Faculty Bibliography

Saccadic slowing as a component of supranuclear saccadic gaze palsy is an important diagnostic sign in multiple neurologic conditions, including degenerative, inflammatory, genetic, or ischemic lesions affecting brainstem structures responsible for saccadic generation. Little attention has been given to the accuracy with which clinicians correctly identify saccadic slowing. We compared clinician (n = 19) judgements of horizontal and vertical saccade speed on video recordings of saccades (from 9 patients with slow saccades, 3 healthy controls) to objective saccade peak velocity measurements from infrared oculographic recordings. Clinician groups included neurology residents, general neurologists, and fellowship-trained neuro-ophthalmologists. Saccades with normal peak velocities on infrared recordings were correctly identified as normal in 57% (91/171; 171 = 9 videos × 19 clinicians) of clinician decisions; saccades determined to be slow on infrared recordings were correctly identified as slow in 84% (224/266; 266 = 14 videos × 19 clinicians) of clinician decisions. Vertical saccades were correctly identified as slow more often than horizontal saccades (94% versus 74% of decisions). No significant differences were identified between clinician training levels. Reliable differentiation between normal and slow saccades is clinically challenging; clinical performance is most accurate for detection of vertical saccade slowing. Quantitative analysis of saccade peak velocities enhances accurate detection and is likely to be especially useful for detection of mild saccadic slowing.

Although visual symptoms are common following concussion, quantitative measures of visual function are missing from concussion evaluation protocols on the athletic sideline. For the past half century, rapid automatized naming (RAN) tasks have demonstrated promise as quantitative neuro-visual assessment tools in the setting of head trauma and other disorders but have been previously limited in accessibility and scalability. The Mobile Interactive Cognitive Kit (MICK) App is a digital RAN test that can be downloaded on most mobile devices and can therefore provide a quantitative measure of visual function anywhere, including the athletic sideline. This investigation examined the feasibility of MICK App administration in a cohort of Division 1 college football players. Participants (n = 82) from a National Collegiate Athletic Association (NCAA) Division 1 football team underwent baseline testing on the MICK app. Total completion times of RAN tests on the MICK app were recorded; magnitudes of best time scores and between-trial learning effects were determined by paired t-test. Consistent with most timed performance measures, there were significant learning effects between the two baseline trials for both RAN tasks on the MICK app: Mobile Universal Lexicon Evaluation System (MULES) (p < 0.001, paired t-test, mean improvement 13.3 s) and the Staggered Uneven Number (SUN) (p < 0.001, mean improvement 3.3 s). This study demonstrated that the MICK App can be feasibly administered in the setting of pre-season baseline testing in a Division I environment. These data provide a foundation for post-injury sideline testing that will include comparison to baseline in the setting of concussion.

BACKGROUND:The diagnosis of multiple sclerosis (MS) requires demyelinating events that are disseminated in time and space. Peripapillary retinal nerve fiber layer (pRNFL) thickness as measured by optical coherence tomography (OCT) distinguishes eyes with a prior history of acute optic neuritis (ON) and may provide evidence to support a demyelinating attack. OBJECTIVE:To investigate whether a deep learning (DL)-based network can distinguish between eyes with prior ON and healthy control (HC) eyes using peripapillary ring scans. METHODS:We included 1033 OCT scans from 415 healthy eyes (213 HC subjects) and 510 peripapillary ring scans from 164 eyes with prior acute ON (140 patients with MS). Data were split into 70% training, 15% validation, and 15% test data. We included 102 OCT scans from 80 healthy eyes (40 HC) and 61 scans from 40 ON eyes (31 MS patients) from an independent second center. Receiver operating characteristic curve analyses with area under the curve (AUC) were used to investigate performance. RESULTS:We used a dilated residual convolutional neural network for the classification. The final network had an accuracy of 0.85 and an AUC of 0.86, whereas pRNFL only had an AUC of 0.77 in recognizing ON eyes. Using data from a second center, the network achieved an accuracy of 0.77 and an AUC of 0.90 compared to pRNFL, which had an AUC of 0.84. INTERPRETATION:DL-based disease classification of prior ON is feasible and has the potential to outperform thickness-based classification of eyes with and without history of prior ON.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Recent studies have suggested that inter-eye differences (IEDs) in peripapillary retinal nerve fiber layer (pRNFL) or ganglion cell+inner plexiform (GCIPL) thickness by spectral-domain optical coherence tomography (SD-OCT) may identify people with a history of unilateral optic neuritis (ON). However, this requires further validation. Machine learning classification may be useful for validating thresholds for OCT IEDs and for examining added utility for visual function tests, such as low-contrast letter acuity (LCLA), in the diagnosis of people with multiple sclerosis (PwMS) and for unilateral ON history. METHODS:Participants were from 11 sites within the International Multiple Sclerosis Visual System (IMSVISUAL) consortium. pRNFL and GCIPL thicknesses were measured using SD-OCT. A composite score combining OCT and visual measures was compared individual measurements to determine the best model to distinguish PwMS from controls. These methods were also used to distinguish those with history of ON among PwMS. ROC curve analysis was performed on a training dataset (2/3 of cohort), then applied to a testing dataset (1/3 of cohort). Support vector machine (SVM) analysis was used to assess whether machine learning models improved diagnostic capability of OCT. RESULTS:Among 1,568 PwMS and 552 controls, variable selection models identified GCIPL IED, average GCIPL thickness (both eyes), and binocular 2.5% LCLA as most important for classifying PwMS vs. controls. This composite score performed best, with AUC=0.89 (95% CI 0.85, 0.93), sensitivity=81% and specificity=80%. The composite score ROC curve performed better than any of the individual measures from the model (p<0.0001). GCIPL IED remained the best single discriminator of unilateral ON history among PwMS (AUC=0.77, 95% CI 0.71,0.83, sensitivity=68%, specificity=77%). SVM analysis performed comparably to standard logistic regression models. CONCLUSIONS:A composite score combining visual structure and function improved the capacity of SD-OCT to distinguish PwMS from controls. GCIPL IED best distinguished those with history of unilateral ON. SVM performed as well as standard statistical models for these classifications. CLASSIFICATION OF EVIDENCE/METHODS:The study provides Class III evidence that SD-OCT accurately distinguishes multiple sclerosis from normal controls as compared to clinical criteria.

