To differentiate distinctive signatures on Volumetric MRI (vMRI) between multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), and myelin oligodendrocyte glycoprotein antibody–associated disease (MOGAD)

Differentiating MS, NMOSD, and MOGAD is challenging due to overlapping MRI features. vMRI offers quantitative biomarkers that may reveal disease-specific patterns, but evidence remains fragmented.

We systematically searched four databases for studies reporting vMRI measures in MS, NMOSD, and MOGAD. Eligible studies assessed brain or spinal cord volumes using validated segmentation and reported sufficient quantitative data. Standardized mean differences (SMD) with 95% confidence intervals (CI) were calculated, and a Bayesian random-effects NMA was conducted using MS as the reference. Global and local inconsistency were assessed, surface under the cumulative ranking curve (SUCRA) values estimated, and sensitivity and subgroup analyses performed by region.

From 68 studies including 5407 patients (MS: 2368; NMOSD: 2369; MOGAD: 670), pairwise analysis showed MS had greater volume loss in white matter, cerebellum, brainstem, and deep grey matter structures (thalamus, caudate, putamen, pallidus, amygdala, accumbens) compared to NMOSD, but not MOGAD. MS demonstrated higher T2 lesion volume than NMOSD (SMD=1.03, p=0.0003) and MOGAD (SMD=0.62, p=0.01). NMOSD showed more pronounced whole brain and grey matter loss than MOGAD (SMD=-0.39, p=0.0002; SMD=-0.40, p=0.0004). NMA rankings indicated MS worst for global brain volume, deep grey matter, and lesion volumes; MOGAD displayed intermediate reductions across structures. Results were consistent across sensitivity analyses without major inconsistency.

Volumetric MRI reveals distinct neuroanatomical signatures in inflammatory demyelinating diseases. MS shows extensive global brain, deep grey matter, and lesion burden; NMOSD preferentially affects cortical and whole-brain grey matter; MOGAD demonstrates intermediate involvement. vMRI could serve as a quantitative biomarker to improve differential diagnosis and understanding of disease-specific neurodegeneration.