@article {pmid41176895,
year = {2025},
author = {Cavallé Garrido, L and de Paula Vernetta, C and Guzmán Calvete, A and Álvarez Arocas, J and Gonçalves, C and Armengot Carceller, M},
title = {Bonebridge active transcutaneous bone conduction hearing implant: Results in the pediatric population.},
journal = {International journal of pediatric otorhinolaryngology},
volume = {199},
number = {},
pages = {112610},
doi = {10.1016/j.ijporl.2025.112610},
pmid = {41176895},
issn = {1872-8464},
abstract = {PURPOSE: This study provides prospective and retrospective data on safety and performance results with the Bonebridge BCI 602 (MED-EL) active transcutaneous bone conduction implant in children.

METHODS: Audiological data were collected at 3 intervals (preoperative, initial activation and 3 months postoperative). Quality of life was assessed with the Speech, Spatial, and Qualities of Hearing (SSQ12/P), KID KINDL and Audio Processor Satisfaction Questionnaire (APSQ) as well as a postoperative questionnaire specifically designed for this study.

RESULTS: 22 pediatric patients (20 conductive/mixed hearing loss (CHL/MHL) and 2 single-sided deafness (SSD)) aged 4-17 received a BCI 602. Three-month post-op pure-tone average (PTA4) functional gain (FG) was 31.9 dB HL for the CHL/MHL group and 11.3 dB HL in the SSD patients. CHL/MHL patients had a mean word recognition score (WRS) improvement of 80.6 ± 23.9 % at initial activation and 83 ± 20.3 % at 3 months post-op. Speech recognition in noise at +5 dB SNR in the CHL/MHL group improved from 24.6 ± 28.3 % unaided to 74.9 ± 26 % aided at 3 months post-op. The mean post-op total scores were 5.5 ± 1.8 on the SSQ12/P and 8.87 ± 0.93 on the APSQ questionnaires. No major complications were noted on the postoperative questionnaire; minor complications were resolved by the end of the study. Stable bone and air conduction thresholds confirmed device safety.

CONCLUSION: The Bonebridge BCI 602 is safe and effective for use in the pediatric population.},
}

@article {pmid41174212,
year = {2025},
author = {Zhou, H and Iramina, K},
title = {Discovery of EEG effective connectivity during visual motor imagery with multi-scale symbolic transfer entropy.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {38200},
pmid = {41174212},
issn = {2045-2322},
mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; Adult ; *Imagination/physiology ; Brain-Computer Interfaces ; Entropy ; Young Adult ; Parietal Lobe/physiology ; *Brain/physiology ; Brain Mapping ; Occipital Lobe/physiology ; },
abstract = {Visual motor imagery (VMI) is an important component of motor imagery, with potential applications in brain-computer interfaces and motor rehabilitation due to its lower training cost compared to kinesthetic motor imagery (KMI). However, the neural mechanisms underlying VMI, particularly the effects of imagery hand and imagery perspective (first-person perspective, 1pp, vs. third-person perspective, 3pp) remain unclear. This study examines the effective connectivity of VMI EEG using multi-scale symbolic transfer entropy. Time-frequency analysis revealed prominent event-related synchronization (ERS) in the alpha and high-beta bands, while connectivity analysis emphasized strong information flow within the parieto-occipital network. Notably, hand effect dominant information flows were found between the motor and posterior parietal-occipital regions, while perspective suggested a more remarkable effect. 1pp imagery significantly enhanced top-down modulation of the occipital cortex, whereas 3pp imagery engaged the right posterior parietal region, suggesting stronger spatial localization processing. These findings provide novel insights into the distinct neural mechanisms of VMI and its potential applications in cognitive neuroscience and brain-machine engineering.},
}

Humans
*Electroencephalography/methods
Male
Female
Adult
*Imagination/physiology
Brain-Computer Interfaces
Entropy
Young Adult
Parietal Lobe/physiology
*Brain/physiology
Brain Mapping
Occipital Lobe/physiology

@article {pmid41173359,
year = {2025},
author = {Chen, Z and Lu, Y and Xu, X},
title = {EEG-SGENet: A lightweight convolutional network integrating SGE for motor imagery brain-computer interfaces.},
journal = {Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuroscience.2025.09.040},
pmid = {41173359},
issn = {1873-7544},
abstract = {In recent years, there has been a significant increase in research activity on electroencephalography (EEG)-based motor imagery brain-computer interfaces (MI-BCI) in the field of deep learning. However, despite achieving high accuracy, the size of models is increasing, requiring significant memory and computational resources. Therefore, finding a balance between accuracy and computational cost has always been a challenge in MI classification research. Convolutional Neural Networks (CNNs) generate feature representations of objects by collecting semantic sub-features. The activation of subfeatures is susceptible to noisy backgrounds. The Spatial Group-wise Enhance (SGE) module adjusts the importance of each sub-feature by generating an attention factor for the spatial location of each semantic group, thus enhancing useful features and suppressing noise. The design of the SGE module is lightweight, with few parameters and computations. Therefore, we introduce the SGE module to improve accuracy and minimize model parameters. In this paper, we propose EEG-SGENet, a novel end-to-end convolutional neural network model that considers both the lightweight model and accuracy. Experimental results on the BCI IV 2a dataset show that EEG-SGENet achieves an accuracy of 80.98% in the four categories of MI. The average classification accuracy for the two-category task of BCI IV 2b is 76.17%. Comparisons with other lightweight models in terms of classification accuracy and other aspects have shown that this model achieves a good balance between decoding performance and computational cost. Overall, experimental results demonstrate that the proposed model is expected to become a new method for decoding EEG signals.},
}

@article {pmid41171945,
year = {2025},
author = {Khanam, T and Siuly, S and Ahmad, K and Wang, H},
title = {A novel channel reduction concept to enhance the classification of motor imagery tasks in brain-computer interface systems.},
journal = {PloS one},
volume = {20},
number = {10},
pages = {e0335511},
pmid = {41171945},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Algorithms ; Neural Networks, Computer ; Male ; Adult ; *Imagination/physiology ; Female ; Movement/physiology ; Brain/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Electroencephalogram (EEG) signals play a critical role in advancing brain-computer interface (BCI) systems, particularly for detecting motor imagery (MI) movements. However, analysing large volume of EEG datasets faces some challenges due to redundant information, and performance degradation. Irrelevant channels introduce noise, which reduces accuracy and slows system performance. To address these issues, this study aims to develop a novel channel selection method to enhance EEG-based MI task performance in BCI applications. Our proposed hybrid approach combines statistical t-tests with a Bonferroni correction-based channel reduction technique, followed by the application of a Deep Learning Regularized Common Spatial Pattern with Neural Network (DLRCSPNN) framework. This framework employs DLRCSP for feature extraction and neural network (NN) algorithm for classification. Our developed method excluded channels with correlation coefficients below 0.5, retaining only significant, non-redundant channels and tested on three real-time EEG-based BCI datasets. This study produces the highest accuracy score in the case of every subjects above 90% for all the applied datasets. In the first dataset, our method achieved the highest accuracy, improving by 3.27% to 42.53% in terms of individual subject compared to seven existing machine learning algorithms. In the second and third dataset, it outperformed existing approaches, with accuracy gains of 5% to 45% and 1% to 17.47% respectively. Comparisons with a CSP and NN framework confirmed DLRCSPNN's algorithms superior performance. These results demonstrate the effectiveness of the approach, offering a new perspective on the identification of MI task performance in EEG based BCI technology. This proposed technique will enable rapid identification of motor-disabled individuals' intentions, supporting patient rehabilitation and improving daily living.},
}

*Brain-Computer Interfaces
Humans
*Electroencephalography/methods
Algorithms
Neural Networks, Computer
Male
Adult
*Imagination/physiology
Female
Movement/physiology
Brain/physiology
Signal Processing, Computer-Assisted

@article {pmid41171651,
year = {2025},
author = {Liu, H and Wang, Z and Li, R and Zhao, X and Xu, T and Zhou, T and Hu, H},
title = {A Novel Binocular-Encoded SSVEP Framework for Efficient VR-Based Brain-Computer Interface.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3626332},
pmid = {41171651},
issn = {2168-2208},
abstract = {This paper presents a novel binocular-encoded SSVEP (beSSVEP) method, leveraging binocular vision in virtual reality (VR) to enhance brain-computer interface (BCI) applications. We introduce the Binocular Periodically Repeated Component Analysis (bPRCA) algorithm, designed to address the unique characteristics of binocular-encoded targets, which include combinations of monocular single-frequency SSVEP units or void units, with frequency units being reused multiple times in the encoded interface. To further optimize performance, we propose the Fusion Component Analysis (FusionCA) framework, which integrates bPRCA with Task-related Component Analysis (TRCA), effectively utilizing both steady-state periodic components and cross-trial aperiodic components. Experimental results demonstrate that ensemble-FusionCA achieves the highest information transfer rate (ITR) with an average accuracy of $71.39\%$ and an ITR of 138.50 bits/min at 0.4 seconds, among the comparison with ensemble-bPRCA and ensemble-TRCA. Compared to traditional SSVEP approaches, beSSVEP significantly enhances frequency utilization, making VR-BCI systems more efficient and practical. This study highlights the application of physiological mechanisms of binocular vision to improve BCI systems, offering a new perspective for developing fast and scalable brain-computer interactions in VR environments.},
}

@article {pmid41170547,
year = {2025},
author = {Ren, J and Mo, WY and Wang, L and Ni, GJ and Yang, JJ},
title = {[Research progress on the role of dopamine system in regulating hippocampal related brain functions].},
journal = {Sheng li xue bao : [Acta physiologica Sinica]},
volume = {77},
number = {5},
pages = {893-904},
doi = {10.13294/j.aps.2025.0055},
pmid = {41170547},
issn = {0371-0874},
mesh = {*Hippocampus/physiology ; *Dopamine/physiology ; Humans ; Animals ; Receptors, Dopamine D2/physiology ; Memory/physiology ; Signal Transduction/physiology ; Neurodegenerative Diseases/physiopathology ; },
abstract = {Dopamine, as a catecholamine neurotransmitter widely distributed in the central nervous system, is involved in physiological functions such as motivation, arousal, reinforcement, and movement through various dopamine signaling pathways. The hippocampus receives dopaminergic neuron projections from regions such as the ventral tegmental area, locus coeruleus, and substantia nigra. Through D1-like and D2-like receptors, dopamine exerts significant regulatory effects such as spatial navigation, episodic memory, fear, anxiety, and reward. This review mainly summarizes the research progress on the functions of dopamine in the hippocampus from aspects including the sources of dopamine, receptor distribution and function, and the association of hippocampal dopamine system dysregulation with neurodegenerative diseases. The aim is to provide insights into the involvement of the dopamine system in hippocampal functions and the diagnosis and treatment of related diseases.},
}

*Hippocampus/physiology
*Dopamine/physiology
Humans
Animals
Receptors, Dopamine D2/physiology
Memory/physiology
Signal Transduction/physiology
Neurodegenerative Diseases/physiopathology

@article {pmid41170533,
year = {2025},
author = {Gherman, DE and Zander, TO},
title = {Towards neuroadaptive chatbots: a feasibility study.},
journal = {Frontiers in neuroergonomics},
volume = {6},
number = {},
pages = {1589734},
pmid = {41170533},
issn = {2673-6195},
abstract = {INTRODUCTION: Large-language models (LLMs) are transforming most industries today and are set to become a cornerstone of the human digital experience. While integrating explicit human feedback into the training and development of LLM-based chatbots has been integral to the progress we see nowadays, more work is needed to understand how to best align them with human values. Implicit human feedback enabled by passive brain-computer interfaces (pBCIs) could potentially help unlock the hidden nuance of users' cognitive and affective states during interaction with chatbots. This study proposes an investigation on the feasibility of using pBCIs to decode mental states in reaction to text stimuli, to lay the groundwork for neuroadaptive chatbots.

METHODS: Two paradigms were created to elicit moral judgment and error-processing with text stimuli. Electroencephalography (EEG) data was recorded with 64 gel electrodes while participants completed reading tasks. Mental state classifiers were obtained in an offline manner with a windowed-means approach and linear discriminant analysis (LDA) for full-component and brain-component data. The corresponding event-related potentials (ERPs) were visually inspected.

RESULTS: Moral salience was successfully decoded at a single-trial level, with an average calibration accuracy of 78% on the basis of a data window of 600 ms. Subsequent classifiers were not able to distinguish moral judgment congruence (i.e., moral agreement) and incongruence (i.e., moral disagreement). Error processing in reaction to factual inaccuracy was decoded with an average calibration accuracy of 66%. The identified ERPs for the investigated mental states partly aligned with other findings.

DISCUSSION: With this study, we demonstrate the feasibility of using pBCIs to distinguish mental states from readers' brain data at a single-trial level. More work is needed to transition from offline to online investigations and to understand if reliable pBCI classifiers can also be obtained in less controlled language tasks and more realistic chatbot interactions. Our work marks preliminary steps for understanding and making use of neural-based implicit human feedback for LLM alignment.},
}

@article {pmid41169537,
year = {2025},
author = {Li, X and Ji, X and Wang, Y and Chen, X},
title = {The influence of different visual eccentricity on SSVEPs elicited by ultra-low frequency visual stimulation in the lower peripheral visual field.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {170},
pmid = {41169537},
issn = {1871-4080},
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been widely explored due to their high information transfer rate (ITR) and minimal training requirements. Traditional SSVEP-based BCIs typically use low- and medium-frequency visual stimuli from the central visual field to induce SSVEPs, but these can easily lead to visual fatigue. In order to improve system's comfort, some studies have attempted to use visual stimuli from the peripheral visual field to elicit SSVEPs. However, few studies have investigated the effects of different visual eccentricities on induced SSVEPs. In this study, we used ultra-low frequency (i.e., 2.00-3.32 Hz) visual stimulation in the lower peripheral visual field to induce SSVEPs. Furthermore, we further explored the effects of different visual eccentricities (i.e., 2.1°, 3.1°, and 4.1°) on induced SSVEPs. Experimental results obtained from twelve participants revealed that all three eccentricity conditions were capable of eliciting SSVEP responses. Moreover, SSVEP amplitude gradually decreased as eccentricity increased. These results provide new parametric references for optimizing the spatial layout of visual stimuli in peripheral SSVEP-based BCI systems.},
}

@article {pmid41167038,
year = {2025},
author = {Schippers, A and Berezutskaya, J and Vansteensel, MJ and Freudenburg, ZV and Crone, NE and Ramsey, NF},
title = {The effect of perceived auditory feedback on speech Brain-Computer Interface decoding performance.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {180},
number = {},
pages = {2111403},
doi = {10.1016/j.clinph.2025.2111403},
pmid = {41167038},
issn = {1872-8952},
abstract = {OBJECTIVE: Brain-Computer Interfaces (BCI) provide alternative means of communication for individuals with severe motor impairment. Implantable speech BCIs have shown great potential, particularly in individuals who could still produce some speech-related movements and/or sounds. As perception of auditory feedback is important for correct speech sound production in able-bodied people, it is conceivable that a complete absence of such feedback in individuals who lost all ability to produce audible speech affects BCI performance. The current study therefore set out to investigate to what extent perception of auditory feedback of self-produced speech contributes to speech decoding performance.

METHODS: In three able-bodied participants, patterns of 65-95 Hz power over sensorimotor cortex were compared between normal speech and speech in which auditory feedback was masked by noise. In addition, decoding accuracy was compared between feedback situations.

RESULTS & CONCLUSIONS: We found subtle differences in brain activity patterns associated with speech production between situations in which participants could versus could not perceive their produced speech. Importantly, absence of such auditory feedback led to lower speech decoding performance in all participants.