BACKGROUND:STRIVE was a prospective, 4-year, multicenter, observational, open-label, single-arm study of natalizumab treatment in anti-JC virus antibody-negative patients with early relapsing-remitting multiple sclerosis (RRMS). OBJECTIVE:Study objectives examined the effects of natalizumab on cognitive processing speed, confirmed disability improvement (CDI), and patient-reported outcomes (PROs). METHODS:Clinical and PRO secondary endpoints were assessed annually over 4 years in STRIVE. The Symbol Digit Modalities Test (SDMT) was used as a measure of cognitive processing speed. PROs were assessed using the Multiple Sclerosis Impact Score (MSIS-29) and the Work Productivity and Activity Impairment Questionnaire (WPAI). RESULTS:At all four annual assessments, the proportion of patients in the intent-to-treat (ITT) population (N = 222) who exhibited clinically meaningful improvement in their SDMT score from baseline (i.e., change ≥ 4 points) ranged from 41.9 to 54.0%. The cumulative probability of CDI at 4 years in patients in the ITT population with a baseline Expanded Disability Status Scale score ≥ 2 (N = 133) was 43.9%. Statistically significant reductions in the mean change from screening in the MSIS-29 physical and psychological scores, indicating improved quality of life, were observed over all 4 years (P ≤ 0.0012 for all). A statistically significant decrease from screening in the impact of MS on regular activities, signifying an improvement in this WPAI measure, was also observed over all 4 years of the study. CONCLUSION/CONCLUSIONS:These results further extend our knowledge of the effectiveness, specifically regarding improvements in cognitive processing speed, disability and PROs, of long-term natalizumab treatment in early RRMS patients. CLINICALTRIALS/RESULTS:GOV: NCT01485003 (5 December 2011).

BACKGROUND/OBJECTIVES/OBJECTIVE:Little is known about trajectories of recovery 12-months after hospitalization for severe COVID. METHODS:We conducted a prospective, longitudinal cohort study of patients with and without neurological complications during index hospitalization for COVID-19 from March 10, 2020-May 20, 2020. Phone follow-up batteries were performed at 6- and 12-months post-COVID symptom onset. The primary 12-month outcome was the modified Rankin Scale (mRS) comparing patients with or without neurological complications using multivariable ordinal analysis. Secondary outcomes included: activities of daily living (Barthel Index), telephone Montreal Cognitive Assessment (t-MoCA) and Neuro-QoL batteries for anxiety, depression, fatigue and sleep. Changes in outcome scores from 6 to 12-months were compared using non-parametric paired-samples sign test. RESULTS:Twelve-month follow-up was completed in N=242 patients (median age 65, 64% male, 34% intubated during hospitalization) and N=174 completed both 6- and 12-month follow-up. At 12-months 197/227 (87%) had ≥1 abnormal metric: mRS>0 (75%), Barthel<100 (64%), t-MoCA≤18 (50%), high anxiety (7%), depression (4%), fatigue (9%) and poor sleep (10%). 12-month mRS scores did not differ significantly among those with (N=113) or without (N=129) neurological complications during hospitalization after adjusting for age, sex, race, pre-COVID mRS and intubation status (adjusted OR 1.4, 95% CI0.8-2.5), though those with neurological complications had higher fatigue scores (T-score 47 vs 44, P=0.037). Significant improvements in outcome trajectories from 6- to 12-months were observed in t-MoCA scores (56% improved, median difference 1 point, P=0.002), and Neuro-QoL anxiety scores (45% improved, P=0.003). Non-significant improvements occurred in fatigue, sleep and depression scores in 48%, 48% and 38% of patients, respectively. Barthel and mRS scores remained unchanged between 6 and 12-months in >50% of patients. DISCUSSION/CONCLUSIONS:At 12-months post-hospitalization for severe COVID, 87% of patients had ongoing abnormalities in functional, cognitive or Neuro-QoL metrics and abnormal cognition persisted in 50% of patients without a prior history of dementia/cognitive abnormality. Only fatigue severity differed significantly between patients with or without neurological complications during index hospitalization. However, significant improvements in cognitive (t-MoCA) and anxiety (Neuro-QoL) scores occurred in 56% and 45% of patients, respectively, between 6- to 12-months. These results may not be generalizable to those with mild/moderate COVID.

