Akul Sharma

What I built and investigated: A complete biomarker discovery pipeline for post-traumatic epilepsy (PTE) in a longitudinal rodent TBI model, covering: rigorous QC (scanner/site effects, sham reference stability, tractography quality), exploratory analysis establishing the general injury phenotype, region- and whole-tract-level statistical and machine learning analysis, and the core methodological contribution: a localized, multimodal normative modeling framework. The pipeline addresses a genuinely hard problem: PTE and TBI-only animals both undergo substantial injury and recovery, so any PTE-specific signal is small and easily masked. What we found: QC revealed real site/scanner effects requiring harmonization, and confirmed the sham group as a stable normative reference despite noisy individual longitudinal metrics. Whole-tract averaging diluted focal injury signal (AUC 0.90 localized vs. 0.63 averaged), and classification collapsed to chance. Our normative framework, representing each tract as a 20-segment × 9-metric profile and modeling deviation from the sham distribution, recovered injury detection far more sensitively, with framework structure. Applied to thalamic tracts, it revealed tract-specific metric signatures (diffusivity-led vs. fiber-fraction-led) and generated concrete, testable hypotheses about injury propagation. Why it matters: This is a full pipeline from data quality through interpretable, localized biomarker discovery and can generalize to other tracts, metrics, and injury or disease models beyond this specific dataset. Beyond the present study, the framework may be more broadly extended to other preclinical rodent imaging studies for discovering biomarkers or evaluating therapeutic interventions.