Evaluating individual sensitivity to propofol through EEG complexity and information integration: from neural...

Area of Science:

• Neuroscience
• Anesthesiology
• Computational Neuroscience

Background:

• Understanding neural mechanisms of consciousness during anesthesia is crucial for advancing anesthesiology and neuroscience.
• Individual variability in anesthetic sensitivity necessitates accurate prediction for clinical anesthesia safety.
• Propofol's effects on neural dynamics and individual responses require further elucidation.

Purpose of the Study:

• To investigate neural complexity, connectivity diversity, and information integration during propofol-induced sedation.
• To stratify participants into low- and high-sensitivity cohorts based on behavioral responsiveness.
• To develop machine learning models for predicting individual propofol sensitivity using EEG metrics.

Main Methods:

• High-density EEG data from 20 participants under propofol sedation were analyzed.
• Metrics including permutation entropy (PE), phase-lag entropy (PLE), and permutation cross mutual information (PCMI) were computed.
• Support vector machines (SVM) and SHapley Additive exPlanations (SHAP) were used for classification and feature interpretability.

Main Results:

• Significant differences in neural complexity and connectivity were observed between high- and low-sensitivity groups.
• A Support Vector Machine model achieved 87.5% accuracy in classifying propofol sensitivity using resting-state metrics.
• Resting-state metrics, such as alpha-band temporal PLE and beta-band frontal-parietal PCMI, were robust predictors of propofol sensitivity.

Conclusions:

• Differential neural dynamics are induced by propofol across varying levels of individual sensitivity.
• Resting-state EEG metrics can effectively predict individual propofol sensitivity.
• Pre-anesthesia brain state assessments show potential for developing personalized anesthesia plans.