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

> **NIH VA I01** · VETERANS AFFAIRS MED CTR SAN FRANCISCO · 2021 · —

## Abstract

Spinal cord injury and disorders (SCI/D) are substantial health concerns impacting veterans at a higher rate
than the civilian population. 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, autonomy, sensory changes,
spasticity, pain and autonomic dysfunction, profoundly impacting quality of life. Restoring these 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 emergent 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 about the performance of therapeutic approaches in a small number of NHPs. Data from this
important model is characterized by the classic ‘3Vs of Big Data’: high volume (large images), high variety
(multi-modal data), and high velocity (robotic rehab; physiology; neuromodulation), 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. As our prior work demonstrates, effective management of VA
NHP big data enables us to effectively harness VA-GMSCIC data to drive new discoveries. However,
integrating these NHP big data requires ongoing data-driven integration of robotic rehab, kinematics, histology,
and medical information. Extraction of meaningful discoveries requires extensive computational work. The
purpose of the proposed renewal is to build on our ongoing success in assembling a data commons for the
VA-GMSCIC by integrating new types of high-resolution data in support of safety/efficacy studies of novel
therapeutics. Our data science team is well positioned to achieve this goal. Our team has provided analytical
support for the VA-GMSCIC, helping to integrate data from UCSD, UCLA, UCI, UC Davis and UCSF for testing
SCI rehab and regenerative therapies in NHPs for over 13 years. We have supported development of different
injury models, behavioral assessments, electrophysiology, kinematic measures, and therapeutic approaches in
100+ subjects. Under our current merit award (ending Nov 2020), our team built on this historical background
to establish a functional primate data commons (PDC-SCI) infrastructure that enables rapid, structured data
sharing, data integration, and analytics support across the VA-GMSCIC sites. The projec...

## Key facts

- **NIH application ID:** 10187442
- **Project number:** 2I01RX002245-05A1
- **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:** 2021
- **Award amount:** —
- **Award type:** 2
- **Project period:** 2016-12-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10187442

## Citation

> US National Institutes of Health, RePORTER application 10187442, Harnessing big-data for plasticity and rehabilitation in translational SCI (2I01RX002245-05A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10187442. Licensed CC0.

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