SIGNIFICANCE: These results underline the need to validate speech BCI efficacy with fully paralyzed individuals, as perceived feedback can influence the attainable speech decoding accuracy.},
}

@article {pmid41165717,
year = {2025},
author = {Lydiatt, WB},
title = {Mind, Machine, and Medicine-Challenges and Opportunities.},
journal = {JAMA otolaryngology-- head & neck surgery},
volume = {},
number = {},
pages = {},
doi = {10.1001/jamaoto.2025.4108},
pmid = {41165717},
issn = {2168-619X},
}

@article {pmid41163348,
year = {2025},
author = {Song, K and Liu, Y and Xu, P},
title = {Acute Effects of Portable Dry-EEG Neurofeedback on Classical Chinese Learning: A Three-Arm Repeated-Measures Study.},
journal = {Brain and behavior},
volume = {15},
number = {11},
pages = {e70977},
pmid = {41163348},
issn = {2162-3279},
support = {2025ZSD017//Shanghai Municipal Education Science Research Project "Special Program for Philosophy and Social Sciences Research in Shanghai Higher Education Institutions"/ ; },
mesh = {Humans ; *Neurofeedback/methods ; Male ; Female ; Young Adult ; *Electroencephalography/methods ; Adult ; *Learning/physiology ; Attention/physiology ; Cognition/physiology ; Comprehension/physiology ; Language ; Adolescent ; East Asian People ; },
abstract = {OBJECTIVE: Dry-electrode electroencephalography (dry-EEG) systems offer promising opportunities for real-time neurofeedback in naturalistic educational settings, yet their effectiveness in supporting complex language learning remains underexplored. This study investigated the acute effects of portable dry-EEG neurofeedback on students' cognitive performance and attentional states during classical Chinese learning, using a repeated-measures design to compare neurofeedback, sham feedback, and device control conditions.

METHODS: A total of 20 undergraduate participants completed three sessions involving a customized semantic disambiguation task after passive reading. EEG signals were acquired using a dry-sensor OpenBCI system from four frontal sites (Fp1, Fp2, F3, F4). Real-time attention indices were computed based on the beta/(alpha+theta) ratio and fed back visually in the neurofeedback condition. Cognitive outcomes included comprehension test scores and semantic conflict resolution performance (RT, accuracy, cognitive load).

RESULTS: Compared to sham and control conditions, neurofeedback significantly improved comprehension accuracy (p < 0.001), reduced reaction times in the interference task (p < 0.05), and lowered subjective cognitive load (p = 0.002). EEG indices of attention were significantly elevated during neurofeedback (p < 0.001) and positively correlated with behavioral gains (r = 0.63, p < 0.05).

CONCLUSIONS: Portable dry-electrode EEG systems can reliably support real-time neurofeedback to enhance attention and cognitive control in complex language learning contexts. This study provides empirical validation for deploying dry-EEG sensors in adaptive educational technologies and contributes to the broader integration of wearable brain-computer interfaces in cognitive augmentation applications.},
}

Humans
*Neurofeedback/methods
Male
Female
Young Adult
*Electroencephalography/methods
Adult
*Learning/physiology
Attention/physiology
Cognition/physiology
Comprehension/physiology
Language
Adolescent
East Asian People

@article {pmid40586414,
year = {2025},
author = {Tian, Y and Wallace, DM and Cederna, PS and Chestek, CA and Kemp, SWP},
title = {Toward Natural Limb Function: A New Era in Prosthetic Innovation.},
journal = {Annals of neurology},
volume = {98},
number = {5},
pages = {913-928},
doi = {10.1002/ana.27287},
pmid = {40586414},
issn = {1531-8249},
mesh = {Humans ; *Artificial Limbs/trends ; *Brain-Computer Interfaces/trends ; *Extremities/physiology ; Electroencephalography ; },
abstract = {The past decade has witnessed groundbreaking clinical implementation of neuroprosthetic limbs driven by signals from peripheral targets (eg, nerves and muscle) and the brain to restore limb function for individuals with limb loss or impairment. In this review, we highlight recent key clinical trials in peripheral neuroprosthetic interfaces directly with nerve, residual muscle, and reinnervated muscle. We then highlight the key advances in brain interfaces, including clinical trials using electroencephalography, electrocorticography, and intracortical electrodes to control neuroprosthetics. Finally, we explore the future of neuroprosthetic control where both peripheral and brain interfaces can be combined to improve neuroprosthetic performance. ANN NEUROL 2025;98:913-928.},
}

Humans
*Artificial Limbs/trends
*Brain-Computer Interfaces/trends
*Extremities/physiology
Electroencephalography

@article {pmid41161815,
year = {2025},
author = {Yang, Y and Liu, C and Liu, S and Ding, P and Bai, R and Chen, G and Li, S and Song, X and Cheng, Y and Xu, J},
title = {Role of combination immunotherapy in restoring brain synergistic functional connectivity in patients with systemic lupus erythematosus without overt neuropsychiatric manifestations.},
journal = {Lupus science & medicine},
volume = {12},
number = {2},
pages = {},
doi = {10.1136/lupus-2025-001771},
pmid = {41161815},
issn = {2053-8790},
mesh = {Humans ; Female ; *Lupus Erythematosus, Systemic/drug therapy/physiopathology ; Adult ; Male ; Cyclophosphamide/therapeutic use/administration & dosage ; Magnetic Resonance Imaging ; *Immunosuppressive Agents/therapeutic use/administration & dosage ; Hydroxychloroquine/therapeutic use/administration & dosage ; *Brain/physiopathology/diagnostic imaging/drug effects ; Glucocorticoids/therapeutic use/administration & dosage ; Middle Aged ; Case-Control Studies ; Drug Therapy, Combination ; Young Adult ; *Immunotherapy/methods ; },
abstract = {OBJECTIVE: To determine whether subclinical brain dysfunction in SLE can be detected by disrupted interhemispheric connectivity and assess its modulation by immunosuppressive regimens.

METHODS: 234 subjects (140 patients with SLE and 94 healthy controls (HCs)) were included. Through stratified analysis, patients with SLE were divided into treatment-naïve group (n=22), glucocorticoid monotherapy group (GC group, n=30) and GC combined with cyclophosphamide (CTX) and/or hydroxychloroquine (HCQ) treatment group (n=50) to assess the differences in voxel-mirrored homotopic connectivity (VMHC) between groups.

RESULTS: SLE group showed lower VMHC than the HC group in bilateral superior temporal gyrus, medial superior frontal gyrus, calcarine fissure and surrounding cortex and middle occipital cortices (Gaussian random field corrected: voxel p<0.005, cluster p<0.01). The VMHC in the bilateral superior temporal gyrus (rs=-0.250, p=0.024) and medial superior frontal gyrus (rs=-0.246, p=0.026) was negatively correlated with the depression score, while the VMHC in the medial superior frontal gyrus was negatively correlated with the anxiety score (rs=-0.239, p=0.031). Three SLE subgroups and HCs had different VMHC in the postcentral/precentral gyrus (F=8.942) and anterior cingulate/paracingulate gyrus (F=9.868). Post hoc analysis found that compared with the HC group, VMHC in the treatment-naïve group was decreased in the bilateral posterior central gyrus (t=-2.953), while in the GC monotherapy group, it decreased in the posterior central gyrus (t=-2.999) and anterior cingulate/paracingulate gyrus (t=-2.999). Compared with GC combined with CTX and/or HCQ group, VMHC in GC monotherapy group was decreased in the postcentral gyrus (t=-2.999).

CONCLUSION: Even without overt neuropsychiatric symptoms, patients with SLE exhibit impaired interhemispheric functional synergy that is partially reversed by combination immunosuppression, suggesting an early targetable brain pathway.},
}

Humans
Female
*Lupus Erythematosus, Systemic/drug therapy/physiopathology
Adult
Male
Cyclophosphamide/therapeutic use/administration & dosage
Magnetic Resonance Imaging
*Immunosuppressive Agents/therapeutic use/administration & dosage
Hydroxychloroquine/therapeutic use/administration & dosage
*Brain/physiopathology/diagnostic imaging/drug effects
Glucocorticoids/therapeutic use/administration & dosage
Middle Aged
Case-Control Studies
Drug Therapy, Combination
Young Adult
*Immunotherapy/methods

@article {pmid41160913,
year = {2025},
author = {Russo, JS and Colebatch, JG and Lin, CS and John, SE and Grayden, DB and Todd, NPM},
title = {Feasibility of decoding cerebellar movement-related potentials for brain-computer interface applications.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae18fa},
pmid = {41160913},
issn = {1741-2552},
abstract = {In Brain-Computer Interface (BCI) applications, signals are conventionally acquired from the cerebrum, and only a subset of the complex interactions that occur in several areas of the brain are collected. One area that has not been investigated for BCI application is the cerebellum, despite its involvement in movement and executive function. The present study aimed to determine the features of movement-related cerebellar electrocerebellography (ECeG) that are most useful for decoding, and how performance compares with conventional electroencephalography (EEG) recordings from the cerebrum. Approach. ECeG and EEG data were collected from six healthy adults to identify useful movement-related features from both cerebrum and cerebellum. Electromyography was used to capture the movements from the muscles. Decoding was conducted in binary movement vs. rest and movement vs. movement systems using support vector machines. Decoding performance was compared between cerebral, cerebellar, a combination of both, and temporal groups. Re-referencing techniques were applied to compensate for possible common reference artefacts or volume conduction effects. Main Results. Movement-related features were decoded from over the cerebellum and the cerebrum. Classification accuracies were similar in both the cerebrum and cerebellum, when classifying movement vs. rest (cerebrum: 0.78 ± 0.02, cerebellum: 0.70 ± 0.01) and movement vs. movement states (cerebrum: 0.76 ± 0.02, cerebellum: 0.71 ± 0.02). The delta band (1-3 Hz) was the most useful feature for decoding. Significance. This study demonstrated, for the first time, that ECeG is a feasible source of movement related signals for implementing a BCI. The present study also demonstrated that the ECeG closely resembled the EEG signals and represents an alternate approach for BCI where the signal from the cerebrum is unreliable either due to disease or injury. .},
}

@article {pmid41160812,
year = {2025},
author = {Li, J and Lu, Y and Li, Z and Jin, L and Zhou, L and Ding, K and Liu, J and Hu, B and Liu, P and An, D and Liang, F and Hu, Y and Shao, Y and Ding, Y and Ma, L and Li, R and Mei, Y and Zhang, R and Song, E},
title = {An Active, Multimodal Neural Interface for Real-Time Monitoring of Cortical Electrical, Thermal, and Optical Dynamics.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e12114},
doi = {10.1002/advs.202512114},
pmid = {41160812},
issn = {2198-3844},
support = {2022ZD0209900//STI 2030-Major Project/ ; 62204057//National Natural Science Foundation of China/ ; 62304044//National Natural Science Foundation of China/ ; 82304124//National Natural Science Foundation of China/ ; U2230108//National Natural Science Foundation of China/ ; 22ZR1406400//Science and Technology Commission of Shanghai Municipality/ ; LGL-8998-09//Lingang Laboratory/ ; SKLICS-K202515//State Key Laboratory of Integrated Chips and Systems/ ; },
abstract = {Chronic neurophysiological monitoring devices facilitate the timely diagnosis and treatment of episodic or recurrent neurological disorders. Compared with passive electrodes, silicon-based active transistors provide intrinsic signal amplification and, when combined with capacitive-coupling measurement mechanisms, enable high-density, high-fidelity recordings. However, most existing systems remain limited to single-modality electrical sensing and fail to address the growing demands of contemporary neurodynamic research. Here, a chronically implantable, large-area cortical interface capable of real-time multimodal monitoring of electrical, thermal, and photodynamic signals is presented. Building upon a silicon-transistor array for neural electrical detection, the device integrates thin-film metal resistors for temperature sensing while preserving mechanical flexibility sufficient for stable, long-term tissue contact. By leveraging the photoelectric effect of silicon transistors and functional multiplexing of active elements, the interface also achieves precise photodynamic measurement. In vitro experiments confirm long-term stability and channel isolation. In vivo evaluation in Sprague-Dawley rats, together with biocompatibility assessments, demonstrates reliable performance under physiological conditions. The technology used in this multifunctional platform has universal applicability in neural interfaces, offering continuous multimodal neurodynamic data acquisition with potential utility in monitoring, diagnosing, and treating chronic neurological conditions such as epilepsy and brain tumors.},
}

@article {pmid41160441,
year = {2025},
author = {Jeong, SY and Lee, JW and Kim, TG},
title = {Comparative analysis across diverse plant species reveals superior antibiofilm efficacy and dose-dependency of root extracts compared to leaf extracts.},
journal = {FEMS microbiology letters},
volume = {},
number = {},
pages = {},
doi = {10.1093/femsle/fnaf116},
pmid = {41160441},
issn = {1574-6968},
abstract = {Although both root- and leaf-derived plant extracts hold potential as antibiofilm agents, research has predominantly focused on leaf tissues. In this study, we systematically compared the antibiofilm efficacy of 158 root and 248 leaf extracts from 360 plant species across five concentrations (0.1, 0.25, 0.5, 1.0, and 2.0 g/L). As concentration increased, the biological control incidence (BCI) of root extracts rose from 68.4% to 94.3%, while leaf extracts showed a smaller increase, from 52.2% to 71.7%. Similarly, the biological control efficacy (BCE) of root extracts increased from 27.6% to 54.2%, whereas leaf extracts ranged from -2.7% to 16.2%. Bootstrapping analysis (10 000 iterations) confirmed significantly higher antibiofilm activity of root extracts at concentrations ≥ 0.5 g/L (P < 0.05). Paired comparisons of species with both extract types further demonstrated the consistent superiority of root extracts across all concentrations (bootstrapped, P < 0.05), despite interspecific variation at higher doses. Linear regression revealed a significantly steeper dose-response slope for root extracts (29.2 ± 2.4) than for leaf extracts (8.1 ± 2.8) (bootstrapped, P < 0.05), indicating a stronger concentration-dependent effect of root extracts. These results suggest that plant roots typically harbor more potent and/or diverse antibiofilm compounds than leaves, underscoring their untapped potential for biofilm control applications.},
}

@article {pmid41160433,
year = {2025},
author = {Chen, Y and Liu, T and Jia, K and Theeuwes, J and Gong, M},
title = {Dual-format attentional template during preparation in human visual cortex.},
journal = {eLife},
volume = {13},
number = {},
pages = {},
doi = {10.7554/eLife.103425},
pmid = {41160433},
issn = {2050-084X},
support = {Major Project 2021ZD0200409//National Science and Technology Innovation 2030/ ; 32371087//National Natural Science Foundation of China/ ; 32300855//National Natural Science Foundation of China/ ; 3200784//National Natural Science Foundation of China/ ; 226-2024-00118//Fundamental Research Funds for the Central University/ ; Non-profit Central Research Institute Fund 2023-PT310-01//Chinese Academy of Medical Sciences/ ; },
mesh = {Humans ; *Attention/physiology ; *Visual Cortex/physiology ; Magnetic Resonance Imaging ; Male ; Female ; Adult ; Young Adult ; *Visual Perception/physiology ; Brain Mapping ; Cues ; Photic Stimulation ; },
abstract = {Goal-directed attention relies on forming internal templates of key information relevant for guiding behavior, particularly when preparing for upcoming sensory inputs. However, evidence on how these attentional templates are represented during preparation remains controversial. Here, we combine functional magnetic resonance imaging with an orientation cueing task to isolate preparatory activity from stimulus-evoked responses. Using multivariate pattern analysis, we found decodable information about the to-be-attended orientation during preparation; yet preparatory activity patterns were different from those evoked when actual orientations were perceived. When perturbing the neural activity by means of a visual impulse ('pinging' technique), the preparatory activity patterns in visual cortex resembled those associated with perceiving these orientations. The observed differential patterns with and without the impulse perturbation suggest a predominantly non-sensory format and a latent, sensory-like format of representation during preparation. Furthermore, the emergence of the sensory-like template coincided with enhanced information connectivity between V1 and frontoparietal areas and was associated with improved behavioral performance. By engaging this dual-format mechanism during preparation, the brain is able to encode both abstract, non-sensory information and more detailed, sensory information, potentially providing advantages for adaptive attentional control. For example, consistent with recent theories of visual search, a predominantly non-sensory template can support the initial guidance and a latent sensory-like format can support prospective stimulus processing.},
}

Humans
*Attention/physiology
*Visual Cortex/physiology
Magnetic Resonance Imaging
Male
Female
Adult
Young Adult
*Visual Perception/physiology
Brain Mapping
Cues
Photic Stimulation

