For each time point, the raw myelin content at each vertex along the midthickness surface was defined by the ratio between the T1-weighted and T2-weighted intensity values of that vertex. In line with HCP processing (Glasser et al. 2013), the normalized myelin content was then computed in order to compare the myelin content across subjects. Specifically, the brain surface containing the raw myelin content was mapped to the 2mm HCP template by aligning sulc features (i.e., average convexity) using multimodal surface matching (Robinson et al. 2014). The raw myelin scores on the 2mm HCP template were then modified so that their corresponding low frequency pattern (i.e., the output of a 14.14 mm Gaussian filter kernel applied to the vertex-wise values) matched the one of the average myelin content across the Conte69 subjects (the reference myelin content) (Glasser and Van Essen 2011). Doing so normalized the distribution of the resulting normalized myelin content to the reference myelin content. The normalized myelin content of the cross-sectional HCP data set was computed by first resampling their skull-stripped, inhomogeneity corrected, structural MRIs to match the resolution of the MRIs of NCANDA (1.2×0.9375×0.9375mm) and then following the procedure for computing normalized myelin content.
