Faculty Bibliography

BACKGROUND:The risk of SARS-CoV-2 infection and severity with disease modifying therapies (DMTs) in multiple sclerosis (MS) remains unclear, with some studies demonstrating increased risks of infection with B-cell-depleting (anti-CD20) therapies and severity, while others fail to observe an association. Most existing studies are limited by a reliance on 'numerator' data (i.e., COVID-19 cases) only. OBJECTIVE:To assess the risks of COVID-19 by DMT, this study aimed to assess both 'numerator' (patients with SARS-CoV-2 infection) and 'denominator' data (all patients treated with DMTs of interest) to determine if any DMTs impart an increased risk of SARS-CoV-2 infection or disease severity. METHODS:We systematically reviewed charts and queried patients during clinic encounters in the NYU MS Comprehensive Care Center (MSCCC) for evidence of COVID-19 in all patients who were on the most commonly used DMTs in our clinic (sphingosine-1-phosphate receptor (S1P) modulators (fingolimod/siponimod), rituximab, ocrelizumab, fumarates (dimethyl fumarate/diroximel fumarate), and natalizumab). COVID-19 status was determined by clinical symptoms (CDC case definition) and laboratory testing where available (SARS-CoV-2 PCR, SARS-CoV-2 IgG). Multivariable analyses were conducted to determine predictors of infection and severe disease (hospitalization or death) using SARS-CoV-2 infected individuals per DMT group and all individuals on a given DMT as denominator. RESULTS:We identified 1,439 MS patients on DMTs of interest, of which 230 had lab-confirmed (n = 173; 75.2%) or suspected (n = 57; 24.8%) COVID-19. Infection was most frequent in those on rituximab (35/138; 25.4%), followed by fumarates (39/217; 18.0%), S1P modulators (43/250; 17.2%), natalizumab (36/245; 14.7%), and ocrelizumab (77/589; 13.1%). There were 14 hospitalizations and 2 deaths. No DMT was found to be significantly associated with increased risk of SARS-CoV-2 infection. Rituximab was a predictor of severe SARS-CoV-2 infection among patients with SARS-CoV-2 infection (OR 6.7; 95% CI 1.1-41.7) but did not reach statistical significance when the entire patient population on DMT was used (OR 2.8; 95% CI 0.6-12.2). No other DMT was associated with an increased risk of severe COVID-19. CONCLUSIONS:Analysis of COVID-19 risk among all patients on the commonly used DMTs did not demonstrate increased risk of infection with any DMT. Rituximab was associated with increased risk for severe disease.

BACKGROUND/UNASSIGNED:The MSBase prediction model of treatment response leverages multiple demographic and clinical characteristics to estimate hazards of relapses, confirmed disability accumulation (CDA), and confirmed disability improvement (CDI). The model did not include Multiple Sclerosis Severity Score (MSSS), a disease duration-adjusted ranked score of disability. OBJECTIVE/UNASSIGNED:To incorporate MSSS into the MSBase prediction model and compare model accuracy with and without MSSS. METHODS/UNASSIGNED:The associations between MSSS and relapse, CDA, and CDI were evaluated with marginal proportional hazards models adjusted for three principal components representative of patients' demographic and clinical characteristics. The model fit with and without MSSS was assessed with penalized r2 and Harrell C. RESULTS/UNASSIGNED:A total of 5866 MS patients were started on disease-modifying therapy during prospective follow-up (age 38.4 ± 10.6 years; 72% female; disease duration 8.5 ± 7.7 years). Including MSSS into the model improved the accuracy of individual prediction of relapses by 31%, of CDA by 23%, and of CDI by 24% (Harrell C) and increased the amount of variance explained for relapses by 49%, for CDI by 11%, and for CDA by 10% as compared with the original model. CONCLUSION/UNASSIGNED:Addition of a single, readily available metric, MSSS, to the comprehensive MSBase prediction model considerably improved the individual accuracy of prognostics in MS.

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.

Departing from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models generate appropriate linguistic responses in a given context. In the current study, nine participants listened to a 30-min podcast while their brain responses were recorded using electrocorticography (ECoG). We provide empirical evidence that the human brain and autoregressive DLMs share three fundamental computational principles as they process the same natural narrative: (1) both are engaged in continuous next-word prediction before word onset; (2) both match their pre-onset predictions to the incoming word to calculate post-onset surprise; (3) both rely on contextual embeddings to represent words in natural contexts. Together, our findings suggest that autoregressive DLMs provide a new and biologically feasible computational framework for studying the neural basis of language.

