages = {},
doi = {10.1227/ons.0000000000002067},
pmid = {42166618},
issn = {2332-4260},
support = {16ME0540//Bundesministerium für Bildung und Forschung/ ; },
abstract = {BACKGROUND AND OBJECTIVES: Chronic implantation of brain-computer interface facilitates stable, high-fidelity neuronal recordings over extended periods of time. Planar microelectrode arrays [Utah arrays (UAs)] are commonly used for intracortical signal acquisition. Here, we describe the surgical workflow for chronic implantation of multiple UAs in 2 patients and report safety and signal-quality outcomes.

METHODS: Two patients (MB, MM) underwent chronic UA implantation within a translational research program. Preoperative planning included magnetic resonance imaging and navigated transcranial magnetic stimulation mapping for localization of functional targets. MB presented with aphasia after a left hemisphere media territory stroke 6 years before implantation and received 4 UAs in speech-related areas. MM presented with tetraparesis after a high level spinal cord injury and received 4 UAs in areas related to grasping functionality, totaling 256 intracortical electrodes for each patient.

RESULTS: The duration of the chronic implantation has currently amounted to 41 months for MB and 4 months for MM. Optimal signal quality has been recorded in MB in 3 of 4 UAs and in MM in all UAs. After 15 months, MB suffered from wound breakdown, necessitating surgical debridement and intravenous antibiotic treatment. Unimpaired signal acquisition resumed after the wound had healed, and no further complications from UA implantation were recorded otherwise.

CONCLUSION: Chronic implantation of UAs across distinct cortical areas is safe. A standardized workflow-combining imaging-based functional navigated transcranial magnetic stimulation mapping, intraoperative neuronavigation, and structured postoperative surveillance-supports reliable, long-term intracortical signal acquisition.},
}

@article {pmid42167136,
year = {2026},
author = {Härmä, V and Palsola, M and Kuusipalo, A and Lindh, E and Melin, M and Nohynek, H},
title = {Lessons from the 2024 avian influenza vaccination campaign in Finland: a qualitative inquiry.},
journal = {Vaccine},
volume = {86},
number = {},
pages = {128736},
doi = {10.1016/j.vaccine.2026.128736},
pmid = {42167136},
issn = {1873-2518},
abstract = {Highly pathogenicity avian influenza H5N1 (HPAI H5N1) viruses cause a continuous threat to wild avian populations. During recent years, spillover to both wild and domestic mammals has occurred with an increasing frequency. As a consequence of the recent developments in the epidemiological situation, the human-animal interface with the risk of human exposure to HPAI H5 has expanded. In 2024, Finland became a global forerunner to offer H5 vaccine to occupational risk groups, specifically fur and poultry workers, following an extensive HPAI H5N1 outbreak in 2023 in fur-farmed minks and foxes. Despite targeted efforts to reach the people at increased risk, only 8,6% of the target population received the first dose and 7,5% completed both doses. To seek a better understanding of the barriers behind low vaccine uptake a Behavioural and Cultural (BCI) insight approach was chosen. A rapid qualitative study was conducted in late 2024 (n = 17), utilising semi-structured interviews with health authorities, industry stakeholders, and risk group representatives in the Ostrobothnia region in Finland. Barriers were identified across three dimensions: (1) logistical failures, including poor timing and difficulties in reaching target groups (2) divergent risk perceptions, where economic livelihood overshadowed personal health risks; and (3) political distrust, stemming from perceived stigmatization by national health authorities. The results will provide vital information for future pre-pandemic communication and implementation strategies and helps to identify key stakeholders and target groups.},
}

@article {pmid42155589,
year = {2026},
author = {Esaian, S and Smith, BA and Oh, J and Espinoza, JC and Vidmar, AP and Goran, MI},
title = {Carbohydrate composition of infant formula and glycemic regulation in early infancy using continuous glucose monitoring: cross-sectional evidence of altered glucose patterns with corn syrup solid-based formulas.},
journal = {The American journal of clinical nutrition},
volume = {},
number = {},
pages = {101325},
doi = {10.1016/j.ajcnut.2026.101325},
pmid = {42155589},
issn = {1938-3207},
abstract = {BACKGROUND: Infant formulas vary widely in carbohydrate composition, yet associations between exposure to nonlactose carbohydrates and glycemic patterns in early infancy remain poorly characterized.

OBJECTIVES: We assessed associations between infant feeding strategy and continuous glucose monitor (CGM)-derived measures of glycemic variability in a cross-sectional observational cohort of infants at 6 mo of age.

