Faculty Bibliography
Searched for:
Department/Unit:Neurology
Total Results:
24319
Dravet syndrome seizure frequency and clustering: Placebo-treated patients in clinical trials
OBJECTIVE:Dravet syndrome is a rare developmental epilepsy syndrome associated with severe, treatment-resistant seizures. Since seizures and seizure clusters are linked to morbidity, reduced quality of life, and premature mortality, a greater understanding of these outcomes could improve trial designs. This analysis explored seizure types, seizure clusters, and factors affecting seizure cluster variability in Dravet syndrome patients. METHODS:Pooled post-hoc analyses were performed on data from placebo-treated patients in GWPCARE 1B and GWPCARE 2 randomized controlled phase III trials comparing cannabidiol and placebo in Dravet syndrome patients aged 2-18 years. Multivariate stepwise analysis of covariance of log-transformed convulsive seizure cluster frequency was performed, body weight and body mass index z-scores were calculated, and incidence of adverse events was assessed. Data were summarized in three age groups. RESULTS:We analyzed 124 placebo-treated patients across both studies (2-5 years: n = 35; 6-12 years: n = 52; 13-18 years: n = 37). Generalized tonic-clonic seizures followed by myoclonic seizures were the most frequent seizure types. Mean and median convulsive seizure cluster frequency overall decreased between baseline and maintenance period but did not change significantly during the latter; variation in convulsive seizure cluster frequency was observed across age groups. Multivariate analysis suggested correlations between convulsive seizure cluster frequency and age (positive), and body mass index (BMI) (negative). INTERPRETATION/CONCLUSIONS:Post-hoc analyses suggested that potential relationships could exist between BMI, age and convulsive seizure cluster variation. Results suggested that seizure cluster frequency may be a valuable outcome in future trials. Further research is needed to confirm our findings.
PMID: 38643658
ISSN: 1525-5069
CID: 5663082
Methodology for classification and definition of epilepsy syndromes with list of syndromes: Report of the ILAE Task Force on Nosology and Definitions ã¦ã‚“ã‹ã‚“症候群ã®åˆ†é¡žã¨å®šç¾©ã®ãŸã‚ã®æ–¹æ³•è«–ã¨ç—‡å€™ç¾¤ä¸€ 覧 ：ILAE疾病分類"¢ 定義作æ¥éƒ¨ä¼šå ±å‘Šæ›¸
SCOPUS:85194188010
ISSN: 0912-0890
CID: 5660442
Autoimmune-associated seizure disorders
With the discovery of an expanding number of neural autoantibodies, autoimmune etiologies of seizures have been increasingly recognized. Clinical phenotypes have been identified in association with specific underlying antibodies, allowing an earlier diagnosis. These phenotypes include faciobrachial dystonic seizures with LGI1 encephalitis, neuropsychiatric presentations associated with movement disorders and seizures in NMDA-receptor encephalitis, and chronic temporal lobe epilepsy in GAD65 neurologic autoimmunity. Prompt recognition of these disorders is important, as some of them are highly responsive to immunotherapy. The response to immunotherapy is highest in patients with encephalitis secondary to antibodies targeting cell surface synaptic antigens. However, the response is less effective in conditions involving antibodies binding intracellular antigens or in Rasmussen syndrome, which are predominantly mediated by cytotoxic T-cell processes that are associated with irreversible cellular destruction. Autoimmune encephalitides also may have a paraneoplastic etiology, further emphasizing the importance of recognizing these disorders. Finally, autoimmune processes and responses to novel immunotherapies have been reported in new-onset refractory status epilepticus (NORSE) and febrile infection-related epilepsy syndrome (FIRES), warranting their inclusion in any current review of autoimmune-associated seizure disorders.
PMID: 38818801
ISSN: 1950-6945
CID: 5663912
Association of Loneliness with Functional Connectivity MRI, Amyloid-β PET, and Tau PET Neuroimaging Markers of Vulnerability for Alzheimer's Disease
BACKGROUND/UNASSIGNED:Loneliness has been declared an "epidemic" associated with negative physical, mental, and cognitive health outcomes such as increased dementia risk. Less is known about the relationship between loneliness and advanced neuroimaging correlates of Alzheimer's disease (AD). OBJECTIVE/UNASSIGNED:To assess whether loneliness was associated with advanced neuroimaging markers of AD using neuroimaging data from Framingham Heart Study (FHS) participants without dementia. METHODS/UNASSIGNED:In this cross-sectional observational analysis, we used functional connectivity MRI (fcMRI), amyloid-β (Aβ) PET, and tau PET imaging data collected between 2016 and 2019 on eligible FHS cohort participants. Loneliness was defined as feeling lonely at least one day in the past week. The primary fcMRI marker was Default Mode Network intra-network connectivity. The primary PET imaging markers were Aβ deposition in precuneal and FLR (frontal, lateral parietal and lateral temporal, retrosplenial) regions, and tau deposition in the amygdala, entorhinal, and rhinal regions. RESULTS/UNASSIGNED:Of 381 participants (mean age 58 [SD 10]) who met inclusion criteria for fcMRI analysis, 5% were classified as lonely (17/381). No association was observed between loneliness status and network changes. Of 424 participants (mean age 58 [SD = 10]) meeting inclusion criteria for PET analyses, 5% (21/424) were lonely; no associations were observed between loneliness and either Aβ or tau deposition in primary regions of interest. CONCLUSIONS/UNASSIGNED:In this cross-sectional study, there were no observable associations between loneliness and select fcMRI, Aβ PET, and tau PET neuroimaging markers of AD risk. These findings merit further investigation in prospective studies of community-based cohorts.
