Figure 1. The pipeline used to calculate an eigenvector centrality map.

A correlation matrix calculated by considering ~ 40,000 grey matter (including brainstem and cerebellum) voxels’ timeseries was squared. The Erdos-Renyi entropy S=log(N)/log((Σd_i)/N), i=1,…N (N being the number of nodes and d_i the degree of the i^th node) of the network was set equal to 2 across subjects. As a result, a binary matrix whose entries were weighted by above threshold r² values (weighted adjacency matrix) was obtained. The eigenvector belonging to the normalized largest eigenvalue of the weighted adjacency matrix was calculated and its entries provided a centrality measure for each voxel.