In adults, MOG-Ab-associated disease extends beyond clinical and radiologic abnormalities in the optic nerve and spinal cord. Despite the relapsing course, the overall visual and motor outcome is better compared with AQP4-Ab-positive patients.

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is the seventh member of the family of coronaviruses that infect humans (1) and induces coronavirus disease 2019 (COVID-19). Human coronaviruses have neuroinvasive capacities and may be neurovirulent by two main mechanisms (2-4): viral replication into glial or neuronal cells of the brain or autoimmune reaction with a misdirected host immune response (5). Thus, a few cases of acute encephalitislike syndromes with human coronaviruses were reported in the past 2 decades (5-8). In regard to COVID-19, current data on central nervous system involvement are uncommon but growing (9-17), demonstrating the high frequency of neurologic symptoms. However, the delineation of a large cohort of confirmed brain MRI parenchymal signal abnormalities (excluding ischemic infarcts) related to COVID-19 has never been performed, and the underlying pathophysiologic mechanisms remain unknown. The purpose of the current study was to describe the neuroimaging findings (excluding ischemic infarcts) in patients with severe COVID-19 and report the clinicobiologic profile of these patients. Materials and Methods This retrospective observational national multicenter study was initiated by the French Society of Neuroradiology in collaboration with neurologists, intensivists, and infectious disease specialists and brought together 16 hospitals. The study was approved by the ethical committee of Strasbourg University Hospital (CE-2020-37) and was in accordance with the 1964 Helsinki Declaration and its later amendments. Because of the emergency in the context of the COVID-19 pandemic responsible for

ObjectiveTo describe neuroimaging findings and to report the epidemiological and clinical characteristics of COVID-19 patients with neurological manifestations.MethodsIn this retrospective multicenter study (10 Hospitals), we included 64 confirmed COVID-19 patients with neurologic manifestations who underwent a brain MRI.ResultsThe cohort included 43 men (67%), 21 women (33%), and the median age was 66 years (range: 20-92). 36 (56%) brain MRIs were considered abnormal, possibly related to SARS-CoV-2. Ischemic strokes (27%), leptomeningeal enhancement (17%), and encephalitis (13%) were the most frequent neuroimaging findings. Confusion (53%) was the most common neurological manifestation, following by impaired consciousness (39%), presence of clinical signs of corticospinal tract involvement (31%), agitation (31%), and headache (16%). The profile of patients experiencing ischemic stroke was different from the other patients with abnormal brain imaging since the former had less frequently acute respiratory distress syndrome (p=0·006) and more frequently corticospinal tract signs (p=0·02). Patients with encephalitis were younger (p=0·007), whereas agitation was more frequent for patients with leptomeningeal enhancement (p=0·009).ConclusionsCOVID-19 patients may develop a wide range of neurological symptoms, which can be associated with severe and fatal complications, such as ischemic stroke or encephalitis. Concerning the meningoencephalitis involvement, even if a direct effect of the virus cannot be excluded, the pathophysiology rather seems to involve an immune and/or inflammatory process given the presence of signs of inflammation in both cerebrospinal fluid and neuroimaging but the lack of virus in cerebrospinal fluid.

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.