some basic graph theoretical measures

· mean characteristic path length

calculated as the average length of the shortest path between two nodes, denotes the level of integration in a given network and is inversely related to the global efficiency of information transfer.

· mean local efficiency

measures global efficiency on the neighbourhood sub-graphs, related to the clustering coefficient.

· clustering coefficient

measures the likelihood of a given node's neighbours to be also connected to each other.

(measures local neighborhood connectivity, calculated as the fraction of a node's neighbors that are neighbors of each other.)

mean local efficiency and clustering coefficient allude to the level of integration/segregation in a network, and howefficient the communication is at the local level.

· betweenness centrality

a measure of node degree(number of connections) and indicates the relative importance of each node calculated as the ratio of shortest paths in the network that passes through a node.

(measures node centrality, calculated as the fraction of all shortest paths in the network that contain a given node.)

· degree

measures the connectivity of each node, calculated as the sum of number/weight of links connected to each node.

· References:

Vatansever, D., et al. (2015). "Default mode network connectivity during task execution." Neuroimage 122: 96-104.

Guo, C. C., et al. (2012). "One-year test–retest reliability of intrinsic connectivity network fMRI in older adults." Neuroimage 61(4): 1471-1483.