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

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

Humans
*Brain-Computer Interfaces
*Brain/physiology

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@article {pmid42262958,
year = {2026},
author = {Mrachacz-Kersting, N and Pasluosta, C and Meyer, B and Nascimento, OFD and Stieglitz, T and Farina, D},
title = {Cortical Activity Associated with Phantom Leg Movements.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2026.3701504},
pmid = {42262958},
issn = {1558-0210},
abstract = {We tested the feasibility for amputees to control artificial limbs using non-invasive electroencephalography (EEG). Thirteen participants engaged in attempts of isometric ankle plantar-flexions using their phantom or intact limb at slow or ballistic speeds. EEG data were analyzed for movement-related cortical potentials (MRCPs), the slow negative potentials related to the planning and execution of movements. We focused on temporal profiles and single-trial classification at electrode location Cz where MRCPs are most prominent. Distinctly different MRCP morphologies were observed for both movement speeds and phantom versus intact limbs. Crucially, time since amputation correlated significantly with classification errors for distinguishing tasks performed with the intact limb from those of the phantom limb (R = 0.36, p =0.004) and movement speed during trials of only the phantom limb (R = -0.33, p = 0.01). Here we show the persistent capacity of amputees to plan and attempt to execute limb motions at varying speeds using their phantom limb. This has implications for understanding neural adaptations over extended post-amputation periods and for the practical implementation of the MRCP in the design of brain-computer interfaces to control prosthetic devices using single-electrode EEG recordings.},
}

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

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

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

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

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

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

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

@article {pmid42265352,
year = {2026},
author = {Busch, EL and Fincke, EC and Lajoie, G and Krishnaswamy, S and Turk-Browne, NB},
title = {Human learning of noninvasive brain-computer interfaces via manifold geometry.},
journal = {Nature neuroscience},
volume = {},
number = {},
pages = {},
pmid = {42265352},
issn = {1546-1726},
support = {1839308//National Science Foundation (NSF)/ ; 2139841//National Science Foundation (NSF)/ ; 2047856//National Science Foundation (NSF)/ ; R01MH069456//U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)/ ; R01GM130847//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; R01GM135929//U.S. Department of Health & Human Services | National Institutes of Health (NIH)/ ; FG-2021-15883//Alfred P. Sloan Foundation/ ; },
abstract = {Brain-computer interfaces (BCIs) promise to restore and enhance human capabilities. Yet, their adoption has been limited by slow and inconsistent learning across users. We show that BCI learning is accelerated by leveraging the naturally occurring geometry, or intrinsic manifold, of brain activity, extracted using data diffusion. Participants were trained with real-time functional magnetic resonance imaging to control an avatar in a video game by self-modulating activity in brain regions supporting spatial navigation. We perturbed the mapping between brain activity and avatar movement to test how neural manifolds constrain human BCI learning. When new mappings relied on directions of significant variance on the intrinsic manifold, participants successfully gained control by realigning brain activity along these directions. When new mappings did not follow the intrinsic manifold, participants could not learn to control the avatar. These findings show how manifold geometry in higher-order brain regions guides human learning of complex cognitive tasks, identifying a principle for improving future neurotechnologies.},
}

@article {pmid42265834,
year = {2026},
author = {Ling, Y and Sun, P and Guo, T and Luo, B},
title = {"Digital eye tracking and plasma biomarkers: Distinguishing functional cognitive impairment from Alzheimer's disease biology".},
journal = {Alzheimer's & dementia : the journal of the Alzheimer's Association},
volume = {22},
number = {6},
pages = {e71574},
doi = {10.1002/alz.71574},
pmid = {42265834},
issn = {1552-5279},
support = {2022C03064//the key Research and Development Program of Zhejiang/ ; 2025ZFJH01//the Fundamental Research for the Central Universities/ ; 2022KY067//Medical and Health Science and Technology Project of Zhejiang Province/ ; 82422027//National Natural Science Foundation of China/ ; U24A20340//National Natural Science Foundation of China/ ; },
}

@article {pmid42259992,
year = {2026},
author = {Fan, Y and Gao, L and Lin, Z and Zhang, T and Bai, X and Chen, L},
title = {The effect of brain-computer interface training on cognitive function in stroke patients: a systematic review and meta-analysis.},
journal = {Journal of neurology},
volume = {273},
number = {7},
pages = {},
pmid = {42259992},
issn = {1432-1459},
support = {82471345//National Natural Science Foundation of China/ ; 2024-LCYJ-MS-17//Clinical Trials from the Affiliated Drum Tower Hospital, Medical School of Nanjing University/ ; NDYGN2025005//Aid project ofJiangsu Ningai Medical Development &Medical Aid Foundation/ ; },
abstract = {OBJECTIVE: This study aims to assess the therapeutic impact of BCI-based interventions on global and domain-specific cognitive functions (attention, memory, and executive function), and activities of daily living in stroke survivors. Furthermore, we seek to identify the potential moderating effects of feedback modes and BCI paradigms on the overall rehabilitative efficacy.

METHODS: A systematic search of PubMed, Embase, Web of Science, the Cochrane Library, and CNKI databases was conducted to identify eligible randomized-controlled trials (RCTs). Meta-analyses were performed by pooling standardized mean differences (SMDs) to synthesize effect sizes. To explore sources of heterogeneity and the effects of potential moderators, subgroup analyses were conducted according to outcome measures, stroke phase, BCI paradigm, and feedback type.

RESULTS: Twelve studies were included. The meta-analysis demonstrated that BCI training significantly improved global cognitive function (SMD = 0.62, P < 0.00001), attention, and executive function, alongside enhanced activities of daily living performance. However, no significant improvement was observed in memory function. Subgroup analyses revealed that superior and more robust effects were associated with subacute patients, active BCI paradigms, and multimodal feedback (visual + auditory + proprioceptive).

