Faculty Bibliography

INTRODUCTION/BACKGROUND:Down syndrome (DS) is caused by a complete or partial trisomy of chromosome 21, resulting in variable intellectual disabilities and a high risk for early-onset Alzheimer's disease (DS-AD). Dysregulation of the mammalian target of rapamycin (mTOR) and insulin (INS) signaling pathways has been reported in DS. We hypothesized that upstream alterations in these two pathways contribute to hippocampal dysfunction in DS-AD. METHODS:We used spatial transcriptomics and localized proteomic techniques to examine mTOR/INS signaling pathways in subregions of the hippocampus in post mortem tissue from individuals with DS-AD and age-matched neurotypical controls. RESULTS:mTOR pathways were significantly altered in specific hippocampal subfields in DS-AD, and INS pathways were significantly altered in dentate granule neurons. DISCUSSION/CONCLUSIONS:These preliminary spatial transcriptomics and localized proteomics findings demonstrate an interplay between select hippocampal mTOR/INS pathways and cellular populations, suggesting potential novel drug targets and early biomarkers for DS.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Patients with epilepsy (PWE), especially temporal lobe epilepsy (TLE), experience impaired memory for personally experienced events. However, current assessments of episodic memory are limited in their ecological validity with a potential to miss detection of subtle cognitive decline. We conducted an exploratory study to determine whether a naturalistic film-viewing task with open-ended spoken recall could detect memory differences between TLE patients and healthy controls (HCs). METHODS:TLE patients (ages 18-60, fluent in English, not legally blind) were recruited from a Level 4 Epilepsy Center (2018-2024). TLE diagnosis was based on seizure semiology, MRI Brain, and EEG. TLE patients scored 22/30 on the Montreal Cognitive Assessment (MOCA); HCs scored 26/30. Subjects watched 6 short films and then freely recalled film details. Spoken responses were recorded, transcribed, segmented, and scored for film- and event-level recall. Recall order was assessed using the Damerau-Levenshtein distance. Semantic and causal centrality were quantified using sentence embeddings and rater-identified causal links, respectively. Beta regression with cluster-robust standard errors assessed group and centrality effects on recall probability. Beta regression evaluated the influence of age, MOCA, and testing platform on sequence recall error. RESULTS:We recruited 51 subjects (27 TLEs; 24 HCs, 70.1% F, mean 29.9 ±8.3 years). TLE patients and HCs showed similar recall of films (HC 89% ±11% vs TLE 88% ±18%, p = 0.54), coarse-grained events (HC 50% ±16% vs TLE 44% ±18%, p = 0.19) and fine-grained events (HC 25%±10% vs. TLE 22%±12%, p=0.17). Both groups recalled high causal centrality events better. However, TLE patients showed significantly greater fine-grained event sequence deviations at recall than HCs (HC 15% ±13% vs TLE 23% ±18%, p = 0.02, Hedges' g = 0.85, Cliff's δ = 0.51), with RTLE demonstrating more sequence deviations than HCs (15%±13 vs. 29%±21% p = 0.021) Age, education, MOCA, and performance on standard verbal and visual memory tasks were unrelated to film, event, and sequence recall performance. DISCUSSION/CONCLUSIONS:We demonstrate that a short film task with spontaneous spoken recall can identify group level differences in episodic memory. TLE patients demonstrate impaired sequence memory despite intact film- and event-level recall compared to healthy controls. Sequence memory may represent a subtle manifestation of memory impairment that is not detected by standard cognitive testing.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Former American football players exposed to repetitive head impacts (RHI) are at a risk of chronic traumatic encephalopathy (CTE), but chronic pain, polypharmacy, and extensive orthopedic surgeries may also contribute to cognitive and behavioral symptoms. This study evaluated associations between chronic pain, centrally acting medications (CAMs), and orthopedic surgeries with cognitive and behavioral symptoms among former American football players. METHODS:The sample included former professional (PRO) and collegiate (COL) football players and unexposed, asymptomatic men (UE) from DIAGNOSE CTE. Number of CAMs, orthopedic surgeries, and average pain scores were compared between the groups. Among former football players, logistic regression tested associations between CAMs, average pain score, and orthopedic surgeries with diagnoses of cognitive impairment and neurobehavioral dysregulation (NBD) using traumatic encephalopathy syndrome (TES) research criteria. Linear regression tested associations between CAMs, average pain score, and orthopedic surgeries with the Montreal Cognitive Assessment (MoCA) and behavioral and mood symptom scales. Covariates included age, education, race, and total years of football. RESULTS:The study included 236 men (120 PRO, 60 COL, 56 UE). The mean ages were 59.1 (PRO), 53.5 (COL) and 59.6 (UE) years. PRO and COL used more CAMs (mean PRO = 0.76, COL = 1.14, UE = 0.14), had higher average pain scores (PRO = 4.22, COL = 3.21, UE = 1.05), and more orthopedic surgeries than the UE (mean PRO = 2.76, COL = 1.22, UE = 0.34). CAMs and average pain scores were associated with increased odds of consensus diagnosed NBD (CAMs OR = 2.15, 95% CI 1.53 to 3.26; average pain score OR = 1.55, 95% CI 1.32 to 1.85). CAMs and average pain scores were associated with increased measures of impulsivity, depression, anxiety, behavioral regulation, and aggression. CAMs and average pain scores were not associated with consensus diagnosed cognitive impairment, but CAMs were negatively associated with MoCA score (estimate = -0.47, 95% CI -0.81 to -0.13). There was no association between number of orthopedic surgeries and cognition or NBD. DISCUSSION/CONCLUSIONS:CAMs and chronic pain are associated with NBD and CAMs are associated with reduced MoCA scores in former American football players.

