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Correction: Frontostriatal circuit dysfunction leads to cognitive inflexibility in neuroligin-3 R451C knockin mice

Molecular psychiatry 2025年第1期30卷 365-366页

作者： Shen Lin Cui-Ying Fan Hao-Ran Wang Xiao-Fan Li Jia-Li Zeng Pei-Xuan Lan Hui-Xian Li Bin Zhang Chun Hu Junyu Xu Jian-Hong Luo Affiliated Mental Health Center & Hangzhou Seventh People's Hospital School of Brain Science and Brain Medicine Zhejiang University School of Medicine Hangzhou China. 11618033@***. Fujian Provincial Institutes of Brain Disorders and Brain Sciences First Affiliated Hospital Fujian Medical University Fuzhou China. 11618033@***. Affiliated Mental Health Center & Hangzhou Seventh People's Hospital School of Brain Science and Brain Medicine Zhejiang University School of Medicine Hangzhou China. Nanhu Brain-Computer Interface Institute Hangzhou China. Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-Machine Integration State Key Laboratory of Brain-Machine Intelligence Zhejiang University Hangzhou China. Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province School of Medicine Hangzhou City University Hangzhou China. Institute for Brain Research and Rehabilitation Key Laboratory of Brain Cognition and Education Sciences of Ministry of Education South China Normal University Guangzhou China. Affiliated Mental Health Center & Hangzhou Seventh People's Hospital School of Brain Science and Brain Medicine Zhejiang University School of Medicine Hangzhou China. junyu@***. Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-Machine Integration State Key Laboratory of Brain-Machine Intelligence Zhejiang University Hangzhou China. junyu@***. NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang University Hangzhou China. junyu@***. Affiliated Mental Health Center & Hangzhou Seventh People's Hospital School of Brain Science and Brain Medicine Zhejiang University School of Medicine Hangzhou China. luojianhong@***. Nanhu Brain-Computer Interface Institute Hangzhou China. luojianhong@***. Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-Machine Integration State Key Laboratory of Brain-Machine Intelligence Zhejiang University Hangzhou China. luojianhong@zju.edu.cn. NHC

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Structural and Statistical Texture Knowledge Distillation and Learning for Segmentation

arXiv

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arXiv 2025年

作者： Ji, Deyi Zhao, Feng Lu, Hongtao Wu, Feng Ye, Jieping MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China China Department of Computer Science and Engineering MOE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Alibaba Group China

Low-level texture feature/knowledge is also of vital importance for characterizing the local structural pattern and global statistical properties, such as boundary, smoothness, regularity, and color contrast, which may not be well addressed by high-level deep features. In this paper, we aim to re-emphasize the low-level texture information in deep networks for semantic segmentation and related knowledge distillation tasks. To this end, we take full advantage of both structural and statistical texture knowledge and propose a novel Structural and Statistical Texture Knowledge Distillation (SSTKD) framework for semantic segmentation. Specifically, Contourlet Decomposition Module (CDM) is introduced to decompose the low-level features with iterative Laplacian pyramid and directional filter bank to mine the structural texture knowledge, and Texture Intensity Equalization Module (TIEM) is designed to extract and enhance the statistical texture knowledge with the corresponding Quantization Congruence Loss (QDL). Moreover, we propose the Co-occurrence TIEM (C-TIEM) and generic segmentation frameworks, namely STLNet++ and U-SSNet, to enable existing segmentation networks to harvest the structural and statistical texture information more effectively. Extensive experimental results on three segmentation tasks demonstrate the effectiveness of the proposed methods and their state-of-the-art performance on seven popular benchmark datasets, respectively. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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An innovative integrated processing framework for enhanced decoding of Mental-Imagery MEG signals

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Biomedical Signal Processing and Control 2025年 109卷

作者： Wang, Fan Wang, Peng Ding, Peng Gong, Anmin Chen, Yanxiao Xue, Yuhang Fu, Yunfa Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Brain Cognition and Brain-computer Intelligence Integration Group Kunming University of Science and Technology Kunming650500 China School of Information Engineering Chinese People's Armed Police Force Engineering University Xi'an710000 China

