Autism spectrum disorder (ASD) is a general neurodevelopmental disorder associated with altered brain connectivity. However, most connectivity analyses in ASD focus on static functional connectivity, largely neglecting brain activity dynamics that have been reported to provide deeper insight into the underlying mechanisms of brain functions. Therefore, we anticipate that the use of dynamic functional connectivity (DFC) with interaction of clustering measures could help characterize ASD severity and reveal more information. In this study, we applied the sliding-window and k-means clustering methods to perform DFC and clustering analyses in ASD and typically developing (TD) groups. Data from 62 ASD and 63 TD children were acquired from the open-access data set Autism Brain Imaging Data Exchange. Our findings revealed higher DFC variability between the posterior cingulate gyrus (PCC) and middle temporal pole (TPOmid) in subjects with ASD. The connection between the PCC and pars opercularis of inferior frontal gyrus (IFGoper) also presented greater variability in ASD, with the increase depending on ASD symptom severity. Furthermore, clustering analysis showed higher averaged dwell time and probability of transition for globally hyper-connected state in the ASD group, which could be related to connection variability between the PCC and IFGoper. Our results demonstrate that both the PCC and IFGoper play crucial roles in characterizing symptom severity and state configuration in ASD, and brain connectivity dynamics may serve as potential indicators of ASD in future studies. Autism Res 2020, 13: 230-243.
Autism spectrum disorder (ASD) is a general neurodevelopmental disorder associated with altered brain connectivity. However, most connectivity analyses in ASD focus on static functional connectivity, largely neglecting brain activity dynamics that have been reported to provide deeper insight into the underlying mechanisms of brain functions. Therefore, we anticipate that the use of dynamic functional connectivity (DFC) with interaction of clustering measures could help characterize ASD severity and reveal more information. In this study, we applied the sliding-window and k-means clustering methods to perform DFC and clustering analyses in ASD and typically developing (TD) groups. Data from 62 ASD and 63 TD children were acquired from the open-access data set Autism Brain Imaging Data Exchange. Our findings revealed higher DFC variability between the posterior cingulate gyrus (PCC) and middle temporal pole (TPOmid) in subjects with ASD. The connection between the PCC and pars opercularis of inferior frontal gyrus (IFGoper) also presented greater variability in ASD, with the increase depending on ASD symptom severity. Furthermore, clustering analysis showed higher averaged dwell time and probability of transition for globally hyper-connected state in the ASD group, which could be related to connection variability between the PCC and IFGoper. Our results demonstrate that both the PCC and IFGoper play crucial roles in characterizing symptom severity and state configuration in ASD, and brain connectivity dynamics may serve as potential indicators of ASD in future studies. Autism Res 2020, 13: 230-243.