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Multiple sclerosis (MS) lesions are heterogeneous with regard to inflammation, demyelination, axonal injury, and neuronal loss. We previously developed a diffusion basis spectrum imaging (DBSI) technique to better address MS lesion heterogeneity. We hypothesized that the profiles of multiple DBSI metrics can identify lesion-defining patterns. Here we test this hypothesis by combining a deep learning algorithm using deep neural network (DNN) with DBSI and other imaging methods.

Myelin oligodendrocyte glycoprotein antibody disease (MOGAD) can radiographically mimic multiple sclerosis (MS) and aquaporin-4 (AQP4) antibody-positive neuromyelitis optica spectrum disorder (NMOSD). Central vein sign (CVS) prevalence has not yet been well-established in MOGAD. The objective of this study is to characterize the magnetic resonance imaging (MRI) appearance and CVS prevalence of MOGAD patients in comparison to matched cohorts of MS and AQP4+ NMOSD.

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Multiple sclerosis (MS) is a central nervous system (CNS) autoimmune demyelinating disease.
Its pathogenesis involves humoral and cellular immunity, with production of pro- and anti-inflammatory
cytokines by T cells.

To identify and characterize myeloid cell populations within the CSF of patients with MS and anti-myelin oligodendrocyte glycoprotein (MOG) disorder by high-resolution single-cell gene expression analysis.