@article {pmid40469097,
year = {2025},
author = {Wang, Z and Wang, Y},
title = {Multi-branch GAT-GRU-transformer for explainable EEG-based finger motor imagery classification.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1599960},
pmid = {40469097},
issn = {1662-5161},
abstract = {Electroencephalography (EEG) provides a non-invasive and real-time approach to decoding motor imagery (MI) tasks, such as finger movements, offering significant potential for brain-computer interface (BCI) applications. However, due to the complex, noisy, and non-stationary nature of EEG signals, traditional classification methods-such as Common Spatial Pattern (CSP) and Power Spectral Density (PSD)-struggle to extract meaningful, multidimensional features. While deep learning models like CNNs and RNNs have shown promise, they often focus on single-dimensional aspects and lack interpretability, limiting their neuroscientific relevance. This study proposes a novel multi-branch deep learning framework, termed Multi-Branch GAT-GRU-Transformer, to enhance EEG-based MI classification. The model consists of parallel branches to extract spatial, temporal, and frequency features: a Graph Attention Network (GAT) models spatial relationships among EEG channels, a hybrid Gated Recurrent Unit (GRU) and Transformer module captures temporal dependencies, and one-dimensional CNNs extract frequency-specific information. Feature fusion is employed to integrate these heterogeneous representations. To improve interpretability, the model incorporates SHAP (SHapley Additive exPlanations) and Phase Locking Value (PLV) analyses. Notably, PLV is also used to construct the GAT adjacency matrix, embedding biologically-informed spatial priors into the learning process. The proposed model was evaluated on the Kaya dataset, achieving a five-class MI classification accuracy of 55.76%. Ablation studies confirmed the effectiveness of each architectural component. Furthermore, SHAP and PLV analyses identified C3 and C4 as critical EEG channels and highlighted the Beta frequency band as highly relevant, aligning with known motor-related neural activity. The Multi-Branch GAT-GRU-Transformer effectively addresses key challenges in EEG-based MI classification by integrating domain-relevant spatial, temporal, and frequency features, while enhancing model interpretability through biologically grounded mechanisms. This work not only improves classification performance but also provides a transparent framework for neuroscientific investigation, with broad implications for BCI development and cognitive neuroscience research.},
}

@article {pmid40469096,
year = {2025},
author = {Li, P and Yu, D and Cheng, L and Wang, K},
title = {Influence of attentional state on EEG-based motor imagery of lower limb.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1545492},
pmid = {40469096},
issn = {1662-5161},
abstract = {INTRODUCTION: Motor imagery (MI) has emerged as a promising technique for enhancing motor skill acquisition and facilitating neural adaptation training. Attention plays a key role in regulating the neural mechanisms underlying MI. This study aims to investigate how attentional states modulate EEG-based lower-limb motor imagery performance by influencing event-related desynchronization (ERD) and the alpha modulation index (AMI) and to develop a real-time attention monitoring method based on the theta/beta ratio (TBR).

METHODS: Fourteen healthy right-handed subjects (aged 21-23) performed right-leg MI tasks, while their attentional states were modulated via a key-press paradigm. EEG signals were recorded using a 32-channel system and preprocessed with independent component analysis (ICA) to remove artifacts. Attentional states were quantified using both the traditional offline AMI and the real-time TBR index, with time-frequency analysis applied to assess ERD and its relationship with attention.

RESULTS: The results indicated a significant increase in ERD during high attentional states compared to low attentional states, with AMI values showing a strong positive correlation with ERD (r = 0.9641, p < 0.01). Cluster-based permutation testing confirmed that this α-band ERD difference was significant (corrected p < 0.05). Moreover, the TBR index proved to be an effective real-time metric, decreasing significantly under focused attention. Offline paired t-tests showed a significant TBR reduction [t (13) = 5.12, p = 2.4 × 10[-5]], and online analyses further validated second-by-second discrimination (Bonferroni-corrected p < 0.01). These findings confirm the feasibility and efficacy of TBR for real-time attention monitoring and suggest that enhanced attentional focus during lower-limb MI can improve signal quality and overall performance.

CONCLUSION: This study reveals that attentional states significantly influence the neural efficiency of lower-limb motor imagery by modulating ERD/AMI and demonstrates that the TBR can serve as a real-time indicator of attention, providing a novel tool for optimizing attentional processes in motor skill training.},
}

@article {pmid40468342,
year = {2025},
author = {Wang, M and Zhou, H and Zhang, X and Chen, Q and Tong, Q and Han, Q and Zhao, X and Wang, D and Lai, J and He, H and Zhang, S and Hu, S},
title = {Alleviating cognitive impairments in bipolar disorder with a novel DTI-guided multimodal neurostimulation protocol: a double-blind randomized controlled trial.},
journal = {BMC medicine},
volume = {23},
number = {1},
pages = {334},
pmid = {40468342},
issn = {1741-7015},
support = {52407261, 82201675//National Natural Science Foundation of China/ ; 52407261, 82201675//National Natural Science Foundation of China/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; No. JNL-2023001B//Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation-"Ten Thousand Talents Program" of Zhejiang Province/ ; 2020R01001//Innovation team for precision diagnosis and treatment of major brain diseases/ ; 2022KTZ004//Chinese Medical Education Association/ ; 226-2022-00193, 226-2022-00002, 2023ZFJH01-01, 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; Double-Blind Method ; Female ; Male ; *Bipolar Disorder/therapy/complications/psychology ; *Diffusion Tensor Imaging/methods ; Adult ; *Transcranial Magnetic Stimulation/methods ; *Cognitive Dysfunction/therapy/etiology/diagnostic imaging ; Middle Aged ; *Transcranial Direct Current Stimulation/methods ; Treatment Outcome ; },
abstract = {BACKGROUND: Traditional neuromodulation strategies show promise in enhancing cognitive abilities in bipolar disorder (BD) but remain suboptimal. This study introduces a novel multimodal neurostimulation (MNS) protocol to improve therapeutic outcomes.

METHODS: The novel MNS protocol used individualized diffusion tensor imaging (DTI) data to identify fiber tracts between the dorsolateral prefrontal cortex and dorsal anterior cingulate cortex. The highest structural connectivity point is selected as the individualized stimulation site, which is then targeted using a combination of optimized transcranial alternating current stimulation (tACS) and robot-assisted navigated repetitive transcranial magnetic stimulation (rTMS). A double-blind randomized controlled trial was conducted to investigate the clinical efficacy of this innovative neuromodulation approach on cognitive abilities in stable-phase BD patients. One hundred BD patients were randomly assigned to four groups: group A (active tACS-active rTMS (MNS protocol)), group B (sham tACS-active rTMS), group C (active tACS-sham rTMS), and group D (sham tACS-sham rTMS). Participants underwent 15 sessions over 3 weeks. Cognitive assessments (THINC integrated tool) were conducted at baseline (week 0) and post-treatment (week 3).

RESULTS: Sixty-six participants completed all 15 sessions. Group A (MNS protocol) showed superior improvements in Spotter CRT, TMT, and DSST scores compared to other groups at week 3. Only group A exhibited significant activation in the left frontal region post-MNS intervention. The novel MNS protocol was well tolerated, with no significant side effects observed.

CONCLUSIONS: The study indicates that DTI-guided multimodal neurostimulation mode significantly improves cognitive impairments and is safe for stable-phase BD patients.

TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT05964777.},
}

Humans
Double-Blind Method
Female
Male
*Bipolar Disorder/therapy/complications/psychology
*Diffusion Tensor Imaging/methods
Adult
*Transcranial Magnetic Stimulation/methods
*Cognitive Dysfunction/therapy/etiology/diagnostic imaging
Middle Aged
*Transcranial Direct Current Stimulation/methods
Treatment Outcome

@article {pmid40467567,
year = {2025},
author = {Pang, J and Xu, J and Chen, L and Teng, H and Su, C and Zhang, Z and Gao, L and Zhang, R and Liu, G and Chen, Y and He, J and Pang, Y and Li, H},
title = {Family history, inflammation, and cerebellum in major depression: a combined VBM and dynamic functional connectivity study.},
journal = {Translational psychiatry},
volume = {15},
number = {1},
pages = {188},
pmid = {40467567},
issn = {2158-3188},
support = {222102310205//Science and Technology Department of Henan Province (Henan Provincial Department of Science and Technology)/ ; 62103377//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Depressive Disorder, Major/physiopathology/diagnostic imaging/pathology/genetics/blood ; Female ; Male ; Adult ; *Cerebellum/diagnostic imaging/physiopathology/pathology ; Magnetic Resonance Imaging ; *Inflammation/blood ; Interleukin-6/blood ; Gray Matter/diagnostic imaging/pathology ; C-Reactive Protein/metabolism/analysis ; Middle Aged ; Prefrontal Cortex/diagnostic imaging/physiopathology ; Case-Control Studies ; Young Adult ; },
abstract = {A family history (FH) of depression significantly influences the progress of major depressive disorder (MDD). However, the underlying neural mechanism of FH remains unclear. This study examined the association between brain structural and connectivity alterations, inflammation, and FH in MDD. A total of 134 MDD patients with (FH group, n = 43) and without (NFH group, n = 91) first-degree FH and 96 demographic-matched healthy controls (HCs) were recruited. Voxel-based morphometry (VBM) and sliding-window dynamic functional connectivity (dFC) analyses were performed, and inflammatory biomarkers (C-reactive protein (CRP) and interleukin-6 (IL-6)) were detected. Compared with HCs, FH and NFH groups showed decreased gray matter volume (GMV) in the left cerebellum posterior lobe and increased dFC between this region and the left inferior parietal lobule. The FH group showed increased dFC between the cerebellum region and medial prefrontal cortex (mPFC) compared to NFH and HCs. The combination of these brain measurements further differentiated between FH and NFH. Moreover, the GMV of the cerebellum was positively correlated with CRP in the NFH group, while the dFC between the cerebellum and mPFC was positively correlated with IL-6 in the FH group. The present findings indicate that cerebellar structure and dynamic function vary according to FH of MDD and are related to inflammatory factors, potentially offering novel insights into the underlying pathogenic mechanisms of MDD.},
}

Humans
*Depressive Disorder, Major/physiopathology/diagnostic imaging/pathology/genetics/blood
Female
Male
Adult
*Cerebellum/diagnostic imaging/physiopathology/pathology
Magnetic Resonance Imaging
*Inflammation/blood
Interleukin-6/blood
Gray Matter/diagnostic imaging/pathology
C-Reactive Protein/metabolism/analysis
Middle Aged
Prefrontal Cortex/diagnostic imaging/physiopathology
Case-Control Studies
Young Adult

@article {pmid40465456,
year = {2025},
author = {Li, C and Hasegawa, I and Tanigawa, H},
title = {Protocol for assisting frequency band definition and decoding neural dynamics using hierarchical clustering and multivariate pattern analysis.},
journal = {STAR protocols},
volume = {6},
number = {2},
pages = {103870},
doi = {10.1016/j.xpro.2025.103870},
pmid = {40465456},
issn = {2666-1667},
abstract = {Traditional fixed frequency band divisions often limit neural data analysis accuracy. Here, we present a protocol for assisting frequency band definition for multichannel neural data using macaque electrocorticography (ECoG) data. We describe steps for performing time-frequency analysis on preprocessed signals and applying hierarchical clustering to frequency power profiles to identify data-informed groupings. We then detail procedures for defining frequency bands guided by these clusters and using multivariate pattern analysis (MVPA) on the derived bands for functional validation via time-series decoding. For complete details on the use and execution of this protocol, please refer to Tanigawa et al.[1].},
}

@article {pmid40463690,
year = {2025},
author = {Rabiee, A and Ghafoori, S and Cetera, A and Abiri, R},
title = {Wavelet Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types.},
journal = {ArXiv},
volume = {},
number = {},
pages = {},
pmid = {40463690},
issn = {2331-8422},
abstract = {This research aims to decode hand grasps from Electroencephalograms (EEGs) for dexterous neuroprosthetic development and Brain-Computer Interface (BCI) applications, especially for patients with motor disorders. Particularly, it focuses on distinguishing two complex natural power and precision grasps in addition to a neutral condition as a no-movement condition using a new EEG-based BCI platform and wavelet signal processing. Wavelet analysis involved generating time-frequency and topographic maps from wavelet power coefficients. Then, by using machine learning techniques with novel wavelet features, we achieved high average accuracies: 85.16% for multiclass, 95.37% for No-Movement vs Power, 95.40% for No-Movement vs Precision, and 88.07% for Power vs Precision, demonstrating the effectiveness of these features in EEG-based grasp differentiation. In contrast to previous studies, a critical part of our study was permutation feature importance analysis, which highlighted key features for grasp classification. It revealed that the most crucial brain activities during grasping occur in the motor cortex, within the alpha and beta frequency bands. These insights demonstrate the potential of wavelet features in real-time neuroprosthetic technology and BCI applications.},
}

@article {pmid40462746,
year = {2025},
author = {Song, J and Chai, X and Zhang, X and Lv, Z and Wan, F and Yang, Y and Shan, X and Liu, J},
title = {HEGNet: EEG and EMG fusion decoding method in motor imagery and actual movement.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-14},
doi = {10.1080/10255842.2025.2512877},
pmid = {40462746},
issn = {1476-8259},
abstract = {The widespread adoption od brain-computer interface (BCI) has been hindered by the limited classification accuracy of electroencephalography (EEG) signals alone. This study proposes a novel BCI model, HEGNet, that addresses this challenge by fusing EEG and electromyography (EMG) signals. HEGNet incorporates an EMG feature extraction component to mitigate the inherent instability and low signal-to-noise ratio limitations of relying solely on EEG data. Additionally, HEGNet employs a feature fusion module to dynamically adjust the focus on EEG and EMG features, thereby enhancing its overall robustness. These findings suggest that EMG information can serve as a valuable supplement to EEG data.},
}

@article {pmid40031188,
year = {2025},
author = {Yu, H and Zeng, F and Liu, D and Wang, J and Liu, J},
title = {Neural Manifold Decoder for Acupuncture Stimulations With Representation Learning: An Acupuncture-Brain Interface.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {6},
pages = {4147-4160},
doi = {10.1109/JBHI.2025.3530922},
pmid = {40031188},
issn = {2168-2208},
mesh = {Humans ; *Electroencephalography/methods ; *Acupuncture Therapy/methods ; Male ; Adult ; *Brain-Computer Interfaces ; Female ; *Signal Processing, Computer-Assisted ; *Deep Learning ; Young Adult ; *Brain/physiology ; Neural Networks, Computer ; },
abstract = {Acupuncture stimulations in somatosensory system can modulate spatiotemporal brain activity and improve cognitive functions of patients with neurological disorders. The correlation between these somatosensory stimulations and dynamical brain responses is still unclear. We proposed a deep learning framework using electroencephalographic activity of stimulated subjects to decode the needling processes of various acupuncture manipulations performed on Zusanli acupoint. Contrastive representation learning integrated with domain adaptation strategy was applied to estimate 3D hand postures and hand joint motion trajectories of acupuncturist with video recordings, by which finite dimensional representations of behavior manifolds for needling operations were inferred. Distinct transition dynamics of behavior manifold were observed for acupuncture with lifting-thrusting and twisting-rotating manipulations. Moreover, latent neural manifolds of acupuncture evoked EEG signals were estimated in low dimensional state space of brain activities with unsupervised manifold learning, which can reliably represent acupuncture stimulations. Furthermore, a nonlinear decoder based on neural networks was designed to transform neural manifolds to behavior manifolds and further predict acupuncture manipulation as well as needling process. Experimental results demonstrated a high performance of the proposed decoding framework for four types of acupuncture manipulations with a precision of 92.42%. The EEG decoder provides an acupuncture-brain interface linking somatosensory stimulations with neural representations, an effective scheme for revealing clinical efficacy of acupuncture treatment.},
}

Humans
*Electroencephalography/methods
*Acupuncture Therapy/methods
Male
Adult
*Brain-Computer Interfaces
Female
*Signal Processing, Computer-Assisted
*Deep Learning
Young Adult
*Brain/physiology
Neural Networks, Computer

@article {pmid40461535,
year = {2025},
author = {He, X and Chen, J and Zhong, Y and Cen, P and Shen, L and Huang, F and Wang, J and Jin, C and Zhou, R and Zhang, X and Wang, A and Fan, J and Wu, S and Tu, M and Qin, X and Luo, X and Zhou, Y and Peng, J and Zhou, Y and Civelek, AC and Tian, M and Zhang, H},
title = {Forebrain neural progenitors effectively integrate into host brain circuits and improve neural function after ischemic stroke.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {5132},
pmid = {40461535},
issn = {2041-1723},
support = {82030049, 32027802//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82102095//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82302262//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82302267//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82394433//National Natural Science Foundation of China (National Science Foundation of China)/ ; LY23H180005//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Animals ; *Neural Stem Cells/transplantation/metabolism/cytology ; Rats ; Humans ; Forkhead Transcription Factors/metabolism/genetics ; *Prosencephalon/cytology ; Nerve Tissue Proteins/metabolism/genetics ; Neurons/metabolism/cytology ; *Ischemic Stroke/therapy/physiopathology/diagnostic imaging ; Induced Pluripotent Stem Cells/cytology/transplantation/metabolism ; Cell Differentiation ; Male ; Stem Cell Transplantation/methods ; Recovery of Function ; Rats, Sprague-Dawley ; Neurogenesis ; Disease Models, Animal ; *Stroke/therapy ; Positron-Emission Tomography ; Synapses ; },
abstract = {Human cortical neural progenitor cell transplantation holds significant potential in cortical stroke treatment by replacing lost cortical neurons and repairing damaged brain circuits. However, commonly utilized human cortical neural progenitors are limited in yield a substantial proportion of diverse cortical neurons and require an extended period to achieve functional maturation and synaptic integration, thereby potentially diminishing the optimal therapeutic benefits of cell transplantation for cortical stroke. Here, we generated forkhead box G1 (FOXG1)-positive forebrain progenitors from human inducible pluripotent stem cells, which can differentiate into diverse and balanced cortical neurons including upper- and deep-layer excitatory and inhibitory neurons, achieving early functional maturation simultaneously in vitro. Furthermore, these FOXG1 forebrain progenitor cells demonstrate robust cortical neuronal differentiation, rapid functional maturation and efficient synaptic integration after transplantation into the sensory cortex of stroke-injured adult rats. Notably, we have successfully utilized the non-invasive [18]F-SynVesT-1 PET imaging technique to assess alterations in synapse count before and after transplantation therapy of FOXG1 progenitors in vivo. Moreover, the transplanted FOXG1 progenitors improve sensory and motor function recovery following stroke. These findings provide systematic and compelling evidence for the suitability of these FOXG1 progenitors for neuronal replacement in ischemic cortical stroke.},
}

Animals
*Neural Stem Cells/transplantation/metabolism/cytology
Rats
Humans
Forkhead Transcription Factors/metabolism/genetics
*Prosencephalon/cytology
Nerve Tissue Proteins/metabolism/genetics
Neurons/metabolism/cytology
*Ischemic Stroke/therapy/physiopathology/diagnostic imaging
Induced Pluripotent Stem Cells/cytology/transplantation/metabolism
Cell Differentiation
Male
Stem Cell Transplantation/methods
Recovery of Function
Rats, Sprague-Dawley
Neurogenesis
Disease Models, Animal
*Stroke/therapy
Positron-Emission Tomography
Synapses

@article {pmid40460359,
year = {2025},
author = {Chen, Z and Zhang, Y and Ding, J and Li, Z and Tian, Y and Zeng, M and Wu, X and Su, B and Jiang, J and Wu, C and Wei, D and Sun, J and Lim, CT and Fan, H},
title = {Hydrogel-Based Multifunctional Deep Brain Probe for Neural Sensing, Manipulation, and Therapy.},
journal = {ACS nano},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsnano.5c03865},
pmid = {40460359},
issn = {1936-086X},
abstract = {Implantable deep brain probes (DBPs) constitute a vital component of brain-machine interfaces, facilitating direct interaction between neural tissues and the external environment. Most multifunctional DBPs used for neural system sensing and modulation are currently fabricated through thermal tapering of polymeric materials. However, this approach faces a fundamental challenge in selecting materials that simultaneously accommodate the thermal stretching process and yet match the modulus of brain tissues. Here, we introduce a kind of multifunctional hydrogel-based fiber (HybF) designed for neural sensing, on-demand deep brain manipulation, and photodynamic therapy, and was achieved by integrating ion chelation/dechelation effects with templating methods throughout the entire wet-spinning process. With a low bending stiffness of approximately 0.3 N/m and a high conductivity of about 97 S/m at 1 kHz, HybF facilitates a high-quality signal recording (SNR ∼10) while minimizing immune rejection. It also effectively mediates deep brain optogenetic stimulation, successfully manipulating the behavior of hippocampal neurons in hSyn-ChrimsonR-tdTomato SD rats. Importantly, by leveraging HybF, this study explores the use of a spatiotemporally controllable photodynamic strategy in antiepilepsy, in which the high-amplitude abnormal electrical discharges were instantaneously eliminated without affecting normal cognitive/memory abilities. The above innovative approach established a distinct paradigm for deep brain manipulation and degenerative disease treatment, providing interesting insights into brain circuits and bioelectronic devices.},
}

@article {pmid40459463,
year = {2025},
author = {Savitz, BL and Dean, YE and Popa, NK and Cornely, RM and Byers, V and Gutama, BW and Abbott, EN and Torres-Guzman, R and Alter, N and Stehr, JD and Hill, JB and Elmaraghi, S},
title = {Targeted Muscle Reinnervation and Regenerative Peripheral Nerve Interface for Myoelectric Prosthesis Control: The State of Evidence.},
journal = {Annals of plastic surgery},
volume = {94},
number = {6S Suppl 4},
pages = {S572-S576},
doi = {10.1097/SAP.0000000000004273},
pmid = {40459463},
issn = {1536-3708},
mesh = {Humans ; *Artificial Limbs ; Electromyography ; *Nerve Regeneration/physiology ; *Muscle, Skeletal/innervation ; *Peripheral Nerves/physiology/surgery ; *Amputation Stumps/innervation ; Phantom Limb/prevention & control ; *Amputation, Surgical/rehabilitation ; },
abstract = {Prosthetic rehabilitation after amputation poses significant challenges, often due to functional limitations, residual limb pain (RLP), and phantom limb pain (PLP). These issues not only affect physical health but also mental well-being and quality of life. In this review, we describe targeted muscle reinnervation (TMR) and regenerative peripheral nerve interface (RPNI) and explore their clinical role in the evolution of myoelectric prosthetic control as well as postamputation pain and neuroma management. Early myoelectric prostheses, which detected electrical potentials from muscles to control prosthetic limbs, faced limitations such as inconsistent signal acquisition and complex control modes. Novel microsurgical techniques at the turn of the century such as TMR and RPNI significantly advanced myoelectric prosthetic control. TMR involves reinnervating denervated muscles with residual nerves to create electromyography (EMG) potentials and prevent painful neuromas. Similarly, RPNI relies on small muscle grafts to amplify EMG signals and distinguish from stochastic noise for refined prosthetic control. Techniques like TMR and RPNI not only improved prosthetic function, but also significantly reduced postamputation pain, making them critical in improving amputees' quality of life. Modern myoelectric prostheses evolved with advancements in microprocessor and sensor technologies, enhancing their functionality and user experience. Today, researchers have developed more intuitive and reliable prosthetic control by utilizing pattern recognition software and machine learning algorithms that may supersede reliance on surgically amplifying EMG signals. Future developments in brain-computer interfaces and machine learning hold promise for even greater advancements in prosthetic technology, emphasizing the importance of continued innovation in this field.},
}

Humans
*Artificial Limbs
Electromyography
*Nerve Regeneration/physiology
*Muscle, Skeletal/innervation
*Peripheral Nerves/physiology/surgery
*Amputation Stumps/innervation
Phantom Limb/prevention & control
*Amputation, Surgical/rehabilitation

@article {pmid40459258,
year = {2025},
author = {Seibert, B and Caceres, CJ and Gay, LC and Shetty, N and Faccin, FC and Carnaccini, S and Walters, MS and Marr, LC and Lowen, AC and Rajao, DS and Perez, DR},
title = {Air-liquid interface model for influenza aerosol exposure in vitro.},
journal = {Journal of virology},
volume = {},
number = {},
pages = {e0061925},
doi = {10.1128/jvi.00619-25},
pmid = {40459258},
issn = {1098-5514},
abstract = {UNLABELLED: Airborne transmission is an essential mode of infection and spread of influenza viruses among humans. However, most studies use liquid inoculum for virus infection. To better replicate natural airborne infections in vitro, we generated a calm-aerosol settling chamber system designed to examine the aerosol infectivity of influenza viruses in different cell types. Aerosol inoculation was characterized for multiple influenza A virus (FLUAV) subtypes, including pandemic 2009 H1N1, seasonal swine H3N2, and avian H9N2, using this exposure system. While each FLUAV strain displayed high infectivity within MDCK cells via liquid inoculation, differences in infectivity were observed during airborne inoculation. This was further observed in recently developed immortalized differentiated human airway epithelial cells (BCi-NS1.1) cultured in an air-liquid interface. The airborne infectious dose 50 for each virus was based on the exposure dose per well. Our findings indicate that this system has the potential to enhance our understanding of the factors influencing influenza transmission via the airborne route. This could be invaluable for conducting risk assessments, potentially reducing the reliance on extensive and costly in vivo animal studies.

