Hidden state dynamics reveal the prolonged inactive state across the adult lifespan

Adult lifespan is accompanied by functional reorganization of brain networks, but the dynamic patterns behind this reorganization remain largely unclear. This study focuses on modelling the dynamic process of spontaneous activity of large-scale networks using hidden Markov model (HMM), and investigates how it changes with age. The HMM with 12 hidden states was applied to temporally concatenated resting state fMRI data from two dataset of 176 / 170 subjects (aged 20-80 years), and each hidden state was characterized by distinct activation patterns of 17 brain networks. Results showed that (a) For both datasets, the elder tended to spend less time on and had less transitions to states showing antagonistic activity between various pairs of networks including default mode network, cognitive control and salience/ventral attention networks. (b) For both datasets, the elder were probable to spend more time on, have less transitions from and have more transitions to an ‘baseline’ state with only moderate-level activation of all networks, the time spent on this state also showed an U-shaped lifespan trajectory. (c) For both datasets, HMM exhibited higher specificity and reproducibility in uncovering the age effects compared with temporal clustering method, especially for age effects in transition probability. (d) These results demonstrate the age-correlated decrease of the anti-correlation between various networks, and further validate the prediction of Naik et al. (2017) that the existence of a particular network state with lower transition probability and higher fractional occupancy in old cohort, which may reflect the shift of the dynamical working point across the adult lifespan.