5.7. CHUV Group-average matrix with finer cortical parcellation - See Figure S13 The pipeline described in Cammoun et al. (2012) makes it possible to parti- tion the cerebral cortex into parcels at several different scales or resolutions. In our main analysis, we used an intermediate scale ( n = 219 cortical parcels, with n = 108 parcels in the right hemisphere). In this section, we repeat our analysis for a composite group-average CHUV matrix generated at the next finest scale, which has n = 223 cortical parcels in the right hemisphere. The group-average matrix was generated using the same procedure as described earlier. In the main text we characterize networks using a number of different graph- theoretic measures. Here we describe those measures in some detail (Rubinov and Sporns, 2010). 5.8. Graph theory 26 • Adjacency matrix : A topology of an n -node network can be described by the matrix A = [ a$_{uv}$ ]. The elements a$_{uv}$ are equal to 1 if nodes u and v are connected and are otherwise equal to 0. • Node degree : A node’s degree counts the total number of connections that node makes. In an undirected network (i.e. a$_{uv}$ = a$_{vu}$ ) a node’s incoming and outgoing degrees are equivalent, and can be calculated as the sum across rows or columns of A , i.e. k$_{u}$ = $^{∑}$$_{v}$a$_{uv}$ . • Network density : A network’s density, ρ , is equal to the fraction of existing connections out of the total number of possible connections. If the total number of connections is equal to 2 m = $^{∑}$$_{u}$k$_{u}$ , then network density is equal to ρ = 2 m n ( n − $_{1)}$.