Researchers interested in autism spectrum disorders (ASD) and the use of diffusion tensor imaging (DTI) in understanding white matter changes in ASD. Brain imaging findings in children with autism spectrum disorder (ASD) suggest the disorder is complex and heterogeneous, making the interpretation of the underlying neurobiology of ASD challenging. Such heterogeneity may suggest that the neuroanatomy of ASD is not restricted to a single brain region or network, rather that differences are subtle and more widespread¹. Thus, consideration of multivariate relationships of the brain may enable better characterization and discrimination of individuals with ASD. Distinguishing these multidimensional brain relationships at both the group and individual level are therefore critical as characterization may be informative for identifying ASD subgroups as well as understanding individual brain variation in ASD. Here, we propose that the Mahalanobis distance², a multidimensional generalization of a standard score (i.e., z-score for univariate statistics), may provide an informative index of characterizing individual brain variation in ASD. The Mahalanobis distance is constructed as the distance from the typically developing control (TD) sample mean in multidimensional feature space weighted by the TD covariance. In this study we used longitudinal trajectories of structural and microstructural brain development to construct the individual Mahalanobis distances and compare the distributions of brain variability between ASD and TD individuals. MRI Acquisition: Longitudinal data from 148 participants (92 ASD and 56 TD) between 3.1 and 34.5 years of age were used for this study. A total of 213 scans from ASD participants and 123 scans from TD individuals were acquired on a 3 Tesla Siemens Tim Trio scanner and consisted of structural T1-weighted (MP-RAGE) and diffusion tensor imaging (DTI) data. Diffusion weighted images were corrected for distortion and head motion and tensors were subsequently fit using the robust estimation algorithm (RESTORE³). Analysis: Volumetric measures from 16 brain regions were computed using FreeSurfer [version 5.1⁴], following the steps for longitudinal processing⁵, while the mean FA were computed from 21 major white matter tracts⁶ and used in subsequent analyses. To construct the Mahalanobis distance metric, longitudinal trajectories were first separately modeled for TD and ASD groups using mixed effects modeling. Distances from the population-averaged TD trajectory were then computed and normalized by the covariance matrix of fit residuals, forming the Mahalanobis distance. Fig. 1 shows a visualization of the distributions of Mahalanobis distances calculated from the ASD and TD groups. Qualitatively, we notice a differential pattern between individuals with and without ASD. In particular, the ASD group’s distribution of Mahalanobis distances is shifted rightward, signifying larger multivariate brain deviation compared to the TD group. Comparing these distributions quantitatively, we find the mean of the ASD distribution (1.2125 [0.3792]) to be significantly larger (p<0.0001) than the mean of the TD distribution (0.4751 [0.2237]). Also of note, the standard deviation of the Mahalanobis distributions was found to be larger in the ASD group compared to the TD group, highlighting the heterogeneity across the ASD population.Previous studies have consistently indicated both gross and microstructural brain differences in ASD compared to typical development^7,8. While univariate measures provide informative insight to specific brain regions and biological mechanisms implicated in ASD, consideration of the multivariate relationships of the brain may enable better characterization and discrimination of individuals with ASD. In this work, we used mixed effects modeling and the Mahalanobis distance to characterize patterns of multivariate brain measures in ASD and TD. Our results suggest that that the distribution of Mahalanobis distances in ASD individuals is larger and more variable compared to TD individuals. These results additionally suggest that the Mahalanobis distance of differing image modalities may be valuable for distinguishing individuals with and without ASD as well as identifying possible subgroups of autism. In this work, we have sought to examine the application of the Mahalanobis distance, to assess multivariate brain measures of ASD and TD brain development. Our results suggest that this measure may provide an informative metric for characterizing and distinguishing the multivariate patterns of brain development between ASD and TD individuals. Moreover, our results raise additional questions in regards to whether specific brain regions or image modalities are particularly sensitive to observed ASD brain differences.