ABSTRACT:A 62-year-old man presented with headache, fever, and malaise. He was diagnosed with Anaplasma phagocytophilum, confirmed by serum polymerase chain reaction, and started on oral doxycycline. After 5 days of treatment, the patient began to experience gait imbalance with frequent falls, as well as myoclonus, and confusion. Examination was notable for opsoclonus-myoclonus-ataxia (OMA) and hypometric saccades. Cerebrospinal fluid (CSF) autoimmune encephalitis panel demonstrated a markedly elevated neuronal intermediate filament (NIF) immunoglobulin G antibody titer of 1:16, with positive neurofilament light- and heavy-chain antibodies. These antibodies were suspected to have been triggered by the Anaplasma infection. Repeat CSF examination 8 days later still showed a positive immunofluorescence assay for NIF antibodies, but the CSF titer was now less than 1:2. Body computed tomography imaging was unrevealing for an underlying cancer. Our patient illustrates a postinfectious mechanism for OMA and saccadic hypometria after Anaplasma infection.

Purpose/UNASSIGNED:Herpes zoster (HZ) has been identified as a potential association with the BNT162b2 COVID-19 vaccination. This study evaluated this possible association in a cohort of patients receiving the vaccination. Methods/UNASSIGNED:Epic electronic health records of adult patients who received at least one COVID-19 vaccination between January 12, 2020 and 9/30/2021 within the NYU Langone Health were reviewed to analyze a new diagnosis of herpes zoster within 3 months before compared to 3 months after vaccination. Results/UNASSIGNED:Of the 596,111 patients who received at least one COVID-19 vaccination, 716 patients were diagnosed with HZ within three months prior to vaccination, compared to 781 patients diagnosed within 3 months afterwards. Using the chi-square test for independence of proportions, there was not a statistically significant difference in frequency of HZ before (proportion: 0.0012, 95% CI: [0.0011, 0.0013]) vs. after vaccination (proportion: 0.0013, 95% CI: [0.0012, 0.0014]); (p = 0.093). Conclusions and importance/UNASSIGNED:This study did not find evidence of an association between COVID-19 vaccination and a new diagnosis of HZ. We encourage health care professionals to strongly recommend COVID-19 vaccinations per Centers for Disease Control (CDC) recommendations and vaccination against HZ according to Food and Drug Administration (FDA) approval for the recombinant zoster vaccine.

INTRODUCTION/BACKGROUND:Neurological complications among hospitalized COVID-19 patients may be associated with elevated neurodegenerative biomarkers. METHODS:Among hospitalized COVID-19 patients without a history of dementia (N = 251), we compared serum total tau (t-tau), phosphorylated tau-181 (p-tau181), glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), ubiquitin carboxy-terminal hydrolase L1 (UCHL1), and amyloid beta (Aβ40,42) between patients with or without encephalopathy, in-hospital death versus survival, and discharge home versus other dispositions. COVID-19 patient biomarker levels were also compared to non-COVID cognitively normal, mild cognitive impairment (MCI), and Alzheimer's disease (AD) dementia controls (N = 161). RESULTS:Admission t-tau, p-tau181, GFAP, and NfL were significantly elevated in patients with encephalopathy and in those who died in-hospital, while t-tau, GFAP, and NfL were significantly lower in those discharged home. These markers correlated with severity of COVID illness. NfL, GFAP, and UCHL1 were higher in COVID patients than in non-COVID controls with MCI or AD. DISCUSSION/CONCLUSIONS:Neurodegenerative biomarkers were elevated to levels observed in AD dementia and associated with encephalopathy and worse outcomes among hospitalized COVID-19 patients.