@article {pmid41110656,
year = {2025},
author = {Huang, X and Xu, S},
title = {Mitigating Choice Overload: The Interactive Effects of Set Size and Overall Preference Revealed by Hierarchical Drift Diffusion Modeling and Electroencephalography.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121542},
doi = {10.1016/j.neuroimage.2025.121542},
pmid = {41110656},
issn = {1095-9572},
abstract = {Excessive choice can overwhelm cognitive resources and trigger choice overload, yet its neurophysiological basis-particularly the moderating role of overall preference level-remains underexplored. This study employed a two-stage experimental paradigm manipulating choice set size (large vs. small) and overall preference level (high vs. low). We integrated event-related potentials (ERPs), multivariate pattern analysis (MVPA), and hierarchical drift diffusion modeling (HDDM) to investigate how these factors interactively shape decision processes. Behavioral and computational modeling results revealed that high-preference conditions enhanced participants' ability to identify satisfactory options, with this advantage persisting and significantly accelerating final selection speed, particularly for large choice sets. Conversely, low-preference conditions amplified choice set size effects, with large sets exacerbating choice overload. ERP analyses showed larger P2 amplitudes for small choice sets, indicating greater early attentional allocation. More negative N2 amplitudes consistently appeared for small sets across both overall preference levels, reflecting elevated conflict and cognitive control demands. Small-set/low-preference conditions elicited the largest P3 amplitudes, suggesting small sets triggered compensatory attentional allocation under low-preference conditions. MVPA identified stable and distinct neural representation patterns across all experimental conditions, confirming that overall preference level modulates neural encoding of choice overload. These findings demonstrate that subjective preference strength functions as a key regulatory factor in mitigating choice overload. Our multimodal approach advances theoretical accounts of value-based decision-making by revealing how internal preferences interact with external complexity to shape the temporal and computational architecture of cognitive control.},
}

@article {pmid41109958,
year = {2025},
author = {Suffian, M and Ieracitano, C and Morabito, FC and Mammone, N},
title = {An Explainable 3D-Deep Learning Model for EEG Decoding in Brain-Computer Interface Applications.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2550073},
doi = {10.1142/S012906572550073X},
pmid = {41109958},
issn = {1793-6462},
abstract = {Decoding electroencephalographic (EEG) signals is of key importance in the development of brain-computer interface (BCI) systems. However, high inter-subject variability in EEG signals requires user-specific calibration, which can be time-consuming and limit the application of deep learning approaches, due to general need of large amount of data to properly train these models. In this context, this paper proposes a multidimensional and explainable deep learning framework for fast and interpretable EEG decoding. In particular, EEG signals are projected into the spatial-spectral-temporal domain and processed using a custom three-dimensional (3D) Convolutional Neural Network, here referred to as EEGCubeNet. In this work, the method has been validated on EEGs recorded during motor BCI experiments. Namely, hand open (HO) and hand close (HC) movement planning was investigated by discriminating them from the absence of movement preparation (resting state, RE). The proposed method is based on a global- to subject-specific fine-tuning. The model is globally trained on a population of subjects and then fine-tuned on the final user, significantly reducing adaptation time. Experimental results demonstrate that EEGCubeNet achieves state-of-the-art performance (accuracy of [Formula: see text] and [Formula: see text] for HC versus RE and HO versus RE, binary classification tasks, respectively) with reduced framework complexity and with a reduced training time. In addition, to enhance transparency, a 3D occlusion sensitivity analysis-based explainability method (here named 3D xAI-OSA) that generates relevance maps revealing the most significant features to each prediction, was introduced. The data and source code are available at the following link: https://github.com/AI-Lab-UniRC/EEGCubeNet.},
}

@article {pmid41109909,
year = {2025},
author = {Yu, J and Chen, J and Zhang, Y and Lyu, H and Ma, T and Huang, H and Wang, Z and Xu, X and Hu, S and Xu, Y},
title = {Emoface: AI-assisted diagnostic model for differentiating major depressive disorder and bipolar disorder via facial biomarkers.},
journal = {Npj mental health research},
volume = {4},
number = {1},
pages = {52},
pmid = {41109909},
issn = {2731-4251},
support = {2025C01104, 2025C02108 and 2021C03107//Key R&D Program of Zhejiang/ ; LZ23H180002 and LQ23F030001//Zhejiang Provincial Natural Science Foundation/ ; 62406280 and 72274170//National Natural Science Foundation of China/ ; 2022RC009//Cao Guangbiao High-tech Development Fund/ ; 20231203A13//Key Projects of Hangzhou Science and Technology Bureau/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; JNL-2023001B//Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation of Zhejiang Province/ ; },
abstract = {Affective disorders, including Major Depressive Disorder (MDD) and Bipolar Disorder (BD), exhibit significant mood abnormalities, making rapid diagnosis essential for social stability and healthcare efficiency. Traditional diagnostic solutions, including medical history collection and psychological assessments, often struggle to differentiate their similar clinical presentations, leading to time-consuming, laborious, and a high rate of misdiagnosis. Here, we propose Emoface, an AI-assisted diagnostic model that reads the emotional activities of faces in affective disorders. By analyzing videos from 353 participants exposed to various emotional stimuli, Emoface identified unique facial digital biomarkers distinguishing BD from MDD. Based on this, Emoface contributed to develop the first digital facial mapping for clinical and teaching use. In clinical practice with 347 patients, Emoface achieved precise diagnosis based on various facial visual signals, with accuracy rates of 95.29% for BD and 87.05% for MDD, offering a reliable face-based AI solution in a new era of affective disorders.},
}

@article {pmid41108907,
year = {2025},
author = {Akazawa, A and Fujita, T and Uraguchi, K and Kitayama, M and Ito, T and Osaki, Y and Shirai, K and Yoshida, H and Yamamoto, N and Doi, K and Iwasaki, S and Oishi, N},
title = {Establishing a comprehensive national auditory implant registry in Japan: Trends and demographics from the first two years (2023-2024).},
journal = {Auris, nasus, larynx},
volume = {52},
number = {6},
pages = {679-686},
doi = {10.1016/j.anl.2025.09.009},
pmid = {41108907},
issn = {1879-1476},
abstract = {OBJECTIVE: To describe the establishment and initial findings of Japan's first comprehensive nationwide registry covering cochlear implants (CIs), active middle ear implants (AMEIs), and bone conduction implants (BCIs), launched in 2023. The registry aims to improve national data collection, support evidence-based policymaking, and track trends in surgical practice and patient demographics.

METHODS: A web-based electronic data capture (EDC) system was implemented to replace the previous paper-based reporting system. Between January 2023 and December 2024, data were voluntarily submitted by participating facilities across Japan. Collected data included patient demographics, implant types, hearing thresholds, etiologies, and manufacturer information. Registry completeness was assessed by comparison with Japan's National Database of Health Insurance Claims (NDB).

RESULTS: A total of 1880 patients were registered, and 1809 patients with surgical information entered from 104 facilities were selected for analysis, comprising 1723 CI cases and 86 AMEI or BCI cases (11 VSB, 22 BB, 53 Baha). Among 605 pediatric CI recipients, early-age implantation was increasingly observed, with 58 patients (10 %) aged under 1 year and 183 (30 %) aged 1 year. Among adult CI recipients, 271 patients were aged 75 years or older, including 40 patients aged 85 years or older. Additionally, simultaneous bilateral CI surgery was performed in 265 patients, of whom 175 were children, reflecting the expanding indications. Patients with better ear thresholds <90 dB HL accounted for 33 % of adults and 29 % of children. Congenital hearing loss predominated in children, while acquired causes were more common in adults. Among cases with a known etiology, hereditary deafness was the most common (24.5 %), although 39.6 % of etiologies were unknown. CI data completeness reached 73 % compared with NDB, indicating strong nationwide participation and a high level of data reliability.

CONCLUSION: This is the first comprehensive report from the national registry in Japan that includes not only CIs but also AMEIs and BCIs. The registry demonstrated reliable data capture and highlighted important trends in patient demographics and surgical practices. Continued data collection will enhance clinical decision-making and support policy development, ultimately improving care for auditory implant recipients.},
}

@article {pmid41107816,
year = {2025},
author = {Chen, B and Gan, H and Yang, L and Yan, X and Lv, X and Zhang, X and Bu, J},
title = {A novel imagery-based retrieval-extinction training for intervention in nicotine addiction.},
journal = {BMC medicine},
volume = {23},
number = {1},
pages = {568},
pmid = {41107816},
issn = {1741-7015},
support = {32471140//National Natural Science Foundation of China/ ; 2021xkjT018//Scientific Research Improvement Project of Anhui Medical University/ ; 2022zhyx-C02//Research Fund of Anhui Institute of Translational Medicine/ ; YQZD2023018//Anhui Province Outstanding Young Teacher Cultivation Key Project/ ; JKS2023013//Research Funds of Center for Big Data and Population Health of IHM/ ; YESS20240007//Young Elite Scientists Sponsorship Program by CAST/ ; 2024AH030021//Excellent Youth of Natural Science Research Projects in Universities of Anhui Province/ ; },
mesh = {Humans ; Male ; *Tobacco Use Disorder/therapy/psychology ; Female ; Adult ; *Imagery, Psychotherapy/methods ; Craving ; *Extinction, Psychological ; Middle Aged ; Electroencephalography ; *Smoking Cessation/methods ; Young Adult ; *Mental Recall ; },
abstract = {BACKGROUND: Retrieval-extinction training based on the theory of memory reconsolidation has promising intervention effects for addiction. However, the conventional conditioned stimuli used in retrieval-extinction training have limitations in lack of contextual and selective activation of memories, which limits intervention efficacy and clinical translation. Therefore, we developed a novel imagery-based retrieval-extinction training (I-RE) and examined its effects on nicotine addiction.

METHODS: This study included 57 nicotine-dependent individuals randomly assigned to either the experimental (n = 29) or control (n = 28) group. Participants were exposed to a 5-min imagery script cue, followed by a 10-min rest period and 60-min extinction training session. Short- and long-term (1 week, 1 month, 3 months, 6 months, 12 months) intervention effects were assessed via the smoking imagery vividness score, smoking craving, and daily cigarette consumption. Electroencephalogram (EEG) data were collected pre- and post-intervention.

RESULTS: Regarding short-term effects, smoking imagery vividness score [pre- vs. post-intervention: p < 0.001; pre- vs. 1-day follow-up (FU): p = 0.003] and craving significantly decreased (pre- vs. post-intervention: p < 0.001; pre- vs. 1-day FU: p < 0.001). Decreased imagery vividness score mediated decreased smoking craving induced by smoking-related I-RE. Moreover, the significant correlation observed between these variables at pre-intervention disappeared at post-intervention. For effects on EEG microstate, a significant decrease was observed in microstate C duration induced by the smoking-related imagery script cue reactivity task post-intervention (p < 0.001). This mediated a decreased smoking craving induced by smoking-related I-RE. Degree of decrease in duration was positively correlated with addict imagery ability (p = 0.035). Consistently, the microstate C occurrence rate significantly decreased during the memory reconsolidation phase (p < 0.001). Regarding long-term effects, the smoking imagery vividness score (1-week FU: p = 0.004; 1-month FU: p < 0.001), smoking craving (1-week FU: p < 0.001; 1-month FU: p < 0.001), and daily cigarette consumption (1-week FU: p < 0.001; 1-month FU: p < 0.001) significantly decreased at 1-week and 1-month FU. Furthermore, decreased smoking craving mediated decreased Daily cigarette consumption in the experimental group. The significant correlation observed between the imagery vividness score and craving at pre-intervention disappeared at the 1-week and 1-month FU.

CONCLUSIONS: This novel I-RE demonstrated significant effects on nicotine addiction for 1 month after a single intervention session, suggesting that it is a promising treatment tool.

TRIAL REGISTRATION: Chinese Clinical Trial Registry identifier: ChiCTR2200064469.},
}

Humans
Male
*Tobacco Use Disorder/therapy/psychology
Female
Adult
*Imagery, Psychotherapy/methods
Craving
*Extinction, Psychological
Middle Aged
Electroencephalography
*Smoking Cessation/methods
Young Adult
*Mental Recall

@article {pmid41107249,
year = {2025},
author = {Zhu, X and Jiang, L and Shi, L and Li, F and Yang, Q and Zhang, M and Li, Y and Yu, Q and Chen, J and Gao, X and Wang, Z and Wang, Y and Xu, P and Lu, L and Deng, J},
title = {Modulation of brain oscillations by continuous theta burst stimulation in patients with insomnia.},
journal = {Translational psychiatry},
volume = {15},
number = {1},
pages = {416},
pmid = {41107249},
issn = {2158-3188},
support = {82271528//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82201646//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; Male ; Female ; *Sleep Initiation and Maintenance Disorders/therapy/physiopathology ; Adult ; *Theta Rhythm/physiology ; Middle Aged ; Electroencephalography ; *Transcranial Magnetic Stimulation/methods ; Polysomnography ; Cross-Over Studies ; Wakefulness/physiology ; },
abstract = {Continuous theta burst stimulation (cTBS) induces long-lasting depression of cortical excitability in motor cortex. In the present study, we explored the modulation of cTBS on resting state electroencephalogram (rsEEG) during wakefulness and subsequent sleep in patients with insomnia disorder. Forty-one patients with insomnia received three sessions active and sham cTBS in a counterbalanced crossover design. Each session comprised 600 pulses over right dorsolateral prefrontal cortex. Closed-eyes rsEEG were recorded at before and after each session. Effects of cTBS in subsequent sleep were measured by overnight polysomnography screening. Power spectral density (PSD) and phase locking value (PLV) were used to calculate changes in spectral power and phase synchronization after cTBS during wakefulness and subsequent sleep. Compared with sham cTBS intervention, PSD of delta and theta bands were increased across global brain regions with a cumulative effect after three active cTBS sessions. PLV of delta and theta bands were enhanced between stimulated frontal area and occipital areas. Efficiency of information communication within frontal-occipital networks was consistently improved through three active sessions. Increased theta power during wakefulness was positively related with that during the first sleep cycle. Active cTBS significantly enhanced the spectral power of delta and theta bands during wakefulness, with a cumulative effect observed over time. This modulation also extended to influence theta power during subsequent sleep onset period. Collectively, these findings provide a robust theoretical foundation for further investigating the therapeutic potential of long-term cTBS in the treatment of insomnia disorders.},
}

Humans
Male
Female
*Sleep Initiation and Maintenance Disorders/therapy/physiopathology
Adult
*Theta Rhythm/physiology
Middle Aged
Electroencephalography
*Transcranial Magnetic Stimulation/methods
Polysomnography
Cross-Over Studies
Wakefulness/physiology

@article {pmid41106071,
year = {2025},
author = {Yasen, A and Sun, W and Gong, Y and Xu, G},
title = {Progress in the combined application of Brain-Computer Interface and non-invasive brain stimulation for post-stroke motor recovery.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {180},
number = {},
pages = {2111383},
doi = {10.1016/j.clinph.2025.2111383},
pmid = {41106071},
issn = {1872-8952},
abstract = {Stroke remains one of the leading causes of disability and death among adults globally. Both Brain-Computer Interface (BCI) and Non-invasive Brain Stimulation (NIBS) have shown significant potential in facilitating motor recovery in stroke patients. The combination of BCI and NIBS enhances brain functional reorganization and accelerates motor recovery post-stroke through a real-time feedback mechanism. By modulating neural plasticity, this combined approach can alter the trajectory of motor recovery, offering a novel therapeutic avenue for stroke rehabilitation. This review examines the application and recent advancements of BCI integrated with NIBS in motor function rehabilitation for stroke patients. Specifically, it outlines the advantages and challenges of this combined approach, including the use of TMS, tDCS, tACS, and other emerging neurostimulation technologies. While the integration of BCI and NIBS is still in the early stages of exploration, a unified, standardized protocol has yet to be established. Future research should focus on optimizing multimodal integration, investigating the underlying neuroplasticity mechanisms, and evaluating the long-term efficacy of BCI combined with NIBS.},
}

@article {pmid41105834,
year = {2025},
author = {Clemesha, J and Chung, M},
title = {A different bimodal: case series of patients with a cochlear implant and a contralateral bone conduction implant.},
journal = {Cochlear implants international},
volume = {},
number = {},
pages = {1-8},
doi = {10.1080/14670100.2025.2571990},
pmid = {41105834},
issn = {1754-7628},
abstract = {INTRODUCTION: An increasing number of long-term users of bone conduction implants (BCI) have been observed to no longer obtain sufficient benefit from their device due to deteriorations in hearing thresholds. At the multidisciplinary auditory implant centre at the University College London Hospitals NHS Trust, these patients are assessed and considered for cochlear implantation (CI). This case series describes the history and outcomes of patients who became bimodal implant users, utilising electrical and vibratory auditory stimulation with a BCI and CI. This unique patient group has seldom been described in the literature.

METHODS: Case series from a retrospective chart review of patients who utilise the combination of electrical and vibratory auditory stimulation with the use of a bone conduction implant and cochlear implant, up to November 2023.

RESULTS: Six bimodal patients were identified from the patient cohort. Their case history and outcome are described.

CONCLUSION: The synergy of electrical and vibratory auditory stimulation observed in this case series provided subjective functional benefits and measurable speech perception benefits for some patients, while others experienced minimal or no measurable benefit and ceased usage.},
}

@article {pmid41105410,
year = {2025},
author = {Saver, JL and Duncan, PW and Stein, J and Cramer, SC and Fox, EJ and Zorowitz, RD and Billinger, SA and Eickmeyer, SM and Kirshblum, SC and Androwis, GJ and Edwards, J and Savitz, SI and Koch, S and Shall, MB and Black-Schaffer, RM and Bonato, P and Cuccurullo, SJ and Barcikowski, J and Cao, N and Bornstein, NM and , },
title = {Electromagnetic Stimulation to Reduce Disability After Ischemic Stroke: The EMAGINE Randomized Clinical Trial.},
journal = {JAMA network open},
volume = {8},
number = {10},
pages = {e2537880},
doi = {10.1001/jamanetworkopen.2025.37880},
pmid = {41105410},
issn = {2574-3805},
mesh = {Humans ; Male ; Female ; Middle Aged ; Double-Blind Method ; Aged ; *Stroke Rehabilitation/methods ; *Ischemic Stroke/therapy/rehabilitation ; *Magnetic Field Therapy/methods ; Treatment Outcome ; Upper Extremity/physiopathology ; Persons with Disabilities/rehabilitation ; },
abstract = {IMPORTANCE: Ischemic stroke remains a leading cause of disability worldwide. Preliminary studies have suggested that noninvasive, frequency-tuned, low-intensity electromagnetic network targeting field (ENTF) stimulation may have recovery benefit for patients with stroke.

OBJECTIVE: To evaluate the safety and effectiveness of ENTF therapy in reducing global disability among patients in the subacute ischemic stroke phase with moderate to severe disability and upper-extremity impairment.

This multicenter, double-blind, sham-controlled, randomized clinical trial was conducted at 15 US-based acute care and inpatient rehabilitation facilities from December 2021 to November 2023. Participants were enrolled 4 to 21 days after a stroke and had a baseline modified Rankin Scale (mRS) score of 3 or 4 (moderate or moderately severe global disability) and Fugl-Meyer Assessment for Upper Extremity score of 10 to 45 (higher scores indicating better arm function). Target sample size was 150 participants. Participants were randomly allocated to receive either active or sham ENTF stimulation. Modified intention-to-treat approach was used in primary efficacy and safety analyses.

INTERVENTION: Participants allocated to the active or sham ENTF stimulation were treated with a proprietary brain-computer interface-based stimulation device paired with an evidence-based, functional, repetitive, home-based physical and occupational exercise regimen for 45 one-hour sessions, 5 times per week within the first 90 days after a stroke.

MAIN OUTCOMES AND MEASURES: The primary end point was change in global disability, assessed with the mRS (score range: 0 [indicating normal or no symptoms] to 6 [indicating death]), from baseline to day 90. Secondary end points were change from baseline to day 90 in upper-limb impairment, arm motor function, gait speed, hand function, and physical and functional limitations as well as day-90 health-related quality of life, each of which was assessed with a specific metric.

RESULTS: The trial was stopped early after enrollment of 100 participants (50 in active group, 50 in sham group) when a promising zone threshold was not attained at planned interim analysis of the first 78 evaluable participants. Participants had a mean age of 59.0 (12.5) years and included 66 males (67.3%). The median (IQR) time from stroke to first ENTF treatment was 14 (12-19) days. Study groups were similar in age, sex, and baseline mRS scores, but imbalances were noted with participants in the active, compared with the sham, group having more right-hemisphere strokes (31 of 49 [63.3%] vs 22 of 49 [44.9%]), more severe upper-extremity impairment (Shoulder Abduction Finger Extension score <5; 31 of 49 [63.3%] vs 24 of 49 [49.0%]), and fewer small-vessel infarcts (14 of 49 [28.6%] vs 21 of 49 [42.9%]). For the primary outcome, the mean (SD) disability reduction on mRS at day 90 was not statistically significantly higher in the active group than in the sham group (-1.96 [0.12] vs -1.72 [0.12]), including mRS score of 0 to 1 attained in 12 participants (26.0%) vs 5 participants (10.0%) (odds ratio, 2.99; 95% CI, 0.96-9.30; P = .05). Point estimates for secondary outcomes favored the active group, although the differences were not statistically significant, in the prespecified analysis. No ENTF device-related serious adverse events were noted.

CONCLUSION AND RELEVANCE: This trial found that ENTF therapy is safe. Although the difference between groups was not statistically significant, ENTF therapy may reduce global disability in patients with severe baseline disability after ischemic stroke. These results warrant confirmation in a higher powered pivotal trial of ENTF therapy.

TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT05044507.},
}

Humans
Male
Female
Middle Aged
Double-Blind Method
Aged
*Stroke Rehabilitation/methods
*Ischemic Stroke/therapy/rehabilitation
*Magnetic Field Therapy/methods
Treatment Outcome
Upper Extremity/physiopathology
Persons with Disabilities/rehabilitation

@article {pmid41104953,
year = {2025},
author = {Kamaleddin, MA},
title = {Simultaneous encoding of sensory features: the role of multiplexing and noise in tactile perception and neural representation.},
journal = {Biological reviews of the Cambridge Philosophical Society},
volume = {},
number = {},
pages = {},
doi = {10.1111/brv.70093},
pmid = {41104953},
issn = {1469-185X},
abstract = {The nervous system's capacity to process complex stimuli has long intrigued neuroscientists, with multiplexing now recognized as a fundamental neural coding strategy. Multiplexing refers to the simultaneous encoding of multiple stimulus features via vi distinct components of neuronal responses, such as firing rates and precise temporal spike patterns. This paper reviews the neural coding mechanisms underlying multiplexing, with a particular emphasis on the somatosensory system and its ability to represent tactile stimuli. The encoding of various sensory attributes, including vibration, texture, motion, and shape, is examined, highlighting the complementary roles of rate and temporal codes in capturing these features. The discussion further addresses how intrinsic and extrinsic noise, often viewed as detrimental, can facilitate multiplexed coding by supporting the concurrent encoding of both stimulus frequency and intensity. The relevance of multiplexing is also considered in translational contexts, such as the development of brain-machine interfaces. By synthesizing recent advances and integrating insights from empirical and theoretical studies, this review establishes multiplexing as a foundational principle in sensory neuroscience and identifies key directions for future research in both basic science and neuroengineering applications.},
}

@article {pmid41104690,
year = {2025},
author = {Chehroudi, C and Chandrasekhar, V and Yu, H and De, S},
title = {Simple Prostatectomy is an Effective Option for BPH Patients With Hypocontractile Bladders.},
journal = {The Prostate},
volume = {},
number = {},
pages = {},
doi = {10.1002/pros.70079},
pmid = {41104690},
issn = {1097-0045},
support = {//The authors received no specific funding for this work./ ; },
abstract = {BACKGROUND: The impact of preoperative bladder function on outcomes of simple prostatectomy (SP) is unknown. The goal of this study was to determine if detrusor contractility affects postoperative catheter-free status in patients undergoing SP for benign prostatic hyperplasia (BPH).