@article {pmid41159356,
year = {2025},
author = {Atan, Y and Doğan, M and Karayel, F and Üzün, İ},
title = {Fatal Isolated Right Ventricular Rupture Without External Chest Injury in a Young Driver: Forensic Autopsy Findings After a One-Sided Vehicle Collision.},
journal = {Archives of Iranian medicine},
volume = {28},
number = {9},
pages = {530-535},
doi = {10.34172/aim.34401},
pmid = {41159356},
issn = {1735-3947},
mesh = {Humans ; Male ; *Accidents, Traffic ; Young Adult ; *Heart Ventricles/injuries/pathology ; Fatal Outcome ; Autopsy ; *Heart Injuries/pathology/etiology ; *Wounds, Nonpenetrating/pathology ; Forensic Pathology ; },
abstract = {Traumatic deaths are common, with cardiac trauma affecting 7‒12% of patients with thoracic injuries. Blunt cardiac injury (BCI), although rare, is associated with a high mortality rate. This report presents a case of blunt cardiac rupture (BCR) observed at autopsy despite the absence of external chest trauma, suggesting the presence of severe internal injuries. A 19-year-old male was found dead in his vehicle which had collided with a wall. At the crime scene investigation, external examination revealed no substantial chest wall injuries in the individual despite significant damage to the vehicle. Autopsy revealed a 2-cm rupture of the right ventricle (heart), accompanied by 400 cc of partially coagulated blood in the pericardial cavity, consistent with cardiac tamponade. Pregabalin was detected in the toxicology analysis, but not in lethal concentrations. Traffic accidents are a major cause of BCI, typically resulting from compression of the heart between the thoracic structures during high-energy impacts. BCR is particularly fatal and often results in rapid death before arrival to the hospital. The absence of external trauma in the current case underscores the need for thorough internal examination in trauma-related deaths.},
}

Humans
Male
*Accidents, Traffic
Young Adult
*Heart Ventricles/injuries/pathology
Fatal Outcome
Autopsy
*Heart Injuries/pathology/etiology
*Wounds, Nonpenetrating/pathology
Forensic Pathology

@article {pmid41157441,
year = {2025},
author = {Moreno-Castelblanco, SR and Vélez-Guerrero, MA and Callejas-Cuervo, M},
title = {Lower-Limb Motor Imagery Recognition Prototype Based on EEG Acquisition, Filtering, and Machine Learning-Based Pattern Detection.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {20},
pages = {},
doi = {10.3390/s25206387},
pmid = {41157441},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods ; *Machine Learning ; Brain-Computer Interfaces ; *Lower Extremity/physiology ; Signal Processing, Computer-Assisted ; Male ; Algorithms ; Adult ; *Imagination/physiology ; Movement/physiology ; *Pattern Recognition, Automated/methods ; Female ; Young Adult ; },
abstract = {Advances in brain-computer interface (BCI) research have explored various strategies for acquiring and processing electroencephalographic (EEG) signals to detect motor imagery (MI) activities. However, the complexity of multichannel clinical systems and processing techniques can limit their accessibility outside specialized centers, where complex setups are not feasible. This paper presents a proof-of-concept prototype of a single-channel EEG acquisition and processing system designed to identify lower-limb motor imagery. The proposed proof-of-concept prototype enables the wireless acquisition of raw EEG values, signal processing using digital filters, and the detection of MI patterns using machine learning algorithms. Experimental validation in a controlled laboratory with participants performing resting, MI, and movement tasks showed that the best performance was obtained by combining Savitzky-Golay filtering with a Random Forest classifier, reaching 87.36% ± 4% accuracy and an F1-score of 87.18% ± 3.8% under five-fold cross-validation. These findings confirm that, despite limited spatial resolution, MI patterns can be detected using appropriate AI-based filtering and classification. The novelty of this work lies in demonstrating that a single-channel, portable EEG prototype can be effectively used for lower-limb MI recognition. The portability and noise resilience achieved with the prototype highlight its potential for research, clinical rehabilitation, and assistive device control in non-specialized environments.},
}

Humans
*Electroencephalography/methods
*Machine Learning
Brain-Computer Interfaces
*Lower Extremity/physiology
Signal Processing, Computer-Assisted
Male
Algorithms
Adult
*Imagination/physiology
Movement/physiology
*Pattern Recognition, Automated/methods
Female
Young Adult

@article {pmid41157340,
year = {2025},
author = {Iadarola, G and Mengarelli, A and Iarlori, S and Monteriù, A and Spinsante, S},
title = {RGB-D Cameras and Brain-Computer Interfaces for Human Activity Recognition: An Overview.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {20},
pages = {},
doi = {10.3390/s25206286},
pmid = {41157340},
issn = {1424-8220},
support = {CUP I33C2200133000//Vitality Project/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Human Activities ; Electroencephalography ; Brain/physiology ; },
abstract = {This paper provides a perspective on the use of RGB-D cameras and non-invasive brain-computer interfaces (BCIs) for human activity recognition (HAR). Then, it explores the potential of integrating both the technologies for active and assisted living. RGB-D cameras can offer monitoring of users in their living environments, preserving their privacy in human activity recognition through depth images and skeleton tracking. Concurrently, non-invasive BCIs can provide access to intent and control of users by decoding neural signals. The synergy between these technologies may allow holistic understanding of both physical context and cognitive state of users, to enhance personalized assistance inside smart homes. The successful deployment in integrating the two technologies needs addressing critical technical hurdles, including computational demands for real-time multi-modal data processing, and user acceptance challenges related to data privacy, security, and BCI illiteracy. Continued interdisciplinary research is essential to realize the full potential of RGB-D cameras and BCIs as AAL solutions, in order to improve the quality of life for independent or impaired people.},
}

Humans
*Brain-Computer Interfaces
*Human Activities
Electroencephalography
Brain/physiology

@article {pmid41156422,
year = {2025},
author = {He, J and Xu, J and Wang, Y},
title = {Non-Linear Modeling and Precision Analysis Approach for Implantable Multi-Channel Neural Recording Systems.},
journal = {Micromachines},
volume = {16},
number = {10},
pages = {},
doi = {10.3390/mi16101176},
pmid = {41156422},
issn = {2072-666X},
support = {2021ZD0200401//STI 2030-Major Project/ ; 2025C01187,2024C03001//Pioneer R&D Program of Zhejiang/ ; 62176232,62336007//National Natural Science Foundation of China/ ; SNZJU-SIAS-002//Starry Night Science Fund of Zhe- 406 jiang University Shanghai Institute for Advanced Study/ ; 2025ZFJH01,226-2024-00127//Fundamental Research Funds for the Central Universities/ ; },
abstract = {High-precision implantable multi-channel neural recording systems are considered as having a crucial role in the diagnosis and treatment of neurological disorders. However, it is a significant design challenge to achieve an optimal trade-off among linear parameters, signal fidelity, power consumption, and circuit area. To address this challenge, a Simulink-based modeling approach has been proposed to incorporate adjustable non-linear parameters across the front-end circuits and analog-to-digital converter (ADC) stages. The model evaluates non-linearity impacts on system performance through both quantitative spike detection accuracy analysis and a neural decoding paradigm based on Chinese handwriting reconstruction. Simulated results show that total harmonic distortion (THD) can be set to -34.32 dB for the low-noise amplifier (LNA), -33.73 dB for the programmable gain amplifier (PGA), and -57.95 dB for the ADC in order to achieve reliable detection accuracy with minimal design cost. Moreover, ADC non-linearity has a greater influence on system performance than that of the LNA and PGA. The proposed approach offers quantitative and systematic hardware design guidance to balance signal fidelity and resource efficiency for future low-power, high-accuracy neural recording systems.},
}

@article {pmid41155027,
year = {2025},
author = {Yao, Y and Wang, X and Hao, X and Sun, H and Dong, R and Li, Y},
title = {Trans-cVAE-GAN: Transformer-Based cVAE-GAN for High-Fidelity EEG Signal Generation.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {10},
pages = {},
doi = {10.3390/bioengineering12101028},
pmid = {41155027},
issn = {2306-5354},
abstract = {Electroencephalography signal generation remains a challenging task due to its non-stationarity, multi-scale oscillations, and strong spatiotemporal coupling. Conventional generative models, including VAEs and GAN variants such as DCGAN, WGAN, and WGAN-GP, often yield blurred waveforms, unstable spectral distributions, or lack semantic controllability, limiting their effectiveness in emotion-related applications. To address these challenges, this research proposes a Transformer-based conditional variational autoencoder-generative adversarial network (Trans-cVAE-GAN) that combines Transformer-driven temporal modeling, label-conditioned latent inference, and adversarial learning. A multi-dimensional structural loss further constrains generation by preserving temporal correlation, frequency-domain consistency, and statistical distribution. Experiments on three SEED-family datasets-SEED, SEED-FRA, and SEED-GER-demonstrate high similarity to real EEG, with representative mean ± SD correlations of Pearson ≈ 0.84 ± 0.08/0.74 ± 0.12/0.84 ± 0.07 and Spearman ≈ 0.82 ± 0.07/0.72 ± 0.12/0.83 ± 0.08, together with low spectral divergence (KL ≈ 0.39 ± 0.15/0.41 ± 0.20/0.37 ± 0.18). Comparative analyses show consistent gains over classical GAN baselines, while ablations verify the indispensable roles of the Transformer encoder, label conditioning, and cVAE module. In downstream emotion recognition, augmentation with generated EEG raises accuracy from 86.9% to 91.8% on SEED (with analogous gains on SEED-FRA and SEED-GER), underscoring enhanced generalization and robustness. These results confirm that the proposed approach simultaneously ensures fidelity, stability, and controllability across cohorts, offering a scalable solution for affective computing and brain-computer interface applications.},
}

@article {pmid41154635,
year = {2025},
author = {Tabish, M and Malik, I and Akhtar, A and Afzal, M},
title = {A Review on Low-Dimensional Nanoarchitectonics for Neurochemical Sensing and Modulation in Responsive Neurological Outcomes.},
journal = {Biomolecules},
volume = {15},
number = {10},
pages = {},
doi = {10.3390/biom15101405},
pmid = {41154635},
issn = {2218-273X},
support = {KSRG-2024-41//King Salman Center for Disability Research/ ; },
mesh = {Humans ; Brain-Computer Interfaces ; Animals ; Artificial Intelligence ; *Biosensing Techniques/methods ; *Nanostructures/chemistry ; Nanotechnology/methods ; Brain ; },
abstract = {Low-Dimensional Nanohybrids (LDNHs) have emerged as potent multifunctional platforms for neurosensing and neuromodulation, providing elevated spatial-temporal precision, versatility, and biocompatibility. This review examines the intersection of LDNHs with artificial intelligence, brain-computer interfaces (BCIs), and closed-loop neurotechnologies, highlighting their transformative potential in personalized neuro-nano-medicine. Utilizing stimuli-responsive characteristics, optical, thermal, magnetic, and electrochemical LDNHs provide real-time feedback-controlled manipulation of brain circuits. Their pliable and adaptable structures surpass the constraints of inflexible bioelectronics, improving the neuronal interface and reducing tissue damage. We also examined their use in less invasive neurological diagnostics, targeted therapy, and adaptive intervention systems. This review delineates recent breakthroughs, integration methodologies, and fundamental mechanisms, while addressing significant challenges such as long-term biocompatibility, deep-tissue accessibility, and scalable manufacturing. A strategic plan is provided to direct future research toward clinical use. Ultimately, LDNHs signify a transformative advancement in intelligent, tailored, and closed-loop neurotechnologies, integrating materials science, neurology, and artificial intelligence to facilitate the next era of precision medicine.},
}

Humans
Brain-Computer Interfaces
Animals
Artificial Intelligence
*Biosensing Techniques/methods
*Nanostructures/chemistry
Nanotechnology/methods
Brain

@article {pmid41154223,
year = {2025},
author = {Du, A and Huang, M and Wang, Z and Zhou, H and Duan, H and Hu, S and Zheng, Y},
title = {Using Low-Intensity Focused Ultrasound to Treat Depression and Anxiety Disorders: A Review of Current Evidence.},
journal = {Brain sciences},
volume = {15},
number = {10},
pages = {},
doi = {10.3390/brainsci15101129},
pmid = {41154223},
issn = {2076-3425},
support = {2023YFC2506200//National Key Research and Development Program of China/ ; 2023YFC2506203//National Key Research and Development Program of China/ ; 2022YFB3204300//National Key Research and Development Program of China/ ; 2022C01002//Zhejiang Provincial Key Research and Development Program of China/ ; },
abstract = {Background: Depression and anxiety disorders impact millions globally. In recent years, low-intensity focused ultrasound (LIFU), characterized by its high precision, deep penetration, and non-invasive nature, has garnered significant interest in neuroscience and clinical practice. To enhance understanding of its effects on mood, therapeutic availability in treatment of depression/anxiety disorders, and potential mechanisms, a systematic review of studies investigating the emotional impact of LIFU on depressive/anxious-like animal models, healthy volunteers, and patients with depression or anxiety disorders has been undertaken. Methods: Relevant papers published before 15 July 2025 were searched across four databases: Web of Science, PubMed, Science Direct, and Embase. A total of 28 papers which met the inclusion and exclusion criteria are included in this review. Results: Our findings indicate that LIFU reversed the depressive/anxious-like behaviors in the animal models and showed antidepressant/anti-anxiety effects among the state-of-art clinical studies. For example, immobility time in FST or TST is reduced in depressive animal models, and HRSD/BAI scales are improved in human studies. Key molecules such as BDNF/5-HT are found restored in animal models, and FC between key brain areas related to depression/anxiety is modulated after LIFU treatment. Notably, no brain tissue damage was observed in animal studies, and only mild adverse effects (such as dizziness and vomiting) were noted in a few human studies. Conclusions: The studies using LIFU to treat depression and anxiety remain in the preliminary stage. The mechanisms underlying LIFU's mood effects-such as activation or inhibition of specific brain regions or neural circuits, anti-inflammatory effects, alterations in functional connectivity, synaptic plasticity, neurotransmitter levels, and BDNF-remain incompletely understood and warrant further investigation. Nevertheless, the LIFU technique holds promise for regulating both cortical and subcortical brain areas implicated in depression/anxiety disorders as a precise neuromodulation tool.},
}

@article {pmid41154218,
year = {2025},
author = {Tan, L and Fang, H and Ding, P and Wang, F and Wei, Y and Fu, Y},
title = {P300 Spatiotemporal Prior-Based Transformer-CNN for Auxiliary Diagnosis of PTSD.},
journal = {Brain sciences},
volume = {15},
number = {10},
pages = {},
doi = {10.3390/brainsci15101124},
pmid = {41154218},
issn = {2076-3425},
support = {82172058, 62376112, 81771926, 61763022, 62366026, 62006246//The National Natural Science Foundation of China under Grant Nos/ ; },
abstract = {Objectives: To address the challenges of subjectivity, misdiagnosis and underdiagnosis in post-traumatic stress disorder (PTSD), this study proposes an objective auxiliary diagnostic method based on P300 signals. Existing studies largely rely on conventional P300 features, lacking the systematic integration of event-related potential (ERP) priors and facing limitations in spatiotemporal feature modeling. Methods: Using common spatiotemporal pattern (CSTP) analysis and quantitative evaluation, we revealed significant spatiotemporal differences in P300 signals between PTSD patients and healthy controls. ERP prior information was then extracted and integrated into a hybrid architecture combining transformer encoders and a convolutional neural network (CNN), enabling joint modeling of long-range temporal dependencies and local spatial patterns. Results: The proposed P300 spatiotemporal transformer-CNN (P300-STTCNet) achieved a classification accuracy of 93.37% in distinguishing PTSD from healthy controls, markedly outperforming traditional approaches. Conclusions: Significant spatiotemporal differences in P300 signals exist between PTSD and healthy control groups. The P300-STTCNet model effectively captures PTSD-related spatiotemporal features, demonstrating strong potential for electroencephalogram-based objective auxiliary diagnosis.},
}

