Preprocessing was carried out using Workbench [50 (link)] and custom code in MATLAB 2017b (MathWorks). For 3T data, to match the steps of preprocessing across rest and task, the version of HCP minimal preprocessing pipeline before FIX denoising was used (i.e., including procedures of registration to MNI space, alignment for motion, fieldmap correction, and MSMAll group registration [47 (link)]). Additional noise regression was applied by in-house code (see details below). For 7T data, as the minimal preprocessed data in both rest and task had performed FIX denoising [47 (link)], no additional noise regression was further applied.
For 3T data, the linear trend for each run was removed, and the nuisance time series (ventricle, white matter, motions along with their first order derivatives) were regressed by using linear regression [3 (link),51 (link)]. The nuisance time series (ventricle and white matter signals) were extracted from volume-based minimal processing. No low-pass temporal filter was applied, given the possibility that frequency specificity might differ between resting and task state [51 (link)].
For 3T data, the linear trend for each run was removed, and the nuisance time series (ventricle, white matter, motions along with their first order derivatives) were regressed by using linear regression [3 (link),51 (link)]. The nuisance time series (ventricle and white matter signals) were extracted from volume-based minimal processing. No low-pass temporal filter was applied, given the possibility that frequency specificity might differ between resting and task state [51 (link)].
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