@article {pmid41557476,
year = {2026},
author = {Kristen, R and Lenarz, T and Keintzel, T and Sprinzl, G and Köstler, C and Knoelke, N and Busch, S and Raffelsberger, T and Magele, A and Schörg, P and Wiek, RJ},
title = {Lifetime Real-World Evidence on Safety and Performance of the First Active Transcutaneous Bone Conduction Implant (BCI), the Bonebridge Covering Conductive to Mixed Hearing Loss (CMHL), and Single-Sided Deafness (SSD): Results From a Long-Term Retrospective Analysis.},
journal = {Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {},
number = {},
pages = {},
doi = {10.1097/MAO.0000000000004837},
pmid = {41557476},
issn = {1537-4505},
abstract = {OBJECTIVE: Confirm the safety and performance of the first partially implantable active transcutaneous Bone Conduction Implant (tBCI) in patients who have been implanted for a minimum of 5 years before 2023.

SETTING: Otolaryngology departments of 4 German and Austrian hospitals.

STUDY DESIGN: Retrospective, multicenter, longitudinal, open-label case series study. Patients: 186 ears treated for conductive and mixed hearing loss (CMHL), or single-sided deafness (SSD) implanted for 5 years (151 aged 18 y or older, 35 aged 5 to 17 y) at the time of implantation.

INTERVENTION: Implantation of the Bonebridge (BB) BCI 601, a partially implantable active middle ear implant (AMEI).

MAIN OUTCOME MEASURES: Patients' audiometric pure-tone average (PTA4) (0.5, 1, 2, 4 kHz) thresholds (bone conduction, sound field) and speech perception (word recognition scores) were retrospectively collected up to 10 years 10 months postoperatively. Complications were recorded with focus on revision surgery and explantations. Subgroups were adults and children.

RESULTS: Safety was established by stable bone conduction (BC) thresholds 5 years after implantation or later with mean paired differences of -5.33 dB for adults and -8.05 dB for children and underscored by a low number of technical failures and high survival rates 10 years after implantation. Paired mean sound field PTA4 thresholds and word recognition scores significantly improved as tested by post hoc analysis 5 years or later after implantation, with functional gains for CMHL of 23.44 dB (adults), 27.69 dB (children), and word recognition scores of 58.22% (adults), 80.00% (children). Furthermore, mean sound field PTA4 thresholds and word recognition scores remain significantly improved over time at 36.37 dB HL and 68.75% 5 years or later after implantation as tested with linear mixed-effects model.

CONCLUSIONS: The findings of this study demonstrate that this tBCI remains safe and effective for up to 10 years.},
}

@article {pmid41555272,
year = {2026},
author = {An, Q and Cao, M and Zhang, J and Liu, D and Jialin, A and Chen, D and Wang, J and Wang, C and Zhao, X and Wang, D and Li, K and Zhang, D and Deng, W},
title = {Spatiotemporal disruption of prefrontal dynamics during affective association in depression: an fNIRS case-control study.},
journal = {BMC psychiatry},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12888-026-07800-z},
pmid = {41555272},
issn = {1471-244X},
}

@article {pmid41554257,
year = {2026},
author = {Althoff, J and Nogueira, W},
title = {Selective auditory attention decoding in bilateral cochlear implant users to music instruments.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae3a1a},
pmid = {41554257},
issn = {1741-2552},
abstract = {Electroencephalography (EEG) data can be used to decode an attended sound source in normal-hearing (NH) listeners, even for music stimuli. This information could steer the sound processing strategy for cochlear implants (CIs) users, potentially improving their music listening experience. The aim of this study was to investigate whether selective auditory attention decoding (SAAD) could be performed in CI users for music stimuli. Approach: High-density EEG was recorded from 8 NH and 8 CI users. Duets containing a clarinet and cello were dichotically presented. A linear decoder was trained to reconstruct audio features of the attended instrument from EEG data. The estimated attended instrument was selected based on which of the two instruments had a higher correlation to the reconstructed instrument. EEG recordings are challenging in CI users, as these devices introduce strong electrical artifacts. We also propose a new artifact rejection technique that employs ICA calculating ICs and automating their selection for removal, which we termed ASICA. Main results: We showed that it was possible to perform SAAD for music in CI users. The decoding accuracies were 59.4 \% for NH listeners and 60 \% for CI users with the proposed algorithm. Using the proposed algorithm, the correlation coefficients between the reconstructed audio feature and the attended audio feature were improved in conditions where artifact was dominating. Significance: Results indicate that selective auditory attention to musical instruments can be effectively decoded, and that this decoding is enhanced by the new artifact reduction algorithm, particularly in scenarios where the cochlear implant's electrical artifact has greater influence. Moreover, these results could be relevant as an objective measure of music perception or for a brain computer interface that improves music enjoyment. Additionally we showed that the stimulation artifact can be suppressed. The ethic's committee of the MHH approved this study (8874_BO_K_2020).},
}

@article {pmid41553892,
year = {2026},
author = {Lim, H and Choi, H and Ahmed, B and Park, Y and Ku, J},
title = {Attention-Adaptive BCI-AOT System Enhances Motor Recovery and Neural Engagement After Stroke.},
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.2026.3654935},
pmid = {41553892},
issn = {1558-0210},
abstract = {Stroke frequently results in long-term motor deficits that impair quality of life. Action observation therapy (AOT) has shown promise for motor recovery through engagement of the mirror neuron system (MNS), yet its passive nature and lack of attentional tracking limit its neuroplasticity efficacy. To address these limitations, we developed a closed-loop Brain-Computer Interface-integrated AOT (BCI-AOT) system employing real-time Steady-State Visual Evoked Potential (SSVEP)-based attention monitoring to dynamically control therapy delivery. In a within-subject crossover study, 22 individuals with hemiplegic stroke completed both BCI-AOT and conventional AOT conditions, each consisting of five daily sessions and separated by a one-week washout. In BCI-AOT, video playback depended on sustained attentional engagement detected via SSVEPs. Behavioral outcomes (Box and Block Test [BBT], Action Research Arm Test [ARAT]) and physiological measures (Motor Evoked Potential [MEP] amplitude and latency, EEG power) were assessed pre- and post-intervention. Significant Condition × Day interactions were found for both BBT and ARAT, indicating greater functional gains over time in the BCI-AOT condition. Both conditions showed increased corticospinal excitability over time, while BCI-AOT additionally exhibited distinct mu and theta modulation over time. Participants also reported greater motivation and attention after BCI-AOT compared to conventional AOT. These results suggest that BCI-AOT elicits stronger neuroplasticity responses and user engagement than standard AOT. This study supports the feasibility and clinical potential of closed-loop, attention-adaptive neurorehabilitation for stroke recovery.},
}

@article {pmid41553833,
year = {2026},
author = {Yang, Y and Su, Z and Liu, X and Song, J and Li, C and Xu, Y and Ye, J and Lin, LL},
title = {A flexible plasmonic SERS hydrogel patch for metabolite sensing on bio-interfaces.},
journal = {Nanoscale},
volume = {},
number = {},
pages = {},
doi = {10.1039/d5nr04403k},
pmid = {41553833},
issn = {2040-3372},
abstract = {The growing demand for real-time, non-invasive monitoring of biochemical molecules has driven the development of advanced, flexible sensing materials. Surface-enhanced Raman spectroscopy (SERS) offers high molecular specificity and ultralow detection limits. While rigid SERS substrates based on plasmonic nanoparticle arrays provide strong signal enhancements, they lack the mechanical compatibility and conformal adhesion required for dynamic biological surfaces, such as human skin or neural tissues. Here, we present a flexible SERS hydrogel patch for the label-free detection of metabolites at bio-interfaces. The patch integrates a self-assembled silver nanoparticle film with an ultrathin polyvinyl alcohol (PVA) hydrogel layer to achieve good plasmonic enhancement, mechanical durability, conformity and reliable SERS stability. The SERS patch allows the detection of metabolites within 6 min upon analyte exposure, enabling the label-free detection of key metabolites, such as glucose, uric acid and urea with concentrations down to 1 μM, 50 μM and 1 mM, respectively. We demonstrate the versatility of this platform by performing ex vivo experiments on porcine brain and muscle tissues to simulate real-world application scenarios in brain-machine interfaces and implantable sensors. This work demonstrates the feasibility of SERS hydrogel-based flexible platforms for the in situ monitoring of metabolites at bio-interfaces.},
}

@article {pmid41553800,
year = {2026},
author = {Jafar, R},
title = {Dimensions of Transparency: How Dys-Appearance Affects BCI Embodiment.},
journal = {AJOB neuroscience},
volume = {17},
number = {1},
pages = {25-27},
doi = {10.1080/21507740.2025.2606297},
pmid = {41553800},
issn = {2150-7759},
}

@article {pmid41553799,
year = {2026},
author = {Zilio, F},
title = {A Multi-Criteria Framework for Transparency in the Design and Use of Brain-Computer Interfaces.},
journal = {AJOB neuroscience},
volume = {17},
number = {1},
pages = {22-25},
doi = {10.1080/21507740.2025.2606296},
pmid = {41553799},
issn = {2150-7759},
}

@article {pmid41553797,
year = {2026},
author = {Barnhart, AJ},
title = {A Phenomenological Photo Finish: Testing Transparency at the Cybathlon Brain-Computer Interface Race.},
journal = {AJOB neuroscience},
volume = {17},
number = {1},
pages = {20-22},
doi = {10.1080/21507740.2025.2606294},
pmid = {41553797},
issn = {2150-7759},
}

@article {pmid41553197,
year = {2026},
author = {Bhargava, EK and Arvaneh, M},
title = {Expanding the olfactory implant paradigm through recent advances in brain-computer interface technology.},
journal = {Rhinology},
volume = {},
number = {},
pages = {},
doi = {10.4193/Rhin25.554},
pmid = {41553197},
issn = {0300-0729},
abstract = {The international opinion paper by Whitcroft et al. provides invaluable guidance for the emerging field of olfactory implants (1). While the authors thoroughly address clinical considerations and current technological approaches, we would like to expand upon Statements 9.1 and 9.3 regarding electrode technology limitations and highlight recent advances in brain-computer interface (BCI) technology that could address key technological challenges around electrode longevity and biocompatibility.},
}

@article {pmid41552876,
year = {2026},
author = {Ding, Y and Lu, Y and Zhao, G and Gong, Z},
title = {Drosophila Larvae Generate Force to Counteract External Mechanical Pressures.},
journal = {The Journal of experimental biology},
volume = {},
number = {},
pages = {},
doi = {10.1242/jeb.250849},
pmid = {41552876},
issn = {1477-9145},
support = {T2293720/ T2293721, 32271041//National Natural Science Foundation of China/ ; 2021ZD0200405//National Major Science and Technology Projects of China/ ; 2022C01022//Key Research and Development Program of Zhejiang Province/ ; },
abstract = {To counteract or to retreat presents a fundamental dilemma for biological organisms when facing adverse abiotic environmental conditions. In many cases, the predominant strategy animals adopt is to retreat. However, if counteraction is possible, and how the choice between counteraction and retreat is decided, are not clear. Here, we report that Drosophila larvae can actively counteract external mechanical pressure, inspired by Drosophila larval cleft-squeezing behaviour. We developed a behavioural paradigm to investigate the counteracting force of larvae in response to external pressures. Instead of retreating by crawling backward, a portion of Drosophila larvae could crawl forward and counteract against the external physical pressure. Under externally applied pressing forces of 25mN, 93.9% of forward peristaltic movements increased the counterforce, while 88.2% of backward peristaltic movements decreased it. The activeness in counteraction force was reflected by the longer inter-wave delay, more oscillation work and longer force wave period during consecutive forward peristaltic waves. As the external pressing force was increased from 25mN to 50mN, 75mN and 100mN, counteraction by forward peristalsis was less frequent, while retreat by backward peristalsis was more frequent when pressure is high. A reduction of the external pressure immediately following the counteracting forward peristalsis, which might serve as rewarding signal, could reinforce the counteraction and induce more ensuing forward peristalsis. The rewarding effect of reducing external pressure by forward crawling was much more than that by backward crawling. Our study sheds light on the intricate mechanisms underlying animal proactive responses to adverse abiotic environmental conditions.},
}

@article {pmid41551232,
year = {2025},
author = {Lin, Z and Choi, J and Mao, R and Zhao, B and Kang, J},
title = {Spatial Adaptive Selection using Binary Conditional Autoregressive Model with Application to Brain-Computer Interface.},
journal = {Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America},
volume = {},
number = {},
pages = {},
pmid = {41551232},
issn = {1061-8600},
abstract = {In medical imaging studies, scalar-on-image regression presents significant challenges due to limited sample sizes and the high-dimensionality of datasets. Additionally, imaging predictors often exhibit spatially heterogeneous activation patterns and complex nonlinear associations with the response variable. To address these issues, we propose a novel Bayesian scalar-on-image regression model with the Spatial Adaptive Selection using Binary Conditional Autoregressive Model (SAS-BCAR) prior. The proposed approach leverages a binary conditional autoregressive model to capture spatial dependencies among feature selection indicators, effectively identifying spatially structured sparsity patterns within image data, while addressing nonlinear relationships between image predictors and the response variable. Furthermore, our SAS-BCAR incorporates an adaptive feature selection mechanism that adjusts to varying spatial dependencies across different image regions, ensuring a more precise and robust feature selection process. Through extensive numerical simulations on benchmark computer vision datasets and analysis of electroencephalography data in brain-computer interface applications, we demonstrate that the SAS-BCAR model achieves superior predictive performance compared to state-of-the-art alternatives, particularly in scenarios with limited training data. Supplementary materials including computer code, R packages, datasets, and additional figures are available online.},
}

@article {pmid41551041,
year = {2025},
author = {Otarbay, Z and Kyzyrkanov, A},
title = {Transfer learning for subject-independent motor imagery EEG classification using convolutional relational networks.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1691929},
pmid = {41551041},
issn = {1662-4548},
abstract = {Motor imagery (MI) based electroencephalography (EEG) classification is central to brain-computer interface (BCI) research but practical deployment remains challenging due to poor generalization across subjects. Inter-individual variability in neural activity patterns significantly limits the development of subject-independent BCIs for healthcare and assistive technologies. To address this limitation, we present a transfer learning framework based on Convolutional Relational Networks (ConvoReleNet) designed to extract subject-invariant neural representations while minimizing the risk of catastrophic forgetting. The method integrates convolutional feature extraction, relational modeling, and lightweight recurrent processing, combined with pretraining on a diverse subject pool followed by conservative fine-tuning. Validation was conducted on two widely used benchmarks, BNCI IV-2a (four-class motor imagery) and BNCI IV-2b (binary motor imagery), to evaluate subject-independent classification performance. Results demonstrate clear improvements over training from scratch: accuracy on BNCI IV-2a increased from 72.22 (±20.49) to 79.44% (±11.09), while BNCI IV-2b improved from 75.10 (±17.17) to 83.85% (±10.30). The best-case performance reached 87.55% on BNCI IV-2a with Tanh activation and 83.85% on BNCI IV-2b with ELU activation, accompanied by reductions in inter-subject variance of 45.9 and 40.0%, respectively. These findings establish transfer learning as an effective strategy for subject-independent MI-EEG classification. By enhancing accuracy, reducing variability, and maintaining computational efficiency, the proposed framework strengthens the feasibility of robust and user-friendly BCIs for rehabilitation, clinical use, and assistive applications.},
}

@article {pmid41548826,
year = {2026},
author = {Gwon, Y and Chung, CK},
title = {Distinct Post-Sentence Neural Patterns Representing Lexical Items vs. Sentence Integration.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121703},
doi = {10.1016/j.neuroimage.2026.121703},
pmid = {41548826},
issn = {1095-9572},
abstract = {While comprehension marks the starting point in daily communication, the process is only fulfilled when suitable responses or inferences are followed. Listeners retain sentence information after initial comprehension. Although comprehension during listening has been widely studied, comparatively little is understood about how and where the brain retains linguistic information beyond the end of a sentence (EOS). A key question is whether the brain retains not only a holistic, sentence-level representation but also independent traces of individual lexical items-and, if so, how and where these dissociable signals are encoded in the brain. By analyzing the high gamma envelope in electrocorticography (ECoG) data from 15 patients with epilepsy, we directly investigated how neural signals encode and retain information about individual lexical items as well as the integrated sentence representation after the EOS. To this end, we employed a question-and-answer paradigm in which participants heard one of four sentences ("Is it alive?", "Is it not alive?", "Is it a part of body?" or "Is it not a part of the body?"), followed by a response prompt. To answer correctly, subjects must retain the relevant linguistic information, so we could trace retained neural representations in post-question periods, that respond either to each lexical item independently-content ("alive" vs. "part of the body") and negation ("positive" vs. "negative")-or to sentence-specific representations integrating both lexical items. Label-based encoding models were fit to predict neural responses from each label, and encoding strength was quantified by the correlation between predicted and observed signals. We found that channels selectively encoding lexical information were distributed across widespread cortical areas. In contrast, sentence-specific encoding was highly localized in the left posterior superior temporal gyrus (pSTG). Furthermore, by applying the same encoding model to neural signals recorded during the subsequent response-preparation period, we found that both lexical-item and integrated sentence information can persist significantly while participants prepared their responses. These findings provide direct evidence for the distinct spatial organization of lexical and sentence-level representations in the human brain after the end of a sentence.},
}

@article {pmid41548791,
year = {2026},
author = {Liu, Y and Wang, S and Zhang, Y and Wan, L and Wang, H and Zhang, L},
title = {Oxidized alginate-based interpenetrated dual-network antibacterial hydrogel for enhanced diabetic wound healing.},
journal = {International journal of biological macromolecules},
volume = {},
number = {},
pages = {150335},
doi = {10.1016/j.ijbiomac.2026.150335},
pmid = {41548791},
issn = {1879-0003},
abstract = {Plagued by a prolonged healing process and recurrent bacterial infections, diabetic wounds pose a significant clinical challenge. This underscores the urgent need to develop advanced dressings to address microbial resistance and dysfunctional healing processes. Herein, we present a self-healing double-network hydrogel that integrates antibacterial activity with enhanced tissue regenerative potential, offering a promising strategy to accelerate diabetic wound repair. The hydrogel was constructed by interpenetrating a stable polyacrylamide (PAM) network into a dynamically crosslinked oxidized alginate-polydopamine (OSPB) network. Owing to the multiple dynamic interactions, including ionic chelation, Schiff base coordination, and hydrogen bonding, the hydrogel exhibits intrinsic self-healing behavior. The compact crosslinked double-network architecture imparted reduced swelling and enhanced mechanical strength while maintaining tissue conformity. Its high stretchability, toughness, and rapid recovery under repetitive stress ensured the hydrogel for dynamic wound protection and long-term wound management. To maximize antibacterial potency, the hydrogel incorporates the antimicrobial Jelleine-1 peptide (J-1), which was deposited at the tissue-adhesive interfaces, imparting strong antibacterial activity. Besides, the enhanced transdermal penetration was confirmed using bovine serum albumin - fluorescein isothiocyanate (BSA-FITC) as the macromolecular model. In vivo studies demonstrated an accelerated wound closure with promoted cell proliferation, migration, and angiogenesis, which consequently improves granulation tissue formation and collagen deposition. Collectively, our work presents a multifunctional hydrogel system for promising clinical treatment of diabetic wounds.},
}

@article {pmid41548584,
year = {2026},
author = {Lim, JH and Kuo, PC},
title = {Enhancing Brain-Computer interface performance through source-level attention mechanism: An EEG motor imagery study.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110666},
doi = {10.1016/j.jneumeth.2025.110666},
pmid = {41548584},
issn = {1872-678X},
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) enable direct communication between humans and machines by translating brain signals into control commands. Electroencephalography (EEG) is a commonly used modality in BCI systems due to its non-invasiveness and high temporal resolution. However, EEG-based BCIs often suffer from low signal-to-noise ratios and limited spatial resolution, primarily due to the small number of recording electrodes. Although source estimation techniques can improve spatial specificity, they typically require subject-specific information such as individual brain anatomy or electrode positions, which may not always be available. This study aims to address these challenges by proposing a framework that enhances task-relevant EEG signals using an attention-guided source estimation approach based on coarse predefined brain regions.

NEW METHOD: We developed an attention-guided neural network that estimates source-level activity most relevant to the BCI task, without requiring subject-specific structural data. The model uses predefined regions of interest to guide attention mechanisms toward informative spatial features.

RESULTS: The framework was validated using publicly available motor imagery EEG datasets, achieving strong performance.

Comparative analyses were conducted against baseline models using traditional EEG signals and standard feature extraction methods. This study presents an effective approach for improving EEG-based BCI performance by integrating an attention-guided source estimation network into the decoding pipeline. The method does not rely on subject-specific anatomical information, making it broadly applicable.