@article {pmid41152182,
year = {2025},
author = {Cao, Y and Xue, Y and Yang, H and Wang, F and Li, T and Zhao, L and Fu, Y},
title = {[Ethical considerations for artificial intelligence-enhanced brain-computer interface].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {5},
pages = {1085-1091},
doi = {10.7507/1001-5515.202507024},
pmid = {41152182},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces/ethics ; *Artificial Intelligence/ethics ; Humans ; Deep Learning ; User-Computer Interface ; Electroencephalography ; },
abstract = {Artificial intelligence-enhanced brain-computer interfaces (BCI) are expected to significantly improve the performance of traditional BCIs in multiple aspects, including usability, user experience, and user satisfaction, particularly in terms of intelligence. However, such AI-integrated or AI-based BCI systems may introduce new ethical issues. This paper first evaluated the potential of AI technology, especially deep learning, in enhancing the performance of BCI systems, including improving decoding accuracy, information transfer rate, real-time performance, and adaptability. Building on this, it was considered that AI-enhanced BCI systems might introduce new or more severe ethical issues compared to traditional BCI systems. These include the possibility of making users' intentions and behaviors more predictable and manipulable, as well as the increased likelihood of technological abuse. The discussion also addressed measures to mitigate the ethical risks associated with these issues. It is hoped that this paper will promote a deeper understanding and reflection on the ethical risks and corresponding regulations of AI-enhanced BCIs.},
}

*Brain-Computer Interfaces/ethics
*Artificial Intelligence/ethics
Humans
Deep Learning
User-Computer Interface
Electroencephalography

@article {pmid41152174,
year = {2025},
author = {Wang, P and Ji, X and Wang, J and Yu, X},
title = {[Brain computer interface nursing bed control system based on deep learning and dual visual feedback].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {5},
pages = {1021-1028},
doi = {10.7507/1001-5515.202504047},
pmid = {41152174},
issn = {1001-5515},
mesh = {Humans ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; *Feedback, Sensory ; Neural Networks, Computer ; Beds ; },
abstract = {In order to meet the need of autonomous control of patients with severe limb disorders, this paper designs a nursing bed control system based on motor imagery-brain computer interface (MI-BCI). In view of the low decoding performance of cross-subjects and the dynamic fluctuation of cognitive state in the existing MI-BCI technology, the neural network structure optimization and user interaction feedback enhancement are improved. Firstly, the optimized dual-branch graph convolution multi-scale neural network integrates dynamic graph convolution and multi-scale convolution. The average classification accuracy is higher than that of multi-scale attention temporal convolution network, Gram angle field combined with convolution long short term memory hybrid network, Transformer-based graph convolution network and other existing methods. Secondly, a dual visual feedback mechanism is constructed, in which electroencephalogram (EEG) topographic map feedback can improve the discrimination of spatial patterns, and attention state feedback can enhance the temporal stability of signals. Compared with the single EEG topographic map feedback and non-feedback system, the average classification accuracy of the proposed method is also greatly improved. Finally, in the four classification control task of nursing bed, the average control accuracy of the system is 90.84%, and the information transmission rate is 84.78 bits/min. In summary, this paper provides a reliable technical solution for improving the autonomous interaction ability of patients with severe limb disorders, which has important theoretical significance and application value.},
}

Humans
*Brain-Computer Interfaces
*Deep Learning
Electroencephalography
*Feedback, Sensory
Neural Networks, Computer
Beds

@article {pmid41152144,
year = {2025},
author = {Bek, J and Aziz, A and Brady, N},
title = {Transcranial Direct Current Stimulation to Augment Motor Imagery Training: A Systematic Review.},
journal = {The European journal of neuroscience},
volume = {62},
number = {8},
pages = {e70280},
doi = {10.1111/ejn.70280},
pmid = {41152144},
issn = {1460-9568},
support = {101034345//H2020 Marie Skłodowska-Curie Actions/ ; },
mesh = {Humans ; *Transcranial Direct Current Stimulation/methods ; *Motor Cortex/physiology ; *Imagination/physiology ; Brain-Computer Interfaces ; *Imagery, Psychotherapy/methods ; Neuronal Plasticity ; *Stroke Rehabilitation/methods ; },
abstract = {Motor imagery training (MIT) is a widely used technique for motor learning and recovery. To optimize training outcomes, researchers have explored the integration of MIT with complementary approaches. One such approach is transcranial direct current stimulation (tDCS), which also shows promise as a method to enhance motor performance and neuroplasticity. This systematic review aimed to synthesize the current evidence on the synergistic effects of MIT combined with tDCS, with a specific focus on behavioral outcomes. Heterogeneous methods across 16 studies with 432 participants in total, including both healthy and clinical populations, yielded mixed results. Nonetheless, the potential of anodal tDCS applied over the primary motor cortex to augment the beneficial effects of MIT for motor performance in healthy participants is suggested by the current literature. The benefits of combining tDCS with MIT in brain-computer interface (BCI) protocols with stroke patients were less clear, which may relate to population differences, timing of stimulation, or the similarity between outcome measures and trained tasks. Overall, small samples and heterogeneous methods limit interpretation of the findings of combined intervention studies, and further research should aim to measure both behavioral and neurophysiological outcomes in larger samples as well as examining longer-term synergistic effects.},
}

Humans
*Transcranial Direct Current Stimulation/methods
*Motor Cortex/physiology
*Imagination/physiology
Brain-Computer Interfaces
*Imagery, Psychotherapy/methods
Neuronal Plasticity
*Stroke Rehabilitation/methods

@article {pmid40967240,
year = {2025},
author = {Del Sesto, MJ and Negoita, S and Bruzzone Giraldez, M and LaJoie, Z and Akhter Sathi, K and Wong, JK and Widge, AS and Okun, MS and Khalifa, A},
title = {Multitarget neurostimulation of the deep brain: clinical opportunities, challenges, and emerging technologies.},
journal = {Journal of neural engineering},
volume = {22},
number = {5},
pages = {},
pmid = {40967240},
issn = {1741-2552},
support = {DP2 EB037188/EB/NIBIB NIH HHS/United States ; },
mesh = {Humans ; *Deep Brain Stimulation/methods/trends/instrumentation ; Animals ; *Brain/physiology ; *Brain-Computer Interfaces/trends ; Electrodes, Implanted/trends ; },
abstract = {Recent computational, pre-clinical, and clinical studies have demonstrated the potential for using neuromodulation through simultaneous targeting of multiple deep brain regions. This approach has already been used for therapeutic and systems neuroscience applications. However, the broad clinical adoption of invasive distributed deep brain interfaces remains in its early stages. This review explores the barriers to implementation by addressing three key questions: do the benefits of implanting multiple electrodes justify the associated risks for specific applications? What is the risk-benefit ratio, and what technological advancements will be necessary to encourage clinical adoption? We also examine next-generation technologies that could enable multi-target brain interfaces, including system-on-chip micro-stimulators as well as nanoparticles. We highlight the role of novel machine learning algorithms in the optimization of stimulation parameters and for the guidance of device placement. Emerging hardware accelerators equipped with on-chip AI have demonstrated functionality that can be used to decode and to classify distributed neuronal data. This advance in hardware accelerators has also contributed to the potential for enhanced closed-loop stimulation control of devices. Despite these advances, significant technological and translational barriers persist, limiting the widespread clinical application of multi-target brain interfaces. This review provides a critical analysis of recent prototypes and novel hardware for use in multi-target systems. We will discuss both clinical and research applications. We will focus on the utilization of multi-site technologies to meet the needs of neurological diseases. We conclude that there exists a critical need for further innovation and integration of multi-site technologies into clinical practice.},
}

Humans
*Deep Brain Stimulation/methods/trends/instrumentation
Animals
*Brain/physiology
*Brain-Computer Interfaces/trends
Electrodes, Implanted/trends

@article {pmid41151221,
year = {2025},
author = {Liu, S and Su, L and He, Q and Qiu, M and Liang, R},
title = {Comparative evaluation of ChatGPT and Gemini in brain-computer interfaces patient education: A multi-dimensional analysis of reliability, accuracy, comprehensibility, and readability.},
journal = {International journal of medical informatics},
volume = {206},
number = {},
pages = {106164},
doi = {10.1016/j.ijmedinf.2025.106164},
pmid = {41151221},
issn = {1872-8243},
abstract = {BACKGROUND: Brain-Computer Interfaces (BCI) are a type of life-altering neurotechnology, but their inherent complexity poses significant challenges to patient education. Large Language Models (LLMs), such as ChatGPT and Gemini, offer new possibilities to address this challenge. This study aims to conduct a multi-dimensional, rigorous comparative analysis of the performance of these two mainstream AI models in responding to common patient questions related to BCI.

METHODS: Through a structured process combining clinical expert consensus, literature review, and online patient community analysis, we identified 13 key patient questions covering the entire BCI treatment cycle. We then obtained responses to these questions from ChatGPT and Gemini on September 1, 2025. An evaluation panel, composed of clinical experts and non-medical professionals, conducted a blinded assessment of the response quality using standardized Likert scales across three dimensions: reliability, accuracy, and comprehensibility. Concurrently, we performed an objective, quantitative analysis of the response texts using the Flesch-Kincaid readability tests.

RESULTS: On core quality metrics such as reliability, accuracy, and comprehensibility, the performance of the two models was generally comparable, both demonstrating a high level of proficiency with only sporadic statistical differences on a few technical questions. However, a clear significant disparity emerged in the dimension of readability: for 12 of the 13 questions, the text generated by Gemini required a significantly lower reading grade level than that of ChatGPT (p < 0.05) and had significantly higher reading ease scores. This difference stemmed from Gemini's tendency to use shorter sentences and simpler vocabulary.

CONCLUSION: AI chatbots possess immense potential in the field of BCI patient education. Although both ChatGPT and Gemini can provide high-quality information, Gemini demonstrates a clear advantage in the accessibility and approachability of information, making it a potentially more suitable tool for initial application across diverse patient populations. Nevertheless, the limitations of AI in handling highly specialized and dynamically changing knowledge underscore the indispensable role of human expert supervision and validation in any clinical application.},
}

@article {pmid41149457,
year = {2025},
author = {Lee, HH and Siu-Li, N and Pagano, I and Fukui, JA},
title = {Examining a Genomic Test in Predicting Extended Endocrine Benefit and Recurrence Risk in a Diverse Breast Cancer Population.},
journal = {Current oncology (Toronto, Ont.)},
volume = {32},
number = {10},
pages = {},
doi = {10.3390/curroncol32100537},
pmid = {41149457},
issn = {1718-7729},
support = {P30CA071789-17S2/CA/NCI NIH HHS/United States ; },
mesh = {Humans ; *Breast Neoplasms/genetics/drug therapy/pathology ; Female ; Middle Aged ; *Neoplasm Recurrence, Local/genetics ; Aged ; Retrospective Studies ; Adult ; *Genomics/methods ; *Antineoplastic Agents, Hormonal/therapeutic use ; },
abstract = {(1) Background: Extended endocrine therapy (EET) beyond five years can reduce distant recurrence in early-stage hormone receptor-positive (HR+) breast cancer. The Breast Cancer Index (BCI) predicts recurrence risk and EET benefits, yet racial/ethnic differences in its results remain unexplored. This study evaluates such differences in a diverse early-stage HR+ breast cancer population. (2) Methods: We retrospectively analyzed demographics, tumor characteristics and BCI scores of 159 women in Hawaii with early-stage HR+ breast cancer, self-identifying as Caucasian, Filipino, Japanese, Native Hawaiian, Other Asian/Pacific Islander, or Other. Tumor characteristics included size, grade, histology, lymph node/receptor status, Oncotype DX score, and laterality. Logistic regression used demographics and tumor features as predictor variables, with BCI's benefit prediction and recurrence risk as outcome variables. (3) Results: Japanese and other Asian/Pacific Islander patients had significantly lower odds of high recurrence risk compared to Caucasian patients. Higher recurrence risk was associated with greater odds of predicted EET. Racial/ethnic differences in EET benefit prediction were not statistically significant. (4) Conclusions: No racial/ethnic differences in EET benefit prediction suggest BCI's applicability in racially and ethnically diverse populations. Findings among Japanese and other Asian/Pacific Islanders point to potential biological or socioeconomic variation. Limitations include sample size and underrepresentation of certain groups. Future studies should address these gaps and adjust for known risk factors to further clarify BCI's racial and ethnic implications.},
}

Humans
*Breast Neoplasms/genetics/drug therapy/pathology
Female
Middle Aged
*Neoplasm Recurrence, Local/genetics
Aged
Retrospective Studies
Adult
*Genomics/methods
*Antineoplastic Agents, Hormonal/therapeutic use

@article {pmid41149344,
year = {2025},
author = {Kucukselbes, H and Sayilgan, E},
title = {Real-Time EEG Decoding of Motor Imagery via Nonlinear Dimensionality Reduction (Manifold Learning) and Shallow Classifiers.},
journal = {Biosensors},
volume = {15},
number = {10},
pages = {},
doi = {10.3390/bios15100692},
pmid = {41149344},
issn = {2079-6374},
mesh = {Humans ; *Electroencephalography/methods ; Adult ; Male ; Signal Processing, Computer-Assisted ; Female ; Machine Learning ; Brain-Computer Interfaces ; Young Adult ; Dimensionality Reduction ; },
abstract = {This study introduces a real-time processing framework for decoding motor imagery EEG signals by integrating manifold learning techniques with shallow classifiers. EEG recordings were obtained from six healthy participants performing five distinct wrist and hand motor imagery tasks. To address the challenges of high dimensionality and inherent nonlinearity in EEG data, five nonlinear dimensionality reduction methods, t-SNE, ISOMAP, LLE, Spectral Embedding, and MDS, were comparatively evaluated. Each method was combined with three shallow classifiers (k-NN, Naive Bayes, and SVM) to investigate performance across binary, ternary, and five-class classification settings. Among all tested configurations, the t-SNE + k-NN pairing achieved the highest accuracies, reaching 99.7% (two-class), 99.3% (three-class), and 89.0% (five-class). ISOMAP and MDS also delivered competitive results, particularly in multi-class scenarios. The presented approach builds upon our previous work involving EEG datasets from individuals with spinal cord injury (SCI), where the same manifold techniques were examined extensively. Comparative findings between healthy and SCI groups reveal consistent advantages of t-SNE and ISOMAP in preserving class separability, despite higher overall accuracies in healthy subjects due to improved signal quality. The proposed pipeline demonstrates low-latency performance, completing signal processing and classification in approximately 150 ms per trial, thereby meeting real-time requirements for responsive BCI applications. These results highlight the potential of nonlinear dimensionality reduction to enhance real-time EEG decoding, offering a low-complexity yet high-accuracy solution applicable to both healthy users and neurologically impaired individuals in neurorehabilitation and assistive technology contexts.},
}

Humans
*Electroencephalography/methods
Adult
Male
Signal Processing, Computer-Assisted
Female
Machine Learning
Brain-Computer Interfaces
Young Adult
Dimensionality Reduction

@article {pmid41146476,
year = {2025},
author = {Yue, X and Lu, L and Liu, H and Zang, Y},
title = {LRR-UNet: A Deep Unfolding Network With Low-Rank Recovery for EEG Signal Denoising.},
journal = {CNS neuroscience & therapeutics},
volume = {31},
number = {10},
pages = {e70632},
doi = {10.1111/cns.70632},
pmid = {41146476},
issn = {1755-5949},
support = {2023YFF1204200//National Key Research and Development Program of China/ ; 62476197//National Natural Science Foundation of China/ ; },
mesh = {*Electroencephalography/methods ; Humans ; *Deep Learning ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Artifacts ; Algorithms ; *Brain/physiology ; },
abstract = {BACKGROUND: Electroencephalogram (EEG) signals are crucial for brain-computer interface research but are highly susceptible to noise contamination, necessitating effective denoising. While deep learning has been widely applied, its "black-box" nature limits interpretability. In contrast, traditional model-based methods like Low-Rank Recovery (LRR) offer strong interpretability by decomposing signals into low-rank and sparse components.

OBJECTIVE: This paper aims to develop an interpretable deep-learning model for EEG denoising that combines the performance of deep learning with the interpretability of traditional LRR methods.

METHODS: We propose LRR-Unet, a deep unfolding network that transforms the traditional iterative LRR algorithm into a neural network architecture. Specifically, the time-consuming Singular Value Decomposition (SVD) and sparse optimization processes in LRR are replaced with learnable neural network modules.

RESULTS: Extensive experiments demonstrate that LRR-Unet outperforms other state-of-the-art models in removing ocular and electromyographic artifacts, achieving superior performance on both quantitative and qualitative metrics. Furthermore, in downstream classification tasks, EEG signals preprocessed with LRR-Unet yield better results across various evaluation indicators.