METHODS: Forty-five infants (28.0 ± 1.2 wk; 47% female) wore CGMs recording interstitial glucose every 15 min for 3 to 8 d. Feeding strategy was categorized as exclusive human milk, formula containing lactose or corn syrup solids (CSS), or mixed human milk/lactose-based formula. Twenty-eight CGM-derived metrics were computed using the R package iglu. Group differences were tested using Freedman-Lane analysis of covariance with permutation-based post hoc tests; effect sizes (η[2]p) and 95% bootstrap confidence intervals (BCI) were reported for all key comparisons. Exploratory hierarchical clustering (Ward's D2) examined glycemic variability subgroups independent of feeding strategy.

RESULTS: Approximately 46% of CGM-derived metrics differed significantly across feeding strategies, all reflecting contrasts between CSS-based formula and other groups; no metrics differed among human milk, lactose-based formula, or mixed feeding. Compared with human milk, CSS-fed infants were associated with greater glycemic variability and large effect sizes (though the study was powered only to detect large effects), including greater time in hyperglycemia (η[2] = 0.21; 95% BCI = -2.59,2.49), glycemic risk assessment diabetes equation (η[2] = 0.31; 95% BCI = -0.25,0.24), J index (η[2] = 0.24; 95% BCI = -1.07,1.08), and mean amplitude of glycemic excursions (η[2] = 0.40; 95% BCI = -6.14,6.03). Exploratory clustering identified 4 glycemic variability subgroups. One subgroup exhibited broadly elevated glucose variability and included ∼36% of CSS-fed infants, with no representation from other feeding strategies.

CONCLUSIONS: Infant feeding strategy was associated with differences in CGM-derived glycemic variability at 6 mo, driven by greater glucose variability among CSS-fed infants. Human milk and lactose-based formula feeding did not differ. Exploratory analyses identified a subgroup with pronounced glycemic variability that included a subset of CSS-fed infants, highlighting interindividual variability in glycemic response.},
}

@article {pmid42158453,
year = {2026},
author = {Li, Y and Yi, R and Hu, Z},
title = {Brain-computer interface technology for motor rehabilitation in severe stroke: a narrative review.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {14},
number = {},
pages = {1822784},
pmid = {42158453},
issn = {2296-4185},
abstract = {This review examines the application of brain-computer interface (BCI) technology for motor rehabilitation in patients with severe stroke-a population often excluded from conventional therapies due to minimal movement. BCIs establish electronic links between the brain and external devices, enabling motor intention recognition without muscular activity. By pairing neural activation with sensory feedback, these systems promote neuroplasticity and strengthen adaptive motor pathways. Compared with standard therapies, preliminary evidence suggests BCI interventions may facilitate additional motor recovery, though current effect size estimates are limited by small sample sizes, high study heterogeneity, and inherent performance biases. Effective modalities include motor imagery with functional electrical stimulation, robotic-assisted training in virtual environments, and multimodal systems. Despite promising results, challenges persist regarding signal reliability, protocol optimization, patient selection, and cost. Emerging research focuses on integrating artificial intelligence, adaptive closed-loop systems, and portable platforms to enhance clinical feasibility. Interdisciplinary collaboration may help transition BCI technology from experimental use to routine rehabilitation, improving outcomes for severely impaired stroke survivors.},
}

@article {pmid42158584,
year = {2026},
author = {Chaiyanan, C and Phukhachee, T and Iramina, K and Kaewkamnerdpong, B},
title = {Toward practical BCIs: a BMNABC-based feature selection and sensor optimization framework for implicit learning detection from multimodal EEG-fNIRS data.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1778884},
pmid = {42158584},
issn = {1662-5161},
abstract = {Implicit learning is a fundamental cognitive process whose identification is critical for understanding human cognition and developing innovative training methodologies. We propose a generalizable feature selection and sensor optimization framework using simultaneous EEG and fNIRS to identify these events. Our approach leverages a two-stage optimization process driven by a binary multi-neighbor artificial bee colony (BMNABC) algorithm. The BMNABC uses the model's classification accuracy to guide the heuristic search for the most discriminative feature subset. First, the framework prioritizes optimal features from high-dimensional, multimodal data using a normalized weighted sum (NWS) metric. Second, it implements a recursive backward elimination mechanism to reduce the number of sensors for practical brain-computer interface (BCIs) applications. Our results demonstrate that the BMNABC framework successfully identifies a superior feature set, leading to a significant improvement in classification accuracy over using either modality alone. Critically, the selected features provided neurophysiological validation, isolating key biomarkers in the prefrontal cortex. We also show that a sparse yet highly effective sensor configuration can be achieved, maintaining high performance with up to 66% fewer sensors. This work not only provides a data-driven method for detecting implicit learning but also advances the design of more efficient and user-friendly BCI systems.},
}