CONCLUSION: By emphasizing task-relevant source activity, the framework enhances signal quality and classification accuracy, thereby advancing the potential of BCIs for precise and practical applications.},
}

@article {pmid41547354,
year = {2026},
author = {Gao, X and Liu, X and Wang, N and Cui, C and Liu, W and Yang, M and Li, Q and Ou, Y and Ning, A and Wei, X and Zhang, M and Qiu, S and Lei, Y and Fu, D and Li, H and Sun, L and Lu, M and Zhang, M and Wang, Y},
title = {Nanoparticles hijack calvarial immune cells for CNS drug delivery and stroke therapy.},
journal = {Cell},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cell.2025.12.008},
pmid = {41547354},
issn = {1097-4172},
abstract = {The rapid accessibility of calvarial immune cells to the brain, in principle, may offer transformative opportunities for overcoming drug delivery barriers in central nervous system (CNS) disorders. Here, we hijacked calvarial immune cells using drug-loaded nanoparticles (NPs) and leveraged their unique migration mechanism through skull-meninges microchannels to bypass the blood-brain barrier (BBB) for CNS drug delivery. We constructed NP-loaded immune cells in situ via intracalvariosseous (ICO) injection, validated their prompt migration in response to CNS perturbation, and targeted therapeutic delivery to CNS lesions. Compared with conventional delivery approaches, this strategy achieved promising therapeutic efficacy in improving both short- and long-term outcomes in preclinical stroke models. Our prospective clinical trial further supports the translational feasibility of ICO immune access in treating malignant stroke. These findings establish skull-based delivery as a promising, clinically translatable route for CNS drug delivery and highlight immune-assisted transport as a potentially transformative strategy for improving therapeutic outcomes in neurological disorders.},
}

@article {pmid41546096,
year = {2026},
author = {Zhang, H and Song, X and Huang, N and Xiong, K and Shao, N and Su, Y and Bian, S and Sawan, M},
title = {A programmable peptide interface for on-demand neural culturing platforms.},
journal = {Journal of nanobiotechnology},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12951-026-04032-x},
pmid = {41546096},
issn = {1477-3155},
support = {W2431058//the National Natural Science Foundation of China/ ; 2024C03002//the "Pioneer" and "Leading Goose" Research and Development Program of Zhejiang/ ; 2023GD004//Project of Westlake Institute for Optoelectronics/ ; },
abstract = {The precise spatial organization of neural cells into two-dimensional networks or three-dimensional spheroids is crucial for advancing neuroscience research and drug discoveries, yet remains challenging with conventional, single-function coatings. Here, we propose a programmable bifunctional peptide that integrates a silica-binding domain with a tunable cell-adhesive Arginine-Glycine-Aspartate (RGD) tripeptide. By systematically improving the RGD variant and linker rigidity, we introduced a single coating material that enables on-demand switching between two distinct functions: guiding the patterned growth of functional neural circuits on glass and facilitating the high-throughput formation of uniform neural spheroids. The latter exhibited high viability, extensive neurite outgrowth, and spontaneous electrophysiological activity, which validates their functional maturity. We establish by this work a versatile and reliable platform for advanced neural interface research, with significant potential for drug discovery and disease modeling.},
}

@article {pmid41545509,
year = {2026},
author = {Li, Y and Li, W and Liu, Y and Chen, Q and Guo, X and Tan, M and Yang, R and Xu, X and Qin, H and Chen, L},
title = {HRV features as potential biomarkers for auxiliary diagnosis in epilepsy.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-025-34682-0},
pmid = {41545509},
issn = {2045-2322},
support = {2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; 2024ZDZX0018//Sichuan Science and Technology Program/ ; },
abstract = {Epilepsy affects around 70 million people worldwide, and diagnosis is often difficult and delayed, exposing patients to avoidable morbidity and psychosocial burden. Heart rate variability (HRV) is a non-invasive marker of autonomic nervous system function that may be altered in epilepsy and may support clinical decision-making. In this single-center case-control study, we recorded short-term HRV during a standardized cardiovascular autonomic reflex test including supine resting, deep-breathing and three challenges (active standing, Valsalva manoeuvre and sustained handgrip) in 200 adults with epilepsy and 200 age- and sex-matched healthy controls. Patients with epilepsy showed consistently lower HRV than controls. Using HRV and demographic features, we developed logistic regression models to distinguish epilepsy from health in an independent test set. A model integrating rest and sustained handgrip achieved the highest performance, although still only moderate (area under the curve 0.68; sensitivity 0.821; specificity 0.484). Standardized multi-paradigm HRV assessment may therefore provide a feasible, low-cost adjunct to support, but not replace, conventional diagnostic evaluation. However, the single-center design, relatively short recordings and inclusion of only healthy controls limit generalizability, and larger multicenter studies including patients with paroxysmal conditions that mimic epilepsy are needed to determine clinical utility.},
}

@article {pmid41544906,
year = {2026},
author = {Hong, T and Su, C and Zhou, H and Geng, F and Hu, Y},
title = {Brain activity inhibition during Short Video Viewing: neurochemical insights.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121722},
doi = {10.1016/j.neuroimage.2026.121722},
pmid = {41544906},
issn = {1095-9572},
abstract = {Cognitive control enables individuals to adapt to the ever-changing environmental demands. The dorsal anterior cingulate cortex (dACC) and the dorsolateral prefrontal cortex (dlPFC) are key regions of the cognitive control network, activated during cognitively demanding tasks. In contrast, the entertaining and habitual nature of short-video consumption for leisure shifts neural processing toward emotional engagement and immediate gratification, contributing to excessive use and diminished self-control in some individuals. This raises a critical question: Does short-video viewing suppress cognitive control regions, and what neurochemical factors may underlie individual differences in this process? To address this question, this preregistered study used proton magnetic resonance spectroscopy ([1]H-MRS) to measure glutamate and γ-aminobutyric acid (GABA) concentrations in the dACC at rest, and employed functional magnetic resonance imaging (fMRI) to examine dACC and dlPFC activity during free viewing of short videos in 56 young adults. We found that both the dACC and the dlPFC exhibited significant deactivation in response to preferred videos that were watched to completion, compared to less-preferred videos that were terminated early. Moreover, resting-state glutamate levels in the dACC were associated with the magnitude of this deactivation, with higher glutamate concentrations associated with less suppression of both dACC and dlPFC activity. Additionally, functional connectivity between the dACC and dlPFC increased during video viewing, particularly for preferred videos. By integrating fMRI with [1]H-MRS, our study provides novel evidence that immersive viewing of preferred short videos deactivates the cognitive control network and that individual differences in this deactivation are linked to glutamate metabolism. These findings enhance our understanding of how digital media consumption interacts with neurochemical processes to influence self-regulation. Our study offers new insights into the neural mechanisms underlying short-video engagement and has implications for understanding excessive digital media use.},
}

@article {pmid32413878,
year = {2020},
author = {Del Campo-Vera, RM and Gogia, AS and Chen, KH and Sebastian, R and Kramer, DR and Lee, MB and Peng, T and Tafreshi, A and Barbaro, MF and Liu, CY and Kellis, S and Lee, B},
title = {Beta-band power modulation in the human hippocampus during a reaching task.},
journal = {Journal of neural engineering},
volume = {17},
number = {3},
pages = {036022},
pmid = {32413878},
issn = {1741-2552},
support = {KL2 TR001854/TR/NCATS NIH HHS/United States ; R25 NS099008/NS/NINDS NIH HHS/United States ; },
mesh = {*Beta Rhythm ; Cerebral Cortex ; Electroencephalography ; Hippocampus ; Humans ; *Movement ; },
abstract = {OBJECTIVE: Characterize the role of the beta-band (13-30 Hz) in the human hippocampus during the execution of voluntary movement.

APPROACH: We recorded electrophysiological activity in human hippocampus during a reach task using stereotactic electroencephalography (SEEG). SEEG has previously been utilized to study the theta band (3-8 Hz) in conflict processing and spatial navigation, but most studies of hippocampal activity during movement have used noninvasive measures such as fMRI. We analyzed modulation in the beta band (13-30 Hz), which is known to play a prominent role throughout the motor system including the cerebral cortex and basal ganglia. We conducted the classic 'center-out' direct-reach experiment with nine patients undergoing surgical treatment for medically refractory epilepsy.

MAIN RESULTS: In seven of the nine patients, power spectral analysis showed a statistically significant decrease in power within the beta band (13-30 Hz) during the response phase, compared to the fixation phase, of the center-out direct-reach task using the Wilcoxon signed-rank hypothesis test (p < 0.05).

SIGNIFICANCE: This finding is consistent with previous literature suggesting that the hippocampus may be involved in the execution of movement, and it is the first time that changes in beta-band power have been demonstrated in the hippocampus using human electrophysiology. Our findings suggest that beta-band modulation in the human hippocampus may play a role in the execution of voluntary movement.},
}

*Beta Rhythm
Cerebral Cortex
Electroencephalography
Hippocampus
Humans
*Movement

@article {pmid41544497,
year = {2026},
author = {Luo, H and Ran, X and Li, Z and Xue, H and Jiang, T and Shen, J and Kärkkäinen, T and Xu, Q and Cong, F},
title = {Key-value pair-free continual learner via task-specific prompt-prototype.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {198},
number = {},
pages = {108576},
doi = {10.1016/j.neunet.2026.108576},
pmid = {41544497},
issn = {1879-2782},
abstract = {Continual learning aims to enable models to acquire new knowledge while retaining previously learned information. Prompt-based methods have shown remarkable performance in this domain; however, they typically rely on key-value pairing, which can introduce inter-task interference and hinder scalability. To overcome these limitations, we propose a novel approach employing task-specific Prompt-Prototype (ProP), thereby eliminating the need for key-value pairs. In our method, task-specific prompts facilitate more effective feature learning for the current task, while corresponding prototypes capture the representative features of the input. During inference, predictions are generated by binding each task-specific prompt with its associated prototype. Additionally, we introduce regularization constraints during prompt initialization to penalize excessively large values, thereby enhancing stability. Experiments on several widely used datasets demonstrate the effectiveness of the proposed method. In contrast to mainstream prompt-based approaches, our framework removes the dependency on key-value pairs, offering a fresh perspective for future continual learning research.},
}

@article {pmid41544454,
year = {2026},
author = {Zheng, L and Lu, Y and Lyu, H and Li, T and Cui, S and Xu, Y and Cai, Z and Hou, Y and Li, Y and Yang, Q and Ye, Z and Yang, G and Xu, K},
title = {Laser fabrication of flexible electrodes for bioelectronics.},
journal = {Biosensors & bioelectronics},
volume = {298},
number = {},
pages = {118386},
doi = {10.1016/j.bios.2026.118386},
pmid = {41544454},
issn = {1873-4235},
abstract = {Bioelectronics lies at the intersection of electronics and biology, enabling real-time signal exchange between living systems and machines. As next-generation applications such as wearable diagnostics, brain-computer interfaces, and closed-loop therapeutic systems desire for soft, miniaturized, and biocompatible platforms, the role of bioelectrodes becomes even more critical. Direct laser writing (DLW) has emerged as a powerful microscale fabrication approach, capable of directly patterning functional electrodes with high spatial resolution on diverse materials. In addition, DLW uniquely offers localized material processing and property modulation, enabling controlled synthesis, phase transition, and surface functionalization. This review presents a comprehensive overview of the underlying mechanisms and advanced material systems that enable DLW. We highlight how DLW enables structural design that impart stretchability and tissue conformity, and how such electrodes are integrated into wearable and implantable bioelectronic systems. Finally, we discuss key challenges and future opportunities for DLW-based bioelectrodes, which are poised to become foundational components of intelligent and adaptive biomedical interfaces.},
}

@article {pmid41544325,
year = {2026},
author = {Gong, C and Zou, L and Li, P and Wu, X and Qiao, Y and Hu, Z and Wang, X and Zhou, Y and Wang, K and Hu, Y and Wang, H},
title = {Rapid spatio-temporal MR fingerprinting using physics-informed implicit neural representation.},
journal = {Medical image analysis},
volume = {109},
number = {},
pages = {103935},
doi = {10.1016/j.media.2026.103935},
pmid = {41544325},
issn = {1361-8423},
abstract = {The potential of Magnetic Resonance Fingerprinting (MRF), which allows for rapid and simultaneous multi-parametric quantitative MRI, is often limited by severe aliasing artifacts caused by aggressive undersampling. Conventional MRF approaches typically treat these artifacts as detrimental noise and focus on their removal, often at the cost of either reduced reconstruction speed or increased reliance on large training datasets. Building on the insight that structured aliasing can be leveraged as an informative spatial encoding mechanism, we propose to extend MRF's encoding capacity to the global spatio-temporal domain by introducing a novel Physics-informed implicit neural MRF (πMRF) framework. πMRF integrates physics-informed spatio-temporal fingerprint modeling with implicit neural representations (INRs), enabling unsupervised, gradient-driven joint estimation of quantitative tissue parameters and coil sensitivity maps (CSMs) with enhanced accuracy and robustness. Specifically, πMRF leverages a scalable component based on physics-informed neural networks (PINNs) to facilitate accurate high-dimensional signal modeling and memory-efficient optimization. In addition, a subspace-guided sensitivity regularization is developed to improve the robustness of CSM estimation in highly undersampled scenarios. Experimental results on simulated, phantom, and in vivo datasets demonstrate that πMRF achieves improved quantitative accuracy and robustness even under highly accelerated acquisitions, outperforming state-of-the-art MRF methods.},
}

@article {pmid41543945,
year = {2026},
author = {Ju, J and Li, H},
title = {Neural Signatures and Multi-Cognitive Decoding of EEGSignals Induced by Shared Stimulus: A Paradigm Study.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3654639},
pmid = {41543945},
issn = {1558-2531},
abstract = {Multi-task decoding from electroencephalogram (EEG) signals is valuable for brain-computer interface (BCI) applications in naturalistic settings. Most existing studies focus on decoding distinctly different tasks, leaving the diversity of cognitive responses elicited by a single stimulus underexplored. We introduced a novel experimental paradigm where a common visual stimulus elicits five distinct cognitive processes: single reach, interception reach, sequence reach, attention reach, and inhibition reach. EEG signatures were analyzed using temporal and spectral methods. A regularized linear discriminant analysis (RLDA) classifier was employed for decoding, utilizing both temporal and event-related spectral perturbation (ERSP) features. Significant neural activation differences (p < 0.05) were observed across tasks and brain regions. The RLDA classifier achieved high decoding accuracy: 91.72% ± 6.10% for classifying the five cognitive states using ERSP features. Furthermore, for the sequence reach task, temporal features enabled classification of normal versus catch trials with 77.96% ± 7.03% accuracy. All these results demonstrate the potential for EEG-based BCI applications to distinguish diverse cognitive states elicited by identical stimuli, offering new insights for improving the naturalness and intelligence of BCI systems. Future work will focus on enhancing decoding performance and extending this research to online applications.},
}

@article {pmid41539931,
year = {2026},
author = {Beste, C and Slagter, HA and Herff, C and Kamitani, Y and Coninx, S and van Wezel, R and Frings, C},
title = {Moving intentions from brains to machines.},
journal = {Trends in cognitive sciences},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.tics.2025.12.003},
pmid = {41539931},
issn = {1879-307X},
abstract = {Brain-computer interface (BCI) research has achieved remarkable technical progress but remains limited in scope, typically relying on motor and visual cortex signals in limited patient populations. We propose a paradigm shift in BCI design rooted in ideomotor theory, which conceptualizes voluntary action as driven by internally represented sensory outcomes. This underused framework offers a principled basis for next-generation BCIs that align closely with the brain's natural intentional and action-planning architecture. We suggest a more intuitive, generalizable, and scalable path by reorienting BCIs around the 'what for' of action-user goals and anticipated effects. This shift is timely and feasible, enabled by advances in neural recording and artificial intelligence-based decoding of sensory representations. It may help resolve challenges of usability and generalizability in BCI design.},
}

@article {pmid41537311,
year = {2026},
author = {Shu, L and Tang, J and Guan, X and Zhang, D},
title = {A comprehensive survey of genome language models in bioinformatics.},
journal = {Briefings in bioinformatics},
volume = {27},
number = {1},
pages = {},
pmid = {41537311},
issn = {1477-4054},
support = {62136004//National Natural Science Foundation of China/ ; 62276130//National Natural Science Foundation of China/ ; 2023YFF1204803//National Key R&D Program of China/ ; BE2022842//Key Research and Development Plan of Jiangsu Province/ ; },
mesh = {*Computational Biology/methods ; *Natural Language Processing ; Humans ; *Genomics/methods ; Deep Learning ; *Genome ; },
abstract = {Large language models have revolutionized natural language processing by effectively modeling complex semantics and capturing long-range contextual relationships. Inspired by these advancements, genome language models (gLMs) have recently emerged, conceptualizing DNA and RNA sequences as biological texts and enabling the identification of intricate genomic grammar and distant regulatory interactions. This review examines the need for gLMs, emphasizing their capacity to overcome the limitations of traditional deep learning approaches in genomic sequence characterization. We comprehensively survey contemporary gLM architectures, including Transformer models, Hyena convolutions, and state space models, as well as various sequence tokenization strategies, assessing their applicability, and effectiveness across diverse genomic applications. Additionally, we discuss foundational pretraining strategies and provide an overview of genomic pretraining datasets spanning multiple species and functional domains. We critically analyze evaluation methodologies, including supervised, zero-shot, and few-shot learning paradigms, as well as fine-tuning approaches. An extensive taxonomy of downstream tasks is presented, alongside a summary of existing benchmarks and emerging trends. Finally, we contemplate key challenges such as data scarcity, interpretability, and the computational demands of genomic modeling, and propose a roadmap to guide future advances in genome language modeling.},
}

*Computational Biology/methods
*Natural Language Processing
Humans
*Genomics/methods
Deep Learning
*Genome

@article {pmid41532318,
year = {2026},
author = {Sun, X and Wang, T and Gong, H and Qiu, Y and Zhang, Y and Chen, M and Xue, J and Ye, G and Mou, R and Teng, P and Li, W and Chen, T and Zhang, L and Guo, X and Mao, W and Zhao, H and Ma, L and Xu, Q},
title = {Circulating CD34[+] Fibroblast Progenitors Engaged in Heart Fibrosis of Allograft.},
journal = {Circulation research},
volume = {},
number = {},
pages = {},
doi = {10.1161/CIRCRESAHA.125.326558},
pmid = {41532318},
issn = {1524-4571},
abstract = {BACKGROUND: Fibrosis is one of the major causes of cardiac allograft malfunction and is mainly driven by fibroblasts. However, the role of recipient-derived cells in generating allograft fibroblasts and the underlying mechanisms remain to be explored.

METHODS: We analyzed human heart allograft samples and used murine transplant models (C57BL/6J, Cd34-CreER[T2]; R26-tdTomato, mRFP mice, Rosa26-iDTR, Postn-CreER[T2]; R26-tdTomato, double-tdTomato, and immunodeficient mice with BALB/c donors). Human progenitor cells were cultivated from blood. Single-cell RNA sequencing, Western blotting, quantitative polymerase chain reaction, and immunohistochemistry, whole-mount staining with 3-dimensional reconstruction, and in vivo/in vitro experiments were applied to characterize allograft cellular composition and communication.

RESULTS: Single-cell RNA sequencing was introduced to delineate the allograft cell atlas of patients and mice. Y chromosome analysis identified that recipient-derived cells contributed to allograft fibroblasts in both patients and murine models. Combining the genetic cell lineage tracing technique, we found that recipient-derived CD34[+] cells could give rise to activated fibroblasts. Bone marrow transplantation and parabiosis models revealed that the recipient's circulating non-bone marrow Cd34[+] cells could generate allograft fibroblasts. Human CD34[+] cells could differentiate into fibroblasts both in vivo and in vitro. CD34[+] fibroblast progenitors were recruited by CXCL12-ACKR3 and MIF-ACKR3 interactions and differentiated via the TGFβ (transforming growth factor beta)/GFPT2 (glutamine-fructose-6-phosphate transaminase 2)/SMAD2/4 axis. Ablation of recipient Cd34[+] cells reduced activated fibroblasts and alleviated allograft fibrosis.

CONCLUSIONS: We identify circulating CD34[+] cells as a novel source of fibroblast progenitors that contribute to cardiac allograft fibrosis, suggesting that targeting recipient CD34[+] cells could be a novel therapeutic potential for treating cardiac fibrosis after heart transplantation.},
}

@article {pmid41531435,
year = {2026},
author = {Lu, X and Chen, Y and Li, Z and Zhao, J and Zhou, Y and Wu, D and Zhang, M},
title = {Electroencephalography Enables Continuous Decoding of Hand Motion Angles in Polar Coordinates.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {7},
number = {},
pages = {0469},
pmid = {41531435},
issn = {2692-7632},
abstract = {Hand movements in task space are typically represented using either Cartesian or polar coordinate systems. While Cartesian coordinates are commonly used in electroencephalography (EEG)-based brain-computer interface (BCI) studies, polar coordinates offer a more natural representation for circular motion by directly encoding angular information. This study investigates the feasibility of continuous decoding of hand motion angles in polar coordinates using EEG signals. In the paradigm, human participants engaged in bimanual circular tracing with a fixed radius while their EEG signals were recorded. To evaluate the feasibility of this approach, 6 deep learning models, including commonly used EEGNet, DeepConvNet, and ShallowConvNet, and their variants incorporating long short-term memory (LSTM) layers, were employed. Performance was assessed using mean squared error (MSE), mean absolute error (MAE), and correlation coefficient (CC) between decoded and actual angles. Across 8 participants, all 6 models significantly outperformed the chance level (P < 0.01), with the best model achieving an MSE of 1.012 rad[2], an MAE of 0.627 rad, and a CC of 0.895. These results demonstrate the feasibility of continuous angular decoding of circular hand motion in polar coordinates using EEG signals. This approach offers a promising alternative to traditional Cartesian-based decoding methods, particularly for applications involving circular or rotational movements.},
}

@article {pmid41520740,
year = {2026},
author = {Liu, Q and Zhang, X and Niu, J and Chen, K and Xia, J and He, Y and Xu, S and Li, W and Chen, H and Zhang, D and Liao, W and Li, J},
title = {Uniformity in happiness and uniqueness in sadness: Naturalistic emotional representation in major depression.},
journal = {NeuroImage},
volume = {326},
number = {},
pages = {121712},
doi = {10.1016/j.neuroimage.2026.121712},
pmid = {41520740},
issn = {1095-9572},
abstract = {Humans develop shared concepts of others' emotions to support adaptive social functioning, yet how these concepts are dynamically represented in major depressive disorder (MDD) during naturalistic movie viewing is not yet fully established. Using functional MRI, we examined patients with MDD (n = 55) and healthy controls (HCs; n = 62) as they freely viewed movie clips depicting happy and sad emotions. Neural similarity was quantified with inter-subject correlation at whole-brain, network, and regional levels, and its association with emotional traits was assessed using inter-subject representational similarity analysis. Compared with HCs, patients with MDD showed significantly reduced whole-brain similarity, particularly during sad contexts. Network analyses revealed that HCs exhibited increased similarity in the limbic network during sadness, reflecting a shared "sadness resonance," whereas patients with higher depressive severity showed widespread disruptions across visual, limbic, dorsal attention, and default mode networks. At the regional level, similarity in the inferior temporal gyrus and lateral occipital cortex was closely linked to individual differences in emotional awareness, with pronounced context- and region-specificity. These findings highlight neural decoupling and heterogeneity as core features of MDD and provide new evidence for potential biomarkers to inform risk assessment and personalized interventions.},
}

@article {pmid41398114,
year = {2025},
author = {Evans, NG and L Gross, M and Shandler, R},
title = {Enhancing Soldiers for Future Warfare: Good Science; Bad Ethics?.},
journal = {Science and engineering ethics},
volume = {32},
number = {1},
pages = {5},
pmid = {41398114},
issn = {1471-5546},
support = {FA9550-21-1-0142//Air Force Office of Scientific Research/ ; W911NF-24-1-0361//Army Research Office/ ; },
abstract = {UNLABELLED: Ethical concerns dog emerging technologies designed to enhance warfighter performance. Brain-computer interfaces, exoskeletons, and mind- or body-altering drugs raise fears about risky, invasive, and experimental medical procedures that offer armies physically and cognitively superior soldiers that will dictate and disrupt the course of future war. What counts as enhancement, however, has been subject to longstanding and passionate debate. This study aims to put an end to this dispute by employing a conjoint experimental design to survey a group of military and professional experts from across the world to explore how definitional perceptions of enhancement influence ethical acceptability. Two main findings emerge. First, we find that there already exists a broad agreement about what constitutes enhancement, and this consensus spans countries, discipline, political orientation, and age. Future policy may now be able to accommodate a definition of enhancement that is widely shared among members of the international community. Second, across the board, ethical acceptability diminishes as medical technologies aim for transhuman warfighting capabilities. Enhancement research and development for military purposes must navigate the conflicting ethical demands of medical experimentation and lawful war. Human enhancement is not morally unacceptable but ethically precarious, requiring regulation, oversight, and transparency.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11948-025-00573-w.},
}

@article {pmid41529886,
year = {2026},
author = {Zhu, H and Gan, Y and Ye, J and Li, Y and Yu, JZ and Li, X},
title = {Effectiveness of brain-computer interface interventions in autism spectrum disorder rehabilitation: a systematic review and meta-analysis protocol.},
journal = {BMJ open},
volume = {16},
number = {1},
pages = {e102277},
doi = {10.1136/bmjopen-2025-102277},
pmid = {41529886},
issn = {2044-6055},
mesh = {Humans ; *Autism Spectrum Disorder/rehabilitation ; *Brain-Computer Interfaces ; Systematic Reviews as Topic ; Meta-Analysis as Topic ; Research Design ; },
abstract = {BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterised by impairments in social interaction, communication and the presence of repetitive behaviours. Recent advancements in brain-computer interface (BCI) technologies have demonstrated potential benefits in enhancing cognitive, social and communication skills in individuals with ASD. However, the effectiveness of BCI-based interventions in ASD rehabilitation remains inconsistent across studies. Therefore, this protocol outlines a systematic review and meta-analysis to synthesise the evidence on the effectiveness of BCI-based interventions for ASD rehabilitation.