METHODS: Patients who underwent SP (either open or minimally invasive) from 2017 to 2024 at our institution and had preoperative urodynamics were identified retrospectively. Bladder contractility index (BCI) was used to categorize patients as normocontractile (BCI ≥ 100) or hypocontractile (BCI < 100). Demographics, preoperative urodynamics, peri-operative characteristics, and postoperative variables were compared between the two groups with postoperative catheter status being the primary outcome.

RESULTS: Among 101 SP patients with preoperative urodynamics, 47 had hypocontractile bladders (median BCI 69 vs. 131). Both groups had similar median age, preoperative prostate specific antigen (PSA), and rates of diabetes. The majority of procedures in both the normocontracile and hypocontractile groups were robot-assisted (83% vs. 81%, respectively). Patients in the hypocontractile group were significantly more likely to be catheter dependent pre-operatively (77% vs. 57%, p = 0.04). There was no difference in preoperative prostate size or use of BPH pharmacotherapy. Overall, 97% of hypocontractile and 100% of normocontractile patients were catheter-free following surgery. There were no differences in postoperative outcomes including pathology tissue weight and post-op PSA.

CONCLUSIONS: This is one of the first studies assessing outcomes of SP in patients with hypocontractile bladders. SP is an effective surgical option for patients with impaired detrusor function including those who are catheter dependent.},
}

@article {pmid41104354,
year = {2025},
author = {Näher, T and Bastian, L and Vorreuther, A and Fries, P and Goebel, R and Sorger, B},
title = {Riemannian geometry boosts functional near-infrared spectroscopy-based brain-state classification accuracy.},
journal = {Neurophotonics},
volume = {12},
number = {4},
pages = {045002},
pmid = {41104354},
issn = {2329-423X},
abstract = {BACKGROUND: Functional near-infrared spectroscopy (fNIRS) has recently gained momentum as a reliable and accurate tool for assessing brain states based on the vascular response to neural activity. This increase in popularity is due to its robustness to movement, non-invasive nature, portability, and user-friendly application. However, compared with other hemodynamic functional brain-imaging methods such as functional magnetic resonance imaging (fMRI), fNIRS is constrained by its limited spatial resolution and coverage with a particularly limited penetration depth. In addition, due to comparatively fewer methodological advancements, the performance of fNIRS-based brain-state classification still lags behind more prevalent methods such as fMRI.

METHODS: We introduce a classification approach grounded in Riemannian geometry for the classification of kernel matrices, leveraging the temporal and spatial relationships between channels and the inherent duality of fNIRS signals, specifically oxygenated and deoxygenated hemoglobin. For the Riemannian-geometry-based models, we compared different kernel matrix estimators and two classifiers: Riemannian Support Vector Classifier and Tangent Space Logistic Regression. These were benchmarked against four models employing traditional feature extraction methods. Our approach was tested on seven participants in two brain-state classification scenarios based on the same fNIRS dataset: an eight-choice classification, which includes seven established plus an individually selected imagery task, and a two-choice classification of all possible 28 two-task combinations.

RESULTS: This approach achieved a mean eight-choice classification accuracy of 65%, significantly surpassing the mean accuracy of 42% obtained with traditional methods. In addition, the best-performing model achieved an average accuracy of 96% for two-choice classification across all task combinations, compared with 78% with traditional models.

CONCLUSION: To our knowledge, we are the first to demonstrate that the proposed Riemannian-geometry-based classification approach is both powerful and viable for fNIRS data, substantially increasing the accuracy in binary and multi-class classification of brain activation patterns.},
}

@article {pmid41104262,
year = {2025},
author = {Bublitz, C and Chandler, JA and Molnár-Gábor, F and Navarro, MS and Kellmeyer, P and Soekadar, SR},
title = {A Moratorium on Implantable Non-Medical Neurotech Until Effects on the Mind are Properly Understood.},
journal = {Neuroethics},
volume = {18},
number = {3},
pages = {46},
pmid = {41104262},
issn = {1874-5490},
abstract = {The development of non-medical consumer neurotechnology is gaining momentum. As companies chart the course for future implanted and invasive brain-computer interfaces (BCIs) in non-medical populations, the time has come for concrete steps toward their regulation. We propose three measures: First, a mandatory Mental Impact Assessment that comprehensively screens for adverse mental effects of neurotechnologies under realistic use conditions needs to be developed and implemented. Second, until such an assessment is developed and further ethical concerns are effectively resolved, a moratorium on placing implantable non-medical devices on markets should be established. Third, implantable consumer neurotech for children should be banned. These measures are initial steps in a process seeking to define the necessary requirements for placing these devices on markets. They are grounded in a human rights-based approach to technology regulation that seeks to promote the interests protected by human rights while minimizing the risks posed to them. Neurotechnologies have the potential to profoundly alter cognitive, emotional, and other mental processes, with implications for the rights to mental health and integrity, and possibly for societal dynamics.},
}

@article {pmid41102402,
year = {2025},
author = {Feng, Y and Zhao, W and Li, Y and Yin, Q and Wang, X and Huang, X and Li, L and Shan, X and Hu, W and Ming, Y and Wang, P and Xiao, J and Chen, H and Duan, X},
title = {Diffusion trajectory of atypical morphological development in autism spectrum disorder.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {1476},
pmid = {41102402},
issn = {2399-3642},
support = {82121003//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82322035//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62333003//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62273076//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62036003//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Autism Spectrum Disorder/diagnostic imaging/pathology/physiopathology ; Child ; Adolescent ; Male ; Female ; *Gray Matter/diagnostic imaging/pathology/growth & development ; *Brain/growth & development/diagnostic imaging/pathology/physiopathology ; Magnetic Resonance Imaging ; },
abstract = {Brain development from childhood through adolescence is crucial for understanding autism spectrum disorder (ASD). Yet how functional networks regulate developmental changes in brain morphology remains unclear. Here, we analyzed gray matter volume (GMV) and functional connectivity (FC) in 301 individuals with ASD and 375 typically developing controls (TDCs), aged 8-18 years, from the Autism Brain Imaging Data Exchange (ABIDE). Using a sliding-window approach, participants were stratified by age, and GMV distribution deviations (DEV) were quantified with Kullback-Leibler divergence and expected value analysis. Network diffusion modeling (NDM) was applied to predict developmental alterations and evaluate how functional networks constrain atypical neurodevelopment. Results revealed a developmental shift in GMV divergence: during early adolescence, ASD participants showed positive GMV deviations relative to TDCs, which shifted to negative in late adolescence. The largest DEV were observed in the superior temporal sulcus, cingulate gyrus, insula, and superior parietal lobule. Furthermore, NDM demonstrated cross-stage predictability, as DEV values of atypical brain regions at preceding age stages significantly predicting subsequent ones, constrained by network architecture. These findings highlight a dynamic developmental shift from GMV overgrowth to delayed maturation during adolescence in ASD and revealing the role of intrinsic functional networks in constraining atypical anatomical development.},
}

Humans
*Autism Spectrum Disorder/diagnostic imaging/pathology/physiopathology
Child
Adolescent
Male
Female
*Gray Matter/diagnostic imaging/pathology/growth & development
*Brain/growth & development/diagnostic imaging/pathology/physiopathology
Magnetic Resonance Imaging

@article {pmid41101308,
year = {2025},
author = {Zheng, D and Xin, Q and Jin, S and Zhou, A and Jia, X and Tan, Y and Hu, H},
title = {Neural mechanism of the sexually dimorphic winner effect in mice.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2025.09.029},
pmid = {41101308},
issn = {1097-4199},
abstract = {The "winner effect," where prior victories increase the likelihood of future wins, profoundly shapes social hierarchy dynamics and competitive motivation. Although human literature suggests a less pronounced winner effect in females, the neural mechanisms underlying these sex differences remain unclear. Here, we show that, compared with male mice, female mice take longer to form social hierarchies and exhibit a weaker winner effect. The dorsomedial prefrontal cortex (dmPFC), crucial for social dominance in males, plays a similar role in female mice. However, female mice exhibit reduced long-term potentiation (LTP) at the mediodorsal thalamus (MDT)-to-dmPFC synapses. In vitro recordings revealed that female mice have heightened excitability of dmPFC parvalbumin interneurons (PV-INs). Modulation of dmPFC PV-IN activity regulates LTP and the winner effect in a sexually dimorphic manner. This work identifies dmPFC PV-INs as a target for enhancing the winner effect, establishing a circuit-level framework for sex differences in competitive behaviors.},
}

@article {pmid41100980,
year = {2025},
author = {Ban, S and Chong, D and Kwon, J and Lee, S and Huang, Y and Yoo, S and Yeo, WH},
title = {Advances in flexible high-density microelectrode arrays for brain-computer interfaces.},
journal = {Biosensors & bioelectronics},
volume = {292},
number = {},
pages = {118102},
doi = {10.1016/j.bios.2025.118102},
pmid = {41100980},
issn = {1873-4235},
abstract = {Recent advances in flexible high-density microelectrode arrays (FHD-MEA) have revolutionized brain-computer interfaces (BCIs) by providing high spatial resolution, mechanical compliance, and long-term biocompatibility. This technology enables stable neural recording and precise stimulation, addressing the shortcomings of conventional rigid BCI arrays. In this review, we outline the challenges of signal acquisition and stimulation of conventional low-density, rigid BCI systems. These include poor spatial resolution, micro-motor-induced instability, electrochemical degradation, wiring bottlenecks, off-target activation, and charge injection hazards. We then describe how these barriers are addressed through advanced materials, device designs, and system-level integration. We summarize representative applications of clinical therapy for sensory enhancement, human-machine interfaces, and neurological diseases, highlighting translational potential. Collectively, this review article presents recent progress and emerging trends in establishing FHD-MEAs as a crucial foundation for next-generation, clinically viable BCIs.},
}

@article {pmid41100231,
year = {2025},
author = {Li, R and Liu, J and Liu, J and Yang, S and Liu, W and Deng, K and Wang, W},
title = {A Novel Grasping Robot Control Method Using Motion Execution BCI Combining Knowledge Reasoning.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3622255},
pmid = {41100231},
issn = {2168-2208},
abstract = {Recently, with the growing number of disabled people, brain-controlled technology offers a novel way to help patients restore their daily abilities. However, the conventional brain-controlled system based on the motion related task lacks intelligence in real-world environments. To address above problem, this study proposed a share controlled system combining a precise hand movement (PHM)-based brain computer interface (BCI) system and knowledge-driven reasoning method. Six types of precise hand movements were selected to design novel motion execution paradigm for BCI system. A feature intermediate fusion convolutional neural network was employed to accurately decode electroencephalogram. Furthermore, a shared control grasping technology based on knowledge based reasoning combined PHM-based BCI system was designed for grasping robot, which enhancing the system's intelligence and versatility in selecting objects. To verify the improvement of proposed method, experiments were conducted with 15 ࣥhealthy subjects and 2 patients. The proposed method achieved an average accuracy of 82.80±6.08%, with the highest accuracy reaching 94.27%. All the experimental results demonstrate the effectiveness of the proposed shared control method.},
}

@article {pmid41096996,
year = {2025},
author = {Ga, YJ and Yeh, JY},
title = {siRNA Cocktail Targeting Multiple Enterovirus 71 Genes Prevents Escape Mutants and Inhibits Viral Replication.},
journal = {International journal of molecular sciences},
volume = {26},
number = {19},
pages = {},
doi = {10.3390/ijms26199731},
pmid = {41096996},
issn = {1422-0067},
support = {2020//Incheon National University/ ; },
mesh = {*Enterovirus A, Human/genetics/physiology ; *Virus Replication/genetics ; *RNA, Small Interfering/genetics/pharmacology ; Humans ; *Mutation ; Enterovirus Infections/virology/genetics ; RNA Interference ; Animals ; Cell Line ; },
abstract = {RNA interference (RNAi) is a powerful mechanism of post-transcriptional gene regulation in which small interfering RNA (siRNA) is utilized to target and degrade specific RNA sequences. In this study, experiments were conducted to evaluate the efficacy of combination siRNA therapy against enterovirus 71 (EV71) and the potential of this therapy to delay or prevent the emergence of resistance in vitro. siRNAs targeting multiple genes of EV71 were designed, and the effects of a cocktail of siRNAs on viral replication were assessed compared to those of single-siRNA treatment. Cotransfection of multiple siRNAs targeting different protein-coding genes of the EV71 genome effectively suppressed escape mutants resistant to RNAi. Combination therapy with siRNAs targeting multiple viral genes successfully prevented viral escape mutations over five passages. By contrast, serial passaging with a single siRNA led to the rapid emergence of resistance, with mutations identified in the siRNA target sites. The combination of siRNAs specifically targeting different regions demonstrated an additive effect and was more effective than individual siRNAs at inhibiting EV71 replication. This study supports the effectiveness of combination therapy using siRNAs targeting multiple genes of EV71 to inhibit viral replication and prevent the emergence of resistant escape mutants. Overall, the findings identify RNAi targeting multiple viral genes as a potential strategy for therapeutic development against viral diseases and for preventing the emergence of escape mutants resistant to antiviral RNAi.},
}

*Enterovirus A, Human/genetics/physiology
*Virus Replication/genetics
*RNA, Small Interfering/genetics/pharmacology
Humans
*Mutation
Enterovirus Infections/virology/genetics
RNA Interference
Animals
Cell Line

@article {pmid41096009,
year = {2025},
author = {von Altdorf, LAWR and Bracewell, M and Cooke, A},
title = {Effectiveness of Electroencephalographic Neurofeedback for Parkinson's Disease: A Systematic Review and Meta-Analysis.},
journal = {Journal of clinical medicine},
volume = {14},
number = {19},
pages = {},
doi = {10.3390/jcm14196929},
pmid = {41096009},
issn = {2077-0383},
abstract = {Background: Electroencephalographic (EEG) neurofeedback training is gaining traction as a non-pharmacological treatment option for Parkinson's disease (PD). This paper reports the first pre-registered, integrated systematic review and meta-analysis of studies examining the effects of EEG neurofeedback on cortical activity and motor function in people with PD. Method: We searched Cochrane Databases, PubMed, Embase, Scopus, Web of Science, PsycInfo, grey literature repositories, and trial registers for EEG neurofeedback studies in people with PD. We included randomized controlled trials, single-group experiments, and case studies. We assessed risk of bias using the Cochrane Risk of Bias 2 and Risk of Bias in Non-Randomized Studies tools, and we used the Grading of Recommendations, Assessment, Development and Evaluations tool to assess certainty in the evidence and resultant interpretations. Random-effects meta-analyses were performed. Results: A total of 11 studies (143 participants; Hoehn and Yahr I-IV) met the criteria for inclusion. A first meta-analysis revealed that EEG activity is modified in the prescribed way by neurofeedback interventions. The effect size is large (SMD = 1.30, 95% CI = 0.50-2.10, p = 0.001). Certainty in the estimate is high. Despite successful cortical modulation, a subsequent meta-analysis revealed inconclusive effects of EEG neurofeedback on motor symptomology. The effect size is small (SMD = 0.10, 95% CI = -1.03-1.23, p = 0.86). Certainty in the estimates is low. Narrative evidence revealed that interventions are well-received and may yield specific benefits not detected by general symptomology reports. Conclusion: EEG neurofeedback successfully modulates cortical activity in people with PD, but downstream impacts on motor function remain unclear. The neuromodulatory potential of EEG neurofeedback in people with PD is encouraging. Additional well-powered and high-quality research into the effects of EEG neurofeedback in PD is warranted.},
}

@article {pmid41095845,
year = {2025},
author = {Kollu, K and Yortanli, BC and Cicek, AN and Susam, E and Karakas, N and Kizilarslanoglu, MC},
title = {Investigation of the Prognostic Value of Novel Laboratory Indices in Patients with Sepsis in an Intensive Care Unit: A Retrospective Observational Study.},
journal = {Journal of clinical medicine},
volume = {14},
number = {19},
pages = {},
doi = {10.3390/jcm14196765},
pmid = {41095845},
issn = {2077-0383},
abstract = {Background: This study aimed to evaluate the prognostic value of some novel laboratory indices in intensive care unit (ICU)-hospitalized sepsis patients. Methods: This retrospective, observational study included 400 patients with sepsis. The indices studied were the C-reactive protein/albumin ratio (CAR), hemoglobin, albumin lymphocyte, and platelet (HALP) score, lymphocyte/monocyte ratio (LMR), prognostic nutritional index (PNI), systemic immune inflammatory index (SII), vitamin B12xC-reactive protein index (BCI), systemic inflammatory response index (SIRI), and platelet/lymphocyte ratio (PLR). The predicting effects of these indices in ICU mortality, along with other clinical outcomes, were investigated. Results: The median age of the study population was 73 (18-95) years and 51.6% were males. The ICU mortality rate was 51.7%. Deceased patients with sepsis had an increased age and high APACHE II and SOFA scores compared to the survivors (p < 0.05 for all). In the multivariate logistic regression analysis, age (HR = 1.069, p = 0.038 in Model 1 vs. HR = 1.053, p = 0.001 in Model 2), SOFA score (HR = 2.145, p < 0.001 in Model 1 vs. HR = 1.740, p < 0.001 in Model 2), phosphorus levels (in Model 1, HR = 0.608, p = 0.037), and CAR (in Model 2, HR = 1.012, p = 0.023) were independent associated factors for ICU mortality. According to the ROC analyses, the SOFA (AUC = 0.879, p < 0.001) and APACHE II (AUC = 0.769, p < 0.001) scores showed high accuracy in predicting ICU mortality, while the PNI (AUC = 0.675, p < 0.001), CAR (AUC = 0.609, p < 0.001), and the BCI (AUC = 0.648, p < 0.001) showed limited accuracy. However, the HALP score did not reach a significant level in predicting ICU mortality (p = 0.067). Conclusions: Excluding the HALP score, the new laboratory indices mentioned above may be prognostic markers for predicting clinical outcomes in intensive care units for patients with sepsis. However, these indices need to be supported by larger patient populations.},
}

@article {pmid41095026,
year = {2025},
author = {Reyes, D and Sieghartsleitner, S and Loaiza, H and Guger, C},
title = {Motor Imagery Acquisition Paradigms: In the Search to Improve Classification Accuracy.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {19},
pages = {},
doi = {10.3390/s25196204},
pmid = {41095026},
issn = {1424-8220},
support = {Bicentenario 1st Call//Colfuturo/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Imagination/physiology ; Algorithms ; Electroencephalography/methods ; Stroke/physiopathology ; Male ; },
abstract = {In recent years, advances in medicine have been evident thanks to technological growth and interdisciplinary research, which has allowed the integration of knowledge, for example, of engineering into medical fields. This integration has generated developments and new methods that can be applied in alternative situations, highlighting, for example, aspects related to post-stroke therapies, Multiple Sclerosis (MS), or Spinal Cord Injury (SCI) treatments. One of the methods that has stood out and is gaining more acceptance every day is Brain-Computer Interfaces (BCIs), through the acquisition and processing of brain electrical activity, researchers, doctors, and scientists manage to transform this activity into control signals. In turn, there are several methods for operating a BCI, this work will focus on motor imagery (MI)-based BCI and three types of acquisition paradigms (traditional arrow, picture, and video), seeking to improve the accuracy in the classification of motor imagination tasks for naive subjects, which correspond to a MI task for both the left and the right hand. A pipeline and methodology were implemented using the CAR+CSP algorithm to extract the features and simple standard and widely used models such as LDA and SVM for classification. The methodology was tested with post-stroke (PS) subject data with BCI experience, obtaining 96.25% accuracy for the best performance, and with the novel paradigm proposed for the naive subjects, 97.5% was obtained. Several statistical tests were carried out in order to find differences between paradigms within the collected data. In conclusion, it was found that the classification accuracy could be improved by using different strategies in the acquisition stage.},
}

Humans
*Brain-Computer Interfaces
*Imagination/physiology
Algorithms
Electroencephalography/methods
Stroke/physiopathology
Male

@article {pmid41094934,
year = {2025},
author = {Zhang, Y and Yin, B and Yuan, X},
title = {TSFNet: Temporal-Spatial Fusion Network for Hybrid Brain-Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {19},
pages = {},
doi = {10.3390/s25196111},
pmid = {41094934},
issn = {1424-8220},
support = {62171152//National Natural Science Foundation of China/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; Spectroscopy, Near-Infrared/methods ; Algorithms ; Brain/physiology ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; },
abstract = {Unimodal brain-computer interfaces (BCIs) often suffer from inherent limitations due to the characteristic of using single modalities. While hybrid BCIs combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offer complementary advantages, effectively integrating their spatiotemporal features remains a challenge due to inherent signal asynchrony. This study aims to develop a novel deep fusion network to achieve synergistic integration of EEG and fNIRS signals for improved classification performance across different tasks. We propose a novel Temporal-Spatial Fusion Network (TSFNet), which consists of two key sublayers: the EEG-fNIRS-guided Fusion (EFGF) layer and the Cross-Attention-based Feature Enhancement (CAFÉ) layer. The EFGF layer extracts temporal features from EEG and spatial features from fNIRS to generate a hybrid attention map, which is utilized to achieve more effective and complementary integration of spatiotemporal information. The CAFÉ layer enables bidirectional interaction between fNIRS and fusion features via a cross-attention mechanism, which enhances the fusion features and selectively filters informative fNIRS representations. Through the two sublayers, TSFNet achieves deep fusion of multimodal features. Finally, TSFNet is evaluated on motor imagery (MI), mental arithmetic (MA), and word generation (WG) classification tasks. Experimental results demonstrate that TSFNet achieves superior classification performance, with average accuracies of 70.18% for MI, 86.26% for MA, and 81.13% for WG, outperforming existing state-of-the-art multimodal algorithms. These findings suggest that TSFNet provides an effective solution for spatiotemporal feature fusion in hybrid BCIs, with potential applications in real-world BCI systems.},
}

*Brain-Computer Interfaces
Humans
Electroencephalography/methods
Spectroscopy, Near-Infrared/methods
Algorithms
Brain/physiology
Signal Processing, Computer-Assisted
Neural Networks, Computer