CONCLUSION: BCI training is an effective intervention for post-stroke cognitive recovery. Early initiation of therapy and the integration of multimodal feedback appear to be critical factors for maximizing therapeutic outcomes.},
}

@article {pmid42260169,
year = {2026},
author = {Su, Z and Gan, KB and Sim, KS},
title = {Decoding Upper-Limb Motor Imagery from EEG Signals: A Systematic Review of Methods and Applications.},
journal = {Annals of biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {42260169},
issn = {1573-9686},
support = {FRGS/1/2024/TK07/UKM/02/14//Ministry of Higher Education, Malaysia/ ; },
abstract = {Brain-computer interfaces (BCIs) have emerged as a promising technology with significant potential across various domains in recent years, including healthcare, industry, and entertainment. Among the many BCI paradigms, motor imagery (MI) based on electroencephalography (EEG) is one of the most commonly used and has been widely applied in medical settings. However, due to the inherently low signal-to-noise ratio and non-stationary nature of EEG signals, current decoding accuracy remains suboptimal-particularly in the classification of movements involving the same limb, where finer motion distinctions and higher decoding precision are urgently needed. This review summarizes the research on upper-limb MI-EEG classification and applications over the past 5 years and analyzes the relevant data extracted from the literature. The objective is to provide a comprehensive overview of the current state of research on decoding hand motor imagery from MI-EEG signals and to examine the challenges encountered in practical applications. We systematically investigate state-of-the-art methods, compare their performance and underlying assumptions, and discuss emerging trends and open challenges. Furthermore, we explore how these decoding methods can be translated into real-world applications, highlighting their potential as well as their limitations. The aim of this work is to provide valuable insights and guidance for researchers and developers in the field of EEG-based BCIs.},
}

@article {pmid42250530,
year = {2026},
author = {C, S and C, S and S, IJ},
title = {Efficient FPGA accelerator for low-power high-speed BCI motor imagery classification using novel deep learning.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {203},
number = {},
pages = {109105},
doi = {10.1016/j.neunet.2026.109105},
pmid = {42250530},
issn = {1879-2782},
abstract = {A brain-computer interface (BCI) is an advanced technology that enables direct communication between the human brain and external systems, eliminating the need for intermediaries. Electroencephalography (EEG) is a commonly used signal for developing BCIs. However, EEG signals have challenges such as a poor signal-to-noise ratio, high dimensionality, nonlinearity, and instability. This necessitates the development of an automated system using deep learning (DL) models for motor imagery (MI) classification. Many researchers have worked on MI classification and developed various algorithms; however, several issues remain unsolved, including achieving high accuracy for EEG data across all groups and unseen data, effective feature extraction, and deploying DL models on edge devices with low power consumption and high-speed MI classification. To address these challenges, a novel DL model, Few-Shot Learning (FSL)-Dual Attention-based SqueezeNet, is designed and tested. FSL enables learning MI classification with a small amount of data and improves accuracy on unseen data. SqueezeNet, combined with a Dual Attention Mechanism (DAM), effectively extracts important temporal and frequency features from EEG signals with low computational cost. The proposed network is evaluated on the BCI Competition IV 2a dataset under intra-session, cross-subject, and inter-session settings. It achieves accuracies of 0.9704, 0.8702, and 0.9568, respectively, outperforming well-known DL models such as EfficientNet (0.9426 in intra-session). Further comparisons with existing methods demonstrate competitive and consistent performance across different evaluation protocols. For generalizability analysis, the proposed network is tested on other public datasets. The proposed network achieves an accuracy of over 98% across all datasets, proving its effectiveness for MI classification using EEG signals. Next, a hardware accelerator is designed to deploy the proposed network on edge devices. The hardware is optimized for fast MI detection and low power consumption by employing a dual-core DPU and a dual-buffer scheme. The proposed accelerator's performance and hardware utilization are analyzed. The hardware design consumes only 12.14 W, which is 4.8 and 6.3 times lower than CPU and GPU power consumption, respectively. It performs MI classification in just 5.01 ms, significantly faster than CPU and GPU inference times. The proposed DL network and hardware accelerator demonstrate that the framework is well-suited for real-time deployment in MI classification tasks.},
}

@article {pmid42250823,
year = {2026},
author = {Márton, A and Benke, J and Markus, M and Hoheisel, W and Bartsch, J and Thommes, M},
title = {Promising advancements to the blue dye ingress test - Quantification of blister integrity by leakage rate.},
journal = {European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V},
volume = {},
number = {},
pages = {115140},
doi = {10.1016/j.ejpb.2026.115140},
pmid = {42250823},
issn = {1873-3441},
abstract = {The most common packaging type for solid dosage forms is the blister package. The critical quality attribute of blisters is the integrity, which is required to be tested. Hereby it is crucial to develop methodologies representing an improvement compared to the current standard, the blue dye ingress test, regarding sensitivity limits and quantification. In this study, two analytical methods (optical emission spectroscopy and a helium mass spectrometry, which rely on a similar principle), were characterized. For the latter a sample preparation procedure was also developed for filling the blister packages with helium tracer gas. Leaky blister packages were prepared via laser drilling, and the leakage rate was measured. Quantification within the experimental space was found to be feasible using optical emission spectroscopy, and partially feasible using helium mass spectrometry. Furthermore, the repeatability was examined and the measurement results were verified with physical and empirical models describing the molecular flow. In conclusion, the two characterized methods represent promising competition to the established standard test due to quantification. Additionally, the procedures can serve as a sensitive reference method for development as well as production.},
}

@article {pmid42253789,
year = {2026},
author = {Heskebeck, F and Bernhardsson, B and Bergeling, C},
title = {Rotation-based metric on the Riemannian manifold of SPD matrices with applications to source data selection for brain-computer interface transfer learning.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1824613},
pmid = {42253789},
issn = {1662-5161},
abstract = {This paper introduces the pole ratio metric and presents a sphere-based view of symmetric positive-definite matrix rotations on the Riemannian manifold of symmetric positive-definite matrices equipped with the affine-invariant Riemannian metric. The pole ratio quantifies whether data from different users lie on this Riemannian manifold in a way that enables effective transfer learning. The sphere-based view provides insight into the rotational step of transfer learning using the Riemannian Procrustes analysis method and highlights the limitations of rotation. For effective transfer learning, selecting appropriate source data is essential for good performance. The pole ratio is shown to be an effective metric for selecting source data. The main contribution of the paper is the insight into the limitations of rotations on a Riemannian manifold; the usefulness of the pole ratio as a source selection metric is a natural extension of this insight. This paper focuses on Brain-Computer Interfaces (BCIs), but the sphere-based view of rotations of symmetric positive-definite matrix data and the pole ratio are applicable to any field that models two-class data using symmetric positive-definite matrices.},
}

@article {pmid42253794,
year = {2026},
author = {Melby, SR and Asok Kumar, JN and Bigus, ER and Kellis, S},
title = {Clinical evaluation of communication brain computer interfaces in amyotrophic lateral sclerosis: a landscape analysis.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1771146},
pmid = {42253794},
issn = {1662-5161},
abstract = {INTRODUCTION: Amyotrophic lateral sclerosis (ALS) is a progressive motor neuron disease that leads to severe motor impairment, including loss of communication ability, and ultimately death. Communication brain computer interfaces (cBCIs) have the potential to restore communication without reliance on motor function, thereby improving quality of life, independence, and palliative care. However, standardized methods to evaluate cBCI efficacy necessary for clinical implementation are not yet established.

