2.5. Independent component analysis

We identified ICN at the group level by running a group ICA in MELODIC after concatenating all participants' FIX‐cleaned dense timeseries grayordinate files in time and reducing the data matrix into a 1,609 dimensional subspace. We requested 27 components, after estimating the ICA's dimensionality in Matlab using HCP code (icaDim.m). Four artifactual noise components were identified through visual inspection and the remaining 23 components were kept for further analyses.

We labeled these ICN based on (a) the similarity of associated time courses obtained through hierarchical clustering of the IC time courses based on the time courses' full correlation network matrices using the Ward method in FSLnets (v0.6.3) (see Figure 2), (b) pairwise comparison of each thresholded and binarized (|z| > 3) ICN with published atlases (the Cole‐Anticevic cortical and subcortical partition, the Yeo 7 and 17 cortical networks, and the Power partition, see Table T1), and (c) visual inspection and comparison with a detailed map of cortical areas (see Figure S1).

Thresholded statistical maps of independent components and their grouping into ICN through hierarchical clustering of associated time courses. The components in the red cluster belong to the executive control and fronto‐parietal network, the components in magenta to the default mode and language networks, the components in light green represent the (visual) occipital network, the components in blue the midcingulo‐insular and dorsal fronto‐parietal “attention” networks, and the components in the darker green cluster represent the somatomotor and auditory networks. The numbers of the components correspond to the order of the ICA output (ordered by variance explained), the ordinal position in the figure was determined by the clustering