More Features in the Mahalanobis Distance Lead to Improved Diagnostic Classification of Autistic and Non-autistic Individuals

Motivation: Heterogeneity of neuroimaging findings remains a challenge to identify specific brain-related phenotypes in ASD. Quantitative metrics of individual deviation across brain measurements are needed for parsing variation and identifying individuals who may or may not have abnormal brain structure. Goal(s): This study aims to quantify individual brain differences in individuals with and without ASD. Approach: We investigated the Mahalanobis distance to characterize multidimensional brain measures of microstructure in individuals with and without ASD in a set of white matter regions. Results: We found multivariate Mahalanobis distance is superior to univariate comparisons at distinguishing between individuals with and without ASD. Impact: Normative modeling and multivariate approaches may provide informative metrics for parsing heterogeneity in the multivariate patterns of brain development in autistic individuals.