@article {pmid42151503,
year = {2026},
author = {Xu, JN and Li, JT and Xu, RX and Wang, YF and Gao, HW and He, HT and Guo, H and Liang, Y and Zhu, YD and Li, XW and Yang, JM and Li, XM and Chen, YH and Gao, TM},
title = {The multilevel exploration test, a novel paradigm to measure exploratory behavior in depression animal models and the involvement of the PL-ZI circuit.},
journal = {Acta pharmacologica Sinica},
volume = {},
number = {},
pages = {},
pmid = {42151503},
issn = {1745-7254},
abstract = {Diminished drive is one of the core symptoms of major depressive disorder (MDD) diagnosis, yet its underlying neural mechanisms remain elusive, primarily due to a lack of appropriate animal models. We developed a novel Multilevel Exploration Test (MET) apparatus to evaluate exploratory behavior, which is captured as a dynamic, stage-dependent process involving "search", "attend/investigate", and "approach" phases. We employed fiber photometry to measure real-time dopamine dynamics in the nucleus accumbens. We further combined cFos staining and neural circuit tracing to identify relevant brain regions and circuits, and employed chemogenetics to selectively modulate prelimbic cortex (PL) inputs to zona incerta (ZI). The MET tests were conducted across five depression models, with ketamine administration to evaluate rescue effects. Machine learning algorithms were utilized to analyze MET data and predict individual emotional states (normal, anxiety-like, depression-like). Here, we developed a novel paradigm to assess exploratory behavior, which demonstrates etiological validity, face validity and predictive validity. Depressed mice exhibited reduced motivation for exploration in this paradigm, while stimulation of the PL-ZI circuit not only restored exploratory deficits but also alleviated other depression-like behaviors in these mice. Furthermore, we established a machine learning-based model to predict individual animals' emotional states by integrating data from the new paradigm, achieving a prediction accuracy of over 92%. The MET provides a functional, high-throughput paradigm for dissecting motivation-related pathology. It facilitates the assessment of depressive-like behaviors, enables the prediction of emotional states, and supports the discovery of novel targets for antidepressant development.},
}

@article {pmid42152642,
year = {2026},
author = {Begum, A and Sultana, A and Bin Heyat, MB and Rahman, K and Akhtar, F and Al-Huda, Z and Banu, T and Khaleeq, K and Bian, S and Sawan, M},
title = {Efficacy of Pimpinella anisum L. in Menopausal Women with Psychological Symptoms: A Randomized Controlled Study Integrated with Machine Learning Analysis.},
journal = {Current pharmaceutical design},
volume = {},
number = {},
pages = {},
doi = {10.2174/0113816128433736260209142133},
pmid = {42152642},
issn = {1873-4286},
abstract = {INTRODUCTION: Menopausal women commonly experience psychological symptoms. These symptoms reduce their quality of life. Pimpinella anisum (anise) is an Unani remedy traditionally used for such problems. This study aimed to test the effects of anise on psychological and menopausal symptoms, along with appraising machine learning models in classifying treatment response between the anise and control groups.

METHODS: A total of 60 menopausal women received either 4 g of anise per day or a matched placebo, administered in two divided doses over an eight-week period. Primary outcomes included the Depression, Anxiety, and Stress Scale 21 (DASS-21) in menopausal women. Secondary outcomes included overall Modified Kuppermen Index (MKI), Vaginal Health Index (VHI), and treatment satisfaction (MS-TSQ). Machine learning classifiers, including Gradient Boosting (GB), AdaBoost (AB), K-Nearest Neighbours (KNN), Naive Bayes (NB), and Random Forest (RF), were utilised. Safety was monitored weekly through interviews. Hepatic and renal function were evaluated at baseline and after 12 weeks.

