Faculty Bibliography

Coronavirus 19 (COVID-19) has infected over 400 million people worldwide. Although COVID-19 causes predominantly respiratory symptoms, it can affect other organs including the brain, producing neurological symptoms. People with epilepsy (PWE) have been particularly impacted during the pandemic with decreased access to care, increased stress, and worsening seizures in up to 22% of them probably due to multiple factors. COVID-19 vaccines were produced in a record short time and have yielded outstanding protection with very rare serious side effects. Studies have found that COVID-19 vaccination does not increase seizures in the majority of PWE. COVID-19 does not produce a pathognomonic EEG or seizure phenotype, but rather 1 that can be seen in other types of encephalopathy. COVID-19 infection and its complications can lead to seizures, status epilepticus and post-COVID inflammatory syndrome with potential multi-organ damage in people without pre-existing epilepsy. The lack of access to care during the pandemic has forced patients and doctors to rapidly implement telemedicine. The use of phone videos and smart telemedicine are helping to treat patients during this pandemic and are becoming standard of care. Investment in infrastructure is important to make sure patients can have access to care even during a pandemic.

The utility of the Victoria Symptom Validity Test (VSVT) as a performance validity test (PVT) has been primarily established using response accuracy scores. However, the degree to which response latency may contribute to accurate classification of performance invalidity over and above accuracy scores remains understudied. Therefore, this study investigated whether combining VSVT accuracy and response latency scores would increase predictive utility beyond use of accuracy scores alone. Data from a mixed clinical sample of 163 patients, who were administered the VSVT as part of a larger neuropsychological battery, were analyzed. At least four independent criterion PVTs were used to establish validity groups (121 valid/42 invalid). Logistic regression models examining each difficulty level revealed that all VSVT measures were useful in classifying validity groups, both independently and when combined. Individual predictor classification accuracy ranged from 77.9 to 81.6%, indicating acceptable to excellent discriminability across the validity indices. The results of this study support the value of both accuracy and latency scores on the VSVT to identify performance invalidity, although the accuracy scores had superior classification statistics compared to response latency, and mean latency indices provided no unique benefit for classification accuracy beyond dimensional accuracy scores alone.

Although mRNA vaccine responses following previous coronavirus disease 2019 (COVID-19) infection have not been assessed in trials, it has been shown that serological evidence of previous COVID-19 generates strong humoral and cellular responses to one dose of mRNA vaccine. We describe a patient with prior COVID-19 infection who developed acute transient encephalopathy with elevated inflammatory markers within 24 h of her first injection of Moderna COVID-19 vaccine. A 69-year-old cognitively normal woman presented with intermittent inattention, disorientation, left/right confusion, weakness, gait instability, and decreased speech. Head CT, brain MRI and MRA, complete blood count, liver enzymes, hepatitis B serology, ammonia, thyroid function, vitamin B12, and pulse oximetry were normal. Electroencephalography performed 48 h after symptom onset showed diffuse triphasic waves, diffuse theta and delta slowing, and no posterior dominant rhythm. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) IgG was positive and inflammatory markers were elevated. On day 5 post-vaccine, she returned to her baseline, without neurological sequelae. The reported patient likely developed a transient inflammatory encephalopathy associated with an abnormal immunologic reaction to one dose of COVID-19 vaccine, in the setting of remote COVID-19 infection (1 year prior), SARS-CoV-2 IgG-positivity, and multiple comorbidities. Physicians should be alert to possible postvaccination reactogenicity in individuals with SARS-CoV-2 IgG-positivity, including risk of neuro-inflammation.