CONCLUSION: The proposed LRR-Unet provides an effective and interpretable solution for EEG denoising. Its superiority in denoising performance and practical utility in enhancing downstream application performance is validated through comprehensive experiments.},
}

*Electroencephalography/methods
Humans
*Deep Learning
*Neural Networks, Computer
*Signal Processing, Computer-Assisted
Signal-To-Noise Ratio
Artifacts
Algorithms
*Brain/physiology

@article {pmid41146424,
year = {2025},
author = {Yang, C and Wang, X and Ye, X and Shen, Y and Tong, J and Zhang, X and Zhou, Y},
title = {Spatiotemporal Immune Dynamics in Experimental Retinal Ganglion Cell Injury Models.},
journal = {Immunity, inflammation and disease},
volume = {13},
number = {10},
pages = {e70284},
doi = {10.1002/iid3.70284},
pmid = {41146424},
issn = {2050-4527},
support = {//This study was supported by a National Natural Science Foundation of China (NSFC) Key Program grant 82430038, Key R&D Program of Zhejiang Province 2025C02109, NSFC grants 82371455 and 82371084, a National Key Research and Development Program of China grant 2023YFC2506200, a China Postdoctoral Science Foundation grant 2024M752831, and the Open Project Program of Shaanxi Provincial Key Laboratory of Biological Psychiatry XJJSHTS-2504./ ; },
mesh = {*Retinal Ganglion Cells/immunology/pathology ; Animals ; *Optic Nerve Injuries/immunology/pathology ; Disease Models, Animal ; *Glaucoma/immunology/pathology ; Humans ; },
abstract = {BACKGROUND: The damage and regeneration of retinal ganglion cells (RGCs) have been extensively studied. Among them, immune cells in different parts of the visual pathway play an important role in injury, regeneration and repair, but a comprehensive analysis of their spatial and temporal distribution is lacking.

PURPOSE: This review emphasizes the unique characteristics of immune cells within the visual input pathway, focusing on their spatiotemporal dynamics in the retina, optic nerve head (ONH), and optic nerve during glaucoma and traumatic optic nerve injury.

METHODS: A comprehensive search was conducted across PubMed and Web of Science up to April 2025. Studies were included if they reported immune cells under glaucoma or optic nerve crush (ONC) animal models.

FINDINGS: Each region of the visual input pathway displays a distinct immune cell composition, including Müller cells, microglia, astrocytes, T cells, and oligodendrocytes, all of which work together to maintain homeostasis and respond to injury. Some immune cells are specific to certain regions, while others are shared across areas. Furthermore, even within a single glial cell type, there are different subtypes with unique developmental origins or marker profiles, reflecting a range of functions. In both glaucoma and traumatic optic nerve injury, retinal immune cells are rapidly activated, regardless of whether the initial impairment occurs in the soma or axon of RGCs, in the subacute or chronic course. The early stages of injury also see the presence of adaptive immune cells, such as T cells and neutrophils. Macrophages and microglia typically play complementary roles, while astrocytes show prolonged activation compared to microglia in the optic nerve, though this pattern does not hold in the retina following ONC.

CONCLUSIONS: Understanding the spatiotemporal dynamics of these immune responses in glaucoma and traumatic optic nerve injury is crucial for developing targeted therapies that can reduce RGC loss, mitigate neurotoxicity, and promote functional recovery, ultimately preventing vision impairment. Targeting specific immune cell subsets may provide new strategies for delaying RGC damage.},
}

*Retinal Ganglion Cells/immunology/pathology
Animals
*Optic Nerve Injuries/immunology/pathology
Disease Models, Animal
*Glaucoma/immunology/pathology
Humans

@article {pmid41146245,
year = {2025},
author = {López-Larraz, E and Sarasola-Sanz, A and Birbaumer, N and Ramos-Murguialday, A},
title = {Uncovering attempted movements of the paralyzed upper limb after stroke through EEG and EMG.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {221},
pmid = {41146245},
issn = {1743-0003},
support = {2422-0-1//Fortüne program - University of Tübingen/ ; E! 113928 SubliminalHomeRehab//Eureka-Eurostars/ ; MAIA 951910//European Union H2020-FETPROACT-EIC-2018-2020/ ; NanoNeuro//Basque IKUR initiative/ ; 01QE2023//Bundesministerium für Bildung und Forschung/ ; },
mesh = {Humans ; *Electroencephalography/methods ; Male ; *Stroke/physiopathology/complications ; Female ; *Electromyography/methods ; Middle Aged ; *Upper Extremity/physiopathology ; Aged ; *Paralysis/physiopathology/etiology/rehabilitation ; Adult ; Movement/physiology ; *Stroke Rehabilitation ; },
abstract = {Detecting attempted movements of a paralyzed limb is a key step for neural interfaces for motor rehabilitation and restoration after a stroke. In this paper, we present a systematic evaluation of electroencephalographic (EEG) and electromyographic (EMG) activity to decode when stroke patients with severe upper-limb paralysis attempt to move their affected arm. EEG and EMG recordings of 35 chronic stroke patients were analyzed. We trained classifiers to discriminate between rest and movement attempt states relying on brain, muscle, or both types of signals combined. Our results reveal that: (i) EEG and residual EMG activity provide complementary information to detect attempted movements, obtaining significantly higher decoding accuracy when both sources of activity are combined; (ii) EMG-based, but not EEG-based, decoding accuracy correlates with the degree of impairment of the patient; and (iii) the percentage of patients that achieve decoding accuracy above the chance level strongly depends on the type of features considered, and can be as low as 50% of them if only ipsilesional EEG is used. These results offer new perspectives to develop improved neurotechnologies that establish a more accurate contingent link between the central and peripheral nervous system after a stroke, leveraging Hebbian learning and facilitating functional plasticity and recovery.},
}

Humans
*Electroencephalography/methods
Male
*Stroke/physiopathology/complications
Female
*Electromyography/methods
Middle Aged
*Upper Extremity/physiopathology
Aged
*Paralysis/physiopathology/etiology/rehabilitation
Adult
Movement/physiology
*Stroke Rehabilitation

@article {pmid41145802,
year = {2025},
author = {Hazrati, H and Daliri, MR},
title = {Decoding covert visual attention of electroencephalography signals using continuous wavelet transform and deep learning approach.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {37503},
pmid = {41145802},
issn = {2045-2322},
mesh = {Humans ; *Deep Learning ; *Electroencephalography/methods ; *Attention/physiology ; *Wavelet Analysis ; Male ; Adult ; Female ; Young Adult ; Brain-Computer Interfaces ; *Visual Perception/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Covert visual attention decoding from EEG signals is a key challenge in cognitive neuroscience and brain-computer interface applications. Traditional approaches often rely on manual feature extraction and handcrafted pipelines, which limit scalability and generalization. In this study, we propose a deep learning-based framework that leverages time-frequency representations, specifically Continuous Wavelet Transform (CWT), to enable end-to-end classification of covert attention states without manual feature engineering. EEG data were recorded from ten healthy participants performing spatial and feature-based attention tasks. Among the tested models, ShallowConvNet achieved 100% accuracy in binary classification and over 90% in four-class conditions. EEGNet also performed competitively, exceeding 97% and 88% accuracy in two- and four-class scenarios, respectively. These findings demonstrate that integrating CWT with deep neural architectures significantly enhances decoding performance compared to conventional raw-signal approaches, offering a scalable and efficient solution for real-time attention monitoring.},
}

Humans
*Deep Learning
*Electroencephalography/methods
*Attention/physiology
*Wavelet Analysis
Male
Adult
Female
Young Adult
Brain-Computer Interfaces
*Visual Perception/physiology
Signal Processing, Computer-Assisted

@article {pmid41145516,
year = {2025},
author = {Wei, Z and Lin, X and Zhang, L and Guo, L and Liu, J and Hu, L and Liu, Y and Kong, Y},
title = {CoSpine open access simultaneous cortico-spinal fMRI database of thermal pain and motor tasks.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1696},
pmid = {41145516},
issn = {2052-4463},
support = {82072010//National Natural Science Foundation of China (National Science Foundation of China)/ ; IS23108//Natural Science Foundation of Beijing Municipality (Beijing Natural Science Foundation)/ ; },
mesh = {*Magnetic Resonance Imaging ; Humans ; *Pain/physiopathology/diagnostic imaging ; Brain/diagnostic imaging ; *Spinal Cord/diagnostic imaging ; },
abstract = {Simultaneous cortico-spinal functional magnetic resonance imaging (fMRI) enables non-invasive investigation of integrated central nervous system function, but acquisition challenges have restricted the availability of public datasets and slowed the development of advanced analytic methods. Here, we introduce the CoSpine database, the first open-access, BIDS-compliant cortico-spinal task-based fMRI resource (N = 61), acquired using a novel single-field-of-view (FOV) imaging protocol covering the whole brain (including cortical, subcortical, brainstem, and cerebellar regions) and cervical spinal cord. The dataset contains raw images, field maps, physiological recordings, and BIDS event files from thermal pain and voluntary motor tasks. An optimized acquisition and preprocessing framework is provided, validated by quality-control metrics such as temporal signal-to-noise ratio and alignment precision. Spanning a broad age range and standardized paradigms, CoSpine serves as a reference for neuroimaging methods development (e.g., hyperalignment) and for artificial intelligence (AI) model benchmarking. Potential applications include sensorimotor phenotyping, studies of age-related neurodegeneration, and exploratory work in neurorehabilitation, while also supporting early-stage development of brain-computer interface (BCI) systems involving spinal activity and personalized neuromodulation strategies.},
}

*Magnetic Resonance Imaging
Humans
*Pain/physiopathology/diagnostic imaging
Brain/diagnostic imaging
*Spinal Cord/diagnostic imaging

@article {pmid41144819,
year = {2025},
author = {Liu, H and Cao, X and Li, J and Zheng, L and Li, J and Li, Q and Xie, M and Li, H and Wang, X and Wu, Y and Zhang, X and Wang, Y and Gao, X and Sheng, T and Du, N and Xu, C and Zhou, K and Xu, J and Yan, C and Liu, L and Gao, L and Li, X and Zhang, M},
title = {Deciphering Neural Mechanisms Underlying Marmoset Dynamic Natural Behaviors Using a Miniaturized Wireless Large-Scale Coverage Neural Recorder.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e07110},
doi = {10.1002/advs.202507110},
pmid = {41144819},
issn = {2198-3844},
support = {2023YFB4705500//National Key Research and Development Program of China/ ; 62350710211//National Natural Science Foundation of China/ ; },
abstract = {Deciphering neural mechanisms underlying dynamic natural behaviors of freely moving species requires long-term recordings of large-scale brain activities. However, most conventional neural recorders are limited by their weights and measures, electrode coverage, and signal throughput, hindering the dissection of underlying neural mechanisms. This study reports real-time large-scale recordings and deciphering of brain activities from frontal and temporal cortices of freely moving marmoset across various natural behavioral repertoire using a miniaturized wireless neural recorder comprising a custom-designed 120-channel flexible µECoG array. Behavior-specific highly resolved spatiotemporal neural dynamics are observed, including alpha-band activations during drinking, anticipatory responses before vocalization, and transient high-gamma increase during vigilance to human intruders. Three phases of drinking behavior are identified using multi-area neural features captured by the recorder with an accuracy exceeding 87%. After over 16 months (March 13, 2024-August 1, 2025, remaining actively recording) of recordings, the neural signals acquired using the recorder maintain high fidelity and low attenuation during both the resting and drinking states, enabling potential long-term dissection of the neural mechanisms of natural behaviors in freely moving marmosets.},
}

@article {pmid41144414,
year = {2025},
author = {Lu, B and Chen, J and Wang, F and Wen, G and Fu, R and Hua, C},
title = {Causality-Driven Convolutional Manifold Attention Network for Electroencephalogram Signal Decoding.},
journal = {IEEE transactions on pattern analysis and machine intelligence},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TPAMI.2025.3625631},
pmid = {41144414},
issn = {1939-3539},
abstract = {Deep learning-based methods have achieved remarkable success in brain-computer interfaces (BCIs). However, its inherent assumption of independent and identically distributed (i.i.d.) data renders it vulnerable to out-of distribution (OOD) scenarios. To address this limitation, the present study proposed a causality-driven convolutional manifold attention network (CD-CMAN) that learned invariant representations from electroencephalogram (EEG) signals to enhance OOD generalization. The framework began with a spatiotemporal convolution module to extract rich temporal and spatial features. Guided by the defined structural causal model and leveraging the strengths of Riemannian geometry and deep learning, dual latent encoders with manifold attention units were crafted to explicitly separate spatiotemporal feature maps into semantic and variation latent factors. A reconstruction module with a dedicated loss was implemented to ensure these factors retaining informative, while the Hilbert-Schmidt independence criterion (HSIC) was introduced to enforce their statistical independence. Further, a variational information bottleneck and gradient reversal layer were incorporated to compress and disentangle the semantic and variation factors. Evaluations on two public datasets under both subject-dependent and subject independent settings demonstrated that CD-CMAN consistently outperforms comparative baselines. These findings suggest that the proposed model could provide a new solution for the practical application of BCI technology.},
}

@article {pmid41143908,
year = {2025},
author = {Xie, X and Hu, F and Yuan, S and Wen, D and Duan, D},
title = {MS-CANet: lightweight multi-scale channel attention network with depthwise residual blocks for EEG-based spatial cognition evaluation.},
journal = {Medical & biological engineering & computing},
volume = {},
number = {},
pages = {},
pmid = {41143908},
issn = {1741-0444},
support = {62276022//National Natural Science Foundation of China/ ; 2023YFF1203702//National Key Research and Development Program of China/ ; 62206014//National Key Research and Development Program of China/ ; FRFBD-25-052//Fundamental Research Funds for the Central Universities/ ; },
abstract = {Objective assessment of spatial cognitive ability is crucial for screening cognitive impairment and in neurorehabilitation. While deep learning methods for electroencephalogram (EEG) analysis show great promise, they increasingly rely on complex, parameter-heavy architectures. This complexity often leads to poor generalization due to overfitting on small datasets and hinders deployment on mobile healthcare devices. To overcome these limitations, we propose a novel lightweight multi-scale channel attention network with depthwise residual blocks. The model incorporates multi-scale convolutional layers to capture diverse temporal and spatial patterns in EEG signals. It then leverages channel attention mechanisms to dynamically prioritize informative channels, focusing on task-critical features. Furthermore, a novel depthwise separable residual block is introduced to significantly reduce computational complexity and maintain stable model performance. Evaluations on a spatial cognition EEG dataset demonstrate that our network achieves higher accuracy than baselines with only 8.453M parameters, making it an efficient and practical solution for mobile deployment. It also holds strong potential for extension to early screening and intervention in a wider range of cognitive disorders.},
}

@article {pmid41142108,
year = {2025},
author = {Peng, Q and Huang, J and Li, C and Jiang, M and Huang, C and Luo, J and Li, H and Yin, T and Cai, M and Fu, S and Ma, G and Liu, Z and Xu, T},
title = {Magnetically Actuated Soft Electrodes for Multisite Bioelectrical Monitoring of Ex Vivo Tissues.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0434},
pmid = {41142108},
issn = {2692-7632},
abstract = {Multisite electrophysiological monitoring of ex vivo tissues and organ models is essential for basic research and drug toxicity evaluation. However, conventional microelectrode arrays with fixed positions and rigid structures are insufficient for dynamic, curved tissue surfaces. Here, we present a magnetically actuated soft electrode (MSE) with precise navigation, adaptive attachment, and high-fidelity signal acquisition. Operating in a "locate-adhere-record-detach" cycle, the MSE enabled continuous multisite detection on beating ex vivo tissues. In isolated rat heart experiments, the MSE demonstrated millimeter-level navigation accuracy, stable contact, and high signal-to-noise ratio (average 28 dB). By integrating magnetic locomotion with electrophysiological sensing, this work establishes a programmable, actively addressable platform for multisite electrophysiological monitoring of organ models, tissue slices, and engineered constructs, offering broad potential for cardiotoxicity screening and cardiovascular research.},
}