Magnetoencephalography (MEG) has emerged as a pivotal tool for developing brain-computer interfaces (BCI) based on mental imagery tasks, owing to its superior spatiotemporal resolution. However, existing decoding strategies for MEG data in mental imagery tasks face two major challenges: (1) inadequate exploitation of the high-dimensional characteristics of MEG signals, and (2) a lack of tailored algorithms for efficient feature extraction and classification. To address these issues, we propose a novel integrated processing framework for enhancing mental imagery-based BCI performance. First, an innovative channel selection method combining correlation coefficient and variance entropy product (CC-VEP) is introduced to identify task-relevant channels while suppressing noise and redundancy. Next, discriminative features are extracted using the DivCSP algorithm with intra-class regularization terms. Subsequently, probabilistic outputs from base classifiers (K-Nearest Neighbors, Random Forests, Support Vector Machines, and Extreme Learning Machines) are fused through a modified multi-criteria decision-based fusion (MCDM-MCF) strategy to generate final class labels. Experimental results on a public MEG dataset demonstrate that the proposed framework achieves a 12.25% improvement in classification accuracy compared to the average accuracy of all base classifiers in traditional methods, and a 7.97% improvement over other existing methods. Moreover, it effectively balances specificity and sensitivity, enhances robustness, and offers a new approach for efficient decoding in mental imagery-based BCI systems. © 2025 Elsevier Ltd

关键词： Support vector machines

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Corrections to “brain-Inspired Computing: A Systematic Survey and Future Trends”

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Proceedings of the IEEE 2025年第12期112卷 1850-1850页

作者： Guoqi Li Lei Deng Huajin Tang Gang Pan Yonghong Tian Kaushik Roy Wolfgang Maass Institute of Automation Chinese Academy of Sciences Beijing China Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China Department of Precision Instrument Tsinghua University Beijing China State Key Laboratory of Brain-Machine Intelligence College of Computer Science and Technology MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Hangzhou China Department of Computer Science Peking University Beijing China School of Electrical and Computer Engineering Purdue University West Lafayette IN USA School of Computer Science Graz University of Technology Graz Austria

Presents corrections to the paper, (Corrections to “brain-Inspired Computing: A Systematic Survey and Future Trends”).

关键词： Artificial intelligence Computational modeling brain modeling Biological system modeling Surveys computer architecture Benchmark testing Bio-inspired computing Neuromorphic engineering Sensors Neural networks Software tools

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CEBRA Method: Decoding brain Activity for Advanced brain-computer Interface Technology 46

CEBRA Method: Decoding Brain Activity for Advanced Brain-Com...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Yang, Jingcheng Kulwa, Frank Liu, Xuanwei Lu, Yiqing Fu, Yufa Li, Guanglin Huai, Yaping Zhang, Xin Li, Yongcheng Shenzhen Dapeng New District Nanao People's Hospital Guangdong Shenzhen518000 China Shenzhen Longhua District Central Hospital Department of Rehabilitation Medicine Guangdong Shenzhen518000 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Brain Cognition and Brain-Computer Intelligence Integration Group Kunming650500 China Cas Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China Shandong Zhongke Advanced Technology Co. Ltd. Jinan250014 China

ISBN: (纸本)9798350371499

关键词： Patient rehabilitation

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CBRAMOD: A CRISS-CROSS brain FOUNDATION MODEL FOR EEG DECODING

arXiv

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arXiv 2024年

作者： Wang, Jiquan Zhao, Sha Luo, Zhiling Zhou, Yangxuan Jiang, Haiteng Li, Shijian Li, Tao Pan, Gang State Key Laboratory of Brain-machine Intelligence Zhejiang University China College of Computer Science and Technology Zhejiang University China Alibaba Group China Department of Neurobiology Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital Zhejiang University School of Medicine China MOE Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang University China

