Fig. 1.

Gray Matter Network construction. After preprocessing, each GM segmentation is divided into 3 × 3 × 3 voxel cubes and 1) similarity between all N cubes within a scan was computed with Pearson’s correlation coefficient and stored as an N by N matrix; 2) the similarity matrix was binarized using a threshold that ensured 5 % chance of spurious connections (corresponding to a significance level of p-value = 0.05 FDR-corrected); 3) five random matrices with similar spatial degree distribution were generated; 4) network properties (size, density, degree, BC, clustering, path length) from the binarized matrix were calculated and 5) normalized properties (gamma, lambda and small-world coefficient) through the comparison with the random networks were computed.