Abnormal Developmental Patterns Of Brain Connectomics In Autism Spectrum Disorder Based On Magnetic Resonance Imaging

Autism spectrum disorder(ASD)is a common and complex neurodevelopmental disorder with aberrant brain connectivity,which is characterized by impairments of social reciprocity and communication,as well as restricted and repetitive patterns of behavirors(RRB).The unclear pathogenesis of ASD,its increasing prevalence,and its poor clinical diagnosis and treatment effect have caused a serious economic and mental burden on patients and their families.How to accomplish the early diagnosis and intervention of ASD has become an urgent scientific problem.Thus,it is of great and practical significance for the clinical diagnosis and intervention of ASD to find out biomarkers that could identify pathogenesis of ASD.Accumulating evidence has suggested that the abnormal synchronization of neuronal activity among brain regions was found in individuals with ASD,and associated with autistic brain development.Recently,magnetic resonance imaging(MRI)technique,as its safety,non-invasiveness,high resolution,and repeatability,has been widely applied in the researches of various mental diseases and further provides an important way for us to explore the synchronization of neuronal activity among brain areas at different levels.In light of the altered brain connectivity between different brain regions with brain development,this dissertation will employ brain connectivity methods based on MRI to explore abnormal structural and functional connectivity patterns,and further identify biomarkers underlying pathological mechanism of ASD.The main research contents consist of the following five parts.1.To explore the abnormally temporal dynamics of functional connectivity from the default mode network(DMN)to the whole brain in young children with ASD.We employed the dynamic functional connectivity(d FC)technique,and found that young children with ASD exhibited decreased d FC variance between the PCC and the right precentral gyrus relative to healthy controls(HC),which is inversely correlated with social motivation and social relating.Additionally,significant differences in functional connectivity patterns were detected between the ASD and HC in discrete temporal states.These findings uncover that atypical dynamic interactions between the PCC and sensorimotor cortex are related to impairments of social behavior in ASD,and imply the vital role of PCC in the social-cognitive impairments of ASD.2.To explore the abnormally temporal dynamics of functional connectivity within DMN subsystems at preadolescence in the autistic brain,we further employed the d FC technique,and found that individuals with ASD,within the medial temporal lobe(MTL)subsystem,exhibited significantly decreased node-wise temporal variability in the left posterior inferior parietal lobule(p IPL.L),but showed increased node-wise temporal variability in the retrosplenial cortex.Meanwhile,within the medial temporal lobe(MTL)subsystem,we also found that individuals with ASD exhibited significantly increased edge-wise temporal variability between the ventral medial prefrontal lobule and the right hippocampal formation as well as decreased edge-wise temporal variability between the p IPL.L and the right temporal pole of dorsal medial prefrontal cortex(d MPFC)subsystem.Moreover,these abnormalities in temporal variability from the MTL subsystem could predict the RRB.In addition,autistic patients showed reduced probability of transitions between state 3 and state 4 relative to healthy controls.These findings suggest that abnormally altered temporal variability of MTL subsystem is associated with symptomatic serverity in ASD and add new knowledge to our understanding of the pathophysiological mechanism of ASD.3.To explore the brain developemental trajectory occurring at preadolescence in the autistic brain,we advanced the brain age estimation analysis based on structurefunction connectomics,and found that brain connectome age(BCA)was remarkably higher than chronological age(Ch A)in childhood ASD,whereas the BCA was remarkably lower than Ch A of adolescent autistic brain.These findings suggest that ASD exhibits accelerated brain development in youth followed by a delay after preadolescence,and highlight the crucial role of BCA in our understanding of abnormally developmental trajectories in ASD and then offer novel insights into the pathophysiological mechanisms of this disorder.4.To explore the disruption of structure-function coupling at preadolescence in the autistic brain connectome,we advanced the multiple-regression model and found that individuals with ASD,compared with HC,exhibited significantly increased coupling between structural connectivity and functional connectivity in the right supplementary motor area(SMA),the right insula,and the left inferior frontal gyrus,which were included in the Module 2.Moreover,the abnormal structure-function coupling relationships can predict the total score of Autism diagnostic observation schedule(ADOS)severity in autistic brain.In addition,the severity of ADOS symptoms in ASD mediated the relationship between structural connectivity from SMA to the whole brain and its functional connectivity.These findings suggest that the disturbed interrelated structural-functional mechanisms can underline symptomatic severity in ASD,and perhaps further provide a new class of putative biomarkers that aid in the diagnosis of autistic symptoms.5.To explore the vital role of abnormal brain connectivity within the different modality at preadolescence in ASD,we advanced the support vector machine(SVM)classification model and multimodal fusion technique,and found a high discriminant accuracy of up to 82.69% for ASD classification when integrating connectivity features from three different modalities.This accuracy rate was higher than those using one-or two-modality connectivity features.Specially,these features for high accuracy discrimimation were mainly involved in the temporal,occipital and parietal networks within diffusion tensor imaging(DTI)modality,the prefrontal and parietal networks within functional magnetic resonance imaging(f MRI)modality,as well as the temporal network within structural magnetic resonance imaging(s MRI)modality.Moverover,the connectivity pattern of these features from three modalities could predict the severity of social communication deficits in individuals with ASD.These findings suggest that multimodal brain connectivity patterns could provide complementary information for understanding the pathology of ASD,and highlight the critical role of the biomarker(i.e.,connectivity patterns from three modalities)in pathophysiological mechanisms of ASD.In summary,to explore the abnormal connectivity patterns in autistic developmental brain,this dissertation,by means of MRI technique,advanced functional and structural connectivity methods from DMN to whole brain network,and constructed multi-modal brain connectivity fusion,classification and predication models.Therefore,this dissertation adds new knowledge to our understanding of the pathophysiological mechanisms underlying ASD,and perhaps offers new clinicall-relevant biomarkers for ASD.