Figure 3.

Small-world properties of functional brain networks. A, B, The global (A) and local (B) efficiency are shown as a function of sparsity for random, regular, and real [healthy controls (CON) and schizophrenia patients (SCZ)] networks. For all networks, global and local efficiency increased with the threshold. The global efficiency curves of the real networks are less than those of random networks, but the local efficiency profiles of the real networks are greater than these of the regular networks over the selected threshold range (i.e., between the vertical dash lines), known as a small-world regime. Significant reductions in local efficiency (p < 0.05), but not in the global efficiency, are found in schizophrenia over the threshold range marked by the gray area in B. The random networks were generated by 50 random rewirings of the edges across nodes while keeping the same number of nodes and degree distribution as in the real networks (Sporns and Zwi, 2004). The regular networks were generated by 50 random rewirings of the edges across nodes along the two sides of the main diagonal (Wang et al., 2009).