Graph spectral analysis using electroencephalography in Alzheimer disease and frontotemporal dementia patients

Abstract

Graph theory has proven to be useful in studying brain dysfunction in Alzheimer´s disease using magnetoencephalography (MEG) and fMRI signals. However, it has not yet been tested enough with reduced sets of electrodes, as in the 10-20 EEG. In this work, we applied techniques from the graph spectral analysis (GSA) derived from EEG signals of patients with Alzheimer, Frontotemporal Dementia and control subjects. A collection of global GSA metrics were computed, accounting for general properties of the adjacency or Laplacian matrices. Also, regional GSA metrics were calculated, disentangling centrality measures in five cortical regions (frontal, central, parietal, temporal and occipital). These two sort of measures were then utilized in a binary AD/controls classification problem to test their utility in AD diagnosis and identify most valuable parameters. The Theta band appeared as the most connected and synchronizable rhythm for all three groups. Also, it was the rhythm with most preserved connections among temporal electrodes, exhibiting the shortest average distances among T_3, T_4, T_5 and T_6. In addition, Theta emerged as the rhythm with the highest classification performances based on regional parameters according to a k=5 cross-validation scheme (mean accuracy=0.74±0.03, mean recall=0.72±0.05 and mean F1-score=0.72pm0.03). In general, regional parameters produced better classification performances for most of the rhythms, encouraging further investigation into GSA parameters with refined spatial and functional specificity.