Electroencephalography (EEG) is a non-invasive technique to measure and record brain electrical activity, widely used in various BCI and healthcare applications. Early EEG decoding methods rely on supervised learning, limited by specific tasks and datasets, hindering model performance and generalizability. With the success of large language models, there is a growing body of studies focusing on EEG foundation models. However, these studies still leave challenges: Firstly, most of existing EEG foundation models employ full EEG modeling strategy. It models the spatial and temporal dependencies between all EEG patches together, but ignores that the spatial and temporal dependencies are heterogeneous due to the unique structural characteristics of EEG signals. Secondly, existing EEG foundation models have limited generalizability on a wide range of downstream BCI tasks due to varying formats of EEG data, making it challenging to adapt to. To address these challenges, we propose a novel foundation model called CBraMod. Specifically, we devise a criss-cross transformer as the backbone to thoroughly leverage the structural characteristics of EEG signals, which can model spatial and temporal dependencies separately through two parallel attention mechanisms. And we utilize an asymmetric conditional positional encoding scheme which can encode positional information of EEG patches and be easily adapted to the EEG with diverse formats. CBraMod is pre-trained on a very large corpus of EEG through patch-based masked EEG reconstruction. We evaluate CBraMod on up to 10 downstream BCI tasks (12 public datasets). CBraMod achieves the state-of-the-art performance across the wide range of tasks, proving its strong capability and generalizability. The source code is publicly available at https://***/wjq-learning/CBraMod. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Inhibition of the inferior parietal lobe triggers state-dependent network adaptations

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Heliyon 2024年第21期10卷 e39735页

作者： Williams, Kathleen A. Numssen, Ole Guerra, Juan David Kopal, Jakub Bzdok, Danilo Hartwigsen, Gesa Lise Meitner Research Group Cognition and Plasticity Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Methods and Development Group “Brain Networks” Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany The Neuro - Montreal Neurological Institute (MNI) McConnell Brain Imaging Centre Department of Biomedical Engineering Faculty of Medicine School of Computer Science McGill University Montreal Canada Mila - Quebec Artificial Intelligence Institute Montreal QC Canada Wilhelm Wundt Institute for Psychology Leipzig University Germany

The human brain comprises large-scale networks that flexibly interact to support diverse cognitive functions and adapt to variability in daily life. The inferior parietal lobe (IPL) is a hub of multiple brain networks that sustain various cognitive domains. It remains unclear how networks respond to acute regional perturbations to maintain normal function. To provoke network-level adaptive responses to local inhibition, we combined offline transcranial magnetic stimulation (TMS) over left or right IPL with neuroimaging during attention, semantic and social cognition tasks, and rest. Across tasks, TMS specifically affected task-active network activity with inhibition and facilitation. Network interaction responses differed between rest and tasks. After TMS over both IPL regions, large-scale network interactions were exclusively facilitated at rest, but mainly inhibited during tasks. Overall, responses to TMS primarily occurred in and between domain-general default mode and frontoparietal subnetworks. These findings elucidate short-term adaptive plasticity in response to network node inhibition. © 2024 The Authors

关键词： cognition cTBS fMRI Functional connectivity Resting state TMS

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CEBRA Method: Decoding brain Activity for Advanced brain-computer Interface Technology

CEBRA Method: Decoding Brain Activity for Advanced Brain-Com...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Jingcheng Yang Frank Kulwa Xuanwei Liu Yiqing Lu Yufa Fu Guanglin Li Yaping Huai Xin Zhang Yongcheng Li CAS Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences (CAS) Shenzhen China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Shenzhen Dapeng New District Nanao People’s Hospital Shenzhen China Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen China Brain Cognition and Brain-Computer Intelligence Integration Group Kunming University of Science and Technology Kunming China Shandong Zhongke Advanced Technology Co. Ltd. Jinan China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The emerging neurorehabilitation technology, brain-computer Interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feature extraction is crutial to build a better decoder. In this study the CEBRA method were employed to extract features first based on electroencephalogram(EEG) data during Motor Execution(ME) and Motor lmagery (Ml) tasks for 20 participants (including 10 stroke patients). The results revealed that, in MI tasks, CEBRA-RF (Random Forests) achieved an average classification accuracy of 91.33%, with an average F1-score of 91.19%, and CEBRA-SVM (Support Vector Machine) achieved an average classification accuracy of 91.32%, with an average F1-score of 90.83%. Compared to other conventional feature extraction methods, CEBRA shows significantly higher accuracy (t-test, p<0.01). However, in ME tasks, CEBRA-RF achieved an average classification accuracy of 75.67%, with an average F1-score of 75.39%, and CEBRA-SVM achieved an average classification accuracy of 76.13%, with an average F1-score of 75.80%. Nevertheless, no significant differences were observed compared to other feature extraction methods. These findings demonstrate the potential of CEBRA in decoding patients’ brain activity. The results of this study hold promise in addressing the current challenges of low decoding accuracy in BCI systems, offering a new approach for designing BCI-assisted rehabilitation systems for stroke patients.