IMPORTANCE: This study presents a significant advancement in influenza research by developing a novel in vitro system to assess aerosol infectivity, a crucial aspect of influenza transmission. The system's ability to differentiate between mammalian-adapted and avian-adapted influenza viruses based on their aerosol infectivity offers a valuable tool for pre-screening the pandemic potential of different strains. This could potentially streamline the risk assessment process and inform public health preparedness strategies. Moreover, the system's capacity to examine aerosol infectivity in human airway epithelial cells provides a more relevant model for studying virus-host interactions in natural airborne infections. Overall, this study provides an accessible platform for investigating aerosol infectivity, which could significantly contribute to our understanding of influenza transmission and pandemic preparedness.},
}

@article {pmid40459142,
year = {2025},
author = {Gao, J and Jiang, D and Wang, H and Wang, X},
title = {Opioid Enantiomers: Exploring the Complex Interplay of Stereochemistry, Pharmacodynamics, and Therapeutic Potential.},
journal = {Journal of medicinal chemistry},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.jmedchem.5c00136},
pmid = {40459142},
issn = {1520-4804},
abstract = {Opioids have been essential in pain management, particularly when other analgesics prove insufficient, but their use is complicated by risks of addiction, tolerance, and a range of adverse effects. These challenges are further exacerbated by the presence of opioid enantiomers that interact in a variety of ways with biological systems. This Perspective provides a comprehensive exploration of opioid enantiomers, focusing on their synthesis, pharmacodynamics, and potential therapeutic applications beyond traditional pain management. It highlights the complexity of synthesizing morphine enantiomers and additional challenges in producing the less-studied (+)-morphine. The Perspective also examines structure-activity relationship studies on (+)-opioid compounds, revealing their potential in modulating neuroinflammatory responses through non-opioid pathways and suggesting new therapeutic avenues for conditions like neuropathic pain and drug addiction. Furthermore, it discusses the differential safety profiles of opioid enantiomers, emphasizing the need for future research to advance precision medicine in opioid therapy, ultimately aiming to develop safer and more effective pain management strategies.},
}

@article {pmid40458259,
year = {2025},
author = {Zhang, W and Wang, T and Qin, C and Xu, B and Hu, H and Wang, T and Shen, Y},
title = {Vibration stimulation enhances robustness in teleoperation robot system with EEG and eye-tracking hybrid control.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {13},
number = {},
pages = {1591316},
pmid = {40458259},
issn = {2296-4185},
abstract = {INTRODUCTION: The application of non-invasive brain-computer interfaces (BCIs) in robotic control is limited by insufficient signal quality and decoding capabilities. Enhancing the robustness of BCIs without increasing the cognitive load remains a major challenge in brain-control technology.

METHODS: This study presents a teleoperation robotic system based on hybrid control of electroencephalography (EEG) and eye movement signals, and utilizes vibration stimulation to assist motor imagery (MI) training and enhance control signals. A control experiment involving eight subjects was conducted to validate the enhancement effect of this tactile stimulation technique.

RESULTS: Experimental results showed that during the MI training phase, the addition of vibration stimulation improved the brain region activation response speed in the tactile group, enhanced the activation of the contralateral motor areas during imagery of non-dominant hand movements, and demonstrated better separability (p = 0.017). In the robotic motion control phase, eye movement-guided vibration stimulation effectively improved the accuracy of online decoding of MI and enhanced the robustness of the control system and success rate of the grasping task.

DISCUSSION: The vibration stimulation technique proposed in this study can effectively improve the training efficiency and online decoding rate of MI, helping users enhance their control efficiency while focusing on control tasks. This tactile enhancement technology has potential applications in robot-assisted elderly care, rehabilitation training, and other robotic control scenarios.},
}

@article {pmid40457127,
year = {2025},
author = {Xiao, Z and She, Q and Fang, F and Meng, M and Zhang, Y},
title = {Auxiliary classifier adversarial networks with maximum subdomain discrepancy for EEG-based emotion recognition.},
journal = {Medical & biological engineering & computing},
volume = {},
number = {},
pages = {},
pmid = {40457127},
issn = {1741-0444},
support = {62371172//National Natural Science Foundation of China/ ; 62271181//National Natural Science Foundation of China/ ; ZY2024025//Wenzhou Institute of Biomaterials and Engineering/ ; },
abstract = {Domain adaptation (DA) is considered to be effective solutions for unsupervised emotion recognition cross-session and cross-subject tasks based on electroencephalogram (EEG). However, the cross-domain shifts caused by individual differences and sessions differences seriously limit the generalization ability of existing models. Moreover, existing models often overlook the discrepancies among task-specific subdomains. In this study, we propose the auxiliary classifier adversarial networks (ACAN) to tackle these two key issues by aligning global domains and subdomains and maximizing subdomain discrepancies to enhance model effectiveness. Specifically, to address cross-domain discrepancies, we deploy a domain alignment module in the feature space to reduce inter-domain and inter-subdomain discrepancies. Meanwhile, to maximum subdomain discrepancies, the auxiliary adversarial classifier is introduced to generate distinguishable subdomain features by promoting adversarial learning between feature extractor and auxiliary classifier. System experiment results on three benchmark databases (SEED, SEED-IV, and DEAP) validate the model's effectiveness and superiority in cross-session and cross-subject experiments. The method proposed in this study outperforms other state-of-the-art DA, that effectively address domain shifts in multiple emotion recognition tasks, and promote the development of brain-computer interfaces.},
}

@article {pmid40456926,
year = {2025},
author = {Slutzky, MW and Vansteensel, MJ and Herff, C and Gaunt, RA},
title = {A brain-computer interface working definition.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {40456926},
issn = {2157-846X},
}

@article {pmid40456256,
year = {2025},
author = {Tangermann, M and Chevallier, S and Dold, M and Guetschel, P and Kobler, R and Papadopoulo, T and Thielen, J},
title = {Learning from Small Datasets - Review of Workshop 6 of the 10th International BCI Meeting 2023.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/addf80},
pmid = {40456256},
issn = {1741-2552},
abstract = {In brain-computer interfacing (BCI), a primary objective is to reduce calibration time by recording as few as possible novel data points to (re-)train decoder models. Minimizing the calibration can be crucial for enhancing the usability of a BCI application with patients, increasing the acceptance by healthy users, facilitating a fast adaptation during non-stationary recordings, or transferring between sessions. At the 10th International BCI Meeting in 2023, our workshop addressed the latest proposed techniques to train classification or regression machine learning models with small datasets. We explored methodologies from both traditional machine learning as well as deep learning. In addition to talks and discussions, we introduced Python toolboxes for all presented methods and for the benchmarking of classification models. This review provides a comprehensive overview of the workshop's content and discusses the insights that were obtained.},
}

@article {pmid40456243,
year = {2025},
author = {Galiotta, V and Caracci, V and Toppi, J and Pichiorri, F and Colamarino, E and Cincotti, F and Mattia, D and Riccio, A},
title = {P300-based Brain-Computer Interface for communication in Assistive Technology centres: influence of users' profile on BCI access.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/addf7f},
pmid = {40456243},
issn = {1741-2552},
abstract = {Assistive technology (AT) refers to any product that enables people to live independently and with dignity and to participate in activities of daily life. A Brain-Computer Interface (BCI) is an AT that provides an alternative output, based on neurophysiological signals, to control an external device. The aim of the study is to screen patients accessing an AT-center to investigate their eligibility for BCI access and the factors influencing the BCI control. Approach. Thirty-five users and 11 healthy subjects were included in the study. Participants were required to operate a P300-speller BCI. We evaluated the influence of clinical diagnosis, socio-demographic factors, level of dependence and disability of users, neuropsychological profile on BCI performance. Main results. The 7.1% of the users controlled the system with a mean accuracy of 93.6±8.0%, while 8 users had an online accuracy below 70%. We found that the neuropsychological profile significantly affected online accuracy and ITR. Significance. The percentage of users who had an accuracy considered functional for communication is an encouraging data in terms of BCI effectiveness. The results regarding accuracy and factors influencing (and not influencing) it, are a contribution to the introduction of BCIs in the AT-centers, considering the BCI for communication both as an AT and as an additional input to provide multimodal access to AT.},
}

@article {pmid40456242,
year = {2025},
author = {Schmid, PR and Sweeney-Reed, CM and Dürschmid, S and Reichert, C},
title = {Stimulus predictability has little impact on decoding of covert visual spatial attention.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/addf81},
pmid = {40456242},
issn = {1741-2552},
abstract = {Brain-computer interfaces (BCI) that are aimed at supporting completely locked-in patients require independence from eye movements. Since visual spatial attention (VSA) shifts precede eye movements, they can be used for non-invasive, gaze-independent BCI control. In VSA tasks, stimuli locations and presentation onsets are commonly unpredictable. In this study we investigated the impact of predictability of potential target stimuli on the decoding accuracy of a BCI. Approach. We presented visual stimuli simultaneously to the left and right visual fields while participants shifted attention to a target stimulus. Using canonical correlation analysis, we decoded the direction of attention under different combinations of temporal and spatial predictability and compared the performance. Main results. We found no variation in decoding accuracies with spatial predictability. In addition, jittered timing did not alter the decoding accuracy compared to a constant stimulus onset asynchrony (SOA). Finally, reducing the SOA enabled faster BCI communication without accuracy loss. Using time-resolved decoding and interpretable models, we show that a later positive difference wave (between 300 ms and 350 ms post-stimulus onset) at occipital sites, rather than the N2pc, primarily contributes to decoding the target receiving attention. Significance. Our results demonstrate that stimulus predictability has no beneficial impact on decoding accuracy, but the paradigm proved robust to alterations in various stimulus parameters, making VSA a promising cognitive process for use in non-invasive, gaze-independent BCI-based communication. .},
}

@article {pmid40456241,
year = {2025},
author = {Pang, Z and Zhang, R and Li, M and Li, Z and Cui, H and Chen, X},
title = {SSVEP-based BCI using ultra-low-frequency and high-frequency peripheral flickers.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/addf82},
pmid = {40456241},
issn = {1741-2552},
abstract = {OBJECTIVE: Existing steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems predominantly employ a flicker frequency range of 8-20 Hz, which often induces visual fatigue in users, thereby compromising system performance. Considering that, this study introduces an innovative paradigm to enhance the user experience of SSVEP-based BCIs while maintaining the performance.

APPROACH: The system encodes 12 targets by integrating ultra-low-frequency (2.00-3.32 Hz) and high-frequency (34.00-35.32 Hz) flickers with peripheral stimulation, and task-related component analysis (TRCA) is employed for SSVEP signal identification.

MAIN RESULTS: The feasibility of the ultra-low-frequency peripheral stimulation paradigm was validated through online experiments, achieving an average accuracy of 89.03 ± 9.95% and an information transfer rate (ITR) of 66.74 ± 15.44 bits/min. For the high-frequency peripheral stimulation paradigm, only the stimulation frequency changed, the paradigm, the signal processing algorithm and the step of frequency and phase were unchanged. The online experiments demonstrated an average accuracy of 93.55 ± 3.02% and an ITR of 51.88 ± 3.74 bits/min.

SIGNIFICANCE: The performance of the proposed system has reached a relatively high level among the current user-friendly SSVEP-based BCI systems. This study successfully innovates the paradigm for SSVEP-based BCIs, offering new insights into the development of user-friendly systems that balance high performance and user comfort.},
}

@article {pmid40456131,
year = {2025},
author = {Jing, S and Dai, Z and Liu, X and Yang, X and Cheng, J and Chen, T and Feng, Z and Liu, X and Dong, F and Xin, Y and Han, Z and Hu, H and Su, X and Wang, C},
title = {Correction: Effectiveness of Neurofeedback-Assisted and Conventional 6-Week Web-Based Mindfulness Interventions on Mental Health of Chinese Nursing Students: Randomized Controlled Trial.},
journal = {Journal of medical Internet research},
volume = {27},
number = {},
pages = {e78147},
doi = {10.2196/78147},
pmid = {40456131},
issn = {1438-8871},
abstract = {[This corrects the article DOI: 10.2196/71741.].},
}

@article {pmid40456094,
year = {2025},
author = {Arpaia, P and Esposito, A and Galdieri, F and Natalizio, A},
title = {Acquisition delay of wireless EEG instruments in time-sensitive applications.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3575695},
pmid = {40456094},
issn = {1558-0210},
abstract = {The aim of this study is to characterize the acquisition delay in wireless EEG instruments and evaluate its impact on the detection of time-locked neural phenomena, such as P300 and movement-related cortical potentials (MRCP). Accurate timing is critical for both research and clinical applications, especially for real-time brain-computer interfaces (BCI). A measurement setup was thus developed to assess acquisition delays and their uncertainty. Delays were measured at both the start and stop of a reference signal generation to investigate the consistency and reliability of the devices. BCI experiments were also performed to evaluate the impact of the measured delay on the detection of the time-locked phenomena. Statistical tests confirmed significant differences in delays across devices and configurations (e.g., from few tens to a hundred ms). These delays directly impacted P300 and MRCP detection, raising concerns about potential misclassification. Nonetheless, the correction of the measured acquisition delay proved beneficial, especially with regard to the P300 latency measured through low-cost instrumentation.},
}

@article {pmid40456080,
year = {2025},
author = {Jin, L and Song, Y and Zhao, H and Cao, J and Cheung, VCK and Liao, WH},
title = {Frequency-Aware Spatial-Temporal Attention Explainable Network for EEG Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3576088},
pmid = {40456080},
issn = {2168-2208},
abstract = {Representation learning in spatial and temporal domains has shown significant potential in EEG decoding, advancing the field of brain-computer interfaces (BCIs). However, the critical role of frequency information, closely tied to the brain's neurological mechanism, has been largely neglected. In this paper, we propose FSTNet, which integrates frequency-spatial-temporal domains synergistically. The network allows broadband EEG signals as input and adaptively learns informative frequency signatures. A frequency-aware module emphasizes the importance of frequency information by selectively assigning weights to latent representations in the frequency space. Subsequently, self-attention captures spatial and temporal dependencies, extracting discriminative neural signatures for EEG decoding. We conducted extensive experiments on EEG datasets for motor imagery and emotion recognition, achieving superior results of 90.17% on SEED, 88.38% on PhysioNet, and 77.02% on the OpenBMI dataset in both individual and cross-subject scenarios. Additionally, visualization reveals that the network captures informative frequency ranges and spatial patterns associated with specific tasks, aligning with known physiological mechanisms. This enhances the transparency of the network's learning process. In conclusion, our method exhibits the potential for decoding EEG and advancing the understanding of neurological processes in the brain.},
}

@article {pmid40455568,
year = {2025},
author = {Chen, Y and Fan, Z and Shi, N and Cheng, B and Huang, C and Liu, X and Gao, X and Liu, R},
title = {MXene-Based Microneedle Electrode for Brain-Computer Interface in Diverse Scenarios.},
journal = {ACS applied materials & interfaces},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsami.5c03798},
pmid = {40455568},
issn = {1944-8252},
abstract = {In this study, we introduce a brain-computer interface (BCI) framework incorporating MXene microneedle EEG electrodes, tailored for versatile deployment. The dry electrodes, configured as 1 mm[2] microneedles, underwent meticulous processing to establish a cohesive integration with the MXene conductive material. The microneedle architecture facilitates epidermal penetration, yielding low contact impedance, enabling the recording of spontaneous EEG and induced brain activity, and ensuring high precision in steady-state visual evoked potential (SSVEP) speller. Simultaneously, the microneedle electrode demonstrates commendable biological compatibility and superior nuclear magnetic resonance compatibility. It exhibits minimal artifact generation and manifests no heating-related adaptations in nuclear magnetic environments. The inherent microneedle electrode structure endows it with robust anti-interference capabilities. In vibrational environments, the SSVEP text input accuracy of the microneedle electrode remains comparable to that of gel electrodes, maintaining consistent impedance and delivering high-fidelity EEG acquisition during real-motion scenarios. The microneedle electrode devised in this study serves as a reliable signal acquisition tool, thereby advancing the development of BCI systems tailored for practical usage scenarios.},
}

@article {pmid40454682,
year = {2025},
author = {Pitt, KM and Mikuls, A and Ousley, CL and Boster, JB and Mahmoudi, M and McCarthy, J and Burnison, J},
title = {Considering whether brain-computer interfaces have prospective potential to support children who have the physical abilities for touch-based AAC access: a forum manuscript.},
journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)},
volume = {},
number = {},
pages = {1-9},
doi = {10.1080/07434618.2025.2495897},
pmid = {40454682},
issn = {1477-3848},
abstract = {Augmentative and alternative communication (AAC) may help address communication challenges for both those with developmental disabilities (DD) and intellectual and developmental disabilities (IDD). This forum manuscript explores the possibility of various future applications of brain-computer interface technology for AAC control (BCI-AAC) by children who have the physical abilities to utilize touch-based AAC access. Due to the early status of BCI-AAC research, the forum focuses on those with DD, though considerations for those with IDD are also discussed. Departing from the prevalent focus on severe speech and physical impairments (SSPI), this work shifts the spotlight toward children who may employ touch selection for AAC access, exploring the challenges and prospective possibilities within this population. Applying the International Classification of Functioning, Disability, and Health (ICF) framework, we explore potential BCI-AAC considerations across Activities and Participation, Functions and Structures, Environmental Factors, and Personal Factors. Proposing prospective BCI-AAC strategies, such as leveraging brain activity for functional intent recognition and emotion detection, this paper is designed to fuel discussion on tailoring AAC interventions to the diverse profiles of children with DD and IDD. Acknowledging the significant hurdles faced by BCI-AAC technology, we support the inclusive consideration of individuals in BCI-AAC development. While not seeking to lay a definitive roadmap, this forum aims to serve as a catalyst for future interdisciplinary dialogues, including those who use AAC and their support network, laying the groundwork for considering diverse BCI-AAC applications in children.},
}

@article {pmid40450930,
year = {2025},
author = {Fan, C and Song, Y and Mao, X},
title = {A classification method of motor imagery based on brain functional networks by fusing PLV and ECSP.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {190},
number = {},
pages = {107684},
doi = {10.1016/j.neunet.2025.107684},
pmid = {40450930},
issn = {1879-2782},
abstract = {In order to enhance the decoding ability of brain states and evaluate the functional connection changes of relevant nodes in brain regions during motor imagery (MI), this paper proposes a brain functional network construction method which fuses edge features and node features. And we use deep learning methods to realize MI classification of left and right hand grasping tasks. Firstly, we use phase locking value (PLV) to extract edge features and input a weighted PLV to enhanced common space pattern (ECSP) to extract node features. Then, we fuse edge features and node features to construct a novel brain functional network. Finally, we construct an attention and multi-scale feature convolutional neural network (AMSF-CNN) to validate our method. The performance indicators of the brain functional network on the SHU_Dataset in the corresponding brain region will increase and be higher than those in the contralateral brain region when imagining one hand grasping. The average accuracy of our method reaches 79.65 %, which has a 25.85 % improvement compared to the accuracy provided by SHU_Dataset. By comparing with other methods on SHU_Dataset and BCI IV 2a Dataset, the average accuracies achieved by our method outperform other references. Therefore, our method provides theoretical support for exploring the working mechanism of the human brain during MI.},
}

@article {pmid40450863,
year = {2025},
author = {Niu, X and Zhang, J and Peng, Y and Kong, Y and Li, Y and Han, Y and Shi, L and Zheng, G},
title = {Extraction and analysis of abnormal EEG features in children with amblyopia.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {175},
number = {},
pages = {2110765},
doi = {10.1016/j.clinph.2025.2110765},
pmid = {40450863},
issn = {1872-8952},
abstract = {OBJECTIVE: Early and accurate diagnosis of amblyopia is crucial for the healthy development of children. Existing clinical diagnostic methods rely on patient cooperation, which can easily lead to misdiagnosis. The commonly used features derived from visual evoked potentials (VEP) only provided limited information for characterizing the whole brain, highlighting the need for integrating additional data sources, such as brain network metrics, to achieve a more comprehensive understanding.

METHODS: We extracted 488 features from 64-channel EEG data recorded from thirty amblyopic children. The features mainly derived from a weighted functional brain network based on coherence across different frequency bands. Feature selection and linear classification techniques were employed to assess their effectiveness in distinguishing amblyopia from normal children.

RESULTS: Abnormal EEG features were distributed not only in the occipital lobe but also in non-visual regions, with a higher prevalence in the alpha and beta bands. Their decoding performance surpassed traditional VEP features, and their combination achieved the highest accuracy (89.00%). Moreover, features beyond the occipital lobe exhibited limited decoding performance when considered individually, yet they still have an obvious contribution.

