Hi Daniel, thank you so much for the reply, here are some more details
What I'm doing on 7T data (1.1mm iso) is:
1) applying motion correction and taking the bias-field-corrected time-average as base for registration (should increase SNR)
2) correcting for distortions using fieldmaps and fsl fugue
3) affine registration epi 2 anat with align_epi_anat.py
4) nonlinear registration epi 2 anat with 3dQwarp
5) concatenation of transforms from steps 1,2,3,4 into a single transform to be applied to original data
I already computed anat 2 template transforms, so now I'm just focusing on epi 2 anat.
I added step 5 because the output of step 4 was very good in the cortex and midbrain, but there were problems in the lowest part of medulla which was usually displaced anteriorly or posteriorly wrt the anatomy