Background/UNASSIGNED:Medical residents must sustain acute sleep deprivation, which can lead to nonfatal and fatal consequences in hospitals due to cognitive decline. Anodal transcranial direct current stimulation (a-tDCS) is a safe noninvasive neuromodulation technique that can induce depolarization of neurons. Previous studies in pilots have shown benefits against fatigue increasing wakefulness and cognitive performance. However, the effects of a-tDCS on cognition in acute sleep deprived healthcare workers remains unknown. Purpose/UNASSIGNED:To evaluate cognitive changes in sleep deprived medical residents after one session of a-tDCS. Methods/UNASSIGNED:Open clinical test-re-test study including 13 medical residents with acute sleep deprivation. Subjects received 1 session of bifrontal a-tDCS (2mAx20min), anodal over the left dorsolateral prefrontal region. Pre-and-post treatment subjects were tested with Beck anxiety inventory, Beck depression and HVLT tests, Rey´s and Taylor´s figures, Trail Making A/B, Stroop, Aleatory Digit retention test (WAIS), Digits and symbols and MoCA tests. Post-intervention was added the Executive functions and Frontal Lobes Neuropsychological Battery (BANFE2) test and changing the Taylor figure for Reyfigure. Results/UNASSIGNED:Twelve medical residents were analyzed; 8 men and 4 women, 29.5 (+/-2.2) years mean age. All had a mean of 21.6 (+/-1.3) hours of sleep deprivation. There were no serious adverse events. We found statistically significant difference in Rey´s/Taylor´s figures (p=0.002), Trail Making Test (p=0.005), WAIS IV symbols (p=0.003), Word Stroop (p=0.021). BANFE-2 showed that the main affected area was the orbito-medial prefrontal region. Conclusion/UNASSIGNED:a-tDCS appears safe and improves working memory, attention, response time and distractors elimination in acute sleep deprived medical residents.

Advanced wearable devices are increasingly incorporating high-resolution multi-camera systems. As state-of-the-art neural networks for processing the resulting image data are computationally demanding, there has been a growing interest in leveraging fifth generation (5G) wireless connectivity and mobile edge computing for offloading this processing closer to end-users. To assess this possibility, this paper presents a detailed simulation and evaluation of 5G wireless offloading for object detection in the case of a powerful, new smart wearable called VIS4ION, for the Blind-and-Visually Impaired (BVI). The current VIS4ION system is an instrumented book-bag with high-resolution cameras, vision processing, and haptic and audio feedback. The paper considers uploading the camera data to a mobile edge server to perform real-time object detection and transmitting the detection results back to the wearable. To determine the video requirements, the paper evaluates the impact of video bit rate and resolution on object detection accuracy and range. A new street scene dataset with labeled objects relevant to BVI navigation is leveraged for analysis. The vision evaluation is combined with a full-stack wireless network simulation to determine the distribution of throughputs and delays with real navigation paths and ray-tracing from new high-resolution 3D models in an urban environment. For comparison, the wireless simulation considers both a standard 4G-Long Term Evolution (LTE) sub-6-GHz carrier and high-rate 5G millimeter-wave (mmWave) carrier. The work thus provides a thorough and detailed assessment of edge computing for object detection with mmWave and sub-6-GHz connectivity in an application with both high bandwidth and low latency requirements.

Importance/UNASSIGNED:Non-communicable chronic diseases (NCDs) such as obesity, type 2 diabetes, heart disease, and cancer were rare among non-western populations with traditional diets and lifestyles. As populations transitioned toward industrialized diets and lifestyles, NCDs developed. Objective/UNASSIGNED:We performed a systematic literature review to examine the effects of diet and lifestyle transitions on NCDs. Evidence Review/UNASSIGNED:We identified 22 populations that underwent a nutrition transition, eleven of which had sufficient data. Of these, we chose four populations with diverse geographies, diets and lifestyles who underwent a dietary and lifestyle transition and explored the relationship between dietary changes and health outcomes. We excluded populations with features overlapping with selected populations or with complicating factors such as inadequate data, subgroups, and different study methodologies over different periods. The selected populations were Yemenite Jews, Tokelauans, Tanushimaru Japanese, and Maasai. We also review transition data from seven excluded populations (Pima, Navajo, Aboriginal Australians, South African Natal Indians and Zulu speakers, Inuit, and Hadza) to assess for bias. Findings/UNASSIGNED:The three groups that replaced saturated fats (SFA) from animal (Yemenite Jews, Maasai) or plants (Tokelau) with refined carbohydrates had negative health outcomes (e.g., increased obesity, diabetes, heart disease). Yemenites reduced SFA consumption by >40% post-transition but men's BMI increased 19% and diabetes increased ~40-fold. Tokelauans reduced fat, dramatically reduced SFA, and increased sugar intake: obesity and diabetes rose. The Tanushimaruans transitioned to more fats and less carbohydrates and used more anti-hypertensive medications; stroke and breast cancer declined while heart disease was stable. The Maasai transitioned to lower fat, SFA and higher carbohydrates and had increased BMI and diabetes. Similar patterns were observed in the seven other populations. Conclusion/UNASSIGNED:The nutrient category most strongly associated with negative health outcomes - especially obesity and diabetes - was sugar (increased 600-650% in Yemenite Jews and Tokelauans) and refined carbohydrates (among Maasai, total carbohydrates increased 39% in men and 362% in women), while increased calories was less strongly associated with these disorders. Across 11 populations, NCDs were associated with increased refined carbohydrates more than increased calories, reduced activity or other factors, but cannot be attributed to SFA or total fat consumption.