CONCLUSIONS: The study identified novel abnormal EEG features associated with amblyopia and demonstrated the potential of multi-channel EEG recordings to assist in the diagnosis of amblyopia.

SIGNIFICANCE: The study suggests amblyopia may impair more abilities beyond visual cognition and further provides objective biomarkers for diagnosing amblyopia, which is essential for effective treatment.},
}

@article {pmid40450806,
year = {2025},
author = {Wosnick, N and Dörfer, T and Turner, M and Nicholls, C and Richardson, M and Génier, I and Hauser-Davis, RA},
title = {Assessing the potential physiological impacts of urban development around lemon shark (Negaprion brevirostris) nurseries: effects on neonate and juvenile health.},
journal = {Marine pollution bulletin},
volume = {218},
number = {},
pages = {118233},
doi = {10.1016/j.marpolbul.2025.118233},
pmid = {40450806},
issn = {1879-3363},
abstract = {Urbanization driven by population growth, development and tourism increasingly threatens even remote areas, potentially impacting biodiversity. This is particularly concerning given the ecological and economic importance of biodiversity, especially for island nations, where ecotourism plays a crucial role in the economy. This study examines urban-driven degradation effects on the nurseries of lemon sharks, a predator with strong site fidelity to its birthing and nursery areas. Six sites in South Eleuthera, The Bahamas, were assessed, analyzing proxies indicative of body condition (triglycerides/cholesterol ratio, body condition index) and energetic stress markers (glucose, β-hydroxybutyrate, triglycerides, total cholesterol) in neonates and juveniles compared across nurseries relative to degradation scores. While TAG/CHOL and BCI were not significantly different between nurseries, energetic markers were overall higher in more degraded nurseries. Moreover, total urban score was a significant predictor for glucose, β-hydroxybutyrate, and triglyceride ciruclating concentrations. These findings, coupled with prior studies carried out in Bimini, suggest that urban development around lemon shark nurseries in The Bahamas may negatively impact shark health. Cooperative monitoring, community initiatives for mangrove preservation, and stronger urbanization laws are required to mitigate these impacts. As urbanization and environmental degradation are universal threats to mangroves worldwide, this approach can be adapted to study urbanization impacts on other species in regions such as Southeast Asia, the Caribbean, the Pacific Islands, and the coasts of Africa and South America, which face similar urban encroachment, habitat degradation, and biodiversity loss challenges.},
}

@article {pmid40450046,
year = {2025},
author = {Maltezou-Papastylianou, C and Scherer, R and Paulmann, S},
title = {Human voices communicating trustworthy intent: A demographically diverse speech audio dataset.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {921},
pmid = {40450046},
issn = {2052-4463},
mesh = {Humans ; Adult ; Middle Aged ; Female ; Male ; *Voice ; Young Adult ; Adolescent ; *Speech ; *Trust ; },
abstract = {The multi-disciplinary field of voice perception and trustworthiness lacks accessible and diverse speech audio datasets representing diverse speaker demographics, including age, ethnicity, and sex. Existing datasets primarily feature white, younger adult speakers, limiting generalisability. This paper introduces a novel open-access speech audio dataset with 1,152 utterances from 96 untrained speakers, across white, black and south Asian backgrounds, divided into younger (N = 60, ages 18-45) and older (N = 36, ages 60+) adults. Each speaker recorded both, their natural speech patterns (i.e. "neutral" or no intent), and their attempt to convey their trustworthy intent as they perceive it during speech production. Our dataset is described and evaluated through classification methods between neutral and trustworthy speech. Specifically, extracted acoustic and voice quality features were analysed using linear and non-linear classification models, achieving accuracies of around 70%. This dataset aims to close a crucial gap in the existing literature and provide additional research opportunities that can contribute to the generalisability and applicability of future research results in this field.},
}

Humans
Adult
Middle Aged
Female
Male
*Voice
Young Adult
Adolescent
*Speech
*Trust

@article {pmid40448829,
year = {2025},
author = {Marques, LM and Strauss, A and Castellani, A and Barbosa, S and Simis, M and Fregni, F and Battistella, L},
title = {Dynamics of sensorimotor-related brain oscillations: EEG insights from healthy individuals in varied upper limb movement conditions.},
journal = {Experimental brain research},
volume = {243},
number = {7},
pages = {160},
pmid = {40448829},
issn = {1432-1106},
support = {#21/05897-5//Fundação de Amparo à Pesquisa do Estado de São Paulo/ ; #21/12790-2//Fundação de Amparo à Pesquisa do Estado de São Paulo/ ; #20/08512-4//Fundação de Amparo à Pesquisa do Estado de São Paulo/ ; #17/12943-8//Fundação de Amparo à Pesquisa do Estado de São Paulo/ ; #17/12943-8//Fundação de Amparo à Pesquisa do Estado de São Paulo/ ; },
mesh = {Humans ; Male ; Female ; Adult ; Young Adult ; Cross-Sectional Studies ; Electroencephalography ; *Sensorimotor Cortex/physiology ; *Upper Extremity/physiology ; *Brain Waves/physiology ; Movement/physiology ; *Motor Activity/physiology ; Brain-Computer Interfaces ; *Psychomotor Performance/physiology ; *Cortical Synchronization/physiology ; Imagination/physiology ; Middle Aged ; },
abstract = {Event-related desynchronization (ERD) and event-related synchronization (ERS) are critical neurophysiological phenomena associated with motor execution and inhibitory processes. Their utility spans neurophysiological biomarker research and Brain-Computer Interface (BCI) development. However, standardized frameworks for analyzing ERD and ERS oscillations across motor tasks and frequency ranges remain scarce. This study conducted a cross-sectional analysis of 76 healthy participants from the DEFINE cohort to explore ERD and ERS variations across four motor-related tasks (Motor Execution, Motor Imagery, Active Observation, and Passive Observation) and six frequency bands (Delta, Theta, Low Alpha, High Alpha, Low Beta, and High Beta) using C3 electrode activity. Repeated measures ANOVA revealed task-sensitive ERD and ERS power modulations, with oscillatory responses spanning the 1-30 Hz spectrum. Beta activity exhibited pronounced differences between tasks, highlighting its relevance in motor control, while other bands showed distinct task-dependent variations. These findings underscore the variability in ERD/ERS patterns across different tasks and frequency bands, reinforcing the importance of further research into standardized analytical frameworks. By refining ERD/ERS analyses, our study contributes to developing reference frameworks that can enhance clinical and Brain-Computer Interface (BCI) applications.},
}

Humans
Male
Female
Adult
Young Adult
Cross-Sectional Studies
Electroencephalography
*Sensorimotor Cortex/physiology
*Upper Extremity/physiology
*Brain Waves/physiology
Movement/physiology
*Motor Activity/physiology
Brain-Computer Interfaces
*Psychomotor Performance/physiology
*Cortical Synchronization/physiology
Imagination/physiology
Middle Aged

@article {pmid40448287,
year = {2025},
author = {Cao, L and Zheng, Q and Wu, Y and Liu, H and Guo, M and Bai, Y and Ni, G},
title = {A dual-modality study on the neural features of cochlear implant simulated tone and consonant perception.},
journal = {Annals of the New York Academy of Sciences},
volume = {},
number = {},
pages = {},
doi = {10.1111/nyas.15380},
pmid = {40448287},
issn = {1749-6632},
support = {2023YFF1203500//National Key Research and Development Program of China/ ; 824B2056//National Natural Science Foundation of China/ ; },
abstract = {Accurately perceiving lexical tones and consonants is critical for understanding speech in tonal languages. Cochlear implant (CI) users exhibit reduced phonetic perception due to spectral loss in CI encoding, yet the underlying neural mechanisms remain unclear. This study combined electroencephalography and functional near-infrared spectroscopy (fNIRS) to investigate the neural processing mechanisms of CI-simulated channelized speech in 26 normal-hearing adults during the processing of tones (T1-T4) and consonants ("ba," "da," "ga," "za"). Results showed that the N1 amplitude in auditory evoked potentials was significantly lower for channelized speech than a natural human voice (NH), particularly for T2 and T4 tones, indicating a weaker perception of channelized speech. Functional connectivity analysis revealed that an NH exhibited significantly higher synchrony in the δ and θ frequency bands than channelized speech, which was more pronounced in the right temporal lobe. This finding was also observed with "za" consonants. fNIRS results showed stronger right temporal lobe activation for channelized speech, suggesting that the brain requires greater auditory effort to process channelized speech. Combining both modalities revealed neural compensatory mechanisms underlying channelized speech-manifesting as "low-efficiency perception with high cognitive load." This study provides potential biomarkers for CI rehabilitation assessment and a foundation for future research.},
}

@article {pmid40446349,
year = {2025},
author = {Wang, S and Chen, G and Xie, J and Yang, R and Wang, X and Shan, Q and Liu, W and Zhao, D and Wang, F and Li, K and Zhang, Q and Guo, Y},
title = {Development and validation of a predictive model for poor initial outcomes after Gamma Knife radiosurgery for trigeminal neuralgia: a prognostic correlative analysis.},
journal = {Journal of neurosurgery},
volume = {},
number = {},
pages = {1-12},
doi = {10.3171/2025.2.JNS242655},
pmid = {40446349},
issn = {1933-0693},
abstract = {OBJECTIVE: The present study aimed to develop a reliable predictive model for identifying preoperative predictors of poor initial outcomes in patients with primary trigeminal neuralgia (PTN) treated with Gamma Knife radiosurgery (GKRS) and further elucidate the clinical significance of these predictors in initial outcomes and long-term pain recurrence.

METHODS: A total of 217 PTN patients were divided into a training set (n = 167) and a validation set (n = 50). The initial outcomes of GKRS treatment were assessed based on the Barrow Neurological Institute pain intensity scale. A predictive model was developed through multivariate regression and validated with repeated sampling. The differences in predictors of long-term pain recurrence were assessed using Kaplan-Meier analysis. The association between predictors was tested using chi-square tests, and subgroup analyses were performed to compare initial outcomes and long-term pain recurrence between two clinically significant correlates.

RESULTS: The training and validation sets showed areas under the curve of 0.85 and 0.88, respectively. Calibration curves and decision curve analysis indicated significant clinical benefits in both sets. Independent risk factors for poor initial outcomes included hyperglycemia, absence of neurovascular contact, carbamazepine insensitivity, and atypical pain (trigeminal neuralgia type 2 [TN2]). Carbamazepine insensitivity was moderately associated with TN2 and predicted long-term pain recurrence. Patients with both phenotypes had significantly worse initial outcomes compared with other subgroups (adjusted p = 0.0125).

CONCLUSIONS: Patients with both TN2 and carbamazepine insensitivity have the poorest initial treatment outcomes and face an increased risk of recurrence. Furthermore, this predictive model is highly accurate and useful, offering a comprehensive method of identifying PTN patients likely to experience poor initial outcomes based on clinical characteristics and imaging perspectives. The authors believe that the nomogram presented in this model enables clinicians to calculate multiple variables and predict the probability of adverse events.},
}

@article {pmid40446280,
year = {2025},
author = {Liang, X and Ding, Y and Yuan, Z and Han, Y and Zhou, Y and Jiang, J and Xie, Z and Fei, P and Sun, Y and Jia, P and Gu, G and Zhong, Z and Chen, F and Si, G and Gong, Z},
title = {Mechanics of Soft-Body Rolling Motion without External Torque.},
journal = {Physical review letters},
volume = {134},
number = {19},
pages = {198401},
doi = {10.1103/PhysRevLett.134.198401},
pmid = {40446280},
issn = {1079-7114},
mesh = {Animals ; Robotics ; Larva/physiology ; *Models, Biological ; Biomechanical Phenomena ; *Drosophila/physiology ; Muscle Contraction/physiology ; Torque ; },
abstract = {The Drosophila larva, a soft-body animal, can bend its body and roll efficiently to escape danger. However, contrary to common belief, this rolling motion is not driven by the imbalance of gravity and ground reaction forces. Through functional imaging and ablation experiments, we demonstrate that the sequential actuation of axial muscles within an appropriate range of angles is critical for generating rolling. We model the interplay between muscle contraction, hydrostatic skeleton deformation, and body-environment interactions, and systematically explain how sequential muscle actuation generates the rolling motion. Additionally, we construct a pneumatic soft robot to mimic the larval rolling strategy, successfully validating our model. This mechanics model of soft-body rolling motion not only advances the study of related neural circuits, but also holds potential for applications in soft robotics.},
}

Animals
Robotics
Larva/physiology
*Models, Biological
Biomechanical Phenomena
*Drosophila/physiology
Muscle Contraction/physiology
Torque

@article {pmid40443843,
year = {2025},
author = {Yang, H and Li, T and Zhao, L and Wei, Y and Chen, X and Pan, J and Fu, Y},
title = {Guiding principles and considerations for designing a well-structured curriculum for the brain-computer interface major based on the multidisciplinary nature of brain-computer interface.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1554266},
pmid = {40443843},
issn = {1662-5161},
abstract = {Brain-computer interface (BCI) is a novel human-computer interaction technology, and its rapid development has led to a growing demand for skilled BCI professionals, culminating in the emergence of the BCI major. Despite its significance, there is limited literature addressing the curriculum design for this emerging major. This paper seeks to bridge this gap by proposing and discussing a curricular framework for the BCI major, based on the inherently multidisciplinary nature of BCI research and development. The paper begins by elucidating the primary factors behind the emergence of the BCI major, the increasing demand for both medical and non-medical applications of BCI, and the corresponding need for specialized talent. It then delves into the multidisciplinary nature of BCI research and offers principles for curriculum design to address this nature. Based on these principles, the paper provides detailed suggestions for structuring a BCI curriculum. Finally, it discusses the challenges confronting the development of the BCI major, including the lack of consensus and international collaboration in the construction of the BCI major, as well as the inadequacy or lack of teaching materials. Future work needs to improve the curriculum design of the BCI major from a competency-oriented perspective. It is expected that this paper will provide a reference for the curriculum design and construction of the BCI major.},
}

@article {pmid40442937,
year = {2025},
author = {Wang, F and Wang, L and Zhu, X and Lu, Y and Du, X},
title = {Neuron-Inspired Ferroelectric Bioelectronics for Adaptive Biointerfacing.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e2416698},
doi = {10.1002/adma.202416698},
pmid = {40442937},
issn = {1521-4095},
support = {B2302045//Shenzhen Medical Research Fund/ ; 52022102//National Natural Science Foundation of China/ ; 52261160380//National Natural Science Foundation of China/ ; 32471042//National Natural Science Foundation of China/ ; 32300845//National Natural Science Foundation of China/ ; 2017YFA0701303//National Key R&D Program of China/ ; Y2023100//Youth Innovation Promotion Association of CAS/ ; RCJC20221008092729033//Fundamental Research Program of Shenzhen/ ; JCYJ20220818101800001//Fundamental Research Program of Shenzhen/ ; 2024A1515010645//Basic and Applied Basic Research Foundation of Guangdong Province/ ; },
abstract = {Implantable bioelectronics, which are essential to neuroscience studies, neurological disorder treatment, and brain-machine interfaces, have become indispensable communication bridges between biological systems and the external world through sensing, monitoring, or manipulating bioelectrical signals. However, conventional implantable bioelectronic devices face key challenges in adaptive interfacing with neural tissues due to their lack of neuron-preferred properties and neuron-similar behaviors. Here, innovative neuron-inspired ferroelectric bioelectronics (FerroE) are reported that consists of biocompatible polydopamine-modified barium titanate nanoparticles, ferroelectric poly(vinylidene fluoride-co-trifluoroethylene) copolymer, and cellular-scale micropyramid array structures, imparting adaptive interfacing with neural systems. These FerroE not only achieve neuron-preferred flexible and topographical properties, but also offer neuron-similar behaviors including highly efficient and stable light-induced polarization change, superior capability of producing electric signals, and seamless integration and adaptive communication with neurons. Moreover, the FerroE allows for adaptive interfacing with both peripheral and central neural networks of mice, enabling regulation of their heart rate and motion behavior in a wireless, non-genetic, and non-contact manner. Notably, the FerroE demonstrates unprecedented structural and functional stability and negligible immune response even after 3 months of implantation in vivo. Such bioinspired FerroE are opening new opportunities for next-generation brain-machine interfaces, tissue engineering materials, and biomedical devices.},
}

@article {pmid40442546,
year = {2025},
author = {Wang, L and Li, T and Li, X and Liu, F and Feng, C},
title = {Mapping trait justice sensitivity in the Brain: Whole-brain resting-state functional connectivity as a predictor of other-oriented not self-oriented justice sensitivity.},
journal = {Cognitive, affective & behavioral neuroscience},
volume = {},
number = {},
pages = {},
pmid = {40442546},
issn = {1531-135X},
support = {2024B0303390003//Research Center for Brain Cognition and Human Development, Guangdong, China/ ; 32020103008//National Natural Science Foundation of China/ ; 32271126//National Natural Science Foundation of China/ ; 81922036//National Natural Science Foundation of China/ ; },
abstract = {Justice sensitivity (JS) reflects personal concern and commitment to the principle of justice, showing considerable heterogeneity among the general population. Despite a growing interest in the behavioral characteristics of JS over the past decades, the neurobiological substrates underlying trait JS are not well comprehended. We addressed this issue by employing a machine learning approach to decode the trait JS, encompassing its various orientations, from whole-brain resting-state functional connectivity. We demonstrated that the machine-learning model could decode the individual trait of other-oriented JS but not self-oriented JS from resting-state functional connectivity across multiple neural systems, including functional connectivity between and within parietal lobe and motor cortex as well as their connectivity with other brain systems. Key nodes that contributed to the prediction model included the parietal, motor, temporal, and subcortical regions that have been linked to other-oriented JS. Additionally, the machine learning model can distinctly distinguish between the distinct roles associated with other-oriented JS, including observer, perpetrator, and beneficiary, with key brain regions in the predictive networks exhibiting both similarities and disparities. These findings remained robust using different validation procedures. Collectively, these results support the separation between other-oriented JS and self-oriented JS, while also highlighting the distinct intrinsic neural correlates among the three roles of other-oriented JS: observer, perpetrator, and beneficiary.},
}

@article {pmid40442206,
year = {2025},
author = {Akama, T and Zhang, Z and Li, P and Hongo, K and Minamikawa, S and Polouliakh, N},
title = {Predicting artificial neural network representations to learn recognition model for music identification from brain recordings.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {18869},
pmid = {40442206},
issn = {2045-2322},
mesh = {*Music ; Humans ; *Neural Networks, Computer ; Electroencephalography ; *Brain/physiology ; Male ; *Auditory Perception/physiology ; Female ; Adult ; Acoustic Stimulation ; Young Adult ; Brain-Computer Interfaces ; },
abstract = {Recent studies have demonstrated that the representations of artificial neural networks (ANNs) can exhibit notable similarities to cortical representations when subjected to identical auditory sensory inputs. In these studies, the ability to predict cortical representations is probed by regressing from ANN representations to cortical representations. Building upon this concept, our approach reverses the direction of prediction: we utilize ANN representations as a supervisory signal to train recognition models using noisy brain recordings obtained through non-invasive measurements. Specifically, we focus on constructing a recognition model for music identification, where electroencephalography (EEG) brain recordings collected during music listening serve as input. By training an EEG recognition model to predict ANN representations-representations associated with music identification-we observed a significant improvement in classification accuracy. This study introduces a novel approach to developing recognition models for brain recordings in response to external auditory stimuli. It holds promise for advancing brain-computer interfaces (BCI), neural decoding techniques, and our understanding of music cognition. Furthermore, it provides new insights into the relationship between auditory brain activity and ANN representations.},
}

*Music
Humans
*Neural Networks, Computer
Electroencephalography
*Brain/physiology
Male
*Auditory Perception/physiology
Female
Adult
Acoustic Stimulation
Young Adult
Brain-Computer Interfaces

@article {pmid40442062,
year = {2025},
author = {Shah, NP and Avansino, D and Kamdar, F and Nicolas, C and Kapitonava, A and Vargas-Irwin, C and Hochberg, LR and Pandarinath, C and Shenoy, KV and Willett, FR and Henderson, JM},
title = {Pseudo-linear summation explains neural geometry of multi-finger movements in human premotor cortex.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {5008},
pmid = {40442062},
issn = {2041-1723},
support = {Milton Safenowtiz Postdoctoral Scholarship//Amyotrophic Lateral Sclerosis Association (ALS Association)/ ; },
mesh = {Humans ; *Fingers/physiology ; *Motor Cortex/physiology/physiopathology ; Male ; Movement/physiology ; Adult ; Quadriplegia/physiopathology ; },
abstract = {How does the motor cortex combine simple movements (such as single finger flexion/extension) into complex movements (such as hand gestures, or playing the piano)? To address this question, motor cortical activity was recorded using intracortical multi-electrode arrays in two male people with tetraplegia as they attempted single, pairwise and higher-order finger movements. Neural activity for simultaneous movements was largely aligned with linear summation of corresponding single finger movement activities, with two violations. First, the neural activity exhibited normalization, preventing a large magnitude with an increasing number of moving fingers. Second, the neural tuning direction of weakly represented fingers changed significantly as a result of the movement of more strongly represented fingers. These deviations from linearity resulted in non-linear methods outperforming linear methods for neural decoding. Simultaneous finger movements are thus represented by the combination of individual finger movements by pseudo-linear summation.},
}

Humans
*Fingers/physiology
*Motor Cortex/physiology/physiopathology
Male
Movement/physiology
Adult
Quadriplegia/physiopathology

@article {pmid40441574,
year = {2025},
author = {Rahman, MH and Mondal, MIH},
title = {Investigation of neem-oil-loaded PVA/chitosan biocomposite film for hydrophobic dressing, rapid hemostasis and wound healing applications.},
journal = {International journal of biological macromolecules},
volume = {},
number = {},
pages = {144712},
doi = {10.1016/j.ijbiomac.2025.144712},
pmid = {40441574},
issn = {1879-0003},
abstract = {The present work aims to develop a hydrophobic dressing with a blood-repellent surface that achieves fast clotting without blood loss, having antibacterial properties, clot self-detachment, and superior wound healing activity. For these reasons, a novel approach was applied by producing a hydrophobic film made of PVA, chitosan, and neem seed oil (NSO). The film had the necessary hydrophobicity, mechanical strength, stability and was able to transmit water vapor to be suitable for the wound skin surface and demonstrated faster blood clotting (BCI = 91.44 % in 5 min and 85.22 % in 10 min). The proportion of red blood cells (2.78 %) and platelets (17.33 %) attached to the film proved its excellent hemostatic activity. It was anti-adhesive, created spontaneous clot detachment, and exhibited antibacterial properties at the wound site, as evidenced by in vivo testing. Moreover, in vivo testing and histopathological findings showed enhanced wound healing activity, greater re-epithelialization, and decreased granulation tissue. Additionally, the film's eco-friendliness was evaluated using a soil burial degradation test, and the results show that it deteriorated into the soil but did so slowly because of its hydrophobic property. Thus, PVA/CS/NSO composite film may be a green biomedical material for hemostasis and wound healing.},
}

@article {pmid40440260,
year = {2025},
author = {Tong, B and Li, G and Bu, X and Wang, Y and Yu, X},
title = {A deep learning-based algorithm for the detection of personal protective equipment.},
journal = {PloS one},
volume = {20},
number = {5},
pages = {e0322115},
pmid = {40440260},
issn = {1932-6203},
mesh = {*Personal Protective Equipment ; *Deep Learning ; Humans ; *Algorithms ; Neural Networks, Computer ; Construction Industry ; },
abstract = {Personal protective equipment (PPE) is critical for ensuring the safety of construction workers. However, site surveillance images from construction sites often feature multi-size and multi-scale targets, leading to low detection accuracy for PPE in existing models. To address this issue, this paper proposes an improved model based on YOLOv8n.By enriching feature diversity and enhancing the model's adaptability to geometric transformations, the detection accuracy is improved.A Multi-Scale Group Convolution Module (MSGP) was designed to extract multi-level features using different convolution kernels. A Multi-Scale Feature Diffusion Pyramid Network (MFDPN) was developed, which aggregates multi-scale features through the Multiscale Feature Focus (MFF) module and diffuses them across scales, providing each scale with detailed contextual information. A customized Task Alignment Module was introduced to integrate interactive features, optimizing both classification and localization tasks. The DCNV2(Deformable Convolutional Networks v2) module was incorporated to handle geometric scale transformations by generating spatial offsets and feature masks from interactive features, thereby improving localization accuracy and dynamically selecting weights to enhance classification precision.The improved model incorporates rich multi-level and multi-scale features, allowing it to better adapt to tasks involving geometric transformations and aligning with the image data distribution in construction scenarios. Additionally, structured pruning techniques were applied to the model at varying levels, further reducing computational and parameter loads. Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. The proposed MFD-YOLO(1.5) model achieves significant progress in detecting personal protective equipment on construction sites, with a substantial reduction in parameter count, making it suitable for deployment on resource-constrained edge devices.},
}

*Personal Protective Equipment
*Deep Learning
Humans
*Algorithms
Neural Networks, Computer
Construction Industry

@article {pmid40438784,
year = {2025},
author = {Du, Y and Yang, X and Wang, M and Lv, Q and Zhou, H and Sang, G},
title = {Longitudinal changes in children with autism spectrum disorder receiving applied behavior analysis or early start denver model interventions over six months.},
journal = {Frontiers in pediatrics},
volume = {13},
number = {},
pages = {1546001},
pmid = {40438784},
issn = {2296-2360},
abstract = {BACKGROUND: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by social communication difficulties, restricted interests, repetitive behaviors, and sensory abnormalities. The rising prevalence of ASD presents a significant public health concern, with no pharmacological treatments available for its core symptoms. Therefore, early and effective behavioral interventions are crucial to improving developmental outcomes for children with ASD. Current interventions primarily focus on educational rehabilitation methods, including Applied behavior Analysis (ABA) and the Early Start Denver Model (ESDM).