@article {pmid41094901,
year = {2025},
author = {Anzalone, A and Acampora, E and Liu, C and Hajra, SG},
title = {Passive Brain-Computer Interface Using Textile-Based Electroencephalography.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {19},
pages = {},
doi = {10.3390/s25196080},
pmid = {41094901},
issn = {1424-8220},
mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; *Textiles ; Support Vector Machine ; Male ; Adult ; Electrodes ; Female ; Machine Learning ; *Brain/physiology ; Cognition/physiology ; },
abstract = {Background: Passive brain-computer interface (pBCI) systems use a combination of electroencephalography (EEG) and machine learning (ML) to evaluate a user's cognitive and physiological state, with increasing applications in both clinical and non-clinical scenarios. pBCI systems have been limited by their traditional reliance on sensor technologies that cannot easily be integrated into non-laboratory settings where pBCIs are most needed. Advances in textile-electrode-based EEG show promise in overcoming the operational limitations; however, no study has demonstrated their use in pBCIs. This study presents the first application of fully textile-based EEG for pBCIs in differentiating cognitive states. Methods: Cognitive state comparisons between eyes-open (EO) and eyes-closed (EC) conditions were conducted using publicly available data for both novel textile and traditional dry-electrode EEG. EO vs. EC differences across both EEG sensor technologies were assessed in delta, theta, alpha, and beta EEG power bands, followed by the application of a Support Vector Machine (SVM) classifier. The SVM was applied to each EEG system separately and in a combined setting, where the classifier was trained on dry EEG data and tested on textile EEG data. Results: The textile EEG system accurately captured the characteristic increase in alpha power from EO to EC (p < 0.01), but power values were lower than those of dry EEG across all frequency bands. Classification accuracies for the standalone dry and textile systems were 96% and 92%, respectively. The cross-sensor generalizability assessment resulted in a 91% classification accuracy. Conclusions: This study presents the first use of textile-based EEG for pBCI applications. Our results indicate that textile-based EEG can reliably capture changes in EEG power bands between EO and EC, and that a pBCI system utilizing non-traditional textile electrodes is both accurate and generalizable.},
}

*Electroencephalography/methods
*Brain-Computer Interfaces
Humans
*Textiles
Support Vector Machine
Male
Adult
Electrodes
Female
Machine Learning
*Brain/physiology
Cognition/physiology

@article {pmid41093880,
year = {2025},
author = {Aguilera-Rodríguez, E and Cuevas-Romero, A and Mendoza-Franco, S and Wornovitzky-Green, J and Rivera-Cerros, E and Villanueva-Cazares, D and Muñoz-Ubando, LA and Ibarra-Zárate, D and Alonso-Valerdi, LM},
title = {An EEG-based Imagined Speech Database for comparing Paradigm Designs.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1644},
pmid = {41093880},
issn = {2052-4463},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography ; *Speech ; Female ; Male ; *Imagination ; Adult ; Video Games ; },
abstract = {Brain-computer interfaces (BCIs) attempt to establish a connection between the human mind and a computer system. While recent computational advances continue to improve these interfaces, human factors have been overlooked. Factors such as fatigue and attention play a key role in brain signal modulation. This arises the need for paradigms designed and implemented in terms of human factors. Therefore, it is proposed to improve the level of engagement to diminish fatigue and increase attention by a video game-based paradigm for an imagined speech BCI. For this purpose, a sample of 15 volunteers (females = 7) was recruited to study the quality of their imagined speech when it is evoked under an abstract scenario (traditional paradigm) and a video-game paradigm. This dataset helps to study the differences in imagined speech signals when using two different paradigms: (1) one that does not consider human factors, and (2) one that does. Additional applications may include designing imagined speech decoding models for BCI and studying the relationship between users' profile and their imagined speech signals.},
}

Humans
*Brain-Computer Interfaces
*Electroencephalography
*Speech
Female
Male
*Imagination
Adult
Video Games

@article {pmid41092418,
year = {2025},
author = {Oliveira, I and Russo, M and Almeida, AI and Vourvopoulos, A and Mendes Pereira, C},
title = {Recommendations for Combining Brain-Computer Interface, Motor Imagery, and Virtual Reality in Upper Limb Stroke Rehabilitation: Qualitative Participatory Design Study.},
journal = {JMIR rehabilitation and assistive technologies},
volume = {12},
number = {},
pages = {e71789},
doi = {10.2196/71789},
pmid = {41092418},
issn = {2369-2529},
abstract = {BACKGROUND: The high incidence and prevalence of upper limb impairment post stroke highlights the need for advancements in rehabilitation. Brain-computer interfaces (BCIs) represent a promising technology by directly training the central nervous system. The integration of motor imagery (MI) and motor observation through virtual reality (VR) using BCIs provides valuable opportunities for rehabilitation. However, the diversity in intervention designs demonstrates the lack of guiding recommendations integrating neurorehabilitation principles for BCIs.

OBJECTIVE: This study aims to develop recommendations for BCI interventions using task specificity and ecological validity through simulated VR tasks for upper limb stroke survivors by gathering tacit knowledge from neurorehabilitation experts, patients' experiences, and engineers' expertise to ensure a comprehensive approach.

METHODS: A multiperspective qualitative study was conducted through collaborative design workshops involving stroke survivors (n=17), neurorehabilitation experts (n=13), and biomedical engineers (n=3), totaling 33 participants. This innovative approach aimed to actively engage stakeholders in developing multifaceted solutions for complex health interventions.

RESULTS: Six themes emerged from the thematic analysis: (1) importance of patient-centered approach, (2) clinical evaluation and patient selection, (3) recommendations for task design, (4) guidelines for structuring BCI intervention, (5) key factors influencing motivation, and (6) technology features. From these themes, the following recommendations (R) are established: (R1) MI-based VR-BCI interventions must be conducted through a patient-centered approach, based on individualized preferences, needs, and goals of the user, by an interdisciplinary team; (R2) selection criteria must include upper limb impairment, cognitive and communication assessment, and clinical traits, such as MI capacity, neglect, and depression must be assessed since they might influence intervention outcomes; (R3) tasks to perform should preferably be based on daily living activities, including unilateral and bilateral tasks, and a variety of tasks must be available for selection to ensure meaningfulness for the user and suitability to clinical traits; (R4) intervention must be structured by different progressing levels starting with simple, gross movements and adding complexity through additional movement features, cognitive demand, or MI difficulty; (R5) optimal levels of motivation must be sustained through task variability, gamification elements, and task demand adequacy; and (R6) multisensorial potential of MI-based VR-BCI must be effectively harnessed through the adequate adjustment of visual, haptic, and proprioceptive feedback modalities to the patient.

CONCLUSIONS: Current results contribute to establishing clear guidelines on patient selection, task design, intervention structuring, motivation factors, and tailoring of sensory feedback. This framework presents a foundation for optimal implementation of VR-BCI-based interventions that associate MI and motor observation, optimizing cortical activity during the intervention, patients' engagement, and clinical outcomes. Future research should explore the application of these guidelines for validation and investigate BCIs' efficacy according to different combinations of patients' profiles, task characteristics, and technology features.},
}

@article {pmid41091050,
year = {2025},
author = {Levy, L and Feinsinger, A},
title = {Participant Engagement, Epistemic Injustice, and Early-Phase Implanted Neural Device Research.},
journal = {The Hastings Center report},
volume = {55},
number = {5},
pages = {18-28},
pmid = {41091050},
issn = {1552-146X},
support = {//Dana Foundation/ ; RF1MH121373/NH/NIH HHS/United States ; },
mesh = {Humans ; *Social Justice ; Motivation ; *Biomedical Research/ethics ; Knowledge ; *Prostheses and Implants ; },
abstract = {In recent years, participant engagement initiatives in research on implanted neural devices have significantly increased. However, there remains little consensus on the motivations, goals, and best practices for engagement efforts. Drawing on the concept of participatory epistemic injustice, we argue that one core ethical motivation for engagement is epistemic in nature. Based on their subject positions, participants should be key knowledge contributors to implanted neurotech research. Therefore, we argue, participants experience participatory epistemic injustice when their insights do not result in changes to or otherwise influence research protocols, device development, and task design. We contend that engagement can resist this type of injustice only if it establishes robust methods not only to gather but also to actively incorporate participant knowledge into the research and development process.},
}

Humans
*Social Justice
Motivation
*Biomedical Research/ethics
Knowledge
*Prostheses and Implants

@article {pmid41090855,
year = {2025},
author = {Liu, Y and Wu, H and Wang, S and Yang, Q and Zhang, B},
title = {The Implantable Electrode Co-Deposited with Iron Oxide Nanoparticles and PEDOT:PSS.},
journal = {Nanomaterials (Basel, Switzerland)},
volume = {15},
number = {19},
pages = {},
pmid = {41090855},
issn = {2079-4991},
support = {5216202252162022//National Natural Science Foundation of China/ ; 2021JJA160015//Guangxi Natural Science Foundation/ ; },
abstract = {Iron oxide nanoparticles (IONs) exhibit biocompatibility, ease of drug loading, and potential for generating forces and heat in a magnetic field, enhancing Magnetic Resonance Imaging (MRI). This study proposes coating IONs on electrode surfaces to improve performance and neuron bonding. Methods included synthesizing IONs, grafting chondroitin sulfate (CS), and co-depositing with poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS). Results showed reduced impedance, increased charge storage, and improved signal quality in vivo.},
}

@article {pmid41089660,
year = {2025},
author = {Dai, Y and Chen, Z and Cao, TA and Zhou, H and Fang, M and Dai, Y and Jiang, L and Tong, J},
title = {A time-frequency feature fusion-based deep learning network for SSVEP frequency recognition.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1679451},
pmid = {41089660},
issn = {1662-4548},
abstract = {INTRODUCTION: Steady-state visual evoked potential (SSVEP) has emerged as a pivotal branch in brain-computer interfaces (BCIs) due to its high signal-to-noise ratio (SNR) and elevated information transfer rate (ITR). However, substantial inter-subject variability in electroencephalographic (EEG) signals poses a significant challenge to current SSVEP frequency recognition. In particular, it is difficult to achieve high cross-subject classification accuracy in calibration-free scenarios, and the classification performance heavily depends on extensive calibration data.

METHODS: To mitigate the reliance on large calibration datasets and enhance cross-subject generalization, we propose SSVEP time-frequency fusion network (SSVEP-TFFNet), an improved deep learning network fusing time-domain and frequency-domain features dynamically. The network comprises two parallel branches: a time-domain branch that ingests raw EEG signals and a frequency-domain branch that processes complex-spectrum features. The two branches extract the time-domain and frequency-domain features, respectively. Subsequently, these features are fused via a dynamic weighting mechanism and input to the classifier. This fusion strategy strengthens the feature expression ability and generalization across different subjects.

RESULTS: Cross-subject classification was conducted on publicly available 12-class and 40-class SSVEP datasets. We also compared SSVEP-TFFNet with traditional approaches and principal deep learning methods. Results demonstrate that SSVEP-TFFNet achieves an average classification accuracy of 89.72% on the 12-class dataset, surpassing the best baseline method by 1.83%. SSVEP-TFFNet achieves average classification accuracies of 72.11 and 82.50% (40-class datasets), outperforming the best controlled method by 7.40 and 6.89% separately.

DISCUSSION: The performance validates the efficacy of dynamic time-frequency feature fusion and our proposed method provides a new paradigm for calibration-free SSVEP-based BCI systems.},
}

@article {pmid41089381,
year = {2025},
author = {Liu, B and Hu, C and Bao, P},
title = {Precision TMS through the integration of neuroimaging and machine learning: optimizing stimulation targets for personalized treatment.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1682852},
pmid = {41089381},
issn = {1662-5161},
abstract = {Transcranial Magnetic Stimulation (TMS), a non-invasive neuromodulation technique based on electromagnetic induction, modulates cortical excitability by inducing currents with a magnetic field. TMS has demonstrated significant clinical potential in the treatment of various neuropsychiatric disorders, including depression, anxiety, and Parkinson's disease. However, conventional TMS targeting methods that rely on anatomical landmarks do not adequately account for individual differences in brain structure and functional networks, leading to considerable variability in treatment responses. In recent years, advances in neuroimaging techniques-such as functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI)-together with the application of machine learning (ML) and artificial intelligence (AI) algorithms in big data analysis, have provided novel approaches for precise TMS targeting and individualized treatment. This review summarizes the latest developments in the integration of multimodal neuroimaging and AI technologies for precision neuromodulation with TMS. It focuses on critical issues such as imaging resolution, AI model generalizability, real-time feedback modulation, as well as data privacy and ethical considerations. Future prospects including closed-loop TMS control systems, cross-modal data fusion, and AI-assisted brain-computer interfaces (BCIs) are also discussed. Overall, AI-driven personalized TMS strategies hold promise for markedly enhancing treatment precision and clinical efficacy, thereby offering new theoretical and practical guidance for individualized treatment in neuropsychiatric and neurodegenerative disorders.},
}

@article {pmid41088329,
year = {2025},
author = {Han, F and Chen, H},
title = {Does brain-computer interface-based mind reading threaten mental privacy? ethical reflections from interviews with Chinese experts.},
journal = {BMC medical ethics},
volume = {26},
number = {1},
pages = {134},
pmid = {41088329},
issn = {1472-6939},
support = {21ZDA017//National Social Science Fund of China/ ; 21ZDA017//National Social Science Fund of China/ ; },
mesh = {Humans ; *Brain-Computer Interfaces/ethics ; China ; *Privacy ; Male ; Female ; Adult ; Interviews as Topic ; Reading ; Neurosciences/ethics ; Qualitative Research ; East Asian People ; },
abstract = {BACKGROUND: The rapid development of brain-computer interface (BCI) technology has sparked profound debates about the right to privacy, particularly concerning its potential to enable mind reading. While scholars have proposed the establishment of neurorights to safeguard mental privacy, questions remain about whether BCIs can genuinely decode inner thoughts and what makes their ethical implications distinctive.

METHODS: This study conducted semi-structured interviews with 20 Chinese experts in the BCI and neuroscience fields to explore their perspectives on the concept, feasibility, and limitations of BCI-based mind reading (BMR). The transcriptions of the interviews were analyzed through reflexive thematic analysis to identify key themes and insights.

RESULTS: The findings reveal a range of expert perspectives on the interpretations and feasibility of BMR. Most participants believe that current BCI technology cannot decode inner thoughts, although they acknowledge the potential for future advancements. Key technical challenges, such as signal quality and reliance on background information, are highlighted.

CONCLUSION: We summarize the interpretations, feasibility, and limitations of BMR and introduce a distinction between "strong BMR" and "weak BMR" to clarify their technical and ethical implications. Based on our analysis, we argue that current BMR does not pose unique ethical challenges compared with other forms of mind reading, and therefore does not yet justify the establishment of a distinct right to mental privacy.},
}

Humans
*Brain-Computer Interfaces/ethics
China
*Privacy
Male
Female
Adult
Interviews as Topic
Reading
Neurosciences/ethics
Qualitative Research
East Asian People

@article {pmid41088296,
year = {2025},
author = {Tang, A and Chen, Y and Ding, J and Li, Z and Xu, C and Hu, S and Lai, J},
title = {Gut microbiota remodeling and sensory-emotional functional disruption in adolescents with bipolar depression.},
journal = {Journal of translational medicine},
volume = {23},
number = {1},
pages = {1083},
pmid = {41088296},
issn = {1479-5876},
support = {82201676//National Natural Science Foundation of China/ ; 82471542//National Natural Science Foundation of China/ ; No. JNL-2023001B//Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; *Gastrointestinal Microbiome/drug effects/physiology ; Adolescent ; *Bipolar Disorder/microbiology/physiopathology/drug therapy/psychology ; Male ; Female ; *Emotions ; Quetiapine Fumarate/therapeutic use/pharmacology ; Magnetic Resonance Imaging ; Case-Control Studies ; Brain/physiopathology/diagnostic imaging ; Neuroimaging ; },
abstract = {BACKGROUND: Adolescence is the peak period of newly-onset bipolar disorder (BD). Accumulating studies have revealed disturbed gut microbiota can interfere with neurodevelopment in adolescents. In this study, we aimed to characterize the gut microbiota in adolescents with BD and its correlation with brain dysfunction.

METHODS: Thirty unmedicated BD adolescents within depressive episode were recruited and underwent four-week quetiapine treatment. Twenty-five age-, gender-, and BMI-matched healthy controls (HCs) were recruited. Fecal samples were collected from HCs and all BD adolescents before and after treatment and analyzed by metagenomic sequencing. Resting-state cranial functional magnetic images were collected from 21 BD adolescents before treatment. Random forest models were used to evaluate the discriminative power of gut microbiota and neuroimaging data for BD and the predictive power of treatment effect.

RESULTS: Although no significant difference was found in alpha-diversity, intra- and inter-group differences in beta-diversity were observed among HCs, pre- and post-treatment patients. Compared to HCs, unmedicated BD adolescents presented a differentiated gut microbial communities, which correlated to the short-chain fatty acids, choline, lipids, vitamins, polyamines, aromatic amino acids metabolic pathways. Four-week quetiapine treatment improved the abundance of specific genus, such as Odoribacter splanchnicus, Oribacterium sinus, Hafnia alvei, Fusobacterium periodonticum, Acidaminococcus interstini and Veillonella rogosae. Neuroimaging analysis revealed sensor-emotional brain regions were associated with BD severity. Finally, random forest models based on gut microbial biomarkers can well distinguish unmedicated BD from HCs (AUC = 91.12%) and predict the treatment effect (AUC = 91.84%). The random forest model integrating gut microbiota and neuroimaging data exhibited a better predictive efficacy than using microbiota data alone.

CONCLUSION: This study first characterized the gut microbiota architecture in adolescent BD. Combining gut microbiota and brain function biomarkers may benefit disease diagnosis and predict treatment outcome. Nonetheless, these findings should be carefully interpreted considering the limitations of a modest sample size and the absence of detailed mechanistic explorations. Trial registration NCT05480150. Registered 29 July 2022-Retrospectively registered, https://clinicaltrials.gov/study/NCT05480150 .},
}

Humans
*Gastrointestinal Microbiome/drug effects/physiology
Adolescent
*Bipolar Disorder/microbiology/physiopathology/drug therapy/psychology
Male
Female
*Emotions
Quetiapine Fumarate/therapeutic use/pharmacology
Magnetic Resonance Imaging
Case-Control Studies
Brain/physiopathology/diagnostic imaging
Neuroimaging

@article {pmid41087533,
year = {2025},
author = {Ge, Y and Dong, Y and Sun, H and Liu, Y and Wang, C},
title = {An incremental adversarial training method enables timeliness and rapid new knowledge acquisition.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {35826},
pmid = {41087533},
issn = {2045-2322},
support = {JJKH20250945KJ//Science and Technology Development Project of the Department of Education of Jilin Province/ ; JJKH20250945KJ//Science and Technology Development Project of the Department of Education of Jilin Province/ ; JJKH20250945KJ//Science and Technology Development Project of the Department of Education of Jilin Province/ ; JJKH20250945KJ//Science and Technology Development Project of the Department of Education of Jilin Province/ ; JJKH20250945KJ//Science and Technology Development Project of the Department of Education of Jilin Province/ ; 2022IT096//New Generation Information Technology Innovation Project of China University Industry, University and Research Innovation Fund/ ; },
mesh = {*Neural Networks, Computer ; Humans ; Algorithms ; *Brain-Computer Interfaces ; *Deep Learning ; },
abstract = {Adversarial training is an effective defense method for deep models against adversarial attacks. However, current adversarial training methods require retraining the entire neural network, which consumes a significant amount of computational resources, thereby affecting the timeliness of deep models and further hindering the rapid learning process of new knowledge. In response to the above problems, this article proposes an incremental adversarial training method (IncAT) and applies it to the field of brain computer interfaces (BCI). Within this method, we first propose a deep model called Neural Hybrid Assembly Network (NHANet) and then train it. Then, based on the original samples and the trained deep model, calculate the Fisher information matrix to evaluate the importance of deep neural network parameters on the original samples. Finally, when calculating the loss of adversarial samples and real labels, an Elastic Weight Consolidation (EWC) loss is added to limit the variation of important weights and bias parameters in the Neural Hybrid Assembly Network (NHANet). The above incremental adversarial training method was applied to the publicly available epilepsy brain computer interface dataset at the University of Bonn. The experimental results showed that when facing three different attack algorithms, including fast gradient sign method (FGSM), projected gradient descent (PGD) and basic iterative method (BIM), the method proposed in this paper achieved robust accuracies of 95.33%, 94.67%, and 93.60%, respectively, without affecting the accuracy of clean samples, which is 5.06%, 4.67%, and 2.67% higher than traditional training methods respectively, thus fully verifying the generalization and effectiveness of the method.},
}

*Neural Networks, Computer
Humans
Algorithms
*Brain-Computer Interfaces
*Deep Learning

@article {pmid41087504,
year = {2025},
author = {Fang, T and Wang, R and Liu, W and Zhang, Y and Guo, Y and Hu, Y and Zhao, X and Chen, Y and Fan, Q and Ming, D},
title = {Edge participation coefficient unveiling the developmental dynamics of neonatal functional connectome.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {1463},
pmid = {41087504},
issn = {2399-3642},
mesh = {Humans ; *Connectome/methods ; Infant, Newborn ; *Brain/growth & development/physiology/diagnostic imaging ; Magnetic Resonance Imaging ; Infant, Premature/growth & development ; Male ; Female ; *Nerve Net/growth & development/physiology ; Infant ; },
abstract = {Understanding how the brain's functional connections develop during infancy is crucial for uncovering the complexities of early neural maturation. Traditional node-based analyses have advanced our knowledge, but may overlook the transient dynamics of interregional connectivity. Leveraging the large neonatal functional MRI dataset from the Developing Human Connectome Project (n = 781, including 494 full-term and 287 preterm infants), we introduce an edge-centric metric to quantify cross-module functional integration. Here we show that preterm infants exhibit higher edge participation coefficients than full-term peers, suggesting delayed network specialization. We mapped developmental changes in edge participation coefficients and found that between-network connections-particularly those involving visual and higher-order systems-undergo the most pronounced changes and are associated with cognitive outcomes at 18 months. By analyzing gene expression in a developing brain, we identified genes involved in neurodevelopmental processes and cellular signalling that may underlie these patterns. Our findings illustrate how interregional diversity evolves in early life and provide insight into the molecular basis of early brain development.},
}

Humans
*Connectome/methods
Infant, Newborn
*Brain/growth & development/physiology/diagnostic imaging
Magnetic Resonance Imaging
Infant, Premature/growth & development
Male
Female
*Nerve Net/growth & development/physiology
Infant

@article {pmid41083759,
year = {2025},
author = {Banaeian Far, S and Chalak Qazani, MR and Imani Rad, A},
title = {Cell-to-cell communication: from physical calling to remote emotional touching.},
journal = {Discover nano},
volume = {20},
number = {1},
pages = {178},
pmid = {41083759},
issn = {2731-9229},
abstract = {The emerging paradigm of cell-to-cell communication represents a transformative shift from device-mediated contact to bio-integrated, emotion-driven interactions. This article introduces a novel, multi-layered framework for enabling biologically integrated communication between cells, devices, and computational systems using the paradigm of Molecular Communication (MC). Moving beyond traditional digital interfaces, the proposed architecture, comprising in-body, on-chip, and external communication layers, models and processes intercellular signaling via molecular emissions, implantable biosensors, and nano-electronic processors. Theoretical foundations are extended to fractional-order diffusion systems and neuromorphic decoding, capturing complex behaviors in realistic biological environments. We further propose a cross-layer molecular digital twin model for context-aware interpretation and feedback. The framework's applications are grounded in the molecular underpinnings of emotion, where neurotransmitters like oxytocin and serotonin mediate prosocial behaviors and affective states through cell-to-cell signaling. For instance, remote emotional interfacing leverages MC to modulate oxytocin release, mimicking natural empathy circuits, while consensual telepathy draws from BCI-mediated neural pattern sharing, extending molecular-level decoding to cognitive-emotional relays. These are not mere metaphors but extensions of established neurochemical pathways, as evidenced by recent studies showing serotonin fluctuations amplify context-specific emotions. This work thus bridges cellular mechanisms to higher-order phenomena, ensuring scientific rigor in bio-digital systems .},
}

