Figure 1.

The pipeline used to calculate an eigenvector centrality map. A correlation matrix calculated by considering ∼40,000 gray matter (including brainstem and cerebellum) voxels' time series was squared. The Erdös–Rényi entropy S = log(N)/log((∑d_i)/N), i = 1, … N (N being the number of nodes and d_i the degree of the ith 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.