OBJECTIVE: This study aims to examine the developmental changes in children with ASD following six months of ABA therapy or ESDM intervention.

METHODS: From December 2021 to December 2023, 30 children receiving ABA therapy at the Zhejiang Rehabilitation Medical Center (40 min/session, 4 sessions/day, 5 days/week), while another 30 children undergoing ESDM training at Hangzhou Children's Hospital (2 h of one-on-one sessions and 0.5 h of group sessions/day, 5 days/week). Both groups participated in their respective interventions for six months. Pre- and post-treatment assessments were conducted using the Psycho-educational Profile-Third Edition (PEP-3).

RESULTS: Both groups showed significant improvements in PEP-3 scores post-treatment, including cognitive verbal/pre-verbal, expressive language, receptive language, social reciprocity, small muscles, imitation, emotional expression, and verbal and nonverbal behavioral characteristics.

CONCLUSION: Both ABA and ESDM interventions were associated with comprehensive improvements in children with ASD over a six-month period.},
}

@article {pmid40438090,
year = {2025},
author = {Ding, W and Liu, A and Chen, X and Xie, C and Wang, K and Chen, X},
title = {Reducing calibration efforts of SSVEP-BCIs by shallow fine-tuning-based transfer learning.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {81},
pmid = {40438090},
issn = {1871-4080},
abstract = {The utilization of transfer learning (TL), particularly through pre-training and fine-tuning, in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has substantially reduced the calibration efforts. However, commonly employed fine-tuning approaches, including end-to-end fine-tuning and last-layer fine-tuning, require data from target subjects that encompass all categories (stimuli), resulting in a time-consuming data collection process, especially in systems with numerous categories. To address this challenge, this study introduces a straightforward yet effective ShallOw Fine-Tuning (SOFT) method to substantially reduce the number of calibration categories needed for model fine-tuning, thereby further mitigating the calibration efforts for target subjects. Specifically, SOFT involves freezing the parameters of the deeper layers while updating those of the shallow layers during fine-tuning. Freezing the parameters of deeper layers preserves the model's ability to recognize semantic and high-level features across all categories, as established during pre-training. Moreover, data from different categories exhibit similar individual-specific low-level features in SSVEP-BCIs. Consequently, updating the parameters of shallow layers-responsible for processing low-level features-with data solely from partial categories enables the fine-tuned model to efficiently capture the individual-related features shared by all categories. The effectiveness of SOFT is validated using two public datasets. Comparative analysis with commonly used end-to-end and last-layer fine-tuning methods reveals that SOFT achieves higher classification accuracy while requiring fewer calibration categories. The proposed SOFT method further decreases the calibration efforts for target subjects by reducing the calibration category requirements, thereby improving the feasibility of SSVEP-BCIs for real-world applications.},
}

@article {pmid40437332,
year = {2025},
author = {Cherukuri, SB and Ramakrishnan, S},
title = {Eye-blink artifact removal in single-channel electroencephalogram using K-means and Savitzky Golay-singular Spectrum Analysis hybrid technique.},
journal = {Physical and engineering sciences in medicine},
volume = {},
number = {},
pages = {},
pmid = {40437332},
issn = {2662-4737},
abstract = {Electroencephalogram (EEG) acquisition systems are used to record the neural condition of humans for diagnosing various neural problems. The eye-blink or Electrooculogram (EOG) artifact caused by eye-lid movements, influences the EEG signal measurements and interferes with the diagnosis. The complete removal of eye-blink artifact while preserving the EEG content is a challenging task that needs highly efficient denoising methods, particularly from Single-Channel EEG which is widely used for Out-Of-Hospital (OOH) neurological patients and for Brain-Computer-Interface (BCI) applications. When compared to multi-channel EEG systems, Single-channel EEG system suffers certain difficulties such as lack of spatial information, redundancy, etc. This paper proposes an innovative hybrid method combining K-Means clustering and Savitzky Golay-Singular Spectrum Analysis (SG-SSA) methods for effective eye-blink artifact removal from single channel EEG. The eye-blink artifact is extracted and then subtracted from the noisy EEG signal, so that the EEG content available in the eye-blink periods are preserved. Through extensive experiments with synthetic as well as real time EEG, we show that our proposed method outperforms the other contemporary methods from literature. Our proposed hybrid approach achieves a significant reduction in Mean Absolute Error (MAE) and Relative Root Mean Square Error (RRMSE) than the Fourier-Bessel Series Expansion based Empirical Wavelet Transform (FBSE-EWT), SSA combined with independent component analysis (SSA-ICA) and Ensemble Empirical Mode Decomposition combined with ICA (EEMD-ICA), proposed in recent literature.},
}

@article {pmid40436265,
year = {2025},
author = {Chopra, M and Kumar, H},
title = {Navigating the complexities of spinal cord injury: an overview of pathology, treatment strategies and clinical trials.},
journal = {Drug discovery today},
volume = {},
number = {},
pages = {104387},
doi = {10.1016/j.drudis.2025.104387},
pmid = {40436265},
issn = {1878-5832},
abstract = {Spinal cord injury (SCI) is a debilitating neurological condition characterized by sensory and motor deficits. It significantly affects patient quality of life and poses a substantial socioeconomic burden. The complex and multifaceted pathophysiology of SCI complicates its effective treatment. Following the primary mechanical insult, a secondary injury cascade disrupts the microenvironment at the injury site, exacerbating the tissue damage. Despite extensive research, no fully effective treatment is currently available. This review explores current pharmacological and non-pharmacological treatment strategies at the preclinical and clinical stages, providing insights into promising interventions. The findings highlight potential avenues for future research aimed at improving SCI management.},
}

@article {pmid40434889,
year = {2025},
author = {Ruszala, BM and Schieber, MH},
title = {Injecting information in the cortical reach-to-grasp network is effective in ventral but not dorsal nodes.},
journal = {Cell reports},
volume = {44},
number = {5},
pages = {115664},
doi = {10.1016/j.celrep.2025.115664},
pmid = {40434889},
issn = {2211-1247},
abstract = {Although control of movement involves many cortical association areas, bidirectional brain-machine interfaces (BMIs) typically decode movement intent from the motor cortex and deliver feedback information to the primary somatosensory cortex (S1). Compared to the S1, the parietal and premotor areas encode more complex information about object properties, hand pre-shaping, and reach trajectories. BMIs therefore might deliver richer information to those cortical association areas than to primary areas. Here, we investigated whether instructions for a center-out task could be delivered via intracortical microstimulation (ICMS) in the anterior intraparietal area (AIP), dorsal posterior parietal cortex (dPPC), or dorsal premotor cortex (PMd) as well as the ventral premotor cortex (PMv) and S1. Two monkeys successfully learned to use AIP, PMv, or S1-ICMS instructions, but neither learned to use dPPC- or PMd-ICMS instructions. The AIP, PMv, and S1 may thus be effective cortical territory for delivering information to the brain, whereas the dPPC or PMd may be comparatively ineffective.},
}

@article {pmid40434816,
year = {2025},
author = {Liu, L},
title = {Did you see it?.},
journal = {eLife},
volume = {14},
number = {},
pages = {},
pmid = {40434816},
issn = {2050-084X},
mesh = {Humans ; *Brain/physiology ; *Consciousness/physiology ; },
abstract = {Cautious reporting choices can artificially enhance how well analyses of brain activity reflect conscious and unconscious experiences, making distinguishing between the two more challenging.},
}

Humans
*Brain/physiology
*Consciousness/physiology

@article {pmid40434551,
year = {2025},
author = {Sokhadze, E},
title = {Neurofeedback and Brain-Computer Interface-Based Methods for Post-stroke Rehabilitation.},
journal = {Applied psychophysiology and biofeedback},
volume = {},
number = {},
pages = {},
pmid = {40434551},
issn = {1573-3270},
abstract = {Stroke has been identified as a major public health concern and one of the leading causes contributing to long-term neurological disability. People suffering from stroke often present with upper limb paralysis impacting their quality of life and ability to work. Motor impairments in the upper limb represent the most prevalent symptoms in stroke sufferers. There is a need to develop novel intervention strategies that can be used as stand-alone techniques or combined with current gold standard post-stroke rehabilitation procedures. There was reported evidence about the utility of rehabilitation protocols with motor imagery (MI) used either alone or in combination with physical therapy resulting in enhancement of post-stroke functional recovery of paralyzed limbs. Brain-Computer Interface (BCI) and EEG neurofeedback (NFB) training can be considered as novel technologies to be used in conjunction with MI and motor attempt (MA) to enable direct translation of EEG induced by imagery or attempted movement to arrange training that has potential to enhance functional motor recovery of upper limbs after stroke. There are reported several controlled trials and multiple cases series that have shown that stroke patients are able to learn modulation of their EEG sensorimotor rhythm in BCI mode to control external devices, including exoskeletons, prosthetics, and such interventions were shown promise in facilitation of recovery in stroke sufferers. A review of the literature suggests there has been significant progress in the development of new methods for post-stroke rehabilitation procedures. There are reviewed findings supportive of NFB and BCI methods as evidence-based treatment for post-stroke motor function recovery.},
}

@article {pmid40433677,
year = {2025},
author = {He, J and Zhou, G and Sun, B and Yan, L and Lang, X and Yang, Y and Hao, H},
title = {Graphene quantum dots induced performance enhancement in memristors.},
journal = {Nanoscale},
volume = {},
number = {},
pages = {},
doi = {10.1039/d5nr00597c},
pmid = {40433677},
issn = {2040-3372},
abstract = {With the rapid development of information technology, the demand for miniaturization, integration, and intelligence of electronic devices is growing rapidly. As a key device in the non-von Neumann architecture, memristors can perform computations while storing data, enhancing computational efficiency and reducing power consumption. Memristors have become pivotal in driving the advancement of artificial intelligence (AI) and Internet of Things technologies. Combining the electronic properties of graphene with the size effects of quantum dots, graphene quantum dot (GQD)-based memristors exhibit potential applications in constructing brain-inspired neuromorphic computing systems and achieving AI hardware acceleration, making them a focal point of research interest. This review provides an overview of the preparation, mechanism, and application of GQD-based memristors. Initially, the structure, properties, and synthesis methods of GQDs are introduced in detail. Subsequently, the memristive mechanisms of GQD-based memristors are presented from three perspectives: the metal conductive filament mechanism, the electron trapping and detrapping mechanism, and the oxygen vacancy conductive filament mechanism. Furthermore, the different application scenarios of GQD-based memristors in both digital and analog types are summarized, encompassing information storage, brain-like artificial synapses, visual perception systems, and brain-machine interfaces. Finally, the challenges and future development prospects of GQD-based memristors are discussed.},
}

@article {pmid40431969,
year = {2025},
author = {You, Z and Guo, Y and Zhang, X and Zhao, Y},
title = {Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {10},
pages = {},
doi = {10.3390/s25103178},
pmid = {40431969},
issn = {1424-8220},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Algorithms ; Machine Learning ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Brain/physiology ; },
abstract = {Driven by the remarkable capabilities of machine learning, brain-computer interfaces (BCIs) are carving out an ever-expanding range of applications across a multitude of diverse fields. Notably, electroencephalogram (EEG) signals have risen to prominence as the most prevalently utilized signals within BCIs, owing to their non-invasive essence, exceptional portability, cost-effectiveness, and high temporal resolution. However, despite the significant strides made, the paucity of EEG data has emerged as the main bottleneck, preventing generalization of decoding algorithms. Taking inspiration from the resounding success of generative models in computer vision and natural language processing arenas, the generation of synthetic EEG data from limited recorded samples has recently garnered burgeoning attention. This paper undertakes a comprehensive and thorough review of the techniques and methodologies underpinning the generative models of the general EEG, namely the variational autoencoder (VAE), the generative adversarial network (GAN), and the diffusion model. Special emphasis is placed on their practical utility in augmenting EEG data. The structural designs and performance metrics of the different generative approaches in various application domains have been meticulously dissected and discussed. A comparative analysis of the strengths and weaknesses of each existing model has been carried out, and prospective avenues for future enhancement and refinement have been put forward.},
}

*Electroencephalography/methods
Humans
*Brain-Computer Interfaces
Algorithms
Machine Learning
Signal Processing, Computer-Assisted
Neural Networks, Computer
Brain/physiology

@article {pmid40431893,
year = {2025},
author = {Sasatake, Y and Matsushita, K},
title = {EEG Baseline Analysis for Effective Extraction of P300 Event-Related Potentials for Welfare Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {10},
pages = {},
doi = {10.3390/s25103102},
pmid = {40431893},
issn = {1424-8220},
support = {JPMJSP2125//JST SPRING/ ; Not Applicable//THERS Make New Standards Program for the Next Generation Researchers/ ; },
mesh = {Humans ; *Event-Related Potentials, P300/physiology ; *Electroencephalography/methods ; Male ; Adult ; Female ; *Brain-Computer Interfaces ; Young Adult ; Photic Stimulation ; Signal Processing, Computer-Assisted ; },
abstract = {Enabling individuals with complete paralysis to operate devices voluntarily requires an effective interface; EEG-based P300 event-related potential (ERP) interfaces are considered a promising approach. P300 is an EEG peak generated in response to specific sensory stimuli recognized by an individual. Accurate detection of this peak necessitates a stable pre-stimulus baseline EEG signal, which serves as the reference for baseline correction. Previous studies have commonly employed either a single-time-point amplitude (e.g., at 100 ms before stimulus onset) or a time-range-averaged amplitude over a specified pre-stimulus period (e.g., 0-200 ms) as a baseline correction method, assuming these provide the most stable EEG reference. However, in assistive P300 interfaces, continuous visual stimuli at 400 ms intervals are typically used to efficiently evoke P300 peaks. Since stimuli are presented before the EEG stabilizes, it remains unclear whether conventional neuroscience baseline correction methods are suitable for such applications. To address this, the present study conducted a P300 induction experiment based on continuous 400 ms interval visual stimuli. Using EEG data recorded from 0 to 1000 ms before each visual stimulus (sampled at 1 ms intervals), we applied three baseline correction methods-single-time-point amplitude, time-range-averaged amplitude, and multi-time-point amplitude-to determine the most effective EEG reference and evaluate the impact on P300 detection performance. The results showed that baseline correction using an amplitude at a single point in time is unstable when the basic EEG rhythm and low-frequency noise remain, while time-range-averaged baseline correction using the 0-200 ms pre-stimulus period led to relatively effective P300 detection. However, it was also found that using only one value averaged over the amplitude from 0 to 200 ms did not result in an accurate EEG reference potential, resulting in an error. Finally, this study confirmed that the multi-time-point baseline correction method, through which the amplitude state from 0 to 200 ms before the visual stimulus is comprehensively evaluated, may be the most effective method for P300 determination.},
}

Humans
*Event-Related Potentials, P300/physiology
*Electroencephalography/methods
Male
Adult
Female
*Brain-Computer Interfaces
Young Adult
Photic Stimulation
Signal Processing, Computer-Assisted

@article {pmid40431780,
year = {2025},
author = {Polo-Hortigüela, C and Ortiz, M and Soriano-Segura, P and Iáñez, E and Azorín, JM},
title = {Time-Frequency Analysis of Motor Imagery During Plantar and Dorsal Flexion Movements Using a Low-Cost Ankle Exoskeleton.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {10},
pages = {},
doi = {10.3390/s25102987},
pmid = {40431780},
issn = {1424-8220},
support = {PID2021-124111OB-C31//MICIU /AEI/10.13039/501100011033 and by ERDF, EU/ ; PRE2022-103336//MICIU/AEI/10.13039 501100011033/ ; //Valencian Graduate School and Research Network of Artificial Intelligence (ValgrAI), Generalitat Valenciana and European Union/ ; //Project "Neurokit" funded by Centro Internacional para la Investigación del Envejecimiento de la Fundación de la Comunitat Valenciana (ICAR)/ ; 101118964//European Union's research and innovation programme under the Marie Skłodowska-Curie/ ; },
mesh = {Humans ; Electroencephalography/methods ; Brain-Computer Interfaces ; Movement/physiology ; Male ; *Ankle/physiology ; *Exoskeleton Device ; Adult ; Biomechanical Phenomena ; Foot/physiology ; Female ; Wearable Electronic Devices ; Fourier Analysis ; },
abstract = {Sensor technology plays a fundamental role in neuro-motor rehabilitation by enabling precise movement analysis and control. This study explores the integration of brain-machine interfaces (BMIs) and wearable sensors to enhance motor recovery in individuals with neuro-motor impairments. Specifically, different time-frequency transforms are evaluated to analyze the correlation between electroencephalographic (EEG) activity and ankle position, measured by using inertial measurement units (IMUs). A low-cost ankle exoskeleton was designed to conduct the experimental trials. Six subjects performed plantar and dorsal flexion movements while the EEG and IMU signals were recorded. The correlation between brain activity and foot kinematics was analyzed using the Short-Time Fourier Transform (STFT), Stockwell (ST), Hilbert-Huang (HHT), and Chirplet (CT) methods. The 8-20 Hz frequency band exhibited the highest correlation values. For motor imagery classification, the STFT achieved the highest accuracy (92.9%) using an EEGNet-based classifier and a state-machine approach. This study presents a dual approach: the analysis of EEG-movement correlation in different cognitive states, and the systematic comparison of four time-frequency transforms for both correlation and classification performance. The results support the potential of combining EEG and IMU data for BMI applications and highlight the importance of cognitive state in motion analysis for accessible neurorehabilitation technologies.},
}

Humans
Electroencephalography/methods
Brain-Computer Interfaces
Movement/physiology
Male
*Ankle/physiology
*Exoskeleton Device
Adult
Biomechanical Phenomena
Foot/physiology
Female
Wearable Electronic Devices
Fourier Analysis

@article {pmid40428890,
year = {2025},
author = {Endzelytė, E and Petruševičienė, D and Kubilius, R and Mingaila, S and Rapolienė, J and Rimdeikienė, I},
title = {Integrating Brain-Computer Interface Systems into Occupational Therapy for Enhanced Independence of Stroke Patients: An Observational Study.},
journal = {Medicina (Kaunas, Lithuania)},
volume = {61},
number = {5},
pages = {},
doi = {10.3390/medicina61050932},
pmid = {40428890},
issn = {1648-9144},
mesh = {Humans ; Male ; Female ; *Brain-Computer Interfaces/standards/trends ; *Occupational Therapy/methods/standards ; *Stroke Rehabilitation/methods/standards ; Middle Aged ; Aged ; Activities of Daily Living/psychology ; Upper Extremity/physiopathology ; Adult ; Stroke/complications ; },
abstract = {Background and Objectives: Brain-computer interface (BCI) technology is revolutionizing stroke rehabilitation by offering innovative neuroengineering solutions to address neurological deficits. By bypassing peripheral nerves and muscles, BCIs enable individuals with severe motor impairments to communicate their intentions directly through control signals derived from brain activity, opening new pathways for recovery and improving the quality of life. The aim of this study was to explore the beneficial effects of BCI system-based interventions on upper limb motor function and performance of activities of daily living (ADL) in stroke patients. We hypothesized that integrating BCI into occupational therapy would result in measurable improvements in hand strength, dexterity, independence in daily activities, and cognitive function compared to baseline. Materials and Methods: An observational study was conducted on 56 patients with subacute stroke. All patients received standard medical care and rehabilitation for 54 days, as part of the comprehensive treatment protocol. Patients underwent BCI training 2-3 times a week instead of some occupational therapy sessions, with each patient completing 15 sessions of BCI-based recoveriX treatment during rehabilitation. The occupational therapy program included bilateral exercises, grip-strengthening activities, fine motor/coordination tasks, tactile discrimination exercises, proprioceptive training, and mirror therapy to enhance motor recovery through visual feedback. Participants received ADL-related training aimed at improving their functional independence in everyday activities. Routine occupational therapy was provided five times a week for 50 min per session. Upper extremity function was evaluated using the Box and Block Test (BBT), Nine-Hole Peg Test (9HPT), and dynamometry to assess gross manual dexterity, fine motor skills, and grip strength. Independence in daily living was assessed using the Functional Independence Measure (FIM). Results: Statistically significant improvements were observed across all the outcome measures (p < 0.001). The strength of the stroke-affected hand improved from 5.0 kg to 6.7 kg, and that of the unaffected hand improved from 29.7 kg to 40.0 kg. Functional independence increased notably, with the FIM scores rising from 43.0 to 83.5. Cognitive function also improved, with MMSE scores increasing from 22.0 to 26.0. The effect sizes ranged from moderate to large, indicating clinically meaningful benefits. Conclusions: This study suggests that BCI-based occupational therapy interventions effectively improve upper extremity motor function and daily functions and have a positive impact on the cognition of patients with subacute stroke.},
}