METHODS: We will conduct a comprehensive literature search across multiple databases, including MEDLINE Ovid, Embase Ovid, Cochrane Central Register of Controlled Trials (CENTRAL), Conference Proceedings Citation Index-Science (CPCI-S), Science Citation Index Expanded (SCI-EXPANDED) and so on, to identify relevant studies published from inception to the present. The search will be supplemented by screening the reference lists of included studies and relevant systematic reviews. Two independent reviewers will screen the titles, abstracts and full texts of identified studies for eligibility based on predefined criteria. Data extraction will be performed using a standardised form, and the risk of bias (RoB) will be assessed using the Cochrane RoB tool. Heterogeneity will be evaluated using the I² statistic, and a random-effects or fixed-effects model will be selected for meta-analysis based on the degree of heterogeneity. Subgroup analyses will be conducted to explore potential sources of heterogeneity, including participant age, ASD severity, type of BCI intervention and duration of the intervention. The review will be conducted from January 2026 to April 2026.

ETHICS AND DISSEMINATION: Ethical approval is not required for this study, as it does not involve the collection of primary data from individual patients. Findings will be disseminated through peer-reviewed publication and conference presentations.

PROSPERO REGISTRATION NUMBER: CRD420251010496.},
}

Humans
*Autism Spectrum Disorder/rehabilitation
*Brain-Computer Interfaces
Systematic Reviews as Topic
Meta-Analysis as Topic
Research Design

@article {pmid41528907,
year = {2026},
author = {Zhao, Y and Cao, D and Yu, H and Liang, G and Chen, Z},
title = {MSHANet: A Multiscale Hybrid Attention Network for Motor Imagery EEG Decoding.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3653824},
pmid = {41528907},
issn = {1558-2531},
abstract = {Brain-computer interface (BCI) technology has significant applications in neuro rehabilitation and motor function restoration, especially for patients with stroke or spinal cord injury. Motor imagery electroencephalog-raphy (MI-EEG) is widely used in BCIs, but its nonlinear dynamics and inter-subject variability limit decoding accuracy. In this paper, a multiscale hybrid attention network (MSHANet) for MI-EEG decoding, which consists of spatiotemporal feature extraction (STFE), talking head self-attention (THSA), dynamic squeeze-and-excitation attention (DSEA), and a temporal convolutional network (TCN), is proposed. MSHANet was evaluated via within-subject experiments using BCI Competition IV Datasets 2a and 2b, as well as EEGMMID, achieving decoding accuracies of 83.56%, 89.75%, and 75.66%, respectively. In cross-subject experiments on the three datasets, the mode lattained accuracies of 69.93% on BCI-2a, 81.85% on BCI-2b, and 79.67% on EEGMMID. In addition, we propose an electrode spatial structure-aware encoder. This technique encodes the spatial positions of electrodes in the original data, enabling the model to obtain richer spatial electrode information at the input stage. In within-subject and cross-subject tasks on BCI-2a, this encoding improved the decoding performance by 2.83% and 2.91%, respectively. Visualization was also employed to elucidate feature distributions and the effec tiveness of its attention mechanisms. Experimental results demonstrate that MSHANet performs exceptionally well in MI-EEG decoding tasks and has high potential for clinical applications, particularly in neurorehabilitation and motor function reconstruction.},
}

@article {pmid41528455,
year = {2026},
author = {Becker, L and Krüger, L and Wolf, M and Alfen, K and Theysohn, J and Lefering, R and Dudda, M and Kamp, O and , },
title = {The necessity of CT scans on pediatric carotid injury after blunt trauma - An analysis of the traumaregister DGU[®].},
journal = {European journal of trauma and emergency surgery : official publication of the European Trauma Society},
volume = {52},
number = {1},
pages = {13},
pmid = {41528455},
issn = {1863-9941},
mesh = {Humans ; Child ; *Wounds, Nonpenetrating/diagnostic imaging/epidemiology ; Child, Preschool ; Adolescent ; Male ; Registries ; Female ; Infant ; Germany/epidemiology ; *Carotid Artery Injuries/diagnostic imaging/epidemiology ; *Tomography, X-Ray Computed ; Injury Severity Score ; Infant, Newborn ; Prevalence ; Risk Factors ; },
abstract = {PURPOSE: Blunt carotid injuries (BCI) in pediatric trauma patients are rare. Using data from the TraumaRegister DGU[®][,] this study aims to identify screening parameters and calculate the prevalence of pediatric BCI. By proposing potential risk factors for a BCI, this research seeks to reduce unnecessary radiation exposure in pediatric trauma cases. These findings may enhance understanding of pediatric BCI and highlight the necessity of cautious diagnostic approaches that balance clinical needs with radiation risks.

METHODS: The TraumaRegister DGU[®] is a multicenter database established in 1993 to document the treatment of severely injured patients from initial injury to hospital discharge. Data are collected in four phases: demographics, injury patterns, treatments, and outcomes. Almost 700 hospitals, primarily from Germany, contribute to the registry annually. Statistical analysis was conducted using SPSS. For analysis, the dataset was divided into two groups: trauma patients diagnosed with BCI and trauma patients without BCI. The complete dataset from the TraumaRegister DGU[®] for 2006-2020 was screened for relevant cases. The dataset was limited to patients between 0 and 15 years old.

RESULTS: Out of 9070 severely injured pediatric trauma patients analysed, 50 cases of pediatric BCI were identified, representing a prevalence of 0.6%. Patients with BCI presented with higher injury severity scores (ISS), lower Glasgow Coma Scale (GCS) scores, and a greater prevalence of head injuries, as well as thoracic, abdominal, and extremity injuries. These patients also experienced higher in-hospital mortality rates (34%) and required more frequent blood transfusions. Full-body CT scans were more commonly performed in patients with BCI.

CONCLUSION: This study highlights the rarity and severity of BCI in pediatric trauma patients, with a prevalence of 0.6%. Significant risk factors for a BCI include high injury severity, head trauma, neurological deficits, and pre-hospital hypotension. The findings emphasise the importance of early diagnosis and targeted diagnostic strategies to balance the need for prompt intervention with reducing unnecessary radiation exposure.},
}

Humans
Child
*Wounds, Nonpenetrating/diagnostic imaging/epidemiology
Child, Preschool
Adolescent
Male
Registries
Female
Infant
Germany/epidemiology
*Carotid Artery Injuries/diagnostic imaging/epidemiology
*Tomography, X-Ray Computed
Injury Severity Score
Infant, Newborn
Prevalence
Risk Factors

@article {pmid41527472,
year = {2026},
author = {Niu, J and Xia, J and He, Y and Li, W and Chen, K and Liu, Q and Li, W and Qiu, J and Chen, H and Li, J and Liao, W},
title = {Controllability of morphometric network colocalize with underlying neurobiology in major depression.},
journal = {Psychological medicine},
volume = {56},
number = {},
pages = {e15},
doi = {10.1017/S0033291725103140},
pmid = {41527472},
issn = {1469-8978},
support = {62473082, 62571105, 82121003, 62036003, 62333003//National Natural Science Foundation of China/ ; ZYGX2022YGRH008, ZYGX2024XJ054//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; *Depressive Disorder, Major/diagnostic imaging/physiopathology/metabolism/pathology ; Female ; Adult ; Male ; Middle Aged ; Case-Control Studies ; *Brain/diagnostic imaging/metabolism/pathology ; *Nerve Net/diagnostic imaging ; Magnetic Resonance Imaging ; Diffusion Tensor Imaging ; Young Adult ; },
abstract = {BACKGROUND: Cognitive and behavioral symptoms of major depressive disorder (MDD) are linked to aberrant changes in the controllability of brain networks. However, previous studies examined network controllability using white matter tractography, neglecting the contributions of gray matter. We aimed to examine differences in the controllability of morphometric networks between patients with MDD and demographic-matched healthy controls and identify the associated neurobiological signatures.

METHODS: Based on the structural and diffusion MRI data from two independent cohorts, we calculated the controllability of morphometric similarity networks for each participant. A generalized additive model was used to investigate the case-control differences in regional controllability and their cognitive and behavioral associations. We investigated the associations between imaging-derived controllability and neurotransmitters, brain metabolism, and gene transcription profiles using multivariate linear regression and partial least squares regression analyses.

RESULTS: In both cohorts, depression-related abnormalities of morphometric network controllability were primarily located in the prefrontal, cingulate, and visual cortices, contributing to memory, sensation, and perception processes. These abnormalities in network controllability were spatially aligned with the distributions of serotonergic transmission pathways as well as with altered oxygen and glucose metabolism. In addition, these abnormalities spatially overlapped with differentially expressed genes enriched in annotations related to protein catabolism and mitochondria in neuronal cells and were disproportionately located on chromosome 22.

CONCLUSIONS: Collectively, neuroimaging evidence revealed aberrant morphometric network controllability underlying MDD-related cognitive and behavioral deficits, and the associated genetic and molecular signatures may help identify the neurobiological mechanisms underlying MDD and provide feasible therapeutic targets.},
}

Humans
*Depressive Disorder, Major/diagnostic imaging/physiopathology/metabolism/pathology
Female
Adult
Male
Middle Aged
Case-Control Studies
*Brain/diagnostic imaging/metabolism/pathology
*Nerve Net/diagnostic imaging
Magnetic Resonance Imaging
Diffusion Tensor Imaging
Young Adult

@article {pmid41526383,
year = {2026},
author = {Wang, D and Shi, Y and Pang, J and Zhu, X and Meng, L and Ming, D},
title = {Data-driven subtyping of early Parkinson's disease via mutual cross-attention fusion of EEG and dual-task gait features.},
journal = {NPJ Parkinson's disease},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41531-026-01258-2},
pmid = {41526383},
issn = {2373-8057},
support = {82372083//National Natural Science Foundation of China/ ; },
abstract = {Parkinson's disease (PD) exhibits marked clinical heterogeneity, which poses challenges for diagnosis, prognosis, and therapeutic precision, especially for early-stage PD patients. Existing subtyping approaches often rely on subjective clinical scales and single-modality data, which limits their sensitivity in capturing subtle but clinically relevant differences across patients. To reveal clinically meaningful PD subtypes, we propose a data-driven multimodal framework that integrates resting-state electroencephalography (EEG) and dual-task gait features using mutual cross-attention (MCA) fusion. Forty idiopathic early-stage PD patients were enrolled in a prospective study. EEG biomarkers were encoded via a convolutional neural network for the prediction of motor severity (MDS-UPDRS-III), while dual-task gait features were derived to capture subtle motor dysfunctions. The MCA enabled bidirectional attention-guided integration of EEG and gait features, which were then clustered using an unsupervised method. The analysis revealed three distinct subtypes, with dual-task-based fusion providing superior clinical separation. Subtype I was characterized by pronounced motor deficits; Subtype II showed moderate symptoms with relatively preserved quality of life; and Subtype III presented mild motor impairments but exhibited poorer cognitive and psychosocial outcomes. Feature contribution analyses highlighted central beta and theta EEG activity, along with dual-task gait metrics (e.g., stride length during turning), as key drivers of subtype differentiation. Longitudinal follow-up demonstrated subtype-specific rehabilitation responses, with Subtype II showing an insufficient response compared to other subtypes. In conclusion, this study enables digital phenotyping of PD with prognostic implications for personalized rehabilitation strategies and accelerates precision medicine.},
}

@article {pmid41525762,
year = {2026},
author = {Ding, W and Chen, X and Liu, A},
title = {Breaking the performance barrier in deep learning-based SSVEP-BCIs: A joint frequency-phase training strategy.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae36f6},
pmid = {41525762},
issn = {1741-2552},
abstract = {OBJECTIVE: Deep learning exhibits considerable potential for steady-state visual evoked potential (SSVEP) classification in electroencephalography (EEG)-based brain-computer interfaces (BCIs). SSVEP signals contain both frequency and phase characteristics that correspond to the visual stimuli. However, existing deep learning training strategies typically focus on either frequency or phase information alone, thus failing to fully exploit these dual inherent properties and substantially limiting classification accuracy.

APPROACH: To tackle this limitation, this study proposes a Joint Frequency-Phase Training Strategy (JFPTS), which comprises two complementary stages with distinct time-window sampling schemes. The first stage adopts a frequency prior-driven sampling scheme to improve frequency component utilization, whereas the second stage employs a phase-locked sampling scheme to enhance intra-category phase consistency. This design enables JFPTS to effectively leverage both frequency and phase properties of SSVEP signals.

MAIN RESULTS: Comprehensive experiments on two well-established public datasets validate the effectiveness of JFPTS. The results demonstrate that the JFPTS-enhanced model achieves a marked superiority over the current state-of-the-art classification approaches, notably surpassing the long-standing performance benchmark set by task discriminative component analysis (TDCA).

SIGNIFICANCE: Overall, JFPTS establishes a new training paradigm that advances deep learning approaches for SSVEP classification and promotes the broader adoption of SSVEP-BCIs.},
}

@article {pmid41525614,
year = {2026},
author = {Jin, J and Wang, C and Xu, R and He, X and Wu, X and Li, J and Chen, W and Wang, X and Cichocki, A},
title = {RUNet: A Zero-Calibration Framework for Cross-Domain EEG Decoding via Riemannian and Unsupervised Representation Learning.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3653024},
pmid = {41525614},
issn = {1558-2531},
abstract = {OBJECTIVE: Inter-session and inter-subject variability in electroencephalography (EEG) signals, resulting from individual differences and environmental factors, poses a major challenge for neural decoding in brain-computer interface (BCI) applications.

METHODS: To address this issue, we propose RUNet, a zero-calibration motor imagery EEG decoding framework based on Riemannian manifold learning and unsupervised representation learning. RUNet incorporates a multi-scale spatiotemporal convolutional module that jointly captures local global spatial and multi-resolution temporal dynamics features. To enhance the robustness of EEG features against non stationarity, a polysynergistic covariance optimization module is employed, which strengthens the covariance matrix representation through multiple regularizations and adaptive fusion. In addition, RUNet integrates the Riemannian Affine Log Mapping layer, based on Affine-Invariant Transformation and Log-Euclidean Mapping, in an end-to-end manner to mitigate cross-domain covariance drift and enhance domain-invariant feature learning. A transfer learning framework is further integrated into RUNet: during pre-training, an unsupervised contrastive loss is applied to resting-state EEG data to learn domain-invariant spatiotemporal features; during retraining, task-specific data are used to enhance discriminability and feature disentanglement.

CONCLUSION: Experimental results on the BCI Competition IV 2a, 2b datasets and a self-collected laboratory dataset show that RUNet achieves average cross-session accuracies of 87.19%, 88.03% and 85.45%, and cross-subject accuracies of 68.09%, 78.29% and 87.25%, respectively. On the PhysioNet dataset, a cross-subject accuracy of 78.14% is achieved. These results demonstrate the effectiveness of RUNet's unified pipeline and its robust cross-domain generalization.},
}

@article {pmid41525559,
year = {2026},
author = {Guan, S and Li, Y and Gao, Y and Yin, R and Luo, Y and Liang, J and Zhang, J and Zhang, Y and Li, R},
title = {Enhanced Mapping of Finger Movement Representations Using Diffuse Optical Tomography: A Systematic Comparison with fNIRS.},
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.2026.3652812},
pmid = {41525559},
issn = {1558-0210},
abstract = {Advancing neuroimaging modalities for motor cortex analysis is critical for understanding the neural mechanisms underlying fine motor tasks and for expanding clinical applications. Functional Near-Infrared Spectroscopy (fNIRS) is widely used for measuring cortical hemodynamic activity due to its portability and accessibility, but its inherent limitations in spatial resolution and noise sensitivity reduce its utility for precise neural mapping. Diffuse Optical Tomography (DOT) has emerged as a promising alternative with superior spatial resolution and sensitivity. In this study, we performed a systematic comparison of DOT and fNIRS in detecting task-evoked neural activation during a finger-tapping paradigm including four conditions varying by finger type (thumb vs. little finger) and frequency (high vs. low). Our results demonstrated that DOT consistently captured robust activation in motor-related brain regions, even during less demanding conditions, while fNIRS exhibited limited sensitivity. Temporal trace analyses revealed that DOT achieved higher contrast-to-noise ratio (CNR) and contrast-to-background ratio (CBR), validating its enhanced signal quality and ability to distinguish subtle hemodynamic responses. Furthermore, statistical comparisons highlighted significant differences in task-related activations detected by the two modalities, particularly in low-effort conditions. These findings underscore the advantages of DOT over fNIRS, particularly in applications requiring high spatial resolution and sensitivity to subtle neural processes. The results contribute to ongoing efforts to refine optical imaging techniques for motor neuroscience and reinforce DOT's potential for clinical translation in motor deficit diagnosis, rehabilitation monitoring, and brain-computer interface development.},
}

@article {pmid41525552,
year = {2026},
author = {Zhu, J and Li, K and Chen, S and Huang, H and Zhang, Y and Hu, L and Li, Y},
title = {Smart Ward Control Based on a Wearable Multimodal Brain-Computer Interface Mouse.},
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.2026.3653138},
pmid = {41525552},
issn = {1558-0210},
abstract = {For patients with severe extremity motor function impairment, traditional smart ward control methods, such as those using joysticks and touchscreens, are frequently unsuitable due to their limited physical abilities. Consequently, developing an effective brain-computer interface (BCI) suitable for their operation has become an immediate concern. This paper presents a wearable multimodal BCI system for smart ward control, which employs a self-designed wearable headband to capture head rotation and blinking movement. By wearing the headband, users can control a computer cursor on the screen only with head rotation and blinking, and further control devices in a smart ward with self-designed graphical user interfaces (GUIs). The system decodes signals from an inertial measurement unit (IMU) to map the head posture to the position of the cursor on the screen and decodes electrooculography (EOG) and electroencephalography (EEG) signals to detect valid blinks for selecting and activating function buttons. Ten participants were recruited to perform two experimental tasks that simulate the daily needs of patients with extremity motor function issues. To our satisfaction, all the participants fully accomplished the simulated tasks, and an average accuracy of 97.0±3.9 % and an average response time of 2.39±0.53 s were achieved. Different from traditional step-controlled BCI nursing beds, we designed a continuous-controlled nursing bed and achieved satisfactory results. Furthermore, workload evaluation using NASA Task Load Index (NASA-TLX) revealed that the participants experienced a low workload when using the system. The experimental results demonstrate the effectiveness of our proposed system, indicating significant potential for practical applications.},
}

@article {pmid41525550,
year = {2026},
author = {Padmaja, GKR and Bhagat, NA and Balasubramani, PP},
title = {Assessing the utility of Fronto-Parietal and Cingulo-Opercular networks in predicting the trial success of brain-machine interfaces for upper extremity stroke rehabilitation.},
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.2026.3653049},
pmid = {41525550},
issn = {1558-0210},
abstract = {Brain-machine interfaces (BMIs) have the potential to improve stroke rehabilitation by actively facilitating sensory-cognitive-motor connections to restore movement. However, individuals with cognitive impairments are often excluded from BMI-based neurorehabilitation due to concerns about impaired cognition, specifically reduced attention and executive control. We propose leveraging the trial-wise dynamics of large-scale cognitive control networks-specifically, the frontoparietal (FPN) and cingulo-opercular (CON) networks-to build neural markers of cognitive control. Using existing BMI datasets, we demonstrate that trial-wise activity within these networks predicts motor task performance, suggesting that cognitive control signals in these networks could serve as adaptive modulations for BMI-based rehabilitation. Our system is able to predict unsuccessful BMI trials at the population level about 84.2% of the time on average, with an overall mean accuracy of 72.2% in a 3-fold cross-validation. Additionally, in a leave-one-subject-out validation, our system achieved 71% specificity on average, with an overall mean accuracy of 68.3%. Notably, model performance varies across subjects, with some individuals showing up to 92% specificity and 100% sensitivity. Unlike previous studies that primarily focus on resting-state data, our findings point toward the untapped potential of incorporating cognitive network state monitoring into BMI systems to optimize online performance through trials. Specifically, we suggest that our pre-trained models can be fine-tuned with subject-specific information to design more targeted rehabilitation programs that enhance motor performance by identifying precise attention and learning tasks to improve the successful response of the network model in patients with significant cognitive impairment.},
}

