Autism Spectrum Disorder Functional Connectivity Study Based On Large Multi-site Dataset

Autism spectrum disorder(ASD)is a type of neurodevelopmental disorder characterized by social-communication deficits and restricted and repetive behaviors.Now the estimated prevalence of ASD is about 1%according to large scale epidemiological investigation.Till now there is no efficient treatment of ASD.ASD brings heavy economic and psychological burden to the parents and society.Although the neural mechanism of ASD is unclear,abundant studies indicate that the symptoms of ASD are related to the atypical brain function.Human brain is one of the most complex system.The cognitive function is not based on neural activity of single brain region but neural synchronization between distinct regions.The emergence of resting-state fMRI provided a powerful technology to explore the neural synchronization pattern(functional connectivity,FC)between distinct brain functional regions,and helped us to study the neural mechanism of ASD at connectivity level.However,results of existing ASD studies based on resting-state fMRI are not consistent.These inconsistent results put an obstacle to translate the research results to clinical use.To solve this problem,reliable large multi?center dataset and new informatics data mining technology are needed.The current study will utilize informatics technology to explore the factors of the inconsistent study results from two aspects of intra subject and inter subject.It consists following parts:1.We explored the effect of frequency to the atypical FC pattern of ASD.We divided the resting-state fMRI signals into sub-frequency bands(Slow-5,0.01-0.027Hz;Slow-4,0.027-0.073Hz)and then constructed the frequency-specific FC networks for each subjects.Using multivariant pattern analysis(MVPA)method,we found that combining information of sub-frequency bands,we could better distinguish ASD and HCs than using single-frequency FC network.This result implied that the atypical FC pattern of ASD is frequency-specific.2.We explored the effect of time states to the atypical FC pattern of ASD.Using the dynamic FC method,we obtained the FC networks of each time point for each subjects.By using k-means cluster method,we divided these dynamic FC networks into different states and then analyses the atypical FC patterns within each state.We found that individuals with ASD showed different atypical FC patterns across different states.This result implied that the atypical FC pattern of ASD is state-specific.3.We explored the thalamo-cortical FC circuit of ASDs to see wether the FC pattern of cortical and subcortical regions would show different atypical patterns.Using granger causality analysis,we charactered the effective connectivity from thalamus to cortical regions for each subject.Finally we found individuals with ASD showed lower effective connectivity compared to HCs.4.We explored the effect of age to the atypical FC pattern of ASD.We recruited children with ASD and matched HCs.By compare the FC networks between ASD children and HCs,we found two atypical FC circuits of ASD.One is related to social function while the other is related to sensorimotor function.The social-related FC circuit showed lower strength in children with ASD while the sensorimotor-related FC circuit showed higher strength in ASDs.This result showed different atypical FC patterns compared to the result based on large adult dataset which implied that age contributed to the heterogeneity of FC patterns of ASDs.5.We explored the effect of brain structure to the atypical FC pattern of ASD.We charactered the neuro anatomical difference map for each ASD subjects and extracted the major anatomical difference patterns of ASD subjects.Using k-means cluster method,we found three ASD subtypes showing different atypical anatomical patterns.By compare the FC networks of individuals with different ASD subtypes,we found different atypical FC patterns across different ASD subtypes.This result proved the neuro anatomical heterogeneity of ASDs and the neuro anatomoical heterogeneity might be one of the factors affecting the FC pattern of ASD.