@article {pmid42320451,
year = {2026},
author = {Li, J and Yang, Y and Huang, Y and Xu, K and Chen, Y and Yu, L and Yao, Z and Fu, Y},
title = {Reconstructing shared visual experiences from human brain activity across individuals.},
journal = {Medical image analysis},
volume = {113},
number = {},
pages = {104157},
doi = {10.1016/j.media.2026.104157},
pmid = {42320451},
issn = {1361-8423},
abstract = {Reconstructing visual experiences from brain activity promises to strengthen brain-computer interfaces and our fundamental understanding of perception. However, current deep learning approaches for functional magnetic resonance imaging (fMRI)-based image synthesis are often person-specific, requiring substantial data to adapt to new individuals, thus limiting their scalability and translational potential. Here, we present MindShow, a unified generative framework for shared-subject fMRI-to-image reconstruction under a cohort-level training setting. The core of MindShow is a Hierarchically-Conditioned Mixture-of-Experts (HiCo-MoE) encoder that disentangles population-shared latent representations from subject-specific neural characteristics, enabling data-efficient target-subject adaptation under limited calibration data. These representations are then processed by our Gated Perceiver Bottleneck (GPB), a gated Perceiver-style tokenization interface that resolves multi-scale representational misalignment by adaptively mapping the fMRI features into distinct, fixed-size image and text latent tokens. To improve semantic and structural consistency, we introduce a multi-granular optimal transport loss (MOT-Align), which regularizes sample- and token-level distributional alignment between brain-derived features and the latent space of a pretrained vision-language model. When guided by these aligned embeddings, a frozen diffusion model synthesizes images that aim to preserve the semantic content and coarse layout of the perceived content. MindShow improves high-level reconstruction metrics while maintaining competitive structural fidelity, representing a methodological step toward scalable shared-subject neural decoding. All implementation code is available on GitHub: https://github.com/AI-NMI/MindShow.},
}

@article {pmid42322017,
year = {2026},
author = {Pavlyk, I and Field, G and Young, M and Carpentier, J and Szlosarek, EA and O'Keeffe-Brown, MR and Crook, T and Syed, N and Bomalaski, JS and Chan, PY and Szlosarek, PW},
title = {Pegargiminase Suppresses the Fanconi Anemia Pathway and Promotes Melphalan-Induced DNA Double-Strand Breaks in Uveal Melanoma.},
journal = {Pigment cell & melanoma research},
volume = {39},
number = {4},
pages = {e70104},
pmid = {42322017},
issn = {1755-148X},
support = {MIMR1A3S//Polaris Pharmaceuticals/ ; },
mesh = {Humans ; *Melanoma/pathology/genetics/drug therapy/metabolism ; *Melphalan/pharmacology ; *Uveal Neoplasms/pathology/genetics/drug therapy/metabolism ; *Polyethylene Glycols/pharmacology ; *DNA Breaks, Double-Stranded/drug effects ; Uveal Melanoma ; Cell Line, Tumor ; *Fanconi Anemia/pathology/metabolism/genetics ; Cell Proliferation/drug effects ; *Signal Transduction/drug effects ; Gene Expression Regulation, Neoplastic/drug effects ; Hydrolases ; },
abstract = {Uveal melanoma is a hard-to-treat arginine-dependent cancer secondary to argininosuccinate synthetase 1 (ASS1) loss with half of patients succumbing to liver-dominant metastases. Arginine deprivation with pegargiminase is a novel antimetabolite strategy for patients with uveal melanoma. We investigated the preclinical rationale for combining pegargiminase with melphalan, an alkylating agent approved recently for the treatment of hepatic-centric disease. Drug sensitivity of ASS1-deficient uveal melanoma cell lines was performed in 2D culture using proliferation and cytotoxicity assays, with analysis of cell death, cell cycle, DNA double-strand breaks, and interrogation of the molecular mechanism of action by RNA-seq. ADI-PEG20 and melphalan suppressed uveal melanoma cell line proliferation and triggered cytotoxicity, effects which were enhanced with the drug combination. ADI-PEG20 downregulated multiple genes of the Fanconi anemia pathway and synergized with melphalan to increase DNA double-strand breaks. Melphalan and pegargiminase is a rational new drug combination that warrants clinical testing in uveal melanoma.},
}

Humans
*Melanoma/pathology/genetics/drug therapy/metabolism
*Melphalan/pharmacology
*Uveal Neoplasms/pathology/genetics/drug therapy/metabolism
*Polyethylene Glycols/pharmacology
*DNA Breaks, Double-Stranded/drug effects
Uveal Melanoma
Cell Line, Tumor
*Fanconi Anemia/pathology/metabolism/genetics
Cell Proliferation/drug effects
*Signal Transduction/drug effects
Gene Expression Regulation, Neoplastic/drug effects
Hydrolases

@article {pmid42323786,
year = {2026},
author = {Khanam, T and Siuly, S and Wang, K and Whittaker, F and Wang, H},
title = {Evaluating multi-level membership inference risk in federated EEG learning.},
journal = {Brain informatics},
volume = {},
number = {},
pages = {},
doi = {10.1186/s40708-026-00313-1},
pmid = {42323786},
issn = {2198-4018},
abstract = {Electroencephalography (EEG) records electrical brain activity from the scalp and is widely used in brain-computer interface (BCI) systems for communication, and assistive technologies. EEG is widely used in motor-imagery (MI) based BCIs, where neural recordings contain highly individual and potentially sensitive information. In this regard, federated learning (FL) is a prominent privacy-enhancing approach which enables collaborative model training without centralising raw signals. However, recent work has shown that FL models still leak private information through membership inference attacks (MIAs). Most existing studies examine only single attack type, so it remains unclear how multiple MIAs together expose different layers of privacy risk in FL-based EEG systems. To address this gap, this study develops a federated MI-EEG classification framework and evaluates privacy leakage across four complementary MIAs: record-level, feature-level, gradient-level, and client-identity inference. Two neural networks were trained using per-subject FL, and differential privacy (DP) with epsilon (ε) ∈ {1, 5, 10}
was applied to client updates. Results showed that standard FL alone provides limited intrinsic protection, while adding DP substantially reduces attack success particularly for gradient and identity-level attacks. Strong privacy settings (ε = 1) offered the greatest leakage reduction but degraded classification accuracy, whereas a moderate privacy budget (ε = 5) achieved the most favourable privacy-utility balance. Overall, the findings demonstrate that FL alone is insufficient as a privacy safeguard for EEG-BCI systems. Explicit privacy mechanisms such as DP are required to mitigate multi-level leakage, supporting the design of trustworthy and secure neural-learning technologies.},
}

@article {pmid42325017,
year = {2026},
author = {Shibamori, K and Kyoda, Y and Shinkai, N and Nofuji, S and Yorozuya, W and Okabe, K and Hashimoto, K and Kobayashi, K and Tanaka, T and Masumori, N},
title = {Differences in Urodynamic Findings of Robot-Assisted Radical Prostatectomy Before and 2 Years After Surgery.},
journal = {Neurourology and urodynamics},
volume = {},
number = {},
pages = {},
doi = {10.1002/nau.70358},
pmid = {42325017},
issn = {1520-6777},
abstract = {BACKGROUND: Numerous studies have investigated changes in urinary function following robot-assisted radical prostatectomy (RARP); however, most studies have primarily focused on identifying predictors of postoperative urinary incontinence. This study aimed to evaluate bladder function and lower urinary tract symptoms through urodynamics examined before and 2 years after RARP.

METHODS: This single-center, retrospective study included patients who underwent RARP for prostate cancer between April 2014 and April 2022. All participants who met our criteria completed questionnaires and underwent uroflowmetry (UFM) and pressure-flow study (PFS) both preoperatively and 2 years postoperatively.

RESULTS: Of the 376 patients, 141 were included in the analysis. Their median age was 68 years. The median prostate volume (PV) was 34.5 mL, and the median PSA level was 6.17 ng/mL. Postoperative evaluations revealed significant reductions in International Prostate Symptom Score (IPSS) items Q1, Q3, Q5, and the total score, while overactive bladder symptom score (OABSS) item Q4 increased significantly. UFM findings demonstrated significant improvements in maximum and average flow rates, flow time, and residual urine volume. PFS results showed significant improvements in voiding phase parameters and the bladder outlet obstruction (BOO) index. However, the bladder contractility index (BCI) significantly declined. Higher preoperative BCI and BOOI, advanced age, and smaller prostate volume were identified as independent risk factors for greater postoperative decline in BCI. Patients with 3-4 risk factors exhibited a significantly greater decline in BCI than those with 0-2 risk factors (median changes in BCI: -28.8 [IQR, -43.8 to -10.6] vs. 2.1 [IQR, -23.7 to 16.2], p < 0.001).

CONCLUSIONS: RARP was associated with improving urinary symptoms and objective urodynamic parameters, including UFM and PFS outcomes. Nevertheless, a decline in the BCI was observed at 2 years postoperatively among older patients. These findings suggest that increased age at the time of surgery might be associated with postoperative deterioration of detrusor function, although causality remains unclear.},
}

@article {pmid42325271,
year = {2026},
author = {Liang, C and Jiang, W and Chen, J and Turner, JA and Calhoun, VD and Abbott, CC and Jiang, R and Fu, Z and Wu, L and Wang, X and Qi, S and Yuan, Y},
title = {Longitudinally altered default mode network and insula multimodal brain pattern in end-stage renal disease during sustained hemodialysis treatment.},
journal = {iScience},
volume = {29},
number = {6},
pages = {116008},
pmid = {42325271},
issn = {2589-0042},
abstract = {Hemodialysis (HD) is the predominant treatment for end-stage renal disease (ESRD). Despite the efficacy of HD, the neurobiological underpinnings underlying high-risk complications remain unclear. In this study, using unsupervised fusion of functional and structural MRI, we identified a longitudinally altered default mode network (DMN)-insula pattern in ESRD receiving HD over 1-year follow-up (n = 39). This pattern was associated with cognition, and its related genes were enriched in biological processes involving DNA damage and repair, energy metabolism, and cellular activation. The baseline DMN-insula pattern demonstrated potential predictive value for follow-up cognition in ESRD. More importantly, these brain-cognition associations were validated in independent high-risk complications cohorts, including major depressive disorder (n = 60), mild cognitive impairment (n = 291), and Alzheimer's disease (n = 77) by extracting the corresponding brain features and assessing their correlations with cognition. Collectively, this study may help researchers better understand the underlying mechanisms of ESRD receiving HD from a multimodal neuroimaging and molecular perspective.},
}

@article {pmid42328775,
year = {2026},
author = {Cubillos, LH and Kelberman, MM and Mender, MJ and Hite, A and Temmar, H and Willsey, M and Kumar, NG and Kung, TA and Patil, PG and Chestek, C and Krishnan, C},
title = {Exploring Synergies in Brain-Machine Interfaces: Compression vs. Performance.},
journal = {Restorative neurology and neuroscience},
volume = {},
number = {},
pages = {9226028261457824},
doi = {10.1177/09226028261457824},
pmid = {42328775},
issn = {1878-3627},
abstract = {Individuals with severe neurological injuries often rely on assistive technologies, but current methods have limitations in accurately decoding multi-degree-of-freedom (DoF) movements. Intracortical brain-machine interfaces (iBMIs) use neural signals to provide a more natural control method but currently struggle with higher-DoF movements: something the brain handles effortlessly. It has been theorized that the brain simplifies high-DoF movement through muscle synergies, which link multiple muscles to function as a single unit. These synergies have been studied using dimensionality reduction techniques like principal component analysis (PCA), non-negative matrix factorization (NMF), and demixed PCA (dPCA) and successfully used to reduce noise and improve offline decoder stability in non-invasive applications. However, their effectiveness in improving decoding and generalizability for implanted recordings across varied tasks is unclear. Here, we evaluated whether brain and muscle synergies can enhance iBMI performance in non-human primates performing a two-DoF finger task. Specifically, we tested if PCA, dPCA, and NMF could compress and denoise brain and muscle data and improve decoder generalization across tasks. Our results showed that while all methods effectively compressed data with minimal loss in decoding accuracy, none improved performance through denoising in our datasets. Additionally, none of the methods enhanced generalization across tasks. These findings suggest that while dimensionality reduction can aid data compression, extracting synergies alone did not provide an advantageous or cleaner control space for linear decoding in our study. Further research with larger sample sizes and more channels in muscle recordings is required to determine whether synergies can be leveraged as an optimal control framework or if alternative approaches are required to enhance decoder robustness in iBMI applications.},
}

@article {pmid42329899,
year = {2026},
author = {Ng, KKW and Birznieks, I and Vickery, RM},
title = {Vibrotactile intensity perception: Predominant influence of afferents recruited remote from stimulus site.},
journal = {The Journal of physiology},
volume = {},
number = {},
pages = {},
doi = {10.1113/JP290236},
pmid = {42329899},
issn = {1469-7793},
support = {APP1028284//National Health and Medical Research Council/ ; 13/RC/2073_P2//Research Ireland/ ; 101081457//HORIZON EUROPE Marie Sklodowska-Curie Actions/ ; DP230100048//Australian Research Council/ ; DP200100630//Australian Research Council/ ; },
abstract = {Sinusoidal vibratory stimuli are frequently used to study human sensory perception but have the limitation that changes in vibration frequency are accompanied by changes in the number and type of activated mechanoreceptive afferents. Here we used trains of brief mechanical pulses to investigate the neural coding of vibrotactile perceived intensity by grouping pulses into bursts. These pulse trains evoke the same perceived frequency, determined by the interval between bursts, as we have previously demonstrated, and held constant across conditions. Subjects rated the perceived intensity using a magnitude estimation task for stimuli varying in the number of pulses per burst (up to four) and stimulation amplitude (5-150 µm). In marked contrast to our previous findings using electrical stimulation, increasing the number of pulses per burst had only a minimal and inconsistent effect on perceived intensity. To explain this we simulated the responses of the afferent population across the hand using the TouchSim computational model. The model revealed that increasing pulse number, without changing amplitude, produced only a modest increase in total population spike count. This occurred because the population response was dominated by large numbers of afferents remote from the stimulation site, many of which failed to respond reliably to each pulse within a burst. In contrast increasing stimulus amplitude enhanced spatial recruitment, leading to greater population spike counts and increased perceived intensity. Together these results highlight the importance of both temporal and spatial summation in shaping tactile intensity perception and argue against a 'hot zone' model of intensity encoding. KEY POINTS: Electrical stimulation of the finger has been shown to change perceived intensity when varying the number of pulses within a stimulus burst, indicating that touch-sensitive nerve fibres encode intensity through the number of impulses they generate within bursts of activity. Here we used mechanical pulses applied to the skin to induce similar bursts of activity. In contrast to expectations, increasing the number of pulses within a burst did not consistently increase perceived intensity. We simulated the neural responses of the entire population of tactile nerve fibres in the hand, revealing that the burst pattern had only a small effect on nerve activity, which was dominated by large numbers of remote fibres that did not reliably follow bursts. These findings argue against a 'hot zone' model, where intensity is determined by nerve activity near the stimulus site, and instead suggest that the number of active fibres and overall activity are most significant.},
}

@article {pmid42329949,
year = {2026},
author = {Feng, L and Xu, B and Duan, L and Jia, S and Jia, Z and Ni, W},
title = {A New Dual-Attention Multi-Node Fusion Network for EEG-fNIRS Motor Imagery Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3706103},
pmid = {42329949},
issn = {2168-2208},
abstract = {Brain-computer interface (BCI) based on motor imagery (MI) can realize the direct control of external devices by decoding different signals. The decoding of MI based on electroencephalogram (EEG) suffers from low spatial resolution and is susceptible to noise. Functional near-infrared spectroscopy (fNIRS) has attracted increasing attention as a complementary modality. There have been attempts to fuse the two types of signals, but their spatio-temporal characteristics have not been fully explored. We propose a new multimodal EEG-fNIRS fusion MI classification and recognition model based on a dual attention mechanism. The model comprises two feature extraction branches and a central fusion network. We set two fusion layers in the central fusion network to exploit the spatio-temporal features of EEG and fNIRS. To reduce redundancy and mine correlation characteristics of multiple sensors, the features are fused in the filter dimension to prevent adverse effects between signals during fusion, thereby enabling the deep network to learn cross modal correlations while reducing mutual interference. The method is evaluated on two multimodal datasets. Experiments show that DAMFNet outperforms STA-Net and M2NN by 4.49% and 2.88% on Dataset1, respectively, and shows competitive performance on Dataset2. The code is available at https://github.com/useflf/DAMFNet.},
}

@article {pmid42329951,
year = {2026},
author = {Liu, Q and Pan, Y and Liu, Q and Zhang, L and Huang, G and Chen, X and Liu, Y and Li, F and Xu, P and Liang, Z},
title = {DuA: Dual Attentive Transformer in Long-Term Continuous EEG Emotion Analysis.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3705860},
pmid = {42329951},
issn = {2168-2208},
abstract = {Affective brain-computer interfaces (aBCIs) are increasingly recognized for their potential in monitoring and interpreting emotional states through electroencephalography (EEG) signals. Current EEG-based emotion recognition methods perform well with short segments of EEG data (known as segment-based emotion analysis). However, these methods encounter significant challenges in real-life scenarios where emotional states evolve over extended periods. To address this issue, we propose a novel Dual Attentive (DuA) transformer framework for long-term continuous EEG emotion analysis. Unlike segment-based approaches, our model processes an entire EEG trial as a whole, identifying emotions at the trial level, referred to as trial-based emotion analysis. This framework is designed to adapt to varying signal lengths, providing a substantial advantage over traditional methods. The DuA transformer incorporates three key modules: the spatial-spectral network module, the temporal network module, and the transfer learning module. The spatial-spectral network module simultaneously captures spatial and spectral information from EEG signals, while the temporal network module detects temporal dependencies within long-term EEG data. The transfer learning module enhances the model's adaptability across different subjects and conditions. To verify the effectiveness of the proposed DuA transformer, we construct a long-term continuous EEG emotion database and extensively evaluate our model using the self-constructed database along with two benchmark EEG emotion databases. On the basis of the trial-based leave-one-subject-out cross-subject cross-validation protocol, our experimental results demonstrate that DuA significantly outperforms existing methods in long-term continuous EEG emotion analysis, with an average improvement of 2.8%. The DuA transformer's ability to adapt to varying signal lengths and its superior performance across diverse subjects and conditions highlight its potential for real-world applications, enhancing the overall user experience and efficacy of aBCI systems.},
}

@article {pmid42330351,
year = {2026},
author = {Emerick, T and Vorenkamp, KE and Francio, VT and Srinivasan, S and Singla, P and Castellanos, J and Ahadian, FM and Chen, Z and Gianlorenco, AL and Sarno, D and Sehgal, N and Murthy, N and Barreveld, AM},
title = {AAPM Consensus Guidelines on Neuromodulation Technologies and Neurocomputer Interfaces for Pain Management and Functional Recovery.},
journal = {Pain medicine (Malden, Mass.)},
volume = {},
number = {},
pages = {},
doi = {10.1093/pm/pnag076},
pmid = {42330351},
issn = {1526-4637},
abstract = {OBJECTIVE: To provide an evidence-based framework for healthcare professionals to use neuromodulation technologies to restore neuromuscular function and relieve pain.

METHODS: An expert panel, convened by the American Academy of Pain Medicine Foundation, conducted a literature review of English-language studies published between 2015 and 2025 using PubMed, the Cochrane Library, Web of Science, and Scopus (detailed in Supplement 2). The panel screened abstracts, extracted key data, and evaluated evidence quality using a modified United States Preventive Services Task Force criteria. A Delphi process was used to achieve expert consensus on clinical recommendations for various neuromodulation technologies: Artificial intelligence-guided and robotic rehabilitation systems, virtual/augmented reality interfaces, brain-computer interfaces, electrical nerve stimulation (encompassing peripheral nerve stimulation transcutaneous electrical stimulation), vagus nerve stimulation, multifidus neurostimulation, surgery (eg,, regenerative peripheral nerve interface), scrambler therapy, spinal cord stimulation for motor restoration, and transcranial magnetic stimulation.

RESULTS: The panel provided clinical recommendations and discussed mechanisms of action, evidence, and clinical considerations for each intervention. Evidence for these technologies is evolving, with some showing promising results in areas like improving upper limb function post-stroke, improving functional spine-related outcomes, and reducing chronic pain.

CONCLUSIONS: Neuromodulation technologies offer a promising approach for neuromuscular restoration, focusing on interventions that promote functional recovery rather than solely providing symptomatic care. Areas for future research include more high-quality, large-scale studies with consistent outcome measures.},
}

@article {pmid42330680,
year = {2026},
author = {Bai, Y and Jiang, S and Yang, J and Hong, C and Zhao, W and Ni, G},
title = {Dynamic neural oscillations underpin audiovisual gain from visual lip cues in speech noise.},
journal = {Hearing research},
volume = {479},
number = {},
pages = {109703},
doi = {10.1016/j.heares.2026.109703},
pmid = {42330680},
issn = {1878-5891},
abstract = {Speech noise substantially impairs speech perception, whereas congruent lip movements provide effective visual support when auditory input is degraded. Using natural continuous Mandarin sentences, the present study recorded behavioral and electroencephalographic (EEG) data under speech noise conditions with different signal-to-noise ratios (SNR) to examine the dynamic neural mechanisms by which visual lip information facilitates speech perception. The results showed that speech noise significantly reduced speech recognition accuracy and weakened δ band activity and functional connectivity in the superior temporal sulcus/posterior superior temporal gyrus (STS/pSTG) and auditory-related regions. Congruent lip movements significantly improved speech recognition in noise and enhanced δ/θ band activity in the STS/pSTG during the early stage after target speech onset. Further analyses showed that, under low-SNR conditions, congruent lip movements increased 11∼15 Hz functional connectivity between the STS/pSTG and motor-related regions. These findings suggest that visual lip information may first facilitate early audiovisual integration by modulating low-frequency neural oscillations and may further recruit sensorimotor networks under difficult listening conditions to provide compensatory support for speech perception in noise.},
}

@article {pmid42330981,
year = {2026},
author = {Yao, Z and Zhou, J and Li, J and Bai, Z and Ji, H and Liu, L and Jin, L},
title = {EEG-EMG spatiotemporal cross-attention fusion network for functional upper-limb movement classification.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ae802c},
pmid = {42330981},
issn = {2057-1976},
abstract = {OBJECTIVE: Electroencephalography (EEG) and electromyography (EMG) are widely used for decoding motor intentions, yet unimodal approaches often suffer from low robustness and limited representational information. EEG-EMG hybrid brain-computer interfaces (BCI) can bridge cortical intention and muscular execution, but challenges remain in signal alignment, fusion modeling, and clinical generalization.

APPROACH: To address these issues, we propose STCAFusion, a spatiotemporal cross-attention framework that integrates EEG and EMG through multi-band based dual-branch convolutional encoders and parallel temporal and spatial cross-attention modules. This design enables detailed modeling of inter-modal correlations across both time and space. We evaluate STCAFusion on a newly collected dataset of synchronous EEG-EMG recordings from 12 subjects, where the data were acquired under two paradigms (Reaching and Lifting) designed from daily functional upper-limb activities to emphasize directional and strength control.

