Decomposing Neuroanatomical Heterogeneity of Autism Spectrum Disorder Across Different Developmental Stages Using Morphological Multiplex Network Model

Abstract

Autism spectrum disorder (ASD) is accompanied by impaired social cognition and behavior. The expense of supporting patients with ASD turns into a significant problem for society. Parsing neurobiological subtypes is a crucial way for delineating the heterogeneity in autistic brains, with significant implications for improving ASD diagnosis and promoting the development of personalized intervention models. Nevertheless, a comprehensive understanding of the heterogeneity in cortical morphology of ASD is still lacking, and the question of whether neuroanatomical subtypes remain stable during cortical development remains unclear. Here, we used T1-weighted images of 515 male patients with ASD, including 216 autistic children (6–11 years), 187 adolescents (12–17 years), and 112 young adults (18–29 years), along with 595 age and gender-matched typically developing (TD) individuals. Cortical thickness (CT), surface area (SA), and volumes of cortical (CV) and subcortical (SV) regions were extracted. A single network layer was established by calculating the covariance of each feature across brain regions between participants, thereby constructing a multilayer intersubject covariance network. Applying a community detection algorithm to multilayer networks derived from different feature combinations, we observed that the network comprising CT and CV layers exhibited the most prominent modular organization, resulting in three subtypes of ASD for each of the three age groups. Subtypes within the corresponding age group significantly differed in terms of brain morphology and clinical scales. Furthermore, the subtypes of children with ASD underwent reorganization with development, transitioning from childhood to adolescence and adulthood, rather than consistently persist. Additionally, subtype categorization largely improved the diagnostic accuracy of ASD compared to diagnosing the entire ASD cohort. These findings demonstrated distinct neuroanatomical manifestations of ASD subtypes across various developmental periods, highlighting the significance of age-related subtyping in facilitating the etiology and diagnosis of ASD.