Exploring the neural mechanisms of mild cognitive impairment in elderly patients with coronary artery disease using machine learning and source-localized EEG

Objective: This study seeks to investigate the electrophysiological mechanisms associated with mild cognitive impairment (MCI) in elderly patients with coronary artery disease (CAD) through the application of source-reconstructed EEG in conjunction with machine learning methodologies. Methods: -test followed by L1-penalized logistic regression. Subsequently, eight machine-learning classifiers were trained using the selected LPS features, with hyperparameters optimized by grid search under five-fold cross-validation. Model interpretability was assessed using SHAP. Results: Baseline demographics and vascular comorbidities were comparable across groups; MoCA scores were lower in the MCI subgroup. Relative to hypertensive controls without CAD, cognitively normal CAD patients showed reduced frontal connectivity, including decreased alpha-band LPS (BA8L-46R) and beta-band LPS (BA44L-44R). Compared with cognitively normal CAD, CAD with MCI exhibited broader multi-band dysconnectivity across alpha, beta, theta, and delta bands, with mixed delta-band changes. In the test set, the Gradient Boosting model achieved the best performance for identifying MCI within CAD (AUC = 0.895). SHAP highlighted the most influential features, led by decreased alpha-band BA8L-46R connectivity, alongside delta- and beta-band alterations. Conclusion: Coronary artery disease is associated with frontal network disruption, which becomes more extensive and frequency-diverse as MCI progresses. Interpretable machine learning further highlights a small set of connectivity abnormalities-particularly within premotor-prefrontal pathways-as candidate markers for MCI classification within a CAD cohort, supporting a vascular-relevant interpretation, which warrants further validation.