@article {pmid41082414,
year = {2025},
author = {Chen, Q and Ye, C and Xiao, R and Pan, J and Li, J},
title = {SemSTNet: Medical EEG Semantic Metric Learning with Class Prototypes Generated by Pretrained Language Model.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3620754},
pmid = {41082414},
issn = {1558-2531},
abstract = {Electroencephalography (EEG) feature learning is crucial for brain-machine interfaces and medical diagnostics. Existing deep learning models for classification often overlook the intrinsic semantic relationships between different EEG classes and rely on overly complex models with a large number of parameters. To address these challenges, we propose SemSTNet, a novel and lightweight framework for EEG analysis. Firstly, we designed an e ficient, lightweight convolutional architecture that decouples spatial and temporal feature extraction. Then we propose a framework which introduces a novel semantic metric learning paradigm that uses class prototypes generated by a pretrained language model to better capture inter-class relationships and enhance intra-class compactness. These prototypes are extracted and stored offline, requiring no additional inference from the language model during training or deployment. This design significantly reduces model complexity, resulting in a model with only 23K parameters-over 100 times fewer than common Transformer-based models. Exten sive experiments demonstrate that SemSTNet outperforms state of-the-art approaches on tasks such as epilepsy classification and sleep staging, highlighting its effectiveness and efficiency. Our work demonstrates that integrating semantic knowledge with a purpose-built lightweight architecture provides a highly effective and efficient solution.},
}

@article {pmid41082173,
year = {2025},
author = {Hodgkiss, DD and Balthazaar, SJT and Gee, CM and Boardley, ID and Janssen, TWJ and Krassioukov, AV and Nightingale, TE},
title = {Electroceuticals for Paralympic Athletes: A Fair Play and Classification Concern?.},
journal = {Sports medicine (Auckland, N.Z.)},
volume = {},
number = {},
pages = {},
pmid = {41082173},
issn = {1179-2035},
support = {NRB123//International Spinal Research Trust/ ; RG2698/21/23//Heart Research UK/ ; SBF009\1126/AMS_/Academy of Medical Sciences/United Kingdom ; },
abstract = {Electroceuticals such as brain computer interfaces and spinal cord stimulation (SCS) represent transformative strategies for neuromodulation. Research has demonstrated that SCS can ameliorate motor and autonomic cardiovascular dysfunctions, particularly in individuals with spinal cord injury (SCI). Notably, SCS has been shown to augment aerobic exercise performance. Owing to the nature of their injury, athletes with SCI are often predisposed to low resting blood pressure and impaired physiological responses to exercise. Therefore, some athletes intentionally induce autonomic dysreflexia ("boosting") to gain a competitive advantage - an act banned by the International Paralympic Committee (IPC). However, the emergence of electroceuticals facilitates an alternative performance enhancement strategy that could be considered unfair without equal access opportunities for all athletes. Currently, the World Anti-Doping Agency and the IPC have not acknowledged the potential impact of electroceuticals in parasport. Herein, we present an argument that the use of SCS meets the criteria for it to be placed on the World Anti-Doping Code Prohibited List (or at the very least be monitored) because collectively: SCS can enhance sport performance, represents a potential health risk to the athlete if misused, and may violate the spirit of sport. Acute and chronic use of SCS may also lead to classification changes, and increased opportunities for athletes to intentionally misrepresent, thereby raising concerns for the IPC. The growing access to electroceuticals (e.g. via clinical trial participation or private healthcare implantation) more than ever increases the likelihood of an athlete using SCS to gain an unfair advantage in parasport.},
}

@article {pmid41082005,
year = {2025},
author = {Kolarijani, NR and Salehi, M and Mirzaii, M and Farahani, MK and Zamani, S and Fazli, M and Alizadeh, M},
title = {Synthesis and characterization of silver nanoparticle-loaded carboxymethylcellulose hydrogels: in vitro and in vivo evaluation of wound healing and antibacterial properties.},
journal = {Cell and tissue banking},
volume = {26},
number = {4},
pages = {46},
pmid = {41082005},
issn = {1573-6814},
mesh = {Animals ; *Silver/pharmacology/chemistry ; *Wound Healing/drug effects ; *Hydrogels/pharmacology/chemistry/chemical synthesis ; *Carboxymethylcellulose Sodium/chemistry/pharmacology ; *Anti-Bacterial Agents/pharmacology/chemistry/chemical synthesis ; *Metal Nanoparticles/chemistry/ultrastructure ; Rats ; Pseudomonas aeruginosa/drug effects ; Staphylococcus aureus/drug effects ; Microbial Sensitivity Tests ; Male ; Hemolysis/drug effects ; },
abstract = {The current research was conducted to assess wound healing activity and antibacterial properties of carboxymethyl cellulose (CMC) hydrogels loaded with silver nanoparticles (AgNPs) against excisional wounds (15 × 15 mm[2]) infected with Pseudomonas aeruginosa and Staphylococcus aureus in a rat model.CMC/AgNPs hydrogels were synthesized using varying concentrations of AgNPs and subsequently lyophilized. A comprehensive range of in vitro tests were conducted, including nanoparticle characterization, scanning electron microscopy (SEM) morphology study, water uptake (WUE) study, blood uptake capacity study (BUC), weight loss study (WLA), pH, hemolysis percentage (HP), blood coagulation index (BCI), antibacterial activity (minimum inhibitory concentration [MIC] and minimum bactericidal concentration [MBC]), and cell viability through the MTT assay. In vivo wound healing studies were conducted using infected excisional wound models in rats. SEM confirmed a porous structure with a mean pore size ranging from 68 to 152 μm. The hydrogels exhibited dosage-dependent swelling and sustained physiological pH (7.4-7.6) for a period of time. The 125 μg/mL AgNPs formulation showed a BUC of 97.68% in 22 h. Hemocompatibility assay showed minimal hemolysis and acceptable coagulation indices for all concentrations of AgNPs. MIC and MBC against both strains of bacteria were found to be 250 μg/mL and 500 μg/mL, respectively. CMC/AgNPs hydrogel with the concentration of 250 μg/mL showed the optimal cell viability and the optimal in vivo wound healing result. The findings indicate that AgNPs-loaded CMC hydrogels possess favorable physicochemical, biocompatible, and antimicrobial properties, suggesting their potential as a wound dressing for managing infected wounds and supporting the wound healing process.},
}

Animals
*Silver/pharmacology/chemistry
*Wound Healing/drug effects
*Hydrogels/pharmacology/chemistry/chemical synthesis
*Carboxymethylcellulose Sodium/chemistry/pharmacology
*Anti-Bacterial Agents/pharmacology/chemistry/chemical synthesis
*Metal Nanoparticles/chemistry/ultrastructure
Rats
Pseudomonas aeruginosa/drug effects
Staphylococcus aureus/drug effects
Microbial Sensitivity Tests
Male
Hemolysis/drug effects

@article {pmid41081225,
year = {2025},
author = {Cao, P and Guo, S and Zhang, G and Zan, X and Wang, J and Zhang, F and Muñoz, J and Lucke-Wold, B and Cheng, R},
title = {Brain-computer interface training for multimodal functional recovery in patients with brain injury: a case series.},
journal = {Quantitative imaging in medicine and surgery},
volume = {15},
number = {10},
pages = {9277-9293},
pmid = {41081225},
issn = {2223-4292},
abstract = {BACKGROUND: Patients with impaired brain function often face sequelae such as limb movement, cognitive, and language impairment, and there are limitations in the efficiency of traditional rehabilitation methods. This study examined whether motor imagery-based brain-computer interface (BCI) training could promote multimodal functional recovery-including limb movement, speech, and cognition-in patients with subacute brain injury. Unlike traditional BCI research focused on single functional domains, we combined multidimensional clinical assessments with multimodal neural analysis to examine cross-network plasticity.

METHODS: Five patients with subacute brain injury (four males and one female; mean age 54.4±10.3 years) underwent 5 weeks of BCI training between 2021 and 2023. Pre- and post-intervention evaluations included the Fugl-Meyer Assessment Scale (FMA), Modified Ashworth Scale (MAS), Western Aphasia Battery (WAB), and Mini-Mental State Examination (MMSE). Neurophysiological metrics included classification accuracy (CA), power spectral density (PSD), and electroencephalography (EEG) topography. Functional connectivity analyses were conducted with functional magnetic resonance imaging (fMRI) and individualized connectomics based on the Human Connectome Project parcellation.

RESULTS: All five patients showed clinical improvement in motor, cognitive, or language functions. The average motor imagery CA increased by 14.2%. PSD flattening and event-related desynchronization (ERD) were observed in the central motor regions. EEG topographies showed enhanced activation converging toward the sensorimotor cortex. Patient-specific functional connectivity analyses revealed strengthened interactions among sensorimotor, language, and attention networks-most notably in one patient with marked clinical gains. Distinct patterns of connectivity reorganization were observed between patients with cortical and subcortical lesions. A critical 3-week time window for neural plasticity was identified.

CONCLUSIONS: Motor imagery-based BCI training may facilitate recovery across motor, language, and cognitive domains in patients with subacute brain injury. Functional gains were supported by neurophysiological and connectomics evidence of cross-network reorganization. These preliminary findings suggest that personalized BCI protocols could represent a promising avenue for multimodal neurorehabilitation.},
}

@article {pmid41079666,
year = {2025},
author = {Jia, Q and Xu, Z and Wang, Y and Duan, Y and Liu, Y and Shan, J and Ma, J and Li, Q and Luo, J and Luo, Y and Wang, Y and Duan, S and Yu, Y and Wang, M and Cai, X},
title = {Targeted-Modified MultiTransm Microelectrode Arrays Simultaneously Track Dopamine and Cellular Electrophysiology in Nucleus Accumbens during Sleep-Wake Transitions.},
journal = {Research (Washington, D.C.)},
volume = {8},
number = {},
pages = {0944},
pmid = {41079666},
issn = {2639-5274},
abstract = {Cellular-level electrophysiological and neurotransmitter signals serve as key biomarkers of sleep depth, offering insights into the dynamic sleep transitions and the neural mechanisms underlying sleep regulation. Microelectrode arrays (MEAs) provide an innovative solution for in situ, simultaneous detection of these signals with high spatial and temporal resolution. However, despite substantial progress in electrode material development, current multimodal MEA systems remain fundamentally constrained by partial integration. This study aims to address the performance limitations of multimodal MEAs by developing a MultiTransm MEA (MT MEA), integrating a 3-electrode system with site-specific surface modifications: platinum nanoparticle (PtNP)/poly(3,4-ethylene dioxythiophene):poly(styrene sulfonate) (PEDOT:PSS)-modified sites for electrophysiology, PtNP/PEDOT:PSS/Nafion-modified sites for dopamine sensing, and iridium oxide (IrOx)-based on-probe reference electrodes. The directional surface modification strategy was employed to enable compact integration, minimize inter-channel crosstalk, preserve high spatiotemporal resolution for both electrophysiological and electrochemical detection, and ensure long-term operational stability. By incorporating electroencephalography (EEG) and electromyography (EMG), MT MEAs enable real-time in vivo monitoring of sleep dynamics within the nucleus accumbens. Three distinct spike types were identified, whose coordinated activity shaped the sleep architecture. In addition, EEG and local field potential (LFP) signals exhibited distinct patterns during wakefulness, indicating region-specific neural processing. Notably, dopamine release was lowest during non-rapid eye movement (NREM) sleep and peaked during wakefulness, suggesting a neuromodulatory role in sleep-wake transitions. These results demonstrate that MT MEAs are powerful tools for probing neural and neurochemical activity across sleep states, offering new insights into the physiological regulation of sleep.},
}

@article {pmid41079401,
year = {2025},
author = {Esteves, D and Vagaja, K and Andrade, A and Vourvopoulos, A},
title = {When embodiment matters most: a confirmatory study on VR priming in motor imagery brain-computer interfaces training.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1681538},
pmid = {41079401},
issn = {1662-5161},
abstract = {BACKGROUND: Virtual Reality (VR) feedback is increasingly integrated into Brain-Computer Interface (BCI) applications, enhancing the Sense of Embodiment (SoE) toward virtual avatars and fostering more vivid motor imagery (MI). VR-based MI-BCIs hold promise for motor rehabilitation, but their effectiveness depends on neurofeedback quality. Although SoE may enhance MI training, its role as a priming strategy prior to VR-BCI has not been systematically examined, as prior work assessed embodiment only after interaction. This study investigates whether embodiment priming influences MI-BCI outcomes, focusing on event-related desynchronization (ERD) and BCI performance.

METHODS: Using a within-subject design, we combined data from a pilot study with an extended experiment, yielding 39 participants. Each completed an embodiment induction phase followed by MI training with EEG recordings. ERD and lateralization indices were analyzed across conditions to test the effect of prior embodiment.

RESULTS: Embodiment induction reliably increased SoE, yet no significant ERD differences were found between embodied and control conditions. However, lateralization indices showed greater variability in the embodied condition, suggesting individual differences in integrating embodied feedback.

CONCLUSION: Overall, findings indicate that real-time VR-based feedback during training, rather than prior embodiment, is the main driver of MI-BCI performance improvements. These results corroborate earlier findings that real-time rendering of embodied feedback during MI-BCI training constitutes the primary mechanism supporting performance gains, while highlighting the complex role of embodiment in VR-based MI-BCIs.},
}

@article {pmid41079152,
year = {2025},
author = {Bassil, K and Jongsma, K},
title = {To Explant or not to Explant Neural Implants: an Empirical Study into Deliberations of Dutch Research Ethics Committees.},
journal = {Neuroethics},
volume = {18},
number = {3},
pages = {45},
pmid = {41079152},
issn = {1874-5490},
abstract = {UNLABELLED: Neural implants such as brain-computer interfaces and spinal cord stimulation offer therapeutic prospects for people with neurological and psychiatric disorders. As neural devices are increasingly tested in clinical research, the decision to explant requires carefully weighing both known and unknown medical and psychological risks, necessitating a thorough evaluation of the benefits and risks of each available option. Research Ethics Committees (RECs) play an important role in assessing research protocols and determining the conditions under which neural implants should be explanted, yet little is understood about how RECs make these decisions. To better understand the role of RECs in explantation decisions of neural implants, we approached REC secretaries within the Netherlands via email, with a list of open-ended questions of which the explantation of neural devices, on informed consent and post-trial care and responsibilities, and psychological harm associated with such trials. The findings highlight the differential technology-specific safety assessments conducted for different types of neural devices. Variability was observed in plans regarding clinical follow-up, post-trial access, and explantation options. While RECs emphasized clear participant information on device maintenance and longevity, the timing of this disclosure varied. Additionally, the psychological impact of explantation was rarely addressed in REC assessments, indicating a gap in ethical oversight. These results shed light on some remaining gaps and suggest the need for improvement in achieving more consistent and comprehensive evaluations of neural device clinical trials, particularly regarding explantation and post-trial access.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12152-025-09619-z.},
}

@article {pmid41076093,
year = {2025},
author = {Althobaiti, M},
title = {Sensitivity Analysis of the Balloon Model Parameters in Functional Near-Infrared Spectroscopy Simulation.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110599},
doi = {10.1016/j.jneumeth.2025.110599},
pmid = {41076093},
issn = {1872-678X},
abstract = {BACKGROUND: Accurate modeling of the hemodynamic response is critical for fNIRS data interpretation. While the Balloon model is a cornerstone for this, the quantitative impact of its key parameters on the fNIRS signal, particularly in the presence of realistic artifacts, remains under-characterized.

NEW METHOD: We developed an end-to-end fNIRS simulation pipeline. It incorporates a neural activity model, the Balloon model for hemodynamics, convolution for signal generation, and realistic motion, cardiac, and respiratory artifacts. We performed a sensitivity analysis by systematically varying Grubb's exponent (α) and transit time (τ).

RESULTS: Both α and τ significantly influence the simulated fNIRS response. α shows a non-linear relationship with peak amplitude, while τ has a more linear effect on signal timing. Regression models quantifying these effects demonstrated a strong statistical fit (p < 0.05, R² > 0.9 for α).

Unlike prior fMRI-focused studies, this is the first quantitative sensitivity analysis specifically for fNIRS signals that incorporates a realistic noise model. Our framework characterizes the forward model's behavior, providing parameter-specific insights not previously available for fNIRS simulations.

CONCLUSIONS: The fNIRS hemodynamic response is highly sensitive to the Balloon model's α and τ parameters. These findings highlight the importance of accounting for physiological variability in fNIRS analysis and provide a robust framework for generating synthetic data to test signal processing algorithms.},
}

@article {pmid41074421,
year = {2025},
author = {Hui, Z and Zhang, Y and Su, Y and Kang, J and Qi, W and Li, S and Zhang, J and Shi, K and Wang, M and Yang, Y and Zhang, G and Yang, L and Chen, G and Li, S and Hu, Y and Zhu, D},
title = {Abnormal Brain Connectivity Patterns in Children with Global Developmental Delay Accompanied by Cognitive Impairment: A Resting-State EEG Study.},
journal = {Journal of integrative neuroscience},
volume = {24},
number = {9},
pages = {44410},
doi = {10.31083/JIN44410},
pmid = {41074421},
issn = {0219-6352},
support = {NHCKLBDP202508//Open Research Program of the NHC Key Laboratory of Birth Defects Prevention/ ; SBGJ202402069//Key Project of Medical Science and Technology Tackling Plan of Henan Province 2024/ ; },
mesh = {Humans ; Male ; Female ; Child ; Electroencephalography ; *Cognitive Dysfunction/physiopathology/etiology ; *Developmental Disabilities/physiopathology/complications ; *Nerve Net/physiopathology ; *Brain Waves/physiology ; *Connectome ; Support Vector Machine ; Child, Preschool ; },
abstract = {BACKGROUND: Global developmental delay (GDD) is a common childhood neurodevelopmental disorder characterized by the core symptoms of cognitive impairment. However, the underlying neural mechanisms of the cognitive impairment remain unclear. This study aimed to both analyze differences in electroencephalography (EEG) connectivity patterns between children with GDD and typical development (TD) using brain functional connectivity and to explore the neural mechanisms linking these differences to cognitive impairment.

METHODS: The study enrolled 60 children with GDD and 60 TD children. GDD participants underwent clinical assessment via the Gesell Developmental Schedule (GDS). Resting-state EEG data were subjected to brain functional connectivity analysis and graph theory metric-based network analysis, with intergroup functional differences compared. Subsequently, correlation analysis characterized the relationships between GDD subject's brain network metrics and GDS-derived cognitive developmental quotient (DQ). Finally, three support vector machine (SVM) models were constructed for GDD classification and feature weight factors were calculated to screen potential EEG biomarkers.

RESULTS: The two groups exhibited complex differences in functional connectivity. Compared with the TD group, the GDD group showed a large number of increased functional connections in the θ, α, and γ-bands, along with a small number of decreased functional connections in the α and γ-bands (all p < 0.025). Brain network analysis revealed lower global efficiency, local efficiency, clustering coefficient and small-world coefficient, as well as higher characteristic path length in GDD children across multiple bands (all p < 0.05). Correlation analysis indicated that global efficiency and small-world coefficient in θ and γ-bands were positively correlated with the DQ, while the characteristic path length in α and γ-bands was negatively correlated with DQ in the GDD group (all p < 0.05). Machine learning models showed that a quantum particle swarm optimization SVM (QPSO-SVM) achieved the highest classification performance, with characteristic path length in the γ-band being the highest weighted metric.

CONCLUSIONS: Children with GDD exhibit abnormal patterns of brain functional connectivity, characterized by global hypo-connectivity and local hyper-connectivity. Specific network metrics under these abnormal patterns are significantly correlated with cognitive impairment in GDD. This study also highlights the potential of the γ-band characteristic path length as an EEG biomarker for diagnosing GDD.},
}

Humans
Male
Female
Child
Electroencephalography
*Cognitive Dysfunction/physiopathology/etiology
*Developmental Disabilities/physiopathology/complications
*Nerve Net/physiopathology
*Brain Waves/physiology
*Connectome
Support Vector Machine
Child, Preschool

@article {pmid41073181,
year = {2025},
author = {Rudroff, T},
title = {Retraction notice to "Decoding thoughts, encoding ethics: A narrative review of the BCI-AI revolution" [Brain Res. 1850 (2025) 149423].},
journal = {Brain research},
volume = {},
number = {},
pages = {149969},
doi = {10.1016/j.brainres.2025.149969},
pmid = {41073181},
issn = {1872-6240},
}

@article {pmid40850344,
year = {2025},
author = {Zhou, S and Liu, Y and Turnbull, A and Tapparello, C and Adeli, E and Lin, FV},
title = {Personalized cognitive enhancement for older adults: An aging-friendly closed-loop human-machine interface framework.},
journal = {Ageing research reviews},
volume = {112},
number = {},
pages = {102877},
doi = {10.1016/j.arr.2025.102877},
pmid = {40850344},
issn = {1872-9649},
mesh = {Humans ; *Cognition/physiology ; Aged ; *Aging/psychology/physiology ; *Precision Medicine/methods ; *Brain-Computer Interfaces ; },
abstract = {Emerging digitally delivered non-pharmacological interventions (dNPIs) offer scalable, low-risk solutions for enhancing cognitive function in older adults, yet their effectiveness remains inconsistent due to a lack of personalization and precise mechanisms of action. Generic, population-based designs often fail to predict individual gains, underscoring the need for more tailored approaches. To address this, we propose a closed-loop human-machine interface (HMI) framework for personalizing dNPIs by optimizing the engagement of neurocognitive resources for cognitive enhancement. Our framework tackles three major challenges: (1) comprehensive and effective neurobehavioral representations for cognitive decoding, (2) tailoring interventions for domain-specific cognitive processes, and (3) ensuring aging-friendly design on usability, validity, and reliability for long-term adherence. We provide reviews and perspectives to guide the development of closed-loop HMIs by outlining the operational details of three key components-sensor, controller, and external actuator-that monitor, analyze, and modulate neurobehavioral activities through real-time adaptive interventions. Centering on neurobehavioral characteristics of older adults, we propose to advance closed-loop HMIs toward (1) deploying multimodal sensor network that captures activities from both central and peripheral nervous systems, (2) artificial intelligence (AI)-powered cognitive decoding and modulation that integrates multi-modal easy-to-acquire neurobehavioral signals and predicts the cross-modal harder-to-acquire signals, and (3) targeting neurobehavioral processes via internal and/or external regulation. We envision that the proposed closed-loop HMI framework could provide personalized dNPI with enhanced effectiveness and scalability for cognitive enhancement in older adults, promoting brain resilience and healthy longevity in the aging population.},
}

Humans
*Cognition/physiology
Aged
*Aging/psychology/physiology
*Precision Medicine/methods
*Brain-Computer Interfaces

