Faculty Bibliography

In analyzing the neural correlates of naturalistic and unstructured behaviors, features of neural activity that are ignored in a trial-based experimental paradigm can be more fully studied and investigated. Here, we analyze neural activity from two patients using electrocorticography (ECoG) and stereo-electroencephalography (sEEG) recordings, and reveal that multiple neural signal characteristics exist that discriminate between unstructured and naturalistic behavioral states such as "engaging in dialogue" and "using electronics". Using the high gamma amplitude as an estimate of neuronal firing rate, we demonstrate that behavioral states in a naturalistic setting are discriminable based on long-term mean shifts, variance shifts, and differences in the specific neural activity's covariance structure. Both the rapid and slow changes in high gamma band activity separate unstructured behavioral states. We also use Gaussian process factor analysis (GPFA) to show the existence of salient spatiotemporal features with variable smoothness in time. Further, we demonstrate that both temporally smooth and stochastic spatiotemporal activity can be used to differentiate unstructured behavioral states. This is the first attempt to elucidate how different neural signal features contain information about behavioral states collected outside the conventional experimental paradigm.

INTRODUCTION/BACKGROUND:Homebound individuals with advanced Parkinson's disease (PD) are underrepresented in research and care. We tested the impact of interdisciplinary, telehealth-enhanced home visits (IN-HOME-PD) on patient quality of life (QoL) compared with usual care. METHODS:Nonrandomized controlled trial of quarterly, structured, telehealth-enhanced interdisciplinary home visits focused on symptom management, home safety, medication reconciliation, and psychosocial needs (ClinicalTrials.gov NCT03189459). We enrolled homebound participants with advanced PD (Hoehn & Yahr (HY) stage ≥3). Usual care participants had ≥2 visits in the Parkinson's Outcomes Project (POP) registry. We compared within- and between-group one-year change in QoL using the Parkinson's Disease Questionnaire. RESULTS:Sixty-five individuals enrolled in IN-HOME-PD (32.3% women; mean age 78.9 (SD 7.6) years; 74.6% white; 78.5% HY ≥ 4) compared with 319 POP controls, with differences in age, race, and PD severity (37.9% women; mean age 70.1 (7.8) years; 96.2% white; 15.1% HY ≥ 4). Longitudinally, the intervention group's QoL remained unchanged (within-group p = 0.74, Cohen's d = 0.05) while QoL decreased over time in POP controls (p < 0.001, Cohen's d = 0.27). The difference favored the intervention (between-group p = 0.04). POP participants declined in 7/8 dimensions while IN-HOME-PD participants' bodily discomfort improved and hospice use and death at home-markers of goal-concordant care-far exceeded national data. CONCLUSIONS:Telehealth-enhanced home visits can stabilize and may improve the predicted QoL decline in advanced PD via continuity of care and facilitating goal-concordant care, particularly among diverse populations. Extrapolating features of this model may improve continuity of care and outcomes in advanced PD.

OBJECTIVES/OBJECTIVE:As part of the COVID-19 and Epilepsy (COV-E) global study, we aimed to understand the impact of COVID-19 on the medical care and well-being of people with epilepsy (PWE) in the United States, based on their perspectives and those of their caregivers. METHODS:Separate surveys designed for PWE and their caregivers were circulated from April 2020 to July 2021; modifications in March 2021 included a question about COVID-19 vaccination status. RESULTS:We received 788 responses, 71% from PWE (n = 559) and 29% (n=229) from caregivers of persons with epilepsy. A third (n = 308) of respondents reported a change in their health or in the health of the person they care for. Twenty-seven percent (n = 210) reported issues related to worsening mental health. Of respondents taking ASMs (n = 769), 10% (n= 78) reported difficulty taking medications on time, mostly due to stress causing forgetfulness. Less than half of respondents received counseling on mental health and stress. Less than half of the PWE reported having discussions with their healthcare providers about sleep, ASMs and potential side effects, while a larger proportion of caregivers (81%) reported having had discussions with their healthcare providers on the same topics. More PWE and caregivers reported that COVID-19 related measures caused adverse impact on their health in the post-vaccine period than during the pre-vaccine period, citing mental health issues as the primary reason. SIGNIFICANCE/CONCLUSIONS:Our findings indicate that the impact of the COVID-19 pandemic in the US on PWE is multifaceted. Apart from the increased risk of poor COVID-19 outcomes, the pandemic has also had negative effects on mental health and self-management. Healthcare providers must be vigilant for increased emotional distress in PWE during the pandemic and consider the importance of effective counseling to diminish risks related to exacerbated treatment gaps.