MAIN RESULTS: With leave-one-run-out cross-validation, STCAFusion achieves average accuracies of 84.15% and 95.22% in the two paradigms, outperforming the strongest competing EEG-EMG fusion baselines by 3.4% in the Reaching paradigm and 1.8% in the Lifting paradigm. Visualization of learned attention weights further reveals meaningful spatiotemporal EEG-EMG coupling patterns, offering insights into neural-muscular coordination patterns relevant to rehabilitation-oriented BCI design.

SIGNIFICANCE: These results highlight the potential of cross-attention-based multimodal physiological signal fusion in building reliable hybrid BCI and wearable devices for upper-limb control and rehabilitation.},
}

@article {pmid42320271,
year = {2026},
author = {Solano-Suarez, KG and Arango-Sabogal, JC and Roy, JP and Molgat, E and Gagnon, CA and Buczinski, S and Dufour, S},
title = {Evaluation of three bulk tank milk enzyme-linked immunosorbent assays to estimate within-herd prevalence of bovine leukosis virus in dairy farms.},
journal = {Preventive veterinary medicine},
volume = {254},
number = {},
pages = {106939},
doi = {10.1016/j.prevetmed.2026.106939},
pmid = {42320271},
issn = {1873-1716},
abstract = {The aim of this study was to estimate the bovine leukosis virus (BLV) herd-level and within-herd prevalence (WHP) in dairy herds and to assess the ability of three bulk tank milk (BTM) ELISA-Ab tests (SVANOVIR, Bovichek, and IDEXX) to predict the BLV WHP. A cross-sectional study was performed on a convenience sample of 93 dairy herds from Québec, Canada, where individual milk samples from all lactating cows and a BTM sample were collected. Individual milk ELISA-Ab results (n = 7612 cows) from a previously validated kit were incorporated into a two-stage hierarchical Bayesian latent class model to estimate BLV herd-level prevalence and WHP. To mitigate potential test saturation, we evaluated the three BTM ELISA-Ab tests with and without sample dilutions. Adjusted BTM ELISA-Ab optical density values were linked to the WHP estimates using a zero-inflated beta regression, and dilution-specific predictive models were compared. The herd-level BLV prevalence was estimated at 87% (95% Bayesian credible interval [BCI]: 78, 93), and BLV WHP ranged from 0% to 90%. The best predictive model-dilution combinations were SVANOVIR 1/10 (nonmonotonic curvilinear), Bovichek 1/5 (linear), and IDEXX 1/50 (nonmonotonic curvilinear). Across tests, optical density values increased with WHP, consistent with higher antibody concentrations in bulk milk. BLV remains highly prevalent at the herd level, with substantial variability in WHP among positive herds. These results indicate that BLV WHP can be reliably estimated from a single BTM sample, reducing the need for individual cow testing.},
}

@article {pmid42319942,
year = {2026},
author = {He, X and Chamberlin, M and Chen, Z and Li, J and Gao, Y and Guo, F and Ma, Y and Wei, X and Luo, X and Zhang, W and Qu, J and Wei, D and Zhu, G and Wang, F and Zhang, H and Yu, X and Chen, X and Yang, Y and Shi, P},
title = {Electronics with switchable flexibility for 3D conforming neural interfaces.},
journal = {Science advances},
volume = {12},
number = {25},
pages = {eaee2752},
doi = {10.1126/sciadv.aee2752},
pmid = {42319942},
issn = {2375-2548},
mesh = {Animals ; Alloys/chemistry ; *Electronics ; *Brain-Computer Interfaces ; },
abstract = {The intricate cortical folds of large primates physically restrict access to substantial portions of neural information via interface devices. Here, we develop a bioelectronic system, sFlex-Fold, with switchable flexibility, representing the neural interface capable of nondestructive three-dimensional (3D) access to both cortical gyri and sulci, providing large-area, nonpenetrative deep tissue coverage. sFlex-Fold is based on an artificial intelligence (AI)-designed liquid metal alloy (LM-alloy), leveraging the phase change of the tailor-made LM-alloy to create neural interfacing electronics with tunable mechanical response to temperatures ranging from 25° to 37°C. The LM-alloy can be patterned into arbitrary circuit layouts with an ~10-micrometer resolution. The flexibility switching happens at the LM melting point, fine-tuned to 36.2°C, upon in vivo tissue contact, causing a three-order-of-magnitude reduction in the effective modulus of the implanted device. As a result, sFlex-Fold has the unique advantages of both a rigid and flexible state and can be morphed into complex, folded, 3D shapes. This enables nondestructive in vivo implantation into deep cortical sulci while maintaining large coverage (>80 square centimeters) over curved brain surfaces with tissue-matching mechanical compliance. Such 3D structural and mechanical mimicking enables high-quality electrical interfacing as quantitatively assessed using rodent and porcine models.},
}

Animals
Alloys/chemistry
*Electronics
*Brain-Computer Interfaces

@article {pmid42311456,
year = {2026},
author = {Chen, Y and Jiang, Y and Wang, X and Zeng, M and Cui, J},
title = {Advances in mechanisms of neuroplasticity induced by multimodal closed-loop brain-computer interfaces after stroke.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1828191},
pmid = {42311456},
issn = {1662-5161},
abstract = {Post-stroke motor dysfunction is one of the leading causes of acquired disability worldwide. The induction and maintenance of neuroplasticity constitute the core mechanisms underlying motor function recovery. Conventional open-loop brain-computer interfaces (BCIs) lack real-time closed-loop feedback and are therefore unable to reliably activate the "temporal contingency" principle required by Hebbian synaptic remodeling, resulting in limited rehabilitation efficacy. Multimodal closed-loop BCIs integrate motor intent decoding, functional electrical stimulation (FES), virtual reality (VR), and exoskeleton-mediated proprioceptive feedback to construct a complete sensorimotor closed-loop circuit. These systems can precisely induce activity-dependent synaptic plasticity, facilitate cortical reorganization, and ameliorate interhemispheric inhibitory imbalance. The present review systematically examines the theoretical foundations of neuroplasticity induction by multimodal closed-loop BCIs following stroke, the constituent system components, electrophysiological and neuroimaging evidence, and the key factors modulating neuroplasticity induction efficacy. Future directions toward personalized adaptive closed-loop systems and long-term home-based rehabilitation are discussed. This review integrates converging evidence from electroencephalography, functional magnetic resonance imaging, transcranial magnetic stimulation, and randomized controlled trials to establish a comprehensive mechanistic framework for multimodal BCI-mediated neuroplasticity, and provides reference for both basic research and clinical translation in this field.},
}

@article {pmid42314246,
year = {2026},
author = {Mohan, A and Anand, RS},
title = {Dynamic wavelet-based augmentation for enhanced EEG-based imagined speech classification.},
journal = {Computers in biology and medicine},
volume = {213},
number = {},
pages = {111810},
doi = {10.1016/j.compbiomed.2026.111810},
pmid = {42314246},
issn = {1879-0534},
abstract = {Brain-computer interfaces allow direct communication between the brain and external devices. They have real potential for assistive technologies and neurorehabilitation. Among the various paradigms, imagined speech decoding aims to translate silent mental speech into actionable outputs. Electroencephalography is used for imagined speech decoding due to its non-invasive nature, high temporal resolution, portability, and affordability compared to other neuroimaging modalities. The noisy and non-stationary characteristics of electroencephalography signals pose challenges for reliable classification. This study proposes a dynamic wavelet basis selection augmentation method. For each electroencephalography epoch, it adaptively chooses the most informative wavelet basis by minimizing wavelet entropy. After basis selection, data augmentation is performed by injecting Gaussian noise into the corresponding wavelet coefficients. The approach helps models handle the noise and differences in electroencephalography signals better than fixed methods. The augmented signals are classified using a convolutional neural network with channel-wise excitation mechanisms to enhance discriminative feature learning. Primary performance evaluation is conducted in an intra-subject setting using a hold-out validation protocol with trial-level separation. Additional verification is performed using cross-validation with 3, 5, and 7 folds to assess robustness. The imagined speech electroencephalography dataset comprises 32 channels, 8 stimuli, and recordings from 10 participants. The highest classification accuracy of up to 98% is achieved for the words-vowels experimental combination, with a Cohen's kappa value of 0.95. Comparatively lower performance is observed in the full eight-class classification. The proposed approach outperforms conventional augmentation strategies and static wavelet-based approaches.},
}

@article {pmid42314707,
year = {2026},
author = {Darie, R and Parker, SR and Calvert, JS and Tiwari, E and Abdelrahman, N and Syed, S and Shaaya, E and Fridley, JS and Merlo, MW and Halpern, I and Borton, DA},
title = {A modular, high-bandwidth, bidirectional implantable device for neural interrogation.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae7f52},
pmid = {42314707},
issn = {1741-2552},
abstract = {OBJECTIVE: Modern neuroelectronic interfaces have shown great potential to diagnose conditions, address neurological dysfunction, and advance neuroscientific knowledge. However, neural interface systems today require tethered connections that restrict mobility, prevent testing across ecological contexts, and inhibit clinical translation to at-home use. Fully implantable commercial systems have previously been developed, but exhibit significant constraints, including limited modularity, low bandwidth, or unidirectional communication. We aimed to close this gap by developing a neuroelectronic interface that can be deployed flexibly with a variety of third-party neural probes.

APPROACH: We have developed the Modular Bionic Interface (MBI), a system composed of a fully implantable device and a worn unit for high-bandwidth, bidirectional interfacing with the nervous system. The MBI can record high fidelity electrophysiological signals and deliver spatiotemporally modulated electrical stimulation for clinical and research purposes through flexible interaction with third party implantable devices.

MAIN RESULTS: We performed benchtop evaluation to validate the recording and stimulation capabilities of the MBI across a diverse range of inputs and outputs. We then evaluated the MBI system in vivo through chronic implantation within a sheep, where results were stable for the length of evaluation, over six months. While connected to an actively powered, third-party high-resolution spinal cord stimulation electrode array, the MBI system was able to deliver stimulation to evoke lower extremity motor responses and record spinal compound action potentials evoked by peripheral nerve and spinal stimulation.

SIGNIFICANCE: We demonstrate a fully implantable system with a small footprint capable of high-resolution, bi-directional communication with the nervous system via modular connections to third-party devices. We expect that modular devices will further our ability to treat complex neurological disease and injury.},
}

@article {pmid42317101,
year = {2026},
author = {Ren, H and Liu, Y and Huang, Y and Tang, Y and Xiao, L and Wu, Y and Liu, S and Yin, Y and Ma, Q and Dai, M and Tao, S and Xie, M and Li, M and Li, T and Wang, Q},
title = {Schizophrenia and bipolar disorder: a comparative analysis of genetic and brain network connectivity.},
journal = {Psychological medicine},
volume = {56},
number = {},
pages = {e202},
doi = {10.1017/S0033291726104413},
pmid = {42317101},
issn = {1469-8978},
support = {82571712//National Natural Science Foundation of China/ ; 82230046//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Bipolar Disorder/genetics/physiopathology/diagnostic imaging ; *Schizophrenia/genetics/physiopathology/diagnostic imaging ; Genome-Wide Association Study ; Magnetic Resonance Imaging ; *Brain/physiopathology/diagnostic imaging ; Female ; Male ; *Nerve Net/physiopathology/diagnostic imaging ; Middle Aged ; Default Mode Network/physiopathology/diagnostic imaging ; Connectome ; UK Biobank ; Genetic Predisposition to Disease ; },
abstract = {BACKGROUND: Schizophrenia (SCZ) and bipolar disorder (BD) are severe psychiatric conditions with overlapping clinical presentations, genetic risk factors, and brain network dysfunction. Whether alterations in large-scale intrinsic brain networks reflect shared or disorder-specific genetic influences remains poorly understood. Clarifying this distinction is essential for refining etiological models and improving diagnostic precision.

METHODS: Genome-wide inferred statistics (GWIS) were applied to decompose the genetic architecture of SCZ and BD into shared and unique components. Using resting-state network (RSN) data from the UK Biobank, functional connectivity (FC) and structural connectivity (SC) were extracted as neuroimaging phenotypes. Causal inference approaches were subsequently employed to infer potential directional relationships between brain network connectivity and each disorder.

RESULTS: Analyses revealed both common and distinct patterns of brain network connectivity associated with SCZ and BD. Notably, SC within the default mode network (DMN) exhibited opposing effects across the two disorders, suggesting divergent structural underpinnings despite clinical overlap. Additionally, SC within the limbic network (LN) and frontotemporal control network demonstrated potential causal relationships with both conditions, implicating these circuits astransdiagnostic neural substrates.

CONCLUSION: These findings illuminate the shared and disorder-specific genetic and neural architecture underlying SCZ and BD. Integrating genome-wide genetic methods with large-scale neuroimaging data offers a powerful framework for disentangling psychiatric comorbidity and may inform more targeted diagnostic criteria and individualized treatment strategies.},
}

Humans
*Bipolar Disorder/genetics/physiopathology/diagnostic imaging
*Schizophrenia/genetics/physiopathology/diagnostic imaging
Genome-Wide Association Study
Magnetic Resonance Imaging
*Brain/physiopathology/diagnostic imaging
Female
Male
*Nerve Net/physiopathology/diagnostic imaging
Middle Aged
Default Mode Network/physiopathology/diagnostic imaging
Connectome
UK Biobank
Genetic Predisposition to Disease

@article {pmid42317238,
year = {2026},
author = {Song, Z and Zhang, X},
title = {A weighted multi-scale attention-enhanced temporal convolutional network for motor imagery EEG decoding in brain-computer interfaces.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {14},
number = {},
pages = {1842610},
pmid = {42317238},
issn = {2296-4185},
abstract = {Accurate decoding of motor imagery electroencephalogram signals plays a critical role in brain-computer interfaces for neurorehabilitation and assistive technologies. However, existing multi-scale temporal methods often overlook scale-specific importance and fail to jointly capture transient and long-term neural dynamics, we propose a Weighted Multi-scale Attention-enhanced Temporal Convolutional Network (WMA-TCNet). The model employs parallel multi-scale temporal convolutions to capture neural patterns associated with distinct EEG rhythms. A global-aware scale attention mechanism adaptively weights each branch to emphasize task-relevant temporal information. A weighted Channel-Preserving Prior Path is introduced to maintain channel-wise dependencies and enhance spatial modeling stability across cortical regions. In addition, a temporal attention-guided TCN jointly captures local and long-range temporal dependencies. Experiments on BCI Competition IV 2a and 2b datasets show that WMA-TCNet achieves accuracies of 85.8% and 90.0% in subject-dependent settings, and 68.6% and 79.5% in cross-subject scenarios. These results demonstrate improved decoding performance and robustness, while providing a biologically meaningful framework for modeling multi-scale neural dynamics, with potential applications in brain-computer interfaces and neurorehabilitation.},
}

@article {pmid42317384,
year = {2026},
author = {Huang, X and Xu, J and Chen, L},
title = {Two decades of AI-driven motion capture in rehabilitation: Mapping research networks, thematic hotspots, and future trajectories.},
journal = {Digital health},
volume = {12},
number = {},
pages = {20552076261462645},
pmid = {42317384},
issn = {2055-2076},
abstract = {BACKGROUND: Motion capture technology integration in artificial intelligence (AI)-driven rehabilitation represents a rapidly expanding interdisciplinary field with significant potential for advancing movement analysis and motor recovery. A comprehensive bibliometric mapping of this domain is currently lacking, limiting systematic understanding of its development trajectory and key contributors.

OBJECTIVE: To provide a WoS-indexed bibliometric analysis of AI applications in motion capture for rehabilitation, identifying research trends, collaboration networks, key contributors, and emerging research frontiers from 2004 to 2023.

METHODS: A total of 3,500 relevant publications indexed in the Science Citation Index Expanded (SCI-EXPANDED) and Social Sciences Citation Index (SSCI) databases were retrieved and analyzed. Bibliometric and visualization analyses were performed using VOSviewer (version 1.6.19) and CiteSpace (version 6.2.4) to map collaboration networks, co-citation relationships, and keyword co-occurrence patterns.

RESULTS: Annual publication output demonstrated consistent growth from 2004 to 2023, with cumulative publications exceeding 3,500. The United States (1,014 publications) and China (722 publications) dominated research output, although collaboration patterns differed substantially. The University of Chinese Academy of Sciences led institutional contributions (52 publications). Keyword clustering revealed prominent research themes centered on brain-computer interfaces, machine learning, EEG-based signal processing, and real-time rehabilitation feedback systems. Temporal analysis demonstrated a paradigm shift from fundamental neurophysiological investigations toward computationally-driven and AI-integrated rehabilitation frameworks.

CONCLUSIONS: This bibliometric analysis provides a WoS-indexed mapping of AI-driven motion capture research in rehabilitation. The identified research hotspots and collaboration patterns offer a foundational reference for future investigations, despite limitations related to database coverage and language scope. Continued interdisciplinary collaboration and standardized methodological frameworks are essential to accelerate clinical translation.},
}

@article {pmid42317731,
year = {2026},
author = {Zhang, W and Xiong, B and Kang, Y and Gao, Y and Yang, R and Wang, Q and Wang, W},
title = {Preliminary clinical and electrophysiological findings of NAc/ALIC deep brain stimulation strategy for treatment-resistant depression.},
journal = {iScience},
volume = {29},
number = {6},
pages = {116032},
pmid = {42317731},
issn = {2589-0042},
abstract = {Effective therapeutic options for treatment-resistant depression remain limited. This study investigated the clinical and electrophysiological effects of dual-target deep brain stimulation of the nucleus accumbens and anterior limb of the internal capsule. In a small patient cohort, the intervention was associated with reduced depressive symptoms and improved cognitive performance. Concurrent electrophysiological recordings revealed that clinical improvement correlated with elevated gamma oscillations in the stimulated target and increased theta-band power in frontal-limbic regions. These integrated findings provide a preliminary framework for understanding therapeutic mechanisms and suggest potential biomarkers for optimizing deep brain stimulation therapy in treatment-resistant depression.},
}

@article {pmid42318605,
year = {2026},
author = {Tor, A and Clarke, SE and Bray, IE and Nuyujukian, P and , },
title = {Material damage to multielectrode arrays after electrolytic lesioning is insignificant.},
journal = {eLife},
volume = {14},
number = {},
pages = {},
doi = {10.7554/eLife.106452},
pmid = {42318605},
issn = {2050-084X},
support = {National Defense Science and Engineering Graduate//United States Department of Defense/ ; 1828993//National Science Foundation/ ; Dean's Postdoctoral Fellowship//Stanford School of Medicine/ ; 828653//American Heart Association/ ; Graduate Research Fellowship Program 1656518//National Science Foundation/ ; R01NS123517/NH/NIH HHS/United States ; R01NS130789/NH/NIH HHS/United States ; U19NS118284/NH/NIH HHS/United States ; ECCS-2026822//National Science Foundation/ ; },
mesh = {Animals ; *Electrodes, Implanted ; Microscopy, Electron, Scanning ; *Electrolysis ; *Motor Cortex/physiology ; Neurons/physiology ; },
abstract = {The quality of stable long-term recordings from chronically implanted electrode arrays is essential for experimental neuroscience and brain-computer interfaces. This work uses scanning electron microscopy (SEM) to image and analyze eight 96-channel Utah arrays previously implanted in motor cortical regions of four subjects (subject H = 2242 days implanted, F = 1875, U = 2680, C = 594), providing important contributions to a growing body of long-term implant research leveraging this imaging technology. Four of these arrays have been used in electrolytic lesioning experiments (H = 10 lesions, F = 1, U = 4, C = 1), a recently developed electrolytic perturbation technique demonstrated compatible with continued neuroelectrophysiology using small direct currents. Previously, our group showed that electrolytic lesioning can be used as a technique to create regions of controlled neuron loss without significantly changing recording quality (Bray, Clarke et al., 2024). Here, by surveying physical damage such as biological debris and material deterioration, we show that electrolytic lesioning causes no statistically significant material damage to the implanted electrode arrays. In addition to surveying physical damage, such as biological debris and material deterioration, this work also analyzes whether electrolytic lesioning created damage beyond what is typical for these arrays. These findings also indicate that there are no statistically significant differences between the damage observed on normal electrodes versus those used for electrolytic lesioning, yielding no evidence that electrolytic lesioning significantly affects the material quality of chronically implanted electrode arrays. Finally, this work also includes the largest collection of single-electrode SEM images for previously implanted multielectrode Utah arrays, spanning 11 different intact arrays and one broken array. As the clinical relevance of chronically implanted electrodes with single-neuron resolution continues to grow, these images may be used to provide the foundation for a larger public database and inform further electrode design and analyses.},
}

Animals
*Electrodes, Implanted
Microscopy, Electron, Scanning
*Electrolysis
*Motor Cortex/physiology
Neurons/physiology

@article {pmid42319573,
year = {2026},
author = {Zhang, Q and Qiao, Y and Li, H and Zeng, J and Chen, J and Zhou, H and Hu, Y and Luo, J},
title = {Male-Biased Social Deficits in Chd8[+/R2219*] Mouse Model of Autism Linked to Hippocampal Abnormalities.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {42319573},
issn = {1995-8218},
abstract = {Autism spectrum disorder (ASD) is a neurodevelopmental condition with a 4:1 male bias, yet the biological basis for this sex difference remains poorly understood. Heterozygous mutations in the chromatin remodeler CHD8 confer high risk for ASD, with phenotypes influenced by sex and genetic background. Here, we generated Chd8[+/R2219*] mice carrying a CRISPR-Cas9-generated mutation orthologous to a human variant. These mice recapitulated core ASD features, including macrocephaly and autistic-like behaviors. Notably, social deficits showed a male preponderance, directly mirroring the human sex bias. Structural magnetic resonance imaging (MRI) confirmed whole-brain enlargement in mutants, with voxel-based morphometry identifying bilateral hippocampal expansion. Crucially, hippocampal volume correlated with social deficit severity exclusively in male mutants. Functional connectivity analyses revealed disrupted hippocampal networks, and connectivity patterns within socially relevant circuits predicted behavioral outcomes. Together, our findings establish this model as exhibiting pronounced sexual dimorphism and implicate aberrant hippocampal structure and connectivity as key neural correlates of male-biased social deficits.},
}

@article {pmid42309989,
year = {2026},
author = {Di Liberto, GM and Ip, EYJ},
title = {Speech Neurophysiology in Realistic Contexts: Big Hype or Big Leap?.},
journal = {The European journal of neuroscience},
volume = {63},
number = {12},
pages = {e70496},
doi = {10.1111/ejn.70496},
pmid = {42309989},
issn = {1460-9568},
support = {13/RC/2106_P2//Research Ireland/ ; 18/CRT/6224//Research Ireland/ ; },
mesh = {Humans ; *Speech/physiology ; *Speech Perception/physiology ; *Brain/physiology ; Brain Mapping/methods ; *Neurophysiology/methods ; },
abstract = {Understanding the neural basis of speech communication is essential for uncovering how sounds are translated into meaning, how that changes with development, ageing and speech-related deficits, as well as contributing to brain-computer interfaces research. While traditional neurophysiological studies have relied on simplified, controlled paradigms, recent advances have shifted the field towards more ecologically valid approaches. Here, we describe the evolving landscape of experimental designs in speech neurophysiology, from discrete to continuous stimuli and from socially isolated listening to dynamic, multiagent communication. Realistic paradigms in that space challenge conventional methods, offering richer insights into neural encoding, functional brain mapping and neural entrainment. At the same time, they introduce significant analytical and technical complexities, particularly when incorporating social interaction. By synthesising findings across studies, we highlight how these ecologically valid speech paradigms have been contributing to refining theories of language processing and open new avenues for research. In doing so, this review critically evaluates of whether the move towards realism in speech neurophysiology represents a technological trend or a transformative leap in understanding the neural underpinnings of speech communication.},
}

