SARS-CoV-2 associated posterior reversible encephalopathy syndrome (PRES) - a review of 82 cases.

OBJECTIVES: Severe, acute, respiratory syndromecoronavirus- 2 (SARS-CoV-2) infections can be complicated by central nervous system (CNS) disease. One of the CNS disorders associated with Coronavirus Disease-19 (COVID- 19) is posterior reversible encephalopathy syndrome (PRES). This narrative review summarises and discusses previous and recent findings on SARS-CoV-2 associated PRES. METHODS: A literature search was carried out in PubMed and Google Scholar using suitable search terms and reference lists of articles found were searched for further articles. RESULTS: By the end of February 2023, 82 patients with SARS-CoV-2 associated PRES were recorded. The latency between the onset of COVID-19 and the onset of PRES ranged from 1 day to 70 days. The most common presentations of PRES were mental deterioration (n=47), seizures (n=46) and visual disturbances (n=18). Elevated blood pressure was reported on admission or during hospitalisation in 48 patients. The most common comorbidities were arterial hypertension, diabetes, hyperlipidemia and atherosclerosis. PRES was best diagnosed by multimodal cerebral magnetic resonance imaging (MRI). Complete recovery was reported in 35 patients and partial recovery in 21 patients, while seven patients died. CONCLUSIONS: PRES can be a CNS complication associated with COVID-19. COVID-19 patients with mental dysfunction, seizures or visual disturbances should immediately undergo CNS imaging through multimodal MRI, electroencephalography (EEG) and cerebrospinal fluid (CSF) studies in order not to miss PRES.

Machine Learning Model for Computer-Aided Depression Screening among Young Adults Using Wireless EEG Headset.

Depression is a disorder that if not treated can hamper the quality of life. EEG has shown great promise in detecting depressed individuals from depression control individuals. It overcomes the limitations of traditional questionnaire-based methods. In this study, a machine learning-based method for detecting depression among young adults using EEG data recorded by the wireless headset is proposed. For this reason, EEG data has been recorded using an Emotiv Epoc+ headset. A total of 32 young adults participated and the PHQ9 screening tool was used to identify depressed participants. Features such as skewness, kurtosis, variance, Hjorth parameters, Shannon entropy, and Log energy entropy from 1 to 5 sec data filtered at different band frequencies were applied to KNN and SVM classifiers with different kernels. At AB band (8-30 Hz) frequency, 98.43 ± 0.15% accuracy was achieved by extracting Hjorth parameters, Shannon entropy, and Log energy entropy from 5 sec samples with a 5-fold CV using a KNN classifier. And with the same features and classifier overall accuracy = 98.10 ± 0.11, NPV = 0.977, precision = 0.984, sensitivity = 0.984, specificity = 0.976, and F1 score = 0.984 was achieved after splitting the data to 70/30 ratio for training and testing with 5-fold CV. From the findings, it can be concluded that EEG data from an Emotiv headset can be used to detect depression with the proposed method.

Cognitive deficit in post-acute COVID-19: an opportunity for EEG evaluation?

BACKGROUND AND PURPOSE: Among the most common post-COVID symptoms, many patients experienced subjective cognitive deficit, commonly named "brain fog," that might be present also in those individuals without severe acute COVID-19 respiratory involvement. Some studies have investigated some of the mechanisms that might be associated with the brain fog with objective techniques including transcranial magnetic stimulation and neuroimaging. METHODS: The aim of this study was to investigate the presence of electroencephalographic (EEG) alterations in people with post-COVID self-reported cognitive deficit. RESULTS: Out of the 90 patients attending the post-COVID neurology ambulatory service, twenty patients presenting brain fog at least 4 weeks after acute non-severe COVID-19 infection, and without previous history of epilepsy, were investigated with 19-channel EEG, Montreal Cognitive Assessment (MoCA), and magnetic resonance imaging (MRI). EEG was found altered in 65% of the sample, among which 69% presented a slowing activity and 31% were characterized by epileptic discharges principally in the frontal areas. None of the patients showed DWI MRI lesions. CONCLUSIONS: These findings highlight the usefulness of EEG analysis to objectively describe possible neurophysiological abnormalities in post-COVID patients presenting subjective cognitive deficit.

COVID-19 Continuous-EEG Case Series: A Descriptive Study.

