Encephalitis in Patients with COVID-19: A Systematic Evidence-Based Analysis.

Although severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) predominantly infects the respiratory system, several investigations have shown the involvement of the central nervous system (CNS) along the course of the illness, with encephalitis being one of the symptoms. The objective of this systematic review was to evaluate the characteristics (clinical, neuro-radiological aspects, and laboratory features) and outcomes of encephalitis in COVID-19 patients. PubMed, Scopus, and Google Scholar databases were searched from 1 December 2019 until 21 July 2022 to identify case reports and case series published on COVID-19 associated with encephalitis. The quality of the included studies was assessed by the Joanna Briggs Institute critical appraisal checklists. This systematic review included 79 studies, including 91 COVID-19 patients (52.7% male) experiencing encephalitis, where 85.6% were adults (49.3 ± 20.2 years), and 14.4% were children (11.2 ± 7.6 years). RT-PCR was used to confirm 92.2% of the COVID-19 patients. Encephalitis-related symptoms were present in 78.0% of COVID-19 patients at the time of diagnosis. In these encephalitis patients, seizure (29.5%), confusion (23.2%), headache (20.5%), disorientation (15.2%), and altered mental status (11.6%) were the most frequently reported neurologic manifestations. Looking at the MRI, EEG, and CSF findings, 77.6%, 75.5%, and 64.1% of the patients represented abnormal results. SARS-CoV-2-associated or -mediated encephalitis were the most common type observed (59.3%), followed by autoimmune encephalitis (18.7%). Among the included patients, 66.7% were discharged (37.8% improved and 28.9% fully recovered), whereas 20.0% of the reported COVID-19-positive encephalitis patients died. Based on the quality assessment, 87.4% of the studies were of high quality. Although in COVID-19, encephalitis is not a typical phenomenon, SARS-CoV-2 seems like a neuropathogen affecting the brain even when there are no signs of respiratory illness, causing a high rate of disability and fatality.

Harmonized-Multinational qEEG norms (HarMNqEEG).

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross-spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects" and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.