Humans
*Speech/physiology
*Speech Perception/physiology
*Brain/physiology
Brain Mapping/methods
*Neurophysiology/methods

@article {pmid42310346,
year = {2026},
author = {Yeom, HG and Choi, WS and An, KM},
title = {A novel transformer architecture for EEG decoding and neuroscientific analysis.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-56405-9},
pmid = {42310346},
issn = {2045-2322},
support = {K207700005//Chosun University/ ; },
abstract = {Deep learning has significantly advanced brain-computer interface (BCI) technology. However, most deep learning models operate as black boxes, limiting their clinical applicability and scientific interpretability. This lack of transparency makes it difficult to determine whether predictions are driven by genuine neural activity or artifacts. To address this limitation, we propose Analformer, a novel Transformer-based architecture designed to achieve both high predictive performance and neuroscientific interpretability. The core component of Analformer is an Analytical Patch Embedding module, which employs fixed, non-trainable Morlet wavelet kernels to extract explainable spatio-temporal-frequency features from raw EEG signals. This structure enables standard neurophysiological analyses-including time-frequency analysis, topography, and F-value time-frequency (FTF) analysis-to be derived directly from the model's internal representations. Furthermore, by analyzing attention weights over these interpretable features, Analformer provides attention-based connectivity that may reflect functional relationships between brain regions. We evaluated Analformer on two large public datasets covering three representative BCI paradigms: Motor Imagery (MI), event-related potentials (ERP), and steady-state visually evoked potentials (SSVEP). Experimental results demonstrate that Analformer achieves competitive performance across all paradigms while producing analysis outputs consistent with established neuroscientific findings. These results suggest that Analformer provides a unified framework that bridges high-performance BCI decoding with interpretable, data-driven scientific analysis.},
}

@article {pmid42310450,
year = {2026},
author = {Deo, DR and Okorokova, EV and Pritchard, AL and Hahn, NV and Card, NS and Nason-Tomaszewski, SR and Jude, J and Hosman, T and Choi, EY and Qiu, D and Meng, Y and Wairagkar, M and Nicolas, C and Kamdar, FB and Iacobacci, C and Acosta, A and Hochberg, LR and Cash, SS and Williams, ZM and Rubin, DB and Brandman, DM and Stavisky, SD and AuYong, N and Pandarinath, C and Downey, JE and Bensmaia, SJ and Henderson, JM and Willett, FR},
title = {A mosaic of whole-body representations on the human precentral gyrus.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {42310450},
issn = {1476-4687},
abstract = {Understanding how the body is represented in the motor cortex is key to understanding how the brain controls movement. Although the motor cortex has been mapped in animal models at a fine scale[1-10], characterization in humans remains primarily limited to low-resolution recording[11-16] and stimulation techniques[17-20]. Here we created a comprehensive map of the human motor cortex at single-neuron resolution, spanning microelectrode array recordings from 20 arrays across 8 individuals with paralysis from spinal cord injury, amyotrophic lateral sclerosis or brainstem stroke, all enrolled in brain-computer interface clinical trials. These arrays broadly sample the crown of the precentral gyrus (PCG; thought to be composed largely of the premotor cortex (Brodmann area 6)). We found that body parts were highly intermixed, such that the entire body was represented in all sampled locations of the PCG, although the relative strength of body parts was roughly consistent with the motor homunculus[17,18]. We also found two speech-preferential areas with a broadly tuned, orofacial-dominant area in between them. Throughout the PCG, movement representations of the four limbs were interlinked, with homologous movements of different limbs (for example, toe curl and hand close) having correlated representations. These data provide evidence consistent with an intermixed, interrelated and behaviour-centred organization of the motor cortex[3,21]. The resulting map also provides important targeting information for brain-computer interfaces that seek to restore motor function.},
}

@article {pmid42311454,
year = {2026},
author = {D'Aquino, A and Schack, T},
title = {Bridging cognition and control through passive eye movement integration in motor imagery brain-computer interfaces.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1849674},
pmid = {42311454},
issn = {1662-5161},
abstract = {Motor Imagery (MI) Brain-Computer Interfaces (BCIs) represent a promising technology for neurorehabilitation and assistive control. However, the clinical viability of these systems is frequently hindered by the inherent limitations of electroencephalography (EEG) with regard to its low signal-to-noise ratio (SNR), non-stationarity, and high inter-subject variability. Standard decoding methods often fail to capture the complexity of user intention leading to unreliable performance and user frustration. This review proposes a solution to these challenges by advocating for the integration of passive eye movements (EM) as a complementary data stream. The theoretical rationale for this approach rests on the neurocognitive principle of functional equivalence. Because imagined actions recruit similar visuomotor networks to those used in physical execution, EM constitute a robust correlate of the underlying neural simulation. We distinguish this approach from conventional hybrid systems that use gaze coordinates for active control. Instead, we argue for a framework of passive monitoring where oculomotor metrics, including pupil diameter, fixation patterns, and saccadic dynamics, serve as a continuous window into the user's cognitive state. We synthesize evidence demonstrating that these passive signals can reliably index cognitive load, attentional allocation, and covert motor planning. By fusing these behavioral metrics with EEG, a BCI can disambiguate uncertain neural patterns and verify user intent without imposing additional task demands. Furthermore, we discuss how this multimodal integration enables the development of adaptive classifiers that respond to fluctuations in user fatigue and engagement. Bridging the gap between cognition and control through passive EM monitoring offers a pathway to create BCI systems that are intrinsically responsive to the user's internal state.},
}

@article {pmid42135191,
year = {2026},
author = {Carleton, M and Oesch, NW},
title = {Inhibitory Circuits Can Restore OFF Pathway Responses in Retinal Prostheses.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {46},
number = {24},
pages = {},
doi = {10.1523/JNEUROSCI.0083-26.2026},
pmid = {42135191},
issn = {1529-2401},
mesh = {Animals ; *Visual Prosthesis ; Mice ; Male ; *Retinal Ganglion Cells/physiology ; *Retinitis Pigmentosa/physiopathology/therapy ; Female ; *Visual Pathways/physiology ; *Neural Inhibition/physiology ; Electric Stimulation ; Amacrine Cells/physiology ; Mice, Inbred C57BL ; Photic Stimulation ; },
abstract = {One of the first steps in processing visual information is to split the light signal captured by photoreceptors into complementary ON and OFF pathways, which separately encode increases and decreases in luminance. In blind patients with retinal degeneration, optoelectronic prostheses can successfully activate the ON pathway and evoke bright percepts; however, patients do not perceive dark features. This indicates that the OFF pathway is not being activated by existing prosthetics. To quantify OFF pathway deficits, we stimulated retinal ganglion cells (RGCs) in a mouse model of retinitis pigmentosa of either sex with electrical stimulation mimicking retinal prosthetic activation and recorded their voltage responses using whole-cell recording. We found that most OFF RGCs respond with incorrect ON responses, except for one specific subtype of RGC, the OFFα, which retained correct OFF-type responses following the termination of stimulation. We found that these preserved OFF responses were driven by postinhibitory rebound excitation, mediated by hyperpolarization-activated cyclic nucleotide-gated channels. Using a combinatorial genetic approach to achieve chemogenetic control, we identified AII amacrine cells as the presynaptic source driving these electrically evoked OFF responses. These insights into how the OFF pathway responds to artificial stimulation suggest new opportunities to improve prosthetic vision restoration through tuning of stimulation parameters.},
}

Animals
*Visual Prosthesis
Mice
Male
*Retinal Ganglion Cells/physiology
*Retinitis Pigmentosa/physiopathology/therapy
Female
*Visual Pathways/physiology
*Neural Inhibition/physiology
Electric Stimulation
Amacrine Cells/physiology
Mice, Inbred C57BL
Photic Stimulation

@article {pmid42303138,
year = {2026},
author = {Gao, X and Lai, Y and Hu, W and Wang, L and Liu, Y and Liao, J},
title = {Spatial variations and risk factors of multimorbidity in China: A population-based spatial modelling study.},
journal = {American journal of preventive medicine},
volume = {},
number = {},
pages = {108481},
doi = {10.1016/j.amepre.2026.108481},
pmid = {42303138},
issn = {1873-2607},
abstract = {INTRODUCTION: Multimorbidity burden is likely to vary across China, but relevant evidence is insufficient. The extent to which individual and provincial factors may affect spatial variations of multimorbidity has not been fully examined. This study aims to estimate the provincial multimorbidity burden among Chinese aged 45 and older, identifying which risk factors remain constant and which vary across China.

METHODS: This study included 18,561 adults aged 45 and older from the China Health and Retirement Longitudinal Study in 2020. A Bayesian spatial varying coefficients model was adopted to estimate the multimorbidity burden and 95% Bayesian credible intervals, using the Chinese Multimorbidity-Weighted Index (CMWI) as a measurement. Partial correlation coefficients between covariates and CMWI in each region were calculated to investigate the need for varying coefficients. Spatial autocorrelation analyses were used to identify clusters of high and low multimorbidity burden.

RESULTS: The estimated CMWI across the 27 provinces in China ranged from 1.76 (95% BCI: 1.64, 1.89) to 4.42 (95% BCI: 4.16, 4.70). High multimorbidity burden areas were clustered in North and Northeast China, while areas with relatively low burden were in southern China. The top three provinces by median CMWI estimates were Neimenggu, Heilongjiang, and Jilin, whereas Guangdong, Zhejiang, and Beijing were among the lowest CMWI estimates. The effect of age and sex showed spatial variation across China, while other risk factors showed fixed effects.

CONCLUSIONS: The burden of multimorbidity varies across China and not all risk factors associated with multimorbidity are consistent across regions, providing valuable insights for chronic disease management.},
}

@article {pmid42303715,
year = {2026},
author = {Zhou, J and Li, W and Luo, S and Chen, K and Xu, S and Liu, Q and Chen, H and Liao, W and Li, J},
title = {Mapping the functional connectome between grey matter and white matter.},
journal = {Communications biology},
volume = {},
number = {},
pages = {},
doi = {10.1038/s42003-026-10483-7},
pmid = {42303715},
issn = {2399-3642},
support = {62473082, 82121003, and 62036003//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Brain white matter (WM) has traditionally been viewed as a passive conduit for neural transmission. However, evidence of blood oxygen level-dependent (BOLD) signals measured from the WM suggests its active participation in grey matter (GM) functional networks. Using 7-Tesla functional MRI (fMRI) data, we constructed a GM-WM functional connectome. We found that GM-WM functional architecture follows the unimodal-transmodal hierarchy of GM and is shaped by distributions of neurotransmitter receptors. Distinct WM networks exhibit unique connectivity profiles with GM, reflecting their roles in specific cognitive domains. Individual variations in this connectome correlated with cognitive performance. Notably, compared with the traditional GM-GM functional connectome, the GM-WM functional connectome shows stronger associations with brain disorders, suggesting greater diagnostic sensitivity as a neuromarker. These findings are replicated in a 3-Tesla fMRI cohort. Our work establishes WM as an integral component of the brain's functional architecture, contributing to hierarchical architecture and supporting higher-order cognition.},
}

@article {pmid42303998,
year = {2026},
author = {Zhang, H and Zhang, D and Wu, J and Li, Y and Lu, J},
title = {A Fine-grained Spatiotemporal ECoG Dataset during Speech Perception in Tonal Language.},
journal = {Scientific data},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41597-026-07619-z},
pmid = {42303998},
issn = {2052-4463},
support = {32371146//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32371154//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {High-density intracranial recordings during naturalistic language processing are critical for advancing models of speech perception. However, open, well-annotated high-density ECoG resources for tonal languages such as Mandarin remain scarce. We present a publicly available high-density ECoG dataset from four participants undergoing awake craniotomy who listened to continuous, sentence-level Mandarin drawn from the Annotated Speech Corpus of Chinese Discourse (ASCCD). Signals were recorded with 128-256-channel subdural grids and synchronized with the audio; ECoG signals were down-sampled to 400 Hz, filtered in the high-gamma range (70-150 Hz), and used to derive high-gamma amplitude. The release follows BIDS-iEEG and is distributed as NWB files, with derivatives including high-gamma amplitude; word- and syllable-level alignments; Pinyin; lexical tone and stress tiers; prosodic break indices; mel-spectrograms; F0 and formants; and electrode localization on individual anatomy with projections to MNI space. This resource supports fine-grained investigations of lexical tone, syllabic structure, and higher-level linguistic representations during naturalistic listening.},
}

@article {pmid42304912,
year = {2026},
author = {Gong, L},
title = {Neuroimaging Mechanisms and Neuromodulation Strategies for Neuropsychiatric Disorders.},
journal = {Current neuropharmacology},
volume = {},
number = {},
pages = {},
doi = {10.2174/011570159X512508260603044250},
pmid = {42304912},
issn = {1875-6190},
}

@article {pmid42305049,
year = {2026},
author = {Li, C and Hu, R},
title = {Caught the 'Catch' of midnolin: structural basis for broad substrate specificity in ubiquitin-independent proteasomal degradation.},
journal = {Acta biochimica et biophysica Sinica},
volume = {58},
number = {6},
pages = {1431-1432},
doi = {10.3724/abbs.2026006},
pmid = {42305049},
issn = {1745-7270},
}

@article {pmid42305781,
year = {2026},
author = {Wang, J and Xu, G and Du, C and Li, Z and Li, H and Chen, S and Han, C and Zhang, S},
title = {Domain-aware domain-class adaptation network for motor execution to motor imagery EEG classification.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1851006},
pmid = {42305781},
issn = {1662-4548},
abstract = {INTRODUCTION: Motor imagery (MI) is one of the most widely used paradigms in electroencephalogram (EEG)-based brain-computer interfaces (BCIs). In recent years, deep learning and transfer learning techniques have been increasingly adopted to further improve MI-EEG decoding performance, thereby facilitating the practical deployment of BCIs. In transfer learning, the similarity between the source and target domains is a critical factor influencing its effectiveness. Given the analogous cortical activation patterns observed in MI and motor execution (ME) tasks, cross-task transfer learning from ME to MI presents a promising yet underexplored direction.

METHODS: To tackle the underexplored problem of cross-task transfer learning from ME to MI, we propose a domain-aware domain-class adaptation network (DDCA Net), which consists of a domain-shared feature extractor, two classifiers, and two domain-specific feature re-weighting blocks. Domain-level alignment is achieved by minimizing the maximum mean discrepancy between source and target feature distributions, while domain-specific feature re-weighting preserves discriminative characteristics unique to each task. In addition, a bi-classifier adversarial learning framework is employed to encourage consistency of decision boundaries across domains, thereby enabling implicit class-level alignment.

RESULTS: Extensive experiments were conducted on a public dataset with over 100 subjects under varying proportions of target-domain training samples. When 80% of target-domain samples are used for training, the proposed DDCA Net significantly outperforms the within-task baseline, achieving a 7.71% improvement in classification accuracy and converting approximately 80% of previously BCI-illiterate subjects into BCI-literate users.

DISCUSSION: To the best of our knowledge, this is the first work to verify the feasibility of applying domain adaptation for cross-task transfer learning in MI-EEG classification. The findings of this study provide new insights for integrating ME and MI in advanced BCIs.},
}

@article {pmid42305825,
year = {2024},
author = {DeRoo, SC and George, I},
title = {Aortic dissection after transcatheter aortic valve replacement.},
journal = {JTCVS structural and endovascular},
volume = {1-2},
number = {},
pages = {100009},
pmid = {42305825},
issn = {2950-6050},
}

@article {pmid42306198,
year = {2026},
author = {Li, X and Zhang, S and Song, Y and Zhang, S and Chen, X and Wang, Y and Gao, X},
title = {Text Sequence Stimulation for High-Speed and Comfortable SSVEP-BCI.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {7},
number = {},
pages = {0612},
pmid = {42306198},
issn = {2692-7632},
abstract = {Steady-state visual evoked potential brain-computer interfaces offer a high-speed communication channel. However, traditional steady-state visual evoked potential paradigms often rely on strong flickering visual stimulation, which can lead to substantial visual fatigue. Moreover, the electroencephalography responses evoked by brightness flicker are spatially constrained and are primarily associated with occipital visual processing. This study presents a novel text sequence stimulation paradigm that combines periodic visual stimulation with orthographic information and elicits distinct occipital and occipitotemporal scalp response patterns relative to conventional brightness flicker. Frequency-sweep experiments were conducted to investigate the temporal, spatial, and spectral characteristics of the evoked responses. A comparison experiment further showed that text sequence stimulation is less sensitive to variations in stimulus size and luminance than conventional brightness flicker. Based on these findings, a 40-target speller was developed and validated through online experiments. The proposed paradigm achieved an information transfer rate of 235.12 ± 30.12 bits/min while significantly improving user comfort, as confirmed by questionnaire evaluations. These results suggest that text sequence stimulation offers a practical design direction for high-speed and more comfortable visual brain-computer interface.},
}

@article {pmid42306266,
year = {2026},
author = {Tagliafierro, M and Hassanabad, AF and Nickles, J and Sevensky, R and Geirsson, A and George, I and Takayama, H and Argenziano, M and Pirelli, L},
title = {A decade of cardiac surgery after transcatheter aortic valve replacement: Short-term clinical outcomes at a high-volume center.},
journal = {JTCVS structural and endovascular},
volume = {10},
number = {},
pages = {100128},
pmid = {42306266},
issn = {2950-6050},
abstract = {BACKGROUND: The growing adoption of transcatheter aortic valve replacement (TAVR) in younger and lower-risk patients has increased the number of patients with other cardiac diseases requiring surgical intervention. Such surgeries pose unique technical challenges due to the presence of the TAVR, often requiring explantation. Despite increasing clinical relevance, outcomes of these operations remain poorly characterized. We sought to assess the incidence, indications, and short-term results of cardiac surgery following TAVR at a high-volume institution.

METHODS: This was a retrospective single-center analysis of patients undergoing any cardiac surgery post-TAVR between 2015 and 2024. Primary endpoints were perioperative all-cause mortality and stroke; secondary endpoints included cardiopulmonary bypass and cross-clamp times, as well as in-hospital and 30-day outcomes.

RESULTS: Among 10,898 surgeries, 61 (0.5%) involved patients with prior TAVR (median age, 72 years; 59% male), including 85% with hypertension, 28% with diabetes, 43% with chronic lung disease, and 15% with cerebrovascular disease. The median time between TAVR and surgery was 20 months, and 57% of the surgeries were urgent or emergent/salvage procedures. Major indications included TAVR dysfunction (28%), infective endocarditis (26%), and aortic pathology (13%). Common procedures were TAVR explant and surgical aortic valve replacement (n = 49), mitral surgery (n = 19), aortic root/arch surgery (n = 12), and multivessel coronary artery bypass grafting (n = 5). The median aortic cross-clamp and cardiopulmonary bypass times were 121 minutes and 160 minutes, respectively. The mortality rate was 13%. Other outcomes included stroke (3%), prolonged ventilation (31%), tracheostomy (7%), de novo dialysis (8%), need for postoperative blood products (61%), cardiac reintervention (10%), discharge to rehabilitation facility (34%), and readmission (13%).

CONCLUSIONS: Cardiac surgery post-TAVR is uncommon and associated with significant morbidity and mortality. Prosthesis dysfunction and endocarditis are the leading indications, and TAVR explant remains a common although highly morbid intervention.},
}

@article {pmid42308156,
year = {2026},
author = {Hui, W and Wang, Y and Wei, M and Yu, S and Zhou, Y and Ka-Meng, L and Yang, N and Zhu, Z},
title = {Cells on IC Chip.},
journal = {ACS sensors},
volume = {},
number = {},
pages = {},
doi = {10.1021/acssensors.6c00959},
pmid = {42308156},
issn = {2379-3694},
abstract = {Cellular signals are essential for sensing the microenvironment and coordinating physiological functions. Their multimodal nature, encompassing electrophysiological, chemical, mechanical, and optical components, helps define cellular functional states and fate. High-resolution spatiotemporal analysis of these weak, dynamic, and heterogeneous signals is critical for elucidating fundamental life processes, uncovering disease mechanisms, and advancing precision medicine. Recent advances in integrated circuit (IC) technology, particularly the co-integration of complementary metal-oxide-semiconductor (CMOS) and micro-electro-mechanical systems (MEMS), have enabled unprecedented capabilities for cellular signal analysis, driving a transition from conventional instruments and microfluidic platforms to chip-level cellular analysis. This review summarizes the key technological foundations, multimodal sensing mechanisms, and emerging applications of IC technology for cellular signal analysis. It explores the core principles of high-sensitivity, long-term stable signal acquisition, focusing on bioelectronic interfaces, biocompatible packaging, and low-noise signal processing. It also reviews breakthroughs in microelectrode arrays, field-effect transistors, CMOS image sensors, MEMS sensors, and multi-parameter chemical sensing chips for single-cell and population-level detection. The review highlights applications in drug screening, clinical diagnostics, single-cell analysis, and brain-computer interfaces. Finally, it addresses challenges such as biocompatibility, crosstalk suppression, and energy efficiency, while outlining future directions in material innovation, three-dimensional integration, and brain-inspired computing.},
}

@article {pmid42308306,
year = {2026},
author = {Sun, R and Wang, W and Liu, W and Yang, M and Zhang, D and Zou, Z and Yang, K and Gao, C and She, J and Zhang, Z and Hu, Z and Hu, F and Jiang, W and Shu, K and Xie, M and Tang, Z and Zhang, L and Yu, C and Luo, Z},
title = {Untethered thin-film neurostimulator wrapped around tiny nerve trunks for wireless neuromodulation.},
journal = {Science advances},
volume = {12},
number = {25},
pages = {eaec9247},
doi = {10.1126/sciadv.aec9247},
pmid = {42308306},
issn = {2375-2548},
mesh = {Animals ; Rats ; *Wireless Technology/instrumentation ; *Vagus Nerve Stimulation/instrumentation/methods ; Vagus Nerve ; *Implantable Neurostimulators ; Disease Models, Animal ; Cytokines/metabolism ; },
abstract = {Electrical stimulation of tiny peripheral nerve trunks for near-organ neuromodulation enables precise electroceutical therapy for refractory diseases, but long-term stable neuromodulation of these delicate, fragile nerve trunks remains an engineering challenge. Here, we report the development of NeuroWrap Ultrasonic Stimulator (NWUS), an untethered, self-adhesive thin-film neurostimulator capable of conformally wrapping around tiny nerve trunks. The untethered architecture can help avoid the damage or rupture of tiny nerves due to uncontrollable micromotion of lead wires during bodily movements. The NWUS is an ultrasound-responsive acoustoelectric converter with optimized impedance matching, which enables highly effective wireless electrical stimulation. The electroceutical application of NWUS was further illustrated in chronic vagus nerve stimulation for immunomodulation therapy in a rat model. The wireless neuromodulation therapy of rat experimental autoimmune myocarditis was proven to restore left ventricular function, suppress proinflammatory cytokine expression as well as macrophage infiltration within cardiac tissue, and promote regulatory T cell recruitment along with increased anti-inflammatory cytokine levels.},
}