Humans
Male
Female
*Brain-Computer Interfaces/standards/trends
*Occupational Therapy/methods/standards
*Stroke Rehabilitation/methods/standards
Middle Aged
Aged
Activities of Daily Living/psychology
Upper Extremity/physiopathology
Adult
Stroke/complications

@article {pmid40428702,
year = {2025},
author = {Ma, X and Miao, T and Xie, F and Zhang, J and Zheng, L and Liu, X and Hai, H},
title = {Development of Wearable Wireless Multichannel f-NIRS System to Evaluate Activities.},
journal = {Micromachines},
volume = {16},
number = {5},
pages = {},
doi = {10.3390/mi16050576},
pmid = {40428702},
issn = {2072-666X},
abstract = {Functional near-infrared spectroscopy is a noninvasive neuroimaging technique that uses optical signals to monitor subtle changes in hemoglobin concentrations within the superficial tissue of the human body. This technology has widespread applications in long-term brain-computer interface monitoring within both traditional medical domains and, increasingly, domestic settings. The popularity of this approach lies in the fact that new single-channel brain oxygen sensors can be used in a variety of scenarios. Given the diverse sensor structure requirements across applications and numerous approaches to data acquisition, the accurate extraction of comprehensive brain activity information requires a multichannel near-infrared system. This study proposes a novel distributed multichannel near-infrared system that integrates two near-infrared light emissions at differing wavelengths (660 nm, 850 nm) with a photoelectric receiver. This substantially improves the accuracy of regional signal sampling. Through a basic long-time mental arithmetic paradigm, we demonstrate that the accompanying algorithm supports offline analysis and is sufficiently versatile for diverse scenarios relevant to the system's functionality.},
}

@article {pmid40428683,
year = {2025},
author = {Hong, S},
title = {Wireless Optogenetic Microsystems Accelerate Artificial Intelligence-Neuroscience Coevolution Through Embedded Closed-Loop System.},
journal = {Micromachines},
volume = {16},
number = {5},
pages = {},
doi = {10.3390/mi16050557},
pmid = {40428683},
issn = {2072-666X},
support = {N/A//Hongik University/ ; },
abstract = {Brain-inspired models in artificial intelligence (AI) originated from foundational insights in neuroscience. In recent years, this relationship has been moving toward a mutually reinforcing feedback loop. Currently, AI is significantly contributing to advancing our understanding of neuroscience. In particular, when combined with wireless optogenetics, AI enables experiments without physical constraints. Furthermore, AI-driven real-time analysis facilitates closed-loop control, allowing experimental setups across a diverse range of scenarios. And a deeper understanding of these neural networks may, in turn, contribute to future advances in AI. This work demonstrates the synergy between AI and miniaturized neural technology, particularly through wireless optogenetic systems designed for closed-loop neural control. We highlight how AI is now revolutionizing neuroscience experiments from decoding complex neural signals and quantifying behavior, to enabling closed-loop interventions and high-throughput phenotyping in freely moving subjects. Notably, AI-integrated wireless implants can monitor and modulate biological processes with unprecedented precision. We then recount how neuroscience insights derived from AI-integrated neuroscience experiments can potentially inspire the next generation of machine intelligence. Insights gained from these technologies loop back to inspire more efficient and robust AI systems. We discuss future directions in this positive feedback loop between AI and neuroscience, arguing that the coevolution of the two fields, grounded in technologies like wireless optogenetics and guided by reciprocal insight, will accelerate progress in both, while raising new challenges and opportunities for interdisciplinary collaboration.},
}

@article {pmid40428114,
year = {2025},
author = {Zheng, Y and Wu, S and Chen, J and Yao, Q and Zheng, S},
title = {Cross-Subject Motor Imagery Electroencephalogram Decoding with Domain Generalization.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {5},
pages = {},
doi = {10.3390/bioengineering12050495},
pmid = {40428114},
issn = {2306-5354},
abstract = {Decoding motor imagery (MI) electroencephalogram (EEG) signals in the brain-computer interface (BCI) can assist patients in accelerating motor function recovery. To realize the implementation of plug-and-play functionality for MI-BCI applications, cross-subject models are employed to alleviate time-consuming calibration and avoid additional model training for target subjects by utilizing EEG data from source subjects. However, the diversity in data distribution among subjects limits the model's robustness. In this study, we investigate a cross-subject MI-EEG decoding model with domain generalization based on a deep learning neural network that extracts domain-invariant features from source subjects. Firstly, a knowledge distillation framework is adopted to obtain the internally invariant representations based on spectral features fusion. Then, the correlation alignment approach aligns mutually invariant representations between each pair of sub-source domains. In addition, we use distance regularization on two kinds of invariant features to enhance generalizable information. To assess the effectiveness of our approach, experiments are conducted on the BCI Competition IV 2a and the Korean University dataset. The results demonstrate that the proposed model achieves 8.93% and 4.4% accuracy improvements on two datasets, respectively, compared with current state-of-the-art models, confirming that the proposed approach can effectively extract invariant features from source subjects and generalize to the unseen target distribution, hence paving the way for effective implementation of the plug-and-play functionality in MI-BCI applications.},
}

@article {pmid40426690,
year = {2025},
author = {Taha, BN and Baykara, M and Alakuş, TB},
title = {Neurophysiological Approaches to Lie Detection: A Systematic Review.},
journal = {Brain sciences},
volume = {15},
number = {5},
pages = {},
doi = {10.3390/brainsci15050519},
pmid = {40426690},
issn = {2076-3425},
abstract = {Background and Objectives: Lie detection is crucial in domains such as security, law enforcement, and clinical assessments. Traditional methods suffer from reliability issues and susceptibility to countermeasures. In recent years, electroencephalography (EEG) and particularly the Event-Related Potential (ERP) P300 component have gained prominence for identifying concealed information. This systematic review aims to evaluate recent studies (2017-2024) on EEG-based lie detection using ERP P300 responses, especially in relation to recognized and unrecognized face stimuli. The goal is to summarize commonly used EEG signal processing techniques, feature extraction methods, and classification algorithms, identifying those that yield the highest accuracy in lie detection tasks. Methods: This review followed PRISMA guidelines for systematic reviews. A comprehensive literature search was conducted using IEEE Xplore, Web of Science, Scopus, and Google Scholar, restricted to English-language articles from 2017 to 2024. Studies were included if they focused on EEG-based lie detection, utilized experimental protocols like Concealed Information Test (CIT), Guilty Knowledge Test (GKT), or Deceit Identification Test (DIT), and evaluated classification accuracy using ERP P300 components. Results: CIT with ERP P300 was the most frequently employed protocol. The most used preprocessing method was Bandpass Filtering (BPF), and the Discrete Wavelet Transform (DWT) emerged as the preferred feature extraction technique due to its suitability for non-stationary EEG signals. Among classification algorithms, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Convolutional Neural Networks (CNN) were frequently utilized. These findings demonstrate the effectiveness of hybrid and deep learning-based models in enhancing classification performance. Conclusions: EEG-based lie detection, particularly using the ERP P300 response to face recognition tasks, shows promising accuracy and robustness compared to traditional polygraph methods. Combining advanced signal processing methods with machine learning and deep learning classifiers significantly improves performance. This review identifies the most effective methodologies and suggests that future research should focus on real-time applications, cross-individual generalization, and reducing system complexity to facilitate broader adoption.},
}

@article {pmid40426631,
year = {2025},
author = {Mao, Q and Zhu, H and Yan, W and Zhao, Y and Hei, X and Luo, J},
title = {MCL-SWT: Mirror Contrastive Learning with Sliding Window Transformer for Subject-Independent EEG Recognition.},
journal = {Brain sciences},
volume = {15},
number = {5},
pages = {},
doi = {10.3390/brainsci15050460},
pmid = {40426631},
issn = {2076-3425},
support = {23JK0556//the Scientific Research Program Founded by Shaanxi Provincial Education Department of China/ ; 61906152, 62376213 and U21A20524//the National Natural Science Foundation of China/ ; },
abstract = {Background: In brain-computer interfaces (BCIs), transformer-based models have found extensive application in motor imagery (MI)-based EEG signal recognition. However, for subject-independent EEG recognition, these models face challenges: low sensitivity to spatial dynamics of neural activity and difficulty balancing high temporal resolution features with manageable computational complexity. The overarching objective is to address these critical issues. Methods: We introduce Mirror Contrastive Learning with Sliding Window Transformer (MCL-SWT). Inspired by left/right hand motor imagery inducing event-related desynchronization (ERD) in the contralateral sensorimotor cortex, we develop a mirror contrastive loss function. It segregates feature spaces of EEG signals from contralateral ERD locations while curtailing variability in signals sharing similar ERD locations. The Sliding Window Transformer computes self-attention scores over high temporal resolution features, enabling efficient capture of global temporal dependencies. Results: Evaluated on benchmark datasets for subject-independent MI EEG recognition, MCL-SWT achieves classification accuracies of 66.48% and 75.62%, outperforming State-of-the-Art models by 2.82% and 2.17%, respectively. Ablation studies validate the efficacy of both the mirror contrastive loss and sliding window mechanism. Conclusions: These findings underscore MCL-SWT's potential as a robust, interpretable framework for subject-independent EEG recognition. By addressing existing challenges, MCL-SWT could significantly advance BCI technology development.},
}

@article {pmid40426214,
year = {2025},
author = {Li, K and Liang, H and Qiu, J and Zhang, X and Cai, B and Wang, D and Zhang, D and Lin, B and Han, H and Yang, G and Zhu, Z},
title = {Reveal the mechanism of brain function with fluorescence microscopy at single-cell resolution: from neural decoding to encoding.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {118},
pmid = {40426214},
issn = {1743-0003},
support = {2024XHSZ-Y08//Zhejiang Health Information Association Research Program/ ; 82401786//National Natural Science Foundation of China/ ; 82201637//National Natural Science Foundation of China/ ; 2024KY246//Zhejiang Provincial Medical and Health Technology Project/ ; BMI2400025//Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence, Zhejiang University/ ; 2024C03150//Key R&D Program of Zhejiang Province/ ; J-202402//Qiushi Youth Program from Scientific Research Cultivation Foundation/ ; },
mesh = {*Brain/physiology/cytology ; Humans ; Microscopy, Fluorescence/methods ; Animals ; *Single-Cell Analysis/methods ; *Neurons/physiology ; Optogenetics ; *Brain Mapping/methods ; },
abstract = {As a key pathway for understanding behavior, cognition, and emotion, neural decoding and encoding provide effective tools to bridge the gap between neural mechanisms and imaging recordings, especially at single-cell resolution. While neural decoding aims to establish an interpretable theory of how complex biological behaviors are represented in neural activities, neural encoding focuses on manipulating behaviors through the stimulation of specific neurons. We thoroughly analyze the application of fluorescence imaging techniques, particularly two-photon fluorescence imaging, in decoding neural activities, showcasing the theoretical analysis and technological advancements from imaging recording to behavioral manipulation. For decoding models, we compared linear and nonlinear methods, including independent component analysis, random forests, and support vector machines, highlighting their capabilities to reveal the intricate mapping between neural activity and behavior. By employing synthetic stimuli via optogenetics, fundamental principles of neural encoding are further explored. We elucidate various encoding types based on different stimulus paradigms-quantity encoding, spatial encoding, temporal encoding, and frequency encoding-enhancing our understanding of how the brain represents and processes information. We believe that fluorescence imaging-based neural decoding and encoding techniques have deepened our understanding of the brain, and hold great potential in paving the way for future neuroscience research and clinical applications.},
}

*Brain/physiology/cytology
Humans
Microscopy, Fluorescence/methods
Animals
*Single-Cell Analysis/methods
*Neurons/physiology
Optogenetics
*Brain Mapping/methods

@article {pmid40425805,
year = {2025},
author = {Liu, CW and Wang, YM and Chen, SY and Lu, LY and Liang, TY and Fang, KC and Chen, P and Lee, IC and Liu, WC and Kumar, A and Kuo, SH and Lee, JC and Lo, CC and Wu, SC and Pan, MK},
title = {The cerebellum shapes motions by encoding motor frequencies with precision and cross-individual uniformity.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {40425805},
issn = {2157-846X},
support = {NTUMC 110C101-011//NTU | College of Medicine, National Taiwan University (College of Medicine, National Taiwan University)/ ; NSC-145-11//National Taiwan University Hospital (NTUH)/ ; 113-UN0013//National Taiwan University Hospital (NTUH)/ ; 108-039//National Taiwan University Hospital (NTUH)/ ; 112-UN0024//National Taiwan University Hospital (NTUH)/ ; 113-E0001//National Taiwan University Hospital (NTUH)/ ; AS-TM-112-01-02//Academia Sinica/ ; NHRI-EX113-11303NI//National Health Research Institutes (NHRI)/ ; 109-2326-B-002-013-MY4//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 107-2321-B-002-020//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 108-2321-B-002-011//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 108-2321-002-059-MY2//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 110-2321-B-002-012//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 111-2628-B-002-036//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 112-2628-B-002-011//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 113-2628-B-002-002//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; R01NS118179//Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)/ ; R01NS104423//Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)/ ; R01NS124854//Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)/ ; },
abstract = {Understanding brain behaviour encoding or designing neuroprosthetics requires identifying precise, consistent neural algorithms across individuals. However, cerebral microstructures and activities are individually variable, posing challenges for identifying precise codes. Here, despite cerebral variability, we report that the cerebellum shapes motor kinematics by encoding dynamic motor frequencies with remarkable numerical precision and cross-individual uniformity. Using in vivo electrophysiology and optogenetics in mice, we confirm that deep cerebellar neurons encode frequencies using populational tuning of neuronal firing probabilities, creating cerebellar oscillations and motions with matched frequencies. The mechanism is consistently presented in self-generated rhythmic and non-rhythmic motions triggered by a vibrational platform or skilled tongue movements of licking in all tested mice with cross-individual uniformity. The precision and uniformity allowed us to engineer complex motor kinematics with designed frequencies. We further validate the frequency-coding function of the human cerebellum using cerebellar electroencephalography recordings and alternating current stimulation during voluntary tapping tasks. Our findings reveal a cerebellar algorithm for motor kinematics with precision and uniformity, the mathematical foundation for a brain-computer interface for motor control.},
}

@article {pmid40425792,
year = {2025},
author = {Chen, ZP and Zhao, X and Wang, S and Cai, R and Liu, Q and Ye, H and Wang, MJ and Peng, SY and Xue, WX and Zhang, YX and Li, W and Tang, H and Huang, T and Zhang, Q and Li, L and Gao, L and Zhou, H and Hang, C and Zhu, JN and Li, X and Liu, X and Cong, Q and Yan, C},
title = {GABA-dependent microglial elimination of inhibitory synapses underlies neuronal hyperexcitability in epilepsy.},
journal = {Nature neuroscience},
volume = {},
number = {},
pages = {},
pmid = {40425792},
issn = {1546-1726},
support = {82373856//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31900824//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32371074//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32071097//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82471481//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32200778//National Natural Science Foundation of China (National Science Foundation of China)/ ; 020813005031//Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation)/ ; 2019M651779//Postdoctoral Research Foundation of China (China Postdoctoral Research Foundation)/ ; },
abstract = {Neuronal hyperexcitability is a common pathophysiological feature of many neurological diseases. Neuron-glia interactions underlie this process but the detailed mechanisms remain unclear. Here, we reveal a critical role of microglia-mediated selective elimination of inhibitory synapses in driving neuronal hyperexcitability. In epileptic mice of both sexes, hyperactive inhibitory neurons directly activate surveilling microglia via GABAergic signaling. In response, these activated microglia preferentially phagocytose inhibitory synapses, disrupting the balance between excitatory and inhibitory synaptic transmission and amplifying network excitability. This feedback mechanism depends on both GABA-GABAB receptor-mediated microglial activation and complement C3-C3aR-mediated microglial engulfment of inhibitory synapses, as pharmacological or genetic blockage of both pathways effectively prevents inhibitory synapse loss and ameliorates seizure symptoms in mice. Additionally, putative cell-cell interaction analyses of brain tissues from males and females with temporal lobe epilepsy reveal that inhibitory neurons induce microglial phagocytic states and inhibitory synapse loss. Our findings demonstrate that inhibitory neurons can directly instruct microglial states to control inhibitory synaptic transmission through a feedback mechanism, leading to the development of neuronal hyperexcitability in temporal lobe epilepsy.},
}

@article {pmid40425030,
year = {2025},
author = {Dehgan, A and Abdelhedi, H and Hadid, V and Rish, I and Jerbi, K},
title = {Artificial neural networks for magnetoencephalography: A review of an emerging field.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/addd4a},
pmid = {40425030},
issn = {1741-2552},
abstract = {Objective: Magnetoencephalography (MEG) is a cutting-edge neuroimaging technique that measures the intricate brain dynamics underlying cognitive processes with an unparalleled combination of high temporal and spatial precision. While MEG data analytics have traditionally relied on advanced signal processing and mathematical and statistical tools, the recent surge in Artificial Intelligence (AI) has led to the growing use of Machine Learning (ML) methods for MEG data classification. An emerging trend in this field is the use of Artificial Neural Networks (ANNs) to address various MEG-related tasks. This review aims to provide a comprehensive overview of the state of the art in this area.Approach: This topical review included studies that applied ANNs to MEG data. Studies were sourced from PubMed, Google Scholar, arXiv, and bioRxiv using targeted search queries. The included studies were categorized into three groups: Classification, Modeling, and Other. Key findings and trends were summarized to provide a comprehensive assessment of the field.Main Results:The review identified 119 relevant studies, with 69 focused on Classification, 16 on Modeling, and 34 in the Other category. Classification studies addressed tasks such as brain decoding, clinical diagnostics, and BCI implementations, often achieving high predictive accuracy. Modeling studies explored the alignment between ANN activations and brain processes, offering insights into the neural representations captured by these networks. The Other category demonstrated innovative uses of ANNs for artifact correction, preprocessing, and neural source localization.Significance: By establishing a detailed portrait of the current state of the field, this review highlights the strengths and current limitations of ANNs in MEG research. It also provides practical recommendations for future work, offering a helpful reference for seasoned researchers and newcomers interested in using ANNs to explore the complex dynamics of the human brain with MEG.},
}

@article {pmid40425024,
year = {2025},
author = {Wolpaw, JR},
title = {Making brain-computer interfaces as reliable as muscles.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/addd47},
pmid = {40425024},
issn = {1741-2552},
abstract = {While BCIs can restore basic communication to people lacking muscle control, they cannot yet restore actions that require the extremely high reliability of natural (i.e., muscle-based) actions. Most BCI research focuses on neural engineering; it seeks to improve the measurement and analysis of brain signals. But neural engineering alone cannot make BCIs reliable. A BCI does not simply decode brain activity; it enables its user to acquire a skill that is produced not by nerves and muscles but rather by the BCI. Thus, BCI research should focus also on neuroscience; it should seek to develop BCI skills that emulate natural skills. A natural skill is produced by a network of neurons and synapses that may extend from cortex to spinal cord. This network has been given the name heksor, from the ancient Greek word hexis. A heksor changes through life; it modifies itself as needed to maintain the key features of its skill, the attributes that make the skill satisfactory. Heksors overlap; they share neurons and synapses. Through their concurrent changes, heksors keep neuronal and synaptic properties in a negotiated equilibrium that enables each to produce its skill satisfactorily. A BCI-based skill is produced by a synthetic heksor, a network of neurons, synapses, and software that produces a BCI-based skill and should change as needed to maintain the skill's key features. A synthetic heksor shares neurons and synapses with natural heksors. Like natural heksors, it can benefit from multimodal sensory feedback, using signals from multiple brain areas, and maintaining the skill's key features rather than all its details. A synthetic heksor also needs successful co-adaptation between its CNS and software components and successful integration into the negotiated equilibrium that heksors establish and maintain. With due attention to both neural engineering and neuroscience, BCIs could become as reliable as muscles.},
}

@article {pmid40425023,
year = {2025},
author = {Wu, D},
title = {Revisiting Euclidean alignment for transfer learning in EEG-based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/addd49},
pmid = {40425023},
issn = {1741-2552},
abstract = {Due to large intra-subject and inter-subject variabilities of electroencephalogram (EEG) signals, EEG-based brain-computer interfaces (BCIs) usually need subject-specific calibration to tailor the decoding algorithm for each new subject, which is time-consuming and user-unfriendly, hindering their real-world applications. Transfer learning (TL) has been extensively used to expedite the calibration, by making use of EEG data from other subjects/sessions. An important consideration in TL for EEG-based BCIs is to reduce the data distribution discrepancies among different subjects/sessions, to avoid negative transfer. Euclidean alignment (EA) was proposed in 2020 to address this challenge. Numerous experiments from 13 different BCI paradigms demonstrated its effectiveness and efficiency. This paper revisits EA, explaining its procedure and correct usage, introducing its applications and extensions, and pointing out potential new research directions. It should be very helpful to BCI researchers, especially those who are working on EEG signal decoding.},
}

@article {pmid40424668,
year = {2025},
author = {Sawyer, A and Brannigan, J and Spielman, L and , and Putrino, D and Fry, A},
title = {Development of a novel clinical outcome assessment: digital instrumental activities of daily living.},
journal = {EBioMedicine},
volume = {116},
number = {},
pages = {105732},
doi = {10.1016/j.ebiom.2025.105732},
pmid = {40424668},
issn = {2352-3964},
abstract = {BACKGROUND: Digital technology is integral to activities of daily living, particularly instrumental activities of daily living (IADLs). However, tools that accommodate digital performance of IADLs are lacking. The aim of this study was to develop a novel Digital IADL Scale.

METHODS: The multi-stage methodology included: (i) deductive item generation via a systematic review and assignment to domains using a Delphi process, (ii) inductive item generation via a survey of individuals with lived experience (IWLE) of severe paralysis, (iii) item refinement via item rating surveys of content experts and IWLE, and (iv) focus group discussions with key opinion leaders.