RESULTS: Baseline variables were similar between the two groups. Anise significantly reduced the DASS-21 scores compared to placebo at 8 weeks (p < 0.0001). At 8 weeks, participants receiving anise demonstrated significant improvements across multiple measures. DASS‑21 scores declined markedly compared with placebo (p < 0.0001), with more than 80% reporting no symptoms of depression, anxiety, or stress. MKI and VHI scores also improved significantly in the anise group (p < 0.0001), while the control group showed no notable change. Satisfaction ratings on the MS‑TSQ were high among anise recipients but low in the placebo arm. No adverse effects were observed. In addition, the KNN model achieved outstanding performance, correctly classifying group membership with 99.20% accuracy.

DISCUSSION: Anise demonstrated significant benefits in reducing psychological and menopausal symptoms, with no adverse effects reported, supporting its potential as a safe non‑hormonal therapy. The strong performance of the KNN model additionally exemplifies how machine learning can improve menopausal research by precisely distinguishing treatment responses. Upcoming studies with larger and more varied populations will be important to endorse these findings and to discover long‑term outcomes.

CONCLUSION: This research indicates that anise is a safe and effective alternative for relieving psychological symptoms in menopausal women. The KNN model reliably classified treatment outcomes, signifying that the integration of anise treatment with AI‑based assessment methods could enrich research on menopause care.},
}

@article {pmid42153184,
year = {2026},
author = {Wang, M and Gong, Z and Li, Y and Li, Y and Zheng, Y},
title = {Robust decoding for MI-EEG: a hybrid transformer network using multi-perspective collaborative attention and dynamic hyperbolic tangent.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {93},
pmid = {42153184},
issn = {1871-4080},
abstract = {Deep learning methods have been extensively applied in brain-computer interface (BCI) systems based on motor imagery (MI) for decoding electroencephalogram (EEG) signals. However, most existing hybrid architectures often struggle to effectively eliminate redundant noise in multi-channel signals and lack adaptability to the inherent non-stationarity and distribution drift of EEG signals. This work proposes a novel end-to-end hybrid attention Transformer network (HATNet) for EEG classification. HATNet first employs a convolutional neural network to extract local spatio-temporal features. To overcome the limitations of existing models, it fuses a Collaborative Attention Mechanism for Lightweight Channels, which dynamically recalibrates feature channels through multidimensional pooling strategies, including entropy pooling, to achieve precise spatial noise suppression. Addressing the non-stationary nature of EEG signals, an innovative Dynamic Hyperbolic Tangent module drives the Transformer encoding layer, adapting in real-time to data distribution drifts and significantly enhancing the model's ability to capture individual variations. Furthermore, cross-layer residual fusion pathways deeply integrate global contextual features with raw local spatio-temporal features. To ensure clear scope definition, experiments explicitly distinguish between primary MI tasks and auxiliary motor execution (ME) tasks. HATNet's performance was evaluated on three primary MI benchmark datasets, namely BCIC-IV-2a, BCIC-IV-2b, and the large-scale OpenBMI, as well as one auxiliary ME dataset, HGD. Experimental results demonstrate that HATNet achieves state-of-the-art performance across all analyses. In subject-dependent evaluations, average accuracy rates reached 81.25%, 86.65%, and 69.57% on the three primary MI datasets respectively, and 96.20% on the auxiliary ME dataset. Furthermore, in subject-independent evaluations, it achieved 60.88%, 80.79%, and 76.28% on the MI datasets respectively, alongside 73.95% on the ME dataset. Through multidimensional feature selection and dynamic adaptive modeling, HATNet exhibits superiority and robustness in enhancing both MI and ME decoding performance.},
}

@article {pmid42154699,
year = {2026},
author = {Fu, R and Fang, Y and Xu, F and Hua, C and Hua, C},
title = {SAND: Spectral-Attention Neural Decoding of Hand Kinematics from Low-Frequency EEG Dynamics.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3694935},
pmid = {42154699},
issn = {1558-2531},
abstract = {Brain-Computer Interface (BCI) technology, integrating neuroscience and artificial intelligence, has been widely applied in neural rehabilitation. However, hand kinematics decoding via electroencephalography (EEG) is constrained by limited precision and cross-subject adaptability. This study proposes the Spectral-Attention Neural Decoder (SAND) - a hybrid framework synergizing spectral decomposition and adaptive deep learning for robust 2D/3D trajectory reconstruction. Systematic analysis of the WAY EEG Grasp-and-Lift dataset revealed that hand movement information is primarily encoded in low-frequency EEG bands. Therefore, a dual-branch continuous decoding architecture was developed: (1) a frequency-domain pathway for noise-resistant spectral embedding, and (2) a temporal-attention pathway utilizing transformer networks to capture dynamic neural modulations. Five-fold cross-validation results demonstrate that SAND achieves state-of-the-art performance in hand-trajectory decoding. The Pearson correlation coefficients for the x, y, and z axes reach 0.9595 ± 0.0148, 0.9534 ± 0.0151, and 0.9293 ± 0.0250, respectively, representing an improvement of 0.07-0.13 over baselines. To assess cross-task generalization, we validate SAND on a self-collected dataset, where it attains average correlation coefficients of 0.90 (x-axis) and 0.96 (y-axis) in 2D trajectory reconstruction. The temporal alignment with ground-truth kinematic recordings was validated by remarkable performance in dynamic time warping analysis. These results confirm SAND as an effective solution for precise hand motion decoding advances non-invasive BCI applications.},
}

