Patients with coronavirus disease 2019 (COVID-19) frequently develop acute encephalopathy and encephalitis, but whether these complications are the result from viral-induced cytokine storm syndrome or anti-neural autoimmunity is still unclear. In this study, we aimed to evaluate the diagnostic and prognostic role of CSF and serum biomarkers of inflammation (a wide array of cytokines, antibodies against neural antigens, and IgG oligoclonal bands), and neuroaxonal damage (14-3-3 protein and neurofilament light [NfL]) in patients with acute COVID-19 and associated neurologic manifestations (neuro-COVID). We prospectively included 60 hospitalized neuro-COVID patients, 25 (42%) of them with encephalopathy and 14 (23%) with encephalitis, and followed them for 18 months. We found that, compared to healthy controls (HC), neuro-COVID patients presented elevated levels of IL-18, IL-6, and IL-8 in both serum and CSF. MCP1 was elevated only in CSF, while IL-10, IL-1RA, IP-10, MIG and NfL were increased only in serum. Patients with COVID-associated encephalitis or encephalopathy had distinct serum and CSF cytokine profiles compared with HC, but no differences were found when both clinical groups were compared to each other. Antibodies against neural antigens were negative in both groups. While the levels of neuroaxonal damage markers, 14-3-3 and NfL, and the proinflammatory cytokines IL-18, IL-1RA and IL-8 significantly associated with acute COVID-19 severity, only the levels of 14-3-3 and NfL in CSF significantly correlated with the degree of neurologic disability in the daily activities at 18 months follow-up. Thus, the inflammatory process promoted by SARS-CoV-2 infection might include blood-brain barrier disruption in patients with neurological involvement. In conclusion, the fact that the levels of pro-inflammatory cytokines do not predict the long-term functional outcome suggests that the prognosis is more related to neuronal damage than to the acute neuroinflammatory process.

Hemichorea and other hyperkinetic movement disorders are a rare presentation of stroke, usually secondary to deep infarctions affecting the basal ganglia and the thalamus. Chorea can also result from lesions limited to the cortex, as shown in recent reports. Still, the pathophysiology of this form of cortical stroke-related chorea remains unknown. We report 4 cases of acute ischemic cortical strokes presenting as hemichorea, with the infarction being limited to the parietal and insular cortex in perfusion computed tomography scans and magnetic resonance imaging. These cases suggest potential dysfunction of pathways connecting these cortical regions with the basal ganglia.

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ObjectiveAn obsessive-compulsive disorder (OCD) subtype has been associated with streptococcal infections and is called pediatric autoimmune neuropsychiatric disorders associated with streptococci (PANDAS). The neuroanatomical characterization of subjects with this disorder is crucial for the better understanding of its pathophysiology; also, evaluation of these features as classifiers between patients and controls is relevant to determine potential biomarkers and useful in clinical diagnosis. This was the first multivariate pattern analysis (MVPA) study on an early-onset OCD subtype.MethodsFourteen pediatric patients with PANDAS were paired with 14 healthy subjects and were scanned to obtain structural magnetic resonance images (MRI). We identified neuroanatomical differences between subjects with PANDAS and healthy controls using voxel-based morphometry, diffusion tensor imaging (DTI), and surface analysis. We investigated the usefulness of these neuroanatomical differences to classify patients with PANDAS using MVPA.ResultsThe pattern for the gray and white matter was significantly different between subjects with PANDAS and controls. Alterations emerged in the cortex, subcortex, and cerebellum. There were no significant group differences in DTI measures (fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity) or cortical features (thickness, sulci, volume, curvature, and gyrification). The overall accuracy of 75% was achieved using the gray matter features to classify patients with PANDAS and healthy controls.ConclusionThe results of this integrative study allow a better understanding of the neural substrates in this OCD subtype, suggesting that the anatomical gray matter characteristics could have an immune origin that might be helpful in patient classification.