Patients with Alzheimer’s disease dementia show partially resilient parietal “HUBS” modeled from resting‐state alpha electroencephalographic rhythms

Abstract

AbstractBackgroundGraph theory models a network by its nodes and connections. “Degree” hubs reflect node centrality, while “connector” hubs are those linked to several clusters of nodes. Here we compared hubs modelled from measures of interdependencies of between‐electrode resting‐state eyes‐closed electroencephalography (rsEEG) rhythms in normal old (Nold) and Alzheimer’s disease dementia (ADD) participants. As ADD is considered as a “network disease” and is typically associated with abnormal rsEEG delta (< 4 Hz) and alpha rhythms (8‐12 Hz) over associative posterior areas, we predicted abnormal posterior hubs from measures of interdependencies of those rhythms in ADD as compared to Nold participants.MethodTo report robust results, we measured interdependencies of rsEEG rhythms from delta to gamma bands (2‐40 Hz) by both bivariate linear lagged connectivity and multivariate (directional) isolated lagged effective coherence. Furthermore, we used three different definitions of “connector” hub.ResultConvergent results showed that in both Nold and ADD groups, there were significant parietal “degree” and “connector” hubs derived from alpha rhythms. These hubs had a prominent outward “directionality” in the two groups, but that “directionality” was lower in ADD than Nold participants (Figure 1).ConclusionIndependent methodologies and hub definitions suggest that ADD patients may be characterized by low outward “directionality” of topologically partially resilient parietal “degree” and “connector” hubs derived from rsEEG alpha rhythms. Combined use of independent methodologies in rsEEG studies may produce robust results in clinical applications in ADD patients.