FINDINGS: The systematic review identified 1250 IADL items from validated IADL measures, of which 353 met criteria. Deduplication reduced the deductive item set to 77, of which 42 remained following the Delphi process. IWLE generated 152 items, of which 132 met criteria. Deduplication reduced the inductive item set to 41. The combined item pool was reduced to 69 following the item rating surveys. Following focus group feedback, a list of nine domains, containing 37 items, and suggested response scale options are presented.

INTERPRETATION: We describe the initial development of a scale to assess functional independence within IADLs that may be completed digitally, which will be submitted to further validation.

FUNDING: Support for this project was provided in kind by the Abilities Research Center. No formal funding was received.},
}

@article {pmid40423756,
year = {2025},
author = {Moeller, A and Andres Porras, JM},
title = {Human enhancement, past and present.},
journal = {Monash bioethics review},
volume = {},
number = {},
pages = {},
pmid = {40423756},
issn = {1836-6716},
abstract = {One important role the medical humanities might and should play relates to public education. In this instance, we mean helping persons to think about their own aims or purposes as potential receivers of enhancement interventions, and similarly helping to inform the developers of said interventions. This article argues that, in the light of real and speculative applications of emerging biotechnologies and artificial intelligence aimed at human enhancement-including germline genetic engineering, the linking of the human brain with an artificial general intelligence by way of a brain-computer interface, and various interventions directed toward life extension-historians would do well to consider the following three practices as they participate in the medical humanities and the shared task of public education: (1) Taking under scrutiny a broad swath of topics and timeframes as it relates to past efforts aimed at human enhancement; (2) Focusing on past engagement with enhancement efforts and their perceived relation to the pursuit of living well; and (3) Entering into debates on enhancement as equal participants. In support of these assertions, this article takes efforts directed towards the prolongation of life in medieval Europe as an illustrative example. It also highlights continuities and discontinuities between past and present justifications for human enhancement, and addresses how similarities and differences can shape and challenge contemporary bioethical arguments.},
}

@article {pmid40423554,
year = {2025},
author = {Brackman, KN and Taychert, MT and Serrell, EC and Gralnek, D and Manakas, C and Knoedler, M and Antar, A and Allen, GO and Grimes, MD},
title = {Clinical Outcomes of HoLEP in Patients with Diminished Bladder Contractility.},
journal = {Urology practice},
volume = {},
number = {},
pages = {101097UPJ0000000000000840},
doi = {10.1097/UPJ.0000000000000840},
pmid = {40423554},
issn = {2352-0787},
abstract = {INTRODUCTION: Bladder outlet obstruction (BOO) due to benign prostatic hyperplasia (BPH) is common in aging men and can be treated with holmium laser enucleation of the prostate (HoLEP). However, diminished bladder contractility (DC) is also highly prevalent (9-48%) and can be clinically indistinguishable from BOO without urodynamics. While HoLEP effectively treats BPH/BOO, clinical outcomes data for DC patients are limited and mixed. We aim to compare the prevalence and risk factors for catheter dependence among patients with and without DC post-HoLEP.

METHODS: A retrospective cohort study was conducted on 179 patients with preoperative urodynamics who underwent HoLEP between June 2018 and December 2023. Diminished contractility was defined as Bladder Contractility Index (BCI) < 100. Statistical analyses included univariate and multivariate logistic regression.

RESULTS: Among 179 patients 103 (57.5%) had DC (BCI <100). Post HoLEP all normal contractility (NC) patients were voiding while 7.8% of DC patients were catheter dependent (p = 0.01) at mean follow up of 28 months. Preoperative BCI was associated with post HoLEP catheter dependence (OR = 0.97, 95% CI 0.95-1.00, p = 0.046). Postoperative international prostate symptom scores were significantly higher in DC compared to NC groups despite similar preoperative scores.

CONCLUSIONS: HoLEP rendered 95.5% (171/179) of patients catheter free. However, DC patients were more likely to require catheterization postoperatively and reported worse urinary symptoms compared to NC patients. Our results support obtaining urodynamics when there is clinical concern for DC, as this may guide shared decision-making prior to pursuing HoLEP.},
}

@article {pmid40422053,
year = {2025},
author = {Avital, N and Shulkin, N and Malka, D},
title = {Automatic Calculation of Average Power in Electroencephalography Signals for Enhanced Detection of Brain Activity and Behavioral Patterns.},
journal = {Biosensors},
volume = {15},
number = {5},
pages = {},
doi = {10.3390/bios15050314},
pmid = {40422053},
issn = {2079-6374},
mesh = {Humans ; *Electroencephalography/methods ; *Brain/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Male ; Female ; Adult ; Young Adult ; },
abstract = {Precise analysis of electroencephalogram (EEG) signals is critical for advancing the understanding of neurological conditions and mapping brain activity. However, accurately visualizing brain regions and behavioral patterns from neural signals remains a significant challenge. The present study proposes a novel methodology for the automated calculation of the average power of EEG signals, with a particular focus on the beta frequency band which is known for its pronounced activity during cognitive tasks such as 2D content engagement. An optimization algorithm is employed to determine the most appropriate digital filter type and order for EEG signal processing, thereby enhancing both signal clarity and interpretability. To validate the proposed methodology, an experiment was conducted with 22 students, during which EEG data were recorded while participants engaged in cognitive tasks. The collected data were processed using MATLAB (version R2023a) and the EEGLAB toolbox (version 2022.1) to evaluate various filters, including finite impulse response (FIR) and infinite impulse response (IIR) Butterworth and IIR Chebyshev filters with a 0.5% passband ripple. Results indicate that the IIR Chebyshev filter, configured with a 0.5% passband ripple and a fourth-order design, outperformed the alternatives by effectively reducing average power while preserving signal fidelity. This optimized filtering approach significantly improves the accuracy of neural signal visualizations, thereby facilitating the creation of detailed brain activity maps. By refining the analysis of EEG signals, the proposed method enhances the detection of specific neural behaviors and deepens the understanding of functional brain regions. Moreover, it bolsters the reliability of real-time brain activity monitoring, potentially advancing neurological diagnostics and insights into cognitive processes. These findings suggest that the technique holds considerable promise for future applications in brain-computer interfaces and advanced neurological assessments, offering a valuable tool for both clinical practice and research exploration.},
}

Humans
*Electroencephalography/methods
*Brain/physiology
Algorithms
Signal Processing, Computer-Assisted
Male
Female
Adult
Young Adult

@article {pmid40421845,
year = {2025},
author = {Pizzolante, S and Covelli, E and Filippi, C and Barbara, M},
title = {Percutaneous Bone Implant Surgery: A MIPS Modified Technique.},
journal = {The Laryngoscope},
volume = {},
number = {},
pages = {},
doi = {10.1002/lary.32192},
pmid = {40421845},
issn = {1531-4995},
abstract = {Since their introduction, passive percutaneous hearing aids have undergone substantial evolution, including changes in implant production, improvements in the sound processor, and simplification of surgical implantation techniques. The latest innovation comes from the minimally invasive technique proposed for the PONTO system (MIPS), which does not involve the creation of a mucoperiosteal flap in order to leave the surrounding soft tissue and vascular microcirculation intact. This study proposes a modified surgical technique compared to the one proposed for the PONTO system in order to overcome some steps of the traditional surgical technique for the placement of the Baha Connect prosthesis. Our technique does not involve any incision but the exposure of the periosteum using a skin punch and subsequent drilling without the use of any protective cannula. The described procedure allows one to overcome some steps of the traditional surgical technique and, consequently, also some post-operative complications. Moreover, a minimally invasive procedure can help reduce surgical time and the invasiveness of the application.},
}

@article {pmid40420994,
year = {2025},
author = {Esteves, D and Valente, M and Bendor, SE and Andrade, A and Vourvopoulos, A},
title = {Identifying EEG biomarkers of sense of embodiment in virtual reality: insights from spatio-spectral features.},
journal = {Frontiers in neuroergonomics},
volume = {6},
number = {},
pages = {1572851},
pmid = {40420994},
issn = {2673-6195},
abstract = {The Sense of Embodiment (SoE) refers to the subjective experience of perceiving a non-biological body part as one's own. Virtual Reality (VR) provides a powerful platform to manipulate SoE, making it a crucial factor in immersive human-computer interaction. This becomes particularly relevant in Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs), especially motor imagery (MI)-BCIs, which harness brain activity to enable users to control virtual avatars in a self-paced manner. In such systems, a strong SoE can significantly enhance user engagement, control accuracy, and the overall effectiveness of the interface. However, SoE assessment remains largely subjective, relying on questionnaires, as no definitive EEG biomarkers have been established. Additionally, methodological inconsistencies across studies introduce biases that hinder biomarker identification. This study aimed to identify EEG-based SoE biomarkers by analyzing frequency band changes in a combined dataset of 41 participants under standardized experimental conditions. Participants underwent virtual SoE induction and disruption using multisensory triggers, with a validated questionnaire confirming the illusion. Results revealed a significant increase in Beta and Gamma power over the occipital lobe, suggesting these as potential EEG biomarkers for SoE. The findings underscore the occipital lobe's role in multisensory integration and sensorimotor synchronization, supporting the theoretical framework of SoE. However, no single frequency band or brain region fully explains SoE. Instead, it emerges as a complex, dynamic process evolving across time, frequency, and spatial domains, necessitating a comprehensive approach that considers interactions across multiple neural networks.},
}

@article {pmid40420178,
year = {2025},
author = {Jiang, M and Luo, Q and Wang, X and Qu, D},
title = {Semantic radicals' semantic attachment to their composed phonograms.},
journal = {BMC psychology},
volume = {13},
number = {1},
pages = {559},
pmid = {40420178},
issn = {2050-7283},
support = {20BYY095//National Social Science Fund of China/ ; 2019YBYY131//Chongqing Social Science Planning Fund/ ; 22SKGH236//Humanities and Social Sciences Research Project Fund of Chongqing Municipal Education Commission/ ; },
mesh = {Humans ; *Semantics ; Female ; Male ; Young Adult ; Reaction Time ; Adult ; Decision Making ; *Reading ; },
abstract = {In Chinese character processing studies, it is widely accepted that semantic radicals, whether character or non-character ones, can undergo semantic activation. However, there is a notable absence of studies dedicated to understanding the nature and operation of the semantic radicals' semantic information. To address this gap, the present study employed a masked semantic priming paradigm combined with a part-of-speech decision task and a lexical decision task across three experiments. Experiment 1 was designed to examine the semantic autonomy and the semantic attachment of semantic radicals in transparent phonograms. Experiment 2 sought to further investigate the degree of semantic autonomy of semantic radicals in opaque phonograms. Experiment 3 was crafted to further probe into the presence of semantic attachment of semantic radicals in pseudo-characters. Results showed significant priming effects in both transparent and opaque phonogram conditions, with faster reaction times and higher accuracy for semantically related prime-target pairs. However, no such priming effect was observed in the pseudo-character condition, indicating that semantic radicals are not activated in non-lexical contexts. These findings suggest that semantic radicals were semantically activated when embedded in both transparent and opaque phonograms, but not when planted in pseudo-characters. The plausible account put forward is that semantic radicals stand on pars with their composed phonograms in possessing their own semantic information, but the former is semantically strongly attached to the latter, such that it cannot live without the latter's semantic company.},
}

Humans
*Semantics
Female
Male
Young Adult
Reaction Time
Adult
Decision Making
*Reading

@article {pmid40419791,
year = {2025},
author = {Chen, Y and Ding, K and Zheng, S and Gao, S and Xu, X and Wu, H and Zhou, F and Wang, Y and Xu, J and Wang, C and Ling, C and Xu, J and Wang, L and Wu, Q and Giamas, G and Chen, G and Zhang, J and Yi, C and Ji, J},
title = {Post-translational modifications in DNA damage repair: mechanisms underlying temozolomide resistance in glioblastoma.},
journal = {Oncogene},
volume = {},
number = {},
pages = {},
pmid = {40419791},
issn = {1476-5594},
support = {82203035//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82403931//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Temozolomide (TMZ) resistance is one of the critical factors contributing to the poor prognosis of glioblastoma (GBM). As a first-line chemotherapeutic agent for GBM, TMZ exerts its cytotoxic effects through DNA alkylation. However, its therapeutic efficacy is significantly compromised by enhanced DNA damage repair (DDR) mechanisms in GBM cells. Although several DDR-targeting drugs have been developed, their clinical outcomes remain suboptimal. Post-translational modifications (PTMs) in GBM cells play a pivotal role in maintaining the genomic stability of DDR mechanisms, including methylguanine-DNA methyltransferase-mediated repair, DNA mismatch repair dysfunction, base excision repair, and double-strand break repair. This review focuses on elucidating the regulatory roles of PTMs in the intrinsic mechanisms underlying TMZ resistance in GBM. Furthermore, we explore the feasibility of enhancing TMZ-induced cytotoxicity by targeting PTM-related enzymatic to disrupt key steps in PTM-mediated DDR pathways. By integrating current preclinical insights and clinical challenges, this work highlights the potential of modulating PTM-driven networks as a novel therapeutic strategy to overcome TMZ resistance and improve treatment outcomes for GBM patients.},
}

@article {pmid40419502,
year = {2025},
author = {Rajabi, N and Zanettin, I and Ribeiro, AH and Vasco, M and Björkman, M and Lundström, JN and Kragic, D},
title = {Exploring the feasibility of olfactory brain-computer interfaces.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {18404},
pmid = {40419502},
issn = {2045-2322},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Odorants/analysis ; Male ; Adult ; Female ; *Smell/physiology ; Feasibility Studies ; Neural Networks, Computer ; Young Adult ; *Olfactory Perception/physiology ; *Brain/physiology ; },
abstract = {In this study, we explore the feasibility of single-trial predictions of odor registration in the brain using olfactory bio-signals. We focus on two main aspects: input data modality and the processing model. For the first time, we assess the predictability of odor registration from novel electrobulbogram (EBG) recordings, both in sensor and source space, and compare these with commonly used electroencephalogram (EEG) signals. Despite having fewer data channels, EBG shows comparable performance to EEG. We also examine whether breathing patterns contain relevant information for this task. By comparing a logistic regression classifier, which requires hand-crafted features, with an end-to-end convolutional deep neural network, we find that end-to-end approaches can be as effective as classic methods. However, due to the high dimensionality of the data, the current dataset is insufficient for either classifier to robustly differentiate odor and non-odor trials. Finally, we identify key challenges in olfactory BCIs and suggest future directions for improving odor detection systems.},
}

Humans
*Brain-Computer Interfaces
Electroencephalography/methods
Odorants/analysis
Male
Adult
Female
*Smell/physiology
Feasibility Studies
Neural Networks, Computer
Young Adult
*Olfactory Perception/physiology
*Brain/physiology

@article {pmid40419488,
year = {2025},
author = {Wang, D and Xue, H and Xia, L and Li, Z and Zhao, Y and Fan, X and Sun, K and Wang, H and Hamalainen, T and Zhang, C and Cong, F and Li, Y and Song, F and Lin, J},
title = {A tough semi-dry hydrogel electrode with anti-bacterial properties for long-term repeatable non-invasive EEG acquisition.},
journal = {Microsystems & nanoengineering},
volume = {11},
number = {1},
pages = {105},
pmid = {40419488},
issn = {2055-7434},
support = {2022 ZD0210700//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; },
abstract = {Non-invasive brain-computer interfaces (NI-BCIs) have garnered significant attention due to their safety and wide range of applications. However, developing non-invasive electroencephalogram (EEG) electrodes that are highly sensitive, comfortable to wear, and reusable has been challenging due to the limitations of conventional electrodes. Here, we introduce a simple method for fabricating semi-dry hydrogel EEG electrodes with antibacterial properties, enabling long-term, repeatable acquisition of EEG. By utilizing N-acryloyl glycinamide and hydroxypropyltrimethyl ammonium chloride chitosan, we have prepared electrodes that not only possess good mechanical properties (compression modulus 65 kPa) and anti-fatigue properties but also exhibit superior antibacterial properties. These electrodes effectively inhibit the growth of both Gram-negative (E. coli) and Gram-positive (S. epidermidis) bacteria. Furthermore, the hydrogel maintains stable water retention properties, resulting in an average contact impedance of <400 Ω measured over 12 h, and an ionic conductivity of 0.39 mS cm[-1]. Cytotoxicity and skin irritation tests have confirmed the high biocompatibility of the hydrogel electrodes. In an N170 event-related potential (ERP) test on human volunteers, we successfully captured the expected ERP signal waveform and a high signal-to-noise ratio (20.02 dB), comparable to that of conventional wet electrodes. Moreover, contact impedance on the scalps remained below 100 kΩ for 12 h, while wet electrodes became unable to detect signals after 7-8 h due to dehydration. In summary, our hydrogel electrodes are capable of detecting ERPs over extended periods in an easy-to-use manner with antibacterial properties. This reduces the risk of bacterial infection associated with prolonged reuse and expands the potential of NI-BCIs in daily life.},
}

@article {pmid40419083,
year = {2025},
author = {Chen, L and Zhang, L and Wang, Z and Li, Q and Gu, B and Ming, D},
title = {Task-related reconfiguration patterns of frontoparietal network during motor imagery.},
journal = {Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuroscience.2025.05.035},
pmid = {40419083},
issn = {1873-7544},
abstract = {Motor imagery (MI) is closely associated with the frontoparietal network that includes prefrontal and posterior parietal regions. Studying task-related network reconfiguration after brain shifts from the resting state to the MI task is an important way to understand the brain's response process. However, how the brain modulates functional connectivity of the frontoparietal network when it shifts to MI has not been thoroughly studied. In this study, we attempted to characterize the frontoparietal network reconfiguration patterns as the brain transitioned to motor imagery tasks. We performed the analysis using EEG signals from 52 healthy subjects during left- and right-hand MI tasks. The results indicated distinct reconfiguration patterns in the frontoparietal network across four typical brain wave rhythms (theta (4 ∼ 7 Hz), alpha (8 ∼ 13 Hz), beta (14 ∼ 30 Hz), and gamma (31 ∼ 45 Hz)). Meanwhile, there was a significant positive correlation between the frontoparietal network reconfiguration and the event-related desynchronization of alpha and beta rhythms in the sensorimotor cortex. We further found that subjects with better MI-BCI performance exhibited greater reconfiguration of the frontoparietal network in alpha and beta rhythms. These findings implied that MI was accompanied by a shift in information interaction between brain regions, which might contribute to understanding the neural mechanisms of MI.},
}

@article {pmid40418615,
year = {2025},
author = {Song, Y and Wang, Y and He, H and Gao, X},
title = {Recognizing Natural Images From EEG With Language-Guided Contrastive Learning.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3562743},
pmid = {40418615},
issn = {2162-2388},
abstract = {Electroencephalography (EEG), known for its convenient noninvasive acquisition but moderate signal-to-noise ratio, has recently gained much attention due to the potential to decode image information. However, previous works have not delivered sufficient evidence of this task, primarily limited by performance and biological plausibility. In this work, we first introduce a self-supervised framework to demonstrate the feasibility of recognizing images from EEG signals. Contrastive learning is leveraged to align the representations of EEG responses with image stimuli. Then, language descriptions of the stimuli generated by large language models (LLMs) help guide learning core semantic information. With the framework, we attain significantly above-chance results on the THINGS-EEG2 dataset, achieving a top-1 accuracy of 19.7% and a top-5 accuracy of 51.5% in challenging 200-way zero-shot tasks. Furthermore, we conduct thorough experiments to resolve the human visual responses with EEG from temporal, spatial, spectral, and semantic perspectives. These results provide evidence of feasibility and plausibility regarding EEG-based image recognition, substantiated by comparative studies with the THINGS-Magnetoencephalography (MEG) dataset. The findings offer valuable insights for neural decoding and real-world applications of brain-computer interfaces (BCIs), such as health care and robot control. The code is available at https://github.com/eeyhsong/NICE-LLM.},
}

@article {pmid40416647,
year = {2025},
author = {Teng, Y and Song, L and Shi, J and Lv, Q and Hou, S and Ramakrishna, S},
title = {Advancing electrospinning towards the future of biomaterials in biomedical engineering.},
journal = {Regenerative biomaterials},
volume = {12},
number = {},
pages = {rbaf034},
pmid = {40416647},
issn = {2056-3418},
abstract = {Biomaterial is a material designed to take a form that can direct, through interactions with living systems, the course of any therapeutic or diagnostic procedure. Growing demand for improved and affordable healthcare treatments and unmet clinical needs seek further advancement of biomaterials. Over the past 25 years, the electrospinning method has been innovated to enhance biomaterials at nanometer and micrometer length scales for diverse healthcare applications. Recent developments include intelligent (smart) biomaterials and sustainable biomaterials. Intelligent materials can sense, adapt to and respond to external stimuli, autonomously adjusting to enhance functionality and performance. Sustainable biomaterials possess several key characteristics, including renewability, a low carbon footprint, circularity, durability, biocompatibility, biodegradability and others. Herein, advances in electrospun biomaterials, encompassing process innovations, working principles and the effects of process variables, are presented succinctly. The potential of electrospun intelligent biomaterials and sustainable biomaterials in specific biomedical applications, including tissue engineering, regenerative medicine, drug delivery systems, brain-computer interfaces, biosensors, personal protective equipment and wearable devices, is explored. More effective healthcare demands further advancements in electrospun biomaterials. In the future, the distinctive characteristics of intelligent biomaterials and sustainable biomaterials, integrated with various emerging technologies (such as AI and data transmission), will enable physicians to conduct remote diagnosis and treatment. This advancement significantly enhances telemedicine capabilities for more accurate disease prediction and management.},
}

@article {pmid40416500,
year = {2025},
author = {Mokienko, OA},
title = {The Potential of Near-Infrared Spectroscopy as a Therapeutic Tool Following a Stroke (Review).},
journal = {Sovremennye tekhnologii v meditsine},
volume = {17},
number = {2},
pages = {73-83},
pmid = {40416500},
issn = {2309-995X},
mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Stroke Rehabilitation/methods ; *Stroke/physiopathology/therapy/diagnosis/diagnostic imaging ; },
abstract = {The advancement of novel technologies for the rehabilitation of post-stroke patients represents a significant challenge for a range of interdisciplinary fields. Near-infrared spectroscopy (NIRS) is an optical neuroimaging technique based on recording local hemodynamic changes at the cerebral cortex level. The technology is typically employed in post-stroke patients for diagnostic purposes, including the assessment of neuroplastic processes accompanying therapy, the study of hemispheric asymmetry, and the examination of functional brain networks. However, functional NIRS can also be used for therapeutic purposes, including the provision of biofeedback during rehabilitation tasks, as well as the navigation method during transcranial stimulation. The effectiveness of therapeutic NIRS application in stroke patients remains insufficiently studied, despite existing scientific evidence confirming its promising potential as a treatment method. The review examines the published literature on the therapeutic applications of NIRS after stroke, evaluating its potential role in the rehabilitation process. The paper describes NIRS features, advantages, and disadvantages, determining its position among other neuroimaging technologies; analyzes the findings of neurophysiological studies, which justified the clinical trials of NIRS technology; and evaluates the results of the studies on the therapeutic use of NIRS in post-stroke patients. Two potential applications of NIRS for therapeutic purposes following a stroke were suggested: the first was to provide real-time feedback during movement training (motor or ideomotor ones, including that in brain-computer interface circuits), and the second was to facilitate navigation during transcranial stimulation. Based on a comprehensive literature review, there were proposed and justified further research lines and development in this field.},
}

