Research On Dynamic Time-varying Brain Networks In Autism Spectrum Disorder Based On Coactivation Patterns

Autism Spectrum Disorder(ASD)is a neurodevelopmental disorder characterized by social interaction disorders,speech communication disorders and repetitive stereotyped behaviors.Extensive studies explored the brain structure and function of ASD,but the neurophysiological mechanism is still being explored.Previous studies have indicated that the dynamic function of ASD brain is abnormal,but most of these studies are based on traditional sliding-window analysis,which require a time window with a fixed window width.Whether there are abnormalities in instantaneous functional activities in ASD and the relationship between such abnormalities and core symptoms of ASD remains unclear.Based on multi-center,large-sample resting-state functional magnetic resonance brain imaging data,this study conducted the following research on the abnormal brain dynamic function of ASD by using coactivation pattern(CAP)analysis:In order to investigate the abnormality of instantaneous functional patterns in the whole brain of ASD,the first research divided the healthy controls into two groups matched for age,gender,handedness,full intelligence quotient and center.Then this research used CAP analysis to obtain the first healthy control group’s CAPs and calculated the state characteristics of the CAPs.Next,based on spatial similarity,this research mapped the first healthy control group’s CAPs to the ASD group and the second healthy control group and compared the inter-group differences between the state characteristics of the CAPs of the ASD group and the second healthy control group.This research classified ASD and healthy controls based on the state time percentage.Our results showed that the CAPs of healthy controls could be divided into two pairs with completely opposite spatial patterns,and the state time percentage of each pair was the same.There were significant inter-group differences in the state time percentage between the ASD group and the second healthy control group,and the state time percentage could effectively classify ASD and healthy controls.In addition,all four CAPs exhibited activation or inhibition of the default mod network.This study revealed the abnormal whole-brain dynamic function of ASD from a single time point perspective and emphasized the importance of the default mode network in the abnormal dynamic functional activity of ASD,suggesting that potential neural markers of ASD may be found from the perspective of brain dynamic function.The second research focused on the abnormal instantaneous functional mode of the default network in ASD,took the medial prefrontal cortex,which is a key region of the default mode network,as the region of interest,and calculated the CAP of the medial prefrontal cortex.Next,this research compared the intergroup differences of CAP between ASD group and healthy control group,and proposed network dissociation index(NDI)to evaluate the degree of functional dissociation within the default mode network and functional integration between default mode network and other networks.Then this research used the network dissociation index as a feature to predict ASD clinical symptoms.The results showed that the functional integration within the default mode network and the functional dissociation between the default mode network and the task-positive network were significantly reduced in ASD,and this abnormality was closely related to clinical symptoms.This study revealed the abnormal functional integration and dissociation of the default mode network in ASD,and provide evidence for the abnormal dynamic functional patterns of the default mode network in ASD at a single time point scale.From the perspective of instantaneous functional activity,this thesis revealed the abnormal brain dynamic functional pattern of ASD and its relationship with clinical symptoms from the whole brain and the default network.This thesis provided important reference index for finding potential neural markers of ASD and deepened our understanding of the neurophysiological mechanisms of ASD.