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

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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.