Exploring the heterogeneity of brain structure in autism spectrum disorder based on individual structural covariance network

Abstract

Abstract Autism spectrum disorder (ASD) is characterized by highly structural heterogeneity. However, most previous studies analyzed between-group differences through a structural covariance network constructed based on the ASD group level, ignoring the effect of between-individual differences. We constructed the gray matter volume-based individual differential structural covariance network (IDSCN) using T1-weighted images of 207 children (ASD/healthy controls: 105/102). We analyzed structural heterogeneity of ASD and differences among ASD subtypes obtained by a K-means clustering analysis based on evidently different covariance edges relative to healthy controls. The relationship between the distortion coefficients (DCs) calculated at the whole-brain, intra- and interhemispheric levels and the clinical symptoms of ASD subtypes was then examined. Compared with the control group, ASD showed significantly altered structural covariance edges mainly involved in the frontal and subcortical regions. Given the IDSCN of ASD, we obtained 2 subtypes, and the positive DCs of the 2 ASD subtypes were significantly different. Intra- and interhemispheric positive and negative DCs can predict the severity of repetitive stereotyped behaviors in ASD subtypes 1 and 2, respectively. These findings highlight the crucial role of frontal and subcortical regions in the heterogeneity of ASD and the necessity of studying ASD from the perspective of individual differences.