Speaker: Prof. Changsong Zhou, Centre for Nonlinear Studies, Life Science Imaging Centre, Hong Kong Baptist University

Time: 10:00-11:30 a.m., Jan 8, 2025, GMT+8

Venue: Rm B101, Lui Che Woo Building, PKU

Abstract: 

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 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. Examples of application of the framework to stress and ADHD are briefly presented.

Source: McGovern Institute for Brain Research at PKU