Conventional static functional connectivity analysis does not capture transient and atypical changes in functional connectivity between neural networks in the autism spectrum disorder (ASD) patients. In this study, we evaluated rsfMRI data of 108 adult ASD patients by dynamic functional network connectivity (dFNC) analysis using sliding time window correlation and K-means clustering methods. Our results showed that higher dwell time and altered functional connectivity between multiple nodes in FNC state 2 correlated with clinical ASD scores. Therefore, our study demonstrates that aberrant and transient functional connectivity changes between neural networks in ASD patients can be evaluated by dFNC analysis.
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