METHODS: We conducted a systematic literature review, semi structured interviews with key opinion leaders (KOLs), and a clinical assessment review panel to (1) identify clinical outcome assessments (COAs) relevant to cBCIs in ALS, (2) obtain expert feedback, and (3) synthesize the current clinical and scientific landscape.

RESULTS: A total of 21 COAs were identified as potentially relevant and may serve as a foundation for cBCI specific measures. However, no existing COA was found to comprehensively capture the clinical benefit or functional impact of cBCIs in ALS.

DISCUSSION: Current COAs are insufficient to evaluate cBCIs in ALS, highlighting a critical gap. Development of cBCI specific outcome measures is needed to support clinical validation, regulatory evaluation, and adoption.},
}

@article {pmid42253796,
year = {2026},
author = {Kojima, S and Kanoh, S},
title = {The ASME-speller: 30-class auditory brain-computer interface speller using stream segregation and the QWERTY layout.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1807535},
pmid = {42253796},
issn = {1662-5161},
abstract = {INTRODUCTION: This study presents the ASME-speller, a novel 30-class auditory brain-computer interface (BCI) speller system that combines auditory stream segregation with the familiar QWERTY keyboard layout to facilitate intuitive and visionfree communication.

METHODS: In the ASME-speller, three distinct auditory streams are presented simultaneously, each corresponding to a row on the QWERTY keyboard. The low-, middle-, and high-frequency streams represent the bottom, middle, and top rows, respectively. Within each stream, alphabet letters and selected symbols are repeatedly presented as spoken voice stimuli. Users are instructed to focus exclusively on the stream corresponding to the row containing the target letter and to selectively attend to that letter within the stream. By leveraging the QWERTY layout and auditory stream segregation, the proposed approach enables users to restrict their attentional focus to a subset of letters by directing selective attention to auditory streams, while the mapping between QWERTY rows and stream pitch facilitates intuitive letter selection. We conducted online experiments with ten healthy participants to evaluate system performance.

RESULTS: The ASME-speller achieved an average classification accuracy of 0.76 and an average information transfer rate (ITR) of 2.16 bits/min. Excluding one participant whose EEG data contained excessive artifacts, these values improved to 0.84 and 2.40 bits/min, respectively. Post-hoc analyses further examined the effects of preprocessing parameters, classification pipelines, and early stopping strategies. Among four pipelines tested, a linear discriminant analysis (LDA) combined with dynamic stopping demonstrated the most robust performance across participants (accuracy of 0.80 and ITR of 4.76 bits/min). For the best participant, a deep learning model (EEGNet4,2) with dynamic stopping achieved accuracy of 1.0 with ITR of 14.44 bits/min.

DISCUSSION: Compared to previous auditory BCI spellers, the ASME-speller demonstrates performance comparable to existing systems, while offering advantages in terms of simplicity, requiring only standard headphones and no visual support. These findings demonstrate the feasibility of the ASME-speller and pave the way toward practical auditory BCI applications for communication.},
}

@article {pmid42256276,
year = {2026},
author = {Yu, M and Guo, T and Han, S and Xue, N and Yang, W and Huang, J and Chen, H and He, C and Ding, J and Xia, L},
title = {Integrating metacognitive mechanisms optimizes EEG generative models via hierarchical regularization.},
journal = {iScience},
volume = {29},
number = {6},
pages = {115785},
pmid = {42256276},
issn = {2589-0042},
abstract = {Obtaining sufficient electroencephalography (EEG) signals for training deep neural networks (DNNs) in brain-computer interfaces (BCIs) is challenging due to individual differences in neural activity, which require large per-participant data to map signals to actions, while factors like movement artifacts often limit data collection. Existing advances mainly leverage generative models with various regularizers to produce sufficient EEG signals. However, selecting appropriate regularizers remains challenging. Inspired by metacognition, the human cognitive process that monitors and regulates learning and decision-making, we propose a metacognitive regulation module including three regularizers that explicitly capture EEG temporal dynamics and functional resolution, thereby improving both the diversity and similarity of generated data. Through extensive theoretical and empirical validation on two datasets, we demonstrate that our module: (1) significantly improves generative models for generating highly complex, realistic EEG activity; (2) improves generalization across different generative models; and (3) endows DNN models with enhanced human-like decision-making and adaptation capabilities.},
}

@article {pmid42256671,
year = {2026},
author = {Enériz, D and Antolín, D and Medrano, N and Calvo, B},
title = {Reproducible testing for embedded BCIs: a demultiplexing PCB and acquisition system for EEG signal emulation.},
journal = {HardwareX},
volume = {26},
number = {},
pages = {e00800},
pmid = {42256671},
issn = {2468-0672},
abstract = {Validating machine learning models for Brain-Computer Interfaces (BCIs) on resource-constrained edge devices is challenging, as traditional methods rely on costly EEG equipment or simulations that fail to capture real-world electronic characteristics. To bridge this gap, we introduce the DEEGMUX, a low-cost, open-source hardware system for high-fidelity, hardware-in-the-loop (HIL) testing of EEG classification algorithms. The system comprises an EEG Demultiplexer Board that converts a multiplexed EEG signal into 8 parallel channels, and an EEG Acquisition and Processing Board featuring an ADS1299 24-bit ADC interfaced with an Arduino Nano 33 BLE. This setup enables the use of real EEG datasets, such as the PhysioNet Motor Imagery dataset, to generate precisely timed electronic signals. Characterization demonstrated high signal fidelity, with a Mean Squared Error of 1.7·10[-10] V[2] and a Signal-to-Noise Ratio of 16 dB relative to the original digital data. Furthermore, an EEGNet motor imagery classifier evaluated on hardware-acquired signals showed a negligible accuracy difference of (-0.3 ± 5)% compared to evaluation on the original data, confirming that the emulation chain preserves classification-relevant features. The DEEGMUX provides a scalable, reproducible, and affordable platform for rigorously testing edge-deployed CNN models against realistic electronic inputs, accelerating the transition from simulation to robust real-world BCI deployment.},
}