@article {pmid42154722,
year = {2026},
author = {Hu, C and Liang, S and Li, R and Ong, ST and Xu, H and Zhang, J},
title = {EEGDTF: Time-Frequency Disentangled Diffusion for High-Fidelity EEG Signal Generation.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3694047},
pmid = {42154722},
issn = {2168-2208},
abstract = {EEG signal generation is hindered by challenges such as complex time-frequency structures, the lack of explicit spectral modeling, limited data availability, and limited generalization across subjects and tasks. To address these issues, we propose EEGDTF, a diffusion-based generative framework for synthesizing high-fidelity EEG signals with improved time-frequency modeling. EEGDTF first employs a multi-scale residual encoder to enhance temporal representation learning and training stability. It further introduces a dual-branch encoder-decoder architecture for time-frequency disentanglement: the frequency branch models both periodic and aperiodic components via power spectral parameterization, while the temporal branch captures waveform continuity and long-range dependencies. A frequency-guided cross-attention mechanism integrates both branches effectively. The model is optimized through a joint waveform and spectral loss, enabling stable clean-signal estimation during reverse sampling. Experiments on four benchmark datasets demonstrate that EEGDTF achieves state-of-the-art performance in both time and frequency domains, particularly under cross-subject conditions. These results underscore the model's robustness and generalizability, positioning EEGDTF as a reliable tool for EEG data augmentation and BCI-related applications.},
}

@article {pmid42155274,
year = {2026},
author = {Houshmand, MH and Pishgoo, B},
title = {Real-time emotion recognition based on EEG signals using a hybrid batch-stream architecture.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {202},
number = {},
pages = {109072},
doi = {10.1016/j.neunet.2026.109072},
pmid = {42155274},
issn = {1879-2782},
abstract = {In recent years, emotion recognition using brain-computer interface (BCI) systems has gained substantial attention. Existing models are typically implemented in either offline (batch) or online (streaming) modes. While batch processing approaches generally achieve higher classification accuracy, they are limited by slow processing speed. In contrast, stream processing approaches offer real-time performance but often compromise accuracy. To address this trade-off, we propose a hybrid batch-streaming framework that integrates the strengths of both paradigms while alleviating their individual limitations. The architecture, features a probabilistic intelligent switching mechanism that estimates the reliability of the streaming module based on its historical performance. This reliability measure dynamically determines the probability of selecting outputs from either the batch or streaming unit. The proposed framework is evaluated on three benchmark datasets (DEAP, AMIGOS, and SEED) achieving classification accuracies of 85%, 94%, and 74%, respectively. Also, experiments were conducted to investigate the performance of the switch mechanism and performance of system components against concept drift. Experimental results demonstrate that our method effectively balances classification accuracy and computational efficiency. It is expected that in the future, such hybrid ideas are widely used in feedback - based systems.},
}

@article {pmid42155486,
year = {2026},
author = {Wimmer, M and Elsayed, N and Thomas, BH and Müller-Putz, GR and Veas, EE},
title = {An online brain-computer interface for detecting incongruity in augmented reality applications.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae6ff1},
pmid = {42155486},
issn = {1741-2552},
abstract = {OBJECTIVE: Augmented reality can provide digital information about physical entities presented within its real-world context. However, this information might disagree with the user's expectations due to factual errors in the data or cognitive biases. Such incongruity can impair user experience and undermine trust in the AR system. To address this issue, we propose detecting inconsistencies between physical objects and digital information through hybrid braincomputer interfaces.

APPROACH: We conducted two complementary experiments. First, we implemented a strategy that integrates eye-tracking and brain signals for incongruity detection in an offline study. Subsequently, we assessed our approach in an online study in which participants received immediate feedback on the classification.

