Figure 1. Overview of the proposed method.

The method consists of two parts: correlation coefficient computation and multiple comparison correction with cluster-based statistics. In the former part, a partial correlation coefficient is calculated for each connection of the brain network with the behavioral measures. In this step, several compounding variables are taken as covariates in order to count their effects on the correlation coefficients. In the later part, we perform cluster-based correction for the multiple comparison of the correlation coefficients by adopting the supra-threshold cluster size test to our problem setting. In this approach, clusters are constructed by grouping together neighboring supra-threshold connections, and the p-values are estimated through permutation testing, forming a null distribution of the maximum cluster extent. The output of this step is a set of sub-networks consisting of neighboring connections that are significantly correlated with the behavioral measures.