Keywords: White Matter, Brain, Autism Spectrum Disorder, Precision Medicine, Replicability
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.
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