Rest-state Never Rest: Transient Brain Dynamics, Cognition and Psychiatric Problems in Children  
报告时间：2022年9月22日 （周四） 9:30-11:30
报告形式：腾讯会议（ID：659-357-826）
报告人：傅泽宁 ？博士
主持人：涂毅恒 ？研究员？

摘要：Children’s brains dynamically adapt to the stimuli from the internal state and the external environment, allowing for changes in the cognitive and mental behavior of individuals. In this work, we performed a large-scale analysis of dynamic functional connectivity (DFC) in children aged 9~11 years, investigating how brain dynamics relate to cognitive performance and mental health during an early age. An adaptive independent component analysis framework was applied to the Adolescent Brain Cognitive Development (ABCD) data containing 10,988 children. We combined a sliding-window approach with k-means clustering to identify five reoccurring brain states with distinct DFC patterns. Interestingly, the occurrence of a strongly connected state was negatively correlated with cognitive performance and positively correlated with dimensional psychopathology in children. Meanwhile, the opposite relationships were observed for a sparsely connected state. The composite scores, the attention score, and the Attention-Deficit/Hyperactivity Disorder score were the most significantly correlated with the DFC states. The cognitive and psychiatric relevance of DFC states were highly reproducible across scans and between longitudinal sessions. Finally, the mediation analysis showed that the attention problems mediate the effect of DFC states on cognitive performance. This investigation unveils the neurological underpinnings of DFC by highlighting their robust associations with behavioral development in childhood. Tracking the patterns of maturation in DFC states may capture delays in development and guide people to provide early intervention to buffer adverse influences in children’s development.
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报告人简介：傅泽宁博士现任美国The Center for Translational Research in Neuroimaging and Data Science研究所助理教授。他于香港大学获得博士学位，并在美国The Mind Research Network开展博士后研究。傅泽宁博士主要从事神经影像分析、静态和动态脑功能连接分析，多模态神经影像数据融合分析，结合模式识别以及大规模数据挖掘研究多种脑神经疾病的复杂病理机制、治疗背后的神经生物学基础。提出了从多维度捕捉脑动态性的方法，并创建了脑图像多种动态特征相关联融合的分析模式，实现对精神分裂症，自闭症，抑郁症等重大脑疾病的个体预测以及临床表现的预估。相关研究发表在了 ？Neurology、 Neuroimage、Neurobiology of Stress、Communications biology，Biological Psychiatry: Cognitive Neuroscience and Neuroimaging等领域内顶级期刊，被引2000次。

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