Hierarchal Modular Organization: Balance of Segregation and Integration in Brain and Cognition
Poster No:
1168
Submission Type:
Abstract Submission
Authors:
Rong Wang1, Changsong Zhou2
Institutions:
1Xi'an Jiaotong University, Xi'an, Shanxi, 2Hong Kong Baptist University, Hong Kong, Hong Kong
First Author:
Co-Author:
Introduction:
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. The modern network neuroscience (NNT) theory of human cognition propsoed that the brain's flexible switching between segregated and integrated processing 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.
Methods:
We developed hierarchical mode analysis method, called nested spectral partition (NSP) (Want,…,Zhou, PRL 2019, Wang,…,Zhou, PNAS2021), to quantify functional segregation, integration, and their balance by decomposing the functional connectivity (FC) network and sorting the eigenvalues in descending order [Fig. 1]. Specifically, the first eigenvector, where all regions share the same sign, represents whole-brain integration. The second eigenvector divides the brain into two distinct modules based on positive and negative signs, reflecting an initial level of segregation. Subsequent eigenvectors further partition each of these modules into submodules, progressively increasing the granularity of segregation until each module comprises only a single region. This methodological approach was applied to various datasets, revealing distinct brain states, like: Human Connectome Project (HCP) S1200 release (Van Essen et al., 2013) and the brain under acute stress (Wang, Zeng, Zhou,Yu, PNAS 2022) and disease with ADHD (Wang, …,Zhou, iScience 2022).
·Fig. 1. Hierarchical segregation and integration in functional connectivity networks and measure of balance.
Results:
Based on HCP large sample of healthy young adults, we discovered that resting brain networks typically maintain a near-balanced state between segregation and integration with dynamic transition between them. Different cognitive functions showed distinct relationships with network organization: network integration correlated with general cognitive ability, while network segregation was associated with crystallized intelligence and processing speed. Notably, optimal memory performance was observed in individuals whose brain networks maintained balance between segregation and integration states. Under stress conditions, the brain exhibited increased integration, reducing neural flexibility by decreasing variance between dissociative and integrated states-a change correlated with cortisol levels and cognitive control mechanisms. In our examination of ADHD patients, we observed a quadratic (inverted U-shaped) relationship between brain network organization and age, with limbic system integration predicting hyperactivity symptoms and salience attention network characteristics indicating inattention symptoms in both adult and pediatric populations.
Conclusions:
Our analysis revealed pronounced hierarchical modular organization in brain structural and functional networks, supporting balanced segregation and integration across multiple levels, enabling flexibility state transition to support diverse function. Individual differences in segregation, integration and the balance of resting state are predictive of diverse cognitive abilities. Conditions like stress and diseases can modify segregation, integration and the balance. These findings highlight the critical role of balanced brain network organization in cognitive function and mental health, offering new perspectives and methods for understanding and intervening in neuropsychological disorders.
Higher Cognitive Functions:
Higher Cognitive Functions Other 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Keywords:
Cognition
Computational Neuroscience
Data analysis
Modeling
1|2Indicates the priority used for review
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[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).
[3] R. Wang, S.S. Zeng, C.S. Zhou and R.J. Yu, Acute stress promotes brain network integration and reduces state transition variability, Proc Natl Acad Sci USA, 119 (24), e2204144119 (2022).
[4] R. Wang*, Y.C. Fan, Y. Wu, Y.-F. Zang, C.S. Zhou, Lifespan associations of resting-state brain functional networks with ADHD symptoms, iScience, 25 (7) (2022).