Figure 2.

Variation in age and SES is associated with whole-brain functional network topology at rest. (a) The clustering coefficient can be used to assess the degree to which neighboring nodes in a graph tend to cluster together. In a binary graph, such as that illustrated here, the clustering coefficient measures the fraction of triangles around a node; the purple node has a high clustering coefficient, and the green node has a low clustering coefficient. In our study, we use an extension of this measure that is appropriate for signed, weighted networks. (b) The average clustering coefficient is significantly higher in the observed functional brain networks than in random network null models in which the average edge weight, degree distribution, and strength distribution have been preserved (p < 1 × 10^-15). (c) Older age is associated with higher average clustering coefficient, controlling for sex, race, head motion, and mean edge weight. Note that the values that are plotted are partial residuals. Partial correlation between age and the average clustering coefficient is shown (Spearman’s ρ, P < 1 × 10⁻⁶). (d) The value of the modularity quality index obtained by maximizing a modularity quality function (see Materials and Methods) is also positively associated with age, controlling for sex, race, head motion, and mean edge weight. Note again that the values that are plotted are partial residuals. Partial correlation between age and the modularity quality index is shown (Spearman’s ρ, P < 1 × 10⁻⁵). (e) High-SES youth display a stronger positive relationship between age and the average clustering coefficient of functional brain networks at rest than low-SES youth over this developmental period. (f) High-SES youth also display a trend towards a stronger positive relationship between age and the modularity quality index of functional brain networks at rest than low-SES youth over this developmental period.