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[Preprint]. 2023 Jun 20:2023.04.18.537240. Originally published 2023 Apr 19. [Version 2] doi: 10.1101/2023.04.18.537240

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 (mm) compcor mask n_compcor ica_aroma demean
baseline desc-preproc_bold True N/A N/A N/A N/A N/A N/A N/A N/A True
simple desc-preproc_bold True full basic N/A N/A N/A N/A N/A N/A True
simple+gsr desc-preproc_bold True full basic basic N/A N/A N/A N/A N/A True
scrubbing.5 desc-preproc_bold True full full N/A 5 0.5 N/A N/A N/A True
scrubbing.5+gsr desc-preproc_bold True full full basic 5 0.5 N/A N/A N/A True
scrubbing.2 desc-preproc_bold True full full N/A 5 0.2 N/A N/A N/A True
scrubbing.2+gsr desc-preproc_bold True full full basic 5 0.2 N/A N/A N/A True
compcor desc-preproc_bold True full N/A N/A N/A N/A anat_combined all * N/A True
compcor6 desc-preproc_bold True full N/A N/A N/A N/A anat_combined 6 N/A True
aroma** desc-smoothAROMAnonaggr_bold True N/A basic N/A N/A N/A N/A N/A full *** True
*

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. (2015). 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 (2015)