PURPOSE/OBJECTIVE:Patients with KRAS-mutant non-small cell lung cancer (NSCLC) with brain metastases (BM) have a poor prognosis. Adagrasib (MRTX849), a potent oral small molecule KRASG12C inhibitor, irreversibly and selectively binds KRASG12C, locking it in its inactive state. Adagrasib has been optimized for favorable pharmacokinetic (PK) properties, including long half-life (~24 hours), extensive tissue distribution, dose-dependent PK, and central nervous system penetration, however BM-specific anti-tumor activity of KRASG12C inhibitors remains to be fully characterized. EXPERIMENTAL DESIGN/METHODS:A retrospective database query identified patients with KRAS-mutant NSCLC to understand their propensity to develop BM. Preclinical studies assessed physiochemical and PK properties of adagrasib. Mice bearing intracranial KRASG12C-mutant NSCLC xenografts (LU99-Luc/H23-Luc/LU65-Luc) were treated with clinically relevant adagrasib doses and levels of adagrasib in plasma, cerebrospinal fluid (CSF), and brain were determined along with anti-tumor activity. Preliminary clinical data were collected from 2 patients with NSCLC with untreated BM who had received adagrasib 600 mg BID in the Phase 1b cohort of the KRYSTAL-1 trial; CSF was collected, adagrasib concentrations measured, and anti-tumor activity in BM evaluated. RESULTS:Patients with KRAS-mutant NSCLC demonstrated high propensity to develop BM ({greater than or equal to}40%). Adagrasib penetrated into CSF and demonstrated tumor regression and extended survival in multiple preclinical BM models. In 2 patients with NSCLC and untreated BM, CSF concentrations of adagrasib measured above the target cellular IC50. Both patients demonstrated corresponding BM regression, supporting potential clinical activity of adagrasib in the brain. CONCLUSIONS:These data support further development of adagrasib in patients with KRASG12C-mutant NSCLC with untreated BM.

Importance/UNASSIGNED:Neuropsychiatric symptoms have been reported as a prominent feature of postacute sequelae of COVID-19 (PASC), with common symptoms that include cognitive impairment, sleep difficulties, depression, posttraumatic stress, and substance use disorders. A primary challenge of parsing PASC epidemiology and pathophysiology is the lack of a standard definition of the syndrome, and little is known regarding mechanisms of neuropsychiatric PASC. Observations/UNASSIGNED:Rates of symptom prevalence vary, but at least 1 PASC neuropsychiatric symptom has been reported in as many as 90% of patients 6 months after COVID-19 hospitalization and in approximately 25% of nonhospitalized adults with COVID-19. Mechanisms of neuropsychiatric sequelae of COVID-19 are still being elucidated. They may include static brain injury accrued during acute COVID-19, neurodegeneration triggered by secondary effects of acute COVID-19, autoimmune mechanisms with chronic inflammation, viral persistence in tissue reservoirs, or reactivation of other latent viruses. Despite rapidly emerging data, many gaps in knowledge persist related to the variable definitions of PASC, lack of standardized phenotyping or biomarkers, variability in virus genotypes, ascertainment biases, and limited accounting for social determinants of health and pandemic-related stressors. Conclusions and Relevance/UNASSIGNED:Growing data support a high prevalence of PASC neuropsychiatric symptoms, but the current literature is heterogeneous with variable assessments of critical epidemiological factors. By enrolling large patient samples and conducting state-of-the-art assessments, the Researching COVID to Enhance Recovery (RECOVER), a multicenter research initiative funded by the National Institutes of Health, will help clarify PASC epidemiology, pathophysiology, and mechanisms of injury, as well as identify targets for therapeutic intervention.

Embedded performance validity tests (PVTs) are key components of neuropsychological evaluations. However, most are memory-based and may be less useful in the assessment of attention-deficit/hyperactivity disorder (ADHD). Four non-memory-based validity indices derived from processing speed and executive functioning measures commonly included in ADHD evaluations, namely Verbal Fluency (VF) and the Trail Making Test (TMT), were cross-validated using the Rey 15-Item Test (RFIT) Recall and Recall/Recognition as memory-based comparison measures. This consecutive case series included data from 416 demographically-diverse adults who underwent outpatient neuropsychological evaluation for ADHD. Validity classifications were established, with ≤1 PVT failure of five independent criterion PVTs as indicative of valid performance (374 valid performers/42 invalid performers). Among the statistically significant validity indicators, TMT-A and TMT-B T-scores (AUCs = .707-.723) had acceptable classification accuracy ranges and sensitivities ranging from 29%-36% (≥89% specificity). RFIT Recall/Recognition produced similar results as TMT-B T-score with 42% sensitivity/90% specificity, but with lower classification accuracy. In evaluating adult ADHD, VF and TMT embedded PVTs demonstrated comparable sensitivity and specificity values to those found in other clinical populations but necessitated alternate cut-scores. Results also support use of RFIT Recall/Recognition over the standard RFIT Recall as a PVT for adult ADHD evaluations.

