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

> **NIH VA I01** · VETERANS AFFAIRS MED CTR SAN FRANCISCO · 2024 · —

## Abstract

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 deform...

## Key facts

- **NIH application ID:** 10702037
- **Project number:** 1I01BX005871-01A2
- **Recipient organization:** VETERANS AFFAIRS MED CTR SAN FRANCISCO
- **Principal Investigator:** ADAM R FERGUSON
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2024
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2023-10-01 → 2027-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10702037

## Citation

> US National Institutes of Health, RePORTER application 10702037, BCCMA: Predicting TBI Pathology with Visual and Blood-based Biomarkers (1I01BX005871-01A2). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10702037. Licensed CC0.

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