@article {pmid41525004,
year = {2026},
author = {Yan, Y and Zhang, Y and Zhao, X and Chen, R and Fang, S and Zhou, Y and Huang, J and Wang, F and Chen, C and Lin, Z and Xu, X},
title = {Life-course body shape trajectories and cerebral oxygen metabolism in community-dwelling older adults.},
journal = {GeroScience},
volume = {},
number = {},
pages = {},
pmid = {41525004},
issn = {2509-2723},
support = {NSFC/72274170//Natural Science Foundation of China/ ; NSFC/82201733//Natural Science Foundation of China/ ; },
abstract = {Obesity and lifelong body-shape fluctuation are associated with late-life structural brain damage, suggesting the involvement of metabolic pathways. The cerebral metabolic rate of oxygen (CMRO2) reflects hemodynamic and oxidative stress and precedes structural atrophy, but its role in adiposity-related brain change remains unclear. We examined whether current and life-course adiposity relate to CMRO2 and to structural change. A total of 303 community-dwelling adults aged 50 years and older were included. Body shape was assessed using Body Mass Index (BMI) and Body Roundness Index (BRI). Global CMRO2 was derived from TRUST and phase-contrast MRI. T1-weighted MPRAGE provided volumetry, and medial temporal atrophy (MTA) grading. General linear models estimated associations of BMI and BRI with CMRO2, including age interactions. Age-stratified mediation tested CMRO2 as a mediator of adiposity to MTA associations. Body-shape trajectories at ages 25, 40, 60, and current age were modeled and related to CMRO2 and metabolism-related regions. Adiposity was associated with lower CMRO2: with overweight (β = -1.12 μmol/100 g/min, 95%CI = (-1.96, -0.28)) and higher BRI (β = -1.31, 95%CI = (-2.36, -0.27)) showing stronger effects with advancing age. Among participants aged 70 years, CMRO2 mediated the association between BMI and MTA (indirect β = 0.06, 95%CI = (0.01, 0.14)). Three adulthood body-shape patterns emerged, and CMRO2 was lower in moderate increasing (β = -11.40; 95%CI = (-20.90, -1.90)) and high-rising (β = - 12.23; 95%CI = (-23.56, -0.90)) groups. Metabolism-related regions were larger in higher-risk patterns, particularly the left hypothalamus. Greater and prolonged adiposity is linked to reduced CMRO2 and related structural differences in older adults.},
}

@article {pmid41523970,
year = {2025},
author = {Xu, C and Kong, L and Mou, T and Tang, A and Hu, S and Lai, J},
title = {Vitamin B12 and Affective Disorders: A Focus on the Gut-Brain Axis.},
journal = {Alpha psychiatry},
volume = {26},
number = {6},
pages = {49138},
pmid = {41523970},
issn = {2757-8038},
abstract = {Accumulating evidence highlights the role of Vitamin B12 (VitB12) in the pathophysiology of affective disorders. However, its influence on brain function and the underlying mechanisms remain incompletely understood. In humans, VitB12 is obtained solely from dietary sources, primarily animal-based foods. VitB12 deficiency leads to the accumulation of homocysteine, a known contributor to emotional and behavioral dysregulation. VitB12 plays a critical role in maintaining neuron stability, synapsis plasticity, and regulating neuroinflammation by modulating key bioactive factors. These processes help to alleviate hippocampal damage, mitigate blood-brain barrier disruption, reduce oxidative stress, and enhance both structural and functional connectivity-collectively contributing to resilience against affective disorders. VitB12 from both diet and microbial sources is essential to gut homeostasis. Within the gut lumen, it stabilizes gut microbial communities, promotes short-chain fatty acid (SCFA) production, and supports neurotransmitter metabolism (e.g., serotonin and dopamine) via its role in S-adenosyl-l-methionine biosynthesis. Crucially, VitB12, gut microbiota, SCFAs, intestinal mucosa, and vagal nerve signaling interact synergistically within the gut-brain axis (GBA) to maintain gut microenvironment stability, protect the gut-blood barrier, and suppress neuroinflammatory cascades, eventually reducing the susceptibility to affective disorders. This review synthesizes current evidence on the involvement of VitB12 in the GBA, its association with mood regulation, and its potential as a nutritional adjunct in managing affective disorders. By elucidating this integrative mechanism, we provide new insights into targeting the GBA to improve clinical outcomes in affective disorders.},
}

@article {pmid41523966,
year = {2025},
author = {Wang, R and Hou, X and Li, R and Cheng, B and Zhou, C and Xue, C and Li, K and Deng, W},
title = {Maintenance of Noninvasive Brain Stimulation for Preventing Relapse in Depression: A Systematic Review and Meta-Analysis.},
journal = {Alpha psychiatry},
volume = {26},
number = {6},
pages = {49140},
pmid = {41523966},
issn = {2757-8038},
abstract = {BACKGROUND: Depression relapse rates remain high after acute treatment; this study evaluates the efficacy of maintenance noninvasive brain stimulation in preventing relapse and identifies optimal treatment parameters.

METHODS: This meta-analysis was conducted following PRISMA guidelines. We conducted a systematic search of PubMed, Embase, Web of Science, Cochrane Library, and PsycINFO databases up to January 5, 2025. The primary outcome was relapse rate.

RESULTS: A total of nine randomized controlled trials with 837 participants were included, six studies used electroconvulsive therapy (ECT) and three studies used repetitive transcranial magnetic stimulation (rTMS). Our findings indicate that ECT combined with pharmacotherapy or rTMS alone demonstrated superiority over pharmacotherapy alone in reducing the relapse of depression during 6, 9, 12-month maintenance treatment periods. Interestingly, ECT alone did not show significant results. In terms of stimulation parameters, the ECT combined with pharmacotherapy group mainly received right unilateral stimulation, while the ECT alone group had bitemporal stimulation. The stimulation frequency was similar between the two groups. In contrast, the rTMS-alone group had significantly higher stimulation frequencies than the ECT groups. We did not find any eligible studies on transcranial direct current stimulation, transcranial alternating current stimulation or magnetic seizure therapy, but they also showed potential in the maintenance treatment of depression, which warrants further investigation.

CONCLUSIONS: ECT combined with pharmacotherapy, or rTMS alone, is more effective than pharmacotherapy alone in preventing relapse of depression during 6 to 12 months of maintenance treatment. Future research should prioritize identifying the optimal treatment regimen and exploring the potential of combination therapies.

THE PROSPERO REGISTRATION: CRD42023490546, https://www.crd.york.ac.uk/PROSPERO/view/CRD42023490546.},
}

@article {pmid41523191,
year = {2026},
author = {van Balen, B and Ramsey, NF and Vansteensel, MJ},
title = {Relational personhood: the missing link for evaluating clinical impact of brain-computer interfaces.},
journal = {Brain communications},
volume = {8},
number = {1},
pages = {fcaf470},
pmid = {41523191},
issn = {2632-1297},
}

@article {pmid41521389,
year = {2026},
author = {Yilmaz Kars, M and Akkar, I and Dogan, MH and Turgut, ZI and Cicek, O and Dikmeer, A and Kollu, K and Cakir Ozden, EC and Kizilarslanoglu, MC},
title = {EXPRESS: The CRP/Albumin Ratio (CAR) may be more strongly linked to delirium than other indices derived from laboratory parameters in older patients in an intensive care unit.},
journal = {Journal of investigative medicine : the official publication of the American Federation for Clinical Research},
volume = {},
number = {},
pages = {10815589261415891},
doi = {10.1177/10815589261415891},
pmid = {41521389},
issn = {1708-8267},
abstract = {The aim of this study is to investigate the association of delirium with laboratory-derived indices and ratios in patients staying in an intensive care unit (ICU). Delirium was diagnosed according to DSM-5 criteria, and laboratory data obtained at the time of diagnosis were retrospectively analyzed. The following indices were calculated: C-reactive protein(CRP)/albumin ratio(CAR), CRP-albumin-lymphocyte(CALLY), B12-CRP(BCI), Systemic Immune-Inflammation(SII), Prognostic Nutritional Index(PNI), Advanced Lung Cancer Inflammation (ALI), Systemic Inflammation Response indices (SIRI) and Glasgow Prognostic Score (GPS). In addition, inflammation markers derived from the complete blood count were also analyzed. They were compared between patients with and without delirium. The study included 215 patients, of whom 104 had delirium (median age 76 years, 51.6% female). Patients with delirium were older than those without delirium(p=0.008). The median CAR index was higher in patients with delirium (3.4 mg/g, 0.02-28.23) compared to those without delirium (2.19 mg/g,0.02-16.74), with borderline statistical significance(p=0.071). No statistically significant differences were found in other indices and laboratory parameters between patients with delirium and those without it (p>0.05 for all). When patients were stratified into tertiles based on CAR levels, the occurrence of delirium was significantly higher in the third tertile than in the other two tertiles (p=0.020). Even after adjusting for all significant confounding factors, CAR remained independently associated with delirium [Odds ratio(OR):1.099, 95% confidence interval(CI):1.002-1.205, p=0.046]. The findings of this study suggest that the CAR index may serve as an independent associated factor for delirium compared to other laboratory-derived markers in critically ill patients.},
}

@article {pmid41521257,
year = {2026},
author = {Wang, S and Song, X and Song, X and Gu, Y and Cong, Z and Shen, Y and Yu, L},
title = {Non-Invasive Brain-Computer Interfaces: Converging Frontiers in Neural Signal Decoding and Flexible Bioelectronics Integration.},
journal = {Nano-micro letters},
volume = {18},
number = {1},
pages = {193},
pmid = {41521257},
issn = {2150-5551},
abstract = {The development of non-invasive brain-computer interfaces (BCIs) relies on multidisciplinary integration across neuroscience, artificial intelligence, flexible electronics, and systems engineering. Recent advances in deep learning have significantly improved the accuracy and robustness of neural signal decoding. Parallel progress in electrode design-particularly through the use of flexible and stretchable materials like nanostructured conductors and novel fabrication strategies-has enhanced wearability and operational stability. Nevertheless, key challenges persist, including individual variability, biocompatibility limitations, and susceptibility to interference in complex environments. Further validation and optimization are needed to address gaps in generalization capability, long-term reliability, and real-world operational robustness. This review systematically examines the representative progress in neural decoding algorithms and flexible bioelectronic platforms over the past decade, highlighting key design principles, material innovations, and integration strategies that are poised to advance non-invasive BCI capabilities. It also discusses the importance of multimodal data fusion, hardware-software co-optimization, and closed-loop control strategies. Furthermore, the review discusses the application potential and associated engineering challenges of this technology in clinical rehabilitation and industrial translation, aiming to provide a reference for advancing non-invasive BCIs toward practical and scalable deployment.},
}

@article {pmid41521019,
year = {2026},
author = {Lv, Z and Li, X and Zhang, X},
title = {Commentary on He et al.: From static association to dynamic causation - a methodological leap in understanding and addressing addiction.},
journal = {Addiction (Abingdon, England)},
volume = {},
number = {},
pages = {},
doi = {10.1111/add.70312},
pmid = {41521019},
issn = {1360-0443},
support = {2024YFF0507600//National Key R&D Program of China/ ; 2021ZD0202101//Chinese National Programs for Brain Science and Brain-like Intelligence Technology/ ; 32571266//National Natural Science Foundation of China/ ; 32171080//National Natural Science Foundation of China/ ; 32400919//National Natural Science Foundation of China/ ; 32200914//National Natural Science Foundation of China/ ; ZSYS(2024)001//Project of Guizhou Key Laboratory of Artificial Intelligence and Brain-inspired Computing QianKeHe Platform/ ; 2408085QC081//Natural Science Foundation of Anhui Province/ ; 24YJCZH014//Ministry of Education of China/ ; //Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; },
}

@article {pmid41518865,
year = {2026},
author = {Hu, W and Xiao, J and Li, L and Zhao, W and Feng, Y and Shan, X and Chen, H and Duan, X},
title = {Developmental organization of neural dynamics supporting social processing: Evidence from naturalistic fMRI in children and adults.},
journal = {Developmental cognitive neuroscience},
volume = {78},
number = {},
pages = {101670},
doi = {10.1016/j.dcn.2026.101670},
pmid = {41518865},
issn = {1878-9307},
abstract = {The development of social cognition underpins significant implications for diagnosing and treating neurodevelopmental disorders such as autism spectrum disorder. This study investigates the dynamic neural organization of social cognition in children (n = 60, ages 3-10) and adults (n = 55) using a naturalistic fMRI paradigm that tracks continuous brain activity during real-world social interactions. We identify four distinct co-activation patterns (CAP) that reflect a functional hierarchy, ranging from basic sensory processing to complex social-cognitive integration. These brain state dynamics reveal significant developmental differences: children exhibit immature transitions, often bypassing intermediate states (e.g., salience-driven filtering, State 3) and prematurely shifting from early sensory encoding (State 1) to internally-directed integration (State 2). Moreover, during mentalizing and pain events, children show reduced modulation of sensory and perceptual brain states, indicating limited cognitive flexibility that is essential for social interaction. Structural equation modeling reveals a developmental cascade linking the maturation of sensory (State 1), perceptual filtering (State 3), and social-cognitive (State 2) processing states. This pathway is mediated by individual differences in Theory of Mind (ToM) development and further predicts empathic abilities. These findings advance our understanding of how brain state reorganization supports social cognitive maturation and offer new insights into neurodevelopmental disorders.},
}

@article {pmid41518463,
year = {2026},
author = {Fernández-Rodríguez, Á and Velasco-Álvarez, F and Vizcaíno-Martín, FJ and Ron-Angevin, R},
title = {Evaluation of video background and stimulus transparency in a visual ERP-based BCI under RSVP.},
journal = {Medical & biological engineering & computing},
volume = {},
number = {},
pages = {},
pmid = {41518463},
issn = {1741-0444},
abstract = {Rapid serial visual presentation (RSVP) is a promising paradigm for visual brain-computer interfaces (BCIs) based on event-related potentials (ERPs) for patients with limited muscle and eye movement. This study explores the impact of video background and stimulus transparency on BCI control, factors that have not been previously examined together under RSVP. Two experimental sessions were conducted with 12 participants each. Four BCI conditions were tested: opaque pictograms, and white background (A255W); opaque pictograms, and video background (A255V); intermediate transparent pictograms, and video background (A085); and highly transparent pictograms, and video background (A028V). The results indicated that the video background had a negative impact on BCI performance. In addition, the intermediate transparent pictograms (A085V) proved to be balanced, as it did not show significant performance differences compared to opaque pictograms (A255V) but was rated significantly better by users on subjective measures related to attending to the video background. Therefore, in applications where users must shift attention between BCI control and their surroundings, balancing stimulus transparency is a suitable option for enhancing system usability. These findings are particularly relevant for designing asynchronous ERP-BCIs using RSVP for patients with impaired oculomotor control.},
}

@article {pmid41518099,
year = {2026},
author = {Berwal, U and Kumar, V},
title = {Exploring assistive technology in adaptive sports: a bibliometric analysis.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {},
number = {},
pages = {1-13},
doi = {10.1080/17483107.2025.2612557},
pmid = {41518099},
issn = {1748-3115},
abstract = {Assistive technology in adaptive sports has become a transformative force for individuals with disabilities. It helps disabled athletes to overcome physical and cognitive barriers to participate in sports. This study presents a bibliometric analysis of assistive technology in adaptive sports to examine its development, key themes, and emerging trends. The analysis used data from 8,660 documents across 2,137 sources retrieved from the Scopus database from 1987 to 2025. The result shows that due to advancements in technology and increased awareness of inclusivity in sports, the research output grows exponentially after 2010. Among these research outputs, the most used theme was rehabilitation. The other emerging topics incorporated into adaptive sports are virtual reality, brain-computer interfaces, wearable technologies. Further, the co-occurrence network analysis reveals that there are strong interdisciplinary connections between rehabilitation, assistive technology, and physical activity. However, several areas remain unexplored such as digital health and telehealth applications in adaptive sports. Thus, bibliometric analysis provides a roadmap for future research by identifying critical trends and gaps. This study highlights the interdisciplinary collaboration and technological innovation in advancing accessibility and inclusivity for athletes with disabilities.},
}

@article {pmid41516662,
year = {2025},
author = {Gomez-Rivera, A and Collazos-Huertas, DF and Cárdenas-Peña, D and Álvarez-Meza, AM and Castellanos-Dominguez, G},
title = {Gaussian Connectivity-Driven EEG Imaging for Deep Learning-Based Motor Imagery Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {1},
pages = {},
doi = {10.3390/s26010227},
pmid = {41516662},
issn = {1424-8220},
support = {91908//Ministerio de Ciencia, Tecnología e Innovación/ ; 57661//Universidad Nacional de Colombia/ ; },
mesh = {*Electroencephalography/methods ; Humans ; Brain-Computer Interfaces ; *Deep Learning ; Normal Distribution ; Neural Networks, Computer ; Brain/physiology ; *Imagination/physiology ; Algorithms ; },
abstract = {Electroencephalography (EEG)-based motor imagery (MI) brain-computer interfaces (BCIs) hold considerable potential for applications in neuro-rehabilitation and assistive technologies. Yet, their development remains constrained by challenges such as low spatial resolution, vulnerability to noise and artifacts, and pronounced inter-subject variability. Conventional approaches, including common spatial patterns (CSP) and convolutional neural networks (CNNs), often exhibit limited robustness, weak generalization, and reduced interpretability. To overcome these limitations, we introduce EEG-GCIRNet, a Gaussian connectivity-driven EEG imaging representation network coupled with a regularized LeNet architecture for MI classification. Our method integrates raw EEG signals with topographic maps derived from functional connectivity into a unified variational autoencoder framework. The network is trained with a multi-objective loss that jointly optimizes reconstruction fidelity, classification accuracy, and latent space regularization. The model's interpretability is enhanced through its variational autoencoder design, allowing for qualitative validation of its learned representations. Experimental evaluations demonstrate that EEG-GCIRNet outperforms state-of-the-art methods, achieving the highest average accuracy (81.82%) and lowest variability (±10.15) in binary classification. Most notably, it effectively mitigates BCI illiteracy by completely eliminating the "Bad" performance group (<60% accuracy), yielding substantial gains of ∼22% for these challenging users. Furthermore, the framework demonstrates good scalability in complex 5-class scenarios, performing competitive classification accuracy (75.20% ± 4.63) with notable statistical superiority (p = 0.002) against advanced baselines. Extensive interpretability analyses, including analysis of the reconstructed connectivity maps, latent space visualizations, Grad-CAM++ and functional connectivity patterns, confirm that the model captures genuine neurophysiological mechanisms, correctly identifying integrated fronto-centro-parietal networks in high performers and compensatory midline circuits in mid-performers. These findings suggest that EEG-GCIRNet provides a robust and interpretable end-to-end framework for EEG-based BCIs, advancing the development of reliable neurotechnology for rehabilitation and assistive applications.},
}

*Electroencephalography/methods
Humans
Brain-Computer Interfaces
*Deep Learning
Normal Distribution
Neural Networks, Computer
Brain/physiology
*Imagination/physiology
Algorithms

@article {pmid41516650,
year = {2025},
author = {Li, J and Yang, H and Xu, M and Wu, Y and Shou, X and Huang, Z and Hao, Y and Wu, F and Ruan, W and Zhang, Y and Cui, Z and Wei, Y},
title = {Task-Dependent Cortical Oscillatory Dynamics in Functional Constipation.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {1},
pages = {},
doi = {10.3390/s26010211},
pmid = {41516650},
issn = {1424-8220},
support = {62373326//National Natural Science Foundation of China/ ; 32471148//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Constipation/physiopathology ; Male ; Electroencephalography/methods ; Female ; Adult ; Middle Aged ; *Cerebral Cortex/physiopathology ; Cognition/physiology ; Defecation/physiology ; Brain/physiopathology ; },
abstract = {Functional constipation (FC) is a common functional gastrointestinal disorder thought to arise from the brain-gut axis dysfunction, yet direct human neurophysiological evidence is lacking. We recorded high-density electroencephalography (EEG) data in 21 FC patients and 37 healthy controls across resting, cognitive, and defecation-related tasks. We observed that FC patients displayed a consistent, task-dependent signature compared with healthy controls. At the regional level, FC patients exhibited increased alpha during both resting and defecation-related tasks, reduced temporal gamma during defecation-related tasks, as well as elevated temporal theta during the cognitive task. At the global level, we found altered network properties, such as global efficiency in the delta and beta band networks during resting and defecation-related tasks. These findings establish a direct neurophysiological link between specific, condition-dependent perturbations in cortical rhythm activity and FC pathophysiology. Our work implicates the brain-gut axis in symptom generation and opens a path toward EEG-based biomarkers and targeted neuromodulatory therapies.},
}

Humans
*Constipation/physiopathology
Male
Electroencephalography/methods
Female
Adult
Middle Aged
*Cerebral Cortex/physiopathology
Cognition/physiology
Defecation/physiology
Brain/physiopathology

@article {pmid41514692,
year = {2025},
author = {Marques, L and Rodrigues, DP and Duarte, RC and Calado, R},
title = {Thermal Limits of the Estuarine Amphipod Melita palmata Under Different Salinities and Its Relevance for Aquaculture Production.},
journal = {Animals : an open access journal from MDPI},
volume = {16},
number = {1},
pages = {},
doi = {10.3390/ani16010004},
pmid = {41514692},
issn = {2076-2615},
support = {10.54499/2022.01620.PTDC//Fundação para a Ciência e Tecnologia/ ; 10.54499/UID/50017/2025//Fundação para a Ciência e Tecnologia/ ; 10.54499/LA/P/0094/2020//Fundação para a Ciência e Tecnologia/ ; },
abstract = {Estuarine organisms experience frequent fluctuations in salinity and temperature, facing major challenges to their physiological homeostasis. Such variability can promote high energetic costs for osmoregulation, potentially reducing tolerance to additional stressors. We investigated the effect of salinity on the thermal tolerance of the estuarine amphipod Melita palmata (Montagu, 1804), a species of growing interest for aquaculture, either as live feed or as a potential source for essential fatty acids in formulated diets. The critical thermal maximum (CTmax) was determined for males and females collected from three sites within a temperate coastal lagoon (Ria de Aveiro, Portugal) characterized by different salinity regimes (15, 20, and 30). Individuals from lower-salinity environments exhibited significantly lower CTmax values than those from higher salinities, indicating that osmoregulatory costs may restrict thermal resistance. No significant sex-based differences in CTmax were detected. However, thermal safety margins (TSMs) increased with salinity, indicating greater thermal tolerance under higher salinity conditions, and differences in body condition index (BCI) between sites suggest salinity-related effects on growth performance. These results highlight that the elevated energetic demands of osmoregulation under hypo-osmotic conditions can constrain the thermal limits of M. palmata, underscoring the complex trade-offs between environmental variability and physiological performance in estuarine habitats. Beyond its ecological implications, understanding the physiological responses of M. palmata to salinity and temperature is key, optimising its use in aquaculture. The species' physiological plasticity under such variable conditions reinforces its suitability for aquaculture production, particularly in earthen ponds in estuarine environments.},
}

@article {pmid41512946,
year = {2026},
author = {Xia, S and Zhao, X and Lv, B and Gan, Y and Kang, Y and Long, J and Liu, F and Hu, X and He, G and Xing, H and Cheng, B},
title = {Functional Gradient Alteration and Structural Remodeling in Postpartum Women.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121702},
doi = {10.1016/j.neuroimage.2026.121702},
pmid = {41512946},
issn = {1095-9572},
abstract = {Postpartum women (PW) undergo profound brain functional and structural reorganization to support maternal adaptation. However, the specific large-scale neural adaptation mechanisms remain unclear. The current study employed a multimodal MRI approach integrating functional gradient analysis, graph-theoretical network metrics, and morphometry to explore the brain connectome reorganization across the postpartum period and its clinical correlates in 209 participants (134 PW and 75 healthy nulliparous women (HNW)). Compared to HNW, PW exhibited a significant contraction of the first two principal functional gradients, reduced local network segregation and less efficient information processing, accompanied by matter volume (GMV) reductions. Mediation analysis revealed that GMV alterations in PW modulate functional gradient reorganization by influencing network integration and segregation. These neural changes were closely linked to clinical symptoms including sleep quality and anxiety. Our findings revealed a large-scale network reconfiguration in PW, simultaneously elucidating neurobiological mechanisms of adaptive plasticity in postpartum period.},
}