PMID: 38820017
ISSN: 1875-8908
CID: 5663972
Trainee highlights
SCOPUS:85192369253
ISSN: 0017-8748
CID: 5661432
Longitudinal trajectories of Alzheimer's disease CSF biomarkers and blood pressure in cognitively healthy subjects
INTRODUCTION/BACKGROUND:We examined whether hypertension (HTN) was associated with Alzheimer's disease-related biomarkers in cerebrospinal fluid (CSF) and how changes in blood pressure (BP) related to changes in CSF biomarkers over time. METHODS:A longitudinal observation of cognitively healthy normotensive subjects (n = 134, BP < 140/90, with no antihypertensive medication), controlled HTN (n = 36, BP < 140/90, taking antihypertensive medication), and 35 subjects with uncontrolled HTN (BP ≥ 140/90). The follow-up range was 0.5to15.6 years. RESULTS:Total tau (T-tau) and phospho-tau181 (P-tau 181) increased in all but controlled HTN subjects (group×time interaction: p < 0.05 for both), but no significant Aβ42 changes were seen. Significant BP reduction was observed in uncontrolled HTN, and it was related to increase in T-tau (p = 0.001) and P-tau 181 (p < 0.001). DISCUSSION/CONCLUSIONS:Longitudinal increases in T-tau and P-tau 181 were observed in most subjects; however, only uncontrolled HTN had both markers increase alongside BP reductions. We speculate cumulative vascular injury renders the brain susceptible to relative hypoperfusion with BP reduction. HIGHLIGHTS/CONCLUSIONS:Over the course of the study, participants with uncontrolled HTN at baseline showed greater accumulation of CSF total tau and phospho-tau181 (P-tau 181) than subjects with normal BP or with controlled HTN. In the group with uncontrolled HTN, increases in total tau and P-tau 181 coincided with reduction in BP. We believe this highlights the role of HTN in vascular injury and suggests decline in cerebral perfusion resulting in increased biomarker concentrations in CSF. Medication use was the main factor differentiating controlled from uncontrolled HTN, indicating that earlier treatment was beneficial for preventing accumulations of pathology.
PMID: 38808676
ISSN: 1552-5279
CID: 5663512
A Comprehensive and Broad Approach to Resting-State Functional Connectivity in Adult Patients with Mild Traumatic Brain Injury
BACKGROUND AND PURPOSE/OBJECTIVE:Several recent works using resting-state fMRI suggest possible alterations of resting-state functional connectivity after mild traumatic brain injury. However, the literature is plagued by various analysis approaches and small study cohorts, resulting in an inconsistent array of reported findings. In this study, we aimed to investigate differences in whole-brain resting-state functional connectivity between adult patients with mild traumatic brain injury within 1 month of injury and healthy control subjects using several comprehensive resting-state functional connectivity measurement methods and analyses. MATERIALS AND METHODS/METHODS:A total of 123 subjects (72 patients with mild traumatic brain injury and 51 healthy controls) were included. A standard fMRI preprocessing pipeline was used. ROI/seed-based analyses were conducted using 4 standard brain parcellation methods, and the independent component analysis method was applied to measure resting-state functional connectivity. The fractional amplitude of low-frequency fluctuations was also measured. Group comparisons were performed on all measurements with appropriate whole-brain multilevel statistical analysis and correction. RESULTS:There were no significant differences in age, sex, education, and hand preference between groups as well as no significant correlation between all measurements and these potential confounders. We found that each resting-state functional connectivity measurement revealed various regions or connections that were different between groups. However, after we corrected for multiple comparisons, the results showed no statistically significant differences between groups in terms of resting-state functional connectivity across methods and analyses. CONCLUSIONS:Although previous studies point to multiple regions and networks as possible mild traumatic brain injury biomarkers, this study shows that the effect of mild injury on brain resting-state functional connectivity has not survived after rigorous statistical correction. A further study using subject-level connectivity analyses may be necessary due to both subtle and variable effects of mild traumatic brain injury on brain functional connectivity across individuals.