OBJECTIVES:Early presentation and workup for acute infectious (IE) and autoimmune encephalitis (AE) are similar. This study aims to identify routine laboratory markers at presentation that are associated with IE or AE. METHODS:This was a multi-center retrospective study at three tertiary care hospitals in New York City analyzing demographic and clinical data from patients diagnosed with definitive encephalitis based on a confirmed pathogen and/or autoantibody and established criteria for clinical syndromes. RESULTS:Three hundred and thirty-three individuals with confirmed acute meningoencephalitis were included. An infectious-nonbacterial (NB) pathogen was identified in 151/333 (45.40%), bacterial pathogen in 95/333 (28.50%), and autoantibody in 87/333 (26.10%). NB encephalitis was differentiated from AE by the presence of fever (NB 62.25%, AE 24.10%; p < 0.001), higher CSF white blood cell (WBC) (median 78 cells/μL, 8.00 cells/μL; p < 0.001), higher CSF protein (76.50 mg/dL, 40.90 mg/dL; p < 0.001), lower CSF glucose (58.00 mg/dL, 69.00 mg/dL; p < 0.001), lower serum WBC (7.80 cells/μL, 9.72 cells/μL; p < 0.050), higher erythrocyte sedimentation rate (19.50 mm/HR, 13.00 mm/HR; p < 0.05), higher C-reactive protein (6.40 mg/L, 1.25 mg/L; p = 0.005), and lack of antinuclear antibody titers (>1:40; NB 11.54%, AE 32.73%; p < 0.001). CSF-to-serum WBC ratio was significantly higher in NB compared to AE (NB 11.3, AE 0.99; p < 0.001). From these findings, the association of presenting with fever, CSF WBC ≥50 cells/μL, and CSF protein ≥75 mg/dL was explored in ruling-out AE. When all three criteria are present, an AE was found to be highly unlikely (sensitivity 92%, specificity 75%, negative predictive value 95%, and positive predictive value 64%). INTERPRETATIONS:Specific paraclinical data at initial presentation may risk stratify which patients have an IE versus AE.

OBJECTIVE:Accreditation Council for Graduate Medical Education (ACGME)-accredited epilepsy fellowships, like other ACGME accredited training programs, use Milestones to establish learning objectives and to evaluate how well trainees are achieving these goals. The ACGME began developing the second iteration of the Milestones 6 years ago, and these are now being adapted to all specialties. Here, we describe the process by which Epilepsy Milestones 2.0 were developed and summarize them. METHODS:A work group of nine board-certified, adult and pediatric epileptologists reviewed Epilepsy Milestones 1.0 and revised them using a modified Delphi approach. RESULTS:The new Milestones share structural changes with all other specialties, including a clearer stepwise progression in professional development and the harmonized Milestones that address competencies common to all medical fields. Much of the epilepsy-specific content remains the same, although a major addition is a set of Milestones focused on reading and interpreting electroencephalograms (EEGs), which the old Milestones lacked. Epilepsy Milestones 2.0 includes a Supplemental Guide to help program directors implement the new Milestones. Together, Epilepsy Milestones 2.0 and the Supplemental Guide recognize advances in epilepsy, including stereo-EEG, neurostimulation, genetics, and safety in epilepsy monitoring units. SIGNIFICANCE:Epilepsy Milestones 2.0 address the shortcomings of the old Milestones and should facilitate the assessment of epilepsy fellowships and fellows by program directors, faculty, and fellows themselves.

There is an urgent need for an in-depth and systematic assessment of a wide range of predictive factors related to populations most at risk for delaying and refusing COVID-19 vaccination as cases of the disease surge across the United States. Many studies have assessed a limited number of general sociodemographic and health-related factors related to low vaccination rates. Machine learning methods were used to assess the association of 151 social and health-related risk factors derived from the American Community Survey 2019 and the Centers for Disease Control and Prevention (CDC) BRFSS with the response variables of vaccination rates and unvaccinated counts in 1,555 ZIP Codes in California. The performance of various analytical models was evaluated according to their ability to regress between predictive variables and vaccination levels. Machine learning modeling identified the Gradient Boosting Regressor (GBR) as the predictive model with a higher percentage of the explained variance than the variance identified through linear and generalized regression models. A set of 20 variables explained 72.90% of the variability of unvaccinated counts among ZIP Codes in California. ZIP Codes were shown to be a more meaningful geo-local unit of analysis than county-level assessments. Modeling vaccination rates was not as effective as modeling unvaccinated counts. The public health utility of this model provides for the analysis of state and local conditions related to COVID-19 vaccination use and future public health problems and pandemics.