@article {pmid42256857,
year = {2026},
author = {Evetović, N and Rosipal, R and Polyanskaya, A and Rošťáková, Z and Dvornák, K and Vankó, M and Korečko, Š and Trejo, LJ},
title = {EEG-based monitoring of mental fatigue during virtual-reality motor imagery tasks.},
journal = {Frontiers in behavioral neuroscience},
volume = {20},
number = {},
pages = {1810723},
pmid = {42256857},
issn = {1662-5153},
abstract = {Prolonged motor-imagery training in immersive virtual-reality environments can induce mental fatigue, reducing engagement and potentially limiting the effectiveness of neurorehabilitation. This study investigated neural markers of mental fatigue by recording electroencephalography (EEG) from healthy participants during extended motor-imagery and control sessions in a head-mounted display setup. Multidimensional analysis was applied to extract spectral, spatial, and temporal features while using a novel deflation step for removing task-related motor components to isolate fatigue-specific activity. Evidence of mental fatigue was consistently seen in parieto-occipital alpha-band modulation, with increases in alpha power corresponding to subjective reports and EEG-based measures of mental fatigue. The derived models were robust to common EEG artifacts and demonstrated consistent fatigue estimation across tasks and sessions. These findings suggest that individualized neural markers can enable real-time monitoring of fatigue (with an accuracy of 83.49 ± 6.34%), allowing adaptive adjustments of task difficulty or pacing in brain-computer interface systems. This work advances understanding of the neurophysiological dynamics of mental fatigue during immersive motor-imagery tasks and provides a foundation for designing more effective, personalized neurorehabilitation protocols.},
}

@article {pmid42258675,
year = {2026},
author = {Wang, Z and He, X and Wang, H and Wu, D},
title = {CKD: Contrastive Knowledge Distillation for Cross-Dataset EEG Classification.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3701548},
pmid = {42258675},
issn = {1558-2531},
abstract = {OBJECTIVE: Cross-dataset transfer in electroencephalography (EEG)-based brain-computer interfaces (BCIs) remains challenging due to substantial distribution shifts across datasets, including differences in subjects, acquisition devices, and recording protocols. This study aims to improve cross-dataset EEG decoding by enhancing knowledge transfer beyond conventional output-level distillation.

METHODS: We propose a contrastive knowledge distillation (CKD) framework for cross-dataset EEG classification. CKD follows a two-stage transfer strategy, consisting of cross-dataset teacher pretraining and cross-subject online adaptation, and jointly exploits logit-level distillation and feature-level contrastive alignment. In this way, the student model is encouraged to inherit both the predictive behavior and representation structure of the teacher.

RESULTS: Experiments on five motor imagery EEG datasets showed that CKD consistently outperformed twelve conventional training, representative knowledge distillation, and domain adaptation baselines under both single-source and multi-source transfer settings. Additional visualizations and quantitative analyses further confirmed that CKD improves teacher-student alignment in terms of feature geometry and distribution consistency, and can be further enhanced by explicit domain adaptation.

CONCLUSION: The proposed CKD provides an effective solution for cross-dataset EEG decoding by jointly improving predictive knowledge transfer and latent feature alignment under severe dataset shifts.

SIGNIFICANCE: This work improves the robustness and generalizability of EEG decoding across heterogeneous datasets, which is important for practical BCI deployment under real-world acquisition conditions.},
}

@article {pmid42259065,
year = {2026},
author = {Motegi, M and Chikamatsu, K},
title = {Management of conductive and mixed hearing loss intolerant to air-conduction hearing aids: A stepwise algorithm and narrative review from a Japanese perspective.},
journal = {Auris, nasus, larynx},
volume = {53},
number = {4},
pages = {567-578},
doi = {10.1016/j.anl.2026.05.001},
pmid = {42259065},
issn = {1879-1476},
abstract = {OBJECTIVE: Conductive and mixed hearing loss in the complicated ear, including chronically inflamed ears with recurrent otorrhea, postoperative cavities, tympanic membrane lateralization, canal stenosis/atresia, and congenital malformations, remains a frequent, consequential problem in cases where conventional air-conduction hearing aids (ACHAs) are unusable or provide insufficient functional benefits. The expansion of the therapeutic landscape from non-implantable (e.g., cartilage conduction hearing aids [CCHA] and adhesive bone-conduction systems) to implantable (e.g., bone-conduction implants [BCIs] and active middle ear implants [Vibrant Soundbridge[®︎,] VSB]) options has not only increased opportunities for personalized rehabilitation but also created a practical "paradox of choice." Japan provides a distinctive clinical context because major implantable auditory devices are reimbursed under defined indications, whereas access to non-implantable options frequently depends on out-of-pocket purchases and/or subsidy programs.

METHOD: This Japan-based narrative review synthesized peer-reviewed evidence and integrated the domestic indication framework to propose a pragmatic, stepwise device-selection algorithm for complicated ears with conductive or mixed hearing loss. Step 1 comprises a Gatekeeper Trial using a non-surgical option (e.g., CCHA/adhesive systems or headband/soft band stimulation) to confirm real-world benefits, identify coupling-related limitations, and provide counseling. Step 2 categorizes the cochlear reserve into zones A, B, or C based on bone-conduction thresholds to align the device output capacity with the inner-ear reserve; this step also incorporates Japan-aligned indications and a high-frequency "B-C border" flag (e.g., >65 dB HL at high frequencies) that can shift the balance between BCI and VSB. Step 3 applies clinically decisive modifiers: ear status and infection-control strategy, imaging-based surgical feasibility, high-frequency listening demands, and patient priorities, such as cosmesis, skin tolerance, maintenance burden, and MRI considerations.

RESULTS: Asymmetric hearing loss is managed as a dedicated differentiator. When appropriate, BCI-mediated transcranial stimulation can add useful contralateral cochlear access to improve speech perception in relevant spatial noise configurations, whereas counseling emphasizes situation-dependent benefits and limited binaural restoration.

