Quantitative electroencephalography to assess post-stroke functional disability: A systematic review and...

Area of Science:

• Neuroscience
• Clinical Neurology
• Biomarkers

Background:

• Quantitative electroencephalography (QEEG) offers a non-invasive method to assess neuronal activity.
• QEEG analysis in acute stroke patients shows potential for predicting both short-term and long-term functional outcomes.
• Previous research has not comprehensively evaluated individual QEEG indices for their predictive value in stroke recovery through meta-analysis.

Purpose of the Study:

• To conduct a meta-analysis evaluating individual QEEG-derived indices for their ability to predict post-stroke disability.
• To identify specific QEEG metrics that correlate with functional outcomes following a stroke.

Main Methods:

• A meta-analysis was performed on nine studies, including 482 participants (predominantly ischemic stroke).
• Included studies required EEG assessment and follow-up outcome measures such as modified Rankin Scale (mRS), National Institute of Stroke Scale (NIHSS), or Fugl-Meyer Scale (FMA).
• Key QEEG indices analyzed included delta-alpha ratio (DAR), delta-theta-alpha-beta ratio (DTABR), brain symmetry index (BSI), and pairwise derived brain symmetry (pdBSI).

Main Results:

• Higher DAR values were significantly associated with worse mRS scores (r=0.26) and higher NIHSS scores (r=0.42).
• Elevated DTABR values also correlated with worse mRS scores (r=0.32) and higher NIHSS scores (r=0.49).
• The findings indicate a clear relationship between increased DAR and DTABR and greater post-stroke functional impairment.

Conclusions:

• QEEG-derived indices, specifically DAR and DTABR, demonstrate significant potential as biomarkers for assessing post-stroke disability.
• Integrating QEEG measures into acute-phase assessments, alongside existing clinical and imaging biomarkers, may enhance the accuracy of predicting stroke recovery trajectories.