# FAIR VISION for TOP-NT

> **NIH NIH UH3** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2021 · $236,892

## Abstract

PROJECT SUMMARY: Trauma to the spinal cord and brain (neurotrauma) together impact over 2.5 million
people per year in the US, with economic costs of $80 billion in healthcare and loss-of-productivity. Yet precise
pathophysiological processes impacting recovery remain poorly understood. This lack of knowledge limits the
reliability of therapeutic development in animal models and limits translation across species and into humans.
Part of the problem is that neurotrauma is intrinsically complex, involving heterogeneous damage to the central
nervous system (CNS), the most complex organ system in the body. This results in a multifarious CNS
syndrome spanning across heterogeneous data sources and multiple scales of analysis. Multi-scale
heterogeneity makes spinal cord injury (SCI) and traumatic brain injury (TBI) difficult to understand using
traditional analytical approaches that focus on a single endpoint for testing therapeutic efficacy. Single
endpoint-testing provides a narrow window into the complex system of changes that describe the holistic
syndromes of SCI and TBI. In this sense, complex neurotrauma is fundamentally a problem that requires big-
data analytics to evaluate reproducibility in basic discovery and cross-species translation. For the proposed
TOP-VISION cooperative agreement we will: 1) integrate preclinical neurotrauma data on a large-scale; 2)
develop novel applications of cutting-edge multidimensional analytics to make sense of complex neurotrauma
data; and 3) validate bio-functional patterns in targeted big-data-to-bench experiments in multi-PI single center
(UG3 phase), and multicenter (UH3 phase) studies. The goal of the proposed project is to develop an
integrated workflow for preclinical discovery, reproducibility testing, and translational discovery both within and
across neurotrauma types. Our team is well-positioned to execute this project given that with prior NIH funding
we built one of the largest multicenter, multispecies repositories of neurotrauma data to-date, housing detailed
multidimensional outcome data on nearly N=5000 preclinical subjects and over 20,000 curated variables. We
will leverage these existing data resources and apply recent innovations from data science to render complex
multidimensional endpoint data into robust syndromic patterns that can be visualized and explored by
researchers and clinicians for discovery, hypothesis-generation and ultimately translational outcome testing.

## Key facts

- **NIH application ID:** 10407811
- **Project number:** 3UH3NS106899-04S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** JACQUELINE C BRESNAHAN
- **Activity code:** UH3 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $236,892
- **Award type:** 3
- **Project period:** 2018-04-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10407811

## Citation

> US National Institutes of Health, RePORTER application 10407811, FAIR VISION for TOP-NT (3UH3NS106899-04S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10407811. Licensed CC0.

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