@article {pmid41141194,
year = {2025},
author = {Jayalaksshme Srinivasan, K and Periasamy, P and Gunasekaran, S},
title = {Motor Imagery and Motor Execution: A Narrative Review of Electroencephalographic (EEG) Signatures, Methodological Consistency, and Translational Applications.},
journal = {Cureus},
volume = {17},
number = {9},
pages = {e93011},
pmid = {41141194},
issn = {2168-8184},
abstract = {This narrative review evaluates when electroencephalography (EEG) signatures elicited by kinesthetic motor imagery (MI) genuinely approximate those of motor execution (ME), appraises methodological consistency across studies, and outlines pragmatic routes to translation in brain-computer interfaces (BCIs) and neurorehabilitation. A keyword-driven search of Web of Science, Scopus, PubMed, and conference repositories was used to extract empirical, English-language EEG studies reporting sensorimotor rhythm (mu 8-13 Hz; beta 13-30 Hz) event-related desynchronization/synchronization (ERD/ERS) metrics and/or decoding performance for MI and/or ME, with structured extraction of task/sample features, imagery protocol, EEG methods/signatures, MI-ME overlap, translational readouts, and limitations. Across convergent datasets, MI reliably evokes contralateral mu/beta ERD with timing and topography akin to ME, typically with smaller amplitudes and broader fields; realistic decoding benchmarks cluster around the mid-70% for MI versus low-80% for ME, with ≈70% a usability threshold and 15%-30% of naïve users below it. Convergence and performance improve with first-person kinesthetic instructions, higher imagery vividness, synchronised action observation, object-oriented tasks, EMG monitoring, and contingent neurofeedback; source-space modelling and synergy-aware features can lift MI accuracy into the ~82%-95% range in constrained settings, though offline gains often overestimate online control. In stroke cohorts, most patients exhibit clear ERD/ERS, and a meaningful subset exceeds operational thresholds; however, calibration-to-online drops (e.g., ~80% to ~70%) are common and partially recover with adaptive retraining. The principal barriers to translation are heterogeneous protocols (band definitions, referencing, validation), small and selective samples, sparse EMG to exclude covert movement, non-stationarity across sessions, and persistent non-responders. To move from plausibility to practice, future studies should standardise mu/beta windows and baselines, report closed-loop outcomes, personalise training with vividness assessment and synchronised action observation, anticipate drift with adaptive algorithms and periodic recalibration, and integrate MI with robotics, functional electrical stimulation, or virtual reality in multisite trials that track durable functional gains.},
}

@article {pmid41140580,
year = {2025},
author = {Boonstra, JT},
title = {Ethical imperatives in the commercialization of brain-computer interfaces.},
journal = {IBRO neuroscience reports},
volume = {19},
number = {},
pages = {718-724},
pmid = {41140580},
issn = {2667-2421},
abstract = {The rapid commercialization of brain-computer interfaces (BCIs) raises urgent ethical and scientific challenges for human research oversight. While BCIs hold transformative potential for treating neurological disorders, their premature translation into consumer markets risks outpacing neuroscientific understanding and ethical frameworks. This essay critically examines the mismatch between commercial claims and the technical limitations of current BCI systems, decoding accuracy and biocompatibility, unresolved ethical dilemmas posed by neural data commodification and procedural risks, and the inadequacy of existing governance to address vulnerabilities in consent, privacy, and long-term safety. Responsible innovation demands proactive measures and robust public engagement to align development with societal values. Without such safeguards, the rush to commercialize BCIs risks prioritizing market interests over patient welfare and eroding public trust in neurotechnology.},
}

@article {pmid41139722,
year = {2025},
author = {Li, J and Chen, T and Yan, X and Luo, L},
title = {The effect of device-based neuromodulation on the motor recovery of patients with spinal cord injury.},
journal = {Spinal cord},
volume = {},
number = {},
pages = {},
pmid = {41139722},
issn = {1476-5624},
abstract = {STUDY DESIGN: This paper systematically analyzes literature from PubMed, MEDLINE, Embase, and Cochrane databases over the past 10 years (up to May 25, 2025). It employs defined search terms, inclusion/exclusion criteria, and a documented search flow to evaluate mechanisms, efficacy, challenges, and future directions of neuromodulation technologies for spinal cord injury rehabilitation. The results synthesize findings from clinical trials, and representative papers.

OBJECTIVE: This review aims to evaluate the mechanisms and clinical applications of device-based neuromodulation technologies in spinal cord injury (SCI) rehabilitation, focusing on their efficacy, challenges, and future directions.

SETTING: The countries and regions worldwide participating in neuromodulation.

METHODS: We systematically analyzed advancements in neuromodulation over the past decade, including brain-spinal interfaces (BSI), brain-computer interfaces (BCI), cranial stimulation techniques (DBS, TMS, tDCS), spinal cord stimulation (SCS), robotic exoskeletons. The review integrates findings from clinical trials.

RESULTS: Neuromodulation technologies demonstrate significant potential in restoring motor and sensory function post-SCI. BSI and BCI improve mobility but face infection and cybersecurity risks. Cranial stimulation techniques enhance neuroplasticity, with DBS and TMS showing efficacy, while tDCS requires further validation. Epidural SCS enables motor recovery in complete paralysis but has high infection rates. Robotic exoskeletons benefit younger patients.

CONCLUSION: Neuromodulation technologies represent promising interventions for SCI, yet challenges remain in precision, safety, and efficacy. Future research should prioritize AI-driven parameter optimization, wearable device development, and multicenter randomized trials to establish these methods as core treatments, ultimately improving patient outcomes and quality of life.},
}

@article {pmid41138930,
year = {2025},
author = {Wang, J and Wang, X and Qiao, S and La, H and Yu, Y and An, X},
title = {Investigation of Fatigue Mechanisms and Detection Methods for Anesthesiologists Based on Multimodal Physiological Signals.},
journal = {Brain research bulletin},
volume = {},
number = {},
pages = {111597},
doi = {10.1016/j.brainresbull.2025.111597},
pmid = {41138930},
issn = {1873-2747},
abstract = {Anesthesiologists are highly susceptible to fatigue due to the demanding intensity and critical responsibility of their work, which poses substantial risks to both clinician health and patient safety. To elucidate fatigue mechanisms, this study systematically assessed cognitive and physiological alterations before and after prolonged high-intensity work. Cognitive performance was evaluated with paradigms targeting attention (0-back), working memory (2-back), and visuospatial processing, complemented by multimodal physiological monitoring with electroencephalogram (EEG) and electrocardiogram (ECG) recordings. Prolonged work was associated with significant declines in n-back accuracy, reflecting impaired attention and working memory, while visuospatial performance showed marked increases in both error rate and reaction time, indicating deterioration of spatial cognition and executive control. Concurrently, physiological analyses revealed enhanced EEG alpha-band connectivity, shortened RR intervals, a reduced LF/HF ratio, and elevated multiscale entropy, collectively indicating autonomic imbalance and central-autonomic dysregulation under fatigue. Building on these mechanistic findings, we applied transfer learning algorithms to statistically significant multimodal physiological features, achieving 99.4% cross-subject classification accuracy. This integration of mechanistic insights with computational modeling underscores the reliability of the proposed strategy and its translational potential for real-world clinical fatigue monitoring.},
}

@article {pmid41137585,
year = {2025},
author = {Zadeh Makouei, ST and Uyulan, C and Erguzel, TT and Tarhan, N},
title = {Advanced Facial Expression Recognition Using Model Averaging Ensembles of Convolutional Neural Networks and CAM Analysis.},
journal = {Clinical EEG and neuroscience},
volume = {},
number = {},
pages = {15500594251366792},
doi = {10.1177/15500594251366792},
pmid = {41137585},
issn = {2169-5202},
abstract = {Facial expressions play a vital role in non-verbal communication, conveying a wide range of emotions and messages. Although prior research achieved notable advances through architecture design or dataset-specific optimization, few studies have integrated multiple advanced techniques into a unified facial expression recognition (FER) pipeline. Addressing this gap, we propose a comprehensive approach that combines (i) multiple pre-trained CNNs, (ii) MTCNN-based face detection for improved facial region localization, and (iii) Grad-CAM-based interpretability. While MTCNN enhances the quality of face localization, it may slightly affect classification accuracy by focusing on cleaner yet more challenging samples. We evaluate four pre-trained models - DenseNet121, ResNet-50, ResNet18, and MobileNetV2 - on two datasets: Raf-DB and Cleaned-FER2013. The proposed pipeline demonstrates consistent improvements in interpretability and overall system robustness. The results emphasize the strength of integrating face detection, transfer learning, and interpretability techniques within a single framework can significantly enhance the transparency and reliability of FER systems. Combining FER with EEG-based systems significantly enhances the emotional intelligence of brain-computer interfaces, enabling more adaptive and personalized user experiences. With this approach the paper bridges the gap between affective computing and cognitive neuroscience, aligning closely EEG-centered interaction methodologies. Besides understanding the relationship between facial expressions of emotions and EEG signals will be an important study for literature.},
}

@article {pmid41136747,
year = {2025},
author = {Zuo, H and Zhang, W and Wang, L and Wu, Y and Zheng, Y and Hao, S and Chen, QY and Cao, P and Ouyang, M and Huang, S and Zhou, W and Xue, YX and Pan, Y and Wei, W and Zhuo, M and Yuan, T and Zha, R and Zhang, Z and Zhang, X},
title = {Transcranial direct current stimulation restores addictive behavior via prefrontal-striatal circuit.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {41136747},
issn = {1476-5578},
support = {32171080//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32400919//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32200914//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Dependence on methamphetamine (METH) is a severe brain disorder characterized by high relapse rates and cognitive decline following detoxification. Recent research suggests that transcranial direct current stimulation (tDCS) may treat addiction, but the underlying neural mechanisms remain unknown. Here, we employed METH-conditioned place preference (CPP) paradigm integrated with fMRI, electrophysiology, chemogenetics, in vivo fiber photometry recordings and a novel rodent tDCS model to examine the neural circuit underlying tDCS modulation on METH-induced addictive behavior. We demonstrated that tDCS targeted at the medial prefrontal cortex (mPFC) prevents relapse. Specifically, tDCS enhanced the activity of neurons in both the infralimbic cortex (IL) and the nucleus accumbens shell (NAcSh) simultaneously. Furthermore, chemogenetic inhibition of the IL-NAcSh circuit eliminated the modulatory effects of tDCS, while activation of the IL-NAcSh circuit was sufficient to suppress the relapse. These findings reveal that the IL-NAcSh pathway functions as a descending regulatory circuit mediating the therapeutic outcomes of tDCS in the treatment of substance use disorder, offering new insights into circuit-based neuro-modulatory treatments for addiction.},
}

@article {pmid41135743,
year = {2025},
author = {Wang, Y and Chen, HJ and Cheng, Y and Xie, Y and Cheng, Y and Zhao, S and Jiang, Y and Bai, T and Huo, Y and Wang, K and Zhang, M and Huang, W and Feng, G and Han, Y and Shu, N},
title = {Multimodal Integration of Plasma Biomarkers, MRI, and Genetic Risk to Predict Cerebral Amyloid Burden in Alzheimer's Disease.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121550},
doi = {10.1016/j.neuroimage.2025.121550},
pmid = {41135743},
issn = {1095-9572},
abstract = {Alzheimer's disease (AD), the most prevalent neurodegenerative disorder, is marked by the accumulation of amyloid-β (Aβ) plaques. Although cerebral Aβ positron emission tomography (Aβ-PET) remains the gold standard for assessing cerebral Aβ burden, its clinical utility is hindered by cost, radiation exposure, and limited availability. Plasma biomarkers have emerged as promising, non‑invasive indicators of Aβ pathology, yet they do not incorporate individual genetic risk or neuroanatomical context. To address this gap, we developed a multimodal machine‑learning framework that integrates plasma biomarkers, MRI‑derived brain structural features (regional volumes, cortical thickness, cortical area and structural connectivity), and genetic risk profiles to predict cerebral Aβ burden. This approach was evaluated in 150 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 101 participants from a domestic Chinese Sino Longitudinal Study of Cognitive Decline (SILCODE). Incorporating multimodal features substantially improved predictive performance: the baseline model using plasma and clinical variables alone achieved an R[2] of 0.56, whereas integrating neuroimaging and genetic information increased accuracy (R[2] = 0.63 with apolipoprotein E genotypes and R[2] = 0.64 with polygenic risk scores). Furthermore, a multiclass classifier trained on the same multimodal features achieved robust discrimination of cognitive status, with area‑under‑the‑curve values of 0.87 for normal controls, 0.76 for mild cognitive impairment, and 0.95 for AD dementia. These findings highlight the value of combining plasma, imaging, and genetic data to non-invasively estimate cerebral Aβ burden, offering a potential alternative to PET imaging for early AD risk assessment.},
}

@article {pmid41135661,
year = {2025},
author = {Pan, Y and Yang, X and Wu, M and Hu, S},
title = {Latent profile analysis of childhood trauma in Chinese individuals with bipolar disorder: Differential associations with suicidality and clinical symptomatology.},
journal = {Journal of affective disorders},
volume = {394},
number = {Pt A},
pages = {120490},
doi = {10.1016/j.jad.2025.120490},
pmid = {41135661},
issn = {1573-2517},
abstract = {BACKGROUND: Childhood trauma is a well-established risk factor for poor clinical outcomes in bipolar disorder (BD), yet most studies have relied on cumulative trauma scores, potentially overlooking heterogeneity in trauma exposure and its differential impact on psychopathology.

METHODS: This study employed latent profile analysis (LPA) to identify distinct subtypes of childhood trauma based on the Childhood Trauma Questionnaire (CTQ) among 725 individuals with BD in a Chinese clinical sample. Differences across trauma profiles were examined in relation to demographic features, psychiatric symptoms (anxiety, depression, mania), and suicidal ideation (Beck Scale for Suicide Ideation, BSSI).

RESULTS: A four-class solution was identified, and the relationship with mental health outcomes was analyzed. Class 4 group, characterized by the most severe emotional abuse and physical neglect, along with the lowest emotional neglect, reported the highest levels of anxiety (HAMA), depression (HAMD), and suicidal ideation (BSSI). In contrast, manic symptoms (YMRS) were present across all groups but did not differ significantly between trauma profiles. Logistic regression indicated that emotional abuse was the strongest predictor of trauma class membership.