CONCLUSION: Finally, we introduce the concept of Device Readiness Surgery, reframing otologic surgery as a staged effort to achieve a safe, dry, and stable ear that enables ACHA use whenever realistic; when ACHA remains ineffective, the ear is optimized for the selected device. This review provides a clinically oriented roadmap to improve the consistency of counseling and device selection in complicated ears and highlights the priorities for prospective validation and comparative-effectiveness research.},
}

@article {pmid42259101,
year = {2026},
author = {Ghafoori, S and Cetera, A and Rabiee, A and Farhadi, MH and Singh, R and Furmanek, M and Shahriari, Y and Abiri, R},
title = {Cross-frequency bispectral EEG analysis of reach-to-grasp planning and execution.},
journal = {Computers in biology and medicine},
volume = {213},
number = {},
pages = {111791},
doi = {10.1016/j.compbiomed.2026.111791},
pmid = {42259101},
issn = {1879-0534},
abstract = {Neural motor control of reach-to-grasp emerges from complex, nonlinear interactions across multiple brain cortices. However, most electroencephalography (EEG)-based motor analysis has largely relied on linear and second-order spectral measures. Here, we investigate whether higher-order cross-frequency dynamics encode meaningful distinctions between motor planning and execution during natural reach-to-grasp movements. Using a cue-based experimental paradigm, EEG was recorded during precision and power plan-to-grasp tasks, enabling stage-resolved analysis of grasp planning and execution-related neural activity. Cross-frequency bispectral analysis was applied to compute complex bicoherence matrices across canonical frequency band pairs, from which magnitude- and phase-based features were extracted. Classification, permutation-based feature selection, and within-subject statistical testing revealed that execution is associated with stronger nonlinear coupling than planning, with dominant contributions from β- and γ-driven interactions. In contrast, decoding of precision versus power grasps showed similar performance across stages, indicating that grasp-type representations emerge during planning and persist into execution. Exploratory single-feature analyses further identified focal, stage-dependent modulation of nonlinear coupling in central motor regions. Informative bispectral features reflected coordinated activity across prefrontal, central, and occipital areas, while feature redundancy enabled dimensionality reduction without loss of performance. Compared with the conventional analytical methods as baselines, bispectral features provided consistent advantages for grasp-type discrimination and multiclass classification, highlighting the value of nonlinear cross-frequency analysis. In summary, our results extend bispectral analysis to clinically relevant grasping tasks and highlight nonlinear cross-frequency coupling as an informative marker of motor stages offering a foundation for future BCI and neuroprosthetic research.},
}

@article {pmid42259478,
year = {2026},
author = {Chen, Y and Li, Z and Wang, Y and Li, Y and Zheng, J and Yang, H and Liu, M and Cukur, T and Fan, Q and Li, Z and Lu, J and Tian, Q},
title = {MicroKAN: Mapping Human Brain Microstructure Using Diffusion MRI and Adaptive Nonlinear Modeling.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {122032},
doi = {10.1016/j.neuroimage.2026.122032},
pmid = {42259478},
issn = {1095-9572},
abstract = {Diffusion magnetic resonance imaging (dMRI) provides powerful insights into brain microstructure, but conventional microstructural modeling methods require long acquisition times for covering sufficient diffusion directions and are computationally intensive. While deep learning has shown promise in reducing the direction requirement and accelerating the modeling, traditional architectures such as CNNs often struggle to capture the highly nonlinear relationships between multi-shell diffusion signals and microstructural properties. We present MicroKAN, a novel framework built upon Kolmogorov-Arnold Networks with adaptive spline-based activations, specifically designed to represent complex biophysical models with enhanced flexibility and efficiency. MicroKAN supports both supervised and self-supervised paradigms: the supervised variant learns mappings from data to reference metrics, while the self-supervised variant estimates model parameters directly by reconstructing signals through the forward diffusion process, eliminating the need for ground-truth labels. Evaluated on diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) across multiple datasets, MicroKAN substantially accelerates acquisition and improves the fidelity of microstructural parameter estimation. Beyond supervised training, its self-supervised formulation shows strong robustness to distribution shifts, enabling reliable performance even without annotations. Furthermore, transfer learning with minimal labeled data preserves high accuracy, underscoring the framework's adaptability to diverse scenarios. These advances establish MicroKAN as a versatile and efficient tool for dMRI analysis, offering new opportunities to accelerate neuroscience research and expand the clinical utility of microstructural imaging. Our source code is available at https://github.com/JustlfC03/MicroKAN.},
}

@article {pmid42246084,
year = {2026},
author = {Fischer, T and Middell, E and Moradi, S and von Lühmann, A},
title = {fNIRS Single-trial decoding improves systematically with higher optode density, model-based noise regression, and image reconstruction.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae782f},
pmid = {42246084},
issn = {1741-2552},
abstract = {Advances in high-density diffuse optical tomography (HD-DOT) promise to overcome long-standing performance limitations in classification of sparse functional Near-Infrared Spectroscopy (fNIRS) signals, but their combined impact on single-trial brain decoding and generalization remains largely unquantified. Here, we systematically evaluate how probe density, physiology removal via short-separation (SS) regression within a general linear model (GLM), and image-space feature representations aligned with brain parcellation schemes shape single-trial decoding accuracy. To enable a structured investigation and validation via realistic ground truth data, we introduce a flexible, easy-to-use framework that allows users to augment their own channel space resting-state fNIRS data with configurable synthetic hemodynamic response functions (HRFs) on target areas of the brain, using a state-of-the art diffuse optical forward model. Using three high-density fNIRS datasets -a whole-head resting-state recording augmented with synthetic HRFs and two motor ball-squeezing datasets -we derive sparse-to-HD optode subsets, integrate GLM-based SS regression into cross-validation, and compare channel-space and parcel-space features derived from HD-DOT image reconstructions. High-density configurations consistently and significantly improve classification accuracy and robustness to focal activations. SS correction yields systematic gains of 4 percentage points in within-subject decoding and more than 10 percentage points in cross-dataset transfer. Parcel-space features outperform channel-space features at matched dimensionality, enabling robust leave-one-subject-out decoding (mean accuracy 79%) and cross-dataset generalization across different probe layouts (72% with SS correction). All methodology is implemented and available in the open-source Cedalion framework. Together, these results demonstrate that HD-DOT, GLM-based SS regression, and parcel-space representations jointly enable significantly more accurate, robust, and probe-independent fNIRS classification pipelines.},
}