Humans
Spectroscopy, Near-Infrared/methods
*Stroke Rehabilitation/methods
*Stroke/physiopathology/therapy/diagnosis/diagnostic imaging

@article {pmid40414967,
year = {2025},
author = {Qian, L and Jia, C and Wang, J and Shi, L and Wang, Z and Wang, S},
title = {The dynamics of stimulus selection in the nucleus isthmi pars magnocellularis of avian midbrain network.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {18260},
pmid = {40414967},
issn = {2045-2322},
support = {2024M752934//China Postdoctoral Science Foundation/ ; },
mesh = {Animals ; *Mesencephalon/physiology ; Neurons/physiology ; Photic Stimulation ; *Nerve Net/physiology ; Birds/physiology ; },
abstract = {The nucleus isthmi pars magnocellularis (Imc) serves as a critical node in the avian midbrain network for encoding stimulus salience and selection. While reciprocal inhibitory projections among Imc neurons (inhibitory loop) are known to govern stimulus selection, existing studies have predominantly focused on stimulus selection under stimuli of constant relative intensity. However, animals typically encounter complex and changeable visual scenes. Thus, how Imc neurons represent stimulus selection under varying relative stimulus intensities remains unclear. Here, we examined the dynamics of stimulus selection by in vivo recording of Imc neurons' responses to spatiotemporally successive visual stimuli divided into two segments: the previous stimulus and the post stimulus. Our data demonstrate that Imc neurons can encode sensory memory of the previous stimulus, which modulates competition and salience representation in the post stimulus. This history-dependent modulation is also manifested in persistent neural activity after stimulus cessation. We identified, through neural tracing, focal inactivation, and computational modeling experiments, projections from the nucleus isthmi pars parvocellularis (Ipc) to "shepherd's crook" (Shc) neurons, which could be either direct or indirect. These projections enhance Imc neurons' responses and persistent neural activity after stimulus cessation. This connectivity supports a Shc-Ipc-Shc excitatory loop in the midbrain network. The coexistence of excitatory and inhibitory loops provides a neural substrate for continuous attractor network models, a proposed framework for neural information representation. This study also offers a potential explanation for how animals maintain short-term attention to targets in complex and changeable environments.},
}

Animals
*Mesencephalon/physiology
Neurons/physiology
Photic Stimulation
*Nerve Net/physiology
Birds/physiology

@article {pmid40414233,
year = {2025},
author = {Paton, NI and Cousins, C and Sari, IP and Burhan, E and Ng, NK and Dalay, VB and Suresh, C and Kusmiati, T and Chew, KL and Balanag, VM and Lu, Q and Ruslami, R and Djaharuddin, I and Sugiri, JJR and Veto, RS and Sekaggya-Wiltshire, C and Avihingsanon, A and Saini, JK and Papineni, P and Nunn, AJ and Crook, AM and , },
title = {Efficacy and safety of 8-week regimens for the treatment of rifampicin-susceptible pulmonary tuberculosis (TRUNCATE-TB): a prespecified exploratory analysis of a multi-arm, multi-stage, open-label, randomised controlled trial.},
journal = {The Lancet. Infectious diseases},
volume = {},
number = {},
pages = {},
doi = {10.1016/S1473-3099(25)00151-3},
pmid = {40414233},
issn = {1474-4457},
abstract = {BACKGROUND: WHO recommends a 2-month optimal duration for new drug regimens for rifampicin-susceptible tuberculosis. We aimed to investigate the efficacy and safety of the 8-week regimens that were assessed as part of the TRUNCATE management strategy of the TRUNCATE-TB trial.

METHODS: TRUNCATE-TB was a multi-arm, multi-stage, open-label, randomised controlled trial in which participants aged 18-65 years with rifampicin-susceptible pulmonary tuberculosis were randomly assigned via a web-based system, using permuted blocks, to 24-week standard treatment (rifampicin, isoniazid, pyrazinamide, and ethambutol) or the TRUNCATE management strategy comprising initial 8-week treatment, then post-treatment monitoring and re-treatment where needed. The four 8-week regimens comprised five drugs, modified from standard treatment: high-dose rifampicin and linezolid, or high-dose rifampicin and clofazimine, or bedaquiline and linezolid, all given with isoniazid, pyrazinamide, and ethambutol; and rifapentine, linezolid, and levofloxacin, given with isoniazid and pyrazinamide. Here, we report the efficacy (proportion with unfavourable outcome; and difference from standard treatment, assessed via Bayesian methods) and safety of the 8-week regimens, assessed in the intention-to-treat population. This prespecified exploratory analysis is distinct from the previously reported 96-week outcome of the strategy in which the regimens were deployed. This trial is registered with ClinicalTrials.gov (NCT03474198).

FINDINGS: Between March 21, 2018, and March 26, 2020, 675 participants (674 in the intention-to-treat population) were enrolled and randomly assigned to the standard treatment group or one of the four 8-week regimen groups. Two 8-week regimens progressed to full enrolment. An unfavourable outcome (mainly relapse) occurred in seven (4%) of 181 participants on standard treatment; 46 (25%) of 184 on the high-dose rifampicin and linezolid-containing regimen (adjusted difference 21·0%, 95% Bayesian credible interval [BCI] 14·3-28·1); and 26 (14%) of 189 on the bedaquiline and linezolid-containing regimen (adjusted difference 9·3% [4·3-14·9]). Grade 3-4 adverse events occurred in 24 (14%) of 181 participants on standard treatment, 20 (11%) of 184 on the rifampicin-linezolid regimen, and 22 (12%) of 189 on the bedaquiline-linezolid regimen.

INTERPRETATION: Efficacy was worse with 8-week regimens, although the difference from standard treatment varied between regimens. Even the best 8-week regimen (bedaquiline-linezolid) should only be used as part of a management strategy involving post-treatment monitoring and re-treatment if necessary.

FUNDING: Singapore National Medical Research Council; UK Department of Health and Social Care; UK Foreign, Commonwealth, and Development Office; UK Medical Research Council; Wellcome Trust; and UK Research and Innovation Medical Research Council.},
}

@article {pmid40411529,
year = {2025},
author = {Sun, Y and Guan, M and Chen, X and Feng, F and He, R and Huang, L and Tong, X and Zhou, H and Liu, X and Ming, D},
title = {Deep learning-based classification and segmentation of interictal epileptiform discharges using multichannel electroencephalography.},
journal = {Epilepsia},
volume = {},
number = {},
pages = {},
doi = {10.1111/epi.18463},
pmid = {40411529},
issn = {1528-1167},
support = {020/0903065111//Tianjin University Innovation Fund/ ; 2021YFF1200602//National Key Technologies Research and Development Program/ ; c02022088//National Defense Science and Technology Innovation Fund of Chinese Academy of Sciences/ ; 0401260011//National Science Fund for Excellent Overseas Scholars/ ; },
abstract = {OBJECTIVE: This study was undertaken to develop a deep learning framework that can classify and segment interictal epileptiform discharges (IEDs) in multichannel electroencephalographic (EEG) recordings with high accuracy, preserving both spatial information and interchannel interactions.

METHODS: We proposed a novel deep learning framework, U-IEDNet, for detecting IEDs in multichannel EEG. The U-IEDNet framework employs convolutional layers and bidirectional gated recurrent units as a temporal encoder to extract temporal features from single-channel EEG, followed by the use of transformer networks as a spatial encoder to fuse multichannel features and extract interchannel interaction information. Transposed convolutional layers form a temporal decoder, creating a U-shaped architecture with the encoder. This upsamples features to estimate the probability of each EEG sampling point falling within the IED range, enabling segmentation of IEDs from background activity. Two datasets, a public database with 370 patient recordings and our own annotated database with 43 patient recordings, were used for model establishment and validation.

RESULTS: The results showed prominent advantage compared with other methods. U-IEDNet achieved a recall of .916, precision of .911, F1-score of .912, and false positive rate (FPR) of .030 on the public database. The classification performance in our own annotated database achieved a recall of .905, a precision of .902, an F1-score of .903, and an FPR of .072. The segmentation performance had a recall of .903, a precision of .916, and an F1-score of .909. Additionally, this study analyzes attention weights in the transformer network based on brain network theory to elucidate the spatial feature fusion process, enhancing the interpretability of the IED detection model.

SIGNIFICANCE: In this paper, we aim to present an artificial intelligence-based toolbox for IED detection, which may facilitate epilepsy diagnosis at the bedside in the future. U-IEDNet demonstrates great potential to improve the accuracy and efficiency of IED detection in multichannel EEG recordings.},
}

@article {pmid40409524,
year = {2025},
author = {Li, Y and Pan, Y and Zhao, D},
title = {Understanding the neurobiology and computational mechanisms of social conformity: implications for psychiatric disorders.},
journal = {Biological psychiatry},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.biopsych.2025.05.011},
pmid = {40409524},
issn = {1873-2402},
abstract = {Social conformity and psychiatric disorders share overlapping brain regions and neural pathways, arousing our interest in uncovering their potentially shared underlying neural and computational mechanisms. Critically, the dynamics of group behavior may either mitigate or exacerbate mental health conditions, highlighting the need to bridge social neuroscience and psychiatry. Our work examines how aberrant neurobiological circuits and computations influence social conformity. We propose a hierarchical computational framework, based on dynamical systems and active inference, to facilitate the interpretation of the multi-layered interplay among processes that drive social conformity. We underscore the significant implications of this hierarchical computational framework for guiding future research on psychiatry, particularly with respect to the clinical translation of interventions such as targeted pharmacotherapy and neurostimulation techniques. The interdisciplinary efforts hold the potential to propel the fields of social and clinical neuroscience forward, fostering the emergence of more efficacious and individualized therapeutic approaches tailored to psychiatric disorders characterized by aberrant social behaviors.},
}

@article {pmid40408764,
year = {2025},
author = {Jing, S and Dai, Z and Liu, X and Yang, X and Cheng, J and Chen, T and Feng, Z and Liu, X and Dong, F and Xin, Y and Han, Z and Hu, H and Su, X and Wang, C},
title = {Effectiveness of Neurofeedback-Assisted and Conventional 6-Week Web-Based Mindfulness Interventions on Mental Health of Chinese Nursing Students: Randomized Controlled Trial.},
journal = {Journal of medical Internet research},
volume = {27},
number = {},
pages = {e71741},
doi = {10.2196/71741},
pmid = {40408764},
issn = {1438-8871},
mesh = {Humans ; *Students, Nursing/psychology ; *Mindfulness/methods ; Female ; Male ; *Mental Health ; China ; *Neurofeedback/methods ; Adult ; Young Adult ; Anxiety/therapy ; *Internet ; Depression/therapy ; *Internet-Based Intervention ; East Asian People ; },
abstract = {BACKGROUND: Nursing students experience disproportionately high rates of mental health challenges, underscoring the urgent need for innovative, scalable interventions. Web-based mindfulness programs, and more recently, neurofeedback-enhanced approaches, present potentially promising avenues for addressing this critical issue.

OBJECTIVE: This study aimed to explore the effectiveness of the neurofeedback-assisted online mindfulness intervention (NAOM) and the conventional online mindfulness intervention (COM) in reducing mental health symptoms among Chinese nursing students.

METHODS: A 3-armed randomized controlled trial was conducted among 147 nursing students in Beijing, China, using a 6-week web-based mindfulness program. Participants received NAOM, COM, or general mental health education across 6 weeks. Electroencephalogram and validated tools such as the Patient Health Questionnaire and the Generalized Anxiety Disorder Questionnaire were used to primarily assess symptoms of depression and anxiety at baseline, immediately after the intervention, and at 1 and 3 months after the intervention. Generalized estimating equations were used to evaluate the effects of intervention and time.

RESULTS: A total of 155 participants enrolled in the study, and 147 finished all assessments. Significant reductions in the symptoms of depression, anxiety, and fatigue were observed in the NAOM (mean difference [MD]=-3.330, Cohen d=0.926, P<.001; MD=-3.468, Cohen d=1.091, P<.001; MD=-2.620, Cohen d=0.743, P<.001, respectively) and the COM (MD=-1.875, Cohen d=0.490, P=.03; MD=-1.750, Cohen d=0.486, P=.02; MD=-2.229, Cohen d=0.629, P=.01, respectively) groups compared with the control group at postintervention assessment. Moreover, the NAOM group showed significantly better effects than the COM group in alleviating depressive symptoms (MD=-1.455; Cohen d=0.492; P=.04) and anxiety symptoms (MD=-1.718; Cohen d=0.670; P=.04) and improving the level of mindfulness (MD=-3.765; Cohen d=1.245; P<.001) at the postintervention assessment. However, no significant difference except for the anxiety symptoms was observed across the 3 groups at the 1- and 3-month follow-ups.

CONCLUSIONS: This 6-week web-based mindfulness intervention, both conventional and neurofeedback-assisted, effectively alleviated mental health problems in the short term among nursing students. The addition of neurofeedback demonstrated greater short-term benefits; however, but these effects were not sustained over the long term. Future research should focus on long-term interventions using a more robust methodological approach.

TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR) ChiCTR2400080314; https://www.chictr.org.cn/bin/project/edit?pid=211845.},
}

Humans
*Students, Nursing/psychology
*Mindfulness/methods
Female
Male
*Mental Health
China
*Neurofeedback/methods
Adult
Young Adult
Anxiety/therapy
*Internet
Depression/therapy
*Internet-Based Intervention
East Asian People

@article {pmid40408491,
year = {2025},
author = {Zhou, H and Wu, J and Li, J and Pan, Z and Lu, J and Shen, M and Wang, T and Hu, Y and Gao, Z},
title = {Event cache: An independent component in working memory.},
journal = {Science advances},
volume = {11},
number = {21},
pages = {eadt3063},
pmid = {40408491},
issn = {2375-2548},
mesh = {*Memory, Short-Term/physiology ; Humans ; Magnetic Resonance Imaging ; Male ; Female ; Adult ; Young Adult ; Brain Mapping ; *Cerebellum/physiology ; Brain/physiology ; },
abstract = {Working memory (WM) has been a major focus of cognitive science and neuroscience for the past 50 years. While most WM research has centered on the mechanisms of objects, there has been a lack of investigation into the cognitive and neural mechanisms of events, which are the building blocks of our experience. Using confirmatory factor analysis, psychophysical experiments, and resting-state and task functional magnetic resonance imaging methods, our study demonstrated that events have an independent storage space within WM, named as event cache, with distinct neural correlates compared to object storage in WM. We found the cerebellar network to be the most essential network for event cache, with the left cerebellum Crus I being particularly involved in encoding and maintaining events. Our findings shed critical light on the neuropsychological mechanism of WM by revealing event cache as an independent component of WM and encourage the reconsideration of theoretical models for WM.},
}

*Memory, Short-Term/physiology
Humans
Magnetic Resonance Imaging
Male
Female
Adult
Young Adult
Brain Mapping
*Cerebellum/physiology
Brain/physiology

@article {pmid40408214,
year = {2025},
author = {Chen, W and Li, Y and Zheng, N and Shi, W},
title = {DenoiseMamba: An Innovative Approach for EEG Artifact Removal Leveraging Mamba and CNN.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3573042},
pmid = {40408214},
issn = {2168-2208},
abstract = {Electroencephalography (EEG) is a widely used tool for monitoring brain activity, but it is often disturbed by various artifacts, such as electrooculography (EOG), electromyography (EMG), and electrocardiography (ECG), which degrade signal quality and affect subsequent analysis. Effective EEG denoising is critical for enhancing the performance of EEG-based applications, including disease diagnosis and brain-computer interfaces (BCIs). While recent deep learning (DL) approaches have shown promise in this area, they often struggle to efficiently model the temporal dependencies inherent in EEG signals, as well as to capture local contextual information simultaneously. In this work, we introduce DenoiseMamba, a novel deep learning-based EEG denoising model. The model incorporates the ConvSSD module, which integrates convolutional neural networks (CNNs) with structured state-space duality (SSD) mechanisms. This allows DenoiseMamba to capture both local and global spatiotemporal features, resulting in more effective artifact suppression. Extensive experiments on three semi-simulated datasets demonstrate that DenoiseMamba outperforms existing methods in EEG reconstruction accuracy, effectively eliminating myoelectric, electrooculographic, and electrocardiographic artifacts while preserving critical EEG signal details.},
}

@article {pmid40408213,
year = {2025},
author = {Lan, Z and Li, Z and Yan, C and Xiang, X and Tang, D and Wu, M and Chen, Z},
title = {MTSNet: Convolution-based Transformer Network with Multi-scale Temporal-Spectral Feature Fusion for SSVEP Signal Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3573410},
pmid = {40408213},
issn = {2168-2208},
abstract = {Improving the decoding performance of steady-state visual evoked (SSVEP) signals is crucial for the practical application of SSVEP-based brain-computer interface (BCI) systems. Although numerous methods have achieved impressive results in decoding SSVEP signals, most of them focus only on the temporal or spectral domain information or concatenate them directly, which may ignore the complementary relationship between different features. To address this issue, we propose a dual-branch convolution-based Transformer network with multi-scale temporal-spectral feature fusion, termed MTSNet, to improve the decoding performance of SSVEP signals. Specifically, the temporal branch extracts temporal features from the SSVEP signals using the multi-level convolution-based Transformer (Convformer) that can adapt to the dynamic fluctuations of SSVEP signals. In parallel, the spectral branch takes the complex spectrum converted from temporal signals by the zero-padding fast Fourier transform as input and uses the Convformer to extract spectral features. These extracted temporal and spectral features are then integrated by the multi-scale feature fusion module to obtain comprehensive features with different scale information, thereby enhancing the interactions between the features and improving the effectiveness and robustness. Extensive experimental results on two widely used public SSVEP datasets, Benchmark and BETA, show that the proposed MTSNet significantly outperforms the state-of-the-art calibration-free methods in terms of accuracy and ITR. The superior performance demonstrates the effectiveness of our method in decoding SSVEP signals, which may facilitate the practical application of SSVEP-based BCI systems.},
}

@article {pmid40408200,
year = {2025},
author = {Ingolfsson, TM and Kartsch, V and Benini, L and Cossettini, A},
title = {A Wearable Ultra-Low-Power System for EEG-based Speech-Imagery Interfaces.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBCAS.2025.3573027},
pmid = {40408200},
issn = {1940-9990},
abstract = {Speech imagery-the process of mentally simulating speech without vocalization-is a promising approach for brain-computer interfaces (BCIs), enabling assistive communication for individuals with speech impairments or to enhance privacy. However, existing EEG-based speech imagery systems remain impractical for use outside specialized laboratories due to their reliance on high-channel-count and resource-intensive machine learning models running on external computing platforms. In this work, we present the first end-to-end demonstration of EEG-based speech imagery decoding on a low-channel, ultra-low-power wearable device. Building on our previous work on vowel imagery, we introduce an extended framework leveraging the BioGAP platform and VOWELNET, a lightweight neural network optimized for embedded speech imagery classification. In particular, we demonstrate state-of- the-art accuracy in the classification of an expanded vocabulary comprising vowels, commands, and rest states (13 classes) with a subject-specific training approach, achieving up to 50.0% for one subject (42.8% average) in multi-class classification. We deploy our model on an embedded biosignal acquisition and processing platform (BioGAP), based on the GAP9 processor, for real-time inference with minimal power consumption (25.93 mW). Our system achieves continuous execution for more than 21 hours on a small LiPo battery while maintaining classification latencies of 40.9 ms. Finally, we also explore the benefits of applying Continual Learning techniques to progressively improve the system's performance throughout its operational lifetime, and we demonstrate that electrodes located on the temporal area contribute the most to the overall accuracy. This work marks a significant step toward practical, real-time, and unobtrusive speech imagery BCIs, unlocking new opportunities for covert communication and assistive technologies.},
}

@article {pmid40407663,
year = {2025},
author = {Astefanei, O and Martu, C and Cozma, S and Radulescu, L},
title = {Cochlear and Bone Conduction Implants in Asymmetric Hearing Loss and Single-Sided Deafness: Effects on Localization, Speech in Noise, and Quality of Life.},
journal = {Audiology research},
volume = {15},
number = {3},
pages = {},
doi = {10.3390/audiolres15030049},
pmid = {40407663},
issn = {2039-4330},
abstract = {BACKGROUND: Single-sided deafness (SSD) and asymmetric hearing loss (AHL) impair spatial hearing and speech perception, often reducing quality of life. Cochlear implants (CIs) and bone conduction implants (BCIs) are rehabilitation options used in SSD and AHL to improve auditory perception and support functional integration in daily life.

OBJECTIVE: We aimed to evaluate hearing outcomes after auditory implantation in SSD and AHL patients, focusing on localization accuracy, speech-in-noise understanding, tinnitus relief, and perceived benefit.

METHODS: In this longitudinal observational study, 37 patients (adults and children) received a CI or a BCI according to clinical indications. Outcomes included localization and spatial speech-in-noise assessment, tinnitus ratings, and SSQ12 scores. Statistical analyses used parametric and non-parametric tests (p < 0.05).

RESULTS: In adult CI users, localization error significantly decreased from 81.9° ± 15.8° to 43.7° ± 13.5° (p < 0.001). In children, regardless of the implant type (CI or BCI), localization error improved from 74.3° to 44.8°, indicating a consistent spatial benefit. In adult BCI users, localization error decreased from 74.6° to 69.2°, but the improvement did not reach statistical significance. Tinnitus severity, measured on a 10-point VAS scale, decreased significantly in CI users (mean reduction: 2.8 ± 2.0, p < 0.001), while changes in BCI users were small and of limited clinical relevance. SSQ12B/C scores improved in all adult groups, with the largest gains observed in spatial hearing for CI users (2.1 ± 1.2) and in speech understanding for BCI users (1.6 ± 0.9); children reported high benefits across all domains. Head shadow yielded the most consistent benefit across all groups (up to 4.9 dB in adult CI users, 3.8 dB in adult BCI users, and 4.6 dB in children). Although binaural effects were smaller in BCI users, positive gains were observed, especially in pediatric cases. Correlation analysis showed that daily device use positively predicted SSQ12 improvement (r = 0.57) and tinnitus relief (r = 0.42), while longer deafness duration was associated with poorer localization outcomes (r = -0.48).