@article {pmid41512029,
year = {2026},
author = {Liu, P and Zhou, L and Xu, D and An, D and Lu, Y and Hu, B and Shao, Y and Huang, N and Guo, C and Chen, L and Li, J and Li, J and Liang, F and Liu, J and Huang, G and Mei, Y and Li, R and Song, E},
title = {A self-wrapping, bioresorbable neural interface for wireless multimodal therapy of localized peripheral nerve injury.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {123},
number = {2},
pages = {e2521817123},
doi = {10.1073/pnas.2521817123},
pmid = {41512029},
issn = {1091-6490},
support = {2022ZD0209900//Ministry of Science and Technology of the People's Republic of China (MOST)/ ; 62204057 62304044//MOST | National Natural Science Foundation of China (NSFC)/ ; 22ZR1406400//Science and Technology Commission of Shanghai Municipality (STCSM)/ ; },
mesh = {Animals ; *Peripheral Nerve Injuries/therapy ; Rats ; *Wireless Technology/instrumentation ; Rats, Sprague-Dawley ; Combined Modality Therapy ; *Absorbable Implants ; Photothermal Therapy/methods ; Drug Delivery Systems/methods ; },
abstract = {High-precision in vivo therapeutic technologies that establish three-dimensional (3D), multimodal neural interfaces with targeted biotissues offer significant clinical potential for the timely treatments of localized peripheral nerve injury (PNI). Current approaches for this purpose such as implantable devices face challenges in terms of percutaneous wires and/or nondegradable designs, and support only single-mode operation that lack microscale spatial resolution. Here, we develop a miniaturized, self-wrapping system that yields wireless, multimodal neural interfaces with 3D adaptation across localized peripheral nerves at scales ranging from tens of micrometers (15 μm) to millimeters. Such platform integrates multilayer architectures that include SiNx layers as the mechanically triggered substrate for 3D wrapping, with multimodal treatments via MXene and drug-loaded layers for photothermal stimulation and pharmacological release. Experimental and computational studies establish operational principle as the basis for the combination of long-term photothermal therapy and transient drug delivery at high spatiotemporal resolution. In vivo tests on living rat models demonstrate that the implantable neural interface can roll up across the localized, dynamic surface of injured nerves, providing sustained treatments over 1 mo in a fully bioresorbable design after the healing process. These findings create future opportunities of such wireless, multimodal system with 3D self-wrapping techniques for precise PNI therapeutic strategies.},
}

Animals
*Peripheral Nerve Injuries/therapy
Rats
*Wireless Technology/instrumentation
Rats, Sprague-Dawley
Combined Modality Therapy
*Absorbable Implants
Photothermal Therapy/methods
Drug Delivery Systems/methods

@article {pmid41510853,
year = {2026},
author = {Zhang, X and Liu, X and Liu, M and Li, Y and Yan, X and Zhang, X and Xu, J},
title = {The Integrated Application and Future Trends of Multimodal Neuromodulation Techniques in Spinal Cord Injury Rehabilitation.},
journal = {Neurology India},
volume = {74},
number = {1},
pages = {3-11},
pmid = {41510853},
issn = {1998-4022},
mesh = {Humans ; *Spinal Cord Injuries/rehabilitation ; *Transcranial Magnetic Stimulation/methods ; *Transcranial Direct Current Stimulation/methods ; Brain-Computer Interfaces ; *Spinal Cord Stimulation/methods ; Recovery of Function/physiology ; Neuronal Plasticity/physiology ; Combined Modality Therapy ; },
abstract = {Spinal cord injury (SCI) remains a severe condition that leads to permanent motor and sensory impairments, significantly affecting patients' quality of life. In recent years, neuromodulation techniques such as spinal cord stimulation (SCS), transcranial magnetic stimulation (TMS), and transcranial direct current stimulation (tDCS) have shown promising results in promoting neural plasticity and functional recovery. However, the limitations of single-modality approaches have spurred the development of multimodal neuromodulation strategies. This review systematically analyzes the integrated application of multimodal neuromodulation techniques in SCI rehabilitation. We first provide an overview of current neuromodulation methods, including SCS, TMS, tDCS, and brain-computer interface (BCI), highlighting their individual mechanisms and clinical outcomes. Next, we discuss the synergistic effects of combining these techniques, such as SCS with TMS or BCI, which act on multiple levels of the nervous system to enhance neuroplasticity, reconstruct neural networks, and modulate neurotransmitter release. Additionally, we explore the mechanisms underlying multimodal neuromodulation, emphasizing its role in promoting axonal regeneration, synaptic reconnection, and adaptive functional recovery. Despite the promising advancements, challenges remain, including technical complexity, safety concerns, and the heterogeneity of SCI patients. Addressing these limitations requires standardized treatment protocols and further clinical validation. Future trends, such as the development of closed-loop systems, artificial intelligence-driven precision rehabilitation, and personalized therapies, will likely drive innovations in this field. In conclusion, multimodal neuromodulation techniques offer a synergistic and integrative approach for SCI rehabilitation, providing new avenues for clinical intervention. This review underscores the importance of combining complementary techniques to optimize neural recovery and highlights the potential for future breakthroughs in neurorehabilitation.},
}

Humans
*Spinal Cord Injuries/rehabilitation
*Transcranial Magnetic Stimulation/methods
*Transcranial Direct Current Stimulation/methods
Brain-Computer Interfaces
*Spinal Cord Stimulation/methods
Recovery of Function/physiology
Neuronal Plasticity/physiology
Combined Modality Therapy

@article {pmid41506085,
year = {2026},
author = {Wang, Y and Xu, S},
title = {Relationship between artificial intelligence tool usage experience and academic stress among college students: Mediating role of loneliness and moderating role of academic self-efficacy.},
journal = {Acta psychologica},
volume = {263},
number = {},
pages = {106220},
doi = {10.1016/j.actpsy.2026.106220},
pmid = {41506085},
issn = {1873-6297},
abstract = {As artificial intelligence (AI) rapidly integrates into higher education, AI tools are increasingly being utilized to support student learning. Although these tools offer efficiency and convenience, their psychological implications-particularly vis-à-vis academic stress-remain unclear. This study investigated the relationship between AI tool usage experience and academic stress among college students, focusing on the potential mediating role of loneliness and the moderating role of academic self-efficacy. Overall, 624 university students were surveyed using the AI Tool Usage Experience Scale, UCLA Loneliness Scale, Academic Stress Scale, and Academic Self-Efficacy Scale. The following three key findings were observed: (1) AI tool usage experience significantly positively predicted students' academic stress. (2) Loneliness partially mediated this relationship. (3) Academic self-efficacy significantly moderated the mediation pathway's first stage. Specifically, AI usage's positive predictive effect on loneliness was stronger (weaker) for students with higher (lower) academic self-efficacy levels. These findings suggest that AI tool usage not only directly influences academic stress but also contributes indirectly through heightened feelings of loneliness, particularly among students with strong self-efficacy beliefs. This study underscores the complex psychological mechanisms underlying students' interactions with AI in educational settings.},
}

@article {pmid41505837,
year = {2026},
author = {Zhang, K and Dong, S and Shi, P and Hu, D and Gao, G and Yang, J and Gan, T and Rao, N},
title = {GenoPath-MCA: Multimodal masked cross-attention between genomics and pathology for survival prediction.},
journal = {Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society},
volume = {128},
number = {},
pages = {102699},
doi = {10.1016/j.compmedimag.2026.102699},
pmid = {41505837},
issn = {1879-0771},
abstract = {Survival prediction using whole slide images (WSIs) and bulk genes is a key task in computational pathology, essential for automated risk assessment and personalized treatment planning. While integrating WSIs with genomic features presents challenges due to inconsistent modality granularity, semantic disparity, and the lack of personalized fusion. We propose GenoPath-MCA, a novel multimodal framework that models dense cross-modal interactions between histopathology and gene expression data. A masked co-attention mechanism aligns features across modalities, and the Multimodal Masked Cross-Attention Module (M2CAM) jointly captures high-order image-gene and gene-gene relationships for enhanced semantic fusion. To address patient-level heterogeneity, we develop a Dynamic Modality Weight Adjustment Strategy (DMWAS) that adaptively modulates fusion weights based on the discriminative relevance of each modality. Additionally, an importance-guided patch selection strategy effectively filters redundant visual inputs, reducing computational cost while preserving critical context. Experiments on public multimodal cancer survival datasets demonstrate that GenoPath-MCA significantly outperforms existing methods in terms of concordance index and robustness. Visualizations of multimodal attention maps validate the biological interpretability and clinical potential of our approach.},
}

@article {pmid41501731,
year = {2026},
author = {Zhang, W and Xiong, B and Shen, D and Wang, W},
title = {Characteristics of resting-state EEG after deep brain stimulation in nucleus accumbens and anterior limb of internal capsule: a pilot study.},
journal = {BMC psychiatry},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12888-025-07681-8},
pmid = {41501731},
issn = {1471-244X},
}

@article {pmid41103211,
year = {2026},
author = {Martín, I and Zamora-López, G and Fousek, J and Schirner, M and Ritter, P and Jirsa, V and Deco, G and Patow, G},
title = {TVB C++: A Fast and Flexible Back-End for The Virtual Brain.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {13},
number = {2},
pages = {e06440},
doi = {10.1002/advs.202406440},
pmid = {41103211},
issn = {2198-3844},
support = {PID2021-122136OB-C22//Ministerio de Ciencia, Innovación y Universidades/ ; PID2022-136216NB-I00//Ministerio de Ciencia, Innovación y Universidades/ ; 785907 (HBP SGA2)//H2020 Excellent Science/ ; Horizon EBRAINS2.0 (101147319)//HORIZON EUROPE Framework Programme/ ; Virtual Brain Twin (101137289)//HORIZON EUROPE Framework Programme/ ; EBRAINS-PREP 101079717//HORIZON EUROPE Framework Programme/ ; AISN - 101057655//HORIZON EUROPE Framework Programme/ ; EBRAIN-Health 101058516//HORIZON EUROPE Framework Programme/ ; EICgrantPHRASE(101058240)//HORIZON EUROPE Framework Programme/ ; DigitalEuropeTEF-Health(101100700)//HORIZON EUROPE Framework Programme/ ; BRIDGE(101219311)//HORIZON EUROPE Framework Programme/ ; SHAIPED(101195135)//HORIZON EUROPE Framework Programme/ ; CoordinaTEF(101168074)//HORIZON EUROPE Framework Programme/ ; SFB 1436 (project ID 425899996)//Kinderherzen Fördergemeinschaft Deutsche Kinderherzzentren/ ; SFB 1315 (project ID 327654276)//Kinderherzen Fördergemeinschaft Deutsche Kinderherzzentren/ ; SFB 936 (project ID 178316478//Kinderherzen Fördergemeinschaft Deutsche Kinderherzzentren/ ; SFB-TRR 295 (project ID 424778381)//Kinderherzen Fördergemeinschaft Deutsche Kinderherzzentren/ ; SPP Computational Connectomics RI 2073/6-1//Kinderherzen Fördergemeinschaft Deutsche Kinderherzzentren/ ; RI 2073/10-2//Kinderherzen Fördergemeinschaft Deutsche Kinderherzzentren/ ; RI 2073/9-1//Kinderherzen Fördergemeinschaft Deutsche Kinderherzzentren/ ; BECAUSE-Y 504745852//DFG Clinical Research Group/ ; CZ.02.01.01/00/22\_008/0004643//ERDF/ ; 2021SGR00917//AGAUR/ ; 2021SGR01035//AGAUR/ ; 945539(HBPSGA3)//Horizon 2020/ ; 425899996//German Research Foundation/ ; 327654276//German Research Foundation/ ; 178316478//German Research Foundation/ ; 424778381//German Research Foundation/ ; RI2073/6-1//SPP Computational Connectomics/ ; RI2073/10-2//SPP Computational Connectomics/ ; RI2073/9-1//SPP Computational Connectomics/ ; ANR-22-PESN-0012//Agence Nationale de la Recherche/ ; NEMESIS(101071900)/ERC_/European Research Council/International ; },
mesh = {Humans ; *Brain/physiology ; *Computer Simulation ; *Brain-Computer Interfaces ; *User-Computer Interface ; *Software ; },
abstract = {This study introduces TVB C++, a streamlined and fast C++ Back-End for The Virtual Brain (TVB), a renowned platform and a benchmark tool for full-brain simulation. TVB C++ is engineered with speed as a primary focus while retaining the flexibility and ease of use characteristic of the original TVB platform. Positioned as a complementary tool, TVB serves as a prototyping platform, whereas TVB C++ becomes indispensable when performance is paramount, particularly for large-scale simulations and leveraging advanced computation facilities like supercomputers. Developed as a TVB-compatible Back-End, TVB C++ seamlessly integrates with the original TVB implementation, facilitating effortless usage. Users can easily configure TVB C++ to execute the same code as in TVB but with enhanced performance and parallelism capabilities. As a consequence, TVB C++ will enable the widespread use of individualized models that will open the possibility of designed tailored solutions at the individual patient level.},
}

Humans
*Brain/physiology
*Computer Simulation
*Brain-Computer Interfaces
*User-Computer Interface
*Software

@article {pmid41499961,
year = {2026},
author = {Pan, L and Wang, K and Yi, W and Zhang, Y and Xu, M and Ming, D},
title = {CTSSP: A temporal-spectral-spatial joint optimization algorithm for motor imagery EEG decoding.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae34ea},
pmid = {41499961},
issn = {1741-2552},
abstract = {OBJECTIVE: Motor imagery brain-computer interfaces (MI-BCIs) hold significant promise for neurorehabilitation, yet their performance is often compromised by EEG non-stationarity, low signal-to-noise ratios, and severe cross-session variability. Current decoding methods typically suffer from fragmented optimization, treating temporal, spectral, and spatial features in isolation.

APPROACH: We propose common temporal-spectral-spatial patterns (CTSSP), a unified framework that jointly optimizes filters across all three domains. The algorithm integrates: 1) multi-scale temporal segmentation to capture dynamic neural evolution, 2) channel-adaptive finite impulse response (FIR) filters to enhance task-relevant rhythms, and 3) low-rank regularization to improve generalization.

MAIN RESULTS: Evaluated across five public datasets, CTSSP achieves state-of-the-art performance. It yielded mean accuracies of 76.9% (within-subject), 68.8% (cross-session), and 69.8% (cross-subject). In within-subject and cross-session scenarios, CTSSP significantly outperformed competing baselines by margins of 2.6-14.6% (p < 0.001) and 2.3-13.8% (p < 0.05), respectively. In cross-subject tasks, it achieved the highest average accuracy, proving competitive against deep learning models. Neurophysiological visualization confirms that the learned filters align closely with motor cortex activation mechanisms.

SIGNIFICANCE: CTSSP effectively overcomes the limitations of decoupled feature extraction by extracting robust, interpretable, and coupled temporal-spectral-spatial patterns. It offers a powerful, data-efficient solution for decoding MI EEG in noisy, non-stationary environments. The code is available at https://github.com/PLC-TJU/CTSSP.},
}

@article {pmid41499837,
year = {2026},
author = {Ge, H and Feng, T and Wu, H and Hu, H and Li, J and Wu, X},
title = {Uncovering the Cognitive Mechanisms of Risk Decision-Making among ICU Nurses in Complex Clinical Contexts.},
journal = {Intensive & critical care nursing},
volume = {93},
number = {},
pages = {104329},
doi = {10.1016/j.iccn.2025.104329},
pmid = {41499837},
issn = {1532-4036},
abstract = {OBJECTIVES: The intensive care unit is a high-stakes, information-intensive environment requiring nurses to make rapid and accurate decisions. This study aimed to elucidate the cognitive and neural mechanisms underlying nurses' risk decision-making under time pressure and complex clinical demands.

METHODS: Thirty ICU nurses participated in a computer-based multitasking experiment simulating concurrent medical multitasking scenarios, with twenty-one valid datasets analyzed. Participants performed priority judgments under high- and low-risk conditions while EEG signals were continuously recorded. Event-related potential components and oscillatory activities across δ, θ, α, and β frequency bands were analyzed. Gaussian Hidden Markov Models were used to characterize cognitive state transition dynamics aligned to task events.

RESULTS: Risk decision-making emerged as a multi-stage, dynamically coordinated process involving four distinct cognitive patterns: monolithic stability progression, compulsory path lock-in, multi-path flexible convergence, and flow separation and premature convergence. Correct decisions were associated with enhanced low-frequency oscillations (δ, θ) and stable HMM transitions, reflecting efficient integration and adaptive cognitive control. In contrast, incorrect decisions exhibited early perceptual inefficiency, unstable state transitions, and premature cognitive closure under high-risk conditions.

CONCLUSIONS: This study is the first to identify four distinct dynamic cognitive patterns of risk decision-making in a simulated ICU multitasking context. The findings indicate that decision accuracy is closely linked to coordinated state-transition dynamics rather than isolated neural activations, highlighting the importance of adaptive cognitive control in clinical judgment.

Although the present findings are exploratory, they may provide a preliminary reference for future research on brain-machine collaboration in clinical nursing contexts. In particular, future work could examine how EEG-decoded cognitive states might be incorporated as input information for robot-assisted systems to characterize nurses' cognitive intentions during risk tasks. Further studies with larger samples and in more realistic clinical settings are needed to validate the model's robustness and generalizability.},
}

@article {pmid41499809,
year = {2026},
author = {Aktaş, FA and Eken, A and Erogul, O},
title = {Explainable AI for Pain Perception: Subject-Independent EEG Decoding Using DeepSHAP and CNNs.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ae34b4},
pmid = {41499809},
issn = {2057-1976},
abstract = {Accurate classification of pain levels is essential for clinical monitoring, particularly in clinical populations with limited verbal communication. This study explores the feasibility of decoding pain from EEG using explainable deep learning. Approach: EEG signals from 50 subjects exposed to low and high pain stimuli were analyzed. A 1D convolutional neural network (CNN) was trained using leave-one-subject-out (LOSO) cross-validation. To enhance interpretability, DeepSHAP was applied to identify frequency-specific contributions of EEG features to the model's decisions. Main Results: The CNN achieved a classification accuracy of 95.85%, outperforming traditional classifiers (SVM, LDA, RF, etc.) on the same dataset. Explainability analysis showed that increased beta activity (14-15 Hz) was associated with high pain, while alpha (11-12 Hz) theta and delta bands correlated with lower pain states. Significance: This work demonstrates the potential of explainable deep learning in real-time, subject-independent pain decoding. The results support the integration of XAI techniques into EEG-based brain-computer interface (BCI) systems for objective pain monitoring.},
}

@article {pmid41499216,
year = {2026},
author = {Zhai, W and Sun, L and Fang, W and Dong, Y and Cheng, C and Liu, Y and Zhou, Y and Ji, J and Wu, L and Pan, A and Gamazon, ER and Pan, XF and Zhou, D},
title = {Cross-ancestry information transfer framework improves protein abundance prediction and protein-trait association identification.},
journal = {Briefings in bioinformatics},
volume = {27},
number = {1},
pages = {},
doi = {10.1093/bib/bbaf707},
pmid = {41499216},
issn = {1477-4054},
support = {82204118//National Natural Sciences Foundation of China/ ; K-20230085//Healthy Zhejiang One Million People Cohort/ ; SN-ZJU-SIAS-0021//Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study/ ; 82473646//National Natural Science Foundation of China/ ; 2024NSFSC0578//Sichuan Provincial Natural Science Foundation/ ; 2024YFC2707602//National Key Research and Development Program of China/ ; YJ202346//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; *Genome-Wide Association Study ; *Proteomics/methods ; *Proteome/genetics ; *Quantitative Trait Loci ; },
abstract = {Genetics-informed proteome-wide association studies (PWASs) provide an effective way to uncover proteomic mechanisms underlying complex diseases. PWAS relies on an ancestry-matched reference panel to model the impact of genetically determined protein expression on phenotype. However, reference panels from underrepresented populations remain relatively limited. We developed a multi-ancestry framework to enhance protein prediction in these populations by integrating diverse information-sharing strategies into a Multi-Ancestry Best-performing Model (MABM). Results indicated that MABM increased the prediction performance with higher performance observed in both cross-validation and an external dataset. Leveraging the Biobank Japan, we identified three times as many significant PWAS associations using MABM as using Lasso model. Notably, 47.5% of the MABM specific associations were reproduced in independent East Asian datasets with concordant effect sizes. Furthermore, MABM enhanced decision-making in gene/protein prioritization for functional validation for complex traits by validating well-established associations and uncovering novel trait-related candidates. The benefits of MABM were further validated in additional ancestries and demonstrated in brain tissue-based PWAS, underscoring its broad applicability. Our findings close critical gaps in multi-omics research among underrepresented populations and facilitate trait-relevant protein discovery in underrepresented populations.},
}

Humans
*Genome-Wide Association Study
*Proteomics/methods
*Proteome/genetics
*Quantitative Trait Loci

@article {pmid41497491,
year = {2026},
author = {Huang, Y and Ding, Q and Chen, Z and Chen, J and Li, Y and Chen, L and Yao, S and Lan, Y and Xu, G},
title = {Brain-Computer Interface Training Enhances Attention Function via Modulating Frontoparietal Connectivity: Evidence From Functional Near-Infrared Spectroscopy.},
journal = {Neural plasticity},
volume = {2026},
number = {},
pages = {8133428},
pmid = {41497491},
issn = {1687-5443},
mesh = {Humans ; *Brain-Computer Interfaces ; Spectroscopy, Near-Infrared/methods ; Male ; Female ; *Parietal Lobe/physiology/diagnostic imaging ; Young Adult ; *Attention/physiology ; Adult ; *Frontal Lobe/physiology ; Prefrontal Cortex/physiology ; Executive Function/physiology ; *Nerve Net/physiology ; },
abstract = {OBJECTIVE: Attention is a critical cognitive function impaired in various neurological disorders, and brain-computer interface (BCI) training shows potential for cognitive improvement. However, the neural mechanisms of BCI training on attention networks remain unclear. This study investigated the effects of BCI training on attention and the underlying neural mechanisms in healthy young adults.

METHODS: Thirty healthy young adults participated in this study. Attention function was assessed using the attention network test (ANT), while brain activation and connectivity were measured using functional near-infrared spectroscopy (fNIRS). Participants underwent the ANT and fNIRS assessments before and after BCI training.