CONCLUSIONS: Distinct trauma profiles in BD are differentially associated with symptom severity and suicide risk. These findings highlight the clinical value of moving beyond cumulative trauma scores to identify trauma-specific subtypes. Early identification of high-risk trauma configurations may inform personalized assessment and intervention strategies for individuals with BD.},
}

@article {pmid41135523,
year = {2025},
author = {Wang, Z and Tang, Q and Li, K and Mou, J and Chen, Y and Kuang, W and Sun, L and Ma, Z and Wei, Y and Bao, R and Sun, X and Wang, S and Lu, W and Xu, GY and Tang, YQ and Duan, S and Ni, JD},
title = {An enteric-DRG pathway for interoception and visceral pain in mice.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2025.09.035},
pmid = {41135523},
issn = {1097-4199},
abstract = {Sensory afferents are major interoceptive pathways for organ-brain communication. Within the distal colon, dorsal root ganglia (DRGs) afferents regulate key gut physiology. Inflammation causes hypersensitivity of DRG pathways, leading to visceral pain. However, whether enteric neurons contribute to interoception and visceral pain remains unclear. Here, we surveyed the DRG innervation along the gastrointestinal tract in mice and found extensive associations between DRG terminals and enteric neurons. Optogenetic activation of different DRG terminals in the distal colon elicited variable degrees of behavioral responses, but only designated subpopulations induced aversion. Notably, optogenetic activation of colon cholinergic, but not nitrergic, enteric neurons signaled through the DRG-spinal pathway to evoke a non-aversive nociceptive-like reflex. Acetylcholine is part of the enteric-DRG signaling. Remarkably, inflammation shifted the nature of the enteric-DRG pathway from non-aversive to aversive. These findings expand the previous understanding of DRG-mediated visceral sensation, highlighting the contribution of enteric neuron-DRG communication to inflammation-induced visceral pain.},
}

@article {pmid41134961,
year = {2025},
author = {Jin, J and Qin, K and Allison, BZ and Li, S and Zhang, Y and Wang, X and Cichocki, A},
title = {A Transfer Learning SSVEP Decoding Algorithm Calibrated With Single-Trial Data.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3617508},
pmid = {41134961},
issn = {2162-2388},
abstract = {Training-based algorithms significantly outperform training-free methods in terms of recognition performance for steady-state visual-evoked potential (SSVEP)-based brain-computer Interfaces (BCIs). However, collecting training data requires calibration experiments that are effort-intensive and often costly. These calibration demands limit the practicality of BCI, as users (and even system operators) may experience fatigue or lose interest in continued use. Transfer learning (TL) offers an effective solution, but it typically relies on either a certain amount of target domain data or extensive source domain data. To address this limitation, we introduce the concept of cross-dataset TL in SSVEP for the first time to extract transfer knowledge from other datasets. During this process, we identified a data mismatch problem that severely compromises the generalizability of transfer knowledge. To overcome this challenge, we propose a TL-SSVEP decoding algorithm calibrated with single-trial data (TL-CSTD). Specifically, we use 2 s of 8 Hz single-trial calibration data from the target domain to obtain matched transfer templates from the source domain. These templates are then corrected to extract holistic and single-period transfer knowledge, which are subsequently employed to construct an efficient TL-SSVEP decoding model for the target subject. Experimental results on three large SSVEP datasets demonstrate that TL-CSTD effectively addresses the data mismatch problem and achieves excellent SSVEP recognition performance using only 2 s of single-trial calibration data, showing its significant application potential and practicality.},
}

@article {pmid41134943,
year = {2025},
author = {Sun, J and Lin, PJ and Zhai, X and Wang, W and Jia, T and Li, Z and Pan, Y and Ji, L and Zhou, B and Li, C},
title = {Multimodal behavioral data predict stroke patient's response to BCI treatment through explainable AI.},
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.2025.3625222},
pmid = {41134943},
issn = {1558-0210},
abstract = {Brain-computer interface (BCI)-based neurorehabilitation holds promise in enhancing motor recovery after stroke. However, recent research has reported heterogeneous results, indicating both responders and non-responders to BCI therapy. Using explainable artificial intelligence (XAI) methods, this study aims to investigate the independent and combined importance of multimodal behavioral data to predict patients' response to BCI therapy. Forty-two subacute stroke patients with lower-limb motor impairment underwent behavioral assessments, and received two-week BCI rehabilitation training. Linear regression, elastic net and artificial neural network models were developed to predict response to BCI therapy. Two XAI techniques, the stepwise method and Shapley additive explanation, were used to interpret model outcomes. The multivariate model (R[2]=0.852, P<0.001) that combines an optimal subset of multimodal behavioral data outperformed the univariate model (R[2]=0.758, P<0.001) trained on a single variable. Elastic net and artificial neural network models both demonstrated high prediction performance, as indicated by classification accuracies of 0.810 and 0.762, and areas under the receiver operating characteristic curve of 0.782 and 0.771. Our results revealed that multimodal behavioral data, including demographic, clinical, and biomechanical characteristics, provided unique and complementary information for interpreting the response of subacute patients to BCI therapy. Particularly, baseline motor impairment, muscle spasticity and balance function were primary predictors. Our findings highlight the core role of XAI methods towards precision medicine, which can help clinicians to identify individual recovery potentials and plan optimal treatment strategies.},
}

@article {pmid41132843,
year = {2025},
author = {Jin, S and Lin, C and Li, P and Wang, X and Wang, Y and Zhang, C and Wang, X and Peng, Y and Li, H and Lu, Y and Wang, X},
title = {Cannabidiol alleviates methamphetamine addiction via targeting ATP5A1 and modulating the ATP-ADO-A1R signaling pathway.},
journal = {Acta pharmaceutica Sinica. B},
volume = {15},
number = {10},
pages = {5261-5276},
pmid = {41132843},
issn = {2211-3835},
abstract = {Cannabidiol (CBD), a non-psychoactive cannabinoid, shows great promise in treating methamphetamine (METH) addiction. Nonetheless, the molecular target and the mechanism through which CBD treats METH addiction remain unexplored. Herein, CBD was shown to counteract METH-induced locomotor sensitization and conditioned place preference. Additionally, CBD mitigated the adverse effects of METH, such as cristae loss, a decline in ATP content, and a reduction in membrane potential. Employing an activity-based protein profiling approach, a target fishing strategy was used to uncover CBD's direct target. ATP5A1, a subunit of ATP synthase, was identified and validated as a CBD target. Moreover, CBD demonstrated the ability to ameliorate METH-induced ubiquitination of ATP5A1 via the D376 residue, thereby reversing the METH-induced reduction of ATP5A1 and promoting the assembly of ATP synthase. Pharmacological inhibition of the ATP efflux channel pannexin 1, blockade of ATP hydrolysis by a CD39 inhibitor, and blocking the adenosine A1 receptor (A1R) all attenuated the therapeutic benefits of CBD in mitigating METH-induced behavioral sensitization and CPP. Moreover, the RNA interference of ATP5A1 in the ventral tegmental area resulted in the reversal of CBD's therapeutic efficacy against METH addiction. Collectively, these data show that ATP5A1 is a target for CBD to inhibit METH-induced addiction behaviors through the ADO-A1R signaling pathway.},
}

@article {pmid41132728,
year = {2025},
author = {Berlet, R and Azapagic, A and Jha, NK and Aksenov, D and Bookwalter, J and Ullah, A and Bobustuc, G and Lee, J and Sant, H and McDaid, J and Walker, M and Shea, J and Graff, D and Barlow, AK and Frigerio, R and Aliee, D and Bailes, C and Gale, BK and Bailes, JE},
title = {An implantable, intracerebral osmotic pump for convection-enhanced drug delivery in glioblastoma multiforme.},
journal = {Frontiers in oncology},
volume = {15},
number = {},
pages = {1676691},
pmid = {41132728},
issn = {2234-943X},
abstract = {BACKGROUND: Glioblastoma multiforme (GBM; WHO Grade 4) is an aggressive brain tumor that invariably recurs after surgical resection, chemoradiation, and adjuvant chemotherapy. Treatment is limited, in part, because the blood-brain barrier (BBB) restricts entry of chemotherapeutic agents to the brain. Introducing drugs directly into the brain circumvents the BBB, but diffusion of these typically large drug molecules within brain parenchyma is limited. Convection-enhanced delivery (CED), based on the principles of bulk flow, can achieve drug distribution over a wider area to target residual cancer cells and thus remains a promising technique for treating GBM and other neuro-oncologic pathologies. Here, we propose a new method that combines direct brain delivery and CED using a fully implantable, microfluidic pump placed at the time of initial resection surgery.

METHODS: In this initial proof-of-concept study, we evaluated the function of a 3D-printed pump in an in vitro system and in vivo in a rat C6 glioma model.

RESULTS: In vitro osmosis-driven distribution of a high molecular-weight marker dye extended up to 18 mm from the pump with minimal reflux, including under simulations of increased intracranial pressure. In vivo, MRI imaging demonstrated wide distribution of superparamagnetic iron oxide particles from a pump implanted after the resection of a C6 glioma. Histological staining indicated that pump implantation did not cause additional inflammatory changes compared to controls.

CONCLUSION: This preliminary study demonstrated the feasibility of using an implantable, osmosis-driven pump to bypass the BBB and provide targeted delivery for treatment of GBM.},
}

@article {pmid41129590,
year = {2025},
author = {Cao, D and Yu, Z and Wang, J and Wu, Y},
title = {SMMTM: Motor imagery EEG decoding algorithm using a hybrid multi-branch separable convolutional self-attention temporal convolutional network.},
journal = {PloS one},
volume = {20},
number = {10},
pages = {e0333805},
pmid = {41129590},
issn = {1932-6203},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Algorithms ; Neural Networks, Computer ; *Imagination/physiology ; },
abstract = {Motor imagery (MI) is a brain-computer interface (BCI) technology with the potential to change human life in the future. MI signals have been widely applied in various BCI applications, including neurorehabilitation, smart home control, and prosthetic control. However, the limited accuracy of MI signals decoding remains a significant barrier to the broader growth of the BCI applications. In this study, we propose the SMMTM model, which combines spatiotemporal convolution (SC), multi-branch separable convolution (MSC), multi-head self-attention (MSA), temporal convolution network (TCN), and multimodal feature fusion (MFF). Specifically, we use the SC module to capture both temporal and spatial features. We design a MSC to capture temporal features at multiple scales. In addition, MSA is designed to extract valuable global features with long-term dependence. The TCN is employed to capture higher-level temporal features. The MFF consists of feature fusion and decision fusion, using the features output from the SMMTM to improve robustness. The SMMTM was evaluated on the public benchmark BCI Comparison IV 2a and 2b datasets, the results showed that the within-subject classification accuracies for the datasets were 84.96% and 89.26% respectively, with kappa values of 0.797 and 0.756. The cross-subject classification accuracy for the 2a dataset was 69.21%, with a kappa value of 0.584. These results indicate that the SMMTM significantly enhances decoding performance, providing a strong foundation for advancing practical BCI implementations.},
}

Humans
*Brain-Computer Interfaces
*Electroencephalography/methods
*Algorithms
Neural Networks, Computer
*Imagination/physiology

@article {pmid41129446,
year = {2025},
author = {Dang, W and Ren, Z and Sun, J and Lv, D and Xiong, Z and Guo, W and Gao, Z and Yu, H},
title = {ML-TGNet: A Multi-Level Topology Guidance Network for Motor Imagery Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3624298},
pmid = {41129446},
issn = {2168-2208},
abstract = {Brain-computer interfaces (BCIs) based on motor imagery electroencephalogram (MI-EEG) signals have been extensively applied in various neural rehabilitation scenarios. However, existing methods primarily focus on designing complex architectures to extract spatio-temporal features from MI-EEG signals, often neglecting the brain dynamics information embedded within them. This oversight leads to the extraction of redundant information, ultimately reducing decoding performance. To address these challenges, we design a multi-level topology-guidance network (ML-TGNet) that leverages topological brain synchronization information to more effectively extract features related to MI tasks. ML-TGNet specifically comprises a multi-level topology guidance module, a feature pool module, and a multi-branch decoding module. To evaluate its performance, extensive experiments are conducted on three publicly available MI datasets: the BCI Competition IV-2a dataset, the High Gamma dataset, and the OpenBMI dataset. ML-TGNet achieves classification accuracies of 82.33%, 96.42%, and 85.26% on these three datasets, respectively, outperforming current state-of-the-art models. These findings confirm the efficacy of using brain synchronization information to guide MI decoding, thereby opening a novel approach for EEG-based brain state decoding by integrating brain dynamics into deep learning.},
}

@article {pmid41129442,
year = {2025},
author = {Wang, Z and Wang, H and Jia, T and He, X and Li, S and Wu, D},
title = {DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3622725},
pmid = {41129442},
issn = {2168-2208},
abstract = {Electroencephalography (EEG)-based brain computer interfaces (BCIs) transform spontaneous/evoked neural activity into control commands for external communication. While convolutional neural networks (CNNs) remain the mainstream backbone for EEG decoding, their inherently short receptive field makes it difficult to capture long-range temporal dependencies and global inter-channel relationships. Recent CNN-Transformer (Con former) hybrids partially address this issue, but most adopt a serial design, resulting in suboptimal integration of local and global features, and often overlook explicit channel-wise modeling. To address these limitations, we propose DBConformer, a dual-branch convolutionalTrans former network tailored for EEG decoding. It integrates a temporal Conformer to model long-range temporal dependencies and a spatial Conformer to extract inter-channel interactions, capturing both temporal dynamics and spatial patterns in EEG signals. A lightweight channel attention module further refines spatial representations by assigning data-driven importance to EEG channels. Extensive experiments under four evaluation settings on three paradigms, including motor imagery, seizure detection, and steady state visual evoked potential, demonstrated that DBCon former consistently outperformed 13 competitive baseline models, with over an eight-fold reduction in parameters than current high-capacity EEG Conformer architecture. Furthermore, the visualization results confirmed that the features extracted by DBConformer are physiologically in terpretable and aligned with prior knowledge. The superior performance and interpretability of DBConformer make it reliable for accurate, robust, and explainable EEG decoding. Code is publicized at https://github.com/wzwvv/ DBConformer.},
}

@article {pmid41129430,
year = {2025},
author = {Bai, Y and Zhang, S and Zhao, R and Han, X and Ni, G and Ming, D},
title = {Cross-Hemispheric Spatial-Temporal Attention Network for Decoding Silent Speech From EEG.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3624878},
pmid = {41129430},
issn = {1558-2531},
abstract = {OBJECTIVE: Speech, as the core of advanced human cognition, is fundamental to social interaction and daily life. Electroencephalogram (EEG)-based speech brain-computer interface (BCI) offers a novel communication pathway for patients with speech disorders, where deep learning has demonstrated significant advantages. Given the established dominance of the left hemisphere in speech processing, exploring methods to extract speech related neural features fully is crucial for enhancing decoding per formance.

APPROACH: In this study, EEG signals were recorded during a silent speech task involving the articulation of 10 distinct Chinese characters. Leveraging the principle of language function lateralization, we proposed a novel deep learning model, the cross hemispheric spatial-temporal attention network (CHSTAN), for EEG-based silent speech recognition. A multiscale temporal con volution block was employed to extract the temporal dynamics of EEG signals. A hemispheric spatial convolutional block was designed to independently process spatial information from the left and right hemispheres. Furthermore, the cross-attention mechanism was introduced to enhance inter-hemispheric feature inter action and specifically reinforce left-hemispheric feature representation for the final classification.

RESULTS: We compared CHSTAN with other existing methods using 5-fold cross-validation on the collected dataset. CHSTAN achieved an average classification accuracy of 49.88% and an average F1-score of 48.75% in decoding the 10 Chinese characters, significantly outperforming other methods.

CONCLUSION: The results indicate that the CHSTAN performs effectively in silent speech EEG classification tasks. Notably, the feature patterns learned through its innovative architecture correspond to neural speech processing mechanism.

SIGNIFICANCE: CHSTAN provides valuable insights and practical solutions for improving the performance of EEG-based speech decoding.},
}

@article {pmid41127543,
year = {2025},
author = {Sun, J and Li, H and Wang, J and Yang, W},
title = {Application of biomimetic approaches in the treatment of neurological disorders.},
journal = {Materials today. Bio},
volume = {35},
number = {},
pages = {102334},
pmid = {41127543},
issn = {2590-0064},
abstract = {Neurological disorders usually involve nerve cell damage or death, and traditional treatments have significant limitations in neural repair. Biomimetic approaches mimic the structure and function of biological systems, providing an important approach to neural repair and regeneration. This paper first summarizes the current challenges in treating neurological disorders. It then explores the applications of bioinspired strategies in drug delivery systems (DDS), neural repair, three-dimensional (3D) printed neural scaffolds, and brain-machine interfaces (BMIs) with neuromodulation. Additionally, it discusses the challenges, strategies, advantages, and prospects of bioinspired methods in neurological disease treatment. The aim is to provide a comprehensive perspective on the potential of biomimicry-based methods in this field.},
}

@article {pmid41127304,
year = {2025},
author = {He, B and Guo, Y and Yang, G},
title = {Integrated Piezoelectric Vibration and In Situ Force Sensing for Low-Trauma Tissue Penetration.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0417},
pmid = {41127304},
issn = {2692-7632},
abstract = {Precision-controlled microscale manipulation tasks-including neural probe implantation, ophthalmic surgery, and cell membrane puncture-often involve minimally invasive membrane penetration techniques with real-time force feedback to minimize tissue trauma. This imposes rigorous design requirements on the corresponding miniaturized instruments with robotic assistance. This paper proposes an integrated piezoelectric module (IPEM) that combines high-frequency vibration-assisted penetration with real-time in situ force sensing. The IPEM features a compact piezoelectric actuator integrated with a central tungsten probe, generating axial micro-vibration (4,652 Hz) to enable smooth tissue penetration while simultaneously measuring contact and penetration forces via the piezoelectric effect. Extensive experiments were conducted to validate the effectiveness and efficacy of the proposed IPEM. Both static and dynamic force-sensing tests demonstrate the linearity, sensitivity (9.3 mV/mN), and accuracy (mean absolute error < 0.3 mN, mean absolute percentage error < 1%) of the embedded sensing unit. In gelatin phantom tests, the module reduced puncture and insertion forces upon activation of vibration. In vivo experiments in mouse brains further confirmed that the system could reduce penetration resistance (from an average of 11.67 mN without vibration to 7.8 mN with vibration, decreased by 33%) through the pia mater and accurately mimic the electrode implantation-detachment sequence, leaving a flexible electrode embedded with minimal trauma. This work establishes a new paradigm for smart surgical instruments by integrating a compact actuator-sensor design with real-time in situ force feedback capabilities, with immediate applications in brain-machine interfaces and microsurgical robotics.},
}

@article {pmid41121568,
year = {2025},
author = {Skarzynski, PH and Cywka, KB and Czaplicka, EA and Skarzynski, H},
title = {The Bonebridge Active Bone Conduction Hearing Implant: Safety, Effectiveness and Outcomes Based on 355 Patients.},
journal = {Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery},
volume = {},
number = {},
pages = {},
doi = {10.1111/coa.70050},
pmid = {41121568},
issn = {1749-4486},
abstract = {OBJECTIVES: This study evaluates the safety and efficacy of the Bonebridge BCI 601 and 602 bone conduction implants in our largest cohort to date of 355 patients. The patients had a wide age range and exhibited conductive, mixed, or single-sided deafness (SSD).