PURPOSE: Corona virus disease 2019 (COVID-19) refers to coronavirus disease secondary to SARS-CoV2 infection mainly affecting the human respiratory system. The SARS-CoV2 has been reported to have neurotropic and neuroinvasive features and neurological sequalae with wide range of reported neurological manifestations, including cerebrovascular disease, skeletal muscle injury, meningitis, encephalitis, and demyelination, as well as seizures and focal status epilepticus. In this case series, we analyzed the continuous video-EEGs of patients with COVID-19 infection to determine the presence of specific EEG features or epileptogenicity. METHODS: All continuous video-EEG tracings done on SARS-CoV2-positive patients during a 2-week period from April 5, 2020, to April 19, 2020, were reviewed. The demographics, clinical characteristics, imaging, and EEG features were analyzed and presented. RESULTS: Of 23 patients undergoing continuous video-EEG, 16 were COVID positive and were included. Continuous video-EEG monitoring was ordered for "altered mental status" in 11 of 16 patients and for "clinical seizure" in 5 of 16 patients. None of the patients had seizures or status epilepticus as a presenting symptom of COVID-19 infection. Instead, witnessed clinical seizures developed as results of COVID-19-related medical illness(es): anoxic brain injury, stroke/hemorrhage, lithium (Li) toxicity (because of kidney failure), hypertension, and renal disease. Three patients required therapeutic burst suppression because of focal nonconvulsive status epilepticus, status epilepticus/myoclonus secondary to anoxic injury from cardiac arrest, and one for sedation (and with concomitant EEG abnormalities secondary to Li toxicity). CONCLUSIONS: In this observational case series of 16 patients with COVID-19 who were monitored with continuous video-EEG, most patients experienced a nonspecific encephalopathy. Clinical seizures and electrographic status epilepticus were the second most commonly observed neurological problem.

Impact of the COVID-19 Pandemic on Continuous EEG Utilization.

PURPOSE: The coronavirus disease 2019 (COVID-19) has significantly impacted healthcare delivery and utilization. The aim of this article was to assess the impact of the COVID-19 pandemic on in-hospital continuous electroencephalography (cEEG) utilization and identify areas for process improvement. METHODS: A 38-question web-based survey was distributed to site principal investigators of the Critical Care EEG Monitoring Research Consortium, and institutional contacts for the Neurodiagnostic Credentialing and Accreditation Board. The survey addressed the following aspects of cEEG utilization: (1) general center characteristics, (2) cEEG utilization and review, (3) staffing and workflow, and (4) health impact on EEG technologists. RESULTS: The survey was open from June 12, 2020 to June 30, 2020 and distributed to 174 centers with 79 responses (45.4%). Forty centers were located in COVID-19 hotspots. Fifty-seven centers (72.1%) reported cEEG volume reduction. Centers in the Northeast were most likely to report cEEG volume reduction (odds ratio [OR] 7.19 [1.53-33.83]; P = 0.012). Additionally, centers reporting decrease in outside hospital transfers reported cEEG volume reduction; OR 21.67 [4.57-102.81]; P ≤ 0.0001. Twenty-six centers (32.91%) reported reduction in EEG technologist coverage. Eighteen centers had personal protective equipment shortages for EEG technologists. Technologists at these centers were more likely to quarantine for suspected or confirmed COVID-19; OR 3.14 [1.01-9.63]; P = 0.058. CONCLUSIONS: There has been a widespread reduction in cEEG volume during the pandemic. Given the anticipated duration of the pandemic and the importance of cEEG in managing hospitalized patients, methods to optimize use need to be prioritized to provide optimal care. Because the survey provides a cross-sectional assessment, follow-up studies can determine the long-term impact of the pandemic on cEEG utilization.

Enhancing the feasibility of cognitive load recognition in remote learning using physiological measures and an adaptive feature recalibration convolutional neural network.

The precise assessment of cognitive load during a learning phase is an important pathway to improving students' learning efficiency and performance. Physiological measures make it possible to continuously monitor learners' cognitive load in remote learning during the COVID-19 outbreak. However, maintaining a good balance between performance and computational cost is still a major challenge in advancing cognitive load recognition technology to real-world applications. This paper introduced an adaptive feature recalibration (AFR) convolutional neural network to overcome this challenge by capturing the most discriminative physiological features (EEG and eye-tracking). The results revealed that the optimal average classification accuracy of the feature combination obtained by the AFR method reached 95.56% with only 60 feature dimensions. Additionally, compared with the best result of the conventional correlation-based feature selection (CFS) method, the introduced AFR algorithm achieved higher accuracy and cheaper computational cost, as well as a 2.06% improvement in accuracy and a 51.21% reduction in feature dimension, which is more in line with the requirements of low delay and real-time performance in practical BCI applications.

