Parsing the heterogeneity of brain-symptom associations in autism spectrum disorder via random forest with homogeneous canonical correlation

Abstract

BackgroundAutism spectrum disorder (ASD) is a highly heterogeneous developmental disorder, but the neuroimaging substrates of its heterogeneity remain unknown. The difficulty lies mainly on the significant individual variability in the brain-symptom association. MethodsT1-weighted magnetic resonance imaging data from the Autism Brain Imaging Database Exchange (ABIDE) (NTDC = 1146) were used to generate a normative model to map brain structure deviations of cases (NASD = 571). Voxel-based morphometry (VBM) was used to compute gray matter volume (GMV). Singular Value Decomposition (SVD) was employed to perform dimensionality reduction. A tree-based algorithm was proposed to identify the ASD subtypes according to the pattern of brain-symptom association as assessed by a homogeneous canonical correlation. ResultsWe identified 4 ASD subtypes with distinct association patterns between residual volumes and a social symptom score. More severe the social symptom was associated with greater GMVs in both the frontoparietal regions for the subtype1 (r = 0.29–0.44) and the ventral visual pathway for the subtype3 (r = 0.19–0.23), but lower GMVs in both the right anterior cingulate cortex for the subtype4 (r = −0.25) and a few subcortical regions for the subtype2 (r = −0.31 to −0.20). The subtyping significantly improved the classification accuracy between cases and controls from 0.64 to 0.75 (p < 0.05, permutation test), which was also better than the accuracy of 0.68 achieved by the k-means-based subtyping (p < 0.01). LimitationsSample size limited the study due to the missing data. ConclusionsThese findings suggest that the heterogeneity of ASD might reflect changes in different subsystems of the social brain, especially including social attention, motivation, perceiving and evaluation.