Characterization of the spontaneous electroencephalographic activity in Alzheimer´s disease using disequilibria and graph theory

The aim of this research was to study the changes that Alzheimer´s disease (AD) elicits in the organization of brain networks. For this task, the electroencephalographic (EEG) activity from 32 AD patients and 25 healthy controls was analyzed. In a first step, a disequilibrium measure, the Euclidean distance (ED), was used to estimate the similarity between the spectral content of each pair of electrodes. In a second step, the similarity matrices were used to generate the corresponding graphs, from which two parameters were computed to characterize the network structure: the mean clustering coefficient and the mean path length. Results revealed significant changes (p<;0.05) in ED values, as well as in the mean clustering coefficient and the mean path length, though they depend on the specific frequency band. Our findings suggest that AD is accompanied by a significant frequency-dependent alteration of brain network organization.