Reconfiguration of Brain Network Dynamics in Autism Spectrum Disorder Based on Hidden Markov Model - Lin, Pingting; Zang, Shiyi; Bai, Yi; Wang, Haixian (Frontiers in Human Neuroscience, 2022)
Autism spectrum disorder (ASD) is a general neurodevelopmental disorder associated with altered brain connectivity. However, most connectivity analysis in ASD focuses on static functional connectivity, largely neglecting brain activity dynamics that have been reported to provide deeper insight into the underlying mechanism of brain functions. Therefore, we developed a Hidden Markov Model (HMM) analysis for resting-state functional magnetic resonance imaging (fMRI) from a large multicenter dataset of 507 male subjects. Specifically, the 507 subjects included 209 subjects with ASD and 298 gender-matched health controls from the Autism Brain Imaging Data Exchange (ABIDE). We identified 19 brain states characterized by the engagement of distinct functional networks that recur over time, then assessed the dynamical configuration of the whole-brain networks and analyzed the community structure of transitions across the brain states. Based on the 19 HMM states, we found that the global temporal statistics of the specific HMM states (including fractional occupancies and lifetimes) were significantly altered in ASD compared to healthy controls. These specific HMM states were characterized by the activation pattern of default mode network (DMN), sensory processing networks(including visual network, auditory network, and sensory and motor network(SMN)). Meanwhile, we also find that the specific modules of transitions between were closely related to ASD. Our findings indicate the temporal reconfiguration of brain networks in ASD and provide novel insights into the dynamical circuit configuration of the whole-brain networks for ASD.