• Jeffrey B Ware, Tessa Hart, John Whyte, Amanda Rabinowitz, John A Detre, and Junghoon Kim.
• 1 Department of Radiology, Hospital of the University of Pennsylvania , Philadelphia, Pennsylvania.
• J. Neurotrauma. 2017 Jul 15; 34 (14): 2243-2253.

AbstractTraumatic brain injury (TBI) is a leading cause of cognitive morbidity worldwide for which reliable biomarkers are needed. Diffusion tensor imaging (DTI) is a promising biomarker of traumatic axonal injury (TAI); however, existing studies have been limited by a primary reliance on group-level analytic methods not well suited to account for inter-subject variability. In this study, 42 adults with TBI of at least moderate severity were examined 3 months following injury and compared with 35 healthy controls. DTI data were used for both traditional group-level comparison and subject-specific analysis using the distribution-corrected Z-score (DisCo-Z) approach. Inter-subject variation in TAI was assessed in a threshold-invariant manner using a threshold-weighted overlap map derived from subject-specific analysis. Receiver operator curve analysis was used to examine the ability of subject-specific DTI analysis to identify TBI subjects with significantly impaired processing speed in comparison with region of interest-based fractional anisotropy (FA) measurements and clinical characteristics. Traditional group-wise analysis demonstrated widespread reductions of white matter FA within the TBI group (voxel-wise p < 0.05, corrected), despite relatively low consistency of subject-level effects secondary to widespread variation in the spatial distribution of TAI. Subject-specific mapping of TAI with the DisCo-Z approach was the best predictor of impaired processing speed, achieving high classification accuracy (area under the curve [AUC] = 0.94). In moderate-to-severe TBI, there is substantial inter-subject variation in TAI, with extent strongly correlated to post-traumatic deficits in processing speed. Significant group-level effects do not necessarily represent consistent effects at the individual level. Better accounting for inter-subject variability in neurobiological manifestations of TBI may substantially improve the ability to detect and classify patterns of injury.

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