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Patients with traumatic brain injuries (TBI), especially those with chronic health symptoms, often experience behavioral impairments attributable to white matter (WM) microstructural degeneration. Diffusion tensor imaging (DTI) is sensitive to these WM abnormalities, which are frequently a consequence of diffuse axonal injuries precipitated by the original trauma. In this study, tract profile analyses using an automated fiber quantification (AFQ) procedure compared diffusion properties along 20 major fiber tracts in a total of 46 participants, including individuals with TBI, who either have (n = 17) or did not have (n = 16) chronic symptoms, and a control group (n = 13). The tract profile analyses indicated that fractional anisotropy (FA) and radial diffusivity (RD) are more sensitive biomarkers of WM integrity than other commonly used diffusion measures, such as the linear anisotropy coefficient (CL) and mean diffusivity (MD). For all participants with TBI, FA was significantly lower and RD significantly higher in the middle portion of the left cingulum hippocampus (as compared to the control group). Additionally, FA was significantly lower in the inside segment of the left uncinate and the posterior segment of the inferior longitudinal fasciculus for TBI participants with chronic symptoms compared to the controls, but this was not the case for individuals with TBI without chronic symptoms. TBI participants with chronic symptoms also exhibited significantly lower fiber volume (VOL) in three other fiber tracts. This study provides evidence that the diffusion properties of discrete segments along the major fiber tracts, as measured by AFQ, can serve as biomarkers of WM abnormalities, especially for individuals with TBI and chronic health symptoms.

Standard MRI methods are often inadequate for identifying mild traumatic brain injury (TBI). Advances in diffusion tensor imaging now provide potential biomarkers of TBI among white matter fascicles (tracts). However, it is still unclear which tracts are most pertinent to TBI diagnosis. This study ranked fiber tracts on their ability to discriminate patients with and without TBI. We acquired diffusion tensor imaging data from military veterans admitted to a polytrauma clinic (Overall n=109; Age: M=47.2, SD=11.3; Male: 88%; TBI: 67%). TBI diagnosis was based on self-report and neurological examination. Fiber tractography analysis produced 20 fiber tracts per patient. Each tract yielded four clinically relevant measures (fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity). We applied receiver operating characteristic (ROC) analyses to identify the most diagnostic tract for each measure. The analyses produced an optimal cutpoint for each tract. We then used kappa coefficients to rate the agreement of each cutpoint with the neurologist's diagnosis. The tract with the highest kappa was most diagnostic. As a check on the ROC results, we performed a stepwise logistic regression on each measure using all 20 tracts as predictors. We also bootstrapped the ROC analyses to compute the 95% confidence intervals for sensitivity, specificity, and the highest kappa coefficients. The ROC analyses identified two fiber tracts as most diagnostic of TBI: the left cingulum (LCG) and the left inferior fronto-occipital fasciculus (LIF). Like ROC, logistic regression identified LCG as most predictive for the FA measure but identified the right anterior thalamic tract (RAT) for the MD, RD, and AD measures. These findings are potentially relevant to the development of TBI biomarkers. Our methods also demonstrate how ROC analysis may be used to identify clinically relevant variables in the TBI population. Keywords: Traumatic brain injury, Concussion, Imaging, Axon degeneration, Neurodegeneration