@article {pmid42246470,
year = {2026},
author = {Grill, WM and Chestek, CA and Wang, Y and Aberra, AS and Destexhe, A and Chan, RHM and Lu, BL and Hanein, Y and Robinson, JT and Whitmire, C and Song, AW and Farina, D and Ferris, DP and Green, R and Goding, J and Asplund, M},
title = {A Roadmap to Navigate the Future of Neural Engineering.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae78c2},
pmid = {42246470},
issn = {1741-2552},
abstract = {A group of leaders in neural engineering collaborated to develop a roadmap to navigate the future of neural engineering. We covered a range of themes, including brain machine interfaces, neural modelling, artificial intelligence and machine learning, neural interfaces, neural imaging, augmented rehabilitation, and neuromaterials. For each topic we reviewed the current status, identified current and future challenges, and speculated on the emerging and necessary advances in science and technology to meet these challenges. Neural engineering will continue to yield the approaches and insights that advance the diagnosis and treatment of nervous system disorders, as well as provide new understanding of neural function. .},
}

@article {pmid42243238,
year = {2026},
author = {Geng, X and Xiong, Z and Yu, P and Dai, T and Wu, S and Wang, H},
title = {ZMW-RSVP: a time-frequency prior-guided normalization-free RSVP-BCI decoding model.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-56317-8},
pmid = {42243238},
issn = {2045-2322},
support = {No. 20240404059ZP//Jilin Province Science and Technology (S&T)/ ; No. 20240404059ZP//Jilin Province Science and Technology (S&T)/ ; No. 20240404059ZP//Jilin Province Science and Technology (S&T)/ ; No. 20240404059ZP//Jilin Province Science and Technology (S&T)/ ; No. 20240404059ZP//Jilin Province Science and Technology (S&T)/ ; No. 20240404059ZP//Jilin Province Science and Technology (S&T)/ ; },
abstract = {Rapid Serial Visual Presentation (RSVP)-based brain-computer interfaces (BCIs) are valuable for target detection and rehabilitation because they require no motor involvement and can elicit reliable event-related potentials (ERP). However, single-trial EEG decoding remains challenging due to low signal-to-noise ratio and substantial inter-subject variability. To address the limitations of conventional approaches that rely on handcrafted features and to further explore internal feature transformation in single-trial ERP decoding, this paper proposes an RSVP-BCI decoding model, termed ZMW-RSVP, by extending a time-frequency Transformer framework with oscillatory gated attention and a normalization-free dynamic activation network. The proposed model incorporates neuroscience-inspired priors through a multi-band oscillatory gating mechanism, with an emphasis on theta-related activity associated with RSVP/P300 processing, and further guides attention allocation via a time-window bias. In addition, the model replaces internal LayerNorm modules with Dynamic Tanh, which provides an element-wise internal transformation without explicit mean-variance normalization and is evaluated as a task-related alternative for ERP-related discriminative feature modeling. Experiments on the Tsinghua RSVP dataset and the NeuBCI Target Retrieval RSVP-EEG dataset demonstrate that ZMW-RSVP achieves improved classification performance under the evaluated cross-subject settings, validating the effectiveness of the proposed approach for single-trial RSVP decoding.},
}

@article {pmid42243338,
year = {2026},
author = {Ga, YJ and Yeh, JY},
title = {Eradication of Mycoplasma contamination in HeLa cells using neomycin resistance gene introduction and aminoglycoside G418 (Geneticin) treatment.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-55513-w},
pmid = {42243338},
issn = {2045-2322},
support = {RS-2025-02307583//Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry/ ; },
abstract = {Mycoplasma contamination remains a persistent problem in continuous cell culture, compromising cellular physiology, altering gene expression, and potentially leading to erroneous experimental conclusions. Mycoplasmas can profoundly affect cultured cells, underscoring the need for efficient eradication strategies. Here, we developed a simple and effective method to eliminate Mycoplasma from HeLa cell lines. A neomycin resistance gene was introduced into Mycoplasma-contaminated cells, conferring resistance to aminoglycoside-induced cytotoxicity. Subsequently, a high concentration of the neomycin analogue G418 (Geneticin), combined with single-cell cloning, was applied to achieve complete removal of the Mycoplasma contamination. Mycoplasma presence and clearance were confirmed by PCR targeting the 16 S rRNA gene and immunofluorescence using a Mycoplasma-specific monoclonal antibody. This genetic-antibiotic combination proved technically simple and highly effective for long-term Mycoplasma eradication in continuous cell lines. To investigate the functional impact of Mycoplasma contamination, we compared protein expression following transient transfection with GFP and FAM-labeled reporters in Mycoplasma-eradicated versus Mycoplasma-contaminated cells. Fluorescence analysis revealed a marked increase in transfection efficiency and reporter expression in Mycoplasma-eradicated cells. Our findings provide an effective strategy for eliminating Mycoplasma contamination in cell line cultures, ensuring the reliability of cell-based research and the accuracy of experimental data.},
}

@article {pmid42245765,
year = {2026},
author = {Shivakumar, D and Gupta, CN and Hazra, B},
title = {Long-range correlations in alpha-band of electroencephalogram: a nonlinear embedding and detrended fluctuation analysis.},
journal = {Frontiers in neuroinformatics},
volume = {20},
number = {},
pages = {1823408},
pmid = {42245765},
issn = {1662-5196},
abstract = {Understanding the temporal organization of brain activity requires methods that capture scale-free dynamics while accounting for the high-dimensional, spatially correlated nature of the electroencephalogram (EEG) data. We propose a novel framework that integrates nonlinear manifold learning (Isometric mapping) with detrended fluctuation analysis (DFA) to quantify long-range temporal correlations (LRTC) in the alpha-band of EEG signals. We applied this framework to two music related EEG datasets, as music is known to evoke different emotions and synchronize brain activity. The first dataset was obtained during live Indian classical music (ICM) listening that included two ragas, Yaman and Puriya Dhanashree. EEG was recorded from 13 healthy volunteers (24 channels, sampled at 500 Hz). The second dataset is the Music BCI dataset (006-2015), which includes Jazz and Synth-pop musical clips, with EEG collected from 11 subjects (64 channels, downsampled to 200 Hz). The EEG data from both datasets were preprocessed, band-limited to 8-13 Hz, and segmented into non-overlapping 2-s windows. Alpha-band power was extracted from each channel to form the feature matrix used for embedding. For the ICM dataset, Isometric mapping (Isomap) produced a low-dimensional representation (d = 3), which we analyzed using two approaches: (i) a norm-based approach and (ii) a mean-based approach. For comparison, an equivalent PCA-based pipeline (d = 5) was implemented. The Isomap mean-based DFA yielded consistent scaling exponents (α) in the range of 0.66-0.70, with higher goodness-of-fit (R [2]) and narrower bootstrap confidence intervals than the norm-based approach. PCA produced similar trends but required more dimensions. Paired t-tests showed that the Isomap mean-based approach detected music-related changes more sensitively than PCA (Yaman p = 0.02; Puriya Dhanashree p = 0.008). Comparable results were also observed for the second Music BCI dataset, where Isomap achieved a compact representation with d = 5, compared to d = 8 for PCA. In this dataset as well, the mean-based DFA yielded α values in the range of 0.62-0.65 and higher goodness-of-fit. Overall, the results suggest that combining nonlinear manifold embeddings with mean-based DFA provides a compact and robust framework for characterizing scale-free temporal structure in EEG data.},
}

