Dynamic functional connectivity analysis in individuals with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that predominantly occurs in children. Previous brain research in ASD has mainly studied biomarkers based on the functional connectivity characterized by the correlation of static temporal signals. However, brain connectivity is dynamic and varies extensively among brain states. The main aim of the paper is to understand the fundamental group differences between ASD patients and typically developing (TD) subjects using dynamic functional connectivity (dFNC) analysis. In this study, we investigated the dFNC between 53 independent components among 188 ASD and 195 TD subjects. We estimated dFNC using sliding window-based approaches and identified four distinct dynamic states through hard-clustering analysis. Hyper-connectivity within the cognitive control domain, between cognitive control and default mode network, has been identified among ASD subjects. Hyper-connectivity within the default mode network has been found among TD individuals. Further, we estimated the dynamic temporal properties such as fractional time spent, and mean dwell time per state and observed significant differences between ASD and TD groups. ASD subjects are found to have significantly longer dwell time in state 4 when compared to TD individuals. We also found a significantly increased occurrence of state 4 in ASD subjects and states 1 and 3 in TD subjects. While there is broad consensus in the brain network profiles between static functional connectivity (sFNC) and dFNC, the temporal profile of brain state dynamics is additionally available with dFNC analysis and may potentially contribute to disease biomarkers.