OBJECTIVES/OBJECTIVE:Distinguishing Idiopathic Generalized Epilepsy (IGE) from focal epilepsy (FE) can be challenging when IGE cases present with asymmetric clinical or electroencephalographic (i.e., atypical) features. We aimed to identify factors that lead to discordance in IGE diagnoses among epileptologists. METHODS:41 patients with IGE and 6 patients with FE or mixed focal and generalized epilepsy followed at the NYU Comprehensive Epilepsy Center for ≥5 years were identified. IGE cases included typical (n = 18) and atypical (n = 23) presentations. Anonymized summaries of patients at initial presentation and 5-year follow-up were presented in random order to epileptologists who categorized them as generalized epilepsy (GE), FE, or other. Factors leading to discordance were identified. RESULTS:Inter-rater agreement was moderate for atypical IGE cases at initial presentation (AC1 = 0.54, 95% CI 0.31-0.77, p < 0.01) and 5-year follow-up (AC1 = 0.48, 95% CI 0.25-0.72, p < 0.01). For atypical cases, asymmetric epileptiform activity was most commonly associated with increased discordance. DISCUSSION/CONCLUSIONS:Epileptologists may underdiagnose IGE when patients have asymmetric EEG or clinical features. Review of one time point (i.e., vignettes) may mask the diversity of features that may support a localized epilepsy when serial reviews reveal a generalized epilepsy.

The introduction of immune checkpoint inhibitors (ICIs) has dramatically changed the landscape of treatment for a variety of cancers. However, there is increasing recognition of neurotoxicity related to ICI use. In this review, we will discuss the peripheral nervous system neurologic immune related adverse events (PNS-nirAEs) of anti-CTLA4, anti-PD1, and anti-PDL1 ICIs, including nerve, neuromuscular junction, muscle, and overlap syndromes. We will discuss the relevant epidemiology, pathophysiology, clinical syndromes, diagnostic approaches, treatment, and prognosis of PNS-nirAEs. We will discuss the safety and efficacy of ICIs when utilized in patients with pre-existing neurologic autoimmune disorders (NAIDs). Lastly, we will discuss relevant treatment options and when to consider rechallenging with ICI after an initial de novo PNS-nirAE or exacerbation of a pre-existing NAID.

