A scalp-EEG network-based analysis of Alzheimer's disease patients at rest

TL;DR

This study uses scalp EEG to analyze brain network alterations in Alzheimer's patients at rest, revealing potential biomarkers through graph-theoretical measures correlated with cognitive scores.

Contribution

It introduces a non-invasive EEG-based method to identify network changes in AD, highlighting the potential of EEG biomarkers for diagnosis.

Findings

Decreased mean connectivity in AD patients

Reduced clustering and global efficiency in lower alpha band

Positive correlation between EEG graph measures and cognitive scores

Abstract

Most brain disorders including Alzheimer's disease (AD) are related to alterations in the normal brain network organization and function. Exploring these network alterations using non-invasive and easy to use technique is a topic of great interest. In this paper, we collected EEG resting-state data from AD patients and healthy control subjects. Functional connectivity between scalp EEG signals was quantified using the phase locking value (PLV) for 6 frequency bands. To assess the differences in network properties, graph-theoretical analysis was performed. AD patients showed decrease of mean connectivity, average clustering and global efficiency in the lower alpha band. Positive correlation between the cognitive score and the extracted graph measures was obtained, suggesting that EEG could be a promising technique to derive new biomarkers of AD diagnosis.