# Combined DDE MRI and Electrophysiology Prediction of Spinal Cord Injury

> **NIH NIH R21** · UNIVERSITY OF LOUISVILLE · 2020 · $443,762

## Abstract

The International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) neurological exam
and magnetic resonance imaging (MRI) —the prevailing methods to assess severity of injury and predict
prognosis— suffer from significant limitations and often fail to accurately predict long-term outcome in
individuals, and neither provides information on the functional and physiological viability of residual cord tissue
altered by the injury. Using standard MRI, physicians may grossly under/overestimate the degree of intrinsic
cord injury or functional connectivity of cord tissue. Thus, these widely used clinical and radiological
assessments are often unable to distinguish between incomplete and complete injuries and more sensitive
determinants of long-term outcomes are critically needed. Diffusion tensor imaging (DTI) is an MRI technique
that has shown promise in estimating the degree of axonal injury in acutely injured cord in both animal and
limited human studies. However, fractional anisotropy (FA), a key DTI parameter that is reduced in acute SCI,
is confounded by edema and hemorrhage making its interpretation and reliability problematic. A novel diffusion
MRI alternative, double diffusion encoding (DDE), has shown high sensitivity to axonal injury with minimal
contribution from edema and accurately predicts outcomes in a rat SCI model. However, rodent models of SCI
are limited, and it remains uncertain if DDE performs as reliably in a large animal model more faithfully
replicating human SCI. Furthermore, the relationship between DDE and electrophysiological activity, which is
critical to establishing the functional integrity or neural conduction block across the injury site, has not been
explored. Our goal is to enhance the translational applicability of DDE using a validated large animal porcine
contusion model currently being used by our group. Preliminary MRI data from our group using this model
matches both the quality and challenges of human SCI imaging. To relate DDE-derived axonal injury index
(ADC||) with neural function, we will conduct both DDE assessments and intraoperative D-wave epidural
electrophysiology in a porcine contusion SCI model with mild (n=6) and severe (n=6) injury. Successful project
completion will lead to direct translation to human SCI patient studies with the long-term goal of improving
patient outcomes from such a devastating injury.

## Key facts

- **NIH application ID:** 10058003
- **Project number:** 1R21NS114982-01A1
- **Recipient organization:** UNIVERSITY OF LOUISVILLE
- **Principal Investigator:** Maxwell Boakye
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $443,762
- **Award type:** 1
- **Project period:** 2020-08-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10058003

## Citation

> US National Institutes of Health, RePORTER application 10058003, Combined DDE MRI and Electrophysiology Prediction of Spinal Cord Injury (1R21NS114982-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10058003. Licensed CC0.

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