@article {pmid41073040,
year = {2025},
author = {Wu, YJ and He, Q and Luo, FG and Li, T and Guo, WJ},
title = {Respiratory Dyskinesia With Refractory Tachypnea and Alkalosis Treated by Vesicular Monoamine Transporter 2 Inhibitor.},
journal = {Chest},
volume = {168},
number = {4},
pages = {e111-e113},
doi = {10.1016/j.chest.2025.04.042},
pmid = {41073040},
issn = {1931-3543},
mesh = {Humans ; Female ; Aged ; *Vesicular Monoamine Transport Proteins/antagonists & inhibitors ; *Tachypnea/drug therapy/diagnosis/etiology ; *Alkalosis/drug therapy/diagnosis ; *Respiration Disorders/drug therapy/diagnosis ; Antipsychotic Agents/adverse effects ; Risperidone/adverse effects/therapeutic use ; Psychotic Disorders/drug therapy ; },
abstract = {We present the case of a 69-year-old woman with a 25-year history of psychosis, managed with risperidone, who developed refractory tachypnea and alkalosis over 2 weeks. Despite multidisciplinary evaluation, she was initially misdiagnosed with psychogenic hyperventilation. Ultimately, a diagnosis of respiratory dyskinesia (RD) was established, and substantial clinical improvement was achieved after initiation of a vesicular monoamine transporter 2 (VMAT2) inhibitor. The substantial effectiveness of this therapy was confirmed over a 7-month follow-up period, with monitoring of both clinical symptoms and arterial blood gas parameters. This case highlights the diagnostic challenges posed by RD and underscores the potential utility of VMAT2 inhibitor as a novel therapeutic option.},
}

Humans
Female
Aged
*Vesicular Monoamine Transport Proteins/antagonists & inhibitors
*Tachypnea/drug therapy/diagnosis/etiology
*Alkalosis/drug therapy/diagnosis
*Respiration Disorders/drug therapy/diagnosis
Antipsychotic Agents/adverse effects
Risperidone/adverse effects/therapeutic use
Psychotic Disorders/drug therapy

@article {pmid41072470,
year = {2025},
author = {Hecker, D and Pillong, L and Reuss, K and Friedrich, KH and Alexandersson, J and Rekrut, M and Linxweiler, M and Bozzato, A and Schick, B and Metzler, P},
title = {[Novel analysis method to determine the neural activation function of the inner hair cell].},
journal = {Laryngo- rhino- otologie},
volume = {},
number = {},
pages = {},
doi = {10.1055/a-2681-5401},
pmid = {41072470},
issn = {1438-8685},
abstract = {Sensorineural hearing loss (SNH) is one of the most common forms of hearing loss. A special form of SNH is hidden hearing loss (HHL) with subjective normal hearing. Current research results indicate that these patients demonstrate a reduced wave I in the averaged signal of brainstem audiometry (ABR). Since the averaging technique is not susceptible to latency jitter and amplitude height variation, a single sweep analysis is required for a deeper insight in HHL.A total of 14 mice with significantly different calcium currents in the IHC at normal hearing thresholds were analysed. For the analysis in order to calculate four new parameters from the single sweeps in the time window of wave I. These parameters also served to describe a neural activation function (NAV).Looking at the wild type all new parameters differ significantly or highly significantly. With the transgenic mouse, there are only non-significant to significant differences. There is also a significant difference in the neural activity demonstrated in the resting EEG between the wild-type mouse and the mutant. There is a negative correlation between the wave amplitudes for the wild mouse - after a strong amplitude follows a weak amplitude and after weak amplitude follows a strong amplitude.Using new parameters based on single sweeps, surprising results are obtained. Obviously the function of the IHC correlates more strongly with the new parameters than it does with the average amplitude of wave I. The new parameters appear to be excellently suited for the diagnosis of hearing disorders even when hearing thresholds are still according to norm values.},
}

@article {pmid41072287,
year = {2025},
author = {Cao, X and Gong, P and Zhang, L and Zhang, D},
title = {EEG-CLIP: A transformer-based framework for EEG-guided image generation.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {194},
number = {},
pages = {108167},
doi = {10.1016/j.neunet.2025.108167},
pmid = {41072287},
issn = {1879-2782},
abstract = {Decoding visual perception from neural signals represents a fundamental step toward advanced brain-computer interfaces (BCIs), where functional magnetic resonance imaging (fMRI) has shown promising results despite practical constraints in deployment and costs. Electroencephalography (EEG), with its superior temporal resolution, portability, and cost-effectiveness, emerges as a promising alternative for real-time brain-computer interface (BCI) applications. While existing EEG-based approaches have advanced neural decoding capabilities, they remain constrained by inadequate architectural designs, limited reconstruction fidelity, and inconsistent evaluation protocols. To address these challenges, we present EEG-CLIP, a novel Transformer-based framework that systematically addresses each limitation: (1) We introduce a specialized EEG-ViT encoder that adeptly captures the spatial and temporal characteristics of EEG signals to augment model capacity, along with a Diffusion Prior Transformer architecture to approximate the image feature distribution. (2) We employ a dual-stage reconstruction pipeline that integrates class contrastive learning and pretrained diffusion models to enhance visual reconstruction quality. (3) We establish comprehensive evaluation protocols across multiple datasets. Our framework operates through two stages: first projecting EEG signals into CLIP image space via class contrastive learning and refining them into image priors, then reconstructing perceived images through a pretrained conditional diffusion model. Comprehensive empirical analysis, including temporal window sensitivity studies and regional brain activation visualization, demonstrates the framework's robustness. We demonstrate through ablations that EEG-CLIP's performance improvements over previous methods result from specialized architecture for EEG encoding and improved training techniques. Quantitative and qualitative evaluations on ThingsEEG and Brain2Image datasets establish EEG-CLIP's state-of-the-art performance in both classification and reconstruction tasks, advancing neural signal-based visual decoding capabilities.},
}

@article {pmid41072285,
year = {2025},
author = {Wu, J and Tang, B and Wang, Y and Li, C and Yang, Q},
title = {A multi-level teacher assistant-based knowledge distillation framework with dynamic feedback for motor imagery EEG decoding.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {194},
number = {},
pages = {108180},
doi = {10.1016/j.neunet.2025.108180},
pmid = {41072285},
issn = {1879-2782},
abstract = {Deep learning has shown promise in motor imagery-based electroencephalogram (MI-EEG) decoding, a critical task in non-invasive brain-computer interfaces (BCIs). In response to the computational complexity of deep learning models to be deployed in practical BCI applications, knowledge distillation (KD) has emerged as a solution for model compression. However, vanilla KD methods struggle to effectively extract and transfer the abundant multi-level knowledge from MI-EEG signals under high compression ratios. This study proposes a novel knowledge distillation framework termed Motor Imagery Knowledge Distillation (MIKD), which compresses deep learning models for MI classification tasks while maintaining high performance. The MIKD framework consists of two key modules: (1) a multi-level teacher assistant knowledge distillation (ML-TAKD) module designed to extract and transfer local representations and global dependencies of MI-EEG signals from the complex teacher network to the much smaller student network, and (2) a dynamic feedback module that allows the teacher assistant to adjust its teaching strategy based on the student's learning progress. Extensive experiments on three public EEG datasets demonstrate that the MIKD framework achieves state-of-the-art performance. The proposed framework improves the baseline student model's accuracy by 6.61 %, 1.91 %, and 3.29 % on the three datasets, while reducing the model size by nearly 90 %.},
}

@article {pmid41072048,
year = {2025},
author = {Li, C and Di, G and Xiong, Z and Sun, L and Li, Q and Li, H and Jiang, X and Wu, J},
title = {Three-dimensional microsurgical anatomy of the basal aspect of the cerebrum: a fiber dissection study.},
journal = {Journal of neurosurgery},
volume = {},
number = {},
pages = {1-13},
doi = {10.3171/2025.5.JNS242560},
pmid = {41072048},
issn = {1933-0693},
abstract = {OBJECTIVE: Due to the unique nature of the basal structures of the cerebrum, only a limited portion is exposed during surgery, leading to potential risk of damage to surrounding structures. The white matter fiber tracts in the basal cerebrum may be more critical than the cortex in determining the extent of resection. A thorough understanding of the 3D anatomy of these fiber tracts is essential for planning safe and precise surgical approaches and provides an anatomical foundation for studying brain function. This study aimed to examine the topographical anatomy of the fiber tracts and subcortical gray matter in the basal cerebrum, as well as their anatomical relationships with the cerebral cortex, ventricles, and associated nuclei.

METHODS: Using fiber dissection techniques and magnification ranging from ×6 to ×40, the authors studied 10 formalin-fixed human brains. The study focused on the fiber tracts and subcortical nuclei in the basal cerebrum, including the hippocampus, amygdala, and nucleus accumbens, and their relationships were documented through 3D photography.

RESULTS: The topographical relationships between the commissural, projection, and association fibers and the significant nuclei in the basal cerebrum were identified. Notable landmarks related to the fiber tracts include the cortical gyri and sulci, major basal nuclei, and lateral ventricles. The fiber tracts also exhibited consistent interrelationships.

CONCLUSIONS: The 3D microsurgical anatomy of the basal cerebrum provides valuable insights for planning precise and safe surgical approaches and offers anatomical evidence for further studies on brain function.},
}

@article {pmid41070190,
year = {2025},
author = {Li, Y and Zhu, L and Huang, A and Zhang, J and Yuan, P},
title = {Multimodal MBC-ATT: cross-modality attentional fusion of EEG-fNIRS for cognitive state decoding.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1660532},
pmid = {41070190},
issn = {1662-5161},
abstract = {With the rapid development of brain-computer interface (BCI) technology, the effective integration of multimodal biological signals to improve classification accuracy has become a research hotspot. However, existing methods often fail to fully exploit cross-modality correlations in complex cognitive tasks. To address this, this paper proposes a Multi-Branch Convolutional Neural Network with Attention (MBC-ATT) for BCI based cognitive tasks classification. MBC-ATT employs independent branch structures to process electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals separately, thereby leveraging the advantages of each modality. To further enhance the fusion of multimodal features, we introduce a cross-modal attention mechanism to discriminate features, strengthening the model's ability to focus on relevant signals and thereby improving classification accuracy. We conducted experiments on the n-back and WG datasets. The results demonstrate that the proposed model outperforms conventional approaches in classification performance, further validating the effectiveness of MBC-ATT in brain-computer interfaces. This study not only provides novel insights for multimodal BCI systems but also holds great potential for various applications.},
}

@article {pmid41066375,
year = {2025},
author = {, },
title = {Correction: Filter bank common spatial pattern and envelope-based features in multimodal EEG-fTCD brain-computer interfaces.},
journal = {PloS one},
volume = {20},
number = {10},
pages = {e0334075},
pmid = {41066375},
issn = {1932-6203},
abstract = {[This corrects the article DOI: 10.1371/journal.pone.0311075.].},
}

@article {pmid41065125,
year = {2025},
author = {Schnitzer, SA and DeFilippis, DM},
title = {Does increasing canopy liana density decrease the tropical forest carbon sink?.},
journal = {Ecology},
volume = {106},
number = {10},
pages = {e70196},
doi = {10.1002/ecy.70196},
pmid = {41065125},
issn = {1939-9170},
support = {DEB 06-13666//National Science Foundation/ ; DEB 20-01799//National Science Foundation/ ; IOS 15-58093//National Science Foundation/ ; },
mesh = {*Tropical Climate ; *Forests ; *Carbon Sequestration/physiology ; Panama ; *Plants/classification ; *Trees/physiology ; *Carbon/metabolism ; Time Factors ; },
abstract = {The ongoing decline in the American tropical forest carbon sink has serious ramifications for atmospheric carbon levels and global climate change. Increasing liana abundance may explain the decaying carbon sink because lianas reduce canopy tree growth and survival, which limits forest carbon storage. However, canopy lianas, not solely understory lianas, would have to be increasing for this hypothesis to be credible because canopy lianas compete especially intensely with canopy trees. We examined the change in canopy lianas over 10 years on Barro Colorado Island (BCI), Panama to test two main hypotheses. (1) Canopy lianas are increasing on BCI. (2) Increasing canopy lianas decrease aboveground canopy tree and forest carbon storage. We found that canopy liana density increased 8.3% over the 10-year period, and canopy lianas outnumbered canopy trees 3.59-1. There was a clear negative relationship between increasing canopy liana density and decreasing canopy tree carbon storage. Where liana density increased, tree carbon decreased, and where canopy lianas decreased, canopy tree carbon increased. Our findings indicate that lianas are the numerically dominant and diverse woody plant group in the BCI canopy, and this dominance is increasing, reducing forest-level carbon storage and possibly explaining the decaying American tropical forest carbon sink.},
}

*Tropical Climate
*Forests
*Carbon Sequestration/physiology
Panama
*Plants/classification
*Trees/physiology
*Carbon/metabolism
Time Factors

@article {pmid41064793,
year = {2025},
author = {Gong, J and Zhao, Z and Niu, X and Ji, Y and Sun, H and Shen, Y and Chen, B and Wu, B},
title = {AI reshaping life sciences: intelligent transformation, application challenges, and future convergence in neuroscience, biology, and medicine.},
journal = {Frontiers in digital health},
volume = {7},
number = {},
pages = {1666415},
pmid = {41064793},
issn = {2673-253X},
abstract = {The rapid advancement of artificial intelligence (AI) is profoundly transforming research paradigms and clinical practices across neuroscience, biology, and medicine with unprecedented depth and breadth. Leveraging its robust data-processing capabilities, precise pattern recognition techniques, and efficient real-time decision support, AI has catalyzed a paradigm shift toward intelligent, precision-oriented approaches in scientific research and healthcare. This review comprehensively reviews core AI applications within these domains. Within neuroscience, AI advances encompass brain-computer interface (BCI) development/optimization, intelligent analysis of neuroimaging data (e.g., fMRI, EEG), and early prediction/precise diagnosis of neurological disorders. In biological research, AI applications include enhanced gene-editing efficiency (e.g., CRISPR) with off-target effect prediction, genomic big-data interpretation, drug discovery/design (e.g., virtual screening), high-accuracy protein structure prediction (exemplified by AlphaFold), biodiversity monitoring, and ecological conservation strategy optimization. For medical research, AI empowers auxiliary medical image diagnosis (e.g., CT, MRI), pathological analysis, personalized treatment planning, health risk prediction with lifespan health management, and robot-assisted minimally invasive surgery (e.g., da Vinci Surgical System). This review not only synthesizes AI's pivotal role in enhancing research efficiency and overcoming limitations of conventional methodologies, but also critically examines persistent challenges, including data access barriers, algorithmic non-transparency, ethical governance gaps, and talent shortages. Building upon this analysis, we propose a tripartite framework ("Technology-Ethics-Talent") to advance intelligent transformation in scientific and medical domains. Through coordinated implementation, AI will catalyze a transition toward efficient, accessible, and sustainable healthcare, ultimately establishing a life-cycle preservation paradigm encompassing curative gene editing, proactive health management, and ecologically intelligent governance.},
}

@article {pmid41064747,
year = {2025},
author = {Yue, J and Xiao, X and Wang, K and Yi, W and Jung, TP and Xu, M and Ming, D},
title = {Augmenting Electroencephalogram Transformer for Steady-State Visually Evoked Potential-Based Brain-Computer Interfaces.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0379},
pmid = {41064747},
issn = {2692-7632},
abstract = {Objective: Advancing high-speed steady-state visually evoked potential (SSVEP)-based brain-computer interface (BCI) systems requires effective electroencephalogram (EEG) decoding through deep learning. However, challenges persist due to data sparsity and the unclear neural basis of most augmentation techniques. Furthermore, effective processing of dynamic EEG signals and accommodating augmented data require a more sophisticated model tailored to the unique characteristics of EEG signals. Approach: This study introduces background EEG mixing (BGMix), a novel data augmentation technique grounded in neural principles that enhances training samples by replacing background noise between different classes. Building on this, we propose the augment EEG Transformer (AETF), a Transformer-based model designed to capture the temporal, spatial, and frequential features of EEG signals, leveraging the advantages of Transformer architectures. Main results: Experimental evaluations of 2 publicly available SSVEP datasets show the efficacy of the BGMix strategy and the AETF model. The BGMix approach notably improved the average classification accuracy of 4 distinct deep learning models, with increases ranging from 11.06% to 21.39% and 4.81% to 25.17% in the respective datasets. Furthermore, the AETF model outperformed state-of-the-art baseline models, excelling with short training data lengths and achieving the highest information transfer rates (ITRs) of 205.82 ± 15.81 bits/min and 240.03 ± 14.91 bits/min on the 2 datasets. Significance: This study introduces a novel EEG augmentation method and a new approach to designing deep learning models informed by the neural processes of EEG. These innovations significantly improve the performance and practicality of high-speed SSVEP-based BCI systems.},
}

@article {pmid41062739,
year = {2025},
author = {Abinaya, G and Dinakaran, K},
title = {ACXNet hybrid deep learning model for cross task mental workload estimation using EEG neural manifolds.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {35178},
pmid = {41062739},
issn = {2045-2322},
mesh = {Humans ; *Electroencephalography/methods ; *Workload/psychology ; *Deep Learning ; Male ; *Cognition/physiology ; Task Performance and Analysis ; Adult ; Female ; Brain/physiology ; Neural Networks, Computer ; Young Adult ; Attention/physiology ; },
abstract = {Mental workload is an interdisciplinary construct that significantly influences human performance, particularly in tasks requiring sustained attention and cognitive processing. Effective mental workload assessment is critical for preventing cognitive overload, which can lead to errors and reduced efficiency in high-stakes environments. The approach leverages topographic neural manifolds (spatial electrode arrangements) and temporal neural manifolds (time-series patterns) to capture comprehensive brain activity representations.Traditional methods rely on subjective reports or task performance, but physiological signals like EEG provide a more objective and continuous means of monitoring cognitive states. Therefore, this paper proposes a hybrid novel approach ACXNet which integrates autoencoder, CNN and XGBoost to learn features of EEG from an individual cross task performance without prior subject-specific calibration or task specific pre-labeled .training data. Utilizing the STEW (Simultaneous Task EEG Workload) dataset, containing recordings from 48 participants experiencing different levels of cognitive demands. Unsupervised feature extraction was carried out using an autoencoder. Subsequently, a CNN was employed to capture the spatial-temporal dependencies in the data, and XGBoost was utilized for efficient mental workload classification. This research adopts a binary classification approach to differentiate between low and high mental workload during SIMKAP and No task. The ACXNet model proposed in this study outperforms the existing methods with an average accuracy of 92.10% for SIMKAP task and 89.94% for No task condition. These findings show that ACXNet significantly improves the robustness and precision of mental workload estimation, providing a scalable solution adaptable to real-world applications, opening new avenues for the development of intelligent systems in human-computer interaction, healthcare, and beyond.},
}

Humans
*Electroencephalography/methods
*Workload/psychology
*Deep Learning
Male
*Cognition/physiology
Task Performance and Analysis
Adult
Female
Brain/physiology
Neural Networks, Computer
Young Adult
Attention/physiology

@article {pmid41061192,
year = {2025},
author = {Bushnell, BD and Jarvis, BT and Jarvis, RC and Piller, CP and Baudier, RS},
title = {Minimal Stiffness After Rotator Cuff Repair With Bioinductive Collagen Implants.},
journal = {Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews},
volume = {9},
number = {10},
pages = {},
pmid = {41061192},
issn = {2474-7661},
support = {N/A//Smith and Nephew/ ; },
mesh = {Humans ; Retrospective Studies ; *Rotator Cuff Injuries/surgery ; *Collagen ; Male ; Female ; Middle Aged ; Aged ; Range of Motion, Articular ; *Postoperative Complications/epidemiology/etiology ; *Prostheses and Implants ; Adult ; Rotator Cuff/surgery ; Reoperation/statistics & numerical data ; },
abstract = {BACKGROUND: Bioinductive collagen implants (BCIs) have been growing in popularity for use in rotator cuff repair (RCR) over the past several years, but recent literature has raised concerns about the implants contributing to postoperative stiffness. The purpose of this study was to investigate the incidence of stiffness over a decade of experience with the BCI.

METHODS: A retrospective review was conducted of all cases of RCR using a BCI performed between September 2014 and December 2023. The primary outcome measure was postoperative range of motion, with significant stiffness defined by parameters in the existing literature. The secondary outcome measure was any revision procedure for stiffness.

RESULTS: After application of inclusion and exclusion criteria to 522 cases of RCR, there were 432 cases (390 individual patients) available for outcome analysis with an average follow-up of 34.9 months (range, 6 months to 9.25 years). There were only 12 cases (2.8%) of significant postoperative stiffness. All of them required additional operative intervention for stiffness, and all but two patients had at least one risk factor for stiffness. Stiffness rates were 4 of 291 (1.4%) for full-thickness tears and 8 of 141 (5.7%) for partial-thickness tears (P = 0.0149).

CONCLUSION: This study, the largest single cohort to date analyzing BCIs in RCR, found a low incidence of significant postoperative stiffness in cases associated with the use of the implant. Stiffness rates were markedly higher for repairs of partial-thickness tears. To further improve understanding of postoperative stiffness after RCR with BCI, better definitions and prospective comparative studies across larger groups are needed.