CONCLUSIONS: CIs and BCIs provide measurable benefits in SSD and AHL rehabilitation. Outcomes vary with age, device, and deafness duration, underscoring the need for early intervention and consistent auditory input.},
}

@article {pmid40406801,
year = {2025},
author = {Moumdjian, RA},
title = {Bioethics of neurotechnologies: a field in effervescence.},
journal = {Neurological research},
volume = {},
number = {},
pages = {1-4},
doi = {10.1080/01616412.2025.2499896},
pmid = {40406801},
issn = {1743-1328},
abstract = {Brain-Computer Interface (BCI) comprises a device that detects brain signals conveying specific intentions and translates them into executable outputs by a machine. It enables neurologically impaired patients to regain some control over their environment, thereby aiding in their rehabilitation. Some authors argue that 'the use of BCI is the greatest ethical challenge that neuroscience faces today. Ethical issues highlighted in the literature include safety, justice, privacy, security, and the balance of risks and benefits.},
}

@article {pmid40403087,
year = {2025},
author = {Essam, AA and Ibrahim, A and Seif Al-Nasr, A and El-Saqa, M and Mohamed, S and Anwar, A and Eldeib, A and Akcakaya, M and Khalaf, A},
title = {Filter bank common spatial pattern and envelope-based features in multimodal EEG-fTCD brain-computer interfaces.},
journal = {PloS one},
volume = {20},
number = {5},
pages = {e0311075},
pmid = {40403087},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Ultrasonography, Doppler, Transcranial/methods ; Male ; Adult ; Female ; Young Adult ; Bayes Theorem ; *Brain/physiology ; Algorithms ; },
abstract = {Brain-computer interfaces (BCIs) provide alternative means of communication and control for individuals with severe motor or speech impairments. Multimodal BCIs have been introduced recently to enhance the performance of BCIs utilizing single modality. In this paper, we aim to advance the state of the art in multimodal BCIs combining Electroencephalography (EEG) and functional transcranial Doppler ultrasound (fTCD) by introducing advanced analysis approaches that enhance system performance. Our EEG-fTCD BCIs employ two distinct paradigms to infer user intent: motor imagery (MI) and flickering mental rotation (MR)/word generation (WG) paradigms. In the MI paradigm, we introduce the use of Filter Bank Common Spatial Pattern (FBCSP) for the first time in an EEG-fTCD BCI, while in the flickering MR/WG paradigm, we extend FBCSP application to non-motor imagery tasks. Additionally, we extract previously unexplored time-series features from the envelope of fTCD signals, leveraging richer information from cerebral blood flow dynamics. Furthermore, we employ a Bayesian fusion framework that allows EEG and fTCD to contribute unequally to decision-making. The multimodal EEG-fTCD system achieved high classification accuracies across tasks in both paradigms. In the MI paradigm, accuracies of 94.53%, 94.9%, and 96.29% were achieved for left arm MI vs. baseline, right arm MI vs. baseline, and right arm MI vs. left arm MI, respectively - outperforming EEG-only accuracy by 3.87%, 3.80%, and 5.81%, respectively. In the MR/WG paradigm, the system achieved 95.27%, 85.93%, and 96.97% for MR vs. baseline, WG vs. baseline, and MR vs. WG, respectively, showing accuracy improvements of 2.28%, 4.95%, and 1.56%, respectively compared to EEG-only results. Overall, the proposed analysis approach improved classification accuracy for 5 out of 6 binary classification problems within the MI and MR/WG paradigms, with gains ranging from 0.64% to 9% compared to our previous EEG-fTCD studies. Additionally, our results demonstrate that EEG-fTCD BCIs with the proposed analysis techniques outperform multimodal EEG-fNIRS BCIs in both accuracy and speed, improving classification performance by 2.7% to 24.7% and reducing trial durations by 2-38 seconds. These findings highlight the potential of the proposed approach to advance assistive technologies and improve patient quality of life.},
}

*Brain-Computer Interfaces
Humans
*Electroencephalography/methods
*Ultrasonography, Doppler, Transcranial/methods
Male
Adult
Female
Young Adult
Bayes Theorem
*Brain/physiology
Algorithms

@article {pmid40402697,
year = {2025},
author = {Chaisaen, R and Autthasan, P and Ditthapron, A and Wilaiprasitporn, T},
title = {AlphaGrad: Normalized Gradient Descent for Adaptive Multi-loss Functions in EEG-based Motor Imagery Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3572197},
pmid = {40402697},
issn = {2168-2208},
abstract = {In this study, we propose AlphaGrad, a novel adaptive loss blending strategy for optimizing multi-task learning (MTL) models in motor imagery (MI)-based electroencephalography (EEG) classification. AlphaGrad is the first method to automatically adjust multi-loss functions with differing metric scales, including mean square error, cross-entropy, and deep metric learning, within the context of MI-EEG. We evaluate AlphaGrad using two state-of-the-art MTL-based neural networks, MIN2Net and FBMSNet, across four benchmark datasets. Experimental results show that AlphaGrad consistently outperforms existing strategies such as AdaMT, GradApprox, and fixed-weight baselines in classification accuracy and training stability. Compared to baseline static weighting, AlphaGrad achieves over 10% accuracy improvement on subject-independent MI tasks when evaluated on the largest benchmark dataset. Furthermore, AlphaGrad demonstrates robust adaptability across various EEG paradigms-including steady-state visually evoked potential (SSVEP) and event-related potential (ERP), making it broadly applicable to brain-computer interface (BCI) systems. We also provide gradient trajectory visualizations highlighting AlphaGrad's ability to maintain training stability and avoid local minima. These findings underscore AlphaGrad's promise as a general-purpose solution for adaptive multi-loss optimization in biomedical time-series learning.},
}

@article {pmid40401160,
year = {2025},
author = {Zhao, L},
title = {Advances in functional magnetic resonance imaging-based brain function mapping: a deep learning perspective.},
journal = {Psychoradiology},
volume = {5},
number = {},
pages = {kkaf007},
pmid = {40401160},
issn = {2634-4416},
abstract = {Functional magnetic resonance imaging (fMRI) provides a powerful tool for studying brain function by capturing neural activity in a non-invasive manner. Mapping brain function from fMRI data enables researchers to investigate the spatial and temporal dynamics of neural processes, providing insights into how the brain responds to various tasks and stimuli. In this review, we explore the evolution of deep learning-based methods for brain function mapping using fMRI. We begin by discussing various network architectures such as convolutional neural networks, recurrent neural networks, and transformers. We further examine supervised, unsupervised, and self-supervised learning paradigms for fMRI-based brain function mapping, highlighting the strengths and limitations of each approach. Additionally, we discuss emerging trends such as fMRI embedding, brain foundation models, and brain-inspired artificial intelligence, emphasizing their potential to revolutionize brain function mapping. Finally, we delve into the real-world applications and prospective impact of these advancements, particularly in the diagnosis of neural disorders, neuroscientific research, and brain-computer interfaces for decoding brain activity. This review aims to provide a comprehensive overview of current techniques and future directions in the field of deep learning and fMRI-based brain function mapping.},
}

@article {pmid40401149,
year = {2025},
author = {Leong, F and Micera, S and Shokur, S},
title = {Optimization frameworks for bespoke sensory encoding in neuroprosthetics.},
journal = {APL bioengineering},
volume = {9},
number = {2},
pages = {020901},
pmid = {40401149},
issn = {2473-2877},
abstract = {Restoring natural sensation via neuroprosthetics relies on the possibility of encoding complex and nuanced information. For example, an ideal brain-machine interface with sensory feedback would provide the user with sensation about movement, pressure, curvature, texture, etc. Despite advances in neural interfaces that allow for complex stimulation patterns (e.g., multisite stimulation or the possibility of targeting a precise neural ensemble), a key question remains: How can we best exploit the potential of these technologies? The increasing number of electrodes coupled with more parameters being explored leads to an exponential increase in the number of possible combinations, making a brute-force approach, such as systematic search, impractical. This Perspective outlines three different optimization frameworks-namely, the explicit, physiological, and self-optimized methods-allowing one to potentially converge faster toward effective parameters. Although our focus will be on the somatosensory system, these frameworks are flexible and applicable to various sensory systems (e.g., vision) and stimulator types.},
}

@article {pmid40399603,
year = {2025},
author = {Wang, Y and Fukuma, R and Seymour, B and Yang, H and Kishima, H and Yanagisawa, T},
title = {Neurofeedback modulation of insula activity via MEG-based brain-machine interface: a double-blind randomized controlled crossover trial.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {770},
pmid = {40399603},
issn = {2399-3642},
support = {JP19dm0307008//Japan Agency for Medical Research and Development (AMED)/ ; 19dm0207070h//Japan Agency for Medical Research and Development (AMED)/ ; JP24wm0625517//Japan Agency for Medical Research and Development (AMED)/ ; JPMJER1801//MEXT | Japan Science and Technology Agency (JST)/ ; JPMJMS2012//MEXT | Japan Science and Technology Agency (JST)/ ; JP20H05705//MEXT | Japan Society for the Promotion of Science (JSPS)/ ; 22H04998//MEXT | Japan Society for the Promotion of Science (JSPS)/ ; 214251/Z/18/Z//Wellcome Trust (Wellcome)/ ; EP/W03509X/1//DH | National Institute for Health Research (NIHR)/ ; 203316//DH | National Institute for Health Research (NIHR)/ ; },
mesh = {Humans ; *Neurofeedback/methods ; *Magnetoencephalography/methods ; Double-Blind Method ; Male ; Cross-Over Studies ; *Brain-Computer Interfaces ; Female ; Adult ; Young Adult ; *Insular Cortex/physiology ; Pain Threshold/physiology ; *Cerebral Cortex/physiology ; },
abstract = {Insula activity has often been linked to pain perception, making it a potential target for therapeutic neuromodulation strategies such as neurofeedback. However, it is not known whether insula activity is under cognitive control and, if so, whether this activity is consequently causally related to pain. Here, we conducted a double-blind randomized controlled crossover trial to test the modulation of insula activity and pain thresholds using neurofeedback training. Nineteen healthy subjects underwent neurofeedback training for upmodulation and downmodulation of right insula activity using our magnetoencephalography (MEG)-based brain-machine interface. We observed significant differences in insula activity between the upmodulation and downmodulation training sessions. Furthermore, resting-state insula activity significantly decreased following downmodulation training compared to following upmodulation training. Compared with upmodulation training, downmodulation training was also associated with increased pain thresholds, albeit with no significant interaction effect. These findings show that humans can cognitively modulate insula activity as a potential route to develop therapeutic MEG neurofeedback systems for clinical testing. However, the present findings do not provide direct evidence of a causal link between modulation of insula activity and changes in pain thresholds.},
}

Humans
*Neurofeedback/methods
*Magnetoencephalography/methods
Double-Blind Method
Male
Cross-Over Studies
*Brain-Computer Interfaces
Female
Adult
Young Adult
*Insular Cortex/physiology
Pain Threshold/physiology
*Cerebral Cortex/physiology

@article {pmid39818881,
year = {2025},
author = {Valle, G and Alamri, AH and Downey, JE and Lienkämper, R and Jordan, PM and Sobinov, AR and Endsley, LJ and Prasad, D and Boninger, ML and Collinger, JL and Warnke, PC and Hatsopoulos, NG and Miller, LE and Gaunt, RA and Greenspon, CM and Bensmaia, SJ},
title = {Tactile edges and motion via patterned microstimulation of the human somatosensory cortex.},
journal = {Science (New York, N.Y.)},
volume = {387},
number = {6731},
pages = {315-322},
pmid = {39818881},
issn = {1095-9203},
support = {R35 NS122333/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Female ; Humans ; Male ; Young Adult ; Bionics ; Cervical Cord/injuries ; *Deep Brain Stimulation/methods ; Hand/physiology ; *Somatosensory Cortex/physiology ; *Touch/physiology ; *Touch Perception/physiology ; Trauma, Nervous System/physiopathology/therapy ; *Brain-Computer Interfaces ; },
abstract = {Intracortical microstimulation (ICMS) of somatosensory cortex evokes tactile sensations whose properties can be systematically manipulated by varying stimulation parameters. However, ICMS currently provides an imperfect sense of touch, limiting manual dexterity and tactile experience. Leveraging our understanding of how tactile features are encoded in the primary somatosensory cortex (S1), we sought to inform individuals with paralysis about local geometry and apparent motion of objects on their skin. We simultaneously delivered ICMS through electrodes with spatially patterned projected fields (PFs), evoking sensations of edges. We then created complex PFs that encode arbitrary tactile shapes and skin indentation patterns. By delivering spatiotemporally patterned ICMS, we evoked sensation of motion across the skin, the speed and direction of which could be controlled. Thus, we improved individuals' tactile experience and use of brain-controlled bionic hands.},
}

Adult
Female
Humans
Male
Young Adult
Bionics
Cervical Cord/injuries
*Deep Brain Stimulation/methods
Hand/physiology
*Somatosensory Cortex/physiology
*Touch/physiology
*Touch Perception/physiology
Trauma, Nervous System/physiopathology/therapy
*Brain-Computer Interfaces

@article {pmid40398442,
year = {2025},
author = {Peterson, V and Spagnolo, V and Galván, CM and Nieto, N and Spies, R and Milone, DH},
title = {Towards subject-centered co-adaptive brain-computer interfaces based on backward optimal transport.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/addb7a},
pmid = {40398442},
issn = {1741-2552},
abstract = {OBJECTIVE: Controlling a motor imagery brain-computer interface (MI-BCI) can be challenging, requiring several sessions of practice. Electroencephalography (EEG)-based BCIs are particularly affected by cross-session variability. In this scenario, it is crucial to implement co-adaptive systems, where the machine adapts the decoding algorithm while the user learns how to control the BCI. To support the user learning process, it is essential to measure and provide real-time feedback on self-modulation skills. This study aims to develop a method for online assessment of MI modulation capability to build co-adaptive BCIs that improve both user performance and system accuracy.

APPROACH: Backward optimal transport for domain adaptation allows across-session MI-BCI usage without classifier retraining. Using the cued label to guide the adaptation, a supportive backward adaptation (SBA) method is defined. The required model effort to perform a trial adaptation is proposed as an online metric of MI modulation skills. We conducted experiments on both real and simulated data to demonstrate that this metric effectively informs about the the discriminability and stability of the EEG patterns related to the MI task. The proposed metric is validated by means of Riemannian distinctiveness metrics.

MAIN RESULTS: Our findings show that the associated effort when applying SBA provides a meaningful way of evaluating EEG patterns discriminability, being significantly correlated with Riemannian distinctiveness metrics.

SIGNIFICANCE: This study introduces a novel framework for co-adaptive BCI learning that performs data adaptation while assessing the MI-BCI skills of the user. The proposed SBA approach can enhance BCI performance by facilitating session-to-session adaptation and empowering users with valuable feedback based on their current MI modulation strategy. This framework represents a significant advancement in developing user-centered, co-adaptive MI-BCIs that effectively support and enhance user capabilities.},
}

@article {pmid40398440,
year = {2025},
author = {Van der Eerden, JHM and Liu, PC and Villalobos, J and Yanagisawa, T and Grayden, DB and John, SE},
title = {Decoding cortical responses from visual input using an endovascular brain-computer interface.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/addb7c},
pmid = {40398440},
issn = {1741-2552},
abstract = {Implantable neural interfaces enable recording of high-quality brain signals that can improve our understanding of brain function. This work examined the feasibility of using a minimally invasive endovascular neural interface (ENI) to record interpretable cortical activity from the visual cortex. Approach: A sheep model (n = 5) was used to record and decode visually evoked potentials from the cortex both with an ENI and a subdural electrode grid. Sets of distinct experimental visual stimuli were presented to attempt decoding from the recorded cortical potentials, using perceptual categories of colour, contrast, movement direction orientation, spatial frequency and temporal frequency. Decoding performances are presented as accuracy scores from K-fold cross-validation of a stratified random forest classification model. The study compared the signal quality and decoding performance between the ENI and electrocorticography electrodes. Main results: Recordings from the ENI array resulted in lower decoding performances than the electrocorticography array, but the classification scores were significantly above chance in the stimuli categories of colour, contrast, direction and temporal frequency. This study is the first report of visually evoked neural activity using a minimally-invasive ENI. Significance: Overall, the results show that implantable macro-electrodes yield sufficient neural signal definition to discern primary visual percepts, using both endo-vascular and intracranial surgical placements.},
}

@article {pmid40398391,
year = {2025},
author = {Huang, W and Shu, N},
title = {AI-powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders.},
journal = {Cell reports. Medicine},
volume = {6},
number = {5},
pages = {102132},
doi = {10.1016/j.xcrm.2025.102132},
pmid = {40398391},
issn = {2666-3791},
mesh = {Humans ; *Precision Medicine/methods ; *Multimodal Imaging/methods ; *Mental Disorders/diagnostic imaging/therapy ; *Artificial Intelligence ; *Neuroimaging/methods ; },
abstract = {Neuropsychiatric disorders have complex pathological mechanism, pronounced clinical heterogeneity, and a prolonged preclinical phase, which presents a challenge for early diagnosis and development of precise intervention strategies. With the development of large-scale multimodal neuroimaging datasets and advancement of artificial intelligence (AI) algorithms, the integration of multimodal imaging with AI techniques has emerged as a pivotal avenue for early detection and tailoring individualized treatment for neuropsychiatric disorders. To support these advances, in this review, we outline multimodal neuroimaging techniques, AI methods, and strategies for multimodal data fusion. We highlight applications of multimodal AI based on neuroimaging data in precision medicine for neuropsychiatric disorders, discussing challenges in clinical adoption, their emerging solutions, and future directions.},
}

Humans
*Precision Medicine/methods
*Multimodal Imaging/methods
*Mental Disorders/diagnostic imaging/therapy
*Artificial Intelligence
*Neuroimaging/methods

@article {pmid40398228,
year = {2025},
author = {Xiao, S and Huang, X and He, X and Chen, Z and Li, X and Wei, X and Liu, Q and Dong, H and Zeng, X and Bai, W},
title = {Interactions between curcumin and fish-/bovine-derived (type I and II) collagens: Preparation of nanoparticle and their application in Pickering emulsions.},
journal = {Food chemistry},
volume = {487},
number = {},
pages = {144781},
doi = {10.1016/j.foodchem.2025.144781},
pmid = {40398228},
issn = {1873-7072},
abstract = {This study aims to elucidate the interaction mechanisms between curcumin (Cur) and four collagen subtypes (fish type I [FCI], bovine type I [BCI], fish type II [FCII], bovine type II [BCII]), with parallel characterization of the structural and functional attributes of their derived nanoparticles. Type I Collagen/Cur nanoparticles exhibited superior solution stability compared to type II. Cur binding significantly enhanced the surface hydrophobicity, absolute ζ potential, and surface tension of collagen, while reduced dynamic interfacial tension. The binding type of Cur to collagen was static, and binding process was enthalpy-driven exothermic reaction. Molecular dynamics simulations revealed that hydrophobic interactions, hydrogen bonds, and electrostatic forces dominated the binding process. The binding affinity followed the order: FCI/Cur > BCI/Cur > FCII/Cur > BCII/Cur. The binding sites of Cur to type I collagen and type II collagen were around Ser129-Glu135 and Asn179-Ser183 residues. Collagen/Cur nanoparticle stabilized emulsions and improved oxidative stability and storage modulus.},
}

@article {pmid40395924,
year = {2025},
author = {Russell, M and Hincks, S and Wang, L and Babar, A and Chen, Z and White, Z and Jacob, RJK},
title = {Visualization and workload with implicit fNIRS-based BCI: toward a real-time memory prosthesis with fNIRS.},
journal = {Frontiers in neuroergonomics},
volume = {6},
number = {},
pages = {1550629},
pmid = {40395924},
issn = {2673-6195},
abstract = {Functional Near-Infrared Spectroscopy (fNIRS) has proven in recent time to be a reliable workload-detection tool, usable in real-time implicit Brain-Computer Interfaces. But what can be done in terms of application of neural measurements of the prefrontal cortex beyond mental workload? We trained and tested a first prototype example of a memory prosthesis leveraging a real-time implicit fNIRS-based BCI interface intended to present information appropriate to a user's current brain state from moment to moment. Our prototype implementation used data from two tasks designed to interface with different brain networks: a creative visualization task intended to engage the Default Mode Network (DMN), and a complex knowledge-worker task to engage the Dorsolateral Prefrontal Cortex (DLPFC). Performance of 71% from leave-one-out cross-validation across participants indicates that such tasks are differentiable, which is promising for the development of future applied fNIRS-based BCI systems. Further, analyses within lateral and medial left prefrontal areas indicates promising approaches for future classification.},
}

@article {pmid40395688,
year = {2025},
author = {Tibermacine, IE and Russo, S and Citeroni, F and Mancini, G and Rabehi, A and Alharbi, AH and El-Kenawy, EM and Napoli, C},
title = {Adversarial denoising of EEG signals: a comparative analysis of standard GAN and WGAN-GP approaches.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1583342},
pmid = {40395688},
issn = {1662-5161},
abstract = {INTRODUCTION: Electroencephalography (EEG) signals frequently contain substantial noise and interference, which can obscure clinically and scientifically relevant features. Traditional denoising approaches, such as linear filtering or wavelet thresholding, often struggle with nonlinear or time-varying artifacts. In response, the present study explores a Generative Adversarial Network (GAN) framework to enhance EEG signal quality, focusing on two variants: a conventional GAN model and a Wasserstein GAN with Gradient Penalty (WGAN-GP).

METHODS: Data were obtained from two distinct EEG datasets: a "healthy" set of 64-channel recordings collected during various motor/imagery tasks, and an "unhealthy" set of 18-channel recordings from individuals with orthopedic impairments. Both datasets underwent comprehensive preprocessing, including band-pass filtering (8-30 Hz), channel standardization, and artifact trimming. The training stage involved adversarial learning, in which a generator sought to reconstruct clean EEG signals while a discriminator (or critic in the case of WGAN-GP) attempted to distinguish between real and generated signals. The model evaluation was conducted using quantitative metrics such as signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), correlation coefficient, mutual information, and dynamic time warping (DTW) distance.

RESULTS: Experimental findings indicate that adversarial learning substantially improves EEG signal fidelity across multiple quantitative metrics. Specifically, WGAN-GP achieved an SNR of up to 14.47 dB (compared to 12.37 dB for the standard GAN) and exhibited greater training stability, as evidenced by consistently lower relative root mean squared error (RRMSE) values. In contrast, the conventional GAN model excelled in preserving finer signal details, reflected in a PSNR of 19.28 dB and a correlation coefficient exceeding 0.90 in several recordings. Both adversarial frameworks outperformed classical wavelet-based thresholding and linear filtering methods, demonstrating superior adaptability to nonlinear distortions and dynamic interference patterns in EEG time-series data.

DISCUSSION: By systematically comparing standard GAN and WGAN-GP architectures, this study highlights a practical trade-off between aggressive noise suppression and high-fidelity signal reconstruction. The demonstrated improvements in signal quality underscore the promise of adversarially trained models for applications ranging from basic neuroscience research to real-time brain-computer interfaces (BCIs) in clinical or consumer-grade settings. The results further suggest that GAN-based frameworks can be easily scaled to next-generation wireless networks and complex electrophysiological datasets, offering robust and dynamic solutions to long-standing challenges in EEG denoising.},
}