EEG has been used to study acute stroke for decades, however due to several limitations, EEG-based measures rarely inform clinical decision-making in this setting. Recent advances in EEG hardware, recording electrodes, and EEG software could overcome these limitations. The current study examined how well dense-array (256-electrode) EEG, acquired with a saline-lead net and analyzed with whole brain partial least squares (PLS) modeling, captured extent of acute stroke behavioral deficits and varied in relation to acute brain injury. In 24 patients admitted for acute ischemic stroke, three minutes of resting-state EEG was acquired at bedside, including in ER and ICU. Traditional quantitative EEG measures (power in a specific lead, in any frequency band) showed a modest association with behavioral deficits (NIHSS score) in bivariate models. However, PLS models of delta or beta power across whole brain correlated strongly with NIHSS score (R2=0.85 to 0.90) and remained robust when further analyzed using cross-validation models (R2=0.72 to 0.73). Larger infarct volume was associated with higher delta power, bilaterally; the contralesional findings were not attributable to mass effect, indicating that EEG captures significant information about acute stroke effects not available from MRI. We conclude (1) dense array EEG data is feasible as a bedside measure of brain function in patients with acute stroke; (2) high-dimension EEG data are strongly correlated with acute stroke behavioral deficits and are superior to traditional single lead metrics in this regard; and (3) EEG captures significant information about acute stroke injury not available from structural brain imaging.
- Copyright © 2015, Journal of Neurophysiology