Estimating interactions of functional brain connectivity by Hidden Markov models

Abstract

The brain activity reflected by functional magnetic resonance imaging (fMRI) is temporally organized as a combination of sensory inputs from environment and its own spontaneous activity. However, temporal patterns of brain activity in a large number of subjects remain unclear. We propose a regularized hidden Markov model (HMM) to estimate dynamic functional connectivity among distributed brain regions and discover repeating connectivity patterns from resting-state functional connectivity across a group of subjects. We found that functional brain connectivity are hierarchically organized and exhibit three repeated patterns across subjects with attention deficit hyperactivity disorder (ADHD). We have examined the temporal characteristics of functional connectivity by its occupancy. And we validated our method by comparing the classification performance with state-of-the-art methods using the same dataset. Experimental results show that our method can improve the classification performance compared to other functional connectivity modelling methods.