To examine the functional connections within and between the large set of regions used in this manuscript we chose to use graph theory. Graph theory is particularly well suited to study large-scale systems organization across development, but requires the data be organized into specific correlation matrices. To do this, for each of the 210 subjects, the resting state BOLD timeseries from each region was correlated with the timeseries from every other region, creating 210 square correlation matrices (34×34). Average group matrices were then created using a sliding boxcar grouping of subjects in age-order (i.e., group1: subjects 1–60, group2: subjects 2–61, group3: subjects 3–62, … group151: subjects 151–210), thus generating a series of groups with average ages ranging from 8.48 years old to 25.48 years old with each group composed of 60 subjects. Average correlation coefficients (r) for each group were generated from the subjects' individual matrices using the Schmidt-Hunter method for meta-analyses of r-values [21],[85],[93]. In cases when the terms “child” or “adult” are used, the matrices or results referred to are the first and last of the sliding boxcar
