We used data from n = 900 individuals from the HCP 1,200 Subject Data Release (aged 22–35 years). All HCP participants provided informed consent. A custom Siemens SKYRA 3.0T MRI scanner and a custom 32-channel head matrix coil were used to obtain high-resolution T1-weighted (MP-RAGE, TR = 2.4 s, 0.7 mm3 voxels) and BOLD contrast sensitive (gradient-echo EPI, multiband factor 8, TR = 0.72 s, 2 mm3 voxels) images from each participant. The HCP used sequences with left-to-right (LR) and right-to-left (RL) phase encoding, with a single RL and LR run on each day for two consecutive days for a total of four runs68 (link). MRI data were preprocessed as previously described62 (link). All HCP data are available at https://db.humanconnectome.org/ .
Similar to the ABCD data, we extracted the timeseries from a total of 394 cortical and subcortical ROIs, correlated and Fisher z-transformed them. Data from the NIH Toolbox were correlated with each edge of the RSFC correlation matrix across participants. Across all NIH Toolbox subscales, the tails of the distributions of the resulting brain–behavioural phenotype correlations were compared to 100 subsampled ABCD brain–behavioural phenotype correlations (n = 877, matching HCP sample size). InSupplementary Fig. 8 , we show the distributions of brain–behavioural phenotype correlations for ABCD and HCP data, for each NIH Toolbox subscale.
Similar to the ABCD data, we extracted the timeseries from a total of 394 cortical and subcortical ROIs, correlated and Fisher z-transformed them. Data from the NIH Toolbox were correlated with each edge of the RSFC correlation matrix across participants. Across all NIH Toolbox subscales, the tails of the distributions of the resulting brain–behavioural phenotype correlations were compared to 100 subsampled ABCD brain–behavioural phenotype correlations (n = 877, matching HCP sample size). In
