文献类型：期刊文献

英文题名：Reorganization of Global Phase Pattern in Complex Brain Network During Aging Revealed by Leading Eigenvector Dynamics Analysis

作者：Tang, Jianwei[1];Wang, Yihong[2];Xu, Xuying[2];Pan, Xiaochuan[2];Wang, Rubin[1]

机构：[1]East China Univ Sci & Technol, Sch Math, Inst Cognit Neurodynam, 130 Meilong Rd, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Inst Cognit Neurodynam, Ctr Intelligent Comp, Sch Math, 130 Meilong Rd, Shanghai 200237, Peoples R China

年份：2026

外文期刊名：INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS

收录：;EI(收录号：20261120279827);WOS:【SCI-EXPANDED(收录号:WOS:001714020400001)】；

基金：This study was supported by the National Natural Science Foundation of China (Grant Nos. 12172132, 12272136 and 12472054) and the Science and Technology Commission of Shanghai Municipality (Grant No. 24JS2810400).

语种：英文

外文关键词：Aging; LEiDA; complex neurodynamics; dynamic functional connectivity; phase coherence

摘要：The brain is a highly complex and nonlinear system, which contains rich biological information through seemingly chaotic dynamic interactions of neural activity across different regions. Resting-state functional Magnetic Resonance Imaging (rs-fMRI) can reveal functional changes in brain networks under different conditions by reflecting brain activity from Blood Oxygen Level-Dependent (BOLD) signal. Meanwhile, age-related cognitive decline has become a significant concern with the aging of the global population. Research has shown that aging disrupts the structural integrity of brain tissue and weakens functional synchronization among neural circuits. Therefore, understanding the effects of aging from the perspective of synchronization and phase coordination in brain networks is of great significance, which are evident dynamical indicators despite the complexity of the brain. However, how aging specifically affects the dynamic changes in phase synchronization patterns within brain networks remains insufficiently studied. In this study, we apply the Hilbert transformation and the Leading Eigenvector Dynamics Analysis (LEiDA) using rs-fMRI data from 32 younger and 28 elder participants. By identifying the recurrent Phase Coherence (PC) states of BOLD signals, we compare intergroup differences in the dynamic features of brain activity and examine the potential associations between these dynamic features and cognitive function. Our results reveal that the occurrence probability and lifetime of PC states in the visual network were reduced in the elder group, whereas the subcortical network exhibited enhanced synchronous activity. Overall, these findings demonstrate that aging leads to a reorganization of phase coupling and synchronization patterns within brain functional networks. This work provides a new perspective for dynamic brain network analysis and contributes to a deeper understanding of the altered spatiotemporal dynamics of functional complex brain network during the aging process.