Animals
Rats
*Wireless Technology/instrumentation
*Vagus Nerve Stimulation/instrumentation/methods
Vagus Nerve
*Implantable Neurostimulators
Disease Models, Animal
Cytokines/metabolism

@article {pmid42309008,
year = {2026},
author = {Xin, Q and Hu, H},
title = {Two circuits, one stress: Dissecting the neural logic of comorbid fear and anhedonia.},
journal = {Neuron},
volume = {114},
number = {12},
pages = {2081-2083},
doi = {10.1016/j.neuron.2026.05.003},
pmid = {42309008},
issn = {1097-4199},
mesh = {*Fear/physiology/psychology ; Animals ; *Anhedonia/physiology ; Humans ; *Stress, Psychological/physiopathology ; *Prefrontal Cortex/physiology ; Neural Pathways/physiology ; Interneurons/physiology ; },
abstract = {In this issue of Neuron, Li and colleagues[1] unveil a circuit-based framework in which parallel insula-prefrontal circuits independently govern stress-induced social fear and novelty preference deficits, and lateral inhibition between these circuits via local parvalbumin interneurons drives their comorbidity.},
}

*Fear/physiology/psychology
Animals
*Anhedonia/physiology
Humans
*Stress, Psychological/physiopathology
*Prefrontal Cortex/physiology
Neural Pathways/physiology
Interneurons/physiology

@article {pmid42309130,
year = {2026},
author = {Patel, RJ and Bryson, B and Carlson, T and Demosthenous, A and Jiang, D},
title = {Stability and neurophysiological validity of graph connectivity features for non-stationary motor imagery BCIs.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae7ead},
pmid = {42309130},
issn = {1741-2552},
abstract = {Motor imagery (MI) Electroencephalography (EEG) Brain-computer interfaces (BCI) degrade under longitudinal non-stationarity, especially in amyotrophic lateral sclerosis (ALS). Functional connectivity (FC) has been proposed as an alternative feature space, but it remains unclear which FC estimators yield stable, class-informative features across sessions. Approach: Using a multi-session ALS EEG dataset, we computed a broad family of FC estimators per trial to form weighted graphs. We extracted edge weights and node strength features, and quantified (i) feature reproducibility and (ii) LH-RH separability using coefficient of variation and symmetric Kullback-Leibler divergence, respectively. We assessed neurophysiological plausibility via spatial topographies, distance-dependence controls, and evaluated selected feature sets in a strictly temporal cross-session decoding protocol against Common Spatial Patterns, Band Power and Riemannian Methods. Main Results: Coherence-based estimators, particularly magnitude-squared coherence, most consistently produced features exhibiting favourable reproducibility-separability trade-offs across subjects. Node-strength discriminability maps showed lateralised sensorimotor structure consistent with known MI physiology. In temporal generalisation, Magnitude Squared Coherence derived features achieved more consistent test performance than baseline methods for most subjects. Significance: Joint reproducibility-separability profiling provides a principled way to select FC feature spaces for longitudinal MI-BCIs and suggests coherence-based connectivity is a stronger sensor-space candidate under drift.},
}

@article {pmid42309345,
year = {2026},
author = {Tozzi, A},
title = {Embedding EEG trajectories in a Möbius-like manifold: An exploratory study.},
journal = {Neuroscience letters},
volume = {},
number = {},
pages = {138665},
doi = {10.1016/j.neulet.2026.138665},
pmid = {42309345},
issn = {1872-7972},
abstract = {Time-frequency decompositions and nonlinear dynamical methods analyze electroencephalographic (EEG) signals as time series evolving in Euclidean state spaces. We explore an alternative representation of EEG dynamics in which neural activity evolves within a Möbius-like state space. While conventional amplitude-phase descriptions represent oscillatory activity within a cylindrical state space, we additionally consider that a shift of half an oscillatory cycle reverses the sign of the waveform, transforming positive amplitudes into negative ones and vice versa. This symmetry introduces a twist into the cylindrical representation, yielding a non-orientable topology analogous to a Möbius strip in which EEG activity evolves as a continuous cyclic trajectory. Using normalized signal amplitude and instantaneous phase derived from the Hilbert transform, we reconstructed three-dimensional trajectories from EEG recordings of a healthy young adult. Our Möbius-like approach describes the geometry of the embedded EEG trajectory in terms of cyclic evolution, phase-dependent symmetry, winding number and torsion. The winding number quantifies cumulative oscillatory phase progression by measuring the number of rotations performed by the trajectory around the manifold, whereas torsion captures local changes in amplitude-phase organization by characterizing how strongly the trajectory twists in three-dimensional space. Together, these descriptors provide complementary assessment of global and local neural dynamics that are not represented by conventional EEG measures based solely on temporal, spectral or statistical properties. Potential applications include characterization of physiological and pathological brain activity, trajectory-based EEG feature extraction, integration with brain-computer interface approaches and comparative analysis of neural dynamics across cognitive and behavioral conditions.},
}

@article {pmid42309710,
year = {2026},
author = {Sun, H and Wang, R and Karako, K and Song, P and He, J},
title = {Clinical translation and accessibility of brain-computer interfaces: From technology development to clinical application.},
journal = {Bioscience trends},
volume = {},
number = {},
pages = {},
doi = {10.5582/bst.2026.01118},
pmid = {42309710},
issn = {1881-7823},
abstract = {Brain-computer interface (BCI) technology establishes a direct communication pathway between neural activity and external devices. Driven by advances in neuroscience, artificial intelligence (AI), neural signal acquisition, decoding algorithms, and implantable system design, BCIs have progressed rapidly from experimental prototypes toward clinically relevant neurotechnologies. However, the translation of these technical advances into routine clinical practice and equitable real-world access remains substantially slower than technological innovation. This review summarizes the major technological pathways of BCIs and their clinical applications, and it then examines BCI development from the perspective of clinical translation and accessibility. We focus on key barriers across the translational chain, including long-term technical stability, quality of clinical evidence, evaluation standards, reimbursement mechanisms, health-economic evidence, and the feasibility of implementation in real-world healthcare settings. We argue that the central challenge in BCI development has shifted from improving technical performance alone to building the translational infrastructure required for safe, effective, affordable, and sustainable clinical integration.},
}

@article {pmid42309711,
year = {2026},
author = {Asakawa, T},
title = {Brain-computer interfaces: A lifeline for paralysis or a Pandora's box for humanity?.},
journal = {Bioscience trends},
volume = {},
number = {},
pages = {},
doi = {10.5582/bst.2026.01142},
pmid = {42309711},
issn = {1881-7823},
abstract = {Recent advances in computerized technologies, neuroscience, and materials and engineering have transformed brain‑computer interfaces (BCIs) from conventional unidirectional signal recording systems (brain-to-device) to bidirectional closed-loop neuromodulation systems (brain-device-brain). BCI-based devices enable direct information exchange between the human central nervous system and external electronic devices, and they are widely used in scenarios such as rehabilitation of patients with dyskinesia or enhancement of the self-care ability of disabled individuals. This editorial discusses the rapidly evolving field of BCIs, highlighting both their transformative potential to restore neurological function and the emerging ethical concerns associated with neural data access, cognitive enhancement, and human autonomy. The academic consensus and future translational prospects are also discussed. This article attempts to provide insightful, balanced, and critical viewpoints to help BCI-related research. Indeed, the future of BCIs will depend not only on technological innovation but also on society's ability to establish robust ethical and regulatory frameworks. Whether BCIs become a lifeline for millions of patients or a source of new societal risks will be determined by the choices made today.},
}

@article {pmid42296267,
year = {2026},
author = {Banta, AR and Fedorov, D and Altilia, A and Malliaras, GG and Barone, DG},
title = {Brain-computer interfaces and neuroprosthetics in the next era of neurosurgery.},
journal = {British journal of neurosurgery},
volume = {},
number = {},
pages = {1-9},
doi = {10.1080/02688697.2026.2682817},
pmid = {42296267},
issn = {1360-046X},
abstract = {PURPOSE: Brain-computer interfaces (BCIs) and neuroprosthetic systems are rapidly advancing from experimental concepts to clinically meaningful technologies capable of restoring communication, movement, sensation, and therapeutic neuromodulation. This review examines the current state of BCI and neuroprosthetic technologies, their neurosurgical applications, emerging frontiers, and the evolving role of neurosurgeons in their clinical translation.

MATERIALS AND METHODS: A narrative review of the contemporary literature was performed, focusing on neural signal acquisition technologies, including intracortical microelectrode arrays, electrocorticography, depth electrodes, and endovascular recording systems. The review also evaluates advances in neural decoding algorithms, closed-loop stimulation paradigms, neuroprosthetic applications, long-term implant stability, cognitive and affective BCIs, and ethical and regulatory considerations relevant to neurosurgical practice.

RESULTS: Recent developments in neural interface design, implantable electronics, adaptive decoding algorithms, and closed-loop neuromodulation have enabled substantial progress in motor restoration, sensory feedback, speech decoding, and therapeutic neuromodulation. Intracortical and minimally invasive recording systems have expanded the range of achievable clinical applications, while adaptive deep brain stimulation and responsive neurostimulation demonstrate the growing importance of closed-loop approaches. Key challenges remain, including foreign body reactions, long-term signal instability, neural signal drift, and ethical concerns related to cognitive applications, privacy, and data security.

CONCLUSION: BCIs and neuroprosthetics are transforming the neurosurgical landscape by providing new opportunities to restore lost neurological function and deliver personalized neuromodulation therapies. Continued advances in biological integration, system adaptivity, and cognitive applications are expected to accelerate clinical adoption. As these technologies mature, neurosurgeons will play a central role in implantation, long-term management, and the responsible clinical translation of neural interface technologies.},
}

@article {pmid42297203,
year = {2026},
author = {Jin, Z and Liu, C and Liu, G and Feng, G and Li, J and Wu, Y and Jia, H and Keefe, DL and Liu, L},
title = {An Asynchronous Production Line of Meiotic Prophase I in the Mouse Fetal Ovary.},
journal = {Experimental cell research},
volume = {},
number = {},
pages = {115099},
doi = {10.1016/j.yexcr.2026.115099},
pmid = {42297203},
issn = {1090-2422},
abstract = {The initiation of meiosis in the female germline of mammals is a gradual process, but there is currently no clear quantitative framework for determining the precise timing of its onset. Here, we attempt to standardize the description of meiotic entry timing through a systematic, quantitative analysis of meiotic entry and progression in the mouse fetal ovary. Using dynamic expression profiling of key regulators Stra8, Sycp1, and Sycp3 alongside proliferation markers, we demonstrate that germ cells enter meiosis asynchronously and continuously between embryonic days E12.5 and E16.5. During this extended period, mitotic proliferation persists, indicating that germ cells are progressively recruited into the meiotic pathway rather than halting division simultaneously. Homologous chromosome synapsis, marked by Sycp1/Sycp3 co-localization, initiates at E14.5 and is completed prenatally by E18.5. Using stage-composition data, we constructed a continuous-time Markov chain model to infer a population-level meiotic stage clock. This model estimates approximately conserved population-level effective intervals from the modeled early-prophase L compartment to pachytene-stage synapsis (∼72 h) and to the late-prophase/dictyate-associated D-state transition (∼91 h) across modeled cohort-start times. Our findings refine the conventional view by quantitatively defining the extended window of meiotic entry and subsequent progression through prophase I.},
}

@article {pmid42297978,
year = {2026},
author = {Card, NS and Singer-Clark, T and Peracha, H and Iacobacci, C and Hou, X and Wairagkar, M and Fogg, Z and Offenberg, EC and Hochberg, LR and Stavisky, SD and Brandman, DM},
title = {Long-term independent use of an intracortical brain-computer interface for speech and cursor control.},
journal = {Nature medicine},
volume = {},
number = {},
pages = {},
pmid = {42297978},
issn = {1546-170X},
support = {N/A//Burroughs Wellcome Fund (BWF)/ ; N/A//Burroughs Wellcome Fund (BWF)/ ; N/A//Achievement Rewards for College Scientists Foundation (ARCS Foundation)/ ; A2295-R//VHA Office of Research and Development | Rehabilitation Research and Development Service (Rehabilitation Research & Development Service)/ ; 1DP2DC021055//U.S. Department of Health & Human Services | NIH | NIH Office of the Director (OD)/ ; AL220043//United States Department of Defense | Office of the Secretary of Defense (OSD)/ ; 23-SGP-652//Amyotrophic Lateral Sclerosis Association (ALS Association)/ ; },
abstract = {Brain-computer interfaces (BCIs) can provide naturalistic communication and digital access to people with severe paralysis by decoding neural activity associated with attempted speech and movement. Recent work has demonstrated highly accurate intracortical BCIs for speech and cursor control, but two critical capabilities needed for practical viability were unmet: independent at-home operation without researcher assistance and reliable long-term performance supporting accurate speech and cursor decoding. Here we demonstrate the independent and near-daily use of a multimodal BCI with novel brain-to-text speech and computer cursor decoders by a man with paralysis and severe dysarthria due to amyotrophic lateral sclerosis. Over nearly 2 years, the participant used the BCI for more than 3,800 h at home with no researchers present to maintain rich interpersonal communication with his family and friends, independently control his personal computer and sustain full-time employment-despite being paralyzed. He communicated 183,060 sentences-totaling 1,960,163 words-at an average rate of 56 words per minute. He labeled 92% of sentences as being decoded at least mostly correctly. In formal quantifications of performance where he was asked to say words presented on a screen, attempted speech was consistently decoded with more than 99% word accuracy (125,000 word vocabulary). The participant also used the speech BCI as keyboard input and the cursor BCI as mouse input to control his personal computer, enabling him to send text messages and emails and to browse the internet. These results demonstrate that intracortical BCIs have the potential to support independent use in the home, marking a critical step toward practical assistive technology for people with severe motor impairment.},
}

@article {pmid42300820,
year = {2026},
author = {Nazif, TM and Simonato, M and Makkar, RR and Thourani, VH and Desai, ND and Babaliaros, V and Greason, K and Rovin, J and Waxman, S and Davidson, C and Kereiakes, DJ and Gupta, A and Satler, L and Schwartz, R and Kapadia, S and Wong, SC and Smalling, RW and Ghani, M and Teirstein, P and George, I and Potluri, S and Szerlip, M and Xu, K and Cohen, DJ and Sharma, RP and Pibarot, P and Hahn, RT and Mack, MJ and Leon, MB and , },
title = {10-Year Outcomes of SAPIEN 3 Transcatheter Aortic Valve Replacement or Surgery in Intermediate-Risk Patients.},
journal = {Journal of the American College of Cardiology},
volume = {87},
number = {23},
pages = {3296-3308},
doi = {10.1016/j.jacc.2026.03.170},
pmid = {42300820},
issn = {1558-3597},
mesh = {Humans ; *Transcatheter Aortic Valve Replacement/methods/mortality ; Female ; *Aortic Valve Stenosis/surgery/mortality/diagnosis ; Male ; Treatment Outcome ; Registries ; Prospective Studies ; Aged, 80 and over ; Follow-Up Studies ; *Heart Valve Prosthesis ; Risk Assessment ; Aged ; Time Factors ; Risk Factors ; Postoperative Complications ; *Aortic Valve/surgery/diagnostic imaging ; },
abstract = {BACKGROUND: Transcatheter aortic valve replacement (TAVR) is an alternative to surgical aortic valve replacement for patients with symptomatic severe aortic stenosis. However, long-term outcomes data are lacking for TAVR, particularly with newer-generation transcatheter heart valves.

OBJECTIVES: The purpose of this study was to compare 10-year outcomes of intermediate-risk patients who underwent TAVR with the third-generation, balloon-expandable SAPIEN 3 valve in the PARTNER 2 SAPIEN 3 Intermediate-risk Registry (P2S3i) with those who underwent surgery in the PARTNER 2A (P2A) randomized trial.

METHODS: Intermediate-risk patients were enrolled in the P2A trial from 2011 through 2013 and in the P2S3i registry in 2014. These prospective, multicenter studies used the same eligibility criteria and stratified patients based on suitability for transfemoral or transthoracic (transapical/transaortic) access. Ten-year outcomes were evaluated, including all-cause mortality, aortic valve reintervention, and core laboratory-adjudicated echocardiographic outcomes. Patient reconsent was required at 5 years for extended 10-year follow-up, and vital status sweeps were implemented to improve data completeness for all-cause mortality. To account for potential baseline differences and reduce confounding, P2S3i TAVR patients were propensity score-matched 1:1 to P2A surgical patients.

RESULTS: Among 2,005 patients who received a valve, 1,069 underwent TAVR in P2S3i and 936 underwent surgery in P2A. After propensity score matching (N = 783 patients in each group), baseline characteristics were similar between groups: mean age was approximately 82 years, 43% were female, and mean Society of Thoracic Surgeons score was 5.5%. At 10 years, all-cause mortality rate was 83.4% after TAVR and 82.3% after surgery, respectively (HR: 1.01 [95% CI: 0.91-1.13]; P = 0.82). Aortic valve reintervention rates adjusted for competing mortality were 2.0% for TAVR and 1.9% for surgery (P = 0.47). Among 32 TAVR and 30 surgical patients with available echocardiographic data at 10 years, mean gradients were 11.0 mm Hg and 12.6 mm Hg, respectively.

CONCLUSIONS: At 10 years, TAVR with the SAPIEN 3 valve and surgery resulted in similar rates of mortality and aortic valve reintervention, and similar hemodynamics in intermediate-risk patients with symptomatic severe aortic stenosis. This analysis highlights challenges associated with extended long-term follow-up of clinical trials, including differential loss to follow-up and the competing risk of mortality in elderly populations. (PARTNER 2A Trial; NCT01314313; PARTNER 2 SAPIEN 3 Intermediate-Risk Registry; NCT03222128).},
}

Humans
*Transcatheter Aortic Valve Replacement/methods/mortality
Female
*Aortic Valve Stenosis/surgery/mortality/diagnosis
Male
Treatment Outcome
Registries
Prospective Studies
Aged, 80 and over
Follow-Up Studies
*Heart Valve Prosthesis
Risk Assessment
Aged
Time Factors
Risk Factors
Postoperative Complications
*Aortic Valve/surgery/diagnostic imaging

@article {pmid42300996,
year = {2026},
author = {Tao, Y and Zhang, F and Zhu, D and Zhang, S and Ma, L},
title = {Engineered Injectable Coaxial Supramolecular Hydrogel for a Minimally Invasive Neural Electrode.},
journal = {ACS applied bio materials},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsabm.6c00499},
pmid = {42300996},
issn = {2576-6422},
abstract = {Implantable neural electrodes with long-term stability remain a central challenge for brain-computer interfaces due to the severe mechanical mismatch between rigid electrodes and soft neural tissue, which triggers chronic inflammation and signal degradation. Herein, we report an injectable coaxial supramolecular hydrogel electrode based on dynamic host-guest interactions between β-cyclodextrin and adamantane, combined with silver nanowire incorporation for enhanced electrical conductivity. The resulting hydrogel exhibits shear-thinning and rapid self-recovery behavior, enabling minimally invasive injection and in situ formation of soft, cylindrical neural electrodes without auxiliary insertion devices. By tuning the supramolecular crosslinker density, the hydrogel achieves tissue-matched mechanical properties comparable to those of brain tissue, effectively mitigating a mechanical mismatch at the electrode-tissue interface. The incorporation of silver nanowires establishes a percolated conductive network, leading to low impedance and stable electrochemical performance. In vivo implantation demonstrates stable impedance and reliable neural signal recording over 14 days. Furthermore, the hydrogel electrodes successfully capture stimulus-evoked neural responses and pathological epileptic activity in a rat model. This work provides a versatile strategy for constructing injectable, mechanically compliant, and electrically robust neural electrodes, offering opportunities for next-generation soft neural interfaces.},
}

@article {pmid42301062,
year = {2026},
author = {Pappas, S},
title = {Xiao Yang: Improving brain-machine interfaces to make them more resilient.},
journal = {Scientific American},
volume = {335},
number = {1},
pages = {58},
doi = {10.1038/scientificamerican072026-2AGY9Pv9CG9vZKawSKhyyi},
pmid = {42301062},
issn = {0036-8733},
}

@article {pmid42301478,
year = {2026},
author = {Gu, B and Zhao, H and Long, F and Li, Q and Zhao, Y and Yu, M and Hu, X and Li, G and Han, F and Tian, Q and Liu, Z and Yu, H},
title = {Advances in printable flexible and stretchable thin-film electrodes: materials, interfaces, technologies and bioelectronic applications.},
journal = {Nanoscale},
volume = {},
number = {},
pages = {},
doi = {10.1039/d5nr05522a},
pmid = {42301478},
issn = {2040-3372},
abstract = {The mechanical mismatch between rigid clinical electrodes and soft biological tissues remains a primary bottleneck restricting the stability of long-term electrophysiological monitoring. Printable flexible thin-film electrodes offer a compelling solution by enabling additive, high-throughput patterning on flexible and stretchable substrates, thereby circumventing the reliance on vacuum environments and high-temperature processing typical of conventional microfabrication. This review synthesizes recent advances in functional inks, ranging from metals and carbon nanomaterials to conductive polymers and ionogels, together with high-resolution printing techniques. Addressing the critical challenge of interfacial failure in flexible devices, we explore engineering strategies to enhance adhesion at both electrode-substrate and electrode-tissue interfaces. Specifically, we analyze the pivotal roles of physical interlocking and chemical anchoring mechanisms in suppressing dynamic delamination and maintaining device integrity. Finally, the review highlights representative applications in wearable electronics, implantable systems, and emerging organoid interfaces, and outlines key translational challenges, including long-term stability and manufacturing reproducibility.},
}

@article {pmid42301851,
year = {2026},
author = {Li, M and Jiang, J and Dong, B and Zhao, R and Wang, B and Wang, K and Yu, H and Wang, C and Xu, M and Ming, D},
title = {Training and transfer effect of evoked brain responses by brain-computer interaction.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3704419},
pmid = {42301851},
issn = {1558-2531},
abstract = {OBJECTIVE: Electroencephalography (EEG)-based neuro feedback training (NFT) guides users to regulate their neural activity via sensory feedback and further modulates their cognitive state or function. It is a crucial approach to improving brain-computer interface (BCI) performance. However, previous studies rarely report cross-task transfer effects of existing NFT approaches beyond the training task, suggesting that they may fail to improve task-related common functions.

METHODS: Here, we propose a steady-state visual evoked potential (SSVEP)-based table hockey BCI game as an NFT approach to improve SSVEP-BCI performance. 40 healthy subjects were randomized into four groups: the 10-frequency NFT group, the 5-frequency NFT and transfer group, the placebo group, and the blank control group. All completed a 10-frequency online SSVEP task before and after five days of training, with EEG and subjective experiences recorded throughout.

RESULTS: The two NFT groups achieved significant improvement in online SSVEP classification accuracy. This was accompanied by increased SSVEP power, inter-trial phase coherence (ITPC), and expansion of activated cortical areas. Notably, enhancements generalized to untrained transfer tasks, likely due to the common impulse response between the training and the transfer tasks.

CONCLUSION: These findings demonstrate that the proposed NFT approach not only improves SSVEP-BCI performance on the trained conditions but also induces transferable neural changes to adjacent frequencies, suggesting training augments the neural population engaged in processing steady-state visual stimuli.