PMID: 38604737
ISSN: 1936-959x
CID: 5657362
Artificial intelligence/machine learning for epilepsy and seizure diagnosis
Accurate seizure and epilepsy diagnosis remains a challenging task due to the complexity and variability of manifestations, which can lead to delayed or missed diagnosis. Machine learning (ML) and artificial intelligence (AI) is a rapidly developing field, with growing interest in integrating and applying these tools to aid clinicians facing diagnostic uncertainties. ML algorithms, particularly deep neural networks, are increasingly employed in interpreting electroencephalograms (EEG), neuroimaging, wearable data, and seizure videos. This review discusses the development and testing phases of AI/ML tools, emphasizing the importance of generalizability and interpretability in medical applications, and highlights recent publications that demonstrate the current and potential utility of AI to aid clinicians in diagnosing epilepsy. Current barriers of AI integration in patient care include dataset availability and heterogeneity, which limit studies' quality, interpretability, comparability, and generalizability. ML and AI offer substantial promise in improving the accuracy and efficiency of epilepsy diagnosis. The growing availability of diverse datasets, enhanced processing speed, and ongoing efforts to standardize reporting contribute to the evolving landscape of AI applications in clinical care.
PMID: 38636146
ISSN: 1525-5069
CID: 5663072
Predicting Risk of Alzheimer's Diseases and Related Dementias with AI Foundation Model on Electronic Health Records
Early identification of Alzheimer's disease (AD) and AD-related dementias (ADRD) has high clinical significance, both because of the potential to slow decline through initiating FDA-approved therapies and managing modifiable risk factors, and to help persons living with dementia and their families to plan before cognitive loss makes doing so challenging. However, substantial racial and ethnic disparities in early diagnosis currently lead to additional inequities in care, urging accurate and inclusive risk assessment programs. In this study, we trained an artificial intelligence foundation model to represent the electronic health records (EHR) data with a vast cohort of 1.2 million patients within a large health system. Building upon this foundation EHR model, we developed a predictive Transformer model, named TRADE, capable of identifying risks for AD/ADRD and mild cognitive impairment (MCI), by analyzing the past sequential visit records. Amongst individuals 65 and older, our model was able to generate risk predictions for various future timeframes. On the held-out validation set, our model achieved an area under the receiver operating characteristic (AUROC) of 0.772 (95% CI: 0.770, 0.773) for identifying the AD/ADRD/MCI risks in 1 year, and AUROC of 0.735 (95% CI: 0.734, 0.736) in 5 years. The positive predictive values (PPV) in 5 years among individuals with top 1% and 5% highest estimated risks were 39.2% and 27.8%, respectively. These results demonstrate significant improvements upon the current EHR-based AD/ADRD/MCI risk assessment models, paving the way for better prognosis and management of AD/ADRD/MCI at scale.
PMCID:11071573
PMID: 38712223
CID: 5662732
Researching COVID to enhance recovery (RECOVER) pediatric study protocol: Rationale, objectives and design
IMPORTANCE/OBJECTIVE:The prevalence, pathophysiology, and long-term outcomes of COVID-19 (post-acute sequelae of SARS-CoV-2 [PASC] or "Long COVID") in children and young adults remain unknown. Studies must address the urgent need to define PASC, its mechanisms, and potential treatment targets in children and young adults. OBSERVATIONS/METHODS:We describe the protocol for the Pediatric Observational Cohort Study of the NIH's REsearching COVID to Enhance Recovery (RECOVER) Initiative. RECOVER-Pediatrics is an observational meta-cohort study of caregiver-child pairs (birth through 17 years) and young adults (18 through 25 years), recruited from more than 100 sites across the US. This report focuses on two of four cohorts that comprise RECOVER-Pediatrics: 1) a de novo RECOVER prospective cohort of children and young adults with and without previous or current infection; and 2) an extant cohort derived from the Adolescent Brain Cognitive Development (ABCD) study (n = 10,000). The de novo cohort incorporates three tiers of data collection: 1) remote baseline assessments (Tier 1, n = 6000); 2) longitudinal follow-up for up to 4 years (Tier 2, n = 6000); and 3) a subset of participants, primarily the most severely affected by PASC, who will undergo deep phenotyping to explore PASC pathophysiology (Tier 3, n = 600). Youth enrolled in the ABCD study participate in Tier 1. The pediatric protocol was developed as a collaborative partnership of investigators, patients, researchers, clinicians, community partners, and federal partners, intentionally promoting inclusivity and diversity. The protocol is adaptive to facilitate responses to emerging science. CONCLUSIONS AND RELEVANCE/CONCLUSIONS:RECOVER-Pediatrics seeks to characterize the clinical course, underlying mechanisms, and long-term effects of PASC from birth through 25 years old. RECOVER-Pediatrics is designed to elucidate the epidemiology, four-year clinical course, and sociodemographic correlates of pediatric PASC. The data and biosamples will allow examination of mechanistic hypotheses and biomarkers, thus providing insights into potential therapeutic interventions. CLINICAL TRIALS.GOV IDENTIFIER/BACKGROUND:Clinical Trial Registration: http://www.clinicaltrials.gov. Unique identifier: NCT05172011.
PMCID:11075869
PMID: 38713673
ISSN: 1932-6203
CID: 5658342
