Hidden state dynamics reveals metastability 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 time-varying 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 of 422 subjects (aged 19-80 years), and each hidden state was characterized by distinct activation patterns of 17 brain networks. Results showed that (a) the elder were probable to spend more time on an 'inactive' state with only mean-level activation of all networks. (b) The elder tended to spend less time on states showing antagonistic activity between various pairs of networks including default mode network, control and salience/ventral attention networks. (c) The transition probabilities between certain pairs of states that default mode or control networks showed opposite activation level exhibited significant increases/decreases with age. (d) Lifespan trajectories of subjects' fractional occupancy and transition probability exhibited U-shaped or linear trends. These results demonstrated that the particular network state with lower transition probability and higher fractional occupancy in old cohort could be detected using HMM, and proved that HMM is a huge potential tool to reveal the metastability, the ability of the system to transition between different cognitive states, across the adult lifespan. Key words: lifespan; hidden markov model; resting state fMRI