BACKGROUND AND PURPOSE/OBJECTIVE:Although visibility of Deep Medullary Veins (DMVs) has been suggested as an imaging biomarker for cerebral small vessel disease (CSVD) and brain atrophy, qualitative visual assessment of DMVs may lack reliability. In this study, we used a rigorous, automated approach to segment DMVs on SWI and to estimate a vein voxel fraction (VVF). We hypothesized that VVF would be associated with vascular risk factors, imaging markers of CSVD, and brain volumes. MATERIAL AND METHODS/METHODS:A retrospective analysis of data from participants enrolled in studies of brain aging and prediction of Alzheimer's disease. All underwent 3T MRI (T1WI, FLAIR, SWI, and arterial spin labeling), clinical evaluations, and laboratory tests to assess vascular risks. A SWI image processing pipeline included denoising, bias field correction, adaptive histogram equalization, and multi-scale Jerman filtering that yielded vesselness maps. The vein voxel fraction (VVF) was calculated as a ratio of DMVs voxels in a periventricular region to the volume of the periventricular region. White matter hyperintensities, microbleeds, brain volumes, and CBF were also assessed. RESULTS:This study included 131 cognitively healthy participants, 71 (65, 76) years (median, Q1, Q3), (51%) female, and a subgroup of subjects with cognitive impairment (n=30, 69 (59, 76) years, 43% female). In the unimpaired group, the VVF positively correlated with systolic blood pressure and body mass index. It showed an inverse association with lateral ventricle volume (all at p < 0.05). It was related to white matter hyperintensities volume only in unadjusted analysis. CONCLUSION/CONCLUSIONS:Vein voxel fraction was associated with increased weight and high blood pressure. Possibly, these observations reflect venous stasis. These factors should be accounted for while evaluating brain venous system with SWI. In addition, subcortical atrophy was related to less visible DMVs.

BACKGROUND:The risk of intracerebral hemorrhage (ICH) hematoma expansion (HE) is highest in the first hours after onset, and coagulopathy is believed to further increase this risk. However, there is a paucity of data comparing the risk of HE over time for various antithrombotic agents. METHODS:We conducted a retrospective study of spontaneous ICH patients enrolled at a comprehensive stroke center between December 2016 and May 2022, excluding those who underwent surgical evacuation. ICH volumes were calculated using ABC/2 methodology and HE was coded for a ≥ 33% and/or ≥ 6-mL increase in ICH volume. Multivariable logistic regression and Cox proportional hazards models were constructed to evaluate risk of HE among patients exposed to the following antithrombotics: aspirin, P2Y12 inhibitors, aspirin + P2Y12 inhibitors, warfarin, oral factor Xa inhibitors, direct thrombin inhibitors, full dose heparinoids, and combined antiplatelet + anticoagulant. RESULTS:Of 319 patients with ICH, 141 (44%) were on an antithrombotic at the time of ICH and 65 (20%) had HE in a median of 10 h (interquartile range (IQR) 7-21) from last known normal (LKN). In multivariable logistic regression analyses, the odds of HE decreased by 2% for every hour from LKN (adjusted odds ratio (aOR) 0.98, 95% CI 0.97-0.99, P = 0.038), and HE occurred significantly more often in patients taking combined antiplatelet + anticoagulant (9/24, 38%) compared with those who were not (56/295, 19%, aOR 2.71, 95% CI 1.09-6.73, P = 0.032). No other antithrombotic was significantly associated with HE. In multivariable Cox analysis adjusting for admission National Institutes of Health Stroke Scale (NIHSS), only antiplatelet + anticoagulant use was associated with significantly increased rates of HE (adjusted HR (aHR) 2.33, 95% CI 1.14-4.75, P = 0.020), with the highest probability of HE occurring immediately after ICH onset. CONCLUSIONS:Use of combined antiplatelet + anticoagulant was associated with a twofold increased hazard of HE, with the highest probability of expansion occurring early after ICH onset. No increased rate of HE was observed for other antithrombotics.

