Hey folks, I’ve been following this discussion with a lot of interest and not a lot of experience with distortion correction. I’ll share some insights. For context, I have dataset A (n=183) with multi-echo rest and single-echo fieldmaps (dir-AP_epi.nii.gz, dir-PA_epi.nii.gz) and dataset B (n=1) with multi-echo rest (dir-AP) and one multi-echo fieldmap encoded in the opposite direction (dir-PA).
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For dataset A, I compared fmriprep + tedana classification with and without distortion correction. I used the default implementation by fmriprep - i.e., I did not follow the recommendation to do SDC after denoising. Using the AIC method for mapca, there was a decrease in the number of ignored components (on average, 9 to 6.5) and increase in the number of rejected components without big change in the total or accepted number of components or the % variance explained. My experience with these ignored components is that some are clearly non-BOLD and some are BOLD-ish. I haven’t systematically evaluated if the former are those that seem to be “reclassified” or know how much change to expect just by re-running tedana regardless of SDC or not.
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As @bpinsard noted above, my understanding of the fMRIprep logs for dataset A is that SDC is being done to each echo separately based on the same AP/PA fmap pair. In dataset B that has a multi-echo fieldmap, it pairs the dir-AP rest echo with it’s corresponding dir-PA fieldmap. Based on the rationale others have given, it seems the approach in dataset A might be slightly more advantageous in terms of having more consistent effect across echoes, right?