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.

The hippocampus is among the first structures affected in Alzheimer's disease (AD). Hippocampal MRI volumetry is a potential biomarker for AD but is hindered by the limitations of manual segmentation. We proposed a fully automatic method using probabilistic and anatomical priors for hippocampus segmentation. Probabilistic information is derived from 16 young controls and anatomical knowledge is modelled with automatically detected landmarks. The results were previously evaluated by comparison with manual segmentation on data from the 16 young healthy controls, with a leave-one-out strategy, and 8 patients with AD. High accuracy was found for both groups (volume error 6% and 7%, overlap 87% and 86%, respectively). In this paper, the method was used to segment 145 patients with AD, 294 patients with Mild Cognitive Impairment (MCI) and 166 elderly normal subjects from the ADNI (Alzheimer's Disease Neuroimaging Initiative) database. Based on a qualitative rating protocol, the segmentation proved acceptable in 94% of the cases. We used the obtained hippocampal volumes to automatically discriminate between AD patients, MCI patients and elderly controls. The classification proved accurate: 76% of the patients with AD, and 71% of the MCI converting to AD before 18 months, were correctly classified with respect to the elderly controls, using only hippocampal volume.