Subtyping Autism Spectrum Disorder Via Joint Modeling of Clinical and Connectomic Profiles.

ABSTRACT

Background: Autism spectrum disorder (ASD) is a highly heterogeneous developmental disorder with diverse clinical manifestations. Neuroimaging studies have explored functional connectivity (FC) of ASD through resting-state functional magnetic resonance imaging studies; however, the findings have remained inconsistent, thus reflecting the possibility of multiple subtypes. Identification of the relationship between clinical symptoms and FC measures may help clarify the inconsistencies in earlier findings and advance our understanding of ASD subtypes. Methods: Canonical correlation analysis was performed on 210 ASD subjects from the Autism Brain Imaging Data Exchange to identify significant linear combinations of resting-state connectomic and clinical profiles of ASD. Then, hierarchical clustering defined ASD subtypes based on distinct brain-behavior relationships. Finally, a support vector machine (SVM) classifier was used to verify that subtypes comprised subjects with distinct clinical and connectivity features. Results: Three ASD subtypes were identified. Subtype 1 exhibited increased intra-network FC, increased Intelligence Quotient (IQ) scores, and restricted and repetitive behaviors. Subtype 2 was characterized by decreased whole-brain FC and more severe Autism Diagnostic Interview-Revised and Social Responsiveness Scale symptoms. Subtype 3 demonstrated mixed FC, low IQ scores, as well as social motivation and verbal deficits. To verify subtype assignment, a multi-class SVM using connectomic and clinical profiles yielded an average accuracy of 71.3% and 65.2% respectively for subtype classification, which is significantly higher than chance (33.3%). Conclusion: The present study demonstrates that combining connectomic and behavioral measures is a powerful approach for disease subtyping and suggests that there are ASD subtypes with distinct connectomic and clinical profiles.