SIGNIFICANCE: This work advances understanding of how self-regulation during NFT improves task-related common functions.},
}

@article {pmid42301910,
year = {2026},
author = {Gu, W and Ju, J and Wang, L},
title = {Structural Designs and Implantation Strategies of Penetrating Flexible Neural Electrodes: From Planar Shanks to Volumetric Neural Interfaces.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {},
number = {},
pages = {e74206},
doi = {10.1002/smll.74206},
pmid = {42301910},
issn = {1613-6829},
support = {LMS25F040004//Natural Science Foundation of Zhejiang Province/ ; 2025A-417-G,2024A-144-G//Yongjiang Talent Program of Ningbo/ ; },
abstract = {This article reviews recent advances in the structural design and implantation strategies of penetrating flexible neural electrodes for intracortical recording. Traditional rigid electrodes suffer from limited long-term stability due to mechanical mismatch with brain tissue. While flexible electrodes improve biocompatibility, they face significant implantation challenges. The article systematically analyzes penetrating-capable structures, including tapered shank electrodes, thread-like electrodes, mesh and bioinspired electrodes, and hollow electrodes, and discusses corresponding implantation strategies such as biodegradable stiffening coatings, shuttle-assisted delivery, syringe injection, as well as stylet or fluid pressure based insertion. It further introduces three-dimensional, high-density electrode arrays enabled by rolling- and assembly-based fabrication techniques, which combine tissue compliance with high electrode density and functionality. Finally, we highlight persistent challenges and future opportunities in scalable fabrication, device reliability, and implantation mechanics, emphasizing the need for integrated approaches to enable next-generation flexible neural interfaces.},
}

@article {pmid42302830,
year = {2026},
author = {Fenoglio, E and Garro, F and Bucchieri, A and Semprini, M},
title = {Motor imagery supported by augmented reality activates motor brain areas similarly to physical movements.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae7e27},
pmid = {42302830},
issn = {1741-2552},
abstract = {OBJECTIVE: Motor imagery (MI) is a well-established cognitive process that may enhance motor skills and recovery during motor rehabilitation. Integrating action observation and augmented reality into MI tasks is a promising approach to enhance brain modulation and improve motor learning, yet it remains underexplored. This study examines the impact of augmented reality on brain modulation during tasks combining MI and action observation, and its implications for motor learning assessed through an implicit sequence learning paradigm.

APPROACH: 35 participants were separated into two groups: a motor execution (ME) group performing a physical reaching task, and a motor imagery (MI) group performing the same task within an augmented-reality-based kinesthetic MI paradigm. Event-related synchronization/desynchronization was analyzed in source-localized electroencephalography (EEG) data between and within groups.

MAIN RESULTS: We found similar brain modulation patterns between ME and MI groups specifically when MI was supported by augmented reality, particularly in alpha and beta bands during movement planning and execution phases. Moreover, we observed high inter-individual variability: a subgroup of MI participants did not produce the expected neural response, showing reduced modulation in motor-related regions compared to those who responded as expected. Furthermore, reaction times were compared during physical movements, through implicit repeated and random sequences of reaching movements. We observed non-significant trends towards faster responses in implicit sequences, which may suggest potential implicit motor learning.

SIGNIFICANCE: These exploratory results offer useful insights into augmented reality-supported MI, particularly for users who may require personalized approaches to benefit from MI paradigms, as in brain-computer interface applications.},
}

@article {pmid41849117,
year = {2026},
author = {Luo, Z and She, Q and Jin, T and Li, Y and Xi, X},
title = {Adaptive graph convolution domain adaptation network for cross-subject EEG emotion recognition.},
journal = {Medical & biological engineering & computing},
volume = {},
number = {},
pages = {},
pmid = {41849117},
issn = {1741-0444},
support = {No. 62371172//National Natural Science Foundation of China/ ; No. 62371178//National Natural Science Foundation of China/ ; },
abstract = {Electroencephalogram (EEG)-based emotion recognition is essential for the advancement of affective brain-computer interface (aBCI) system. However, in cross-subject scenarios, the dynamic nature and subject-specific characteristics of EEG signals significantly hinder knowledge transfer, thereby leading to reduced model performance on previously unseen target domain. To overcome these limitations, we design an innovative domain adaptation model, the adaptive graph convolution domain adaptation network (AGCDAN), to capture the dynamic spatial information of EEG signals and reduce inter-domain distribution discrepancies by aligning both marginal and conditional distributions through domain adaptation. Specifically, adaptive graphs are firstly constructed based on differential entropy features extracted from EEG signals to extract dynamic frequency-spatial representations. A multi-branch neural network is then employed to extract customized feature representations tailored to each source domain and the target domain individually. Subsequently, discriminator-free adversarial learning is employed to align marginal distributions, and introduces subdomain metric learning guided by label information to achieve conditional distribution alignment. Finally, domain-specific classifiers, combined with a decision fusion strategy, produce the final emotion predictions. We evaluate AGCDAN on three datasets (SEED, SEED-IV, DEAP) under a multi-source domain adaptation setting for cross-subject emotion recognition. Achieving recognition accuracies of 89.68%, 68.61%, and 68.13%, respectively, demonstrating superior performance over current state-of-the-art (SOTA) domain adaptations techniques in cross-subject emotion recognition, showcasing its strong capability in modeling dynamic emotional states and reducing negative transfer effects.},
}

@article {pmid42285527,
year = {2026},
author = {Chen, P and Ma, M and Liu, X and Ju, Y and Zhang, Y and Liao, M and Liu, S and Ming, D},
title = {Neural abnormalities in cognitive subprocesses of emotional conflict control in bipolar II disorder: Evidence from ERPs and brain functional networks.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {122124},
doi = {10.1016/j.jad.2026.122124},
pmid = {42285527},
issn = {1573-2517},
abstract = {Patients with bipolar disorder (BD) exhibit deficits in emotional conflict control. These abnormalities may be related to alterations in distinct cognitive subprocesses involved in emotional conflict processing; however, the specific stages affected remain unclear. Given the temporal and stage-dependent nature of emotional conflict control, examining specific processing stages may clarify the mechanisms underlying these deficits in BD. Therefore, this study combined a face-word emotional Stroop task with EEG, integrating event-related potentials (ERPs) and brain functional network analyses to characterize the cognitive subprocesses involved in emotional conflict control in bipolar II disorder (BD-II). BD-II patients showed significant abnormalities in early cognitive stages, including emotional stimulus perception and conflict monitoring (p < 0.05). These abnormalities were mainly reflected by reduced N200 amplitudes, right temporal region (T8)-centered network changes, and alterations in both global topology and frontal network organization. Machine learning analysis further suggested that these abnormal electrophysiological features may contain information relevant to distinguishing BD-II patients from healthy controls (HC), yielding an accuracy of 83.3% on the held-out test set. In summary, this study suggests that emotional conflict control deficits in BD-II are mainly reflected in early-stage electrophysiological abnormalities, with ERP amplitude changes and T8-centered right temporal network alterations representing the core findings. These findings provide candidate EEG features for further investigation of emotional conflict control abnormalities in BD-II, but require validation in larger independent samples.},
}

@article {pmid42285811,
year = {2026},
author = {Cleary, HL and Bernard, MD and Davenport, DL and Bernard, AC},
title = {Diagnostic value of serum troponin in stable patients at risk for blunt cardiac injury.},
journal = {Injury},
volume = {},
number = {},
pages = {113448},
doi = {10.1016/j.injury.2026.113448},
pmid = {42285811},
issn = {1879-0267},
abstract = {INTRODUCTION: There are no gold standard criteria for diagnosing blunt cardiac injury (BCI). While the combination of electrocardiogram (ECG) and serum troponin is considered the most sensitive screening method for those being considered for discharge, little evidence exists regarding the diagnostic value and clinical utility of initial and serial serum troponin levels in stable patients who are being admitted with a possible BCI. Therefore, this study seeks to determine if troponin level is an independent predictor of adverse cardiac events in stable, admitted patients at risk for BCI.

METHODS: This was a five-year retrospective study using the trauma database at a University Level I Trauma Center. The study population included adult trauma patients presenting with a physician diagnosis of BCI or a sternal fracture who met prespecified stability criteria (SBP ≥ 90 mmHg, HR < 110bpm, shock index < 1, GCS ≥ 14). The data collected included all troponin values and ECG interpretations as well as echocardiogram interpretations, when performed. A patient was classified as having an adverse cardiac event if they were diagnosed with a new arrhythmia requiring treatment, had cardiac surgery, or suffered cardiac-related mortality. Sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratios were calculated to analyze the diagnostic performance of the different BCI screening modalities. Diagnostic tests were compared using the Exact Mcnemar's test and the exact binomial test.

RESULTS: 350 patients met inclusion criteria. There were 12 adverse cardiac events; each were new arrhythmias requiring treatment with one patient also requiring synchronized cardioversion. Only one adverse cardiac event occurred in a patient with a normal ECG and troponin (n = 160). No patients with a normal ECG and abnormal troponin had an adverse cardiac event (n = 83). Patients with an abnormal ECG were more likely to have an adverse cardiac event (p < 0.001).

DISCUSSION: In stable patients in this cohort, troponin level did not predict adverse cardiac events. Therefore, admitted patients at risk for BCI who meet stability criteria might be safely observed without measurement of serum troponin.},
}

@article {pmid42286462,
year = {2026},
author = {Dai, W and Jahangir, M and Li, T and Guo, WJ},
title = {Early-life stress and adolescent circadian dysrhythmia drives unique behavioral and microbial profiles in rats.},
journal = {BMC microbiology},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12866-026-05287-y},
pmid = {42286462},
issn = {1471-2180},
support = {82171487//National Natural Science Foundation of China/ ; 2024C03006//"Pioneer" and "Leading Goose" R&D Program of Zhejiang/ ; TD2024003//Leading innovation and entrepreneurship team of Hangzhou/ ; },
abstract = {OBJECTIVES: Early life adversity and circadian disruptions are known to impact neurodevelopment and physiology. This study investigated the effects of maternal separation (MS), adolescent circadian dysrhythmia, and their combination (double hit) on anxiety-like behavior and gut microbiota composition in rats.

METHODS: Rats were divided into four groups: CL (control group: normal early-life conditions with a standard light/dark cycle during adolescence), MS + N (maternal separation (MS) with a standard light/dark cycle (N=normal)) during adolescence), N + ALD (normal early-life conditions (N) with an altered light/dark cycle (ALD) during adolescence), and MS + ALD (combined exposure: MS with an altered light/dark cycle (ALD) during adolescence). Anxiety-like behavior and locomotor activity were assessed using the Open Field Test. Gut microbial diversity and taxonomic composition were analysed to identify microbial shifts across groups.

RESULTS: Behavioral analysis indicated that the combined stress group (MSLD) spent significantly (p < 0.05) more time in the center of the arena compared to the CL, MS + N, and N + ALD groups, suggesting a compromise in risk assessment ability due to dual stress exposure. Microbiome profiling revealed that while a core microbiome was conserved, each stressor generated a unique taxonomic signature. The N + ALD group appeared as the most distinct outlier, characterized by the lowest number of unique features and a specific enrichment of the viral species of phylum Uroviricota. Conversely, the MS + ALD group was distinguished by an enrichment of Bacteroidota species, including Muribaculum intestinale and Phocaeicola vulgatus, while the MS + N group showed enrichment in Bacteroides acidifaciens. Mycobiome analysis showed that early-life stress was the primary driver of fungal restructuring, distinguishing maternal separation groups by the loss of Neocallimastix species and the competitive expansion of Piromyces finnis. While adolescent circadian disruption alone largely preserved the baseline mycobiome, the cumulative dual-hit stress (MS + ALD) generated a distinct dysbiotic profile evident by the unique proliferation of Anaeromyces robustus.

CONCLUSIONS: In conclusion, the developmental timing of stress exposure drives distinct dysbiotic shifts. Specifically, adolescent circadian disruption selectively targets the virome, whereas early-life stress causes shift in the microbiome which endures a long-term foundation for adolescent psychiatric vulnerability. Notably, the cumulative effect of early life and adolescence stressors results in a unique microbial and behavioral profile, highlighting that the specific developmental window of exposure is a decisive factor in gut-brain axis dysfunction.},
}

@article {pmid42287979,
year = {2026},
author = {Jin, Z and Li, D and Zhou, Q and Wang, Z},
title = {NEURAL-VOX: NEURal auditory language decoding for voice and text reconstruction.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {204},
number = {},
pages = {109221},
doi = {10.1016/j.neunet.2026.109221},
pmid = {42287979},
issn = {1879-2782},
abstract = {Neural decoding of perceived linguistic content from non-invasive brain recordings remains a profound scientific challenge with transformative implications for assistive technologies. Existing approaches often struggle to generate intermediate representations, such as mel spectrograms or phonemes, and seldom integrate multi-modal information to enhance text decoding. This study presents a framework for decoding non-invasive brain activity into text, phoneme sequences, and mel-spectrogram-based acoustic representations, termed NEURAL-VOX. Leveraging a three-stage training strategy, NEURAL-VOX not only improves the accuracy of brain-to-text decoding, but also enables text generation to benefit from joint optimization with speech synthesis. By incorporating multi-scale frequency-domain analysis, our model more effectively captures the hierarchical structure of language processing in neural activity. Experiments across multiple datasets demonstrate that NEURAL-VOX achieves substantial gains over existing methods. The learned phoneme representations encode rich linguistic information and further strengthen text decoding, while model interpretability analysis reveals strong alignment with neurobiological patterns.},
}

@article {pmid42288545,
year = {2026},
author = {Nechchad, M and Elkari, B and Midaoui, IE and Cadi, SAE and M'Hifed, Z and Ourabah, L and Workneh, AD and Chaibi, Y and Irshad, SM and El-Barbary, ZMS and Yessef, M},
title = {EEG blink and gaze control using random forest classification for accessible assistive robotic navigation in real world conditions.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-56416-6},
pmid = {42288545},
issn = {2045-2322},
abstract = {This study presents an EEG-based Brain-Computer Interface for intuitive robotic navigation driven by ocular activity. A real multi-subject dataset collected from 15 participants was used to extract blink- and gaze related EEG features for five control commands. Eight supervised classifiers were evaluated under stratified 5-fold cross-validation: Random Forest, multilayer perceptron, support vector machine, k-nearest neighbors, RUSBoost, Naive Bayes, decision tree, and linear discriminant analysis. Among them, Random Forest achieved the best overall performance, reaching 98.74 ± 1.19% accuracy, 0.9874 Macro-F1, and 0.9993 macro-AUC, demonstrating excellent robustness and class separability across the five ocular classes. The decoded commands were transmitted wirelessly to an embedded Raspberry Pi platform, where they were converted into safe motor actions for wheelchair type robot navigation. Real world experiments in indoor environments confirmed stable motion control, reliable command execution, and successful obstacle avoidance without physical interaction from the user. These findings support the feasibility of a low-cost, portable, and non-invasive BCI solution based on realistic multi-subject EEG data for assistive mobility applications. Although the full control loop operated online, the end-to-end system latency was not quantitatively benchmarked in the present study and remains an important limitation to be addressed in future work. Future developments will focus on expanding the command set, improving robustness under more variable conditions, and validating the system in broader assistive mobility scenarios.},
}

@article {pmid42292038,
year = {2026},
author = {Wang, NN and Cao, F and Xu, D},
title = {Hydrogels in Neurological Disorders: Emerging Diagnostic and Therapeutic Applications.},
journal = {International journal of nanomedicine},
volume = {21},
number = {},
pages = {618081},
pmid = {42292038},
issn = {1178-2013},
mesh = {*Hydrogels/chemistry/therapeutic use ; Humans ; *Nervous System Diseases/diagnosis/therapy/drug therapy ; Animals ; Drug Delivery Systems/methods ; Brain-Computer Interfaces ; Electroencephalography/methods ; Blood-Brain Barrier ; },
abstract = {The clinical management of neurological disorders remains a major challenge worldwide, constrained by fundamental limitations in both diagnosis and therapy. Electroencephalography (EEG), the cornerstone of neurological assessment, is limited by low spatial resolution and inconsistent signal quality. Therapeutically, the blood-brain barrier (BBB) restricts drug delivery to the brain, resulting in subtherapeutic intracerebral concentrations. These convergent diagnostic and delivery bottlenecks underscore an urgent imperative for innovative materials and technologies. Hydrogels, characterized by biomimetic three-dimensional (3D) architectures, have emerged as a versatile material platform to bridge this gap. From a diagnostic perspective, hydrogels-based electrodes exhibit exceptional biocompatibility and low interfacial impedance, enabling high-fidelity EEG acquisition while minimizing insult to sensitive neural and skin tissues. From a therapeutic perspective, their 3D architecture provides versatile scaffolds for therapeutic agents, supporting high loading efficiency and programmable release profiles for neurological interventions. In this review, we first outline the physicochemical properties and fabrication techniques of hydrogels. We then discuss their applications, with particular emphasis on neural bio-electrodes, brain-computer interfaces (BCIs), drug delivery, and neuro-bioengineering. Finally, we examine the challenges impeding the clinical translation of hydrogels and outline prospective mitigation strategies. The integration of these functionalities is anticipated to advance closed-loop therapeutic systems for the precise management of complex neurological disorders.},
}

*Hydrogels/chemistry/therapeutic use
Humans
*Nervous System Diseases/diagnosis/therapy/drug therapy
Animals
Drug Delivery Systems/methods
Brain-Computer Interfaces
Electroencephalography/methods
Blood-Brain Barrier

@article {pmid42292814,
year = {2026},
author = {Wang, Z and Zhao, W and Chen, X and Zhao, S and Li, X and Yang, Q and Zong, F and Zhang, H},
title = {Activation of GLP-1R ameliorates alcohol withdrawal induced anxiety-like behavior by regulating neuronal mitochondrial quality control.},
journal = {Frontiers in pharmacology},
volume = {17},
number = {},
pages = {1820128},
pmid = {42292814},
issn = {1663-9812},
abstract = {INTRODUCTION: Alcohol use disorder (AUD) is a specific psychological state induced by repeated heavy drinking, and withdrawal symptoms such as anxiety are closely related to relapse after withdrawal. While neuronal damage caused by alcohol is considered a significant precipitating factor for withdrawal-induced anxiety, the underlying molecular mechanisms remain unclear.

METHODS: In this study, we established a mouse model of alcohol withdrawal through 3 months of chronic ethanol exposure (CEE) followed by withdrawal. Mice were treated with semaglutide (0.03 mg/kg) via intraperitoneal injection and subjected to behavioral, biochemical, and morphological analyses.

RESULTS: Our results demonstrate that the glucagon-like peptide-1 receptor (GLP-1R) agonist semaglutide alleviates anxiety-like behaviors in CEE withdrawal mice and reverses the downregulation of GLP-1R and its downstream effector CREB in the mitochondria of prefrontal cortex (PFC) neurons. Enhancing the GLP-1R/CREB pathway regulates mitochondrial quality control, including fission, fusion, and mitophagy, to maintain mitochondrial function and ameliorate synaptic impairment.

DISCUSSION: These findings suggest that activation of GLP-1R ameliorates alcohol withdrawal-induced anxiety-like behaviors by regulating neuronal mitochondrial function, providing a potential therapeutic target for AUD.},
}

@article {pmid42294101,
year = {2026},
author = {Sümer-Arpak, E and Saini, R and Chakladar, DD and Varun, SK and Simistira Liwicki, F},
title = {The current status of foundation models in decoding inner speech from non-invasive brain signals: a mini review.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1838064},
pmid = {42294101},
issn = {1662-5161},
abstract = {Inner speech (IS), or imagined speech without overt articulation, is a promising target for brain-computer interfaces (BCIs) aimed at restoring communication in individuals with severe speech impairments, such as locked-in syndrome. Foundation models (FMs), typically trained using self-supervised learning (SSL) on large-scale datasets, offer new opportunities for learning transferable and robust representations from neural signals. This mini review provides an overview of FM-based approaches for IS decoding using non-invasive neuroimaging modalities, including functional magnetic resonance imaging, electroencephalography, magnetoencephalography, and functional near-infrared spectroscopy, highlighting architectural trends, pretraining strategies, and model adaptation techniques. We discuss how recent models move beyond task-specific classification toward scalable representation learning and semantic-level decoding. Despite these advances, several challenges remain, including the weak, noisy, and non-stationary nature of neural signals, variability in data acquisition, and limitations in dataset scale, standardization, computational resources, interpretability, and evaluation metrics. Ethical and privacy considerations are also critical. Overall, FMs provide a promising paradigm for non-invasive IS decoding, addressing neurophysiological, methodological, and ethical challenges is essential for developing scalable and reliable BCI systems.},
}

@article {pmid42294231,
year = {2026},
author = {Uszko, JM and Abibu, SA and Eichhorn, SJ and Patil, AJ and Hall, SR},
title = {Cellulose Biofilms, New Biotemplates in the Synthesis of Cuprate Superconductors.},
journal = {ACS omega},
volume = {11},
number = {22},
pages = {32391-32399},
pmid = {42294231},
issn = {2470-1343},
abstract = {Bacterial cellulose (BC), obtained from fermented food byproducts (Symbiotic Culture of Bacteria and Yeast, and Nata de Coco), was successfully used as a template for the synthesis of a YBa2Cu3O6+δ (YBCO) superconductor. As previous studies have shown, a dry template is needed to ensure the maximum uptake of the precursor solution. BC used is obtained in a wet state; it must be dried before use as a template. A variety of template drying techniques were investigated to assess the efficacy. This included air, oven, freeze, and solvent exchange drying. Among these, freeze-drying proved to be the most effective method as it best preserved the porous internal structure of the template. The addition of ethylenediaminetetraacetic acid (EDTA), a polychelating acid, also had a beneficial effect on the synthesis, improving both phase purity and the contribution of the superconducting phase. Waste-derived BC was shown to be a suitable substrate for the sol-gel synthesis of cuprate superconductors, providing an alternative to the ionic-liquid/nanocellulose-based approach used previously.},
}

@article {pmid42295582,
year = {2026},
author = {Li, J and Li, Y and Shi, W and Kang, R and Wang, H},
title = {A Multiscale Decoding Approach of Subject-Independent Motor Imagery EEG Signal Combined with Data Alignment Strategy.},
journal = {Annals of biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {42295582},
issn = {1573-9686},
support = {62373108//the National Nature Science Foundation of China/ ; },
abstract = {PURPOSE: Brain-computer interface (BCI) leverages artificial intelligence (AI) and wearable electroencephalography (EEG) sensors to decode brain signals, significantly enhancing quality of life. EEG-based motor imagery (MI) brain signals are widely used in various BCI applications, including smart healthcare, intelligent vehicle, smart homes, and robotics control. However, the substantial individual variability in EEG data distribution poses a challenge in applying constructed models to new subjects.

METHODS: To address this issue, this study proposes a novel deep learning framework, namely Multiscale Spatiotemporal Convolutional Neural Network (MSTCNN) based on Euclidean Space Data Alignment (ESDA), which can effectively decode MI tasks in subject-independent scenarios. First, to address the data distribution variability for different subjects, the proposed ESDA method is utilized to align all MI task data, reducing the discrepancies caused by physiological differences. Subsequently, an end-to-end multiscale model is constructed to extract multi-level MI task-related feature information from EEG signals. Additionally, the Squeeze-and-Excitation (SE) attention mechanism is incorporated to highlight critical local features, further improving MI task-decoding performance.

