Mahalanobis distance tractometry (MaD-Tract) - a framework for personalized white matter anomaly detection applied to TBI

Imaging-based quantitative measures from diffusion-weighted MRI (dMRI) offer the ability to non-invasively ex-tract microscopic information from human brain tissues. Group-level comparisons of such measures represent an important approach to investigate abnormal brain conditions. These types of analyses are especially useful when the regions of abnormality spatially coincide across subjects. When this is not true, approaches for individual-ized analyses are necessary. Here we present a framework for single-subject multidimensional analysis based on the Mahalanobis distance. This is conducted along specific white matter pathways represented by tractography-derived streamline bundles. A definition for abnormality was constructed from Wilk's criterion, which accounts for normative sample size, number of features used in the Mahalanobis distance, and multiple comparisons. One example of a condition exhibiting high heterogeneity across subjects is traumatic brain injury (TBI). Using the Mahalanobis distance computed from the three eigenvalues of the diffusion tensor along the cingulum, uncinate, and parcellated corpus callosum tractograms, 8 severe TBI patients were individually compared to a norma-tive sample of 49 healthy controls. For all TBI patients, the analyses showed statistically significant deviations from the normative data at one or multiple locations along the analyzed bundles. The detected anomalies were widespread across the analyzed tracts, consistent with the expected heterogeneity that is hallmark of TBI. Each of the controls subjects was also compared to the remaining 48 subjects in the control group in a leave-one-out fashion. Only two segments were identified as abnormal out of the entire analysis in the control group, thus the method also demonstrated good specificity.

Automated summary

Imaging-based quantitative measures from diffusion-weighted MRI can extract microscopic information from human brain tissues. Group-level comparisons are useful for investigating abnormal brain conditions, but individualized analyses are necessary when abnormalities do not spatially coincide across subjects. A framework for single-subject multidimensional analysis based on the Mahalanobis distance is presented in this study. Using this framework, statistically significant deviations from normative data were detected in severe TBI patients, indicating the heterogeneity of TBI. The method also demonstrated good specificity in the control group.