We usually preform single voxel analysis in the subject’s EPI space. We also usually pool data across runs/sessions.
When visualizing results we transform them to the subject’s T1w.
We do this by:
1)Aligning all volumes to a single target EPI volume (for example the first volume of the first run, which I think is consistent with SPM)
2)Estimate a single transformation from the target EPI volume to the subject’s T1w. This transformation is applied when visualizing the results.
I think our approach is similar to the second use case mentioned here:
Fmriprep and native EPI space.
My (potentially incorrect) understanding is that fmriprep:
1) aligns each run to some type of mean image,
2) transforms each run to the desired output space (T1w, fsaverage. Or, if using the ‘func’ option, then the output is in the ‘native’ EPI space but not aligned across sessions/runs )
I have done some quick comparisons between fmriprep (output in T1w) and our existing pipeline and found that fmriprep indeed results in lower signal to noise ratio (estimated as response reliability). Happy to share more details about this as/if required.
We think that maybe the additional inter-run alignments in fmriprep hurts the signal to noise ratio, though we cannot rule out something else.
I know that there are some related things in the works, (https://github.com/poldracklab/fmriprep/issues/1604, https://github.com/poldracklab/fmriprep/issues/620, https://github.com/poldracklab/fmriprep/issues/1294) but I can’t make out if these fixes will result in that sort of flexibility.
All and all I’ve found fmriprep really excellent and, aside from the reduced signal to noise ratio, much better than my current processes. It’d be great if this extra alignment option was available.
Cheers and apologies if I missed something obvious.