TABLE 1.

MST descriptive statistics.

Symbol	Concept	Explanation	Formula
N	Nodes	Number of nodes in MST	–
M	Links	Number of links in the MST	–
ki	Degree	Number of links for a given node. Degreemax represents the maximum of all node degrees. It may be considered a feature of “hubs,” i.e., crucial regions in the functional brain network.	ki = ∑j ∈ Naij
Lf	Leaf fraction	Measured based on the leaf number (the number of nodes that have only one connection). In the formula, L represents the total number of leaves of an MST. When the leaf fraction is high, communication is largely dependent on hub nodes.	Lf = L/M
D	Diameter	A measure of the efficiency of global network organization. In the formula, d represents the maximum path length in an MST. In a network with a small diameter, information is efficiently processed between remote brain regions.	D = d/M
E	Eccentricity	The longest optimal path from a reference node to any other node in the MST, where d(i,j) represents the optimal (shortest) path between node i and node j. The average eccentricity represents the average value of the eccentricity of all nodes. Low average eccentricity means that the nodes of the MST are closer to the hub nodes.	Ei = max⁡{d(i,j)|j ∈ N}
BC	Betweenness centrality	Fraction of all shortest paths that pass through a particular node.ρhjis the number of shortest paths between h and j, and ρhj(i) is the number of shortest paths between h and j that pass through i. BCmax represents the maximum BC value of all nodes of an MST. It describes the importance of the most central node, which is a measure of central network organization.	$\text{BC}i={\displaystyle \frac{1}{(n-1)(n-2)}}{\displaystyle \sum _{\begin{array}{c}h,j\in N\hfill \\ h\ne i,j\ne i\hfill \end{array}}}{\displaystyle \frac{\mathrm{\rho }h{j}^{(i)}}{\mathrm{\rho }hj}}$
Th	Tree hierarchy	A hierarchical metric that quantifies the trade-off between large scale integration in the MST and central node overloading.	$Th={\displaystyle \frac{L}{2\text{MBC}\max }}$