关键词： Support vector machines brain Accuracy Stroke (medical condition) Feature extraction Motors brain-computer interfaces Neurorehabilitation Decoding Paralysis

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Music, Computing, and Health: A Roadmap for the Current and Future Roles of Music Technology for Health Care and Well-Being

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Music and Science 2021年 4卷

作者： Agres, Kat R. Schaefer, Rebecca S. Volk, Anja van Hooren, Susan Holzapfel, Andre Dalla Bella, Simone Müller, Meinard de Witte, Martina Herremans, Dorien Ramirez Melendez, Rafael Neerincx, Mark Ruiz, Sebastian Meredith, David Dimitriadis, Theo Magee, Wendy L. Yong Siew Toh Conservatory of Music National University of Singapore Singapore Social and Cognitive Computing Department Institute of High Performance Computing A*STAR Singapore Institute for Psychology Health Medical Neuropsychology Unit Leiden University Leiden Netherlands Leiden Institute for Brain and Cognition Leiden University Leiden Netherlands Academy of Creative and Performing Arts Leiden University Leiden Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Faculty of Health care Department of Arts Therapies Zuyd University of Applied Sciences Heerlen Netherlands KenVaK Research Centre for the Arts Therapies and Psychomotricity Heerlen Netherlands Faculty of Psychology Open University of The Netherlands Heerlen Netherlands Division of Media Technology and Interaction Design KTH Royal Institute of Technology Stockholm Sweden International Laboratory for Brain Music and Sound Research (BRAMS) Outremont QC Canada Department of Psychology University of Montreal Montreal QC Canada Centre for Research on Brain Language and Music (CRBLM) Montreal QC Canada University of Economics and Human Sciences in Warsaw Warsaw Poland International Audio Laboratories Erlangen Friedrich-Alexander Universität Erlangen-Nürnberg Erlangen Germany HAN University of Applied Sciences Department of Arts Therapies and Psychological Studies Nijmegen Netherlands University of Amsterdam Research Institute of Child Development and Education Amsterdam Netherlands Treatment Centre for People with Mild Intellectual Disabilities and Psychiatric and Behavioral Disorders Gennep Netherlands Information Systems Technology and Design Singapore University of Technology and Design Singapore Music and Machine Learning Lab Music Technology Group Universitat Pompeu Fabra Barcelona Spain Faculty of EEMCS Interactive Intelligence Group Delft University of Technology Delft Netherlands Centre for Digital Music School of Electronic Engineerin

The fields of music, health, and technology have seen significant interactions in recent years in developing music technology for health care and well-being. In an effort to strengthen the collaboration between the involved disciplines, the workshop “Music, Computing, and Health” was held to discuss best practices and state-of-the-art at the intersection of these areas with researchers from music psychology and neuroscience, music therapy, music information retrieval, music technology, medical technology (medtech), and robotics. Following the discussions at the workshop, this article provides an overview of the different methods of the involved disciplines and their potential contributions to developing music technology for health and well-being. Furthermore, the article summarizes the state of the art in music technology that can be applied in various health scenarios and provides a perspective on challenges and opportunities for developing music technology that (1) supports person-centered care and evidence-based treatments, and (2) contributes to developing standardized, large-scale research on music-based interventions in an interdisciplinary manner. The article provides a resource for those seeking to engage in interdisciplinary research using music-based computational methods to develop technology for health care, and aims to inspire future research directions by evaluating the state of the art with respect to the challenges facing each field. © The Author(s) 2021.

关键词： health care interdisciplinarity MedTech music information retrieval (MIR) music neuroscience Music psychology music technology music therapy well-being

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