# Understanding Behavioral Variability in Outcome After SCI

> **NIH NIH R21** · EMORY UNIVERSITY · 2022 · $426,183

## Abstract

PROJECT SUMMARY
Opportunities now exist to implement a paradigm shift in health management towards individualized physio-
behavioral (biometric) monitoring - to predict, to prevent, and to better manage disease using wearable
technologies, as well as embedded sensor technologies within wheelchairs as well as within the home.
Our broad objective is to interpret collected combinatorial changes in the same biometric variables captured
noninvasively during the progression of SCI in naturally behaving mice. In well-controlled animal studies, we
propose to apply machine learning algorithms to identify ‘digital biosignatures’ that are predictive to disease
emergence and/or expression, and therefore of use in feedback-based mitigation. To achieve this, we have
engineered specialty instrumented mouse home-cages with commercially available sensors that enable continuous
long-term noninvasive home cage capture of these biometrics to prototype development of such digital
biosignatures.
Emphasis is on understanding temporal interrelations in the emergence of sleep disturbances, neuropathic pain,
thermoregulatory dysfunction, cardiorespiratory dysfunction and autonomic crises (autonomic dysreflexia) after
SCI. Accordingly, home cage sensor-based capture includes all motor events, respiration, heart rate, 3-state sleep,
skin temperature thermography and sensory preference testing.
Our overarching hypothesis is that combined continuous capture several variables during the progression of SCI
will identify novel ‘digital biosignatures’ that link to emergent dysfunction. The longer-term goal is to incorporate
capture of digital biosignatures into real-time feedback-based approaches that limit disease expression.
Two SCI models will be used to quantify variability in emergent dysfunction with the temporal correspondence
of alterations in measured biometrics: [1] T9-10 contusion SCI and [2] T2-3 complete transection. For both
experimental series, variables will be continuously captured in specialty instrumented home cages located in
environmentally controlled chambers both before and for 10 weeks after SCI or sham surgery. Captured
biometrics will be further categorized for machine learning based on measures of SCI -induced dysfunction from
more conventional tests of sensory and autonomic dysfunction to link noninvasive biometric digital biosignatures
with established measures physio-behavioral dysfunction after SCI.
If successful, capturing digital biosignatures of dysfunction in real time may have translational impact on
individualized medicine applications in SCI individuals. This is because acquired biosignatures may then serve a
template recognition function from analogously captured biometrics obtained from embedded/wearable sensors
in clinical populations.

## Key facts

- **NIH application ID:** 10528065
- **Project number:** 1R21NS125496-01A1
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** SHAWN HOCHMAN
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $426,183
- **Award type:** 1
- **Project period:** 2022-08-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10528065

## Citation

> US National Institutes of Health, RePORTER application 10528065, Understanding Behavioral Variability in Outcome After SCI (1R21NS125496-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10528065. Licensed CC0.

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