RESULTS: Experiments conducted on the publicly available BCI Competition IV dataset 2a demonstrate that the proposed multiscale model achieves an average accuracy of 67.5% in subject-independent scenarios, with a relatively small number of parameters (4.4 × 10[3]). Moreover, the highest decoding accuracy for individual subjects reaches 81.4%, surpassing many other state-of-the-art (SOTA) methods. The ablation experiments further validate the model's performance and robustness.

CONCLUSION: This study may provide a solid theoretical foundation for advancing BCI system applications in Internet of Things units, clinical, and other areas.},
}

@article {pmid42295948,
year = {2026},
author = {Zhang, Z and Hamada, H and Rangpong, P and Connelly, A and Yagi, T},
title = {Auditory Perception as a Surrogate for Auditory Imagery in EEG-Based BCI Training: Neural Evidence and a Feasibility Study.},
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.2026.3703988},
pmid = {42295948},
issn = {1558-0210},
abstract = {Brain-computer interface (BCI) systems are commonly classified as reactive or active. Reactive BCIs rely on responses to external stimuli, simplifying decoding but limiting direct user control. In contrast, active BCIs enable intuitive control through spontaneous mental activity but impose greater signal-processing challenges. Speech BCIs, a subset of active systems, hold promise for individuals with severe neurological impairments such as locked-in syndrome (LIS), potentially enabling communication through imagined speech. However, their adoption is hindered by the demanding pre-training required to collect imagery data, which often induces fatigue, and the difficulty many users face in generating consistent, high-quality imagery. To address these limitations, we propose a hybrid BCI framework that uses passive listening tasks as a surrogate for auditory imagery tasks during model training. As a first step, this proof-of-concept feasibility study examined neural responses to five Japanese vowels (/a/, /i/, /u/, /e/, /o/) under listening and imagery conditions in a limited experimental setting with healthy male participants, using event-related potentials (ERPs), topographical mapping, and event-related spectral perturbations (ERSPs), followed by statistical analysis of shared and distinct neural patterns. In the primary three-class and binary classification analyses, classification accuracies for imagery signals obtained from models trained solely on listening data were comparable to, and in some settings higher than, those trained directly on imagery data, with both outperforming chance level. These findings indicate that passive listening may provide effective training data for active auditory imagery BCIs within the tested setting, potentially reducing cognitive demands without compromising performance. Further validation in larger and more diverse cohorts is required before clinical application.},
}

@article {pmid42295950,
year = {2026},
author = {Fang, C and Xu, G and Chen, R and Li, H and Zhang, X and Xie, J and Tian, P and Yang, Z and Han, C and Zhang, S},
title = {Noise-Driven Feature Enhancement of High-Frequency SSVEP through Underdamped Second-order Stochastic Resonance Energy Transfer.},
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.2026.3703399},
pmid = {42295950},
issn = {1558-0210},
abstract = {Brain-computer interfaces (BCIs) system leveraging high-frequency steady-state visual evoked potentials (SSVEP) enhance user comfort, but their signals suffer from significant amplitude attenuation, which challenges precise detection. To address this, we proposed a novel weak-feature enhancement framework, FBUSSR-CCA, which integrates a monostable (single-well) underdamped second-order stochastic resonance (USSR) model into the filter-bank processing pipeline. This model strategically converts noise energy to nonlinearly amplify weak, high-frequency target components. Validation was performed on two public high-frequency SSVEP datasets (stimulus frequencies of 40-43Hz and 40-41.5Hz, respectively). The proposed FBUSSR-CCA was compared against six benchmark methods, including the best-performing FBCCA. Results indicated that the proposed framework attained superior performance across all three experimental conditions: the mean accuracy and ITR reached 83.61 ± 13.16 % / 17.92 ± 7.81 bits min[-1], 98.89 ± 2.17 % / 28.77 ± 2.30 bits min[-1], and 70.27 ± 19.02 % / 11.88 ± 8.62 bits min[-1], respectively. Further analysis confirmed the USSR module is a general-purpose tool (enhancing all six methods) and is parametrically robust to perturbations. These findings show that the monostable USSR framework is a reliable solution for enhancing high-frequency SSVEP, advancing the potential for more comfortable and efficient BCI systems.},
}

@article {pmid42296064,
year = {2026},
author = {Tai, YD and Villalobos, J and Wickramasinghe, N and Widdicombe, B and Unnithan, RR and Grayden, DB and John, SE},
title = {Toward a fully wireless endovascular neural interface: Evaluating power transfer efficacy.},
journal = {PloS one},
volume = {21},
number = {6},
pages = {e0351138},
doi = {10.1371/journal.pone.0351138},
pmid = {42296064},
issn = {1932-6203},
mesh = {Animals ; *Wireless Technology/instrumentation ; Sheep ; *Endovascular Procedures/instrumentation/methods ; Equipment Design ; Brain ; },
abstract = {BACKGROUND: Endovascular neural interfaces (ENIs) offer a minimally invasive approach for neural stimulation and recording without the need for open brain surgery. However, current generation devices have long transvascular wires from the implant site to the chest. Eliminating these wires will unlock clinical usability, including lowering infection risk from transvascular wires, reducing the risk of thrombosis from altered hemodynamics, and improving mechanical reliability. However, removing these transvascular wires would require efficient power transfer across the skull and tissue while meeting specific absorption rate (SAR) limits, which is a significant challenge in the field.

OBJECTIVE: This work designed and evaluated endovascular receiver (Rx) and transmitter (Tx) coils within endovascular geometric and biological constraints to maximize wireless power transfer.

METHODS: This study evaluated the optimal operating frequencies, quantified coupling, coil quality factors, power transfer efficiency, and SAR using computational modeling, benchtop, and in-vivo testing. The study also assessed the tolerance to coil misalignment and load mismatch. We evaluated each case with and without ferrites with measurements in air, sheep tissue, and in vivo in sheep.

RESULTS: The results showed that inductive power transfer delivered power to endovascular geometry devices at clinically relevant depths. The maximum power transfer efficiency (PTE) reached 11% at 15 mm and 2% at 30 mm, with up to 72 mW delivered at 30 mm under SAR safety limits. The rectangular planar coil pair performed best at ≤15 mm, whereas the ferrite-core flux-pipe Tx with a helical Rx outperformed beyond ~20 mm and was more tolerant to misalignment.

CONCLUSION: This study demonstrated the feasibility of wirelessly powering multichannel ENIs using coils that can be placed inside a blood vessel and powered inductively. Making an endovascular neural interface fully wireless has the potential to transform the technology by improving both safety and reliability.},
}

Animals
*Wireless Technology/instrumentation
Sheep
*Endovascular Procedures/instrumentation/methods
Equipment Design
Brain

@article {pmid42296109,
year = {2026},
author = {Janapati, R and Maram, B and Saidhbi, S and Desai, U and Nayak, P},
title = {SET Net A Hybrid Deep Learning Framework For EEG Based Attention Relaxation Classification In Brain Computer Interface Applications.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {231},
pages = {},
doi = {10.3791/70700},
pmid = {42296109},
issn = {1940-087X},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Deep Learning ; *Attention/physiology ; },
abstract = {Brain Computer Interface (BCI) technology is used as a tool to provide a direct communication channel between the human brain and the external environment. This has applications in assistive and interactive systems. The non-invasive acquisition of neural activity using Electroencephalography (EEG) is standard with BCI systems. However, it is difficult to effectively classify cognitive states, including attention and relaxation, because the EEG signal is non-stationary and noisy. To overcome these shortcomings, this study proposes a new hybrid deep learning architecture namely, squeeze-and-excitation (SE) transformer network (SET-Net). In this method, EEG signals are preprocessed and divided into temporal windows to analyze the significant spectrogram representations. The proposed model achieves a classification accuracy of 93.7%, with F1-score of 0.93 and ROC-AUC of 0.98. This demonstrates the effectiveness of the hybrid deep learning model with enhanced discrimination of EEG-based attention-relaxation state. This offers a scalable system for real-time BCI applications as assistive technology in human-computer interaction.},
}

*Brain-Computer Interfaces
Humans
*Electroencephalography/methods
*Deep Learning
*Attention/physiology

@article {pmid42296181,
year = {2026},
author = {Wang, L and Chen, H and Gong, X and Shi, Y and Lu, Z and Zhang, L and Chen, X and Chen, D and Zhou, H and Cheng, L},
title = {Research on Pathological Voice Recognition Based on XGBoost.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {231},
pages = {},
doi = {10.3791/68784},
pmid = {42296181},
issn = {1940-087X},
mesh = {Humans ; Boosting Machine Learning Algorithms ; *Voice Disorders/diagnosis ; *Pattern Recognition, Automated/methods ; },
abstract = {With the continuous growth of human social communication, the number of people suffering from voice disorders is also increasing. Due to the objective and non-invasive advantages of acoustic detection methods for pathological voice, the use of speech signal analysis for pathological voice recognition has become a research hotspot. This article first selected 101 continuous vowels /a/ from the German SVD database as the research object. Secondly, using wavelet packet technology for time-frequency analysis, four nonlinear dynamic parameters, namely approximate entropy, sample entropy, fuzzy entropy, and permutation entropy, are extracted from the sub signals as the feature parameter set for the pathological voice classifier. Finally, the machine learning algorithm XGBoost is selected as the pattern recognition method to establish a pathological voice classifier, and the classification performance is verified using five fold cross validation and ROC curve. Experimental results have shown that the accuracy of XGBoost's pathological voice classifier is 0.857, the F1 score is 0.875, and the AUC value is 0.944, all of which are higher than the classifier constructed by SVM, indicating that XGBoost has better performance in pathological voice recognition.},
}

Humans
Boosting Machine Learning Algorithms
*Voice Disorders/diagnosis
*Pattern Recognition, Automated/methods

@article {pmid42035123,
year = {2026},
author = {Xi, L and Liu, Q and Li, H and Li, W and He, D and Yao, L and Yang, X},
title = {Comparative efficacy of motor imagery augmented with central non-invasive brain stimulation versus peripheral electrical stimulation for upper extremity rehabilitation post-stroke: a systematic review and network meta-analysis.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {23},
number = {1},
pages = {},
pmid = {42035123},
issn = {1743-0003},
support = {zx2019-04-02//Rehabilitation Clinical Medical Centre of Yunnan Province/ ; 2019IC034//Jiajie Expert Workstation of Yunnan Province/ ; 202203AC100007-6//Study on a New Model of Comprehensive Intervention in Rehabilitation and Psychology of "Brain and Heart together"/ ; 202305AF150032//Science and Technology Talent and Platform Program (Academician and Expert Workstation)/ ; 2022YFC2009700//Research and Development of Integrated Chinese and Western Medicine Rehabilitation Technology and Multi-modal Monitoring System for movement Disorders/ ; 2018YFC2002301//National Key Research and Development Program of China/ ; 2024XKTDTS18//the Neurorehabilitation Team of Kunming Medical University/ ; FWCY-ZNT2024011//Systematic Development and Industrial Applications of Balneotherapy in Stroke Rehabilitation: A Translational Research Framework/ ; 202402AA310058//Application and Innovative Research of Balneotherapy in Chronic Disease Management/ ; 2024J0383//Scientific Research Fund project of Education Department of Yunnan Province/ ; 2023BS01//Doctoral research project of the Second Affiliated Hospital of Kunming Medical University/ ; 2025KFZD006//Investigating the Impact of iTBS on the HPA Axis in Stroke Patients via Stimulation of Different Brain Regions Using Resting-State EEG/ ; 82560452//National Natural Science Foundation of China/ ; 202501AY070001-185//Investigating the Modulation of Negative Emotions by the Deep Cerebellar Nuclei-Hippocampal Neural Circuit Using Transcranial Magnetic Stimulation/ ; },
abstract = {BACKGROUND: Upper limb dysfunction is a common and debilitating consequence of stroke, severely affecting patients’ activities of daily living and quality of life. Motor imagery (MI) has emerged as a promising rehabilitation technique, and its combination with various forms of non-invasive stimulation, both central (e.g., repetitive transcranial magnetic stimulation, rTMS; transcranial direct current stimulation, tDCS) and peripheral (e.g., functional electrical stimulation, FES), has been increasingly investigated. While previous meta-analyses have confirmed the general benefit of combined interventions, the relative efficacy of different MI-based combination strategies remains unclear. This systematic review and network meta-analysis aimed to directly and indirectly compare the effectiveness of MI augmented with different non-invasive central or peripheral stimulation modalities for upper extremity recovery post-stroke.

METHODS: We registered the study on PROSPERO (CRD420251131264) and followed the PRISMA guidelines. Randomized controlled trials (RCTs) were searched in PubMed, Cochrane Library, EMBASE, Scopus, CNKI, and Wanfang databases from inception until August 4, 2025. The included RCTs involved adult stroke patients with upper limb dysfunction receiving MI combined with any non-invasive stimulation. The primary outcome was the change in upper limb motor function measured by the Fugl-Meyer Assessment (FMA or FMA-UE). A frequentist network meta-analysis was performed using random-effects models. Risk of bias was assessed using the Cochrane RoB 2 tool. Subgroup, sensitivity, and meta-regression analyses were conducted to explore heterogeneity.

RESULTS: Seventeen RCTs involving 846 participants were included in the systematic review, with 13 studies forming the network for meta-analysis, comparing 9 intervention strategies. Network meta-analysis for the FMA outcome showed that MI combined with low-frequency rTMS (MI-LF-rTMS) showed a statistically significant difference compared to conventional rehabilitation alone (Standardized Mean Difference, SMD = 1.755, 95% CI 0.631 to 2.879, p  = 0.002). No other intervention, including MI-tDCS, MI-FES, or any single therapy, showed a statistically significant difference compared to conventional rehabilitation. MI-LF-rTMS also showed a statistically significant difference in upper limb functional activity (Action Research Arm Test). Subgroup analyses indicated that the statistically significant difference for MI-LF-rTMS was also observed across intervention durations ≤ 4 weeks, disease stages ≤ 3 months post-stroke, and in protocols not using brain-computer interface technology. Meta-regression identified that the use of a brain-computer interface, publication year, and patient mean age were significant sources of heterogeneity.

CONCLUSION: Among the intervention strategies evaluated in this network meta-analysis, motor imagery combined with low-frequency repetitive transcranial magnetic stimulation (MI-LF-rTMS) showed a statistically significant difference compared to conventional rehabilitation. This regimen integrates central neuromodulation with cognitive training and may be a clinically feasible option, particularly for patients in the early phase after stroke. Future research should focus on parameter optimization, mechanistic exploration, and validation in larger, more diverse populations.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12984-026-02002-w.},
}

@article {pmid42284173,
year = {2026},
author = {Meunier, A and Zak, MR and Munz, L and Garkot, S and Eder, M and Xu, J and Grosse-Wentrup, M},
title = {A Conversational Brain-Artificial Intelligence Interface.},
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.2026.3703091},
pmid = {42284173},
issn = {1558-0210},
abstract = {We introduce Brain-Artificial Intelligence Interfaces (BAIs) as a new class of Brain-Computer Interfaces (BCIs). Unlike conventional BCIs, which rely on intact cognitive capabilities, BAIs leverage the power of artificial intelligence to replace parts of the neuro-cognitive processing pipeline. BAIs allow users to accomplish complex tasks by providing high-level intentions, while a pre-trained AI agent determines low-level details. This approach enlarges the target audience of BCIs to individuals with cognitive impairments, a population often excluded from the benefits of conventional BCIs. We present the general concept of BAIs and illustrate the potential of this new approach with a Conversational BAI based on electroencephalography (EEG), termed EEGChat. In particular, we show in an experiment with simulated phone conversations that the Conversational BAI enables complex communication without the need to be able to generate language. Our work thus demonstrates the ability of a speech neuroprosthesis to enable fluent communication in realistic scenarios with non-invasive technologies.},
}

@article {pmid42277056,
year = {2026},
author = {Wang, Y and Lin, Z and Wu, D and Yang, Y and Zhang, J},
title = {Associations of DTI-ALPS index and choroid plexus volume with clinical severity across the Parkinson's disease spectrum.},
journal = {NPJ Parkinson's disease},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41531-026-01432-6},
pmid = {42277056},
issn = {2373-8057},
support = {82571418//National Natural Science Foundation of China/ ; 32530027//National Natural Science Foundation of China/ ; 2024C03098//Key Research and Development Program of Zhejiang Province/ ; },
abstract = {Impaired clearance of pathogenic proteins may contribute to Parkinson's disease (PD) progression, but the clinical relevance of brain clearance-associated processes in humans remains incompletely understood. Using cross-sectional data from 1,861 participants (704 PD, 997 prodromal, 160 healthy controls) in Parkinson Progression Marker Initiative, we investigated whether the magnetic resonance imaging (MRI)-based indirect markers linked to brain clearance-related processes, diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index and normalized choroid plexus volume (NCPV), are associated with disease stage and motor/non-motor manifestations across the PD spectrum. The ALPS index declined with age, was lower in males, and showed a stepwise reduction from controls to prodromal individuals and PD patients. Clinically, lower ALPS index correlated with greater motor severity in prodromal and PD groups, with stronger associations at advanced stages. Lower ALPS index also correlated with rapid eye movement sleep behavior disorder, cognitive impairment, and depressive symptoms. NCPV showed a complementary trend, demonstrating positive associations with clinical severity measures and a significant negative correlation with the ALPS index. Together, these findings suggest that MRI markers linked to brain clearance processes are associated with clinical progression across the PD continuum and may provide imaging biomarkers relevant to disease staging and pathophysiology.},
}

@article {pmid42277539,
year = {2026},
author = {Xu, R and Huang, X and Chen, Z and Mohamed, HA and He, X and Lu, F and Ou, Y and Li, G and Zhang, K},
title = {Application Mechanisms and Clinical Prospects of Brain-Computer Interface Technology in Radiation-Induced Brain Injury.},
journal = {Cellular and molecular neurobiology},
volume = {},
number = {},
pages = {},
doi = {10.1007/s10571-026-01758-y},
pmid = {42277539},
issn = {1573-6830},
support = {2024A1515011451//Natural Science Foundation of Guangdong Province/ ; 2023-CCA-TCM-033//Li Xin Traditional Chinese Medicine Research and Innovation Fund/ ; BYPDF2411213//Wohua Research Fund/ ; 82271395//the National Natural Science Foundation of China/ ; 0011/2025/RIA1//the Science and Technology Development Fund of Macau/ ; 2023A1515030073//the Guangdong Basic and Applied Basic Research Foundation/ ; 2025A04J4740//the Guangzhou Science and Technology Plan Project/ ; KY0120220133 and DFJHBF202111//the Special Project of Dengfeng Program of Guangdong Provincial People's Hospital/ ; KY012026190//Excellence Initiative Project of the National Natural Science Foundation of China/ ; },
abstract = {Radiation-induced brain injury (RIBI) is a common complication of radiotherapy that leads to neurological symptoms, significantly impairing patients' daily functioning and long-term rehabilitation. Consequently, the development of effective therapeutic strategies has received considerable attention. As an emerging approach in the management of neurological disorders, brain-computer interface (BCI) technology shows substantial potential for both the assessment and treatment of RIBI. This review synthesizes evidence retrieved from electronic databases and examines RIBI from the perspectives of its mechanisms and clinical manifestations. Current findings suggest that BCI technology holds promise for several applications in RIBI, including early diagnosis, mitigation of neuroinflammation, alleviation of associated symptoms, and prediction and management of complications. The implementation of BCI is likely to play a significant role in early assessment and treatment processes for RIBI. Furthermore, with ongoing technological advancements, the development of next-generation BCI is expected to enable more targeted treatment that address additional pathological mechanisms of RIBI, thereby progressively improving the quality of life for affected patients.},
}

@article {pmid42280831,
year = {2026},
author = {Zini, S and Bidone, F and Napoletano, P},
title = {A Robust Multi-Branch CNN-LSTM Architecture for Cross-Subject Motor Imagery Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {11},
pages = {},
doi = {10.3390/s26113310},
pmid = {42280831},
issn = {1424-8220},
support = {PNC0000003//AdvaNced Technologies for Human-centrEd Medicine (ANTHEM)/ ; },
abstract = {Brain-computer interfaces (BCIs) based on motor imagery (MI) aim to convert electroencephalographic (EEG) activity into reliable device commands across users and recording setups. However, low signal-to-noise ratio and strong inter-subject variability still limit true "plug-and-play" deployment without lengthy calibration. To address these challenges, we propose a multi-branch convolutional long short-term memory (CNN-LSTM) architecture that jointly performs multi-scale temporal feature extraction and within-trial sequence modeling. The model employs four parallel 1D convolutional branches with distinct kernel sizes, each followed by an LSTM module and late fusion, combined with group normalization and supervision over sequences of sub-windows within each trial. We evaluate the approach on the EEG Motor Movement/Imagery (EEGMMI) dataset from PhysioNet under strictly subject-independent conditions, and on the ISLab-MI Dataset, a 32-channel wearable-EEG collection designed to assess cross-setup robustness. On EEGMMI, the network achieves up to 82.63% accuracy for binary left/right MI and 74.10% for a four-class task using 4 s trials under 5-fold cross-validation, outperforming an EEGNet-style baseline by 1-10% depending on class count and window length. Under a leave-one-subject-out protocol, the model attains 74.9% mean accuracy for a three-class MI task. Zero-shot transfer to ISLab-MI yields 64.60% and 63.02% accuracy in three- and four-class settings, respectively, while brief subject-specific fine-tuning using only 20% of each session improves performance to 81.38% and 73.48%. These findings show that combining multi-scale convolutional feature extraction with explicit sequence modeling and robust normalization yields accurate, data-efficient, and portable MI decoders suitable for practical BCI applications.},
}

@article {pmid42280857,
year = {2026},
author = {Shen, X and Zhong, H and Gu, Y and Han, R},
title = {DO-PI-EATCNet: Efficient-Attention- and Dream-Optimization-Based Channel Selection for EEG Motor Imagery Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {11},
pages = {},
doi = {10.3390/s26113336},
pmid = {42280857},
issn = {1424-8220},
support = {Grant No. BK20251914//Jiangsu Province Science and Technology Department/ ; Grant No. JC2023072//the Nantong Natural Science Foundation/ ; },
abstract = {Existing deep-learning-based motor imagery (MI) electroencephalogram (EEG) decoding methods face challenges in generalizing across sessions and providing channel-level physiological interpretability. These limitations hinder the practical application of MI-EEG systems. Accordingly, DO-PI-EATCNet (Dream-Optimization-Enhanced, Physics-Inspired, Efficient-Attention Temporal Channel Network) is proposed to improve generalization and interpretability in MI-EEG classification. Unlike models that simply combine multiple components, DO-PI-EATCNet assigns distinct roles to feature representation, temporal channel modeling, temporal regularization, and channel compactness. Latent-Projected Attention (LPA) enhances spatiotemporal discriminability by aligning attention in a low-dimensional latent space, and Temporal Channel Cascaded Collaborative Attention (TCCA) refines dependencies between time and channels. Fractional-Order Difference Temporal Consistency Loss (FD-TCL) is introduced as a neurodynamics-inspired temporal regularizer to reduce high-frequency fluctuations in prediction sequences and improve within-subject cross-session prediction stability. The Multi-Population Dream Optimization Algorithm (MPDOA) is used for channel selection to obtain a compact EEG channel subset and reduce computational load, although it introduces a slight accuracy decrease compared with the uncompressed full model. Under a within-subject cross-session protocol on the BCI Competition IV-2a four-class MI dataset, the final compact model achieves an average accuracy of 84.4% and Cohen's κ of 0.790, outperforming the reimplemented baselines. Compared with the uncompressed LPA-TCCA-FD-TCL variant, MPDOA slightly decreases accuracy from 84.9% to 84.4%, but reduces EEG channels from 22 to about 15 and decreases MACs by 27%. Scalp topographies and selected-channel visualizations provide qualitative support for channel-level anatomical plausibility, as the selected electrodes are mainly located over expected sensorimotor-related regions, while t-SNE offers a descriptive visualization of the learned feature distributions.},
}