BACKGROUND/UNASSIGNED:Hyperglycemia has been associated with worse survival in glioblastoma. Attempts to lower glucose yielded mixed responses which could be due to molecularly distinct GBM subclasses. METHODS/UNASSIGNED:Clinical, laboratory, and molecular data on 89 IDH-wt GBMs profiled by clinical next-generation sequencing and treated with Stupp protocol were reviewed. IDH-wt GBMs were sub-classified into RTK I (Proneural), RTK II (Classical) and Mesenchymal subtypes using whole-genome DNA methylation. Average glucose was calculated by time-weighting glucose measurements between diagnosis and last follow-up. RESULTS/UNASSIGNED:= .02). Methylation clustering did not identify unique signatures associated with high or low glucose levels. Metabolomic analysis of 23 tumors showed minimal variation across metabolites without differences between molecular subclasses. CONCLUSION/UNASSIGNED:Higher average glucose values were associated with poorer OS in RTKI and Mesenchymal IDH-wt GBM, but not RTKII. There were no discernible epigenetic or metabolomic differences between tumors in different glucose environments, suggesting a potential survival benefit to lowering systemic glucose in selected molecular subtypes.

Objective/UNASSIGNED:Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality. Although lots of effort has been made in identifying clinical risk factors for SUDEP in the literature, there are few validated methods to predict individual SUDEP risk. Prolonged postictal EEG suppression (PGES) is a potential SUDEP biomarker, but its occurrence is infrequent and requires epilepsy monitoring unit admission. We use machine learning methods to examine SUDEP risk using interictal EEG and ECG recordings from SUDEP cases and matched living epilepsy controls. Methods/UNASSIGNED:This multicenter, retrospective, cohort study examined interictal EEG and ECG recordings from 30 SUDEP cases and 58 age-matched living epilepsy patient controls. We trained machine learning models with interictal EEG and ECG features to predict the retrospective SUDEP risk for each patient. We assessed cross-validated classification accuracy and the area under the receiver operating characteristic (AUC) curve. Results/UNASSIGNED:The logistic regression (LR) classifier produced the overall best performance, outperforming the support vector machine (SVM), random forest (RF), and convolutional neural network (CNN). Among the 30 patients with SUDEP [14 females; mean age (SD), 31 (8.47) years] and 58 living epilepsy controls [26 females (43%); mean age (SD) 31 (8.5) years], the LR model achieved the median AUC of 0.77 [interquartile range (IQR), 0.73-0.80] in five-fold cross-validation using interictal alpha and low gamma power ratio of the EEG and heart rate variability (HRV) features extracted from the ECG. The LR model achieved the mean AUC of 0.79 in leave-one-center-out prediction. Conclusions/UNASSIGNED:Our results support that machine learning-driven models may quantify SUDEP risk for epilepsy patients, future refinements in our model may help predict individualized SUDEP risk and help clinicians correlate predictive scores with the clinical data. Low-cost and noninvasive interictal biomarkers of SUDEP risk may help clinicians to identify high-risk patients and initiate preventive strategies.

Background and objectives/UNASSIGNED:) in a group of cognitively normal middle-aged and older adults. Methods/UNASSIGNED:Our study was a retrospective analysis of prospectively collected data. Subjects were enrolled for studies assessing the role of hippocampal hemodynamics as a biomarker for AD among cognitively healthy elderly individuals (age > 50). Participants without cognitive impairment, stroke, and active substance abuse were recruited between January 2008 and November 2017 at the NYU Grossman School of Medicine, former Center for Brain Health. All subjects underwent medical, psychiatric, and neurological assessments, blood tests, and MRI examinations. To estimate CVR, we increased their carbon dioxide levels using a rebreathing protocol. Relationships between BMI and brain measures were tested using linear regression. Results/UNASSIGNED:in women (β = -0.20, unstandardized B = -0.08, 95% CI -0.13, -0.02). Discussion/UNASSIGNED:These findings lend support to the notion that obesity is a risk factor for hippocampal hemodynamic impairment and suggest targeting obesity as an important prevention strategy. Prospective studies assessing the effects of weight loss on brain hemodynamic measures and inflammation are warranted.

Although tardive and neuroleptic-induced movement disorders are not typically viewed as neurologic emergencies, in rare instances they may manifest in ways that can produce severe bodily discomfort or even threaten vital functions like breathing and swallowing. The continued widespread use of dopamine receptor-blocking agents in the hospital and outpatient setting has necessitated their recognition, as prompt diagnosis and treatment are critical for the prevention of sometimes life-threatening complications. In this chapter, we review the history, clinical presentation, and management of neuroleptic-induced respiratory and gastrointestinal phenomena and oculogyric crisis.
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