RESULTS: BCI training significantly improved the efficiency of the executive control network (p = 0.016). Nodal efficiency in the right posterior parietal cortex (PPC) was decreased (p  = 0.044). In the resting state, effective connectivity (EC) analysis showed decreased connectivity from the right PPC to the left PPC in the resting state (p  = 0.047). In the task state, the EC from the right prefrontal cortex (PFC) to the right PPC was significantly increased (p  = 0.016), and the connectivity from the left PFC to the right PFC was significantly decreased (p  = 0.023).

CONCLUSION: BCI training optimized connectivity within frontoparietal networks (FPNs), leading to enhanced executive control function. These findings suggest that BCI training could be an effective cognitive intervention for improving the function of FPNs. Future studies should explore the long-term effects of BCI training and its potential application in clinical populations, such as patients with attention deficit hyperactivity disorder and stroke.},
}

Humans
*Brain-Computer Interfaces
Spectroscopy, Near-Infrared/methods
Male
Female
*Parietal Lobe/physiology/diagnostic imaging
Young Adult
*Attention/physiology
Adult
*Frontal Lobe/physiology
Prefrontal Cortex/physiology
Executive Function/physiology
*Nerve Net/physiology

@article {pmid41495620,
year = {2026},
author = {Li, Y and Feng, Y and Liu, X and Yuan, R and Chen, S and Wang, J and Pan, C and Li, G and Tang, Z},
title = {Functional near-infrared spectroscopy: Systematic mapping of abnormal brain function features in neurological disorders.},
journal = {Neural regeneration research},
volume = {},
number = {},
pages = {},
doi = {10.4103/NRR.NRR-D-25-00595},
pmid = {41495620},
issn = {1673-5374},
abstract = {Functional near-infrared spectroscopy quantifies cerebral hemodynamic signals by capturing oxygenation-dependent changes in hemoglobin in a noninvasive, portable, and ecologically valid manner, providing a unique insight into neurovascular coupling. However, functional imaging biomarkers with high ecological validity for neurological disorders such as stroke, Parkinson's disease, dementia, amyotrophic lateral sclerosis, epilepsy, spinal cord injury, and traumatic brain injury are lacking, limiting the mechanistic understanding, treatment evaluations, and individualized interventions. The aim of this review is to systematically summarize evidence from the past decade on the use of functional near-infrared spectroscopy under the aforementioned conditions, synthesize its value for revealing neural mechanisms and assessing therapeutic responses, and identify current technical bottlenecks and future directions for advancement. Collectively, the findings demonstrate that functional near-infrared spectroscopy possesses substantial and far-reaching potential for uncovering the neural mechanisms underlying disease and for evaluating treatment-induced changes in brain function. Equipped with wearable probes, functional near-infrared spectroscopy can continuously and noninvasively monitor brain activity in naturalistic environments for extended periods, thereby overcoming the limitations of conventional imaging modalities that can only acquire data under restricted settings. This capability can furnish unprecedented objective neuroimaging evidence for neuroregenerative therapy research. Moreover, the portability of functional near-infrared spectroscopy allows it to be integrated into neurofeedback training systems: hemoglobin signals can be fed back to participants within milliseconds, enabling targeted, individualized, closed-loop modulation of brain function and considerably expanding the scope of hemodynamics-based neurofeedback. When combined with other brain function assays (such as electroencephalography) and intervention techniques (such as transcranial magnetic stimulation and transcranial direct current stimulation), functional near-infrared spectroscopy also supplies high-temporal-resolution hemodynamic information, laying a critical foundation for the construction of high-precision noninvasive brain-computer interfaces, real-time cognitive-state decoding, and adaptive neuromodulation. Admittedly, almost all existing functional near-infrared spectroscopy studies are still observational and have small sample sizes, short follow-ups, and insufficient controls-shortcomings that together produce low-grade evidence. Therefore, there is still a significant gap before clinical translation can be achieved. Technically, the limited penetration depth of functional near-infrared spectroscopy restricts sampling to the superficial cortex, leaving deep nuclei largely unreachable. In addition, no consensus exists across devices regarding optode layout, light-source choice, motion-artifact correction, or analytical pipelines, creating pronounced heterogeneity that undermines reproducibility. With artificial intelligence and big data analytics advancing rapidly, functional near-infrared spectroscopy embedded within multimodal fusion frameworks is now poised to systematically map aberrant brain function signatures of neurological disorders, identify pathological regions suitable for targeted intervention, and provide real-time assessments of functional changes produced by neuroregenerative therapies.},
}

@article {pmid41495200,
year = {2026},
author = {Li, D and Cui, G and Yang, K and Lu, C and Jiang, Y and Zhang, L and Wu, Q and Dixit, D and Zhu, Z and Gimple, RC and Gu, D and Gao, J and Lin, Q and Yu, H and Shi, Z and Chen, Y and Wang, Q and Jin, G and Lin, F and Shao, J and Zhou, Q and Liu, C and Li, C and You, Y and Zhang, N and Zhang, J and Qian, X and Zhang, Q and Rich, JN and Wang, X},
title = {Inhibiting macrophage-derived lactate transport restores cGAS-STING signalling and enhances antitumour immunity in glioblastoma.},
journal = {Nature cell biology},
volume = {},
number = {},
pages = {},
pmid = {41495200},
issn = {1476-4679},
support = {82525047//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82573312//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Glioblastoma (GBM) is a malignancy with a complex tumour microenvironment (TME) dominated by GBM stem cells (GSCs) and infiltrated by tumour-associated macrophages (TAMs) and exhibits aberrant metabolic pathways. Lactate is a critical glycolytic metabolite that promotes tumour progression; however, the mechanisms of lactate transport and lactylation in the TME of GBM remain elusive. Here we show that lactate is transported from TAMs to GSCs via MCT4-MCT1. TAMs provide lactate to GSCs, promoting GSC proliferation and inducing lactylation of the non-homologous end joining protein KU70 at lysine 317 (K317), which inhibits cGAS-STING signalling and remodels the immunosuppressive TME. Inhibition of lactate transport or targeting the lactylation of KU70, in combination with the immune checkpoint blockade, demonstrates additive therapeutic benefits in immunocompetent xenograft models. This study unveils TAM-derived lactate and lactylation as critical regulators in GSCs to enforce an immunosuppressive microenvironment, opening avenues for developing combinatorial therapy for GBM.},
}

@article {pmid41494647,
year = {2026},
author = {Luckie, DB and Green, MA and Hami, DW and Zawisa, HL},
title = {CURE lecture too: MCAT, BCI & tracking data show students who regularly discussed research data in lecture learned more than peers using traditional textbooks.},
journal = {Advances in physiology education},
volume = {},
number = {},
pages = {},
doi = {10.1152/advan.00002.2025},
pmid = {41494647},
issn = {1522-1229},
support = {//Cystic Fibrosis Foundation (CFF)/ ; //Pennsylvania Cystic Fibrosis Inc./ ; //National Science Foundation (NSF)/ ; },
abstract = {The purpose of this study was to examine the impact of an intervention, a "CURE lecture" approach, which introduced course-based undergraduate research experience (CURE) strategies into the lecture setting. Rather than learning biological explanations from a traditional textbook, instead students studied primary literature curated in a reformed research-focused textbook and had discussions of data and experimental design. In control cohorts, reformed active and cooperative pedagogies were used in lecture to engage students in learning traditional textbook content. In experimental cohorts, "lecture" format was replaced with active and cooperative "journal club" discussions of published experiments. Prior studies examined use of research-focused Integrating Concepts in Biology (ICB) textbook readings in two sequential introductory biology courses. In this study assessments focused on student learning gains after a single semester. Klymkowsky's Biology Concept Inventory with known misconceptions as distractors, and Loznak's MCAT instrument used for over a decade prior, joined longitudinal tracking to evaluate impact of intervention. The ICB student cohort had higher scores (46.3% versus 34.3%) than controls on the Concept Inventory, and on the MCAT questions performed comparably in the range achieved by peer controls since the year 2000. Longitudinal tracking revealed ICB students immediately outperformed peers in their next biology course the following semester. The literature suggested a two-semester ICB experience helped students better succeed, and these findings support even a shorter exposure, of just a single semester, to the "CURE Lecture" strategy is impactful to students.},
}

@article {pmid41494544,
year = {2026},
author = {Jiang, H and He, J and Zhou, B and Guo, Y and Gan, X and Fan, X and Wang, X and Ferraro, S and Vatansever, D and Kendrick, KM and Li, L and Becker, B},
title = {Adolescents with non-suicidal self-injury exhibit increased pain empathic neural reactivity and personal distress to physical but not affective pain.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {121145},
doi = {10.1016/j.jad.2025.121145},
pmid = {41494544},
issn = {1573-2517},
abstract = {BACKGROUND: Non-suicidal self-injury (NSSI) in adolescents represents a critical public health issue. While symptomatic links between NSSI and alterations in pain and social processing have been established, changes in neural responses and everyday reactivity to others' pain remain unknown.

METHODS: This pre-registered study examined pain empathic processing in unmedicated adolescents with NSSI (n = 29) and healthy controls (n = 33) using functional magnetic resonance imaging (fMRI). A validated paradigm assessed neural responses to physical pain versus affective pain observation and was combined with both univariate and machine learning analytic approaches.

RESULTS: NSSI participants exhibited significantly increased neural reactivity during physical pain empathy in lateral prefrontal, insular, temporal, and the somatomotor network regions (all p < 0.05, FDR-corrected), while affective pain processing remained intact. Machine learning analysis revealed distinguishable whole-brain signatures, with a physical pain empathic pattern achieving superior discrimination in NSSI. NSSI participants reported elevated personal distress to others' negative experiences in everyday life, which was associated with enhanced limbic reactivity during physical pain empathy.

CONCLUSIONS: Findings identify domain-specific neural hyperreactivity to others' physical pain in NSSI adolescents and elevated personal distress in daily life. These characteristics may represent predisposing alterations that facilitate engagement in self-harm or consequences of repeated engagement in NSSI that impact everyday social behavior.},
}

@article {pmid41494206,
year = {2026},
author = {Qi, W and Wang, X and Yang, W and Wang, W},
title = {ACFSENet: an adaptive cross-frequency global sparse encoding network for end-to-end EEG emotion recognition.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ae33c7},
pmid = {41494206},
issn = {2057-1976},
abstract = {End-to-end EEG-based emotion recognition is attracting increasing attention due to its potential in human-computer interaction, mental health, and affective brain-computer interfaces (aBCIs). However, most existing methods overlook cross-frequency interactions in neural oscillations and suffer from high computational complexity, limiting their applicability in real-time or resource-constrained scenarios. To this end, we propose ACFSENet, a novel end-to-end neural architecture that integrates adaptive cross-frequency modeling with global sparse encoding. ACFSENet employs an adaptive frequency-aware mechanism to dynamically focus on subject- and task-specific local brain dynamics, thereby enhancing the flexibility of emotional representation. In parallel, it incorporates a sparse attention mechanism with a temporal distillation structure to reduce computational complexity while preserving the ability to model long-range temporal dependencies. We evaluate ACFSENet using cross-block validation on three benchmark datasets: DEAP, SEED, and SEED-IV. Results demonstrate that ACFSENet outperforms state-of-the-art methods and achieves a favorable balance between recognition performance and computational efficiency.},
}

@article {pmid41494035,
year = {2026},
author = {Chia, R and Lin, CT},
title = {Biologically-constrained spiking neural network for neuromodulation in locomotor recovery after spinal cord injury.},
journal = {PLoS computational biology},
volume = {22},
number = {1},
pages = {e1013866},
doi = {10.1371/journal.pcbi.1013866},
pmid = {41494035},
issn = {1553-7358},
abstract = {Presynaptic inhibition after spinal cord injury (SCI) has been hypothesised to disproportionately affect flexion reflex loops in locomotor spinal circuitry. Reducing gamma-aminobutyric acid (GABA) inhibitory activity increases the excitation of flexion circuits, restoring muscle activation and stepping ability. Conversely, nociceptive sensitisation and muscular spasticity can emerge from insufficient GABAergic inhibition. To investigate the effects of neuromodulation and proprioceptive sensory afferents in the spinal cord, a biologically constrained spiking neural network (SNN) was developed. The network describes the flexor motoneuron (MN) reflex loop with inputs from ipsilateral Ia- and II-fibres and tonically firing interneurons. The model was tuned to a Baseline level of locomotive activity before simulating an inhibitory-dominant and body-weight supported (BWS) SCI state. Electrical stimulation (ES) and serotonergic agonists were simulated by the excitation of dorsal fibres and reduced conductance in excitatory neurons. ES was applied across all afferent fibres without phase- or muscle-specific protocols. The present computational findings suggest that reducing stance-phase GABAergic inhibition on flexor motoneurons could facilitate more physiological flexor activation during locomotion. The model further predicts that neuromodulatory therapy, together with body-weight support, modulates the balance of synaptic excitation and inhibition in ankle flexor motoneurons to mitigate excessive inhibitory drive in the ankle flexor circuitry.},
}

@article {pmid41493973,
year = {2026},
author = {Gao, M and Zang, S and Zhu, Y and Xi, K and Du, Y and Cheng, S and Miao, L and Lu, Y and Mao, C and Zhang, Y and Ma, X},
title = {Structural insights into the activation mechanism of the human metabolite receptor HCAR1.},
journal = {Science signaling},
volume = {19},
number = {919},
pages = {eadw1483},
doi = {10.1126/scisignal.adw1483},
pmid = {41493973},
issn = {1937-9145},
mesh = {Humans ; Cryoelectron Microscopy ; *Receptors, G-Protein-Coupled/chemistry/metabolism/genetics/agonists ; *Lactic Acid/metabolism/chemistry ; Ligands ; Binding Sites ; Protein Binding ; Signal Transduction ; },
abstract = {Hydroxycarboxylic acid receptor 1 (HCAR1) is a class A G protein-coupled receptor (GPCR) that is activated by the endogenous metabolite l-lactate and that plays an important role in various metabolic and inflammatory disorders. HCAR1 uses distinct ligand recognition and self-activation mechanisms to mediate specific pathophysiological functions through Gαi/o and β-arrestin signaling pathways. To support effective drug development targeting HCAR1, we investigated ligand recognition and activation mechanisms through cryo-electron microscopy (cryo-EM) structures of the HCAR1-Gαi1 complex in the apo state or with l-lactate or with the synthetic agonist CHBA. Compared with other HCARs, HCAR1 has a more compact binding pocket, which is stabilized by three unique disulfide bonds. l-lactate exhibited a flexible binding mode and relatively weak intermolecular interactions, thus requiring millimolar concentrations for receptor activation. In contrast, the binding of CHBA was more stable because of its chlorinated benzene ring, thus resulting in improved agonist potency. Structural comparisons with HCAR2 identified critical residues that restrict the size of the binding pocket of HCAR1 and influence ligand selectivity. Self-activation of HCAR1 is driven by conformational rearrangements within extracellular loop 2, with Phe168[ECL2] playing a pivotal role as the key agonist. Together, these results clarify the mechanisms underlying HCAR1 activation, self-activation, and ligand selectivity, providing a structural framework for the design of high-affinity, selective agonists and inverse agonists with minimized off-target effects.},
}

Humans
Cryoelectron Microscopy
*Receptors, G-Protein-Coupled/chemistry/metabolism/genetics/agonists
*Lactic Acid/metabolism/chemistry
Ligands
Binding Sites
Protein Binding
Signal Transduction

@article {pmid41493559,
year = {2026},
author = {Andrade, P and Mercado, R and Jimenez, F and Visser-Vandewalle, V},
title = {[Neuroprosthetics].},
journal = {Chirurgie (Heidelberg, Germany)},
volume = {},
number = {},
pages = {},
pmid = {41493559},
issn = {2731-698X},
abstract = {Neuroprosthetics represents a dynamic field at the interface of neurosciences, engineering and neurosurgery that is based on implanted devices for restoration or extension of neurological functions. Important advances involve brain-computer and brain-spine interfaces that enable communication, motor and sensory feedback in paralyzed or anarthric patients. Intracortical arrays, subdural electrocorticographic lattices and endovascular electrodes provide different access routes, supplemented by strategies, such as spinal neuromodulation and functional electrostimulation. Recent studies confirmed the restoration of grasping movements, standing and walking as well as fluid speech and text communication, sometimes via avatars. Bidirectional systems with sensory feedback enhance the naturalness and precision. There are challenges in signal stability, longevity and minimally invasive access routes. With interdisciplinary cooperation and technical maturity neuroprostheses can enrich the routine neurosurgical care in the future.},
}

@article {pmid41490776,
year = {2026},
author = {Bao, M and Feng, S and Wang, J and Ye, J and Wang, J and Li, W and Jiang, K and Yao, L},
title = {Efficacy and Safety of a Video Game-Like Digital Therapy Intervention for Chinese Children With Attention-Deficit/Hyperactivity Disorder: Single-Arm, Open-Label Pre-Post Study.},
journal = {JMIR serious games},
volume = {14},
number = {},
pages = {e76114},
doi = {10.2196/76114},
pmid = {41490776},
issn = {2291-9279},
abstract = {BACKGROUND: The digital therapy of attention-deficit/hyperactivity disorder (ADHD) based on a "self-adaptive multitasking training paradigm" has been developed to improve the cognitive functional impairments and attention deficits of children with ADHD. However, the efficacy and safety of such treatment for Chinese patients remain untested.

OBJECTIVE: This study aimed to preliminarily evaluate the actual intervention effects of a video game-like training software (ADHD-DTx) for children with ADHD aged 6-12 years as the first nationally certified digital therapeutics medical device for ADHD in China. We performed a single-arm, open-label efficacy and safety study.

METHODS: This is a single-arm, open-label, pre-post efficacy and safety study. A total of 97 participants were included in the analysis. Participants received digital therapy (ADHD-DTx) and basic behavioral parent training for 4 weeks (25 min/day, ≥5 times/week) without medication. The efficacy outcomes included the Test of Variables of Attention (TOVA), Swanson, Nolan, and Pelham Questionnaire, version 4 (SNAP-IV), Weiss Functional Impairment Rating Scale (WFIRS), and Conner's Parent Symptom Questionnaire (PSQ). Safety-related events were monitored during and after the trial.

RESULTS: From day 0 (baseline) to day 28, the population TOVA Attention Performance Index exhibited statistically significant improvement (from mean -4.15, SE of the mean [SEM] 0.32 to mean -1.70, SEM 0.30; t94=-8.78; n=95; P<.001); the population total, inattention (AD), hyperactivity/impulsivity (HD), and oppositional defiant disorder (ODD) scores of SNAP-IV all significantly improved (total: from mean 1.33, SEM 0.05 to mean 1.09, SEM 0.05; t96=5.32; P<.001; AD: from mean 1.71, SEM 0.06 to mean 1.44, SEM 0.06; t96=4.44; P<.001; HD: from mean 1.38, SEM 0.07 to mean 1.05, SEM 0.06; t96=5.96; P<.001; ODD: mean 0.84, SEM 0.05 to mean 0.75, SEM 0.05; Z=2.47; P=.03; n=97); for WFIRS results, domains of "family" and "social activities" showed significant population improvement (family: from mean 0.75, SEM 0.05 to mean 0.65, SEM 0.04; Z=2.80; P=.01; social activities: from mean 0.56, SEM 0.05 to mean 0.45, SEM 0.05; Z=2.91; P=.01; n=97); for PSQ results, domains of "learning problem," "psychosomatic problem," "impulsivity-hyperactivity," and "hyperactivity index" showed significant improvement (learning problem: from mean 1.72, SEM 0.06 to mean 1.57, SEM 0.06; Z=2.42; P=.03; psychosomatic problem: from mean 0.40, SEM 0.03 to mean 0.32, SEM 0.03; Z=2.66; P=.02; impulsivity-hyperactivity: from mean 0.94, SEM 0.06 to mean 0.80, SEM 0.06; Z=2.49; P=.03; hyperactivity index: from mean 1.06, SEM 0.05 to mean 0.92, SEM 0.05; Z=2.90; P=.01; n=97). No device-related adverse event or severe adverse event was observed or reported during or after the intervention.

CONCLUSIONS: This study preliminarily suggested the significant improvements of ADHD symptoms and attention function after 4 weeks of ADHD-DTx digital therapy combining basic behavioral parent training with satisfying safety outcomes.},
}

@article {pmid41489950,
year = {2026},
author = {Chung, CM and Tsai, CH and Chu, YL and Hsu, CH and Lu, JB and Hsu, YC and Su, YJ and Wu, Y and Hung, CF and Wang, YT},
title = {3D printed watermill-like semi-dry electrodes for BCI applications.},
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.2026.3650950},
pmid = {41489950},
issn = {1558-0210},
abstract = {Wet electrodes with conductive gel are widely applied as the gold standard for recording EEG signals due to their low impedance between the scalp and the electrode. However, their extensive preparation time before data collection and the required cleaning afterward make them impractical for real-world Brain-Computer Interface (BCI) applications. Recent advancements in semi-dry electrodes, which use a minimal amount of conductive material and achieve a comparable signal-to-noise quality to wet electrodes, present an alternative approach for continuous EEG monitoring when comparing to dry electrodes. Our prior study introduced a potential solution for overcoming challenges related to hair-layer penetration and dose control through 3D-printed, watermill-shaped EEG electrodes. Based on those promising results, this study prototypes three designs of watermill-shaped EEG electrodes and refines the fabrication process to scale production and accommodate diverse hairstyles in real-world scenarios. Eight different wig styles which were made of either human or synthetic hair were tested in offline experiments to evaluate hair-layer penetration performance and gel-applying application efficiency. In the real-world experiment, 15 participants with varying hairstyles were recruited in neurophysiological experiments. Statistical analysis revealed that the watermill electrodes consumed significantly less gel than wet electrodes (p<0.001), with the star electrode requiring the fewest mean rolls to achieve target impedance (1.94 rolls). The results demonstrate that the watermill-shaped electrode effectively works across different hairstyles, ensuring consistent hair-layer penetration and controlled application of conductive material. These findings establish the proposed electrode as a viable semi-dry solution for real-world BCI applications.},
}

@article {pmid41489217,
year = {2026},
author = {Lu, J and Zhan, G and Jia, J and Zhang, L and Kang, X},
title = {Automated source domain EEG analysis based on graph theory for healthy controls and stroke patients in different tasks.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-19},
doi = {10.1080/10255842.2025.2609653},
pmid = {41489217},
issn = {1476-8259},
abstract = {This study aimed to compare functional brain networks and identify recovery markers in 12 stroke patients (SG) and 14 healthy controls (HG) using EEG during three fist-task paradigms. Analyzing clustering coefficient (CC), characteristic path length (CPL), small-world index (SWI), and frontal node strength across frequency bands, passive task revealed significant alpha band differences in CC/CPL/SWI between groups. Lower SG strength in alpha/mu vs. controls predicted better recovery. An automated source imaging pipeline reduced volume conduction effects, providing new insights into stroke rehabilitation outcomes. Large-scale source imaging shows promise for broader disease applications.},
}

@article {pmid41488688,
year = {2025},
author = {Cao, Y and Ding, J and Zhao, Z and He, Y and Fu, M and Liu, X and Lyv, X},
title = {Improved filter bank common spatial pattern algorithm based on the sparrow search algorithm.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1679329},
pmid = {41488688},
issn = {1662-5161},
abstract = {INTRODUCTION: The application of motor imagery in human-computer interaction and rehabilitative medicine has attracted growing attention due to recent advances in brain-computer interface technologies. However, traditional EEG decoding paradigms based on fixed frequency-band segmentation often exhibit limited performance because they fail to capture individual variability in brain rhythms.