MAIN RESULTS: The grand average event-related potentials revealed consistent electroencephalographic responses to incongruent augmentations, specifically a centroparietal N400 effect, across both experiments. We could further distinguish between congruent and incongruent information with an average balanced accuracy of 70 % in the online study.

SIGNIFICANCE: These findings demonstrate the feasibility of detecting incongruity online, allowing for autonomous system adaptation, like presenting information in a more accessible format or providing contextual support.},
}

@article {pmid42144446,
year = {2026},
author = {Liu, JY and Yang, DL and Liu, HY and Xiang, SF and Cao, J and Yang, B and He, QJ and Wang, JH and Shao, XJ and Ying, MD},
title = {Advances in targeted therapies for pediatric tumors.},
journal = {Acta pharmacologica Sinica},
volume = {},
number = {},
pages = {},
pmid = {42144446},
issn = {1745-7254},
abstract = {Pediatric tumors represent a major cause of disease-related mortality in children and exhibit biological features that differ markedly from those of adult cancers. Pediatric malignancies display unique molecular architectures, with lower mutation frequency, higher frequency of chromosomal alterations such as gene rearrangement and amplification, a distinct alteration spectrum marked by dysregulated developmental genes, as well as a characteristic pattern of differentiation blockage. These alterations often arise during developmental windows and sustain tumor dependency, providing unique drug targets for targeted therapy. This review first describes the molecular characteristics and oncogenic drivers of pediatric tumors, as well as the potential mechanisms underlying the formation of oncogenic driver events in these tumors. It subsequently systematically synthesizes recent advances in targeted therapeutic strategies for pediatric tumors, categorizing strategies by disease type and oncogenic driver events, including oncofusion-directed inhibitors, agents targeting amplified or mutated genes, differentiation-inducing approaches, antibody-based therapies, and cellular therapies. We highlight both pediatric-specific drug development and the extrapolation of adult therapies to pediatric patients, while underscoring persistent challenges in clinical translation. This work advocates for a biology-driven framework to accelerate the development of effective targeted therapies for pediatric tumors.},
}

@article {pmid42145810,
year = {2026},
author = {Benachour, A and Syrov, N and Lebedev, M},
title = {Motor imagery affects both cortical and spinal circuitry: a transcranial and trans-spinal magnetic stimulation study.},
journal = {Frontiers in neural circuits},
volume = {20},
number = {},
pages = {1809125},
pmid = {42145810},
issn = {1662-5110},
mesh = {Humans ; *Transcranial Magnetic Stimulation/methods ; Male ; *Imagination/physiology ; Adult ; *Evoked Potentials, Motor/physiology ; Female ; *Motor Cortex/physiology ; *Spinal Cord/physiology ; Young Adult ; Electromyography ; Muscle, Skeletal/physiology ; Brain-Computer Interfaces ; },
abstract = {INTRODUCTION: Motor imagery (MI), the mental rehearsal of movement without physical execution, is a key technique in brain-computer interfaces (BCIs), known for eliciting cortical modulations similar to those exhibited during real movement. Beyond cortical effects, MI could also modulate spinal cord processing, which offers additional potential for neurorehabilitation in conditions like spinal cord injury (SCI) and stroke, where BCIs are used for therapy.

MATERIAL AND METHODS: To investigate the interactions of MI with both the cortex and the spinal cord, we employed both transcranial magnetic stimulation (TMS) and trans-spinal magnetic stimulation (TSMS) while recording brain and muscle activities.

RESULTS AND CONCLUSION: With proper coil orientation, TSMS elicited lateralized MEPs in ipsilateral forearm muscles at significantly shorter latencies than M1-evoked MEPs, confirming direct spinal cord activation. Importantly, right-hand kinesthetic MI selectively facilitated TSMS-evoked MEPs in the stimulated ipsilateral side only, providing direct evidence that MI modulates spinal cord excitability. Moreover, TSMS-evoked cortical responses were modulated by imagery, demonstrating that MI increases cortical processing of the ascending spinal volley. This within-group demonstration of MI affecting both cortical and spinal circuitry underscores its potential as a powerful strategy for BCI-driven neurorehabilitation, including pairing MI with spinal magnetic stimulation.},
}

Humans
*Transcranial Magnetic Stimulation/methods
Male
*Imagination/physiology
Adult
*Evoked Potentials, Motor/physiology
Female
*Motor Cortex/physiology
*Spinal Cord/physiology
Young Adult
Electromyography
Muscle, Skeletal/physiology
Brain-Computer Interfaces