Hidden Markov Modeling Reveals Prolonged “Baseline” State and Shortened Antagonistic State across the Adult Lifespan
Keyu Chen, Chaofan Li, Wei Sun, Yun-Yun Tao, Ruidi Wang, Hou Wen, Dongqiang Liu
Abstract
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.