Network modularity is a key feature for efficient information processing in the human brain. This information processing is however dynamic and networks can reconfigure at very short time period, few hundreds of millisecond. This requires neuroimaging techniques with sufficient time resolution. Here we use the dense electroencephalography, EEG, source connectivity methods to identify cortical networks with excellent time resolution, in the order of millisecond. We identify functional networks during picture naming task. Two categories of visual stimuli were presented, meaningful (tools, animals) and meaningless (scrambled) objects.
In this paper, we report the reconfiguration of brain network modularity for meaningful and meaningless objects. Results showed mainly that networks of meaningful objects were more modular than those of meaningless objects. Networks of the ventral visual pathway were activated in both cases. However a strong occipitotemporal functional connectivity appeared for meaningful object but not for meaningless object. We believe that this approach will give new insights into the dynamic behavior of the brain networks during fast information processing.
Dissociable neural representations of adversarially perturbed images in convolutional neural networks and the human brain
National Digital Switching System Engineering and Technological Research Center（国家数字交换系统工程技术研究中心）；People's Hospital of Henan Province（河南省人民医院）；Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属精神卫生中心）；Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University（上海交通大学心理与行为科学研究院）
BrainSymphony: A parameter-efficient multimodal foundation model for brain dynamics with limited data
Turner Institute for Brain and Mental Health（大脑与心理健康Turner研究所）；School of Psychological Sciences, Monash University（墨尔本大学心理学科学学院）；Cortical Labs（皮层实验室）；CIFAR Azrieli Global Scholars Program（CIFAR阿兹里埃利全球学者计划）
Deep reinforcement learning guided graph neural networks for brain network analysis
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Macquarie University（麦考瑞大学）；Beihang University（北京航空航天大学）；National Acoustic Laboratories（国家声学实验室）；CAEIT（中国电子科学研究院）
Brain-Semantoks: Learning Semantic Tokens of Brain Dynamics with a Self-Distilled Foundation Model
Hertie Institute for AI in Brain Health, University of Tübingen（图宾根大学脑健康人工智能研究所）；Tübingen AI Center, University of Tübingen（图宾根大学图宾根人工智能中心）；Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy（柏林夏里特医学院心理治疗系）
Identifying Influential nodes in Brain Networks via Self-Supervised Graph-Transformer
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；School of Life Science and Technology, University of Electronic Science and Technology of China（电子科技大学生命科学与技术学院）；School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）
Brain2Text Decoding Model Reveals the Neural Mechanisms of Visual Semantic Processing
Ministry of Education Center for Studies of Psychological Application（教育部心理应用研究中心）；Center for Studies of Psychological Application（心理应用研究中心）；Key Laboratory of Brain, Cognition and Education Sciences（脑认知与教育科学重点实验室）；School of Psychology（心理学学院）；Guangdong Key Laboratory of Mental Health and Cognitive Science（广东省心理健康与认知科学重点实验室）
