Abstract
Abstract Traumatic brain injury (TBI) presents a major global health concern, characterized by a variety of negative long term neurological outcomes. Current diagnostic tools lack the sensitivity to fully capture the complex pathophysiology of TBI and predict long-term consequences, underscoring the need for robust methods for biomarker detection. This study, conducted within the multicenter Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) framework, used a standardized lateral fluid-percussion injury (FPI) model to produce TBI in the left hemisphere of adult male Sprague-Dawley rats across three sites: University of Eastern Finland, Monash University, and the University of California, Los Angeles. This study utilized a novel kernel regression method for improved estimation of fiber orientations and streamline tractography to derive diffusion tensor imaging (DTI) metrics of 36 white matter tracts which were used as features to classify TBI vs of sham-operated rodents scanned at 2 days (30 sham, 87 TBI), 9 days (29 sham, 84 TBI), 1 month (28 sham, 81 TBI), and 5 months (25 sham, 65 TBI) post-injury using Elastic Net Regression regularization. A mean area under the curve (AUC) of 0.92 was achieved in correctly classifying the TBI rats in a leave-one-out cross-validation (LOOCV) framework. The results revealed delayed, region-specific effects on the microstructure of the left fimbria and left thalamic subcortical projections at 5 months following TBI. By integrating multi-compartment modeling, tractography, and harmonization, this study advances our understanding of the temporal evolution of TBI pathogenesis, paving the way for development of translational prognostic biomarkers for the risk of PTE.
Published Version
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