Sleep spindles changes in people with previous COVID-19 infection.

Stage 2 sleep spindles are considered useful biomarkers for the integrity of the central nervous system and for cognitive and memory skills. We investigated sleep spindles patterns in subjects after 12 months of their hospitalization in the intensive care unit (ICU) of the Padova Teaching Hospital due to COVID-19 between March and November 2020. Before the nap, participants (13 hospitalized in ICU - ICU; 9 hospitalized who received noninvasive ventilation - nonlCU; 9 age and sex-matched healthy controls - CTRL, i.e., not infected by COVID-19) underwent a cognitive and psychological as-sessment. During the nap, high-density electroencephalography (EEG) recordings were acquired. Slow (i.e., [9]-[12] Hz) and fast (i.e.,]12-16] Hz) spindles were automatically detected. Spindle density and spindle source reconstruction in brain grey matter were extracted. The psychological assessment revealed a statistical difference comparing CTRL and nonlCU in Beck Depression Inventory score and in the Physical Quality of Life index (pvalue = 0.03). The cognitive assessment revealed a trend of worsening results in executive functions in COVID-19 survivors. Slow spindle density significantly decreased comparing CTRL to COVID-19 survivors (pvalue= 0.001). There were statistically significant differences in EEG source-waveforms fast spindle amplitude onset among the three groups, mainly between CTRL and nonlCU. Clinical Relevance- Our results suggest that nonlCU were more susceptible to the hospitalization experience than ICU participants with a slight effect on cognitive tests. This impacted the spindle generation revealing a decreased density of slow spindles and affecting the generators of fast spindles in COVID-19 survivors especially in nonlCU.

Changes in EEG Measures of a Recipient of the mRNA COVID-19 Vaccine - A Case Study.

The current study is aimed to evaluate the effect of COVID-19 vaccine on human EEG and the persistence of the effect. Within a one-year-long resting EEG study period, the healthy male subject was administered two Comirnaty doses three weeks apart to prevent COVID-19. Fourteen recordings were acquired from the subject in one year: twelve reference and two post-vaccination recordings after administrating the second dose of Comirnaty. The changes in absolute powers of EEG frequency bands, EEG spectral asymmetry index (SASI), and Higuchi's fractal dimension (HFD) were analyzed. The results indicated a statistically significant increase in absolute gamma power, SASI and HFD values on the fifth day after the vaccination, while the EEG had restored its normal character on the twelfth day after vaccination. These measures seem to have higher sensitivity for the detection of the effects of the vaccine Clinical Relevance- This is the first study evaluating COVID-19 vaccine effect on healthy human EEG. The study indicated that the vaccine disturbs EEG but the impact is not long-lasting.

The value of EEG attenuation in the prediction of outcome in COVID-19 patients.

INTRODUCTION: During the COVID-19 pandemic, electroencephalography (EEG) proved to be a useful tool to demonstrate brain involvement. Many studies reported non-reactive generalized slowing as the most frequent pattern and epileptiform activity in a minority of patients. OBJECTIVE: To investigate the prevalence of diffuse unreactive background attenuation or suppression and its correlation with outcome in a cohort of COVID-19 patients. METHODS: The EEGs recorded during the first year of the COVID-19 pandemic were retrospectively evaluated to identify the main pattern and focus on the occurrence of a low-voltage background, either attenuated (10-20 µV) or suppressed (< 10 µV). We sought a correlation between in-hospital mortality and low-voltage EEG. In a subsample of patients, biomarkers of inflammation, hypoxemia and organ failure were collected. Brain imaging was also evaluated. RESULTS: Among 98 EEG performed in 50 consecutive patients, diffuse unreactive slowing was the most prevalent pattern (54%), followed by unreactive attenuation or suppression pattern (26%), being the latter significantly correlated with an unfavourable outcome (p = 0.0004). Survivors showed significantly lower interleukine-6 values compared to non-survivors. Patients with attenuated EEG and non-survivors also showed lower PaO2/FiO2 values. Neuroradiological findings were very heterogeneous with a prevalence of lesions suggestive of a microangiopathic substrate. CONCLUSIONS: EEG attenuation or suppression may be more frequent than previously reported and significantly associated with a poor outcome. SARS-CoV-2 infection may result in encephalopathy and reduced EEG voltage through mechanisms that are still unknown but deserve attention given its negative impact on prognosis.

Development of a brain wave model based on the quantitative analysis of EEG and EEG biofeedback therapy in patients with panic attacks during the COVID-19 pandemic.