LEVEL OF EVIDENCE: Level IV, retrospective cohort with no comparison group.},
}

Humans
Retrospective Studies
*Rotator Cuff Injuries/surgery
*Collagen
Male
Female
Middle Aged
Aged
Range of Motion, Articular
*Postoperative Complications/epidemiology/etiology
*Prostheses and Implants
Adult
Rotator Cuff/surgery
Reoperation/statistics & numerical data

@article {pmid41061070,
year = {2025},
author = {Huang, K and Fu, P and Zhu, H and Feng, J and Zhang, L and Wang, B and Lu, Y and Zhang, D and Yao, M and Chen, L and Ying, Y and Chen, J and Li, X and Wu, Y and Xiong, W and Li, J and Wu, Y and Sun, J and Zhang, H and Lin, L},
title = {High-speed photoacoustic and ultrasonic computed tomography of the breast tumor for early diagnosis with enhanced accuracy.},
journal = {Science advances},
volume = {11},
number = {41},
pages = {eadz2046},
pmid = {41061070},
issn = {2375-2548},
mesh = {Humans ; *Breast Neoplasms/diagnostic imaging/diagnosis ; Female ; *Photoacoustic Techniques/methods ; *Tomography, X-Ray Computed/methods ; *Early Detection of Cancer/methods ; Middle Aged ; Adult ; Aged ; Ultrasonography, Mammary/methods ; },
abstract = {We have developed a high-speed dual-modal imaging system (HDMI), designed to concurrently reveal anatomical and hematogenous details of the human breast within seconds. Through innovative system design and technical advancements, HDMI integrates large-view photoacoustic and ultrasonic computed tomography with standardized scanning and batch data processing for computer-aided diagnosis. It achieves dual-modal imaging at a 10-hertz frame rate and completes a whole-breast scan in 12 seconds, providing penetration up to 5 centimeters in vivo. In a clinical study involving 170 patients with 186 breast tumors, we developed a diagnostic model leveraging combined photoacoustic and ultrasound features. In a triple-blinded comparison using pathological diagnosis as the ground truth, HDMI significantly improved diagnostic specificity from 22.5 to 75.0% compared to clinical ultrasonography. This technology shows strong potential for early breast tumor diagnosis, offering enhanced accuracy without the need for ionizing radiation, exogenous contrast agents, pain, invasiveness, operator dependence, or extended examination times.},
}

Humans
*Breast Neoplasms/diagnostic imaging/diagnosis
Female
*Photoacoustic Techniques/methods
*Tomography, X-Ray Computed/methods
*Early Detection of Cancer/methods
Middle Aged
Adult
Aged
Ultrasonography, Mammary/methods

@article {pmid41060851,
year = {2025},
author = {Nguyen, MD and Do, T and Tran, XT and Nguyen, QT and Lin, CT},
title = {Edge AI-Brain-Computer Interfaces System: A Survey.},
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.2025.3618688},
pmid = {41060851},
issn = {1558-0210},
abstract = {Edge artificial intelligence (Edge AI) has emerged as a transformative paradigm for enhancing the performance, portability, and autonomy of brain-computer interface (BCI) systems. By integrating advanced AI capabilities directly into electroencephalography (EEG)-based devices, Edge AI enables real-time signal processing, reduces dependence on external computational resources, and improves data privacy. However, deploying AI on resource-constrained hardware introduces challenges related to computational capacity, power consumption, and system latency. This survey provides a comprehensive examination of Edge AI-enabled BCI systems, covering the full pipeline from EEG hardware specifications and on-device data acquisition to signal preprocessing techniques and lightweight deep learning models optimized for embedded platforms. We review existing frameworks, specialized hardware accelerators, and energy-efficient AI approaches that facilitate real-time BCI processing at the edge. Furthermore, the paper reviews state-of-the-art solutions, examines key technical challenges, and outlines future research directions in hardware-software co-design and application development. This work aims to serve as a reference for researchers and practitioners seeking to design efficient, portable, and practical Edge AI-powered BCI systems.},
}

@article {pmid41060788,
year = {2025},
author = {Rosenthal, IA and Bashford, L and Bjanes, D and Pejsa, K and Lee, B and Liu, C and Andersen, RA},
title = {Visual context affects the perceived timing of tactile sensations elicited through intra-cortical microstimulation: a case study of two participants.},
journal = {Journal of neurophysiology},
volume = {},
number = {},
pages = {},
doi = {10.1152/jn.00518.2024},
pmid = {41060788},
issn = {1522-1598},
support = {N/A//T&C Chen Brain-Interface Center/ ; N/A//James G. Boswell Foundation (Boswell Foundation)/ ; U01NS123127//HHS | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; T32 NS105595/NS/NINDS NIH HHS/United States ; },
abstract = {Intra-cortical microstimulation (ICMS) is a technique to provide tactile sensations for a somatosensory brain-machine interface (BMI). A viable BMI must function within the rich, multisensory environment of the real world, but how ICMS is integrated with other sensory modalities is poorly understood. To investigate how ICMS percepts are integrated with visual information, ICMS and visual stimuli were delivered at varying times relative to one another. Both visual context and ICMS current amplitude were found to bias the qualitative experience of ICMS. In two tetraplegic participants, ICMS and visual stimuli were more likely to be experienced as occurring simultaneously in a realistic visual condition compared to an abstract one, demonstrating an effect of visual context on the temporal binding window. The peak of the temporal binding window varied but was consistently offset from zero, suggesting that multisensory integration with ICMS can suffer from temporal misalignment. Recordings from primary somatosensory cortex (S1) during catch trials where visual stimuli were delivered without ICMS demonstrated that S1 represents visual information related to ICMS across visual contexts. This study was a part of a clinical trial (NCT01964261).},
}

@article {pmid41060749,
year = {2025},
author = {Ji, J and Luo, H and Su, J and Wang, S and Chen, X and Song, J},
title = {Multisensory electronic skin with decoupled pressure-temperature-sensing capabilities for similar object recognition.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {41},
pages = {e2519693122},
doi = {10.1073/pnas.2519693122},
pmid = {41060749},
issn = {1091-6490},
support = {2022YFC2401901//MOST | National Key Research and Development Program of China (NKPs)/ ; 12225209//MOST | National Natural Science Foundation of China (NSFC)/ ; 12321002//MOST | National Natural Science Foundation of China (NSFC)/ ; U21A20502//MOST | National Natural Science Foundation of China (NSFC)/ ; Smart Grippers for Soft Robotics (SGSR) Programme under the National Research Foundation Prime Min//Prime Minister's Office Singapore (PMO)/ ; },
mesh = {Humans ; Pressure ; Touch/physiology ; Temperature ; Robotics ; Skin ; *Touch Perception/physiology ; *Wearable Electronic Devices ; *Thermosensing/physiology ; },
abstract = {Multisensory electronic skin (e-skin), which mimics the tactile capabilities of human skin, is pivotal in equipping robots with intelligent perceptual functions. Despite numerous advances in multifunctional perceptions, e-skin with combined mechano- and thermosensation capabilities for accurately recognizing objects with similar characteristics is still challenging. Here, we report a multisensory e-skin with a skin-like multilayer construction for smart perceptions, which features the patterned protrusion texture mimicking the skin texture to enhance the pressure-sensing sensitivity, the temperature-sensing component mimicking the thermoreceptors, the pressure-sensing component mimicking the mechanoreceptors, and the heater mimicking the body heat source. This multisensory e-skin exhibits excellent decoupled sensing performances of pressure and temperature, enabling the development of a haptic perception system for evaluating some discernible characteristics (e.g., shape and size) and experience-driven features (e.g., modulus and thermal conductivity) of objects through a simple grasp. Demonstrations of accurate recognition and automatic classification of various objects even with extremely similar surface features highlight the significant potential of this multisensory e-skin in applications such as intelligent soft robotics, prosthetics, and other related fields.},
}

Humans
Pressure
Touch/physiology
Temperature
Robotics
Skin
*Touch Perception/physiology
*Wearable Electronic Devices
*Thermosensing/physiology

@article {pmid41059626,
year = {2025},
author = {Meng, W and Hou, F and Chen, K and Ma, L and Liu, Q},
title = {Visually-Inspired Multimodal Iterative Attentional Network for High-Precision EEG-Eye-Movement Emotion Recognition.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2550072},
doi = {10.1142/S0129065725500728},
pmid = {41059626},
issn = {1793-6462},
abstract = {Advancements in artificial intelligence have propelled affective computing toward unprecedented accuracy and real-world impact. By leveraging the unique strengths of brain signals and ocular dynamics, we introduce a novel multimodal framework that integrates EEG and eye-movement (EM) features synergistically to achieve more reliable emotion recognition. First, our EEG Feature Encoder (EFE) uses a convolutional architecture inspired by the human visual cortex's eccentricity-receptive-field mapping, enabling the extraction of highly discriminative neural patterns. Second, our EM Feature Encoder (EMFE) employs a Kolmogorov-Arnold Network (KAN) to overcome the sparse sampling and dimensional mismatch inherent in EM data; through a tailored multilayer design and interpolation alignment, it generates rich, modality-compatible representations. Finally, the core Multimodal Iterative Attentional Feature Fusion (MIAFF) module unites these streams: alternating global and local attention via a Hierarchical Channel Attention Module (HCAM) to iteratively refine and integrate features. Comprehensive evaluations on SEED (3-class) and SEED-IV (4-class) benchmarks show that our method reaches leading-edge accuracy. However, our experiments are limited by small homogeneous datasets, untested cross-cultural robustness, and potential degradation in noisy or edge-deployment settings. Nevertheless, this work not only underscores the power of biomimetic encoding and iterative attention but also paves the way for next-generation brain-computer interface applications in affective health, adaptive gaming, and beyond.},
}

@article {pmid41059099,
year = {2025},
author = {Zhang, C and Liu, Y and Wu, X},
title = {TFANet: a temporal fusion attention neural network for motor imagery decoding.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1635588},
pmid = {41059099},
issn = {1662-4548},
abstract = {INTRODUCTION: In the field of brain-computer interfaces (BCI), motor imagery (MI) classification is a critically important task, with the primary objective of decoding an individual's MI intentions from electroencephalogram (EEG) signals. However, MI decoding faces significant challenges, primarily due to the inherent complex temporal dependencies of EEG signals.

METHODS: This paper proposes a temporal fusion attention network (TFANet), which aims to improve the decoding performance of MI tasks by accurately modeling the temporal dependencies in EEG signals. TFANet introduces a multi-scale temporal self-attention (MSTSA) mechanism that captures temporal variation in EEG signals across different time scales, enabling the model to capture both local and global features. Moreover, the model adaptively adjusts the channel weights through a channel attention module, allowing it to focus on key signals related to motor imagery. This further enhances the utilization of temporal features. Moreover, by integrating the temporal depthwise separable convolution fusion network (TDSCFN) module, TFANet reduces computational burden while enhancing the ability to capture temporal patterns.

RESULTS: The proposed method achieves a within-subject classification accuracy of 84.92% and 88.41% on the BCIC-IV-2a and BCIC-IV-2b datasets, respectively. Furthermore, using a transfer learning approach on the BCIC-IV-2a dataset, a cross-subject classification accuracy of 77.2% is attained.

CONCLUSION: These results demonstrate that TFANet is an effective approach for decoding MI tasks with complex temporal dependencies.},
}

@article {pmid41058890,
year = {2025},
author = {Benachour, A and Medvedev, V and Zinchenko, O},
title = {Mouse-tracking as a tool for investigating strategic behavior in Public Goods Game: an experimental pilot study.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1635677},
pmid = {41058890},
issn = {1664-1078},
abstract = {INTRODUCTION: Recent research has demonstrated the potential of utilizing mouse-tracking as a viable alternative method for examining attention-related attributes within the context of a multifaceted activity.

METHODS: In this study, a mouse-tracking technique was utilized to gather data from individuals who were involved in an online format of the Public Goods Game.

RESULTS: It was observed that participants exhibited distinct approaches to acquiring information while formulating decisions to propose high, moderate, or low offers. The mouse-tracking algorithm effectively distinguished between various types of offers made toward group funding, as evidenced by the measured distance of the cursor.

DISCUSSION: These findings suggest that mouse-tracking is a valuable tool for capturing decision-making processes and differentiating behavioral patterns in economic game contexts, offering insights into attention and choice mechanisms.},
}

@article {pmid41056741,
year = {2025},
author = {Yin, Y and Zhang, Y and Xu, S},
title = {The influence of money priming on conformity consumption: The distinct roles of self-sufficiency and self-control.},
journal = {Acta psychologica},
volume = {260},
number = {},
pages = {105682},
doi = {10.1016/j.actpsy.2025.105682},
pmid = {41056741},
issn = {1873-6297},
abstract = {Despite the pervasive role of money in society and the known psychological effects of money priming, research into its influence on consumer choices, especially regarding conformity behavior in consumption, remains limited. This study examines the impact of money priming on individual conformity behaviors within the context of Chinese consumption through three behavioral studies. Study 1 revealed that priming with money concepts reduces the tendency to conform. Study 2 investigated how feelings of monetary abundance and deprivation, elicited by money priming, affect conformity in consumption. The findings showed that a perceived sense of monetary abundance decreases conformity in consumption, whereas a sense of deprivation increases it. While product types did affect conformity consumption, they did not significantly interact with monetary primes. Study 3 explored the mediating roles of self-sufficiency and self-control, confirming that monetary abundance decreases conformity by enhancing self-sufficiency, and monetary deprivation increases conformity by diminishing self-control. These results suggest that money priming can trigger distinct feelings of abundance and deprivation, each having differential effects on conformity consumption. Understanding these effects can enable marketers to tailor strategies for personalized marketing or group purchasing initiatives, effectively addressing different market segments.},
}

@article {pmid41055454,
year = {2025},
author = {Xiang, Y and He, X and Cheng, T and Zhu, W and Pang, J and Cao, Y and Wu, M and Pei, R and Cao, Y},
title = {A Zwitterionic Conductive Hydrogel Interface for Enhanced Electrocorticography Signal Fidelity via High Conductivity, Antifouling, and Brain-Matched Mechanics.},
journal = {Biomacromolecules},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.biomac.5c01412},
pmid = {41055454},
issn = {1526-4602},
abstract = {Electrocorticography (ECoG) holds considerable promise for neural signal monitoring with high spatiotemporal resolution. However, conventional rigid ECoG electrodes are often hampered by poor mechanical compliance and insufficient resistance to biofouling, leading to high interfacial impedance and compromised signal quality. While integrating conductive hydrogels into ECoG interface offers a potential solution, concurrently achieving high conductivity, mechanical compatibility with brain tissue, biosafety, and robust antifouling remains a significant challenge. This study introduces SPP@NaCl, a novel zwitterionic conductive hydrogel synthesized by doping a poly(sulfobetaine methacrylate) (pSB) hydrogel matrix with poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) and employing NaCl as a Lewis acid to induce phase separation, thereby promoting an interconnected PEDOT network. The resultant SPP@NaCl hydrogel exhibits a compelling combination of properties: high electrical conductivity (∼9 S·m[-][1]), a low Young's modulus (1.74 kPa) that closely matches brain tissue, excellent conformability, and markedly reduced protein adsorption attributable to its zwitterionic structure. When integrated with commercial ECoG electrodes, the optimized SPP@NaCl-8 hydrogel dramatically lowers interfacial impedance. The resulting Au-SPP@NaCl electrodes enabled high-fidelity, real-time monitoring of cortical epileptiform discharges in a rat seizure model and demonstrated stable, long-term neural signal acquisition in anesthetized healthy rats. This work presents a new strategy for constructing ECoG interfaces that simultaneously deliver high conductivity, mechanical compliance, biosafety, and antifouling capabilities, highlighting the significant potential of these hydrogel-integrated ECoG electrodes for advanced brain-computer interface applications.},
}

@article {pmid41054887,
year = {2025},
author = {Ye, Y and Chen, S and Zhang, Y and Zhang, T and Liao, T and Ren, Z and Chen, W and Hu, W},
title = {Mechano-Locking Strategy for Broad-Spectrum SARS-CoV-2 Neutralization.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {},
number = {},
pages = {e05582},
doi = {10.1002/smll.202505582},
pmid = {41054887},
issn = {1613-6829},
support = {T2394511//National Science Foundation of China/ ; T2394510//National Science Foundation of China/ ; 92359303//National Science Foundation of China/ ; 92269101//National Science Foundation of China/ ; LY23A020002//Natural Science Foundation of Zhejiang Province/ ; },
abstract = {Viral entry into host cells is typically initiated by interactions between viral surface proteins and host cell receptors. Conventional neutralization strategies aim to disrupt these interactions but often lose effectiveness against rapidly mutating viral strains. This challenge extends beyond SARS-CoV-2 to other viruses such as HIV and influenza. To overcome this limitation, a novel mechano-locking strategy is proposed, using SARS-CoV-2 as a model system, in which bispecific antibodies (bsAbs) lock the spike protein in its prefusion conformation by preventing force-induced conformational changes. These bsAbs demonstrate broad-spectrum neutralization efficacy against multiple SARS-CoV-2 variants in pseudoviral assays. Single-molecule magnetic tweezers experiments further reveal that these bsAbs significantly raise the mechanical force threshold required for S1-S2 dissociation, thereby enhancing spike protein mechano-stability. This stabilization mechanism offers a mutation-resistant approach to neutralization and introduces a new design paradigm for antiviral therapeutics. These findings establish a mechanistically driven framework for developing biomechanically enhanced strategies potentially applicable to a wide range of mechanically activated enveloped viruses.},
}

@article {pmid41052978,
year = {2025},
author = {Liang, R and Fang, T and Wang, L and Ren, J and Meng, L and Zhao, M and Zheng, C and Fan, Q and Chen, Y and Yang, J and Ming, D},
title = {Multi-connectomics underpin emotional dysfunction in mouse exposed to simulated space composite environment.},
journal = {Translational psychiatry},
volume = {15},
number = {1},
pages = {359},
pmid = {41052978},
issn = {2158-3188},
mesh = {Animals ; Mice ; *Connectome ; Male ; Magnetic Resonance Imaging ; *Prefrontal Cortex/diagnostic imaging/physiopathology ; *Emotions/physiology ; Mice, Inbred C57BL ; Space Flight ; Behavior, Animal/physiology ; Gray Matter/diagnostic imaging/pathology/physiopathology ; *Space Simulation ; *Brain/diagnostic imaging/physiopathology ; Nerve Net/physiopathology/diagnostic imaging ; },
abstract = {Long-duration space exploration, including missions to the Moon and Mars, demands strategies to preserve astronauts' emotional well-being for optimal performance. This study combines behavioral phenotyping, multimodal MRI, in vivo calcium imaging, and brain-wide genomics to bridge macroscopic brain function with mesoscopic neural activity and microscopic genetic processes, providing a dynamic characterization of the mouse connectome under simulated spaceflight conditions. We observed a reduction in gray matter volume, particularly in the prefrontal cortex, with prolonged exposure. Simulated space composite environment (SSCE) disrupted multi-scale functional connectivity and altered the macro-organizational functional gradient, reversing the relationship between brain function and emotional behaviors. Neural activity in the medial prefrontal cortex demonstrated exposure-time-dependent changes across emotional tasks, while genetic analyses linked SSCE-induced alterations in functional profiles to synaptic function and ion channel activity. Our findings reveal how extreme environments impact emotional behaviors, brain networks, and neural activity, offering insights for interventions to maintain brain integrity during extended space missions.},
}

Animals
Mice
*Connectome
Male
Magnetic Resonance Imaging
*Prefrontal Cortex/diagnostic imaging/physiopathology
*Emotions/physiology
Mice, Inbred C57BL
Space Flight
Behavior, Animal/physiology
Gray Matter/diagnostic imaging/pathology/physiopathology
*Space Simulation
*Brain/diagnostic imaging/physiopathology
Nerve Net/physiopathology/diagnostic imaging

@article {pmid41052270,
year = {2025},
author = {Lu, Y and Xiong, T and Liu, Y and Zhou, H and Xie, B and Guo, G and Pan, C and Ma, W and Yu, P},
title = {Gate Capacitance-Dependent Neuromorphic Functions of Organic Electrochemical Transistors.},
journal = {The journal of physical chemistry letters},
volume = {},
number = {},
pages = {10678-10684},
doi = {10.1021/acs.jpclett.5c02510},
pmid = {41052270},
issn = {1948-7185},
abstract = {Neuromorphic functions of organic electrochemical transistors (OECTs) have attracted enormous research attention due to their promising application in the field of brain-mimicking computing and brain-computer interfaces. However, the essential role of gate electrodes in the neuromorphic functions of these synaptic transistors remains unclear. Herein, we systematically investigated the influence of gate electrodes on the neuromorphic functions of synaptic OECTs by rationally choosing four kinds of typical gate electrodes: bare glass carbon electrode (Bare-GCE), carbon nanotube-modified GCE (CNT-GCE), PEDOT:PSS modified GCE (PEDOT:PSS-GCE), and Ag/AgCl electrode. Evaluations of the neuromorphic functions indicated that gate capacitance controlled the performance of synaptic OECTs by tuning the electrical field distribution and doping kinetics in the ionic circuits. This systematic exploration of the gate electrode influences on the OECTs offers rational guidance for the structural design of synaptic OECTs.},
}

@article {pmid41052170,
year = {2025},
author = {Zhang, M and Zhao, S and Xie, L and Liu, T and Yao, D and Yin, E},
title = {Self-Supervised Contrastive Pre-Training for EEG-Based Recognition via Cross Device Representation Consistency.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3613730},
pmid = {41052170},
issn = {1558-2531},
abstract = {Electroencephalography (EEG) has emerged as a powerful tool for modeling human brain states. However, the widespread adoption of EEG-based recognition systems is hindered by low signal-to-noise ratios and the scarcity of labeled data. While existing studies often tackle these challenges in isolation, we propose a novel Cross-Device Representation Consistency (CDRC) pretraining paradigm that addresses both issues simultaneously. CDRC leverages self-supervised signals derived from representation distances and is trained through contrastive estimation. Specifically, our approach employs a transformer based dual-branch single-view embedding prediction task, combining with a contrastive feature alignment module to extract robust and discriminative representations. We first evaluate the CDRC model on a low signal-to-noise ratio emotion classification task involving wearable dry electrodes. Furthermore, we extend CDRC to a multimodal fusion setting to address a cross-device vigilance regression task involving heterogeneous physiological modalities. Extensive experiments on the PaDWEED and SEED-VIG datasets demonstrate that CDRC achieves performance comparable to fully supervised methods and reaches the stat-of-the-art results of existing self-supervised methods, setting a new benchmark in this field. Notably, its strong performance on subject-independent tasks highlights its effectiveness in mitigating subject variability. These results underscore the potential of CDRC to significantly enhance the practicality and scalability of EEG-based recognition systems, marking a meaningful step toward real-world brain-computer interfaces.},
}

@article {pmid41044400,
year = {2025},
author = {Serafini, ERDS and Guerrero-Mendez, CD and Blanco-Diaz, CF and da Silva Fiorin, F and de Albuquerque, TS and A Dantas, AFO and Delisle-Rodriguez, D and do Espírito-Santo, CC},
title = {Cortical modulation through robotic gait training with motor imagery brain-computer interface enhances bladder function in individuals with spinal cord injury.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {34633},
pmid = {41044400},
issn = {2045-2322},
mesh = {Humans ; *Spinal Cord Injuries/physiopathology/rehabilitation/complications ; Male ; *Brain-Computer Interfaces ; Adult ; *Gait/physiology ; Middle Aged ; *Robotics/methods ; *Urinary Bladder, Neurogenic/physiopathology/etiology/rehabilitation/therapy ; *Urinary Bladder/physiopathology ; Electroencephalography ; *Imagery, Psychotherapy/methods ; Neurofeedback ; },
abstract = {Neurogenic bladder (NB) dysfunction in individuals with complete spinal cord injury (SCI) is a condition that significantly affects quality of life. Despite the prevalence of interventions, there is a substantial gap in effective treatments for this dysfunction. This study proposes robotic-assisted gait training combined with motor imagery (MI)-based brain-computer interface (BCI) to induce improved cortical modulation, and consequently improve bladder function in patients with SCI. The study involved seven men with complete and chronic SCI in a protocol comprising 24 sessions of robotic-assisted walking with BCI and MI. This regimen was designed to teach both mu (µ, 8-12 Hz) and beta (β, 15-20 Hz) modulation through MI practices using multi-channel EEG neurofeedback (NFB), focusing on sensorimotor rhythm (SMR) activation. Clinical outcomes were measured using the neurogenic bladder symptom score (NBSS), which revealed substantial improvements in bladder control among participants. EEG analysis confirmed a significant correlation between modulation of µ and β rhythms with decreased NBSS scores. Our findings support that robotic-assisted gait training combined with MI-based BCI effectively modulates with more precision the cortical µ and β rhythms and improves NB dysfunction in SCI individuals.},
}

Humans
*Spinal Cord Injuries/physiopathology/rehabilitation/complications
Male
*Brain-Computer Interfaces
Adult
*Gait/physiology
Middle Aged
*Robotics/methods
*Urinary Bladder, Neurogenic/physiopathology/etiology/rehabilitation/therapy
*Urinary Bladder/physiopathology
Electroencephalography
*Imagery, Psychotherapy/methods
Neurofeedback

@article {pmid41044308,
year = {2025},
author = {Chen, Z and Cao, Y and Fu, Q and Hou, L},
title = {Hierarchical attention enhanced deep learning achieves high precision motor imagery classification in brain computer interfaces.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {34555},
pmid = {41044308},
issn = {2045-2322},
mesh = {*Brain-Computer Interfaces ; Humans ; *Deep Learning ; Electroencephalography/methods ; *Attention/physiology ; Male ; Adult ; *Brain/physiology ; Female ; *Imagination/physiology ; },
abstract = {Motor imagery-based Brain-Computer Interfaces (BCIs) hold transformative potential for individuals with severe motor impairments, yet their clinical deployment remains constrained by the inherent complexity of electroencephalographic (EEG) signal decoding. This study presents a systematic investigation of hierarchical deep learning architectures for motor imagery classification, introducing a novel attention-enhanced convolutional-recurrent framework that achieves state-of-the-art accuracy of 97.2477% on a custom four-class motor imagery dataset comprising 4,320 trials from 15 participants. By synergistically integrating spatial feature extraction through convolutional layers, temporal dynamics modeling via long short-term memory networks, and selective attention mechanisms for adaptive feature weighting, our approach significantly outperforms conventional methods while providing interpretable insights into the spatiotemporal signatures of motor imagery. Beyond demonstrating competitive performance, this work elucidates the critical role of attention mechanisms in capturing task-relevant neural patterns amidst the high-dimensional, non-stationary nature of EEG signals. Our findings demonstrate that biomimetic computational architectures that mirror the brain's own selective processing strategies can substantially enhance BCI reliability, offering immediate implications for neurorehabilitation technologies and broader applications in restorative neuroscience. Our code is available at https://github.com/Laboratory-EverythingAI/-EEG_Classification .},
}

