Abstract
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: We retrospectively analyzed clinical data and resting-state 64-channel EEG recorded during hospitalization at The First Hospital of Changsha. Participants included primary hypertension without CAD (n = 53) and CAD with primary hypertension (n = 117), with CAD stratified by Montreal Cognitive Assessment (MoCA) into MCI (n = 49) and cognitively normal (n = 68). EEG sources were reconstructed using an ICBM152-based head model and BEM forward modeling, yielding 82 Brodmann-atlas ROIs; functional connectivity was quantified using lagged phase synchronization (LPS) in delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) bands. Group comparisons applied false discovery rate correction. For MCI classification among patients with CAD, the dataset was randomly split into training and testing sets (7:3). Feature selection was performed in the training set using an independent-samples t-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.