The current global crisis facing the world is the COVID-19 pandemic. Infection from the SARS-CoV-2 virus leads to serious health complications and even death. As it turns out, COVID-19 not only physically assails the health of those infected, but also leads to serious mental illness regardless of the presence of the disease. Social isolation, fear, concern for oneself and one's loved ones, all of this occurs when a pandemic overloads people. People exhibit numerous neurological disorders that have never happened to them before. Patients are diagnosed with frequent panic attacks, the result of which can be seen in their Quantitative Electroencephalogram results. This test may be one of the main diagnostic tools of the COVID-19 pandemic. From the results obtained, it is possible to compare and draw conclusions. This method of testing effectively allows EEG biofeedback training and observes its effect on brain activity. The feedback received in this way gives us the opportunity to properly tailor a protocol for the patient and their conditions. Numerous studies support the effectiveness of EEG biofeedback for panic attacks and other psychiatric disorders. The purpose of our study was to show the effectiveness of EEG biofeedback with a Quantitative Electroencephalogram of the brainwave pattern after having COVID-19 and what symptoms may result.

A Bimodal Deep Learning Architecture for EEG-fNIRS Decoding of Overt and Imagined Speech.

OBJECTIVE: Brain-computer interfaces (BCI) studies are increasingly leveraging different attributes of multiple signal modalities simultaneously. Bimodal data acquisition protocols combining the temporal resolution of electroencephalography (EEG) with the spatial resolution of functional near-infrared spectroscopy (fNIRS) require novel approaches to decoding. METHODS: We present an EEG-fNIRS Hybrid BCI that employs a new bimodal deep neural network architecture consisting of two convolutional sub-networks (subnets) to decode overt and imagined speech. Features from each subnet are fused before further feature extraction and classification. Nineteen participants performed overt and imagined speech in a novel cue-based paradigm enabling investigation of stimulus and linguistic effects on decoding. RESULTS: Using the hybrid approach, classification accuracies (46.31% and 34.29% for overt and imagined speech, respectively (chance: 25%)) indicated a significant improvement on EEG used independently for imagined speech (p = 0.020) while tending towards significance for overt speech (p = 0.098). In comparison with fNIRS, significant improvements for both speech-types were achieved with bimodal decoding (p<0.001). There was a mean difference of ∼12.02% between overt and imagined speech with accuracies as high as 87.18% and 53%. Deeper subnets enhanced performance while stimulus effected overt and imagined speech in significantly different ways. CONCLUSION: The bimodal approach was a significant improvement on unimodal results for several tasks. Results indicate the potential of multi-modal deep learning for enhancing neural signal decoding. SIGNIFICANCE: This novel architecture can be used to enhance speech decoding from bimodal neural signals.

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.

Genetic Programming-Based Feature Selection for Emotion Classification Using EEG Signal.

The COVID-19 has resulted in one of the world's most significant worldwide lock-downs, affecting human mental health. Therefore, emotion recognition is becoming one of the essential research areas among various world researchers. Treatment that is efficacious and diagnosed early for negative emotions is the only way to save people from mental health problems. Genetic programming, a very important research area of artificial intelligence, proves its potential in almost every field. Therefore, in this study, a genetic program-based feature selection (FSGP) technique is proposed. A fourteen-channel EEG device gives 70 features for the input brain signal; with the help of GP, all the irrelevant and redundant features are separated, and 32 relevant features are selected. The proposed model achieves a classification accuracy of 85% that outmatches other prior works.

A Near-Global Slowing of Background Activity and Epileptic Discharges in Children With Mild to Moderately Symptomatic COVID-19 Infection: An Electro-Neurophysiological Study.

Background. To assess the functional involvement of the central nervous system (CNS) via quantitative electroencephalography (EEG) analysis in children with mild to moderate COVID-19 infection who were otherwise previously healthy children. Methods. This prospective, case-control study was conducted between June and September 2020. Sleep EEG records of at least 40 min were planned for children who tested positive for COVID-19 using real-time PCR analysis and within 4-6 months post-recovery. All of the EEG analyses in this study were performed on an Ubuntu 20.04.2 LTS Operating System with the developed software using Python 3.7.6. The quantitative analysis of the epileptic discharges within the EEG records was performed using random forest after elimination of the artifacts with a model training accuracy of 98% for each sample data point. The frequency analysis was performed using the Welch method. Results. Among the age and sex-matched groups, the global mean frequency was significantly lower among the COVID-19 patients, with a P-value of 0.004. The spike slow-wave and sharp slow-wave indices were significantly higher in the patients when compared to the controls. The mean frequency values were significantly lower in almost all of the electrodes recording the frontal, central, and occipital areas. For the temporal and parietal areas, those significantly low mean frequencies were limited to the right hemisphere. Conclusion. A near-global involvement of background activity with decreased frequency, in addition to epileptic discharges, was recorded in mild to moderately COVID-19 infected children post-infection.

