BCCMA: Predicting TBI Pathology with Visual and Blood-based Biomarkers

The purpose of this collaborative project is to identify robust biomarkers that predict chronic neuronal dysfunction following traumatic brain injury (TBI). Evaluating TBI-induced pathology and dysfunction in the brain can be challenging. We hypothesize that the retina can serve as a surrogate to monitor

the rate of neurodegeneration occurring in the brain following injury. Furthermore, we hypothesize that blood-based biomarkers correlate with TBI-mediated neurodegeneration in the retina and brain. The first phase of this project is the discovery phase with the objective of identifying biomarkers that are predictive

of chronic neuronal damage. Three distinct proposals will examine both blast-mediated and impact TBI using overlapping assessments of retinal function and structure. Each proposal will also utilize unique outcomes including proteomic analysis of the blood and serum, cognitive function, brain imaging modalities,

and histology in both animal models and Veterans. A fourth project will use data from the first three projects to apply tissue modeling and informatic approaches to fully characterize TBI injury in the retina and brain. The second phase will validate the candidate biomarkers identified by each site by testing animals/tissue, and

human biofluid samples in the other laboratories. This phase will determine the robustness of each biomarker across studies. In the third phase, we will test the most promising biomarkers in mice exposed to live blast explosions, providing translational value to the biomarkers. Project 4 (this project) will manage big data and analytics (statistics, computational modeling, and

machine learning/AI) for the Linked Merit. The rich TBI measures produced by Project 1-3 will create a specific type data management challenge: big data variety1. We will manage this data variety across projects using data science and advanced analytics to assess multiple injury severities and types in a unified pipeline. By

pooling and aligning diverse data at a granular level, it becomes possible to make TBI biomarker data Findable, Accessible, Interoperable and Reusable (FAIR)2, making complex data manageable, improving biomarker discovery, enhancing quality control (QC) and reproducibility, and enabling advanced analytics

to drive translational therapeutic development. The present Linked Merit leverages our prior efforts to build infrastructure enabling FAIR data sharing, data citation and query, and multidimensional analytics; repurposing them to promote biomarker discovery, assess reproducibility, and cross-validate findings. We

will establish a TBI Open Data Commons pipeline for biomarkers (Aim 1). We will ingest and cross-curate animal data from Project 1 [Harper], Project 2 [Feola], Project 3 [Wang] and Co-I Thao (Vicky) Nguyen will use sensor data from these studies to develop biofidelic computational models of blunt TBI, shock-tube

blast, and open field blast experiments to determine and compare the stresses and deformation of the tissues of the eye (retina, optic nerve head) and optic tract at the base of the rat/mouse brain (Aim 2). We will then work closely with Project PIs and clinician-scientist Co-I Fanny Elahi to perform clinicopathological

analysis of retinal and biofluid biomarkers as reproducible and cross-species translational predictors of functional outcomes in TBI (Aim 3), harnessing a combination of traditional statistics and machine learning/ machine intelligence tools. This approach will ensure rigor and reproducibility of validated retinal and blood-

based biomarker associations for diverse TBI types. Through this work the Linked merit will take tangible steps toward realizing the potential of biomarkers to predict neurodegeneration and drive precision therapeutic targeting.