OBJECTIVE:Long COVID (LC) remains poorly understood, and there is a critical need for advanced computational tools to better identify and characterize patients. In this study, we use summarized symptom reports by RECOVER-Adult cohort participants linked to EHR data to characterize patients and train a computable phenotype algorithm of LC. MATERIALS AND METHODS/METHODS:The study included adult participants with linked FHIR-sourced EHR data. We characterized EHR diagnoses, procedures, medications, lab tests, and vital sign features associated with LC. A computable phenotyping algorithm was trained and validated against patient-reported symptoms. MAIN OUTCOME AND MEASURES/METHODS:We assessed model discrimination and calibration in a held-out test set. We describe important model features and evaluate model discrimination and calibration. RESULTS:The study included 1,501 RECOVER-Adult cohort participants with linked EHR data. 376 (25%) met criteria for highly symptomatic LC based on the RECOVER Long COVID Research Index (LCRI). EHR features associated with LC included clinician diagnosis of shortness of breath, malaise and fatigue, and cardiac dysrhythmias; documented treatment with albuterol, gabapentin, or duloxetine; or elevated heart rate. The algorithm identifying patients with highly symptomatic LC had an AUROC of 0.80 (95% confidence interval (CI) 0.74-0.85), and AUPRC of 0.58 (95% CI, 0.47-0.69). CONCLUSION AND RELEVANCE/CONCLUSIONS:These findings demonstrate that, using EHR data, a machine-learning model can accurately select patients with sets of self-reported LC symptoms. The model could help identify patients within a health system with the highest probability of the condition and facilitate screening, recruitment for clinical trials, and etiologic studies.

INTRODUCTION/BACKGROUND:Juvenile myoclonic epilepsy (JME) is a genetic generalized epilepsy syndrome with onset typically in adolescence and a chronic course requiring long-term antiseizure medications (ASMs). Valproate (VPA) is the most effective treatment for seizure control in JME but use is limited by metabolic, cognitive, and teratogenic adverse effects (AEs). Levetiracetam (LEV) is an alternative ASM when VPA is contraindicated or not tolerated. Comparisons of the efficacy and long-term tolerability of VPA and LEV remain limited. METHODS:statistic. RESULTS: = 21.2%). Sensitivity analyses confirmed the robustness of the pooled estimates. CONCLUSION/CONCLUSIONS:VPA was associated with higher seizure remission rates and lower treatment discontinuation compared with LEV; however, these findings must be interpreted with caution given the substantial heterogeneity, the predominance of observational studies, and the serious risk of bias identified in most included studies VPA also demonstrated lower rates of treatment discontinuation, despite a higher burden of cognitive impairment and weight gain. No relevant differences were observed regarding dizziness. Large-scale randomized trials with standardized outcome definitions and longer follow-up are needed to define the comparative risk-benefit profiles of LEV and VPA in JME.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Multiple-choice questions are the primary assessment format for neurosurgical board certification. Creating high-quality examination questions requires significant expert time and resources. The goal of this study was to develop an automated system to generate board-style neurosurgical multiple-choice questions using state-of-the-art vision-language models and compare their quality with authentic self-assessment questions. METHODS:articles. We generated 89 587 synthetic questions: 45 689 with GPT-4o and 43 898 with Claude. Each question was associated with a single image extracted from the articles' figures. We evaluated the quality of synthetic questions through 5 surveys comparing 20 synthetic questions (10 from each model) with 10 authentic questions from the Self-Assessment for Neurological Surgeons (SANS) question bank. Each survey was completed by a neurosurgery resident and an attending who guessed the source [human vs artificial intelligence (AI)-generated] and rated suitability for board examination use. We also evaluated the question-answering performance of the generalist GPT-4o and the specialized CNS-Obsidian. RESULTS:). CONCLUSION/CONCLUSIONS:Although quality gaps exist between AI-generated and human-created neurosurgical board examination questions, our approach demonstrates the potential of vision-language models to augment assessment development in specialized medical fields, reducing the burden on examination boards and credentialing organizations.