# Leveraging Heterogeneity in Preclinical Traumatic Brain Injury to Drive Discovery and Reproducibility

> **NIH NIH F32** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2020 · $69,810

## Abstract

Traumatic brain injury (TBI) is a leading cause of neurological disorders and affects over 2.5 million people each
year, yet no treatment has successfully translated from bench to clinic. TBI is a broad term and encompasses
an extremely heterogeneous set of injuries differing by cause, severity, biomechanics, and the varied, complex
secondary injury responses that collectively result in chronic disabilities. Current preclinical research circumvents
the issue of TBI heterogeneity by relying on specific preclinical animal models that mimic subpopulations of
patients and particular secondary injury mechanisms with each study focusing on limited, individual pathways.
This proposal instead aims to tackle TBI heterogeneity by approaching TBI as a “big data” problem and
aggregating and analyzing the multidimensional data collectively. A framework for data harmonization and
curation will be developed, and datasets from a consortium of preclinical labs employing a variety of preclinical
TBI models will be collected and curated into an open data commons (ODC-TBI). Utilizing machine learning and
multidimensional analytics, the proposed research will directly leverage TBI heterogeneity in the merged dataset
to identify persistent features of TBI to empower translational research. By creating a preclinical TBI ODC and
applying machine learning to integrate the heterogeneity of preclinical TBI models, the project will reveal
multidimensional features of TBI across heterogeneous injuries and characterize how diverse secondary injury
mechanisms interact and ultimately affect injury outcome. Throughout the project's timeline, new datasets will
continue to be harmonized into the ODC-TBI according to the established framework. The ODC-TBI will be the
first open multicenter, multi-model repository of preclinical TBI data and will enable the application of data science
to the field of TBI. Furthermore, the ODC-TBI and the methods implemented throughout the project will be openly
shared to improve reproducibility of TBI research. Together with the multidimensional analysis that will provide
quantitative and qualitative understanding of TBI heterogeneity, the project aims to ultimately accelerate data-
driven discovery and precision medicine for TBI.

## Key facts

- **NIH application ID:** 10042756
- **Project number:** 1F32NS117728-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Austin C Chou
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $69,810
- **Award type:** 1
- **Project period:** 2020-07-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10042756

## Citation

> US National Institutes of Health, RePORTER application 10042756, Leveraging Heterogeneity in Preclinical Traumatic Brain Injury to Drive Discovery and Reproducibility (1F32NS117728-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10042756. Licensed CC0.

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