摘要

The brain is a highly nonlinear complex network system supporting diverse cognitive abilities. The locally segregated and globally integrated processing are the two basic foundations to cognition. However, how does the brain organizes the effective processing of neural information in the local and global scales, so as to support diverse cognitive tasks is not well understood. A physical hypothesis is that the brain system is in a dynamic critical state at rest and can support the balance of separation and integration. The modern network neuroscience (NNT) theory of human cognition propsoed  that the brain’s flexible switching between local information processing (segregation) and global processing (integration) promotes the development of general intelligence, i.e., the segregation-integration balance corresponds to a higher general intelligence. However, there has been no clear evidence on whether the resting brain is in the segregation-integration balance at the whole-brain scale, and the NNT theory also urgently needs to be further verificated. 

    We address the above open interdisciplinary question using an eigenmode-based approach to identify hierarchical modules in structural and functional brain networks. The structural brain network displays hierarichal modular organization inherently supporting multilevel segregation and integration modes. We found that the critical state can best recruit such hierarichal modes to maximize the diversity in the functional connectivity. We further apply the hierarical mode analysis to functional network to quantify the functional segregation, integration and their balance. In a large sample of healthy young adults (n=991) from the Human Connectome Project (HCP), we demonstrate that resting brain networks are on average close to a balanced state. This state allows for a balanced time dwelling at segregated and integrated configurations, and highly flexible switching between them. Meanwhile, we demonstrate that network segregation, integration and their balance in resting brains predict individual differences in diverse cognitive phenotypes. More specifically, stronger integration is associated with better general cognitive ability, stronger segregation fosters crystallized intelligence and processing speed, and individual’s tendency towards balance supports better memory. Our current work further show that weak links in the brain structural connectivity that are largely ignored in brain connectome study are cruicial to maintain the balance of segregation and integration and to induce the induce individual differences. Our findings provide a systems level understanding of the brain’s functioning principles in supporting diverse functional demands and cognitive abilities, and advance modern network neuroscience theories of human cognition, which may shed light on dysfunctional segregation and integration in neurodegenerative diseases and neuropsychiatric disorders. 

References: 

[1] R. Wang, P. Lin, M.X. Liu, Y. Wu, T. Zhou and C.S. Zhou. Hierarchical Connectome Modes and Critical State Jointly Maximize Human Brain Functional Diversity. Phys. Rev. Lett. 123, 038301 (2019). 

[2] R. Wang⁺, M.X. Liu⁺, X. Cheng, Y. Wu, A. Hildebrandt, and C.S. Zhou. Segregation, integration and balance of large-scale resting brain networks configure different cognitive abilities. Proc Natl Acad Sci USA, 118 (23), e2022288118 (2021). 

个人简介 

周昌松, 物理学博士，香港浸会大学物理系教授、系主任，浸会大学非线性研究中心主任,计算及理论研究所副所长。1992年获南开大学物理学士, 1997年获南开大学物理博士，1997-2007年在新加坡、 香港、 德国等地从事访问研究, 是洪堡基金获得者。 2007年加入香港浸会大学物理系， 2011年获浸会大学“杰出青年研究者校长奖”，2021年获“杰出研究表现校长奖”。周昌松博士致力于复杂系统动力学基础研究及其应用，特别是网络的复杂联结结构与体系的动态行为的关系和相互作用。近几年与国际国内系统和认知神经科学家合作，把这些理论进展应用到大脑的复杂联结结构和活动以及认知功能及障碍的分析和建模等方面研究中。周昌松博士对生物神经网络复杂结构、动力学及其高成本效益如何启发类脑智能具有浓厚的兴趣。在国际交叉学术刊物 PNAS，PRL，Physics Reports，National Science Review, J Neuroscience, NeuroImage, Cerebral Cortex, PLoS Computational Biology等发表论文150余篇 (Google Scholar引用16000余次，H-因子为48)。任Scientific Reports 编委，PLoS One，Cognitive Neurodynamics学术编辑，及多种国际期刊常任审稿人。