Effects of Electroencephalogram Biofeedback on Emotion Regulation and Brain Homeostasis of Late Adolescents in the COVID-19 Pandemic.

PURPOSE: The purpose of this study was to examine the effects of electroencephalogram (EEG) biofeedback training for emotion regulation and brain homeostasis on anxiety about COVID-19 infection, impulsivity, anger rumination, meta-mood, and self-regulation ability of late adolescents in the prolonged COVID-19 pandemic situation. METHODS: A non-equivalent control group pretest-posttest design was used. The participants included 55 late adolescents in the experimental and control groups. The variables were evaluated using quantitative EEG at pre-post time points in the experimental group. The experimental groups received 10 sessions using the three-band protocol for five weeks. The collected data were analyzed using the Shapiro-Wilk test, Wilcoxon rank sum test, Wilcoxon signed-rank test, t-test and paired t-test using the SAS 9.3 program. The collected EEG data used a frequency series power spectrum analysis method through fast Fourier transform. RESULTS: Significant differences in emotion regulation between the two groups were observed in the anxiety about COVID-19 infection (W = 585.50, p = .002), mood repair of meta-mood (W = 889.50, p = .024), self-regulation ability (t = -5.02, p < .001), self-regulation mode (t = -4.74, p < .001), and volitional inhibition mode (t = -2.61, p = .012). Neurofeedback training for brain homeostasis was effected on enhanced sensory-motor rhythm (S = 177.00, p < .001) and inhibited theta (S = -166.00, p < .001). CONCLUSION: The results demonstrate the potential of EEG biofeedback training as an independent nursing intervention that can markedly improve anxiety, mood-repair, and self-regulation ability for emotional distress during the COVID-19 pandemic.

A Modified LSTM Framework for Analyzing COVID-19 Effect on Emotion and Mental Health during Pandemic Using the EEG Signals.

COVID-19, a WHO-declared public health emergency of worldwide concern, is quickly spreading over the world, posing a physical and mental health hazard. The COVID-19 has resulted in one of the world's most significant worldwide lockdowns, affecting human mental health. In this research work, a modified Long Short-Term Memory (MLSTM)-based Deep Learning model framework is proposed for analyzing COVID-19 effect on emotion and mental health during the pandemic using electroencephalogram (EEG) signals. The participants of this study were volunteers that recovered from COVID-19. The EEG dataset of 40 people is collected to predict emotion and mental health. The results of the MLSTM model are also compared with the other literature classifiers. With an accuracy of 91.26%, the MLSTM beats existing classifiers when using the 70-30 partitioning technique.

Electroencephalography at the height of a pandemic: EEG findings in patients with COVID-19.

OBJECTIVE: To characterize continuous video electroencephalogram (VEEG) findings of hospitalized COVID-19 patients. METHODS: We performed a retrospective chart review of patients admitted at three New York City hospitals who underwent VEEG at the peak of the COVID-19 pandemic. Demographics, comorbidities, neuroimaging, VEEG indications and findings, treatment, and outcomes were collected. RESULTS: Of 93 patients monitored, 77% had severe COVID-19 and 40% died. Acute ischemic or hemorrhagic stroke was present in 26% and 15%, respectively. Most common VEEG indications were encephalopathy/coma (60%) and seizure-like movements (38%). Most common VEEG findings were generalized slowing (97%), generalized attenuation (31%), generalized periodic discharges (17%) and generalized sharp waves (15%). Epileptiform abnormalities were present in 43% and seizures in 8% of patients, all of whom had seizure risk factors. Factors associated with an epileptiform VEEG included increasing age (OR 1.07, p = 0.001) and hepatic/renal failure (OR 2.99, p = 0.03). CONCLUSIONS: Most COVID-19 patients who underwent VEEG monitoring had severe COVID-19 and over one-third had acute cerebral injury (e.g., stroke, anoxia). Seizures were uncommon. VEEG findings were nonspecific. SIGNIFICANCE: VEEG findings in this cohort of hospitalized COVID-19 patients were those often seen in critical illness. Seizures were uncommon and occurred in the setting of common seizure risk factors.

