Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimers disease

In this study we examined changes in the large-scale structure of resting-state brain networks in patients with Alzheimers disease compared with non-demented controls, using concepts from graph theory. Magneto-encephalograms (MEG) were recorded in 18 Alzheimers disease patients and 18 non-demented control subjects in a no-task, eyes-closed condition. For the main frequency bands, synchronization between all pairs of MEG channels was assessed using a phase lag index (PLI, a synchronization measure insensitive to volume conduction). PLI-weighted connectivity networks were calculated, and characterized by a mean clustering coefficient and path length. Alzheimers disease patients showed a decrease of mean PLI in the lower alpha and beta band. In the lower alpha band, the clustering coefficient and path length were both decreased in Alzheimers disease patients. Network changes in the lower alpha band were better explained by a Targeted Attack model than by a Random Failure model. Thus, Alzheimers disease patients display a loss of resting-state functional connectivity in lower alpha and beta bands even when a measure insensitive to volume conduction effects is used. Moreover, the large-scale structure of lower alpha band functional networks in Alzheimers disease is more random. The modelling results suggest that highly connected neural network hubs may be especially at risk in Alzheimers disease.