OFSEP Very High Definition Cohort

Multiple sclerosis (MS) is the most common acquired neurological disease leading to disability in young adults. MS often leads to the development of a physical and/or cognitive impairment that disables patients in their daily lives. Early use of disease modifying treatments for patients at risk of developing disability is therefore essential. However, disability progression is very heterogeneous between patients and currently impossible to predict at the individual level. Thus, numerous studies, particularly epidemiological and imaging studies, have identified prognostic factors for the development of disability such as age, gender, number of relapses during the first years of the disease, existence of a residual disability after a first relapse, number of gadolinium-enhancing lesions on initial MRI, early brainstem and spinal cord lesions. However, these different factors only explain incompletely the progression of the physical or cognitive disability in MS patients. In particular, some components of MS pathophysiology, more related to the progressive development of disability, such as axonal degeneration or the existence of chronic inflammation of the central nervous system (CNS) are usually not measured by these biomarkers. In this research project, the investigators will test promising biomarkers, focused on these components of the disease, on a large cohort of patients in a multicenter setting, in order to evaluate their added value to predict disability progression, in comparison with more classical biomarkers such as clinical characteristics, and brain and spinal cord lesion load. In particular, the investigators will test: * Imaging biomarkers extracted from brain and spinal cord MP2RAGE, brain and spinal cord QSM, brain and spinal cord relaxometry, brain diffusion and spinal cord magnetization transfer sequences * Biomarkers extracted from optical coherence tomography (OCT) * Biological biomarkers (serum neurofilament-light chain (NFL) and Glial Fibrillary Acidic Protein (GFAP))