*Brain-Computer Interfaces
Humans
*Deep Learning
Electroencephalography/methods
*Attention/physiology
Male
Adult
*Brain/physiology
Female
*Imagination/physiology

@article {pmid41043460,
year = {2025},
author = {Rasheed, S and Bennett, J and Yoo, PE and Burkitt, AN and Grayden, DB},
title = {Decoding saccadic eye movements from brain signals using an endovascular neural interface.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae0f52},
pmid = {41043460},
issn = {1741-2552},
abstract = {An Oculomotor Brain-Computer Interface (BCI) records neural activity from brain regions involved in planning eye movements and translates this activity into control commands. While previous successful studies have relied on invasive implants in non-human primates or electrooculography (EOG) artefacts in human electroencephalogram (EEG) data, this study aimed to demonstrate the feasibility of an oculomotor BCI using a minimally invasive endovascular StentrodeTM device implanted near the supplementary motor area of a patient with Amyotrophic Lateral Sclerosis (ALS). Approach. One participant performed self-paced visually-guided and free-viewing saccade tasks in four directions (left, right, up, down) while endovascular EEG and eye gaze recordings were collected. Visually-guided saccades were cued with visual stimuli, whereas free-viewing saccades were self-directed without explicit cues. Brain signals were pre-processed to remove cardiac artefacts, downsampled, and classified using a Random Forest algorithm. For saccade onset classification (fixation vs. saccade), features in time and frequency domains were extracted after xDAWN denoising, while for saccade direction classification, the downsampled time series were classified directly without explicit feature extraction. Main results. The neural responses of visually-guided saccades overlapped with cue-evoked potentials, while free-viewing saccades exhibited saccade-related potentials that began shortly before eye movement, peaked approximately 50 ms after saccade onset, and persisted for around 200 ms. In the frequency domain, these responses appeared as a low-frequency synchronisation below 15 Hz. Saccade onset classification was robust, achieving mean area under the receiver operating characteristic curve (AUC) scores of 0.88 within sessions and 0.86 across sessions. Saccade direction decoding yielded within-session AUC scores of 0.67 for four-class decoding and up to 0.75 for the best performing binary comparisons (left vs. up and left vs. down). Significance. This proof-of-concept study demonstrates the feasibility of an endovascular oculomotor BCI in a patient with ALS, establishing a foundation for future oculomotor BCI studies in human subjects.},
}

@article {pmid41042834,
year = {2025},
author = {Guo, M and Zhang, J and Liu, H and Bai, Y and Ni, G},
title = {Signal-to-Noise Ratio Effects Frontoparietal Network Lateralization: Electroencephalogram Evidence in Underwater Auditory Target Recognition.},
journal = {Annals of the New York Academy of Sciences},
volume = {},
number = {},
pages = {},
doi = {10.1111/nyas.70081},
pmid = {41042834},
issn = {1749-6632},
support = {2023YFF1203500//National Key Research and Development Program of China/ ; },
abstract = {Accurately recognizing auditory targets within background interference remains challenging at a low signal-to-noise ratio (SNR). Using an oddball paradigm, this electroencephalogram study investigated the impact of SNR (0, -10, and -20 dB) on psychophysiological processes underlying underwater auditory target recognition in twenty normal-hearing participants. Reduced SNR impaired the N1-P2 component and led to P300 variations, with delayed latencies (N1: p = 0.0355; P300: p = 0.0075) and reduced amplitudes (P2: p = 0.0075; P300: p = 0.0277), indicating increased attentional demands. Microstate analysis highlighted 300-400 ms frontoparietal activation for attention orientation and sensory information integration. Reduced accuracy correlates with alpha-band activity and phase variations over frontoparietal areas (event-related spectral perturbation [ERSP]: p = 0.0388; inter-trial coherence [ITC]: p = 0.0059), implying suppression of task-relevant processing. Gamma-band activity and phase at lower SNR levels suggest changes in the parietal network's function (ERSP: p = 0.0183; ITC: p = 0.0113), influencing reaction times due to increased integration difficulty. Right-lateralized alpha- and gamma-band network shifts support the functional advantages of the right hemisphere in noise, with enhanced local efficiency (frontal alpha: p = 0.0100; parietal-occipital gamma: p = 0.0116). These findings provide insights into the psychophysiological mechanisms underlying auditory target recognition in noise.},
}

@article {pmid41042451,
year = {2025},
author = {Huang, Y and Ke, Y and Li, J and Liu, S and Ming, D},
title = {Frontal Theta Modulation in Sequential Working Memory: the Impact of Spatial Regularity and Scenario.},
journal = {Brain topography},
volume = {38},
number = {6},
pages = {74},
pmid = {41042451},
issn = {1573-6792},
support = {No. 2021YFF1200603//the National Key Research and Development Program of China/ ; No. 62276184 and 61806141//the National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Memory, Short-Term/physiology ; *Theta Rhythm/physiology ; Male ; Female ; Young Adult ; Adult ; Electroencephalography ; *Frontal Lobe/physiology ; *Space Perception/physiology ; },
abstract = {Humans can quickly extract spatial regularities from sequences to reduce working memory (WM) load, yet the electrophysiological mechanisms remain unclear. Although previous studies have underscored the role of frontal-midline theta (FM-theta) in sequential WM processing, whether and how spatial regularity modulates FM-theta is unknown. To investigate this, we varied the spatial relation between successive items-more repetitions of the same displacement yielded fewer unique chunks and thus higher regularity-while sequence length stayed fixed. Participants were asked to encode, maintain and reproduce the temporal order of sequences utilizing their spatial structures. To enhance ecological validity, we further embedded the task in a complex scenario that included meaningful contexts, dispersed layouts, and variable stimulus sizes. Behavioral data revealed that sequences with higher regularity and the simple scenario yielded higher accuracy, confirming successful manipulations of regularity and scenario difficulty. The overall temporal dynamics of EEG data showed prominent theta enhancement and concurrent alpha/beta suppression during encoding and maintenance. Subsequent analyses across the 4-30 Hz and delay period demonstrated that theta power increased while alpha/beta power declined monotonically with sequence complexity. Notably, regularity-modulated alpha power differed in two scenarios. Moreover, the results found that only sequence regularity-not scenario difficulty-modulated fronto-posterior theta connectivity and slowed the FM-theta frequency. In sum, FM-theta, operating through long-range connectivity and frequency modulation, exclusively tracks spatial-regularity demands in sequential WM, while such neural mechanisms remain impervious to variations in scenario difficulty. These findings suggest that FM-theta may serve as a specific neural marker for spatial regularity processing, rather than a general index of task difficulty, thereby offering a concrete target for future neuromodulatory interventions.},
}

Humans
*Memory, Short-Term/physiology
*Theta Rhythm/physiology
Male
Female
Young Adult
Adult
Electroencephalography
*Frontal Lobe/physiology
*Space Perception/physiology

@article {pmid41042091,
year = {2025},
author = {Sato, K and Tanaka, R and Ota, K},
title = {BCI-Mediated Warfare, Psychological Distance, and the Duty to Care.},
journal = {AJOB neuroscience},
volume = {16},
number = {4},
pages = {344-346},
doi = {10.1080/21507740.2025.2557822},
pmid = {41042091},
issn = {2150-7759},
}

@article {pmid41040967,
year = {2025},
author = {Wood, C and Wang, H and Yang, WJ and Xi, Y},
title = {Facing the possibility of consciousness in human brain organoids.},
journal = {Patterns (New York, N.Y.)},
volume = {6},
number = {9},
pages = {101365},
pmid = {41040967},
issn = {2666-3899},
abstract = {Human brain organoids (HBOs) have emerged as transformative models for neurodevelopment and disease, yet ethical concerns persist regarding their potential to develop consciousness. Since 2020, a growing cohort of neuroscientists and philosophers has dismissed these concerns as unscientific, citing limited structural complexity, absence of bodily integration and environmental interaction, and a prevailing neuroscientific consensus against the feasibility of any, or any near-future, emergence of HBO consciousness, thus challenging any suggested revisions of ethical guidelines and safeguards. We argue that this dismissal is premature. Drawing on neuroscientific benchmarks, comparisons to the developing human brain, contemporary theories of consciousness, and principles of natural developmental progression, we question the basis for selectively excluding consciousness from among HBOs' expanding functional repertoire. We caution against enshrining such skepticism into dogma or using it to defer ethical engagement. Instead, we advocate for proactive, ongoing assessment of the moral implications of advancing HBO capabilities.},
}

@article {pmid41040697,
year = {2025},
author = {Chetty, N and Kacker, K and Feldman, AK and Yoo, PE and Bennett, J and Fry, A and Tal, I and Hardy, NF and Ebrahimi, S and Echavarria, C and Sawyer, A and Schone, HR and Harel, NY and Nogueira, RG and Majidi, S and Levy, EI and Kandel, A and Hill, KK and Opie, NL and Lacomis, D and Collinger, JL and Oxley, TJ and Putrino, DF and Weber, DJ},
title = {Signal properties and stability of a chronically implanted endovascular brain computer interface.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.09.19.25335897},
pmid = {41040697},
abstract = {BACKGROUND: Implanted brain-computer interfaces (iBCIs) establish direct communication with the brain and hold the potential to enable people with severe disability to achieve control of digital devices, enabling communication and digital activities of daily living. The ability to access brain signals reliably and continuously over many years post-implantation is crucial for iBCIs to be effective and feasible. This study investigates the signal characteristics and long-term stability of neural activity recorded with a stent-electrode array over 1 year post-implant.

METHODS: We report on five participants with paralysis who were enrolled in an early feasibility clinical trial of an endovascular iBCI (Stentrode; ClinicalTrials.gov, NCT05035823). Each participant was implanted with a 16-channel stent-electrode array, deployed in the superior sagittal sinus to record bilaterally from the primary motor cortices. Neural activity was recorded during home-based sessions while the participants performed a set of standardized tasks. Metrics including motor signal strength during attempted movement, resting state signal features, and electrode impedances were quantified over time.

RESULTS: Motor-related modulation in neural activity was exhibited in the high-frequency bands (30-200 Hz) during attempted movements, with rest and attempted movement states showing sustained differentiation over time. Impedance and resting state band power for most channels did not change significantly over time.

CONCLUSIONS: These findings provide strong evidence that the endovascular BCIs may be suitable for long-term neural signal acquisition in the home environment, demonstrating the ability to record movement-related modulation over one year.},
}

@article {pmid41040692,
year = {2025},
author = {Schone, HR and Yoo, P and Fry, A and Chetty, N and Sawyer, A and Herbers, C and Liu, F and Moon, CH and Hill, K and Majidi, S and Harel, NY and Nogueira, RG and Levy, E and Putrino, DF and Lacomis, D and Oxley, TJ and Weber, DJ and Collinger, JL},
title = {Motor Cortex Coverage Predicts Signal Strength of a Stentrode Endovascular Brain-Computer Interface.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.09.19.25335875},
pmid = {41040692},
abstract = {Brain-computer interfaces (BCIs) are an emerging assistive technology for individuals with motor impairments, enabling the command of digital devices using neural signals. The Stentrode BCI is an implant, positioned within the brain's neurovasculature, that can record movement-related electrocortical activity. Over 5 years, 10 participants (8 amyotrophic lateral sclerosis, 1 primary lateral sclerosis, 1 brainstem stroke) have been implanted with a Stentrode BCI and significant inter-participant variability has been observed in the recorded motor signal strength. This variability warrants a critical investigation to characterize potential predictors of signal strength to promote more successful BCI control in future participants. Therefore, we investigated the relationship between Stentrode BCI motor signal strength and a variety of user-specific factors: (1) clinical status, (2) pre-implant functional activity, (3) peri-implant neuroanatomy, (4) peri-implant neurovasculature, and (5) Stentrode device integrity. Data from 10 implanted participants, including clinical demographics, pre- and post-implant neuroimaging and longitudinal Stentrode BCI motor signal assessments were acquired over a year. Across all potential predictors, the strongest predictor of Stentrode motor signal strength was the degree to which the Stentrode BCI's deployment position overlapped with primary motor cortex (M1). These findings highlight the importance of targeting M1 during device deployment and, more generally, provides a scientific framework for investigating the role of user-specific factors on BCI device outcomes.},
}

@article {pmid41040179,
year = {2025},
author = {Rigotti-Thompson, M and Nason-Tomaszewski, SR and Bechefsky, P and Acosta, A and Hahn, N and Avansino, D and Richards, B and Nicolas, C and Ali, YH and Henderson, JM and Hochberg, LR and AuYong, N and Pandarinath, C},
title = {Preparatory encoding of diverse features of intended movement in the human motor cortex.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.09.24.678356},
pmid = {41040179},
issn = {2692-8205},
abstract = {Over the course of a voluntary movement, motor cortical activity exhibits a transition from preparation to execution, with markedly different activity across these phases. Preparatory activity in particular might be used to improve brain-computer interfaces (BCIs) that harness brain activity to control external assistive devices, for example by anticipating a user's intended movement trajectory for quick and fluid performance. However, to leverage preparatory activity for clinical BCIs, we must first understand which features of upcoming movements are encoded by preparatory activity in humans. In this work, we collected intracortical recordings from 3 research participants in the BrainGate2 clinical trial to investigate whether diverse features of movement, such as direction, curvature, and distance, are encoded by preparatory activity in the human motor cortex. We first show that preparatory activity is tuned to the direction of upcoming movements, and this tuning is largely preserved across movements with different effectors. Further investigation demonstrated this preparatory activity is also informative of initial and endpoint directions of curved movement trajectories, and encodes movement distance and speed independently. Finally, we present an online control paradigm that leverages preparatory activity to predict movements towards intended directions in advance, yielding rapid, self-paced control of a computer cursor by human participants. Altogether, these results demonstrate that preparatory activity in the human motor cortex encodes rich features of upcoming movement, highlighting its potential use for high performance brain-computer interface applications.},
}

@article {pmid41039114,
year = {2025},
author = {},
title = {High-resolution brain-computer interface with electrode scalability and minimally invasive surgery.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {41039114},
issn = {2157-846X},
}

@article {pmid41039113,
year = {2025},
author = {Hettick, M and Ho, E and Poole, AJ and Monge, M and Papageorgiou, D and Takahashi, K and LaMarca, M and Trietsch, D and Reed, K and Murphy, M and Rider, S and Gelman, KR and Byun, YW and Miller, JS and Hanson, T and Tolosa, V and Lee, SH and Bhatia, S and Konrad, PE and Mager, M and Mermel, CH and Rapoport, BI},
title = {Minimally invasive implantation of scalable high-density cortical microelectrode arrays for multimodal neural decoding and stimulation.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {41039113},
issn = {2157-846X},
abstract = {High-bandwidth brain-computer interfaces rely on invasive surgical procedures or brain-penetrating electrodes. Here we describe a cortical 1,024-channel thin-film microelectrode array and we demonstrate its minimally invasive surgical delivery that avoids craniotomy in porcine models and cadavers. We show recording and stimulation from the same electrodes to large portions of the cortical surface, and the reversibility of delivering the implants to multiple functional regions of the brain without damaging the cortical surface. We evaluate the performance of the interface for high-density neural recording and visualizing cortical surface activity at spatial and temporal resolutions and total spatial extents. We demonstrate accurate neural decoding of somatosensory, visual and volitional walking activity, and achieve focal neuromodulation through cortical stimulation at sub-millimetre scales. We report the feasibility of intraoperative use of the device in a five-patient pilot clinical study with anaesthetized and awake neurosurgical patients, characterizing the spatial scales at which sensorimotor activity and speech are represented at the cortical surface. The presented neural interface demonstrates the highly scalable nature of micro-electrocorticography and its utility for next-generation brain-computer interfaces.},
}

@article {pmid41039090,
year = {2025},
author = {Zhou, H and Wang, M and Qi, S and Chen, Q and Lai, J and Wu, Z and Liu, R and Wang, L and Zhou, H and Zhang, S and Hu, S},
title = {Transcranial temporal interference stimulation for treating bipolar disorder with depressive episodes: a feasibility Study.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {41039090},
issn = {1476-5578},
support = {52407261//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Bipolar depression (BD-D) is a significant clinical challenge associated with high disease burden. Transcranial temporal interference stimulation (tTIS), a novel and noninvasive approach for targeting deep brain structures, was investigated for its efficacy and safety in BD-D patients in this trial. Thirty-six patients were recruited for a single-arm, open-label trial, and 25 completed the 5-day intervention consisting of 10 tTIS sessions targeting the left nucleus accumbens. Each session lasted 20 min, with a maximum current intensity of 2 mA and an envelope stimulation frequency of 40 Hz. Significant symptom reductions were observed following treatment, with mean HAMD-17 scores decreasing from 23.36 to 16.16 (p < 0.0001), MADRS scores from 39.12 to 31.28 (p < 0.01), HAMA scores from 19.68 to 15.44 (p < 0.05), and QIDS scores from 13.52to 9.68 (p < 0.001). Eleven participants (44.0%) met improvement criteria and seven (28.0%) achieved response. Cognitive assessments indicated improvements in memory and executive function, and changes in reward-related brain activity correlated positively with symptom reduction. Adverse events were mild, mainly transient scalp discomfort. These findings provide preliminary evidence supporting the efficacy and safety of tTIS for alleviating depressive symptoms and cognitive impairments in BD-D.},
}

@article {pmid40788303,
year = {2025},
author = {Shotbolt, M and Bryant, J and Liang, P and Khizroev, S},
title = {Mechanism and applications of magnetoelectric nanoparticles in cancer therapy.},
journal = {Nanomedicine (London, England)},
volume = {20},
number = {19},
pages = {2469-2481},
pmid = {40788303},
issn = {1748-6963},
mesh = {Humans ; *Neoplasms/drug therapy/therapy ; *Nanoparticles/chemistry/therapeutic use ; Animals ; Drug Delivery Systems/methods ; Nanomedicine/methods ; Magnetic Fields ; *Antineoplastic Agents/therapeutic use/administration & dosage ; },
abstract = {Cancer remains a major clinical challenge, with current therapies often hampered by off-target effects, drug resistance, and incomplete tumor eradication. There is a pressing need for more precise and effective treatment strategies. This review explores the mechanisms and applications of magnetoelectric nanoparticles (MENPs) in cancer therapy. MENPs, typically composed of magnetostrictive and piezoelectric materials in a core-shell structure, generate electric fields in response to magnetic fields, enabling targeted and noninvasive therapeutic actions. The literature search included recent advances in MENP synthesis, optimization of material composition and morphology, and preclinical studies demonstrating their ability to enhance drug delivery, disrupt tumor cell membranes, and induce tumor regression without systemic toxicity. Relevant studies were identified by searching electronic databases, including PubMed, Web of Science, Scopus, and Google Scholar. The search employed a combination of keywords and phrases such as "magnetoelectric nanoparticles," "MENPs," "cancer therapy," "nanomedicine," "core-shell nanoparticles," "magnetostrictive," "piezoelectric," "drug delivery," "magnetic field," "nano-electroporation," and "reactive oxygen species.." MENPs represent a promising option for precision oncology, offering remote control over therapeutic effects and the potential to overcome limitations of conventional treatments. Ongoing research should focus on optimizing MENP design for selectivity and efficacy, as well as advancing their clinical translation for cancer therapy.},
}

Humans
*Neoplasms/drug therapy/therapy
*Nanoparticles/chemistry/therapeutic use
Animals
Drug Delivery Systems/methods
Nanomedicine/methods
Magnetic Fields
*Antineoplastic Agents/therapeutic use/administration & dosage

@article {pmid41038246,
year = {2025},
author = {Xie, H and Xu, H and Xu, K and Yu, C and Yang, W and Yang, C},
title = {Rat Robot Autonomous Border Detection Based on Wearable Sensors.},
journal = {Bioinspiration & biomimetics},
volume = {},
number = {},
pages = {},
doi = {10.1088/1748-3190/ae0ee8},
pmid = {41038246},
issn = {1748-3190},
abstract = {Bio-robots, a novel type of robots created based on brain-machine interface, have shown great potential in search and rescue tasks. However, current research focuses on the bio-robot itself, such as locomotion, localization and navigation, but lacks interactions with the external environment. In this paper, we proposed a new system for rat robot to autonomously explore the border of unknown field out of sight, and then get the boundary map. We invented a wearable backpack, which is an embedded system with laser-ranging sensors, IMU and ultra-wide band (UWB) module, for the rat robot. Based on the wearable system, a classification method for motion states based on random forest (RF) and a navigation algorithm based on finite state machine (FSM) were developed for the autonomous exploration of border and tested in the locomotion experiment. Besides, with the localization and distance data from UWB and laser-ranging sensors, we mapped the distribution of the border, using Ramber-Douglas-Peucker (RDP) algorithm. The results show that the system could effectively navigate the rat robot to explore the field and accurately detect the border. The accuracy of classification reaches 97.86% and the error rate of border detection is 5.90%. This work provides a novel technology that has potential for practical applications such as prospect for minerals and search tasks. .},
}

@article {pmid41038061,
year = {2025},
author = {Wang, X and Li, X and Li, J and Fu, Y and Zhang, D and Peng, Y},
title = {RimeSleepNet: A hybrid deep learning network for s-EEG sleep stage classification.},
journal = {Sleep medicine},
volume = {136},
number = {},
pages = {106835},
doi = {10.1016/j.sleep.2025.106835},
pmid = {41038061},
issn = {1878-5506},
abstract = {Sleep stage classification is essential for sleep research and clinical diagnostics. However, frequency aliasing in sleep electroencephalogram (s-EEG) signals remains a significant challenge, existing methods have yet to effectively address this issue. This study proposes a hybrid deep-learning model, RimeSleepNet, comprising four key components. First, the rime optimization algorithm adaptively tunes variational mode decomposition (VMD) to reduce frequency aliasing by generating intrinsic mode functions (IMFs). Second, a convolutional neural network (CNN) automatically extracts stage-specific features from IMFs. A multi-head self-attention (MHSA) mechanism then dynamically weights these features to prioritize stage-specific patterns, followed by long short-term memory (LSTM) networks that model temporal dynamics for robust classification of NREM, REM, and WAKE stages. Evaluated on the Chengdu People's Hospital and Sleep-EDF datasets, RimeSleepNet achieves the highest F1 scores of 0.94, 0.89, and 0.92 for NREM, REM, and WAKE stages, respectively, with an AUC of 0.92, outperforming baseline models like CNN and LSTM. Cross-dataset validation confirms its robust generalization (Cohen's κ = 0.90), and it reduces validation loss by 53 % compared to LSTM, providing an advanced tool for automated sleep stage analysis in sleep disorder diagnosis and personalized monitoring.},
}

@article {pmid41036535,
year = {2025},
author = {Arif, S and Rehman, MZU and Mushtaq, Z},
title = {Editorial: Advancements in smart diagnostics for understanding neurological behaviors and biosensing applications.},
journal = {Frontiers in computational neuroscience},
volume = {19},
number = {},
pages = {1693327},
doi = {10.3389/fncom.2025.1693327},
pmid = {41036535},
issn = {1662-5188},
}

@article {pmid41035957,
year = {2025},
author = {Wang, W and Liu, Y and Shi, P and Zhang, J and Wang, G and Li, Y and Liu, W and Ming, D},
title = {Altered tactile abnormalities in children with ASD during tactile processing and recognition revealed by dynamic EEG features.},
journal = {Frontiers in psychiatry},
volume = {16},
number = {},
pages = {1611438},
pmid = {41035957},
issn = {1664-0640},
abstract = {INTRODUCTION: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by sensory processing abnormalities, particularly in tactile perception, highlighting the need for objective screening methods beyond current subjective behavioral assessments.

