Table 1.

Network measures (Bassett and Bullmore, 2006; Rubinov and Sporns, 2010).

Network-level measures
Characteristic path length (L) is the average shortest path length (i.e., the minimal number of edges that form a direct connection between two nodes) between all pairs of nodes in the network and is a measure of functional integration. A short L indicates a more compact network and more efficient global information processing.
Global efficiency (Eglob) is the average inverse shortest path length between all pairs of nodes in the network and is a measure of functional integration, and represents the functional efficiency of brain networks for information transmission between multiple parallel paths.
Transitivity (T) is the ratio of triangles to triplets in the network, and was a measure of clustering or functional segregation.
Modularity (M) quantifies how well the network can be subdivided into non-overlapping groups of nodes or modules and is a measure of functional segregation.
Node-level measures
Clustering coefficient (C) is a measure of the number of edges between a node’s nearest neighbors or the fraction of triangles around a node, and is a measure of functional segregation. High C represents clustered connectivity at the node.
Local efficiency (Eloc) is the global efficiency computed on node neighborhoods, is related to C, and is a measure of functional segregation.
Degree (d) is the number of edges connected to a node, and provides information related to the centrality of a node by determining nodes with a large number of connections.
Betweenness centrality (CB) is the fraction of all the shortest paths in the network that contain a given node. Nodes with high CB participate in a large number of shortest paths and may connect distinct parts of the network.
Participation coefficient (P) is a measure of the inter-modular connections of nodes and indicates centrality.