Graph Theory Applied to Brain Network Analysis in Alzheimer's Disease

Alzheimer's disease (AD) is an incurable brain disorder which affects especially elderly. Over the years, the analysis of the brain functional connectivity from EEG signals has been exploited for promoting an early diagnosis of AD. Graph theory provides helpful tools to describe complex brain networks. In this work, starting from High-Density EEGs, we estimated the functional connectivity by the Lagged Linear Connectivity (LLC) parameter, for 84 Regions of Interest (ROIs), and analyzed the brain networks properties for three groups of subjects: control subjects (CNT), Mild Cognitive Impairment patients (MCI) and AD patients. We computed three network parameters: the Clustering Coefficient, the Characteristic Path Length and the Randic Index. The results showed that the functional connectivity of MCI and even more of AD patients declines in comparison to healthy people. Moreover, the results deriving from the Randic Index about robustness of brain networks outperform those deriving from the Connection Density Index, commonly used for brain network analysis.