Although graph theory for network analysis is a promising tool for investigating the topological properties of functional and structural connections within brain, the number of network measures can be influenced by the number of nodes and the average degree in the network (van Wijk et al., 2010). For example, network characteristics computed from different numbers of nodes might differ from each other even if the network topology is not changed. Thus, we chose the same number of nodes (29 channels) for each group to address the node issue. However, the choice of degree is also important to consider in the comparison two networks, even when using a weighted connectivity matrix. Since the raw SL connectivity matrix in this study has continuous values with full connections by mathematical definition, it could also include spurious connections with low SL values. In this case, thresholding and binarization of the network matrix has been a typical approach to eliminate the weak connections. However, without an optimal threshold, the matrix binarization tends to overestimate the contrast-to-noise ratio of the network in that it enhances connectivity
