During resting-state scans, subjects are instructed to keep their eyes fixated on a crosshair, relax and “think of nothing in particular”. Resting-state are identified using ICA (independent component analysis33, 75), which identifies components within the data that are spatially independent (where a component comprises a spatial map and a single associated time course). Following the pre-processing described above, resting-state fMRI data for each subject is further “cleaned” using an ICA-based algorithm for automatically identifying and removing structured artefacts76. This data is fed into group-level ICA (including an initial group-level dimensionality reduction77), which is used to parcellate the data set into sets of 25 and (separately) 100 spatially-independent components. Where a small (<30) number of components is estimated78, it is common to consider each component as a separate “network” in its own right; each component will often include several non-contiguous regions, all having the same timecourse (according to the model). If a higher number of components is estimated79, these are more likely to be smaller regions (parcels), which can then be considered as nodes for use in network analysis80, where the