METHODS: This work proposes an adaptive method that integrates the sparrow search algorithm (SSA) with Filter Bank Common Spatial Pattern (FBCSP) to optimize sub-band segmentation for motor imagery EEG decoding. SSA adaptively searches for optimal sub-band boundaries, enabling individualized frequency-band selection.

RESULTS: Experiments on the BCI Competition IV 2a dataset under a cross-session evaluation protocol (training on session T, testing on session E) demonstrated that SSA-FBCSP effectively improves frequency-band adaptability. The SSA-FBCSP approach was further combined with Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and k-Nearest Neighbor (KNN) classifiers to evaluate the influence of different downstream classifiers.

CONCLUSION: Among them, SSA-FBCSP-LDA achieved the best performance, outperforming the conventional uniform sub-band approach by 21.76% and reaching an average accuracy of 89.92%. The adaptively selected sub-bands closely matched the ERD/ERS distribution, confirming the method's effectiveness in frequency-band optimization. Compared with recent deep-learning-based MI-EEG models, the proposed technique offers a balance of accuracy, interpretability, and computational efficiency, providing a promising direction for personalized brain-computer interface systems.},
}

@article {pmid41486740,
year = {2026},
author = {Zhang, T and Ngetich, RK and Zhang, J and Jin, Z and Li, L},
title = {The role of emotion in economic decision making: behavioral and neurophysiological evidence from the Wheel of Fortune Gambling Task.},
journal = {Reviews in the neurosciences},
volume = {},
number = {},
pages = {},
pmid = {41486740},
issn = {2191-0200},
abstract = {Decision making is frequently influenced by factors such as an individual's emotional state, cognitive biases, social influences, and environmental constraints. Understanding how these factors influence the way decisions are made is essential for optimizing and improving this cognitive process. Therefore, this review examines the theoretical basis of emotion-influenced decision making. Here, we integrate insights from eye-tracking, electroencephalography (EEG), and magnetic resonance imaging (MRI) evidence, as well as behavioral findings. We specifically review evidence from studies applying the Wheel of Fortune Gambling Task paradigm. Through critical and reflective synthesis, we (1) present suggestions for distinguishing between emotion types in decision-making theoretical models, (2) identify key research gaps, and (3) explore innovative applications of emerging technologies. In essence, our review highlights the role of diverse emotions in decision making across theoretical models and neural mechanisms, utilizing the Wheel of Fortune Gambling Task paradigm to link clinical disorders with decision-making impairments. This knowledge may have implications for predicting and intervening in behavioral addictions and cognitive disorders through strategies such as the neuromodulation. Additionally, by synthesizing existing knowledge and proposing new avenues for research, this review aims to deepen understanding of emotion-driven decision making and inspire further exploration into this vital area of cognitive science.},
}

@article {pmid41486339,
year = {2026},
author = {Li, S and Wang, X and Zheng, J and Xu, H},
title = {Subparafascicular Thalamic Nucleus: An Integration Center for Sexual Motivation and Physical Contact in Mating Behaviour.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {41486339},
issn = {1995-8218},
}

@article {pmid41486068,
year = {2026},
author = {Yamada, S and Sato, M and Osawa, T and Harabayashi, T and Miki, J and Kobayashi, T and Hashine, K and Kawashima, A and Matsumoto, T and Mochizuki, T and Taoka, R and Urabe, F and Tatarano, S and Sawada, A and Kojima, T and Takahashi, A and Yokomizo, A and Suekane, S and Hashimoto, K and Hashimoto, Y and Yatsuda, J and Morita, K and Kobayashi, K and Satake, Y and Sazawa, A and Matsui, Y and Ito, YM and Shimizu, S and Fukuhara, S and Nishiyama, H and Kitamura, H and Shinohara, N and , },
title = {Longitudinal Impact of Urinary Diversion on Health-Related Quality of Life After Radical Cystectomy: A Multicenter Study in Japan.},
journal = {Cancer science},
volume = {},
number = {},
pages = {},
doi = {10.1111/cas.70289},
pmid = {41486068},
issn = {1349-7006},
support = {2019-67//Japan Urological Association, Young Researcher Promotion Grant/ ; },
abstract = {This multicenter longitudinal study was conducted across 24 institutions in Japan to examine the impact of urinary diversion on health-related quality of life (HRQOL) among bladder cancer patients who underwent radical cystectomy (RC). We evaluated bladder cancer-specific HRQOL and general HRQOL via the bladder cancer index (BCI) and the QOL General (QGEN-8), respectively, before the operation and at 3, 6, and 12 months postoperatively. The scores were compared across urinary diversion groups as well as across different time points within each urinary diversion group with linear mixed-effects models. Data from 227 patients were analyzed (151 with ileal conduits, 45 with ureterostomy, and 31 with neobladders). Neobladder patients were more likely to experience longitudinal impacts of their urinary diversion on urinary function than ileal conduit or ureterostomy patients were. Compared with that at baseline, the bowel function of neobladder patients remained impaired 12 months after surgery. All urinary diversion groups had worse sexual function scores at 3 and 6 months than at baseline, and the ileal conduit and neobladder groups had significantly worse sexual function scores at 12 months than at baseline. On the other hand, there was no significant difference in bother scores in the urinary, bowel, or sexual domain. The generic HRQOL was maintained from the preoperative to the postoperative period in all urinary diversion groups. This study explored longitudinal changes in HRQOL after RC, and the findings may help inform patient counseling regarding possible QOL trajectories.},
}

@article {pmid41485025,
year = {2026},
author = {Ying, W and Wang, X and Yu, J and Wang, J and He, Q and Yang, B and Chen, Y and Ying, M},
title = {Fusion oncoproteins orchestrate tumorigenesis and sustain malignant progression via a positive feedback mechanism.},
journal = {Cell & bioscience},
volume = {},
number = {},
pages = {},
doi = {10.1186/s13578-025-01523-6},
pmid = {41485025},
issn = {2045-3701},
support = {No. 82272677//National Natural Science Foundation of China/ ; No. LR23H310001//Natural Science Fund for Distinguished Young Scholars of Zhejiang Province/ ; No. GZC20232321//Postdoctoral Fellowship Program of CPSF/ ; No. 2024C03181//Pioneer and Leading Goose R&D Program of Zhejiang Province/ ; No. 226-2025-00136//Fundamental Research Funds for the Central Universities/ ; },
abstract = {Chromosomal translocations are prevalent genetic events across multiple pediatric cancers, notably in CNS tumors, solid tumors, and leukemias. For decades, Fusion oncoproteins resulting from chromosomal translocations have been proposed as a hallmark of cancers, some of which can drive the process of cancers as the initial event of the disease. In addition, studies have shown that some tumor cells become addicted to the activity of fusion proteins, and cell death occurs when the fusion proteins are depleted. These researches suggest that fusion oncoproteins are one of the most promising targets for cancer treatment. Although fusion proteins are already recognized as critical oncogenic drivers, increasing evidence suggests that they can also form positive feedback loops with other proteins. In cancer patients, positive feedback loops have been shown to activate various oncogenic signals to drive tumor development, and influencing tumor cells' sensitivity to different therapies. Therefore, these loops not only amplify the functions of the fusion proteins but also render single-agent targeting of the fusion protein insufficient to suppress tumor growth, highlighting the therapeutic potential of combination strategies in treating fusion-positive tumors. This review highlights the oncogenic roles of fusion protein-driven positive feedback loops in tumor initiation and progression, outline the molecular mechanisms underlying their formation and function, and summarize emerging therapeutic strategies targeting these circuits, offering new insights into the treatment of fusion-positive cancers.},
}

@article {pmid41484139,
year = {2026},
author = {Gao, J and Liu, Y and Li, Z and Huang, K and Wang, F and Xu, J and Zhao, L and Li, T and Fu, Y},
title = {An EEG Dataset for Visual Imagery-Based Brain-Computer Interface.},
journal = {Scientific data},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41597-025-06512-5},
pmid = {41484139},
issn = {2052-4463},
support = {No.62366026, No.62376112, No.82172058, No.81771926, No.61763022, No. 62006246//National Natural Science Foundation of China (National Science Foundation of China)/ ; No.62366026, No.62376112, No.82172058, No.81771926, No.61763022, and No. 62006246//National Natural Science Foundation of China (National Science Foundation of China)/ ; No.62366026, No.62376112, No.82172058, No.81771926, No.61763022, and No. 62006246//National Natural Science Foundation of China (National Science Foundation of China)/ ; No.62366026, No.62376112, No.82172058, No.81771926, No.61763022, and No. 62006246//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {With the advancement of non-invasive brain-computer interface (BCI) technologies, decoding high-level cognitive activity has become pivotal for expanding human-machine interaction. Visual imagery-based BCI (VI-BCI) enable voluntary activation of specific brain regions without external cue, offering novel pathways for immersive applications. However, research on the neural representation of such complex cognitive tasks is still limited, and most existing electroencephalogram (EEG) datasets primarily target motor imagery, hindering the development of robust VI decoding models. Here we present an EEG dataset recorded from 22 participants performing visual imagery tasks involving ten commonly recognized images across three categories: figures, animals, and objects. Each participant completed two sessions, with EEG recorded from 32-channels at 1000 Hz. This resource helps overcome data homogeneity issues in VI studies and provides a foundation for exploring neuroplasticity, adaptive decoding algorithms, and cross-subject generalization, facilitating the transition from controlled experiments to real-world applications.},
}

@article {pmid41483149,
year = {2026},
author = {Zhu, L and Hong, H and Qian, M and Cao, W and Luo, Z and Gong, J and Zou, W and Kang, L},
title = {Hierarchical Channel System Drives Stimulus Specificity and Polymodal Encoding in A Mechano-Cold Sensory Neuron.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {41483149},
issn = {1995-8218},
abstract = {Polymodal sensory neurons integrate diverse stimuli for environmental perception, but their modality discrimination mechanisms remain unclear. We focused on Caenorhabditis elegans inner labial type 1 (IL1) neurons, key polymodal neurons mediating mechanical and cold responses, and identified a hierarchical channel system supporting their multimodal function. Specifically, DEG-1 sodium channels are dedicated mechanotransduction receptors; GLR-3 glutamate receptors are the main rapid cold sensors, driving cold-induced calcium signals and behaviors; TRPA-1 bidirectionally modulates mechanical adaptation via calcium signaling and promotes cold-related longevity. This framework reveals a polymodal design logic: dedicated channels (DEG-1/GLR-3) process discrete modalities in parallel for specificity, while TRPA-1 regulates both. Our work provides a molecular blueprint for IL1's precise stimulus processing, offering insights into conserved multimodal integration mechanisms across lineages.},
}

@article {pmid41482611,
year = {2026},
author = {Nochalabadi, A and Khazaei, M and Kadivarian, S and Rezakhani, L},
title = {Innovative Herbal-Based Decellularization of Pericardium for Advanced Polymeric Skin Substitutes.},
journal = {Artificial organs},
volume = {},
number = {},
pages = {},
doi = {10.1111/aor.70087},
pmid = {41482611},
issn = {1525-1594},
support = {//Kermanshah University of Medical Sciences/ ; },
abstract = {INTRODUCTION: Tissue engineering has opened new horizons with the introduction of biological scaffolds obtained by decellularization techniques as novel tools in regenerative medicine. Chemical agents such as SDS, although effective in cell removal, can cause cytotoxicity. Herbal agents can be a safer and more biocompatible alternative. This study aimed to investigate the efficacy of Acanthophelium extract (ACP) as a herbal agent in decellularization of sheep pericardium and compare it with SDS for use in skin engineering.

METHODS: Pericardial tissues were decellularized with different concentrations of ACP (5, 7.5% and 10%) and SDS (1%), as well as the combination of ACP + SDS. Tissue staining, biocompatibility (MTT), hemolysis, blood clotting index (BCI), scanning electron microscopy (SEM), ATR-FTIR spectroscopy, mechanical testing, contact angle, and antibacterial activity were performed.

RESULTS: Complete cell removal was observed in the ACP + SDS combination groups, while the ECM structure was preserved. Biocompatibility was more than 90% in all groups. ACP-based scaffolds had less hemolysis, a more favorable coagulation index, preserved protein structure, higher porosity, and higher hydrophilicity. Although the mechanical properties were slightly reduced, they remained acceptable. The 10% ACP + 0.1% SDS group reported the highest antibacterial effect.

CONCLUSIONS: ACP extract, as a plant agent in pericardial decellularization, has an effective and biocompatible function, and in combination with a small amount of SDS, it can provide a balanced scaffold with desirable properties for skin engineering.},
}

@article {pmid41481676,
year = {2026},
author = {Proverbio, AM and Zanetti, A},
title = {Reinstating motivational states: Electrical signatures of craving and neural mind reading.},
journal = {PloS one},
volume = {21},
number = {1},
pages = {e0315068},
pmid = {41481676},
issn = {1932-6203},
mesh = {Humans ; Male ; Female ; Electroencephalography ; Adult ; *Motivation/physiology ; Young Adult ; Evoked Potentials/physiology ; *Craving/physiology ; *Brain/physiology ; Brain Mapping ; },
abstract = {The aim of this electroencephalogram (EEG) study was to identify electrical neuro-markers of 12 different motivational and physiological states such as visceral craves, affective and somatosensory states, and secondary needs. Event-related potentials (ERPs) were recorded in 30 right-handed participants while recalling a specific state upon the presentation of an auditory verbal command incorporating an evocative sound background consistent with that state (e.g., the chirping of cicadas associated with the verbal complaint about feeling hot). ERP data showed larger amplitude N400 responses in the affective and somatosensory states, while the P400 component displayed greater amplitudes for the secondary and visceral states. Furthermore, the two components were also discernibly responsive to the 12 micro-categories (e.g., joy vs. pain or hunger), by providing a distinctive electric pattern for mostly all microstates. The reconstruction of the intracranial generators of surface signals revealed common imagery-related activations, including the middle and superior frontal gyri, the fusiform and lingual gyri, supramarginal, and middle occipital regions, as well as the middle temporal region. Additionally, specific regions were identified that were active for distinct mentally represented content, such as that visceral needs were associated with activations in the medial and inferior frontal gyri, uncus, precuneus, and cingulate gyrus. Affective states were associated with activations in the medial frontal, superior temporal, and middle temporal gyri. Somatosensory states (e.g., pain or cold) activated regions in the parietal cortex and the crave for music was linked to activations in the auditory and motor regions. These findings support the use of ERP markers for BCI applications.},
}

Humans
Male
Female
Electroencephalography
Adult
*Motivation/physiology
Young Adult
Evoked Potentials/physiology
*Craving/physiology
*Brain/physiology
Brain Mapping

@article {pmid41480666,
year = {2026},
author = {Hu, X and Li, N and Pang, M and Bai, S and Mo, J and Yao, S and Lu, Y and Huang, M and Di, J and Kang, Y and Tang, J and Zhang, H and Zhao, T and He, J and He, L and Xie, R and Liu, B and Xu, G and Hu, X and Rong, L},
title = {Brain-Computer Interface-Controlled Exoskeleton Training for Lower-Limb Rehabilitation in Spinal Cord Injury: A Pilot Randomized Clinical Trial.},
journal = {Annals of neurology},
volume = {},
number = {},
pages = {},
doi = {10.1002/ana.78144},
pmid = {41480666},
issn = {1531-8249},
support = {U22A20297//National Natural Science Foundation of China/ ; 202206060003//Key Research and Development Program of Guangzhou/ ; GZC20251372//Postdoctoral Fellowship Program and China Postdoctoral Science Foundation/ ; },
abstract = {OBJECTIVE: This study aimed to evaluate the efficacy of brain-computer interface (BCI)-controlled exoskeleton training on lower-limb functional recovery, psychological outcomes, and neural plasticity in patients with spinal cord injury (SCI).

METHODS: We conducted a single-center, prospective, randomized, single-blind pilot trial (ChiCTR2300074503) including 21 patients with SCI. Participants were randomized to a BCI-exoskeleton group (B + E, n = 10) or an exoskeleton-only group (E, n = 11) for lower-limb training. Both groups received conventional rehabilitation plus 30 minutes of training, 6 days per week, for 4 weeks. The primary outcomes were Walking Index for Spinal Cord Injury II (WISCI II) scoring. Secondary outcomes included Lambert-Eaton myasthenic syndrome (LEMS), Spinal Cord Independence Measure version III (SCIM III), International Association of Neurorestoratology Spinal Cord Injury Functional Rating Scale (IANR-SCIFRS), 10-Meter Walk Test (10MWT), 6-Minute Walk Test (6MWT), and Hospital Anxiety and Depression Scale (HADS). Cortical plasticity was assessed by electroencephalography (EEG) and magnetic resonance imaging (MRI).

RESULTS: The B + E group showed a significant improvement in LEMS (p = 0.003), whereas both groups improved in IANR-SCIFRS (p < 0.05). The B + E group demonstrated significant within-group gains in walking speed (10MWT, p < 0.001) and endurance (6MWT, p = 0.031), although between-group differences were not significant. Compared with the E group, the B + E group had larger reductions in HADS scores (p = 0.003). EEG analyses revealed stronger μ/β desynchronization and increased network efficiency, whereas MRI showed no structural changes.

INTERPRETATION: BCI-controlled exoskeleton training enhanced motor function, walking performance, and depressive symptoms more than exoskeleton training alone, likely through cortical reorganization. Extended training may further consolidate these benefits, supporting BCI-exoskeleton integration as a promising rehabilitation strategy for SCI. ANN NEUROL 2026.},
}

@article {pmid41476655,
year = {2025},
author = {Li, Y and Miao, Y and Wei, L and Li, W and Shan, M and Jiang, Q and Wang, F and Wang, L and Zhang, Z and Song, J and Zhu, Y and Mao, J},
title = {An Anisotropic and Stable-Conductance Patch for Mechanical-Electrical Coupling With Infarcted Myocardium.},
journal = {Exploration (Beijing, China)},
volume = {5},
number = {6},
pages = {20250021},
pmid = {41476655},
issn = {2766-2098},
abstract = {Polymeric conductive patches have conventionally been employed to facilitate the repair of infarcted myocardium by enhancing myocardial electrical conduction and providing mechanical support. However, it remains a challenge to restore the electrical conduction and diastolic-systolic functions with stable and anisotropic mechanical and electrical cues in the dynamic physiological environment. Herein, inspired by the hierarchical myocardial fiber microscopic striated structure, we established a weaving-based processing method to compound a striated polypyrrole conductive coating on the surface of highly oriented elastic fiber bundles. This unique design endows the patch with exceptional stretchability (elongation at break > 400%), stable conductance (ΔR/R 0 = 0.04 within 20% strain), and excellent fatigue resistance (ΔR/R 0 = 0.01 after 1,000,000 cycles). In addition, the precision process grounded on woven molding accomplished the tunable mechanical and electrical properties of the patch, which facilitates the achievement of long-term, stable, and anisotropic mechanical-electrical coupling with the infarcted myocardium. The rat MI model experiments demonstrated that this anisotropic conductive patch can not only improve cardiac function and electrical activity over an extended period, but also effectively inhibit myocardial inflammation and fibrosis and promote angiogenesis. This study proposes a promising MI-treatment patch and highlights the potential of woven technology in processing biomaterials composed of both rigid and elastic materials.},
}

@article {pmid41474622,
year = {2025},
author = {Chen, J and Xu, T and Xiong, X and Yang, X and Wang, Y and Qi, Y},
title = {Surrogate deep neural networks reveal hierarchical handwriting encoding in the human motor cortex.},
journal = {Cell reports},
volume = {45},
number = {1},
pages = {116837},
doi = {10.1016/j.celrep.2025.116837},
pmid = {41474622},
issn = {2211-1247},
abstract = {Skilled fine movements are essential for daily life. Although prior work has identified motor cortical tuning to low-level kinematic features like velocity and position, these findings fall short of explaining the precision underlying complex motor behaviors. Critically, it remains unclear whether and how the motor cortex (MC) represents higher-level features of movement. Using single-unit recordings from the human MC during handwriting, we employed surrogate deep neural networks (DNNs) as a tool to investigate these mechanisms. We found that surrogate DNNs capture key aspects of neural activity at both single-unit and population levels. Through this approach, we demonstrate that the MC encodes hierarchical information of movement, including both low-level kinematics and high-level features related to the written content. These results uncover neural encoding behind dexterous motor execution and provide a framework for studying the neural basis of complex behavior.},
}

@article {pmid41472918,
year = {2025},
author = {Sedi Nzakuna, P and D'Auria, E and Paciello, V and Gallo, V and Kamavuako, EN and Lay-Ekuakille, A and Kyamakya, K},
title = {Real-world evaluation of deep learning decoders for motor imagery EEG-based BCIs.},
journal = {Frontiers in systems neuroscience},
volume = {19},
number = {},
pages = {1718390},
pmid = {41472918},
issn = {1662-5137},
abstract = {INTRODUCTION: Motor Imagery (MI) Electroencephalography (EEG)-based control in online Brain-Computer Interfaces requires decisions to be made within short temporal windows. However, the majority of published Deep Learning (DL) EEG decoders are developed and validated offline on public datasets using longer window lengths, leaving their real-time applicability unclear.

METHODS: To address this gap, we evaluate 10 representative DL decoders, including convolutional neural networks (CNNs), filter-bank CNNs, temporal convolutional networks (TCNs), and attention- and Transformer-based hybrids-under a soft real-time protocol using 2-s windows. We quantify performance using accuracy, sensitivity, precision, miss-as-neutral rate (MANR), false-alarm rate (FAR), information-transfer rate (ITR), and workload. To relate decoder behavior to physiological markers, we examine lateralization indices, mu-band power at C3 vs. C4, and topographical contrasts between MI and neutral conditions.