@article {pmid41963527,
year = {2026},
author = {Gayathri, T and Manjula, G and Kenchannavar, HH and Sudha, D and Jankatti, SK and Kaur, R and Venkateswarlu, B},
title = {QuantumNeuroXAI: a quantum-inspired deep learning framework with explainability for brain signal analysis and neurological disorder detection.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {},
pmid = {41963527},
issn = {2045-2322},
abstract = {Electroencephalography (EEG) is a non-invasive, high-temporal-resolution method for diagnosing and monitoring neurological disorders. Deep learning has recently substantially enhanced the state of the art for automated EEG analysis. However, many of the currently applied paradigms are still challenged by limited generalisation across datasets, vulnerability to noise or preprocessing changes, and the absence of interpretable decision rules. Additionally, many deep learning models operate like black boxes, which limits their use in clinical settings where interpretability and trust are key. Although the potential of quantum-inspired learning has recently been demonstrated through improved feature separability in high-dimensional signal spaces, its scope of applicability does not yet extend to deep temporal modelling and explainable artificial intelligence applications. We address these limitations by introducing QuantumNeuroXAI, a quantum-inspired deep learning framework implemented on classical hardware that leverages structured feature encoding inspired by quantum neural networks to provide inherent explainability for EEG-based diagnosis of neurological disorders. This framework hybridises quantum-inspired feature encoding with a deep-learning architecture that blends temporal convolutional and attention-based recurrent modelling to capture local and long-range patterns of dependencies in EEG signals. The framework incorporates a multi-level explainability module relevant at the signal, model, and quantum-representation levels, allowing predictions to be interpreted in a clinically meaningful and transparent fashion. We conduct extensive experiments on three publicly available EEG datasets (TUH EEG, CHB-MIT, and BCI Competition IV-2a) to evaluate the proposed framework. These quantitative results show that QuantumNeuroXAI achieves statistically significant and large effect sizes, with macro-F1 improvements of up to 5.2% over classical machine learning, deep learning, and hybrid baseline models. Additional robustness and scalability analyses further validate stable performance against dataset shift and across various preprocessing configurations. In summary, QuantumNeuroXAI is an interpretable and reproducible solution for EEG-based neurological analysis, demonstrating promise for clinical decision support and future scalability to multimodal brain signal applications. It is important to note that the proposed framework does not rely on quantum hardware and is fully implemented using classical computational resources. The implementation of the proposed framework is publicly available at: https://github.com/venkateshwarlu-bondu/QuantumNeuroXAI.},
}

@article {pmid41968126,
year = {2026},
author = {Kuroda, N and Sato, Y and Harada, S and Teraoka, R and Teramoto, W},
title = {Bayesian causal inference reveals declined proprioception, increased integration bias underlie older adults' stronger visual bias in hand position perception.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {},
pmid = {41968126},
issn = {2045-2322},
support = {23K18980//Japan Society for the Promotion of Science/ ; 20H05801 and 23H00076//Japan Society for the Promotion of Science/ ; },
abstract = {UNLABELLED: Self-localization is fundamental to bodily self-consciousness across the lifespan. Humans estimate body-part position by integrating afferent signals such as vision and proprioception. Rubber and mirror hand illusions highlight the dominant role of vision in hand position perception. Although older adults rely more heavily on visual information, the computational mechanisms underlying age-related increases in visual bias remain unclear. Here, we examined age-related changes in visuo-proprioceptive integration using a Bayesian causal inference (BCI) model. Two experiments introduced spatial discrepancies between visual and proprioceptive hand positions to manipulate the likelihood of integration. Participants reached toward a target after the visual hand disappeared, allowing the BCI model to estimate sensory reliabilities and the prior probability of a common cause ([Formula: see text]). Decision-making strategies were also compared within the BCI framework. Older adults exhibited reduced proprioceptive reliability and a higher [Formula: see text], indicating a stronger tendency to infer a shared source for visual and proprioceptive signals. No age-related differences were observed in decision-making strategy. These findings suggest that age-related visual bias reflects changes not only in sensory reliability but also in causal inference during multisensory integration.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1038/s41598-026-45797-3.},
}

@article {pmid42236468,
year = {2026},
author = {Wu, M and Di, Y and Kuang, S and Hu, J and Zhang, J and Wang, K and Zhang, J and Chen, C and Zhou, J and Li, T and Luo, B and Ding, N},
title = {Neural Response to Familiar Names Predicts Outcome of Comatose ICU Patients: A Prospective Observational Cohort Study.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-73878-4},
pmid = {42236468},
issn = {2041-1723},
support = {LQN26H090007//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
abstract = {Predicting the outcome of comatose patients in the intensive care unit (ICU) can inform decision making but remains challenging. Recent studies suggest that task-state electroencephalography (EEG) can detect covert cognition and facilitate patient prognosis. This study aimed to predict the outcome of comatose patients, by assessing covert processing of familiar names using a state-of-the-art EEG frequency tagging approach. Eighty-nine comatose patients following acute brain injury were recruited from five ICUs. Patients were presented with a rapid stream of familiar names and acoustically matched but unintelligible control sounds. EEG responses tracking the familiar names and control sounds were extracted in the frequency domain and utilised to predict the outcome of each patient, which was assessed at 1, 3, and 6 months post-injury using the Glasgow Outcome Scale-Extended (GOSE). Name-tracking EEG responses positively correlated with GOSE scores. A machine learning model integrating EEG responses and clinical characteristics achieved AUCs of 0.86, 0.88, and 0.86 in the test set, and 0.91, 0.90, and 0.85 in the external validation set, for predicting outcomes at 1, 3, and 6 months, respectively. These findings underscore that EEG assessment of residual processing of familiar names relates to patient outcomes and has the potential to predict outcome of comatose ICU patients.},
}

