# SCI Consortium Study: Harnessing big-data for plasticity and rehabilitation in translational SCI

> **NIH VA I01** · VETERANS AFFAIRS MED CTR SAN FRANCISCO · 2020 · —

## Abstract

Spinal cord injury and disorders (SCI/D) are a substantial health concern impacting about 1.2 million
Americans, and 45,000 veterans. The total economic burden of SCI/D is estimated at $9 billion/year to $400
billion in lifetime medical and loss-of-productivity costs. The most common clinical presentation is high cervical
SCI/D which produces a broad spectrum of issues, including loss of hand function and autonomy, sensory
changes, spasticity, pain and autonomic dysfunction, profoundly impacting quality of life. Restoring these broad
functions is the goal of regenerative and rehabilitative therapeutic approaches for SCI/D. The VA Gordon
Mansfield Spinal Cord Injury Consortium (VA-GMSCIC) is a VA-funded effort to develop late-stage
translational therapeutics in a nonhuman primate (NHP) model in preparation for testing emerging therapeutic
approaches clinically. Prior and current funding has focused on multifaceted data collection on each subject,
with 5 different centers collaboratively collecting data, each within their specific domain of expertise
(physiology, behavior, histology, neurorehabilitation, and molecular biology). This is an ideal use of the NHP
model, as maximal information is collected on the performance of therapeutic approaches in a small number of
NHPs. Yet the data from VA-GMSCIC is high-volume, high-complexity, and high-heterogeneity, providing both
a challenge and opportunity for novel discoveries. Application of modern data science tools can help deliver on
the promise of translational precision medicine for SCI/D. However, the VA-GMSCIC does not have dedicated
funding to support modern big-data infrastructure for data integration, analysis and visualization. The proposed
project will address this gap to deliver much-needed data science tools through an NHP Data Commons in
collaboration with the Neuroscience Information Framework (NIF) to create open-source software for the data
commons, an NHP ontology built on top of the NIF-Standard Ontology (NIFSTD), and a data analysis and
visualization services. NIF is an NIH-Blueprint funded initiative that maintains the largest federated repository
of neuroscience data, biomedical resources and neuroscience ontologies on the web. Our data-science team
is uniquely-positioned to develop the proposed big-data-to-knowledge pipeline for SCI/D. Over the past 5
years we built the first multicenter, multispecies data repository for SCI/D, known as VISION-SCI (housing data
on N>3500 SCI/D animals tracked on >20,000 variables). In the process of building VISION-SCI we developed
tools and workflows for large-scale data curation, federated database systems, and cutting edge machine
learning analytics for SCI/D discovery in mice, rats, monkeys and de-identified human clinical data. This prior
work demonstrates proof-of-concept that a translational SCI/D data commons can deliver new discoveries
about the nature of plasticity and recovery, as well as cross-species translation. For the proposed Aims ...

## Key facts

- **NIH application ID:** 9812192
- **Project number:** 5I01RX002245-04
- **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:** 2020
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2016-12-01 → 2020-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9812192

## Citation

> US National Institutes of Health, RePORTER application 9812192, SCI Consortium Study: Harnessing big-data for plasticity and rehabilitation in translational SCI (5I01RX002245-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9812192. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*