METHODS: This study developed a portable electro-tactile stimulation system with EEG to evaluate tactile processing differences in children with ASD (n=36) versus typically developing controls (n=36).

RESULTS: Revealing significantly reduced ERP amplitudes at key processing stages: P200 at FP2 (F(1,70)=10.82, p=0.0454), N200 at F3 (F(1,70)=58.33, p<0.0001), and P300 at C4 (F(1,70)=45.62, p<0.0001). Topographic analysis identified pronounced group differences (>10ìV) across frontal, central, and parietal regions (F8, FC5/6, CP1/2/5/6, Pz, Oz), with ASD children exhibiting prolonged but less efficient tactile discrimination and compensatory prefrontal activation (FP2 CV: p=0.043). The paradigm demonstrated strong reliability (CV ICC: ASD=0.779, TD=0.729) and achieved 85.2% classification accuracy (AUC=0.91) using ANN, with optimal performance from F8 P300 features (sensitivity=87.5%, specificity=83.7%).

DISCUSSION: These findings provide an objective, efficient (15-minute) screening method that advances understanding of tactile processing abnormalities in ASD and supports the development of physiological biomarkers for early identification, overcoming limitations of questionnaire-based approaches.},
}

@article {pmid41035905,
year = {2025},
author = {Xue, Y and Chen, Y and Wang, F and Zhao, L and Li, T and Gong, A and Nan, W and Fu, Y},
title = {Applications and interrelationships of brain function detection, brain-computer interfaces, and brain stimulation: a comprehensive review.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {161},
pmid = {41035905},
issn = {1871-4080},
abstract = {Brain-Computer Interface (BCI), Brain Function Detection (BFD), and Brain Stimulation (BS) are three pivotal technological domains in neuroscience and neuroengineering. Each plays a critical role in fundamental research, clinical applications, and human-computer interaction paradigms. Despite their distinct developmental pathways and application focuses, these technologies are frequently conflated or ambiguously referenced in both academic discourse and industrial practice, potentially leading to conceptual misinterpretations, suboptimal system designs, and clinical misapplications. Prior literature reviews have predominantly concentrated on BCI as a standalone subject, covering its historical evolution, specific neurophysiological signal modalities, or emergent technological trends. This manuscript's core contribution is critiquing the overuse of "passive BCI" (labeling feedback-absent monitoring as BCI). Through an application-oriented lens, it clarifies boundaries between BCI, BFD, and BS to resolve conceptual confusion. Further, the review interrogates the convergences and divergences among these modalities and critically evaluates the practical feasibility and challenges associated with their integrative deployment in clinical and experimental settings. Ultimately, this work aspires to provide a lucid, systematic, and conceptually coherent framework to support neuroscientific novices, interdisciplinary investigators, and clinical practitioners. By fostering precise comprehension and judicious utilization of BCI, BFD, and BS, it aims to propel their standardized advancement and enhance their translational impact across both research and clinical domains.},
}

@article {pmid41034549,
year = {2025},
author = {Di, S and Luo, N and Shi, W and Yang, Z and Sui, J and Jiang, R and Cui, Y and Du, Z and Zhang, J and Ma, Y and Wang, H and Chu, C and Zhong, Y and Li, W and Lu, Y and Yan, H and Liao, J and Zhang, D and Calhoun, V and Song, M and Jiang, T},
title = {Physical Activity and Depressive Mood Share the Structural Connectivity Between Motor and Reward Networks.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {41034549},
issn = {1995-8218},
abstract = {In various studies, exercise has been revealed to have a positive effect on alleviating depressive symptoms. However, the neural basis behind this phenomenon remains unknown, as well as its underlying biological mechanism. In this study, we used a large neuroimaging cohort [n = 1,027, major depressive disorder (MDD)/healthy controls (HCs) = 492/535] from the UK Biobank to identify structural connectivity (SC) patterns simultaneously linked with physical activity and depression, as well as the biological interpretation. An SC pattern linked with exercise was identified to be both significantly correlated with depressive mood and group discrimination between MDDs and HCs, primarily located between the motor-related regions and reward-related regions. This pattern was associated with multiple neurotransmitter receptors, such as serotonin and GABA receptors, and enriched in pathways like synaptic signaling and the astrocyte cell type. The SC pattern and genetic results were also replicated in another independent MDD dataset (n = 3,496) and present commonalities with bipolar disorder (n = 81). Overall, these findings not only initially identified a reproducible shared SC pattern between physical activity and depressive mood, but also elucidated the underlying biological mechanisms, which enhance our understanding of how exercise helps alleviate depression and may inform the development of novel neuromodulation targets.},
}

@article {pmid41034219,
year = {2025},
author = {Griggs, WS and Norman, SL and Tanter, M and Liu, C and Christopoulos, V and Shapiro, MG and Andersen, RA},
title = {Functional ultrasound neuroimaging reveals mesoscopic organization of saccades in the lateral intraparietal area.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {8752},
pmid = {41034219},
issn = {2041-1723},
support = {F30EY032799//U.S. Department of Health & Human Services | NIH | National Eye Institute (NEI)/ ; T32GM008042//U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)/ ; R01NS123663//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; },
mesh = {Animals ; *Saccades/physiology ; *Parietal Lobe/physiology/diagnostic imaging ; Male ; Macaca mulatta ; Ultrasonography/methods ; *Functional Neuroimaging/methods ; Brain Mapping/methods ; Magnetic Resonance Imaging ; },
abstract = {The lateral intraparietal cortex (LIP), contained within the posterior parietal cortex (PPC), is crucial for transforming spatial information into saccadic eye movements, yet its functional organization for movement direction remains unclear. Here, we used functional ultrasound imaging (fUSI), a technique with high sensitivity, large spatial coverage, and good spatial resolution, to map movement direction encoding across the PPC by recording local changes in cerebral blood volume within PPC as two male monkeys performed memory-guided saccades. Our analysis revealed a heterogeneous organization where small patches of neighboring LIP cortex encoded different directions. These subregions demonstrated consistent tuning across several months to years. A rough topography emerged where anterior LIP represented more contralateral downward movements and posterior LIP represented more contralateral upward movements. These results address two fundamental gaps in our understanding of LIP's functional organization: the neighborhood organization of patches and the stability of these populations across long periods of time. By tracking LIP populations over extended periods, we developed mesoscopic maps of direction specificity previously unattainable with fMRI or electrophysiology methods.},
}

Animals
*Saccades/physiology
*Parietal Lobe/physiology/diagnostic imaging
Male
Macaca mulatta
Ultrasonography/methods
*Functional Neuroimaging/methods
Brain Mapping/methods
Magnetic Resonance Imaging

@article {pmid41034198,
year = {2025},
author = {Singh, A and Thomas, T and Li, J and Hickok, G and Pitkow, X and Tandon, N},
title = {Transfer learning via distributed brain recordings enables reliable speech decoding.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {8749},
pmid = {41034198},
issn = {2041-1723},
mesh = {Humans ; *Brain-Computer Interfaces ; *Speech/physiology ; *Brain/physiology ; Electroencephalography/methods ; Male ; Female ; Adult ; Young Adult ; Middle Aged ; Learning ; },
abstract = {Speech brain-computer interfaces (BCIs) combine neural recordings with large language models to achieve real-time intelligible speech. However, these decoders rely on dense, intact cortical coverage and are challenging to scale across individuals with heterogeneous brain organization. To derive scalable transfer learning strategies for neural speech decoding, we used minimally invasive stereo-electroencephalography recordings in a large cohort performing a demanding speech motor task. A sequence-to-sequence model enabled decoding of variable-length phonemic sequences prior to and during articulation. This enabled development of a cross-subject transfer learning framework to isolate shared latent manifolds while enabling individual model initialization. The group-derived decoder significantly outperformed models trained on individual data alone, enabling decoding robustness despite variable coverage and activation. These results highlight a pathway toward generalizable neural prostheses for speech and language disorders by leveraging large-scale intracranial datasets with distributed spatial sampling and shared task demands.},
}

Humans
*Brain-Computer Interfaces
*Speech/physiology
*Brain/physiology
Electroencephalography/methods
Male
Female
Adult
Young Adult
Middle Aged
Learning

@article {pmid41033466,
year = {2025},
author = {Dong, Z and Xiang, Y and Wang, S},
title = {High - Quality Decoding of RGB Images from the Neuronal Signals of the Pigeon Optic Tectum.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110595},
doi = {10.1016/j.jneumeth.2025.110595},
pmid = {41033466},
issn = {1872-678X},
abstract = {BACKGROUND: Decoding neural activity to reverse-engineer sensory inputs advances understanding of neural encoding and boosts brain-computer interface and visual prosthesis tech. A major challenge is high-quality RGB image reconstruction from natural scenes, which this study tackles using pigeon optic tectum neurons.

NEW METHOD: We built a neural response dataset via microelectrode arrays capturing tectal neurons' ON-OFF responses to RGB images. A modular decoding algorithm, integrating a convolutional encoding network, linear decoder, and image enhancement network, enabled inverse RGB image reconstruction from neural signals.

RESULTS: Experimental results confirmed high-quality RGB image reconstruction by the proposed algorithm. For all test set reconstructions, average metrics were: correlation coefficient (R) of 0.853, structural similarity index (SSIM) of 0.618, peak signal-to-noise ratio (PSNR) of 19.94dB, and feature similarity index (FSIMc) of 0.801. These results confirm accurate recapitulation of both color and contour details of the original images.

In terms of key quantitative metrics, the proposed algorithm achieves a significant improvement over traditional linear reconstruction methods, with the correlation coefficient (R) increased by 12.65%, the structural similarity index (SSIM) increased by 38.92%, the peak signal-to-noise ratio (PSNR) increased by 12.65%, and the feature similarity index (FSIMc) increased by 9.28%.

CONCLUSIONS: This research provides a novel technical pathway for high-quality visual neural decoding, with robust experimental metrics validating its effectiveness. It also offers experimental evidence to support investigations into the information processing mechanisms of the avian visual pathway.},
}

@article {pmid41033328,
year = {2025},
author = {Deng, X and Fan, Z and Dong, W},
title = {MEFD dataset and GCSFormer model : Cross-subject emotion recognition based on multimodal physiological signals.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ae0e28},
pmid = {41033328},
issn = {2057-1976},
abstract = {Cross-subject emotion recognition is an important research direction in the fields of affective computing and brain-computer interfaces, aiming to identify the emotional states of different individuals through physiological signals such as functional near-infrared spectroscopy (fNIRS) and electroencephalogram (EEG). Currently, most EEG-based emotion recognition datasets are unimodal or bimodal, which may overlook the emotional information reflected by other physiological signals of the subjects. In this paper, a multimodal dataset named Multimodal Emotion Four Category Dataset (MEFD) is constructed, which includes EEG, Heart Rate Variability (HRV), Electrooculogram (EOG), and Electrodermal Activity (EDA) data from 34 participants in four emotional states: sadness, happiness, fear, and calm. This will contribute to the development of multimodal emotion recognition research. To address the recognition difficulty caused by individual differences in cross-subject emotion recognition tasks, a classification model named Global Convolution Shifted Window Transformer (GCSFormer) composed of an EEG-Swin Convolution module and an improved Global Adaptive Transformer (GAT) module is proposed. By using a parallel network, the feature discrimination ability and generalization ability are enhanced. The model is applied to classify the EEG data in the self-built MEFD dataset, and the results are compared with those of mainstream methods. The experimental results show that the proposed EEG classification method achieves the best average accuracy of 85.36%, precision of 85.23%, recall of 86.35%, and F1 score of 84.52% in the cross-subject emotion recognition task. The excellent performance of GCSFormer in cross-subject emotion recognition task was verifie.},
}

@article {pmid41032544,
year = {2025},
author = {Ju, J and Zhuang, Y and Yi, C},
title = {An EEG-EMG-based Hybrid Brain-Computer Interface for Decoding Tones in Silent and Audible Speech.},
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.2025.3616276},
pmid = {41032544},
issn = {1558-0210},
abstract = {Speech recognition can be widely applied to support people with language disabilities by enabling them to communicate through brain-computer interfaces (BCIs), thus improving their quality of life. Despite the essential role of tonal variations in conveying semantic meaning, there have been limited studies focusing on the neural signatures of tones and their decoding. This paper systematically investigates the neural signatures of the four tones of Mandarin. It explores the feasibility of tone decoding in both silent and audible speech using a multimodal BCI based on electroencephalography (EEG) and electromyography (EMG). The time-frequency analysis of EEG has revealed significant variations in neural activation patterns across various tones and speech modes. For example, in the silent speech condition, temporal-domain analysis shows significant tone-dependent activation in the frontal lobe (ANOVA p = 0.000, Tone1 vs Tone2: p = 0.000, Tone1 vs Tone4: p = 0.000, Tone2 vs Tone3: p = 0.000, Tone3 vs Tone4: p = 0.001) and in channel F8 (ANOVA p=0.008, Tone1 vs Tone2: p=0.014, Tone2 vs Tone3: p=0.034). Spectral analysis shows significant differences between four tones in event-related spectral perturbation (ERSP) in the central region (p = 0.000) and channel C6 (p = 0.000). EMG analysis identifies a significant tone-related difference in activation of the left buccinator muscle (p = 0.023), and ERSP from the mentalis muscle also shows a marked difference across tones in both speech conditions (p = 0.00). Overall, tone-related neural differences were more pronounced in the audible speech condition than in the silent condition. For tone classification, RLDA and SVM classifiers achieved accuracies of 71.22% and 72.43%, respectively, using EEG temporal features in both speech modes. Additionally, the RLDA classifier with temporal features achieves binary tone classification accuracies of 90.92% (audible tones) and 91.00% (silent tones). The combination of EEG and EMG yields the highest speech modes decoding accuracy of 81.33%. These findings provide a potential strategy for speech restoration in tonal languages and further validate the feasibility of a speech brain-computer interface (BCI) as a clinically effective treatment for individuals with tonal language impairment.},
}

@article {pmid41031916,
year = {2025},
author = {Liu, M and Guo, X and Cao, L and Cui, H and Li, Z and Lin, Y and Yin, Z and Quan, W and Feng, C and Ma, T and Zhao, Z and Yang, L and Yao, L and Zhang, X and Wang, G},
title = {Revolutionizing brain-computer interfaces: Compact and high-speed wireless neural signal acquisition.},
journal = {The Review of scientific instruments},
volume = {96},
number = {10},
pages = {},
doi = {10.1063/5.0287033},
pmid = {41031916},
issn = {1089-7623},
mesh = {*Brain-Computer Interfaces ; *Wireless Technology/instrumentation ; Animals ; *Signal Processing, Computer-Assisted/instrumentation ; Mice ; Electroencephalography/instrumentation ; Signal-To-Noise Ratio ; *Brain/physiology ; Humans ; },
abstract = {A brain-computer interface (BCI) facilitates the connection between the human brain and external devices by decoding neurophysiological signals, thereby enabling seamless interaction between humans and machines. However, existing neural signal acquisition systems often suffer from limited channel counts, low sampling rates, and challenges in miniaturization and wireless bandwidth, which restrict their ability to support large-scale and real-time neural recordings. Given the rapid advancements in BCI technologies and the increasing demand for high-resolution neural data, there is an imperative need for BCI systems that are high-throughput, high-speed, and miniaturized. This paper presents a wireless neural signal acquisition system based on FPGA technology, supporting 1024 channels at 32 kSPS and employing a stacked architecture for compact, low-power wireless transmission. Following the creation of the functional prototype, laboratory electrical performance tests were conducted. The system exhibited a noise voltage of 8.56 μVrms, which is in close proximity to the 6 μVrms specified by the chip. In addition, the system accurately captured weak sine wave inputs in both time and frequency domains, confirming its ability to record weak bioelectrical signals. Subsequent animal experiments involving mice implanted with EEG electrodes demonstrated that the system could reliably acquire brain neural signals in real time. The maximum and minimum values of signal-to-noise ratios among the channels were measured at 28.66 and 30.56 dB, thereby providing additional validation for the system's signal quality and consistency.},
}

*Brain-Computer Interfaces
*Wireless Technology/instrumentation
Animals
*Signal Processing, Computer-Assisted/instrumentation
Mice
Electroencephalography/instrumentation
Signal-To-Noise Ratio
*Brain/physiology
Humans

@article {pmid41031500,
year = {2025},
author = {Sisubalan, N and Vijay, N and Kesika, P and Newbegin, M and Shalini, R and Sivamaruthi, BS and Chaiysut, C},
title = {The Contribution of Wearable Devices and Artificial Intelligence to Promoting Healthy Aging.},
journal = {Current pharmaceutical biotechnology},
volume = {},
number = {},
pages = {},
doi = {10.2174/0113892010390500250911104231},
pmid = {41031500},
issn = {1873-4316},
abstract = {INTRODUCTION: Healthy aging involves consistently maximizing opportunities to maintain and enhance physical and mental well-being, fostering independence, and sustaining a high quality of life. This review examines recent technological innovations aimed at promoting the well-being of older adults. The scope encompasses wearable devices and telemedicine, showcasing their potential to enhance the health and overall well-being of older individuals. The review highlights the crucial role of assistive technologies, including mobility aids, hearing aids, and adaptive home devices, in addressing the specific challenges associated with aging.

METHODS: The relevant literature was collected and selected based on the objective of the study and reviewed.

RESULTS: Digital technologies, including brain-computer interfaces (BCIs), are explored as potential solutions to enhance communication between healthcare providers and aging patients, considering engagement levels and active interaction. Sophisticated BCIs, such as electroencephalograms, electrocorticography, and signal modeling for real-time identification, play a crucial role in event detection, with machine learning algorithms enhancing signal processing for accurate decoding. The exploration of smart wearable systems for health monitoring emerges as a dynamic and promising field in the context of aging.

DISCUSSION: Fitbit® showcases accurate step counting, making it suitable for monitoring physical activity in older adults engaged in slow walking. ActiGraph™ is evaluated for accuracy in monitoring physical activity in older adults, with results indicating reliable concurrence with Fitbit® devices. The study identifies several limitations, including sample size constraints, challenges in keeping pace with technological advancements, and the need for further investigation into the suitability of fitness trackers for individuals with significant mobility impairments.

CONCLUSION: The evolving landscape of wearable technologies, exemplified by Fitbit®, Acti- Graph™, and other interventions, holds substantial promise for reshaping healthcare approaches for the aging population. Addressing the limitations will be crucial as research progresses to ensure the effective and ethical integration of wearables into geriatric care, maximizing their potential benefits.},
}

@article {pmid41028971,
year = {2025},
author = {Korkmaz, I and Tepe, C},
title = {EEG-based motor execution classification of upper and lower extremities using machine learning.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-17},
doi = {10.1080/10255842.2025.2566260},
pmid = {41028971},
issn = {1476-8259},
abstract = {This study classifies upper- and lower-extremity motor execution from electroencephalography (EEG). We compared two feature extractors, statistical features and Common Spatial Patterns (CSP), and four classifiers: K-Nearest Neighbors, Linear Discriminant Analysis (LDA), Multilayer Perceptron, and Support Vector Machine. Metrics were accuracy, F1, precision, and recall. CSP with LDA achieved the best, most consistent performance (72.5% accuracy); statistical features underperformed. We report real-time feasibility benchmarks, post-cue time-window analysis, and significance tests for classifiers. Findings support BCI and neuroprosthesis development, while noting subject variability and dataset specificity. Future work is real-time use, cross-dataset generalization, and hybrid deep learning.},
}

@article {pmid41028569,
year = {2025},
author = {Du, X and Liu, J and Wang, X},
title = {The transformational power of psychedelics: catalysts for creativity, consciousness, and mental health.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {41028569},
issn = {1476-5578},
support = {T2350008//National Natural Science Foundation of China (National Science Foundation of China)/ ; JCYJ20220804182935001//Shenzhen Science and Technology Innovation Commission/ ; },
abstract = {Psychedelics, such as psilocybin, lysergic acid diethylamide (LSD), ketamine, and N,N-dimethyltryptamine (DMT), have captured the attention of scientists, artists, and seekers alike for their profound ability to alter consciousness and inspire creativity. The concept of "creation" encompasses multiple interpretations-ranging from generating novel ideas to fostering personal transformation. This perspective explores how psychedelics interact with the concept of creation, examining their role in enhancing artistic inspiration, facilitating spiritual experiences, and driving therapeutic breakthroughs in mental health treatment. By integrating findings from neurobiological research, clinical applications, and cultural analysis, we offer a holistic view of how psychedelics may catalyze innovative modes of thinking and awaken the mind's creative and transformative potential. As these substances gain prominence as tools for reshaping our understanding of consciousness and psychological healing, their broader integration into society requires careful consideration of legal complexities, ethical responsibilities, and cultural contexts to ensure their use is evidence-based, respectful, and responsibly guided.},
}

@article {pmid41025886,
year = {2025},
author = {Chaudhary, J and Gupta, E and Singh, PK and Yadav, RK and Chaudhary, M and Singh, S},
title = {Designing behavioural change intervention module for tobacco cessation counselling among pregnant tobacco users in India: a methodology paper.},
journal = {Health education research},
volume = {40},
number = {6},
pages = {},
doi = {10.1093/her/cyaf041},
pmid = {41025886},
issn = {1465-3648},
support = {2020-5325//Indian Council of Medical Research, New Delhi/ ; },
mesh = {Humans ; Female ; Pregnancy ; India ; *Counseling/methods ; Prenatal Care/methods ; Adult ; *Tobacco Use Cessation/methods ; *Smoking Cessation/methods ; Tobacco Use ; },
abstract = {Tobacco use has detrimental effects on women's reproductive health and is associated with poor pregnancy outcomes. Antenatal care (ANC) check-ups provide health professionals with a unique opportunity to screen and counsel pregnant tobacco users to quit. Currently, in India, pregnant women are not being screened for tobacco use during antenatal care visits and healthcare providers lack formal training to provide tobacco cessation advice. This article describes the designing and development of a tailored behaviour change intervention (BCI) module for tobacco cessation and its delivery to pregnant women attending antenatal clinics. The BCI module was designed to incorporate the components of the Capability, Opportunity and Motivation Model and the Behaviour Change Wheel guide. The development was done in three steps-understanding the behaviour, developing intervention model, and identifying implementation options along with monitoring and evaluation strategies. The module has three tools-counselling flipbook for healthcare provider, take home pamphlets, and information posters for patient waiting areas. A gender- and culture-specific BCI module was developed and implemented to screen and counsel 105 pregnant tobacco users during antenatal visits, leading to high self-reported tobacco quit rate (69%) which corroborated with urine cotinine levels at baseline and end line.},
}

Humans
Female
Pregnancy
India
*Counseling/methods
Prenatal Care/methods
Adult
*Tobacco Use Cessation/methods
*Smoking Cessation/methods
Tobacco Use