Parsing brain structural heterogeneity in males with autism spectrum disorder reveals distinct clinical subtypes

• Published in: Human brain mapping Vol. 40; no. 2; pp. 628 - 637
• Main Authors: Chen, Heng, Uddin, Lucina Q., Guo, Xiaonan, Wang, Jia, Wang, Runshi, Wang, Xiaomin, Duan, Xujun, Chen, Huafu
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.02.2019
• Subjects: Adolescent; Adult; Anatomy; Autism; autism spectrum disorder; Autism Spectrum Disorder - classification; Autism Spectrum Disorder - diagnostic imaging; Autism Spectrum Disorder - pathology; Autism Spectrum Disorder - physiopathology; Brain; Brain architecture; Child; Child, Preschool; Classification; Cluster Analysis; Clustering; Connectome; Data exchange; Databases, Factual; data‐driven; Gray Matter - diagnostic imaging; Gray Matter - pathology; Gray Matter - physiopathology; Heterogeneity; Humans; Magnetic Resonance Imaging; Male; Males; Morphometry; Neural networks; neuroanatomical heterogeneity; Neurodevelopmental disorders; Neuroimaging; searchlight classification; Severity of Illness Index; Substantia grisea; Young Adult; searchlight classification; autism spectrum disorder; neuroanatomical heterogeneity; data-driven
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.24400

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with considerable neuroanatomical heterogeneity. Thus, how and to what extent the brains of individuals with ASD differ from each other is still unclear. In this study, brain structural MRI data from 356 right‐handed, male subjects with ASD and 403 right‐handed male healthy controls were selected from the Autism Brain Image Data Exchange database (age range 5–35 years old). Voxel‐based morphometry preprocessing steps were conducted to compute the gray matter volume maps for each subject. Individual neuroanatomical difference patterns for each ASD individual were calculated. A data‐driven clustering method was next utilized to stratify individuals with ASD into several subtypes. Whole‐brain functional connectivity and clinical severity were compared among individuals within the ASD subtypes identified. A searchlight analysis was applied to determine whether subtyping ASD could improve the classification accuracy between ASD and healthy controls. Three ASD subtypes with distinct neuroanatomical difference patterns were revealed. Different degrees of clinical severity and atypical brain functional connectivity patterns were observed among these three subtypes. By dividing ASD into three subtypes, the classification accuracy between subjects of two out of the three subtypes and healthy controls was improved. The current study confirms that ASD is not a disorder with a uniform neuroanatomical signature. Understanding neuroanatomical heterogeneity in ASD could help to explain divergent patterns of clinical severity and outcomes.