@article {pmid42236749,
year = {2026},
author = {Hazen, M and Cushing, SL and Gordon, KA},
title = {Impacts of hearing history, etiology, vestibular and balance function, and socioeconomic marginalization on developmental outcomes in children with cochlear implants.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {},
pmid = {42236749},
issn = {2045-2322},
mesh = {Humans ; Female ; *Cochlear Implants ; Child, Preschool ; Male ; Child ; Adolescent ; *Postural Balance/physiology ; Memory, Short-Term ; *Vestibule, Labyrinth/physiopathology ; Socioeconomic Disparities in Health ; *Hearing Loss/etiology/physiopathology ; },
abstract = {Children with bilateral cochlear implants (BCIs) remain vulnerable to developmental challenges relative to typically developing peers (TD), even when access to sound through acoustic or amplified hearing is achieved within the first year of life. This study evaluated contributions of hearing history, etiology, vestibular/balance function, and socioeconomic marginalization to language, working memory, and academic outcomes. Ninety-six children aged 4.65-17.85 years participated: 66 with BCIs (mean [SD] age, 11.54 [3.57]) and 30 typically developing (TD) peers (mean [SD], 11.69 [2.68]). Standardized assessments included the CELF, Dot Matrix, Corsi Block, Digit Span, and WIAT-III subtests. Regression and principal component analysis (PCA) identified predictors of developmental outcomes. Children with BCIs scored significantly lower than TD peers in language (p = 0.003), visuospatial working memory (p = 0.001), math (p < 0.001), and word reading (p = 0.048). PCA identified four components: hearing loss history, auditory experience/resources, social marginalization, and vestibular/balance function. Only auditory experience predicted developmental outcomes across domains (p's < 0.05). Vestibular and balance function were impaired in the BCI group (p < 0.001) but did not predict language, working memory, or academic scores. Deficits were most pronounced in children with congenital cytomegalovirus, cochleovestibular anomalies, and genetic hearing loss. Results emphasize the importance of sustained early auditory access together with etiology-informed, multidisciplinary support to optimize developmental outcomes in children with BCIs.},
}

Humans
Female
*Cochlear Implants
Child, Preschool
Male
Child
Adolescent
*Postural Balance/physiology
Memory, Short-Term
*Vestibule, Labyrinth/physiopathology
Socioeconomic Disparities in Health
*Hearing Loss/etiology/physiopathology

@article {pmid42239131,
year = {2026},
author = {Yang, L and Zhang, J and Wang, J and Huang, HH and Han, H and Razansky, D and , and Rominger, A and Lu, J and Ni, R},
title = {Divergent scalp-to-region distance alteration patterns in autism spectrum disorders, Parkinson's disease and Alzheimer's disease.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.05.14.725296},
pmid = {42239131},
issn = {2692-8205},
abstract = {UNLABELLED: Brain stimulation is increasingly recognized as an effective and important therapeutic intervention for many brain diseases. Distance between the scalp and other brain regions is a pivotal variable for neurostimulation planning and the development of new techniques, but alterations in the distance between the scalp and other regions in brain diseases are largely unknown. In this study, we developed an automatic pipeline to calculate scalp-to-region distance (SRD) values from T1 MR images and applied it to a total of 1382 participants, including patients with autism spectrum disorder (ASD), Parkinson's disease (PD), Alzheimer's disease (AD), and cognitively normal controls (CNs). Cloud points were uniformly sampled on the automatically extracted scalp surface and cortex surface, on which the point-wise distance maps were generated. The brain was then coregistered with the BCI-DNI atlas, and SRD value for each brain region was extracted. Analysis of covariance (ANCOVA) was performed for SRD in each brain region, with age and sex as covariates. Compared with CNs, ASD patients showed widespread SRD decreases across the brain with prominent involvement of the frontal lobe, especially the orbitofrontal cortex and adjacent regions. In contrast, in AD patients, significantly increased SRD values were observed in various regions of the frontal gyrus. No significant SRD alteration was found in PD patients after correction. The automatic SRD calculation pipeline and the different patterns of SRD alterations in these diseases might be helpful for future neurostimulation planning in clinical practice.

HIGHLIGHTS: Automatic pipeline enables scalp-to-region distance (SRD) measurement, facilitates brain stimulation planning.ASD patients show widespread SRD decreases, especially in the orbitofrontal cortex and adjacent regions.AD patients present increased SRD in the frontal gyrus and decreased SRD in the parahippocampal gyrus.},
}

@article {pmid42239466,
year = {2026},
author = {Welton, TA and Currie, T and Fontaine, A and Caldwell, J and Weir, RF and Restrepo, D and Gibson, EA},
title = {Multi-site temporal control of optogenetic stimulation enhances firing frequencies in peripheral nerves.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.05.15.724667},
pmid = {42239466},
issn = {2692-8205},
abstract = {We find that multi-site temporal control of optogenetic photostimulation in peripheral nerves can enhance firing rates by overcoming the intrinsic limitation of opsin photophysics. The benefits of multi-site optogenetic stimulation were demonstrated with three approaches: (1) in silico modeling, (2) ex vivo in the sciatic nerve, and (3) in vivo in the vagus nerve. An in silico model of multi-site optogenetic stimulation was developed in two Hodgkin and Huxley type neuron models, that supported our hypothesis. The ex vivo sciatic nerve showed an increase in firing frequency that is physiologically relevant for functional control. The technique was then applied in vivo for optogenetic vagus nerve stimulation resulting in significant changes in heart rate compared with standard methods of single-site stimulation. Improving the control of optogenetically induced neural firing will have broad impacts for future developments in optical nerve interfaces and brain-machine interfaces.},
}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

CLINICAL TRIAL NUMBER: Not applicable.},
}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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