Modeling brain functional dynamics via hidden Markov models

Functional connectivities constructed via resting state fMRI (R-fMRI) data have been widely used to study the brain´s functional activities and to characterize the brain´s states. However, the temporal dynamic transition patterns of the brain´s functional states have been rarely investigated before. In this paper, we present a novel algorithmic framework to cluster and label the brain´s functional states, and learn their hidden Markov models (HMMs). Here, the brain´s functional state is compactly represented by a large-scale functional connectivity matrix, called functional connectome state (FCS), and the temporal FCS sequences are derived via an overlapping sliding time window approach. The best-matched HMM learned for ADHD patients revealed a meaningful phenomenon of psychiatric conditions, that is, the tendency to enter into, and inability to disengage from, a negative mood state. Experimental results demonstrated 87% of ADHD patients and 89% of normal controls are successfully classified via multiple HMMs by using majority voting.