@article {pmid42280920,
year = {2026},
author = {Zhang, Y and Gong, X and Yuan, X},
title = {MGFNet: A Multi-Granularity Fusion Network with Coupling-Guided Sparse Routing for Hybrid EEG-fNIRS Decoding.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {11},
pages = {},
doi = {10.3390/s26113402},
pmid = {42280920},
issn = {1424-8220},
support = {62171152//National Natural Science Foundation of China/ ; },
abstract = {Hybrid brain-computer interfaces (BCIs) have attracted growing research attention because they combine the millisecond-level temporal resolution of electroencephalography (EEG) with the spatially informative hemodynamic responses of functional near-infrared spectroscopy (fNIRS). However, most existing deep fusion methods rely on static late-fusion strategies, which tend to underexploit latent cross-modal dependencies and are vulnerable to modality-specific signal degradation. To address these limitations, we propose MGFNet, a multi-granularity fusion network for hybrid BCI decoding. MGFNet contains three components: (1) intra-modal encoders that learn modality-specific spatiotemporal representations from EEG, oxygenated hemoglobin (HbO), and deoxygenated hemoglobin (HbR) signals; (2) cross-modal interaction encoders that temporally align paired modalities and use dilated convolutions to capture long-range EEG-fNIRS dependencies; and (3) a Coupling-Guided Sparse Component Routing (CGSCR) module that estimates sample-specific cross-modal coupling and performs adaptive discrete routing. We further introduce a deep supervision strategy to stabilize optimization and improve branch-level discriminability. Under a within-subject held-out evaluation protocol on a public benchmark dataset, MGFNet achieved classification accuracies of 99.40% on the n-back task and 99.03% on the word generation (WG) task, outperforming representative comparison methods evaluated under a matched protocol. Ablation studies further confirmed the contributions of the intra-modal encoders, the cross-modal interaction encoders, and the CGSCR module. Under controlled EEG corruption with additive white Gaussian noise at -10 dB, MGFNet outperformed a static-fusion variant by 9.23 percentage points on the n-back task and 6.31 percentage points on the WG task. These results support the effectiveness of MGFNet in the present offline within-subject setting and indicate improved robustness under controlled single-modality degradation.},
}

@article {pmid42282632,
year = {2026},
author = {Yoon, S and Avansino, DT and Madugula, S and Levin, AD and Fan, C and Abramovich Krasa, B and Singh, A and Vo, C and Hahn, NV and Card, NS and Fogg, Z and Wairagkar, M and Nason-Tomaszewski, SR and Jacques, BG and Bechefsky, PH and Iacobacci, C and Deo, DR and Hochberg, LR and Brandman, DM and Stavisky, SD and Au Yong, N and Pandarinath, C and Henderson, JM and Willett, FR},
title = {Neural decoding of speech using deep neural ensembles.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.06.02.729705},
pmid = {42282632},
issn = {2692-8205},
abstract = {Speech brain-computer interfaces (BCIs) can restore rapid communication to people with paralysis, but decoding errors still limit performance. In recent brain-to-text decoding competitions, deep ensemble methods, which combine predictions from multiple independently trained decoders, have delivered striking accuracy improvements and account for the largest gains over baseline approaches. However, these methods have not previously been tested in real-time, require substantial computational resources, and their performance under various clinically relevant constraints remains poorly understood. Here, we present the first closed-loop test of deep ensembles in a participant with bilateral intracortical microelectrode arrays, demonstrating a reduction in word error rate from 33.7% to 26.0% on a large-vocabulary task. Using additional data from three participants, we then assess how these gains depend on baseline error rate, training dataset size, and ensemble size, including the resource-accuracy tradeoffs most relevant for real-world deployment. Finally, we introduce a computationally efficient pseudoensembling approach based on test-time augmentation that improves decoding accuracy while requiring only a single base decoder, greatly reducing the computational burden of ensembling. Together, these results show that the benefits of deep ensembling can be realized in real time and under practical resource constraints, bringing speech BCIs closer to broader clinical adoption.},
}

@article {pmid42283210,
year = {2026},
author = {Ding, W and Li, L and Liu, H and Liu, Y and Wang, J and Wang, Z and Tao, L},
title = {BOOI-Defined Obstruction Stratifies Early Outcomes After ThuLEP in Men With Detrusor Underactivity: A Retrospective Complete-Case Cohort Study.},
journal = {Lower urinary tract symptoms},
volume = {18},
number = {4},
pages = {e70073},
doi = {10.1111/luts.70073},
pmid = {42283210},
issn = {1757-5672},
support = {2022cg29//Wuhu Municipal Science and Technology Bureau/ ; },
abstract = {BACKGROUND: The benefit of transurethral outlet surgery in men with benign prostatic enlargement/benign prostatic obstruction (BPE/BPO) and detrusor underactivity (DU) remains uncertain, particularly when bladder outlet obstruction (BOO) is not demonstrated by pressure-flow testing.

OBJECTIVE: To evaluate early outcomes after transurethral thulium laser enucleation of the prostate (ThuLEP) in men with DU and BPE/BPO-related voiding dysfunction, and to examine whether BOOI-defined obstruction status stratifies early postoperative improvement.

METHODS: We retrospectively reviewed 189 ThuLEP records from a single centre and constructed a 100-patient complete-case cohort with interpretable preoperative pressure-flow studies and 3-month follow-up. DU was defined as bladder contractility index (BCI) < 100. BOO status was classified using bladder outlet obstruction index (BOOI) as definite BOO, equivocal BOO or no BOO. Functional, symptom, catheter-removal, anatomical and safety outcomes were compared descriptively across BOO strata.

RESULTS: Ninety-one patients (91.0%) had DU: 56 had definite BOO, 25 had equivocal BOO and 10 had no BOO. In the DU cohort, mean Qmax increased from 3.88 ± 1.76 mL/s at baseline to 9.98 ± 3.68 mL/s at 1 month and 11.03 ± 4.02 mL/s at 3 months (both p < 0.001). Mean PVR decreased from 267.8 ± 167.4 to 142.6 ± 115.3 mL and 124.7 ± 103.4 mL, respectively (both p < 0.001). IPSS and QoL also improved. Because enucleated specimen weight was unavailable, postoperative prostate volume was analyzed as an anatomical surrogate; mean 3-month postoperative prostate volume was 18.8 ± 4.3 mL, corresponding to an absolute volume reduction of 38.0 ± 14.2 mL and a percentage reduction of 65.4% ± 6.3% in the DU cohort. Catheter-removal success was 83.9% in definite BOO, 68.0% in equivocal BOO and 40.0% in no BOO patients (descriptive p = 0.009).

CONCLUSIONS: Preoperative BOOI-based stratification may help counsel men with DU regarding expected early functional improvement after ThuLEP. BOOI-defined no-BOO patients in this cohort were clinically selected after counseling, and the findings should be interpreted as exploratory because of the retrospective design, complete-case selection, short follow-up, and the small no-BOO subgroup.},
}

@article {pmid42182433,
year = {2026},
author = {Cattabriga, M and Alamri, AH and Hobbs, TG and Emonds, AMX and Sobinov, AR and Gaunt, RA and Greenspon, CM and Valle, G},
title = {Spatiotemporal encoding of touch signals in the human somatosensory and motor cortices.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {42182433},
issn = {2692-8205},
abstract = {The sense of touch is fundamental for dexterous manipulation, object interaction, and body awareness. It is primarily processed in the somatosensory cortex (SC), yet our understanding of how tactile information is encoded at the level of neural populations and single neurons in humans remains limited. It is unclear how natural tactile signals are represented in SC and how they may be influenced by visual inputs, as well as how closely sensory and motor cortices interact during passive touch. Here, we investigated the neural basis of touch in the human SC using chronically implanted microelectrode arrays in three participants. By delivering controlled mechanical stimuli, we characterized neural responses to natural touch and mapped detailed somatotopic receptive fields (the patch of skin that elicits neural responses when stimulated) in humans, including multidigit representations. Surprisingly, we also found strong, clearly somatotopic activation in the motor cortex (MC) during passive touch, even in the absence of movement, highlighting a tight and functionally relevant sensorimotor coupling. We further examined how vision shapes tactile processing by comparing neural activity during actual touch with and without vision, and during observation of touch on another person's hand. While touch to the participants' hands elicited robust, event-locked, and somatotopically organized responses in the SC, observation of tactile actions alone did not produce significant activation, suggesting limited vicarious encoding at this level. These findings provide a detailed characterization of human touch processing at the level of neuronal populations and give insights for the design of microstimulation strategies of the SC for the restoration of touch.},
}

@article {pmid42246522,
year = {2026},
author = {Rodionova, KN and Vigovskaya, EA and Novosad, YA and Shabunin, AS and Vissarionov, SV},
title = {[Materials and technologies in neural interfaces: optimization ways for chronic implantation].},
journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova},
volume = {126},
number = {5},
pages = {21-28},
doi = {10.17116/jnevro202612605121},
pmid = {42246522},
issn = {1997-7298},
mesh = {Humans ; *Brain-Computer Interfaces ; *Brain/physiology ; },
abstract = {A neural interface is a set of tools that enable information exchange between the brain and an external device. Such systems are widely used in biomedicine, including the recovery of nervous system functions. This review summarizes the operating principles of neurointerfaces, reviews the materials used in their design, and presents examples of this technology's use in medicine, including chronic implantation.},
}

Humans
*Brain-Computer Interfaces
*Brain/physiology

@article {pmid42266219,
year = {2026},
author = {Gu, T and Li, J and Chen, T and Pan, Y and Yu, Q and Sha, J},
title = {Effect of biofeedback electrical stimulation combined with HoLEP on surgical outcomes in patients with benign prostatic hyperplasia complicated with detrusor underactivity: a retrospective cohort study.},
journal = {Frontiers in surgery},
volume = {13},
number = {},
pages = {1847062},
pmid = {42266219},
issn = {2296-875X},
abstract = {OBJECTIVE: To investigate the clinical efficacy and safety of biofeedback electrical stimulation combined with holmium laser enucleation of the prostate (HoLEP) in the treatment of patients with benign prostatic hyperplasia (BPH) complicated by detrusor underactivity (DUA).

METHODS: A retrospective analysis was conducted on 100 patients with BPH and DUA who had surgical indications and were treated in the Department of Urology of our hospital from January 2023 to June 2025. Patients were divided into an intervention group (n = 51) and a control group (n = 49) according to the treatment modality they received. Patients in the intervention group underwent HoLEP followed by biofeedback electrical stimulation therapy (three times per week for a total of 10 sessions), whereas those in the control group received HoLEP alone. The International Prostate Symptom Score (IPSS), Quality of Life score (QOL), maximum urinary flow rate (Qmax), bladder contractility index (BCI), bladder outlet obstruction index (BOOI), maximum detrusor pressure (Pdetmax), post-void residual volume (PVR), voiding efficiency (VE), and postoperative complications were compared between the two groups before surgery and at 3 months postoperatively.

RESULTS: Baseline characteristics were comparable between the two groups (P > 0.05). At 3 months postoperatively, the intervention group showed significantly higher Qmax (14.38 ± 1.47 mL/s vs. 10.01 ± 0.85 mL/s, P < 0.001) and BCI (111.68 ± 10.15 vs. 93.96 ± 8.42, P < 0.001), significantly lower IPSS (10.8 ± 1.9 vs. 18.6 ± 2.1, P < 0.001) and QOL scores (2.1 ± 0.8 vs. 3.0 ± 0.6, P < 0.001), significantly lower PVR (21.8 ± 5.8 mL vs. 40.2 ± 7.5 mL, P < 0.001), and significantly higher VE (77.8 ± 6.2% vs. 61.9 ± 5.8%, P < 0.001) compared with the control group. The proportion of patients achieving Qmax ≥15 mL/s at 3 months postoperatively was 39.2% in the intervention group vs. 20.8% in the control group (P = 0.022). At 90 days postoperatively, the incidence rates of urinary tract infection (13.7% vs. 28.6%, P = 0.047), urinary incontinence (9.8% vs. 24.5%, P = 0.039), and indwelling catheter reinsertion (2.0% vs. 12.2%, P = 0.037) were significantly lower in the intervention group than in the control group. No significant differences were observed in the incidence of postoperative bleeding or urethral stricture between the two groups (P > 0.05).

CONCLUSION: Biofeedback electrical stimulation combined with HoLEP significantly improves voiding function, clinical symptoms, and quality of life in patients with BPH and DUA, enhances bladder contractility, and reduces the risk of postoperative complications, offering clear clinical benefits and a favorable safety profile, warranting broader clinical adoption.},
}

@article {pmid42266278,
year = {2026},
author = {Achanccaray, D and Clodic, A and Roy, RN},
title = {Error-related potentials detection to enhance human-robot collaboration: a mini review.},
journal = {Frontiers in neuroergonomics},
volume = {7},
number = {},
pages = {1769098},
pmid = {42266278},
issn = {2673-6195},
abstract = {Error-related potentials (ErrPs) have been studied to evaluate wrong decisions or actions in several contexts. An ErrP is an electrical potential on the scalp generated by the perception of errors and occurs unwittingly. In human-robot collaboration (HRC), ErrP detection can be used to trigger a feedback or an action to adapt the system to the user. This contributes to the improvement of HRC, taking into account user performance. However, to our knowledge, the detection of ErrPs in HRC has not been widely explored, resulting in only a few studies. This systematic review will present work on ErrP-based interfaces related to adaptation, control, and neuroergonomics for HRC. Thirteen articles were included after the exclusion criteria of the review stages. The average accuracy of ErrP detection was between 54 and 87.2%. In most cases, the authors simulated the occurrence of unexpected behavior of the robot. The robot mistakes occurred randomly between 20 and 35% of the total trials. Some works focused on the robot learning process and adaptation between humans and robots. The mental model and the robot behavior policy were updated based on the decoded ErrPs during collaborative interactions. Control-related works have included ErrPs detection/features as input inside the control loop or algorithm. Other studies assessed the influence of mental workload variability in the adaptation process, given that a high mental workload affects the cognitive processes needed to perceive errors. Thus, ErrPs present advantages for enhancing HRC, and this review opens the way to further developments in the robotic domain.},
}

@article {pmid42269199,
year = {2026},
author = {Wei, J and Ye, H and Shao, B and Ma, L and Ding, Q and Zhou, Z and Cao, X and Zhong, J and He, H},
title = {A spatiotemporal dependency-aware lightweight CNN-ViT network for 3D MRF with a balanced acceleration strategy.},
journal = {Medical image analysis},
volume = {113},
number = {},
pages = {104147},
doi = {10.1016/j.media.2026.104147},
pmid = {42269199},
issn = {1361-8423},
abstract = {The push for rapid MRI acquisition aims to enhance clinical efficiency and diagnostic consistency by shortening scan times. 3D Magnetic Resonance Fingerprinting (MRF) has emerged as a promising technique for fast, multi-parametric quantitative imaging. However, its accuracy and relatively long acquisition time remain a limiting factor for clinical adoption. Accelerating MRF while preserving quantitative accuracy constitutes a crucial research objective. Deep learning approaches have recently been applied to accelerate MRF parameter quantification, but existing methods still exhibit notable limitations in both acceleration scheme design and the ability to model the complex contextual information embedded in MRF data. To address these limitations, we propose a lightweight spatiotemporal attention enhanced network (LiST-UNet) that integrates convolutional neural networks with lightweight Vision Transformer components to model long-range spatiotemporal dependencies in 3D MRF. A precursor-successor network is included to model interrelationships among tissue parameters, improving T2 quantification accuracy, while a balanced k-space and temporal-frame acceleration strategy significantly reduces errors compared with single-dimension undersampling schemes. Experimental results demonstrate that the proposed method enables whole-brain MRF imaging in approximately 1.25 min, achieving an eightfold acceleration over conventional 10-minute acquisitions with superior quantification accuracy and image quality compared to previously proposed deep learning methods. This work combines architectural improvements in MRF reconstruction with an acceleration strategy, supporting the future clinical translation of 3D MRF.},
}

@article {pmid42270932,
year = {2026},
author = {Shen, JJ and Yang, YJ and Tang, YY and Yan, S and Ying, MD and Chen, WH and Xu, LL},
title = {m[6]A-modified Mid1 promotes sevoflurane-induced cognitive impairment in neonatal mice by ubiquitin-mediated degradation of Syngap1.},
journal = {Experimental & molecular medicine},
volume = {},
number = {},
pages = {},
pmid = {42270932},
issn = {2092-6413},
abstract = {Investigating the cognitive effects of sevoflurane exposure during early development is essential due to its potential long-term neurodevelopmental impacts. This investigation systematically explored the molecular basis of sevoflurane-induced cognitive impairment, with emphasis on m[6]A RNA modifications and ubiquitin-dependent proteostasis involving Mid1 and Syngap1. Using integrated approaches, including methylated RNA immunoprecipitation sequencing (MeRIP-seq), transcriptomic profiling, neurobehavioural testing and molecular analyses, 2091 m[6]A methylation sites were identified that were differentially regulated. Mechanistically, Mid1 was found to orchestrate Syngap1 degradation via the ubiquitin-proteasome pathway, establishing a direct link between protein stability control and cognitive outcomes. Behavioural phenotyping demonstrated that Mid1 suppression ameliorated learning and memory deficits in sevoflurane-exposed mice, which was corroborated by improved neuronal viability and attenuated apoptotic signalling in biochemical assays. Epigenetic regulation studies further revealed that the m[6]A eraser ALKBH5 and the reader YTHDF2 collaboratively modulate Mid1 mRNA stability, thereby contributing to neuropathological progression. Pathway analysis uncovered Mid1-Syngap1 axis-mediated dysregulation of MAPK signalling cascades, proposing this network as a potential therapeutic target. Collectively, the present findings delineated a novel m[6]A-ubiquitin regulatory circuit centred on Mid1 that drives sevoflurane-associated cognitive dysfunction, offering mechanistic insights for the development of neuroprotective interventions against anaesthesia-related neurotoxicity in paediatric and other at-risk populations.},
}

@article {pmid42270979,
year = {2026},
author = {Wang, J and Yang, C and Chang, S and Jiao, D and Lin, J and Yang, X and Cai, W and Ma, D and Ding, ZJ and Huang, J and Huang, J and Fan, M and Hu, M and Wang, Y and Xu, H and Su, N and Guo, J},
title = {Cryo-EM structures of Drosophila OR67d-Orco complexes reveal insect pheromone sensing mechanism.},
journal = {Cell research},
volume = {},
number = {},
pages = {},
pmid = {42270979},
issn = {1748-7838},
support = {32371204//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32421001//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32371300//National Natural Science Foundation of China (National Science Foundation of China)/ ; 2024T170801//China Postdoctoral Science Foundation/ ; },
abstract = {Pheromones mediate intraspecific communication to regulate the physiology and behavior of animals, particularly insects. The detection of pheromones is initiated by the binding of pheromone molecules, e.g., 11-cis-vaccenyl acetate (cVA) in Drosophila, to specific receptor proteins in chemosensory neurons, but the underlying molecular mechanisms remain unclear. Here, we report structures of Drosophila pheromone receptor OR67d-Orco complexes in apo closed, pheromone-bound open, and synthetic agonist VUAA1-bound open conformations. OR67d and Orco assemble into a hetero-tetrameric channel with a 1:3 stoichiometry. In OR67d, the inverted L-shaped cVA or its analog binds into a deep and bent hydrophobic pocket, inducing both local and global conformational changes that lead to an asymmetrical opening of the channel gate. By comparison, VUAA1 binds to Orco instead of OR67d to cause a similar asymmetrical opening. Together, our studies reveal the structural basis for pheromone activation of hetero-tetrameric pheromone receptors.},
}

@article {pmid42271499,
year = {2026},
author = {Shi, Z and Yuan, Z and Gao, L and Hu, Y and Ni, G and Liao, W and Xie, Y and He, J and Xiao, D and Chen, X and Wang, Z},
title = {Mixed reality assisted target localization for transcranial magnetic stimulation navigation: a feasibility study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-026-02045-z},
pmid = {42271499},
issn = {1743-0003},
support = {3502Z20254ZD1008//Xiamen Municipal Guiding Project for Medical and Health Services/ ; },
abstract = {BACKGROUND: Transcranial magnetic stimulation (TMS), as a non-invasive neurostimulation technique, modulates neural activity by applying electromagnetic fields to specific areas of the brain. It is clinically used for several approved indications, including major depressive disorder, obsessive‑compulsive disorder, and migraine with aura, and is under active investigation for other neurological and psychiatric conditions. Accurate stimulation targeting is crucial for the effectiveness of TMS. Existing targeting methods, such as generic brain localization caps and the international 10-20 electroencephalogram (EEG) system, generally provide only rough localization, leading to significant targeting errors. In recent years, significant progress has been made in the application of mixed reality (MR) technology in medicine, particularly in surgical navigation, offering new ideas and possibilities for developing a simple, low-cost, and efficient TMS navigation system.

OBJECTIVE: This study proposes, for the first time, a portable MR navigation system for non-invasive neural modulation target localization. The aim is to evaluate its localization accuracy and operational efficiency in TMS through preclinical validation. This system seeks to provide a simple and high-precision localization solution for other non-invasive technologies, with the goal of improving localization accuracy and simplifying the operational workflow in clinical applications.

METHODS: The system is based on Microsoft HoloLens 2 and features three specifically designed interaction tools. Five different types of simulation head models were selected, and ten target points were set on each head model. CT scanning was used to obtain imaging data for each head model. Three researchers used the system to perform target localization and repeated the verification process by adjusting the head model posture (from standing to lying) to assess localization accuracy and efficiency.

RESULTS: The validation conducted by the three researchers showed the following results: In the standing position of the simulated head model, the measurement errors were 2.4 (IQR: 1.4-2.7) mm, 2.3 (IQR: 1.7-2.7) mm, and 2.6 (IQR: 1.9-3.0) mm, respectively. In the lying position of the simulated head model, the measurement errors were 1.9 (IQR: 1.6-2.4) mm, 2.0 (IQR: 1.4-3.0) mm, and 2.5 (IQR: 1.9-2.9) mm, respectively. There was a significant difference between researchers (p < 0.05), but no significant difference within the same researcher (p > 0.05).

