Figure 1.

Approaches for analyzing brain networks. Brains can be represented as graphs consisting of nodes (regions) and connections between those nodes (connectivity; a), and connection strengths can be mathematically represented in adjacency matrices where each cell represents the strength of the connection between a pair of regions (b). Community detection algorithms take adjacency matrices and partition the brain into modules that contain greater (or stronger) within-community edges than expected in a statistical null model (c). Graphical approaches to studying brains can be extended across time (d). Dynamic networks capture how frequently brain regions (represented in rows) change their allegiance from one community to another (indexed by color), identifying what regions are inflexible (largely the same community affiliation across time steps) versus flexible (changing communities frequently across time steps; e).