An externalizing PS computed with a p-value threshold cutoff of <.50 was the primary variable used in all analyses, selected based on prior work (Sadeh et al., 2016). We also report associations between the neural network connectivity measures and externalizing PSs calculated with other p-value thresholds for reference. To identify network connections that varied with polygenic variation, connectivity matrices were entered as dependent variables into the Network Based Statistic (NBS) toolbox (Zalesky et al., 2010), with the PS as the independent variable of interest. An individual connection level threshold of t = 2.9 was used with intensity-based correction for multiple comparisons (5000 permutations) and an overall corrected α < .05. Pairwise LiNGAM 31) was used to gain initial insight into the overall direction of influence of those connections observed in NBS analyses between cortical and subcortical structures. First, we tested the significance of the mean direction (across participants) for each connection via a one-sample t-test. Next, we tested whether the PS moderated the direction of influence for each connection via a regression with the same covariates used in the NBS analyses. False discovery rate was used to correct (across connections) for multiple comparisons.