CONCLUSION: The TMS-Guide, based on mixed reality technology, is a portable and simple navigation solution that provides higher localization accuracy than traditional manual targeting. It shows promising potential for broader applications in non-invasive neural modulation and brain-computer interface fields.},
}

@article {pmid42271590,
year = {2026},
author = {Imura, T},
title = {[Communication Support for Neurological Disorders].},
journal = {Brain and nerve = Shinkei kenkyu no shinpo},
volume = {78},
number = {6},
pages = {701-705},
doi = {10.11477/mf.188160960780060701},
pmid = {42271590},
issn = {1881-6096},
abstract = {Communication support for patients with neurological disorders extends beyond high-technology communication aids. It also includes non-aided and low-technology methods and requires flexible selection and the combined use of these methods depending on the situation. Gaining experience with various communication strategies in a stepwise manner from an early stage enables the smoother introduction of advanced communication devices when necessary. Effective support must be tailored to the disease stage, as communication abilities and needs change over time. In this context, collaboration among multiple professionals is essential. Such interprofessional collaboration enables appropriate assessment, timely intervention, and continuity of care across disease stages. A team-based, continuous support system benefits patients, and caregivers and professionals involved in their care. By sharing knowledge, skills, and responsibilities within a support team, the burden on individual supporters can be reduced, and the quality and consistency of communication support can be enhanced. Looking to the future, further development of emerging technologies such as eye-gaze input systems, personalized speech synthesis, and brain-machine interfaces is highly anticipated. However, careful consideration of their characteristics, limitations, and potential risks is necessary to ensure their safe and effective use in clinical practice.},
}

@article {pmid42272373,
year = {2025},
author = {Aabideen, M and Ashokan, A and Nivargi, SM and Ramzan, M and Boddu, D and Aboobacker, F and Odat, A and Taher, M and Mohamed, R and Cingapagu, R and Al Shamsi, HO and Aabideen, Z},
title = {Single Centre Experience of Treating Children with Cancer in the United Arab Emirates.},
journal = {The Gulf journal of oncology},
volume = {1},
number = {49},
pages = {80-84},
pmid = {42272373},
issn = {2521-3881},
abstract = {INTRODUCTION: Cancer is a leading cause of death in children. Advancements in medical sciences have significantly improved childhood cancer outcome. However, the pattern of malignancy and outcomes of childhood cancer in the UAE have not been published in the literature in the recent period. Therefore, we aim to investigate this in a leading cancer institute in Abu Dhabi, United Arab Emirates (UAE).

METHODOLOGY: This is a retrospective study. We collected data including diagnosis; age at diagnosis; treatments used e.g., chemotherapy, radiation, surgery, immunotherapy and bone marrow transplant (BMT) and outcomes. Overall survival (OS) and Event-Free Survival (EFS) were estimated using the Kaplan Meier method.

RESULTS: There are 82 children with cancer. The male-tofemale ratio is 1.2. Most patients (45%) were diagnosed between one to five years of age. The most common malignancies are B cell Acute Lymphoblastic Leukaemia (32, 39%), brain tumours (12, 15%); Neuroblastoma (9, 10%), Hodgkin Lymphoma (8, 9%) and Wilms Tumour (5, 6%); Acute Myeloid Leukaemia (3, 4%); Non-Hodgkin Lymphoma (3, 4%); Ewing Sarcoma (3, 4%); T ALL (2, 2%); Osteosarcoma (2, 2%); Angiosarcoma (1, 1%); Synovial Sarcoma (1, 1%). 28 (34%) of patients had completed all treatment at Burjeel Cancer Institute (BCI); 15 (18%) had completed their treatment at another centre and attending follow-up at BCI; and 5 (6%) commenced their treatment at BCI and were transferred to another centre. 34 (41%) but currently still undergoing treatment at BCI. The abandonment rate is 0%. Overall survival is 94% and event-free survival is 90%.

DISCUSSION: The rapid progression of UAE cancer care over the past four decades has contributed massively to our favourable survival outcomes. Radiation therapy, bone marrow stem cell transplantation, strict medication regulation and monitoring by the UAE Department of Health have been established in recent years to further enhance treatment for cancer patients in the UAE.

CONCLUSION: The results of our study are comparable to the international standard. More studies involving multiple centres in the UAE are needed to ascertain the exact pattern of paediatric malignancy and outcomes in the UAE.},
}

@article {pmid42274110,
year = {2026},
author = {Chen, HL and Zou, S and Zheng, LL},
title = {Brain-Computer Interface Applications in Craniofacial Nerve Functional Reconstruction: A Narrative Review.},
journal = {The Journal of craniofacial surgery},
volume = {},
number = {},
pages = {},
doi = {10.1097/SCS.0000000000013038},
pmid = {42274110},
issn = {1536-3732},
abstract = {BACKGROUND: Brain-computer interface (BCI) technology is increasingly relevant to craniofacial nerve functional reconstruction because it can decode cortical motor intent and convert it into physical or digital output when peripheral motor pathways are impaired. Facial nerve palsy, dysphagia, and oromandibular motor dysfunction remain difficult to treat when conventional nerve repair, muscle transfer, or electrical stimulation cannot restore coordinated and natural movement.

METHODS: This narrative review synthesized peer-reviewed literature on BCI-related craniofacial functional reconstruction. A targeted search of PubMed, Embase, Web of Science, and Google Scholar was performed, covering English-language articles published from 2014 to April 12, 2026. Eligible core articles addressed BCI-based facial motor restoration, swallowing or oromandibular BCI paradigms, speech or orofacial neuroprosthetics, neural interface integration in craniofacial surgery, flexible facial bioelectronic sensing, or functional electrical stimulation systems with direct relevance to craniofacial nerve recovery. Background literature was cited separately to contextualize disease burden, conventional reconstruction, dysphagia, outcome assessment, calibration, and neuroethical issues.

RESULTS: Twenty-two core articles were included in the final thematic synthesis and organized into 3 domains: facial expression motor reconstruction, oromandibular and swallowing rehabilitation, and neural interface integration in craniofacial surgery. EEG-based facial-expression decoding has shown promising accuracy under controlled laboratory conditions, speech neuroprosthetics provide potentially transferable frameworks for orofacial motor decoding that remain unproven in facial palsy or dysphagia rehabilitation, swallowing motor-imagery studies support physiological feasibility for dysphagia-oriented BCI, and flexible facial biosensors may support future closed-loop systems.

CONCLUSIONS: BCI technology should be regarded as a potential complement to conventional craniofacial reconstruction rather than a replacement for established surgical techniques. Current evidence supports technical feasibility, but clinical translation will require naturalistic decoding, durable interfaces, faster patient-specific calibration, meaningful outcome measures, and early attention to ethical issues.},
}

@article {pmid42274641,
year = {2026},
author = {Shahbaz, H and Sherman, AB and Shaikh, FA and Elsawwah, JK and Charles, EJ and Curran, T and Nemeth, ZH},
title = {Risk factors and outcomes of blunt cardiac injury in adult motor vehicle collision patients.},
journal = {Traffic injury prevention},
volume = {},
number = {},
pages = {1-5},
doi = {10.1080/15389588.2026.2674238},
pmid = {42274641},
issn = {1538-957X},
abstract = {OBJECTIVES: Motor vehicle protective equipment, such as seatbelts and airbags, has improved occupant safety. However, while seatbelts reduce facial and abdominal injuries, they may not significantly prevent head, neck, or thoracic trauma. Limited data exist on blunt cardiac injury (BCI). This study evaluated patterns of BCI, associated thoracic injuries, and hospital outcomes in adult trauma patients following motor vehicle collisions (MVCs).

METHODS: We analyzed the 2023 American College of Surgeons Trauma Quality Improvement Program (ACS-TQIP) database for adult MVC occupants. Abbreviated Injury Scale codes 4208xx.x, 4404xx.x, 4410xx.x, 4412xx.x, 4413xx.x, and 4416xx.x identified patients with BCI. Those without BCI formed the reference cohort. A 1:1 propensity score match (PSM) on Injury Severity Score (ISS) was performed using RStudio to balance collision severity.

RESULTS: In the overall cohort, the incidence of BCI was 1.2% (1,914/161,446). After PSM, 1,914 patients remained in each cohort with a mean ISS of 22.7. Both seatbelt plus airbag use and airbag use alone were independently associated with increased odds of BCI. BCI was strongly associated with thoracic injuries, including sternum fracture (odds ratio [OR] 3.492; 95% CI 2.95-4.14), hemothorax (OR 2.928; 95% CI 2.29-3.75), thoracic aortic injury (OR 1.773; 95% CI 1.29-2.44), and pulmonary contusion (OR 1.382; 95% CI 1.18-1.62). In multivariable analysis with BCI as the outcome, mortality (OR 2.325; 95% CI 1.93-2.79) and cardiac arrest (OR 1.827; 95% CI 1.29-2.59) were independently associated with BCI.

CONCLUSION: Protective equipment use correlates with BCI and thoracic trauma. In MVC patients using seatbelts and airbags, concomitant chest injuries should heighten suspicion for BCI and prompt further evaluation.},
}

@article {pmid42275342,
year = {2026},
author = {Abdollahpour, N and Artan, NS},
title = {EC-Transformer: Connectivity-Informed Embeddings and Adaptive Gating for fNIRS.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3702505},
pmid = {42275342},
issn = {2168-2208},
abstract = {Functional Near-Infrared Spectroscopy (fNIRS) provides a non-invasive modality for monitoring brain activity, yet jointly modeling temporal dynamics and inter-regional interactions remains challenging for accurate brain-computer interface (BCI) decoding. This study proposes an Effective Connectivity Transformer (EC-Transformer), which integrates connectivity-informed representations into transformer-based modeling of fNIRS signals. The architecture combines a time- wise embedding that captures temporal dynamics using positional encoding and bidirectional LSTMs with a connectivity-based embedding that encodes low-frequency directed dependency patterns. An adaptive gating mechanism dynamically fuses these representations during classification. The model was evaluated using leave-one-subject-out validation on two public fNIRS datasets involving mental arithmetic and motor execution tasks, achieving accuracies of $76.83 \pm 2.4$ and $76.03 \pm 2.00$, respectively. The proposed framework demonstrates competitive performance relative to existing transformer-based approaches while maintaining substantially lower model complexity (approximately 0.7 M parameters compared to 1.7M-3.5 M in prior models). Ablation and control analyses further suggest that EC-based embeddings provide connectivity-informed representations that complement temporal modeling while maintaining competitive decoding performance. Interpretability analyses revealed task-related connectivity patterns broadly consistent with distributed cognitive and motor-related networks. Overall, the findings suggest that incorporating connectivity-informed representations can provide physiologically structured complementary information for transformer-based fNIRS decoding while maintaining competitive performance and computational efficiency.},
}

@article {pmid42275895,
year = {2026},
author = {Chen, W and Daly, I and Chen, Y and Li, J and Wu, X and Zhao, R and Wang, X and Cichocki, A and Jin, J},
title = {EDSF-Net : An enhanced dynamic spatiotemporal-frequency attention network for robust EEG decoding in motor imagery.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {204},
number = {},
pages = {109197},
doi = {10.1016/j.neunet.2026.109197},
pmid = {42275895},
issn = {1879-2782},
abstract = {Motor imagery is a non-invasive process that operates independently of external stimuli, and can be used to establish a direct connection between the brain and external devices solely through the imagination of a specific movement. Nonetheless, the complexity and variability of neural patterns pose substantial challenges, as accurately decoding motor imagery from electroencephalography signals remains a significant obstacle. This paper introduces an enhanced dynamic spatiotemporal -frequency attention convolutional neural network (EDSF-Net) for the precise decoding of motor imagery. EDSF-Net employs a refined spatiotemporal attention mechanism, grounded in enhanced dynamic convolution (EDConv), to emphasize localized spatial features alongside high and low-frequency temporal characteristics. Subsequently, EDConv is utilized for global spatial feature extraction. Following this, group convolutions formed by EDConv are implemented to fuse the extracted features effectively. Ultimately, a synchronized channel-frequency attention mechanism is employed to capture critical channel and frequency domain information, facilitating the model's focus on features most pertinent to the task throughout the learning process. We conducted a comprehensive evaluation of the performance of EDSF-Net on two public datasets, BCI Competition IV 2a and OpenBMI. In the hold-out session experiments, EDSF-Net achieved decoding accuracies of 84.26% and 75.14%, respectively. In the leave-one-subject-out experiments, EDSF-Net attained decoding accuracies of 66.78% and 82.24%, respectively. These results show that EDSF-Net has robust generalization capabilities, affirming its efficacy in addressing complex pattern recognition tasks, with significant potential for diverse applications.},
}

@article {pmid42275898,
year = {2026},
author = {Cheng, C and Zhang, J and Cheng, Y and Jia, Z and He, W},
title = {MS-STGAN: A dual-branch multi-scale spatio-temporal generative adversarial framework for incomplete EEG-based emotion recognition.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {204},
number = {},
pages = {109203},
doi = {10.1016/j.neunet.2026.109203},
pmid = {42275898},
issn = {1879-2782},
abstract = {Electroencephalography (EEG) enables high-resolution emotion recognition but often suffers from incomplete data in real-world scenarios due to sensor failures or preprocessing errors. To this end, we propose a Multi-Scale Spatio-Temporal Generative Adversarial Network (MS-STGAN). Specifically, we first apply random masking to the EEG channels data to simulate missing data conditions in practical environments. Then, we design a spatio-temporal dual-branch generator to reconstruct complete representations: the spatial branch employs graph convolutional networks (GCNs) to capture robust inter-regional dependencies, while the temporal branch leverages the BiMamba state space model to encode the dynamic evolution of emotions. To further enhance feature learning, multi-scale 2D convolution and deconvolution layers are incorporated before and after both branches, enabling the extraction of diverse spatio-temporal features. Additionally, we introduce a generative adversarial framework, where the generator restores informative features from incomplete inputs and the discriminator enforces the authenticity of reconstructed data. Finally, a fusion module integrates the outputs of both branches for downstream classification. Extensive experiments on the DEAP and SEED-IV datasets validate the effectiveness of each component and demonstrate that MS-STGAN achieves superior performance and strong generalization ability.},
}

@article {pmid42276068,
year = {2026},
author = {Kistler, W and Fakhreddine, R and Rodriguez, GR and Hayward, M and Buch, ER and Bestmann, S and Cohen, LG},
title = {Early skill learning is shaped by the offline emergence of expert synergies.},
journal = {Current biology : CB},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cub.2026.05.032},
pmid = {42276068},
issn = {1879-0445},
abstract = {Everyday skilled actions depend on the formation of coordinated motor synergies that integrate multiple digits into stable, low-dimensional control units. Although initial practice of a new skill leads to rapid performance improvements, it is unclear whether the underlying movement kinematics reorganize on a similar timescale or in a way that directly relates to these gains. It also remains uncertain whether such reorganization occurs mainly during active practice or instead during brief rest breaks. Here, we tracked the temporal evolution of multi-digit synergy formation during early learning of a naturalistic keypress skill. Initial practice rapidly sculpted the motor repertoire toward higher-order, temporally compressed, and overlapping multi-digit synergies. These synergies emerged after only minutes of practice and continued to be expressed throughout the full training session. Notably, they were primarily shaped across brief rest breaks and robustly predicted individual skill proficiency. Across learning, distinct synergy subtypes emerged that differed in their heuristic prevalence. Rarely expressed synergies reflected transient novice patterns, synergies expressed at intermediate levels could index exploratory and trial-initiation strategies, and highly expressed synergies emerged later to dominate performance, reflecting the consolidation and expansion of skilled motor control. Together, these findings indicate that skilled performance is supported by the early formation of a compact repertoire of expert multi-digit synergies that emerge preferentially across rest periods and predict subsequent skill gains. They further raise the hypothesis that explicitly training such expert synergies alongside task goals could enhance learning in domains such as the arts, sports, and neurorehabilitation.},
}

@article {pmid42262704,
year = {2026},
author = {Luo, N and Yang, Z and Song, M and Di, S and Chu, C and Shi, W and Yue, W and Zhang, Y and Yan, H and Zhang, X and Zhang, D and Sui, J and Calhoun, V and Jiang, T},
title = {Measuring the Impacts of Urbanicity and Different Exposome Factors on Human Brain through Exposure Network Mapping.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {42262704},
issn = {1995-8218},
abstract = {While urbanicity increases the risk of mental health issues, its effects on brain networks are heterogeneous and underexplored in relation to different exposome factors. Using a coordinate network mapping strategy termed exposure network mapping (ENM) across eight datasets, this study first consolidated heterogeneous findings of urbanicity to a significant, replicable network involving the middle frontal gyrus, orbital gyrus, and anterior cingulate gyrus. Afterwards, among the other factors examined (air pollution, noise, income, stress, green space), only stress converged into a distinct common network, highlighting the orbital gyrus, caudate, anterior/middle cingulate gyrus, hippocampus, and middle frontal gyrus. This ENM-stress map exhibited the highest correlation with both the ENM-urbanicity map (r = 0.77) and a transdiagnostic map (r = 0.72). In addition, sleep-related coordinates also formed a consistent network, involving the middle cingulate gyrus, orbital gyrus, caudate, and putamen, which correlated strongly with urbanicity (r = 0.75), stress (r = 0.80), and the transdiagnostic pattern (r = 0.55). Collectively, this study highlights the potential risks of urbanicity and stress, as well as the protective role of sleep on brain networks, which may offer new insights for preventing mental health issues in urban environments.},
}

@article {pmid42262958,
year = {2026},
author = {Mrachacz-Kersting, N and Pasluosta, C and Meyer, B and Nascimento, OFD and Stieglitz, T and Farina, D},
title = {Cortical Activity Associated with Phantom Leg Movements.},
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.2026.3701504},
pmid = {42262958},
issn = {1558-0210},
abstract = {We tested the feasibility for amputees to control artificial limbs using non-invasive electroencephalography (EEG). Thirteen participants engaged in attempts of isometric ankle plantar-flexions using their phantom or intact limb at slow or ballistic speeds. EEG data were analyzed for movement-related cortical potentials (MRCPs), the slow negative potentials related to the planning and execution of movements. We focused on temporal profiles and single-trial classification at electrode location Cz where MRCPs are most prominent. Distinctly different MRCP morphologies were observed for both movement speeds and phantom versus intact limbs. Crucially, time since amputation correlated significantly with classification errors for distinguishing tasks performed with the intact limb from those of the phantom limb (R = 0.36, p =0.004) and movement speed during trials of only the phantom limb (R = -0.33, p = 0.01). Here we show the persistent capacity of amputees to plan and attempt to execute limb motions at varying speeds using their phantom limb. This has implications for understanding neural adaptations over extended post-amputation periods and for the practical implementation of the MRCP in the design of brain-computer interfaces to control prosthetic devices using single-electrode EEG recordings.},
}

@article {pmid42263493,
year = {2026},
author = {Offenberg, EC and Berezutskaya, J and Müller, L and Freudenburg, ZV and Ramsey, NF and Vansteensel, MJ},
title = {Optimal positioning and size of high-density electrocorticography grids for speech brain-computer interfaces.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {190},
number = {},
pages = {2111940},
doi = {10.1016/j.clinph.2026.2111940},
pmid = {42263493},
issn = {1872-8952},
abstract = {OBJECTIVE: Speech-based brain-computer interfaces (BCIs) can offer an intuitive communication method for those who have lost the ability to speak due to paralysis. Significant progress has been made in classifying individual words from high numbers of electrocorticographic (ECoG) electrodes on the sensorimotor cortex (SMC). As implantations of larger grids with more ECoG electrodes are associated with higher surgical risk, we investigated whether confined electrode configurations can match the classification accuracy of larger grids.

METHODS: We analyzed data from eight able-bodied participants with high-density ECoG grids (64 to 128 electrodes) performing a 12-word repetition task in Dutch.

RESULTS: Word pronunciation elicited high frequency band activity in two SMC foci: one ventral, one dorsal. Smaller, rectangular configurations with surface areas of 325 mm[2] to 561 mm[2] (32 electrodes) could achieve similar word classification accuracies as larger grids: 76 ± 16% versus 75 ± 17% across participants, respectively (practical chance level 16.7%). The best configurations were oriented vertically and centered on the central sulcus.

CONCLUSION: These findings indicate that a 32-electrode ECoG grid placed optimally can be sufficient for achieving high word classification accuracy on a closed set of words.

SIGNIFICANCE: These findings support the targeted placement of small ECoG grids, reducing surgical demands on end users and justifying energy- and complexity-efficient designs of fully implantable BCI devices for individuals with severe paralysis.},
}

@article {pmid42264810,
year = {2026},
author = {Haroon, N and Jabbar, H and Jeong, TT and Khan, US and Rashid, N and Naseer, N},
title = {Investigating cognitive fatigue recovery through mechanical massage and binaural beats: An AI-driven fNIRS study.},
journal = {Journal of bodywork and movement therapies},
volume = {47},
number = {},
pages = {305-331},
doi = {10.1016/j.jbmt.2026.04.004},
pmid = {42264810},
issn = {1532-9283},
abstract = {Cognitive fatigue is a state of reduced mental performance resulting from prolonged periods of cognitive activity. It is characterized by a sense of tiredness that reduces decision-making abilities. To date, there remains a significant gap in classifying cognitive fatigue under the influence of mechanical massage via massage chair and binaural beats brain massage aided by functional Near-Infrared Spectroscopy. Our aim is to explore the impact of mechanical and binaural brain massage on cognitive fatigue recovery whilst carrying out an extensive comparative analysis of the efficacy of the existing Deep Learning (DL) models alongside conventional Machine Learning (ML) models. The experimental paradigm is consisted of two treatments: Treatment A (Control (General Rest) Group) and B (Experimental Group). Real-time data acquisition of 10 test subjects before and after both treatments is being done. Following a meticulous features extraction protocol, a comprehensive set of 8 DL and 8 ML models is utilized, and their performance is evaluated through a comparative analysis. The categorical results unequivocally demonstrate that Temporal Convolutional Network achieves superior performance by outperforming other DL models, boasting a remarkable accuracy of 97% and 96.52% for Treatment A and B, respectively. Likewise, Support Vector Machine with Radial Basis Function overtakes other ML models by yielding 91.00% and 87.50% accuracy for Treatment A and B, respectively. Upon evaluation of models' performance in Brain-Computer Interface application, it's been concluded that mechanical massage along with binaural beats significantly helps to relieve mental fatigue, enhance working memory, and mental vigilance.},
}

@article {pmid42265105,
year = {2026},
author = {Gracia, DI and Iáñez, E and Ortiz, M and Azorín, JM},
title = {An Electrospinography Database of Gait-Related Tasks and Motor Imagery Exercises.},
journal = {Scientific data},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41597-026-07592-7},
pmid = {42265105},
issn = {2052-4463},
abstract = {This study presents a dataset of electrospinography (ESG) signals recorded from the human spinal cord during gait-related activities and motor imagery tasks. The dataset was acquired as part of a broader initiative to develop a spinal-machine interface (SMI) for closed-loop control of lower limb exoskeletons. ESG signals were collected using high-density surface electromyography (HD-sEMG) electrodes from fourteen able-bodied participants performing baseline trials (2), movement execution tasks (12) and motor imagery tasks conducted both in static conditions (5) and during movement (5). The dataset encompasses multiple electrode configurations targeting the brachial and lumbar plexuses, as well as surrounding musculature, across three experimental protocols. A total of 10 sessions were recorded for Experiment 1 (one 64-electrode matrix), 10 sessions for Experiment 2 (two 32-electrode matrices) and 5 sessions for Experiment 3 (two-32 electrode matrices). Preprocessing techniques were applied to mitigate cardiac and motion artifacts. The data provides a valuable and pionneering resource for advancing neurorehabilitation research, allowing the refining of exoskeleton control strategies and improving artifact removal methods.},
}