Table 2.

List of estimated weighted indicators to determine the characteristics of each network analyzed.

	Description	Calculations
Functional Integration (FI)
Number of communities	Number of independent communities detected in a group of specific ROIs. Estimated maximum number of statistically significant clusters in a random network.
Mean of the path lengths	The path length of a node i (Li) is the average number of edges that must be crossed to go from node i to the remaining nodes in the network	$Li={\displaystyle \sum _{\mathrm{i}ϵN}}\left(\frac{1}{n-1}{\displaystyle \sum _{\mathrm{j}ϵN,\mathrm{j}\ne \mathrm{i}}}dij\right)$ where N is the total number of nodes in the network, n is the number of nodes involved and dij is the shortest path length between node i and j.
Standard deviation of the path lengths	The characteristic path length is a global measure of the network, i.e., there is only one value for the entire network. It consists of the average path length of each node in the network.	$L=\frac{1}{N}{\displaystyle \sum _{\mathrm{i}ϵN}}Li$
Functional Segregation (FS)
Global clustering coefficient	This is the average value of the clustering coefficients, which is the fraction of triangles around a node, and is equivalent to the fraction of neighbors of the node that are neighbors among them.	$C=\frac{\sum \mathsf{\Gamma }\mathrm{i}}{\sum ki\left(ki-1\right)}$
Number of triangles	This is the number of connected triangles that can be estimated within a network in Euclidean space.	$G=\left(\mathrm{V},\mathrm{E}\right)$ An ordered pair in which V is a nonempty set of vertices and E is a set of edges. Where E consists of unordered pairs of vertices such as {x, y} E, then x and y are said to be adjacent.
Other measures
Density	The network density (D) is the number of edges in the network in proportion to the total number of possible edges.	$D=\frac{\mathrm{K}}{N\left(N-1\right)}$ where K is the number of edges in the network and N is the total number of nodes in the network.
Small world (Watts–Strogatz)	Networks that present a higher clustering coefficient than expected by chance and that, in addition, have a characteristic shortest path length.	$S=\frac{Cnorm}{Lnorm}=\frac{C/Crandom}{L/Lrandom}$ A network is said to represent this type of organization if the calculated index is greater than 1.
Complexity	The number of nodes and alternative paths that exist within a specific network