Global and local network analysis

Prior to brain network quantification, a sparsity threshold of 0.25 (i.e., which is the ratio of the number of actual edges to the maximum possible number of edges in a structural network)⁸⁵, was applied to individual networks to remove the weakest connections subject to experimental noise⁸⁸. The specific threshold selection procedure followed that of our previous network study²³. Global and local network properties were analyzed using the Brain Connectivity Toolbox⁸⁹ and GRETNA software (http://www.nitrc.org/projects/gretna/)⁹⁰.

Graph metrics were used to quantify brain global (global efficiency, E_glob; local efficiency, E_loc; modularity, Q; small-worldness, S; normalized clustering coefficient, C_p; normalized shortest path length, L_p)⁸⁹ and local (betweenness centrality, BC; degree centrality, DC; nodal clustering coefficient, NC_p; nodal shortest path length, NL_p; nodal efficiency, L_e; nodal local efficiency, NL_e)^91,92 connectivity. Global metrics were computed for 1,000 random networks with conserved number of nodes, number of edges, and degree distribution at predefined sparsity thresholds²³. Local network metrics were used as indicators of neonatal and children brain development, and employed to elucidate clinical implications^23,93–96. Details of the described graph-theoretical measures can be found in supplementary text ¹.