Intermittent theta burst stimulation over the parietal cortex has a significant neural effect on working memory.

The crucial role of the parietal cortex in working memory (WM) storage has been identified by fMRI studies. However, it remains unknown whether repeated parietal intermittent theta-burst stimulation (iTBS) can improve WM. In this within-subject randomized controlled study, under the guidance of fMRI-identified parietal activation in the left hemisphere, 22 healthy adults received real and sham iTBS sessions (five consecutive days, 600 pulses per day for each session) with an interval of 9 months between the two sessions. Electroencephalography signals of each subject before and after both iTBS sessions were collected during a change detection task. Changes in contralateral delay activity (CDA) and K-score were then calculated to reflect neural and behavioral WM improvement. Repeated-measures ANOVA suggested that real iTBS increased CDA more than the sham one (p = .011 for iTBS effect). Further analysis showed that this effect was more significant in the left hemisphere than in the right hemisphere (p = .029 for the hemisphere-by-iTBS interaction effect). Pearson correlation analyses showed significant correlations for two conditions between CDA changes in the left hemisphere and K score changes (ps <.05). In terms of the behavioral results, significant K score changes after real iTBS were observed for two conditions, but a repeated-measures ANOVA showed a nonsignificant main effect of iTBS (p = .826). These results indicate that the current iTBS protocol is a promising way to improve WM capability based on the neural indicator (CDA) but further optimization is needed to produce a behavioral effect.

Brain structure and function in people recovering from COVID-19 after hospital discharge or self-isolation: a longitudinal observational study protocol.

BACKGROUND: The detailed extent of neuroinvasion or deleterious brain changes resulting from COVID-19 and their time courses remain to be determined in relation to "long-haul" COVID-19 symptoms. Our objective is to determine whether there are alterations in functional brain imaging measures among people with COVID-19 after hospital discharge or self-isolation. METHODS: This paper describes a protocol for NeuroCOVID-19, a longitudinal observational study of adults aged 20-75 years at Sunnybrook Health Sciences Centre in Toronto, Ontario, that began in April 2020. We aim to recruit 240 adults, 60 per group: people who contracted COVID-19 and were admitted to hospital (group 1), people who contracted COVID-19 and self-isolated (group 2), people who experienced influenza-like symptoms at acute presentation but tested negative for COVID-19 and self-isolated (group 3, control) and healthy people (group 4, control). Participants are excluded based on premorbid neurologic or severe psychiatric illness, unstable cardiovascular disease, and magnetic resonance imaging (MRI) contraindications. Initial and 3-month follow-up assessments include multiparametric brain MRI and electroencephalography. Sensation and cognition are assessed alongside neuropsychiatric assessments and symptom self-reports. We will test the data from the initial and follow-up assessments for group differences based on 3 outcome measures: MRI cerebral blood flow, MRI resting state fractional amplitude of low-frequency fluctuation and electroencephalography spectral power. INTERPRETATION: If neurophysiologic alterations are detected in the COVID-19 groups in our NeuroCOVID-19 study, this information could inform future research regarding interventions for long-haul COVID-19. The study results will be disseminated to scientists, clinicians and COVID-19 survivors, as well as the public and private sectors to provide context on how brain measures relate to lingering symptoms.

Elective inpatient video-EEG monitoring during the COVID-19 pandemic.

To evaluate the safety and feasibility of admission for elective video-EEG monitoring during the SARS-CoV-2 pandemic. We performed a retrospective review of elective inpatient epilepsy monitoring unit admissions at our institution from May 3rd, 2020 to August 12th, 2020. All patients were screened by telephone for symptoms concerning infection or recent diagnosis of SARS-CoV-2 or excess medical risk prior to admission. Patients deemed eligible for admission underwent testing via a nasopharyngeal swab for SARS-CoV-2 within three days of admission, and were directed to self-quarantine between testing and admission. The community seven-day case rate for SARS-CoV-2 (new cases per 100,000 population) ranged from 2.8 to 28.9 during the study period in our region. A total of 95 patients (63 adults and 32 children) were admitted. One adult patient developed mild SARS-CoV-2 infection and one adult patient tested positive for asymptomatic SARS-CoV-2 infection. These findings illustrate that inpatient epilepsy monitoring can be safely performed in carefully selected patients when appropriate processes are in place, even in the setting of the SARS-CoV-2 pandemic. There is a risk of nosocomial spread, and the potential benefits of admission should be balanced against the risks of infection.