With regard to the functional brain network in each subject, we averaged the global network parameters and the regional nodal parameters over the small-world regime (0.2≤t≤0.35) as the summary network parameters [39]. We applied a general linear model (GLM) to analyze the effects of age, sex, and their interaction on the summary network parameters. To detect the development trajectories of the linear and quadratic age-related changes in each summary network parameter (indicated as Y in the following equations), we used two multiple linear regressions (Model I and II) that modeled mean value, age, and age2 as predictors, with sex as a covariate. We then determined the best model among the two regressions based on Akaike's information criterion (AIC) [49].(I) (II)To detect the sex-related difference in each summary network parameter and its development, we performed another multiple linear regression analysis (Model III), which included age, sex, and age-by-sex interaction, to examine both positive (male>female) and negative (male<female) contrasts as well as positive and negative age-by-sex interactions.(III)If a significant age-by-sex interaction were found, a Pearson's correlation analysis was further performed between age
