期刊
CEREBRAL CORTEX
卷 32, 期 2, 页码 439-453出版社
OXFORD UNIV PRESS INC
DOI: 10.1093/cercor/bhab220
关键词
adult lifespan; hidden Markov model; resting state fMRI
资金
- National Natural Science Foundation of China [81201083]
- MOE (Ministry of Education in China) Project of Humanities and Social Sciences [16YJCZH057]
- Scientific Research Project of Department of Education of Liaoning Province [LQ2019031]
The study utilized a hidden Markov model to model the dynamic activity of brain networks, finding that older adults exhibit less antagonistic instances between the DMN and attention systems, as well as a prolongation of inactive periods for all networks.
The brain networks undergo functional reorganization across the whole lifespan, but the dynamic patterns behind the reorganization remain largely unclear. This study models the dynamics of spontaneous activity of large-scale networks using hidden Markov model (HMM), and investigates how it changes with age on two adult lifespan datasets of 176/157 subjects (aged 20-80 years). Results for both datasets showed that 1) older adults tended to spend less time on a state where default mode network (DMN) and attentional networks show antagonistic activity, 2) older adults spent more time on a baseline state with moderate-level activation of all networks, accompanied with lower transition probabilities from this state to the others and higher transition probabilities from the others to this state, and 3) HMM exhibited higher sensitivity in uncovering the age effects compared with temporal clustering method. Our results suggest that the aging brain is characterized by the shortening of the antagonistic instances between DMN and attention systems, as well as the prolongation of the inactive period of all networks, which might reflect the shift of the dynamical working point near criticality in older adults.
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