Related Experiment Video
Updated: Aug 26, 2026
A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia
Published on: September 16, 2017
Correlation of quantitative EEG in acute ischemic stroke with 30-day NIHSS score: comparison with diffusion and
Simon P Finnigan1, Stephen E Rose, Michael Walsh
1Centre for Magnetic Resonance, University of Queensland, Brisbane, Queensland, Australia. finnigan@cmr.uq.edu.au.
Background And Purpose:
Magnetic resonance imaging (MRI) methods such as diffusion- (DWI) and perfusion-weighted (PWI) imaging have been widely studied as surrogate markers to monitor stroke evolution and predict clinical outcome. The utility of quantitative electroencephalography (qEEG) as such a marker in acute stroke has not been intensively studied. The aim of the present study was to correlate ischemic cortical stroke patients' clinical outcomes with acute qEEG, DWI, and PWI data.
Materials And Methods:
DWI and PWI data were acquired from 11 patients within 7 and 16 hours after onset of symptoms. Sixty-four channel EEG data were obtained within 2 hours after the initial MRI scan and 1 hour before the second MRI scan. The acute delta change index (aDCI), a measure of the rate of change of average scalp delta power, was compared with the National Institutes of Health Stroke Scale scores (NIHSSS) at 30 days, as were MRI lesion volumes.
Results:
The aDCI was significantly correlated with the 30-day NIHSSS, as was the initial mean transit time (MTT) abnormality volume (rho=0.80, P<0.01 and rho=0.79, P<0.01, respectively). Modest correlations were obtained between the 15-hour DWI lesion volume and both the aDCI and 30-day NIHSSS (rho=0.62, P<0.05 and rho=0.73, P<0.05, respectively).
Conclusions:
In this small sample the significant correlation between 30-day NIHSSS and acute qEEG data (aDCI) was equivalent to that between the former and MTT abnormality volume. Both were greater than the modest correlation between acute DWI lesion volume and 30-day NIHSSS. These preliminary results indicate that acute qEEG data might be used to monitor and predict stroke evolution.
