The aim of this pilot study was to analyze spontaneous electroencephalography (EEG) activity in Alzheimer's disease (AD) by means of Cross-Sample Entropy (Cross-SampEn) and two local measures derived from graph theory: clustering coefficient (CC) and characteristic path length (PL). Five minutes of EEG activity were recorded from 37 patients with dementia due to AD and 29 elderly controls. Our results showed that Cross-SampEn values were lower in the AD group than in the control one for all the interactions among EEG channels. This finding indicates that EEG activity in AD is characterized by a lower statistical dissimilarity among channels. Significant differences were found mainly for fronto-central interactions (p < 0.01, permutation test). Additionally, the application of graph theory measures revealed diverse neural network changes, i.e. lower CC and higher PL values in AD group, leading to a less efficient brain organization. This study suggests the usefulness of our approach to provide further insights into the underlying brain dynamics associated with AD.
['Carlos Gómez', 'Jesús Poza', 'Javier Gomez-Pilar', 'Alejandro Bachiller', 'Celia Juan-Cruz', 'Miguel Angel Tola-Arribas', 'Alicia Carreres', 'Mónica Martín Cano', 'Roberto Hornero']
Analysis of spontaneous EEG activity in Alzheimer's disease using cross-sample entropy and graph theory