within a network and the presence of network clusters indicates segregated functional dependencies in the brain. We selected the clustering coefficient (C and γ; the fraction of neighboring nodes being also nodes each other) as a measure of network segregation. The importance of individual nodes was addressed by network centrality. Because the important brain regions usually interact with many other regions, we chose the strength (s; the sum of all neighboring connection weights) and betweenness centrality (b; the number of shortest paths from all nodes to all others passing through the node) as the centrality measures. Small-worldness (σ; to what degree the network is highly clustered with short path lengths) was also computed to characterize the property of integration and segregation simultaneously. Mathematical definitions of the network measures can be found in the supplementary material. To compute the network measures for the 29 × 29 matrix of SL, a fraction of the total number of connections was fixed constant by applying a different threshold for each participant and for each frequency band. The same number of matrix elements was necessary to allow a comparison of network measures obtained from different network topologies (Bullmore and Sporns, 2009). In this study, we