RESULTS: Results show shifts in performance ranking between offline and online BCI settings, along with a pronounced increase in inter-subject variability. Best online means were FBLight ConvNet 71.7% (±2.1) and EEG-TCNet 70.0% (±5.3), with attention/Transformer designs less stable. Errors were mainly Left-Right swaps while Neutral was comparatively stable. Lateralization indices/topomaps revealed subject-specific μ/β patterns consistent with class-wise precision/sensitivity.

DISCUSSION: Compact spectro-temporal CNN backbones combined with lightweight temporal context (such as TCNs or dilated convolutions) deliver more stable performance under short-time windows, whereas deeper attention and Transformer architectures are more susceptible to variation across subjects and sessions. This study establishes a reproducible benchmark and provides actionable guidance for designing and calibrating online-first EEG decoders that remain robust under real-world, short-time constraints.},
}

@article {pmid41471452,
year = {2025},
author = {Gao, D and Zhao, Y and Zhou, J and Zhang, H and Li, H},
title = {MCRBM-CNN: A Hybrid Deep Learning Framework for Robust SSVEP Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {24},
pages = {},
pmid = {41471452},
issn = {1424-8220},
abstract = {The steady-state visual evoked potential (SSVEP), a non-invasive EEG modality, is a prominent approach for brain-computer interfaces (BCIs) due to its high signal-to-noise ratio and minimal user training. However, its practical utility is often hampered by susceptibility to noise, artifacts, and concurrent brain activities, complicating signal decoding. To address this, we propose a novel hybrid deep learning model that integrates a multi-channel restricted Boltzmann machine (RBM) with a convolutional neural network (CNN). The framework comprises two main modules: a feature extraction module and a classification module. The former employs a multi-channel RBM to unsupervisedly learn latent feature representations from multi-channel EEG data, effectively capturing inter-channel correlations to enhance feature discriminability. The latter leverages convolutional operations to further extract spatiotemporal features, constructing a deep discriminative model for the automatic recognition of SSVEP signals. Comprehensive evaluations on multiple public datasets demonstrate that our proposed method achieves competitive performance compared to various benchmarks, particularly exhibiting superior effectiveness and robustness in short-time window scenarios.},
}

@article {pmid41471422,
year = {2025},
author = {Ammar, S and Triki, N and Karray, M and Ksantini, M},
title = {A Multidimensional Benchmark of Public EEG Datasets for Driver State Monitoring in Brain-Computer Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {24},
pages = {},
doi = {10.3390/s25247426},
pmid = {41471422},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Automobile Driving ; Benchmarking ; Male ; Female ; Adult ; },
abstract = {Electroencephalography (EEG)-based brain-computer interfaces (BCIs) hold significant potential for enhancing driver safety through real-time monitoring of cognitive and affective states. However, the development of reliable BCI systems for Advanced Driver Assistance Systems (ADAS) depends on the availability of high-quality, publicly accessible EEG datasets collected during driving tasks. Existing datasets lack standardized parameters and contain demographic biases, which undermine their reliability and prevent the development of robust systems. This study presents a multidimensional benchmark analysis of seven publicly available EEG driving datasets. We compare these datasets across multiple dimensions, including task design, modality integration, demographic representation, accessibility, and reported model performance. This benchmark synthesizes existing literature without conducting new experiments. Our analysis reveals critical gaps, including significant age and gender biases, overreliance on simulated environments, insufficient affective monitoring, and restricted data accessibility. These limitations hinder real-world applicability and reduce ADAS performance. To address these gaps and facilitate the development of generalizable BCI systems, this study provides a structured, quantitative benchmark analysis of publicly available driving EEG datasets, suggesting criteria and recommendations for future dataset design and use. Additionally, we emphasize the need for balanced participant distributions, standardized emotional annotation, and open data practices.},
}

*Brain-Computer Interfaces
Humans
*Electroencephalography/methods
*Automobile Driving
Benchmarking
Male
Female
Adult

@article {pmid41471369,
year = {2025},
author = {Ga, YJ and Yeh, JY},
title = {Does Coxsackievirus B3 Require Autophagosome Formation for Replication? Evidence for an Autophagosome-Independent Mechanism: Insights into Its Limited Potential as a Therapeutic Target.},
journal = {Pharmaceuticals (Basel, Switzerland)},
volume = {18},
number = {12},
pages = {},
doi = {10.3390/ph18121880},
pmid = {41471369},
issn = {1424-8247},
support = {RS-2025-02304897//Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries/ ; },
abstract = {Background/Objectives: Coxsackievirus B3 (CVB3), a neurotropic enterovirus, is a major causative agent of viral encephalitis and myocarditis, yet no protective vaccine or effective antiviral therapy is currently available. Autophagy plays a dual role in viral infections, acting as both an antiviral defense and a process that can be exploited by certain viruses. Although CVB3 has been proposed to utilize autophagosomes as replication platforms, the underlying mechanisms remain controversial. Methods: In this study, we investigated the relationship between CVB3 replication and autophagosome formation under starvation-induced conditions and in ATG5 knockout cells. Results: While nutrient deprivation robustly induced autophagy, CVB3 infection did not trigger autophagosome formation. Moreover, viral replication proceeded efficiently in ATG5-deficient cells lacking autophagosomes. Pharmacological modulation of autophagy using rapamycin, a potent autophagy inducer, did not alter intracellular viral titers or protein expression, although extracellular viral release was modestly reduced. These results indicate that CVB3 replication occurs independently of autophagosome formation, suggesting that pharmacological targeting of autophagy provides limited therapeutic benefit. Conclusions: This study refines our understanding of autophagy as an antiviral target and highlights the need to identify alternative host-directed pathways for antiviral drug development.},
}

@article {pmid41470530,
year = {2025},
author = {Wang, C and Cheng, B and Tang, Q and Wu, R and Li, H},
title = {Design and Validation of a Brain-Controlled Hip Exoskeleton for Assisted Gait Rehabilitation Training.},
journal = {Micromachines},
volume = {16},
number = {12},
pages = {},
pmid = {41470530},
issn = {2072-666X},
support = {GXXT2022053//the Collaborative Innovation Program for Universities in Anhui Province/ ; 2023A3112//Huainan City Science and Technology Plan Project/ ; 2023AHIMB05//Base for Innovative Methods Promotion Application and Demonstration of Anhui Province/ ; },
abstract = {This study presents an integrated micro-system solution to address the challenges of gait instability in patients with impaired hip motor function. We developed a novel wearable hip exoskeleton, where a flexible support unit and a parallel drive mechanism achieve self-alignment with the biological hip joint to minimize parasitic forces. The system is driven by an active brain-computer interface (BCI) that synergizes an augmented reality visual stimulation (AR-VS) paradigm for enhanced motor intent recognition with a high-performance decoding algorithm, all implemented on a real-time embedded processor. This integration of micro-sensors, control algorithms, and actuation enables the establishment of a gait phase-dependent hybrid controller that optimizes assistance. Online experiments demonstrated that the system assisted subjects in completing 10 gait cycles with an average task time of 37.94 s, a correlated instantaneous rate of 0.0428, and an effective output ratio of 82.17%. Compared to traditional models, the system achieved an 18.64% reduction in task time, a 28.31% decrease in instantaneous rate, and a 7.36% improvement in output ratio. This work demonstrates a significant advancement in intelligent micro-system platforms for human-centric rehabilitation robotics.},
}

@article {pmid41469392,
year = {2025},
author = {Jilderda, MF and Bartlett, JMS and Liefers, GJ and Zhang, Y and Dunn-Davies, H and Rebattu, V and Salunga, R and Meershoek-Klein Kranenbarg, E and de Munck, L and Hasenburg, A and Markopoulos, C and Dirix, L and van de Velde, CJH and Rea, D and Anderson, AKL and Bastiaannet, E and Treuner, K and Taylor, KJ},
title = {Validation of minimal risk of recurrence classification by the Breast Cancer Index in early stage breast cancer.},
journal = {NPJ breast cancer},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41523-025-00885-x},
pmid = {41469392},
issn = {2374-4677},
abstract = {The Breast Cancer Index (BCI) was previously shown to identify ~20% of postmenopausal patients with early stage, hormone receptor positive (HR+), node negative (N0) breast cancer with minimal (<5%) risk of 10-year distant recurrence (DR) even without receiving adjuvant endocrine therapy (ET). This prospective-retrospective study further validated the BCI minimal risk classification in postmenopausal patients with early-stage, HR + HER2- N0 breast cancer from the Netherlands Cancer Registry (NCR) and the Tamoxifen and Exemestane Adjuvant Multinational (TEAM, NCT00279448, NCT00032136) randomized trial who received 5 years of primary adjuvant ET. BCI classified approximately 15% of patients as minimal risk. In the NCR cohort (n = 1264 out of 15,053 HR+ patients in the registry), risks of DR in the minimal, low, intermediate, and high groups were 4.8%, 3.3%, 8.0%, and 12.4%, respectively (P < 0.001). In the TEAM cohort (n = 978 out of 3544 in the BCI study), DR risks were 3.8%, 8.3%, 12.6% and 22.7% (P < 0.001). In multivariate analyses, BCI risk scores provided independent information over standard prognostic factors (P < 0.001). This study confirmed the ability of the adjusted BCI model to identify postmenopausal women with HR + HER2- N0 breast cancer who are at minimal risk of DR and may consider de-escalating adjuvant ET.},
}

@article {pmid41469234,
year = {2026},
author = {Ge, H and Gu, X and Wang, Z and Tan, S and Jiao, B and Zhang, L and Yang, Y and Li, W and Xie, J and Bai, R},
title = {Anesthetics Modulate Cerebrospinal Fluid Efflux Pathways in Mice by Altering Perineural and Perivascular Spaces.},
journal = {NMR in biomedicine},
volume = {39},
number = {2},
pages = {e70222},
doi = {10.1002/nbm.70222},
pmid = {41469234},
issn = {1099-1492},
support = {2022ZD0206000//STI2030-Major Projects of China/ ; 92359303//National Natural Science Foundation of China/ ; 82222032//National Natural Science Foundation of China/ ; 2025ZD0215000//Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project/ ; },
mesh = {Animals ; *Cerebrospinal Fluid/drug effects/metabolism ; *Anesthetics/pharmacology ; Male ; Mice ; Magnetic Resonance Imaging ; *Glymphatic System/drug effects ; Mice, Inbred C57BL ; },
abstract = {The brain-wide glymphatic transport system facilitates cerebrospinal fluid (CSF) circulation and the clearance of metabolic waste, processes largely influenced by sleep and sleep-like anesthesia. Recent research indicates that different anesthetic agents modulate CSF dynamics in distinct ways; however, their effects on CSF efflux pathways remain unclear. This study utilized dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and structural MRI to investigate CSF efflux pathways in mice under three anesthesia protocols (n = 6 per group): isoflurane alone (ISO), isoflurane combined with dexmedetomidine (DEXI), and ketamine/xylazine (K/X). Additionally, blood vessel diameters and CSF volume fractions were quantified. Our results demonstrate that ISO induced vasodilation in the anterior brain, slowing CSF flow to the dorsal brain while substantially accelerating CSF efflux across the cribriform plate and nasal mucosa toward the nasopharyngeal lymphatic plexus compared with DEXI and K/X (p < 0.001). However, ISO reduced CSF outflow through the spinal subarachnoid space primarily due to a decreased spinal subarachnoid CSF volume (ISO vs. DEXI, p = 0.0373; ISO vs. K/X, p = 0.0436). K/X considerably impaired CSF efflux via the cervical ganglia relative to DEXI and ISO, likely resulting from a lower CSF volume fraction within the peri-cranial nerve space (ISO vs. K/X, p = 0.0328, K/X vs. DEXI, p = 0.023). In conclusion, different anesthesia protocols modulate CSF efflux pathways by altering perineural and perivascular CSF spaces. These findings suggest that anesthetic agents influence glymphatic function by modulating distinct CSF efflux routes.},
}

Animals
*Cerebrospinal Fluid/drug effects/metabolism
*Anesthetics/pharmacology
Male
Mice
Magnetic Resonance Imaging
*Glymphatic System/drug effects
Mice, Inbred C57BL

@article {pmid41468722,
year = {2025},
author = {Ye, QY and Zhang, SY and He, XL and Yang, YQ and Ni, K and Yang, HX and Wei, W and Preece, DA and Chan, RCK and Li, BM and Cai, XL},
title = {Interrelationships between childhood trauma, alexithymia, and depressive symptoms: A network analysis and replication.},
journal = {Child abuse & neglect},
volume = {172},
number = {},
pages = {107877},
doi = {10.1016/j.chiabu.2025.107877},
pmid = {41468722},
issn = {1873-7757},
abstract = {BACKGROUND: Childhood trauma has been found to increase the risk of developing alexithymia and depressive symptoms. However, the complex interplay between childhood trauma, alexithymia, and depressive symptoms remains unclear.

OBJECTIVE: To understand how different facets of childhood trauma, alexithymia across positive and negative emotions, and depressive symptoms interact with each other, this study adopted the network analysis approaches to examine this complex relationship.

PARTICIPANTS AND SETTING: An initial sample of 2918 Chinese college students completed a set of psychometric questionnaires measuring childhood trauma, alexithymia and depressive symptoms. Another independent sample (n = 858) was used to investigate the replicability of our results.

METHODS: Undirected networks were estimated to explore the most relevant connections between the above variables. Bayesian network analysis was further used to explore the potential causal directions between the variables.

RESULTS: Findings from the initial dataset showed that childhood trauma was positively correlated with both alexithymia and depressive symptoms in the undirected networks. Physical abuse was the most central node. The Bayesian network analysis indicated that externally orientated thinking and depressed mood may be key drivers for activating other symptoms. Physical abuse might affect suicide ideation through difficulties in describing negative emotions. The replication dataset showed similar network structures as the initial dataset.

CONCLUSIONS: The findings suggest that childhood trauma, especially physical abuse, plays an important role in developing later depressive symptoms via valenced components of alexithymia. This study clarifies how early adversities link to depressive symptoms through emotional functioning and informs clinical interventions targeting influential symptoms in trauma-exposed populations.},
}

@article {pmid41467724,
year = {2025},
author = {Wang, C and Allison, BZ and Wu, X and Li, J and Zhao, R and Chen, W and Wang, X and Cichocki, A and Jin, J},
title = {Multi-Domain Dynamic Weighting Network for Motor Imagery Decoding.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2650005},
doi = {10.1142/S012906572650005X},
pmid = {41467724},
issn = {1793-6462},
abstract = {In motor imagery (MI)-based brain-computer interfaces (BCIs), convolutional neural networks (CNNs) are widely employed to decode electroencephalogram (EEG) signals. However, due to their fixed kernel sizes and uniform attention to features, CNNs struggle to fully capture the time-frequency features of EEG signals. To address this limitation, this paper proposes the Multi-Domain Dynamic Weighted Network (MD-DWNet), which integrates multimodal complementary feature information across time, frequency, and spatial domains through a branch structure to enhance decoding performance. Specifically, MD-DWNet combines multi-band filtering, spatial convolution, and temporal variance calculation to extract spatial-spectral features, while a dual-scale CNN captures local spatiotemporal features at different time scales. A dynamic global filter is designed to optimize fused features, improving the adaptive modeling capability for dynamic changes in frequency band energy. A lightweight mixed attention mechanism selectively enhances salient channel and spatial features. The dual-branch joint loss function adaptively balances contributions through a task uncertainty mechanism, thereby enhancing optimization efficiency and generalization capability. Experimental results on the BCI Competition IV 2a, IV 2b, OpenBMI, and a self-collected laboratory dataset demonstrate that MD-DWNet achieves classification accuracies of 83.86%, 88.67%, 75.25% and 84.85%, respectively, outperforming several advanced methods and validating its superior performance in MI signal decoding.},
}

@article {pmid41467584,
year = {2026},
author = {Wang, M and He, Q and Zhu, S and Cao, T and Wang, N and Jia, Y and Wu, H and Liang, J and Niu, H and Xu, Z and Cui, Z and Yang, Y and Zhao, J},
title = {Global White Matter Damage in Focal Brainstem Injury Patients With Disorders of Consciousness: A Diffusion Tensor Tractography Study.},
journal = {European journal of neurology},
volume = {33},
number = {1},
pages = {e70476},
doi = {10.1111/ene.70476},
pmid = {41467584},
issn = {1468-1331},
support = {2022ZD0205300//Science and Technology Innovation 2030/ ; Z221100002722014//International (Hong Kong, Macao, and Taiwan) Science and Technology Cooperation Project/ ; 2022-NKX-XM-02//Chinese Institute for Brain Research Youth Scholar Program/ ; 82371197//National Natural Science Foundation of China/ ; 7232049//Natural Science Foundation of Beijing Municipality/ ; },
mesh = {Humans ; Diffusion Tensor Imaging ; Female ; Male ; *White Matter/pathology/diagnostic imaging ; Adult ; *Consciousness Disorders/etiology/pathology/diagnostic imaging ; *Brain Stem/pathology/diagnostic imaging/injuries ; Middle Aged ; Retrospective Studies ; Young Adult ; Neural Pathways/pathology/diagnostic imaging ; Aged ; },
abstract = {BACKGROUND: Disorders of consciousness (DoC) pose significant challenges in clinical diagnosis and treatment. This study aims to investigate the relationship between consciousness levels and the brainstem-cortical white matter tracts in DoC patients resulting from focal brainstem injury using diffusion tensor imaging (DTI).

METHODS: DTI data of DoC patients with focal brainstem injury and healthy volunteers were retrospectively collected. White matter tractography was performed to reconstruct brainstem-cortical projections. The number of streamlines, total volume, and fractional anisotropy (FA) were analyzed from the perspective of global brain, physiological pathways, and functional networks. The relationship between these measurements and consciousness levels was investigated.

RESULTS: A cohort of 28 DoC patients and 32 healthy controls were included in the analysis. DoC patients exhibited significant reductions in the number of streamlines in global brainstem-cortical projections compared to controls. However, the total volume and FA of these fibers were relatively preserved. Specific pathways such as the corticospinal tract and frontoparietal tract showed marked reductions in streamline counts. Significant reductions in streamline counts were also observed in the somatomotor and frontoparietal networks. No significant changes in mean FA were observed across different physiological pathways and brain networks. Correlation analyses revealed significant associations between consciousness levels and structural connections in the frontoparietal tract and frontoparietal network.

CONCLUSION: This study highlights the impact of focal brainstem injury on global brain structural connectivity in DoC patients. Despite significant reductions in streamline counts, the preservation of FA suggests maintained microstructural integrity in surviving fibers.},
}

Humans
Diffusion Tensor Imaging
Female
Male
*White Matter/pathology/diagnostic imaging
Adult
*Consciousness Disorders/etiology/pathology/diagnostic imaging
*Brain Stem/pathology/diagnostic imaging/injuries
Middle Aged
Retrospective Studies
Young Adult
Neural Pathways/pathology/diagnostic imaging
Aged

@article {pmid41466537,
year = {2025},
author = {Selcuk, C and Boulgouris, NV},
title = {Dynamic graph representation of EEG signals for speech imagery recognition.},
journal = {Journal of neural engineering},
volume = {22},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ae2ccb},
pmid = {41466537},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Imagination/physiology ; *Brain-Computer Interfaces ; *Speech/physiology ; Algorithms ; *Pattern Recognition, Automated/methods ; Male ; Female ; },
abstract = {Objective. Speech imagery recognition from electroencephalography (EEG) signals is an emerging challenge in brain-computer interfaces, and has important applications, such as in the interaction with locked-in patients. In this work, we use graph signal processing for developing a more effective representation of EEG signals in speech imagery recognition.Approach. We propose a dynamic graph representation that uses multiple graphs constructed based on the time-varying correlations between EEG channels. Our methodology is particularly suitable for signals that exhibit fluctuating correlations, which cannot be adequately modeled through a static (single graph) model. The resultant representation provides graph frequency features that compactly capture the spatial patterns of the underlying multidimensional EEG signal as well as the evolution of spatial relationships over time. These dynamic graph features are fed into an attention-based long short-term memory network for speech imagery recognition. A novel EEG data augmentation method is also proposed for improving training robustness.Main results. Experimental evaluation using a range of experiments shows that the proposed dynamic graph features are more effective than conventional time-frequency features for speech imagery recognition. The overall system outperforms current state-of-the-art approaches, yielding accuracy gains of up to 10%.Significance. The dynamic graph representation captures time-varying spatial relationships in EEG signals, overcoming limitations of static graph models and conventional feature extraction. Combined with data augmentation and attention-based classification, it demonstrates substantial improvements over existing methods in speech imagery recognition.},
}

Humans
*Electroencephalography/methods
*Imagination/physiology
*Brain-Computer Interfaces
*Speech/physiology
Algorithms
*Pattern Recognition, Automated/methods
Male
Female

@article {pmid41464697,
year = {2025},
author = {Kimmeyer, M and Buijze, GA and Soares, MN and Rab, P and Colombini, AG and Diot, R and Macken, A and Lafosse, T},
title = {Arthroscopic Bioinductive Collagen Scaffold Augmentation in High-Risk Posterosuperior Rotator Cuff Tears: Clinical and Radiological Outcomes.},
journal = {Journal of clinical medicine},
volume = {14},
number = {24},
pages = {},
doi = {10.3390/jcm14248797},
pmid = {41464697},
issn = {2077-0383},
abstract = {Background/Objectives: Bioinductive bovine collagen implants (BCI) have been introduced to enhance tendon biology and promote tissue regeneration in rotator cuff (RC) repairs. This study aimed to assess the clinical and radiological outcomes of arthroscopic posterosuperior rotator cuff (psRC) repair with BCI augmentation in full-thickness tears at increased risk of retear. Methods: This case series analyzed 30 patients with psRC tears who were classified as being at high risk of failure according to a predefined set of parameters, including patient history, radiological findings and intraoperative assessments, and the presence of psRC retears. All patients subsequently underwent arthroscopic psRC repair with BCI augmentation, compromising 21 primary and 9 secondary repairs. Clinical outcomes were assessed using Subjective Shoulder Value (SSV), American Shoulder and Elbow Surgeons (ASES) shoulder score, and Constant score at 6 and 12 months postoperatively. Tendon integrity was assessed using the Sugaya classification. Results: At 12 months, magnetic resonance imaging revealed complete tendon healing in 56.7%, partial healing in 16.7%, and insufficient healing in 26.7%. Significant improvements in SSV (45.3 to 83.5), ASES (40.6 to 77.8), and Constant score (36.6 to 71.7) were observed at 12 months postoperatively, with all outcome measures exceeding their respective minimally clinically important differences. Two patients (6.7%) developed secondary shoulder stiffness, and 1 patient (3.3%) required revision surgery for bicipital groove pain. Conclusions: Augmentation with a BCI in arthroscopic repair of high-risk psRC tears demonstrate promising short-term results. Patients achieve significant improvements in pain and shoulder function, accompanied by satisfactory tendon healing on MRI.},
}