Exploring Functional Brain Networks in Alzheimer’s Disease Using Resting State EEG Signals

Background/Objectives: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that disrupts functional brain connectivity, leading to cognitive and functional decline. Electroencephalography (EEG), a noninvasive and cost-effective technique, has gained attention as a promising tool for studying brain network alterations in AD. This study aims to leverage EEG-derived connectivity metrics to differentiate between healthy controls (HC), subjective cognitive decline (SCD), mild cognitive impairment (MCI), and AD, offering insights into disease progression. Methods: Using graph theory-based analysis, we extracted key connectivity metrics from resting-state EEG signals, focusing on the betweenness centrality and clustering coefficient. Statistical analysis was conducted across multiple EEG frequency bands, and discriminant analysis was applied to evaluate the classification performance of connectivity metrics. Results: Our findings revealed a progressive increase in theta-band betweenness centrality and a concurrent decrease in alpha- and beta-band centrality, reflecting AD-related network reorganization. Among the examined metrics, theta-band betweenness centrality exhibited the highest discriminative power in distinguishing AD stages. Additionally, classification performance using connectivity metrics was comparable to advanced deep learning models, highlighting their potential as predictive biomarkers. Conclusions: EEG-derived connectivity metrics demonstrate strong potential as noninvasive biomarkers for the early detection and monitoring of AD progression. Their effectiveness in capturing network alterations underscores their value in clinical diagnostic workflows, offering a scalable and interpretable alternative to deep learning-based models for AD classification.