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 versus 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 post-traumatic epilepsy (PTE).

Biophysical modeling of diffusion MRI (dMRI) offers the exciting potential of bridging the gap between the macroscopic MRI resolution and microscopic cellular features, effectively turning the MRI scanner into a noninvasive in vivo microscope. In brain white matter, the Standard Model (SM) interprets the dMRI signal in terms of axon dispersion, intra- and extra-axonal water fractions, and diffusivities. However, for SM to be fully applicable and correctly interpreted, it needs to be carefully evaluated using histology. Here, we perform a comprehensive histological validation of the SM parameters, by characterizing white matter (WM) microstructure in sham and injured rat brains using volume electron microscopy and ex vivo dMRI. Sensitivity is evaluated by how well each SM metric correlates with its histological counterpart, and specificity by the lack of correlation with other, non-corresponding histological features. Compared to previously developed SM estimators with constraints, our results show that SMI is the most sensitive and specific. Furthermore, we derive the functional form of the fiber orientation distribution based on its exponentially decreasing rotational invariants. This comprehensive comparison with histology may facilitate the clinical adoption of in vivo dMRI-derived SM parameters as biomarkers for neurological disorders.