DESIGN: All patients underwent Bonebridge implantation. Pre- and post-implantation evaluations included pure-tone audiometry, speech recognition tests, and free-field audiometry. Word recognition was measured using the Polish Monosyllabic Word Test, while speech reception in noise was assessed using the Polish Sentence Matrix Test. Subjective benefit was assessed using the APHAB questionnaire. Follow-up tests were performed 3-6 months after activation.

RESULTS: Revision surgery was required in 17 patients (4.8%) due to complications, including implant removal in 5 cases. Reimplantation was successful in 4 of these. The APHAB questionnaire showed improved hearing function and all hearing tests also showed significant improvement.

CONCLUSION: Active bone conduction implantation is an effective method for the rehabilitation of conductive hearing loss, mixed hearing loss, and unilateral deafness. This large cohort study confirms significant hearing improvement and subjective benefits. The low complication rate supports the reliability of the Bonebridge system.},
}

@article {pmid41121378,
year = {2025},
author = {Chen, HJ and Dong, X and Wang, Y and Wang, K and Feng, G and Bai, T and Zhang, M and Gan, K and Peng, JJ and Huang, W and Zhang, Z and Shu, N and Ma, G},
title = {Polygenic risk for Alzheimer's disease in healthy aging: age-related and APOE-driven effects on brain structures and cognition.},
journal = {Genome medicine},
volume = {17},
number = {1},
pages = {126},
pmid = {41121378},
issn = {1756-994X},
support = {2022ZD0213300//STI2030-Major Projects/ ; },
mesh = {Humans ; *Alzheimer Disease/genetics/pathology ; *Multifactorial Inheritance ; Male ; Aged ; Female ; *Cognition ; Middle Aged ; *Apolipoproteins E/genetics ; *Brain/pathology ; *Healthy Aging/genetics ; *Genetic Predisposition to Disease ; Aged, 80 and over ; Risk Factors ; Gray Matter/pathology ; White Matter/pathology ; Magnetic Resonance Imaging ; },
abstract = {BACKGROUND: Alzheimer's disease (AD) is characterized by progressive neurodegeneration and cognitive decline with age. The genetic architecture of AD involves multiple loci, including the apolipoprotein E gene (APOE). The polygenic risk scores for AD (AD-PRS) provide a comprehensive genome-wide assessment of AD risk, yet their age-related effects on brain structures and cognitive function in cognitively unimpaired individuals remain largely undefined.

METHODS: We analyzed cognitively unimpaired, genetically unrelated Caucasians from the UK Biobank (N = 21,236, 64.5 ± 7.6 years). AD-PRS was derived using a Bayesian approach incorporating approximately 5 million genetic variants (UK Biobank's standard PRS). Brain structures were measured with regional gray matter (GM) volumes and tract-wise microstructural white matter (WM) integrity. Cognitive performance was evaluated with executive function, visuospatial function, reasoning, and memory. Sliding window analyses were performed to investigate age-related polygenic effects on brain structures, and mediation analyses tested whether structural changes mediated the gene-cognition relationship across different age groups. Analyses were replicated using two custom PRSs-one including APOE and the other excluding APOE regions-calculated with the clumping-and-thresholding approach.

RESULTS: High AD-PRS was associated with accelerated GM atrophy (particularly in the hippocampus, thalamus, and parahippocampus), increased cerebral ventricular volume, and reduced WM integrity (especially in the fornix, cingulum, and superior fronto-occipital fasciculus). These polygenic effects demonstrated significant age-related amplification (pBonf < 0.05), with the strongest effects in individuals aged ≥ 75. Elevated AD-PRS was linked to lower cognitive performance across aging, especially in executive function, reasoning, and memory, which were significantly mediated by structural brain changes in subcortical and posterior limbic regions and their WM connections, predominantly in late aging (p < 0.05). Sensitivity analyses confirmed the robustness of these findings, emphasizing the dominant contribution of APOE, while also identifying age-specific effects from non-APOE variants.

CONCLUSIONS: High polygenic risk for AD may be associated with accelerated cognitive decline in healthy aging, mediated by structural changes within hippocampal-thalamic regions and their connecting WM tracts. We provide insights into the early pathogenesis of AD and support the potential for age-targeted screening and early intervention for individuals at high genetic risk.},
}

Humans
*Alzheimer Disease/genetics/pathology
*Multifactorial Inheritance
Male
Aged
Female
*Cognition
Middle Aged
*Apolipoproteins E/genetics
*Brain/pathology
*Healthy Aging/genetics
*Genetic Predisposition to Disease
Aged, 80 and over
Risk Factors
Gray Matter/pathology
White Matter/pathology
Magnetic Resonance Imaging

@article {pmid41120372,
year = {2025},
author = {He, S and Li, Z and Dang, J and Luo, Y and Zhang, G},
title = {CIRE: A Chinese EEG Dataset for decoding speech intention modulated by prosodic emotion.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1664},
pmid = {41120372},
issn = {2052-4463},
support = {No. 62276185, No. 61876126//National Natural Science Foundation of China (National Science Foundation of China)/ ; No. 62276185, No. 61876126//National Natural Science Foundation of China (National Science Foundation of China)/ ; No. 62276185, No. 61876126//National Natural Science Foundation of China (National Science Foundation of China)/ ; No. 62276185, No. 61876126//National Natural Science Foundation of China (National Science Foundation of China)/ ; No. 62276185, No. 61876126//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Electroencephalography ; *Emotions ; *Speech ; Brain-Computer Interfaces ; China ; Machine Learning ; Speech Perception ; Intention ; Female ; Male ; Adult ; East Asian People ; },
abstract = {Neural decoding of speech intention could advance the development and application of brain-computer interface (BCI) technology. Currently, lack of dataset limited the research on decoding the true speech intention, especially the diverse intentions expressed by the same text when no context is given. This study provides an EEG dataset, CIRE, on spoken language interaction intention featuring aligned textual expressions with divergent intentional meanings due to the differences in prosodic emotion. The dataset comprises preprocessed high-density (128-channel) EEG recordings from 38 participants engaged in comprehension of attitude-conveying speech stimuli, accompanied by Wav2vec2-derived acoustic embeddings of the listening materials. To validate our dataset through cognitive neuroscience studies and binary intent classification, we applied signal processing pipelines, cognitive analysis frameworks, and machine learning (ML) approaches. Our baseline model achieved a cross-subject classification accuracy of 68.2%, with differences exhibiting interpretable neurophysiological correlates. The high-density and high temporal resolution EEG data offer broader application areas, both in cognitive neuroscience and speech BCI, and can also contribute to the brain-inspired algorithms.},
}

Humans
*Electroencephalography
*Emotions
*Speech
Brain-Computer Interfaces
China
Machine Learning
Speech Perception
Intention
Female
Male
Adult
East Asian People

@article {pmid41117243,
year = {2025},
author = {Mou, T and Lai, J and Kong, L},
title = {Effects of Paliperidone on Serum D-dimer Levels: Clinical and Experimental Findings.},
journal = {Actas espanolas de psiquiatria},
volume = {53},
number = {5},
pages = {959-966},
pmid = {41117243},
issn = {1578-2735},
mesh = {*Paliperidone Palmitate/therapeutic use/pharmacology ; Humans ; *Antipsychotic Agents/therapeutic use/pharmacology ; *Fibrin Fibrinogen Degradation Products/analysis ; Animals ; Male ; Adult ; *Schizophrenia/drug therapy/blood ; Female ; Mice ; Mice, Inbred C57BL ; Middle Aged ; Young Adult ; },
abstract = {BACKGROUND: Dysregulation of coagulation function associated with antipsychotic treatment remains poorly understood. This study investigates the potential impact of paliperidone on serum D-dimer levels during the early stages of treatment.

METHODS: Nine patients diagnosed with first-episode schizophrenic spectrum disorder were assessed for serum D-dimer levels before and after a 2-week paliperidone regimen. Additionally, eight adult C57 mice in the experimental group (EG) received 3 mg/kg of paliperidone daily for 10 consecutive days, while eight mice in the control group (CG) were untreated. Venous blood was collected and analyzed for D-dimer at baseline, and on the 5th and 10th days in the EG, as well as on the 10th day for the CG.

RESULTS: No significant differences were observed in serum D-dimer levels before and after paliperidone treatment in the patient cohort. In animal experiments, compared to the CG on the 10th day, serum D-dimer levels in the EG on the 10th day showed no significant difference (p > 0.05), while the level in the EG on the 5th day was significantly lower (p < 0.05). Compared to its baseline, serum D-dimer levels within the EG on the 5th day was significantly decreased (p < 0.05).

CONCLUSION: Short-term paliperidone treatment had minimal effects on serum D-dimer levels in both human participants and mice, though transient changes were noted early in treatment. Nonetheless, the potential for drug-induced coagulation disruption should be considered in clinical practice.},
}

*Paliperidone Palmitate/therapeutic use/pharmacology
Humans
*Antipsychotic Agents/therapeutic use/pharmacology
*Fibrin Fibrinogen Degradation Products/analysis
Animals
Male
Adult
*Schizophrenia/drug therapy/blood
Female
Mice
Mice, Inbred C57BL
Middle Aged
Young Adult

@article {pmid41115264,
year = {2025},
author = {Ye, Y and Zhang, Y and Li, J and Wu, P and Zhang, T and Liao, T and Neculai, D and Lou, J and Li, Z and Chen, W and Hu, W},
title = {Nanoscale Mechanical Force Primes NOD1-LRR for Efficient Pathogen Recognition.},
journal = {The journal of physical chemistry letters},
volume = {},
number = {},
pages = {11196-11205},
doi = {10.1021/acs.jpclett.5c02875},
pmid = {41115264},
issn = {1948-7185},
abstract = {Detecting pathogens requires molecular sensors that can rapidly and precisely respond to local threats. While cytosolic innate immune receptors such as NOD1 are known as biochemical detectors, their ability to interpret physical cues remains a critical unknown. Here, we combine piconewton-resolution single-molecule manipulation, molecular dynamics simulations, and structural modeling to demonstrate that NOD1 is not a passive detector but an active nanomechanical sensor. We show that the receptor's LRR domain, with its curved, horseshoe-like nanoarchitecture, functions as a mechanical force concentrator. Physiologically relevant piconewton-scale forces, such as those at the membrane-cytosol interface, are concentrated into a high-stress hotspot that primes the domain for a conformational transition. This force-induced priming acts as an allosteric nanoswitch, transducing mechanical energy into a biochemical output: a dramatic increase in binding strength and sensitivity for its bacterial ligand iE-DAP. This mechanochemical coupling positions NOD1 as a force-responsive sensor, enabling rapid and spatially restricted immune activation. Our work establishes a new paradigm for cytosolic pathogen recognition and suggests that force-sensing LRR domains represent a generalizable design principle in nanobiology, bridging a conceptual gap between mechanobiology and innate immunity.},
}

@article {pmid41112520,
year = {2025},
author = {Shi, J and Wang, J and Fei, W and Feleke, AG and Bi, L},
title = {Neuroanatomy-Informed Brain-Machine Hybrid Intelligence for Robust Acoustic Target Detection.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0438},
pmid = {41112520},
issn = {2692-7632},
abstract = {Sound target detection (STD) plays a critical role in modern acoustic sensing systems. However, existing automated STD methods show poor robustness and limited generalization, especially under low signal-to-noise ratio (SNR) conditions or when processing previously unencountered sound categories. To overcome these limitations, we first propose a brain-computer interface (BCI)-based STD method that utilizes neural responses to auditory stimuli. Our approach features the Triple-Region Spatiotemporal Dynamics Attention Network (Tri-SDANet), an electroencephalogram (EEG) decoding model incorporating neuroanatomical priors derived from EEG source analysis to enhance decoding accuracy and provide interpretability in complex auditory scenes. Recognizing the inherent limitations of stand-alone BCI systems (notably their high false alarm rates), we further develop an adaptive confidence-based brain-machine fusion strategy that intelligently combines decisions from both the BCI and conventional acoustic detection models. This hybrid approach effectively merges the complementary strengths of neural perception and acoustic feature learning. We validate the proposed method through experiments with 16 participants. Experimental results demonstrate that the Tri-SDANet achieves state-of-the-art performance in neural decoding under complex acoustic conditions. Moreover, the hybrid system maintains reliable detection performance at low SNR levels while exhibiting remarkable generalization to unseen target classes. In addition, source-level EEG analysis reveals distinct brain activation patterns associated with target perception, offering neuroscientific validation for our model design. This work pioneers a neuro-acoustic fusion paradigm for robust STD, offering a generalizable solution for real-world applications through the integration of noninvasive neural signals with artificial intelligence.},
}

@article {pmid41110663,
year = {2025},
author = {Gordleeva, S and Grigorev, N and Pitsik, E and Kurkin, S and Kazantsev, V and Hramov, A},
title = {Detection and rehabilitation of age-related motor skills impairment: neurophysiological biomarkers and perspectives.},
journal = {Ageing research reviews},
volume = {},
number = {},
pages = {102923},
doi = {10.1016/j.arr.2025.102923},
pmid = {41110663},
issn = {1872-9649},
abstract = {Age-related decline in motor control, manifesting as impaired posture, gait, and slowed movement execution, significantly diminishes the quality of life in older adults. These functional deficits are associated with alterations in neurophysiological data, which are analyzed using advanced techniques including spectral analysis, complexity measures, and functional connectivity network analysis. These methodologies provide valuable insights into the neurobiological mechanisms underpinning age-related motor function impairments, linking physiological changes to non-invasively recorded electrophysiological and hemodynamic responses. Recent investigations have demonstrated an age-dependent impairment in access to working memory during motor tasks, evidenced by significant correlations between electroencephalographic biomarkers and neural response latencies. Furthermore, these functional biomarkers are associated with the degradation of motor learning abilities in older individuals. There is a broad consensus that non-invasive assessment of brain activity accurately reflects the processes underlying age-related motor decline, thereby opening avenues for targeted intervention strategies. A key area of investigation is the utilization of motor system function for the early detection of neurodegenerative diseases. Seemingly, simple motor tasks engage cortical regions responsible for attention, vision, and memory through a process known as sensorimotor integration. Sensorimotor training implemented via brain-computer interfaces with neurofeedback demonstrates potential for ameliorating both cognitive and motor deficits in both healthy older adults and those with age-related conditions. This review synthesizes current research on age-related changes revealed through neuroimaging data analysis, highlighting how biomarkers derived from brain electrical and hemodynamic activity reflect both normative and pathological aging processes. Finally, we emphasize the considerable potential of neurophysiological data analysis for advancing the field of aging research. Digital medicine platforms, including brain-computer interfaces and a range of wearable monitoring devices, hold significant promise for transforming the diagnosis of age-related diseases. These technologies empower continuous, objective monitoring of older adults, paving the way for personalized, precision-based medical interventions.},
}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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