Table 2. Strategies examined in the benchmark and associated parameters applied to load_confounds.
| strategy | image | high_pass | motion | wm_csf | global_signal | scrub | fd_thresh | std_dvars_threshold | compcor | n_compcor | ica_aroma |
|---|---|---|---|---|---|---|---|---|---|---|---|
| baseline | desc-preproc_bold | True | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| simple | desc-preproc_bold | True | full | basic | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| simple+gsr | desc-preproc_bold | True | full | basic | basic | N/A | N/A | N/A | N/A | N/A | N/A |
| scrubbing.5 | desc-preproc_bold | True | full | full | N/A | 5 | 0.5 | None | N/A | N/A | N/A |
| scrubbing.5+gsr | desc-preproc_bold | True | full | full | basic | 5 | 0.5 | None | N/A | N/A | N/A |
| scrubbing.2 | desc-preproc_bold | True | full | full | N/A | 5 | 0.2 | None | N/A | N/A | N/A |
| scrubbing.2+gsr | desc-preproc_bold | True | full | full | basic | 5 | 0.2 | None | N/A | N/A | N/A |
| compcor | desc-preproc_bold | True | full | N/A | N/A | N/A | N/A | N/A | anat_combined | all | N/A |
| compcor6 | desc-preproc_bold | True | full | N/A | N/A | N/A | N/A | N/A | anat_combined | 6 | N/A |
| aroma | desc-smoothAROMAnonaggr_bold | True | N/A | basic | N/A | N/A | N/A | N/A | N/A | N/A | full |
* 50% variance explained.
** In Ciric et al. (2017), there was a variation of the ICA-AROMA strategy including global signal regressor. The global signal regressor generated by fMRIPrep does not follow the recommendation of Pruim et al. [32]. The result of ICA-AROMA+GSR can be found in the first version of the preprint.
*** Referring to the non-aggressive implementation in Pruim and colleagues’ work [32]