# Using quantitative gradient echo MRI to distinguish MOG antibody disorder from multiple sclerosis

> **NIH NIH R03** · WASHINGTON UNIVERSITY · 2021 · $157,500

## Abstract

Project Summary/Abstract
 Myelin oligodendrocyte glycoprotein (MOG) is a protein exclusive to the central nervous system (CNS)
found on the external surface of oligodendrocytes and CNS myelin. The presence of an autoantibody to MOG
in its native conformation in patients with a CNS demyelinating syndrome defines MOG antibody disorder
(MOGAD). This recently-characterized condition overlaps clinically with multiple sclerosis (MS), the
prototypical CNS demyelinating condition, and it can be challenging to differentiate MOGAD patients from MS
patients using only clinical features and conventional MRI.
 Quantitative gradient recalled echo (qGRE) is a novel imaging technique developed at Washington
University. qGRE technique can detect microscopic damage in CNS white matter (WM), gray matter (GM),
and normal appearing WM (NAWM) and GM (NAGM) in MS and other neurologic diseases including
Alzheimer’s disease. Because qGRE generates naturally co-registered images of different contrasts from a
single scan, it is also ideally suited to detect the central vein sign (CVS), an advanced imaging feature thought
to differentiate MS lesions from those of other neuroinflammatory conditions. It is notable that high-resolution
(1 mm3 voxel) qGRE acquisition takes less than 10 minutes on any standard 3T MRI scanner, deposits little
energy, is reproducible, and does not require contrast agent administration.
 Here, we hypothesize that quantitative tissue damage within lesions, NAWM, and NAGM, as well as the
prevalence of CVS within lesions, will together differentiate MOGAD from MS. To assess our hypothesis, we
will apply the qGRE approach to MOGAD CNS imaging to provide quantitative information on regional brain
tissue integrity and CVS prevalence.
 In Aim 1, we will obtain qGRE imaging data in 20 MOGAD patients to characterize and quantify these
advanced imaging features. MOGAD patient imaging data will be compared to already acquired data from 20
age- and sex-matched MS patients.
 In Aim 2, we will correlate this qGRE data in MOGAD patients with clinical test results and compare
these results to those in MS. A comparison of clinical test scores measuring physical dysfunction in legs and
arms and tests of cognition to the qGRE findings may allow us to understand the distribution and severity of
MOGAD pathology, which has not yet been well-defined. This will be done in comparison to data we have
already acquired linking these same test results and qGRE data in MS patients.
 Upon successful completion of our aims, we expect that qGRE could be used clinically to simplify and
improve the diagnostic accuracy for both MOGAD and MS, limiting misdiagnoses. We also expect that qGRE
will help to better understand the disease processes underlying MOGAD.

## Key facts

- **NIH application ID:** 10193051
- **Project number:** 1R03NS121960-01
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** DOROTHY ANNE CROSS
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $157,500
- **Award type:** 1
- **Project period:** 2021-04-01 → 2023-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10193051

## Citation

> US National Institutes of Health, RePORTER application 10193051, Using quantitative gradient echo MRI to distinguish MOG antibody disorder from multiple sclerosis (1R03NS121960-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10193051. Licensed CC0.

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