ArticleStroke2023
A Measure of Neural Function Provides Unique Insights into Behavioral Deficits in Acute Stroke.
Article in Stroke, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A bibliometric analysis of electroencephalogram research in stroke: current trends and future directions.Frontiers in neurology · 2025Pooled it
- Detection of early neurological deterioration using a quantitative electroencephalography system in patients with large vessel occlusion stroke after endovascular treatment.Journal of neurointerventional surgery · 2025Trial
- Quantitative EEG Metrics for Determining HD-tDCS Induced Alteration of Brain Activity in Stroke Rehabilitation.Restorative neurology and neuroscience · 2024Article
- Quantitative electroencephalography (qEEG), apolipoprotein A-I (APOA-I), and apolipoprotein epsilon 4 (APOE ɛ4) alleles for the diagnosis of mild cognitive impairment and Alzheimer's disease.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Article
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Authors and funding
6 authors.
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Abstract
backgroundClinical and neuroimaging measures incompletely explain behavioral deficits in the acute stroke setting. We hypothesized that electroencephalography (EEG)-based measures of neural function would significantly improve prediction of acute stroke deficits.
methodsPatients with acute stroke (n=50) seen in the emergency department of a university hospital from 2017 to 2018 underwent standard evaluation followed by a 3-minute recording of EEG at rest using a wireless, 17-electrode, dry-lead system. Artifacts in EEG recordings were removed offline and then spectral power was calculated for each lead pair. A primary EEG metric was DTABR, which is calculated as a ratio of spectral power: [(Delta*Theta)/(Alpha*Beta)]. Bivariate analyses and least absolute shrinkage and selection operator (LASSO) regression identified clinical and neuroimaging measures that best predicted initial National Institutes of Health Stroke Scale (NIHSS) score. Multivariable linear regression was then performed before versus after adding EEG findings to these measures, using initial NIHSS score as the dependent measure.
resultsAge, diabetes status, and infarct volume were the best predictors of initial NIHSS score in bivariate analyses, confirmed using LASSO regression. Combined in a multivariate model, these 3 explained initial NIHSS score (adjusted r
conclusionsEEG measures of neural function significantly add to clinical and neuroimaging for explaining initial NIHSS score in the acute stroke emergency department setting. A dry-lead EEG system can be rapidly and easily implemented. EEG contains information that may be useful early after stroke.
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