Hi @Bennett1, and welcome to neurostars!
I believe a basic step-by-step is something like:
Using RAW data:
- Run SynBOLD-DisCo without topup (
--no_topup) - Take the synthesized undistorted BOLD-contrast image (
BOLD_s_3D.nii.gz), place in thefmapfolder, give it a name such assub-xx_ses-xx_acq-synbold_dir-PA/AP_epi.nii.gz, where thedir-<>label is the opposite of what your original BOLD is. - Make your own JSON. Just needs a few fields: (1)
TotalReadoutTime- make it whatever you define at runtime or the default (1 second), (2)PhaseEncodingDirection- make it the opposite as your original image (so add or remove the minus sign), (3) Add theIntendedForand/orB0FieldSource/Identifierfields to link the fieldmap to your BOLD image. The JSON should be calledsub-xx_ses-xx_acq-synbold_dir-PA/AP_epi.json. - Run fMRIPrep as normal (of course, do not
--ignore fieldmapsor--use-syn-sdc, otherwise the fmaps may be not used)
Best,
Steven
EDIT: While this probably works fine with just 1 T1 image, I now wonder if it would be better to use the preprocessed T1 template in the case of multiple T1s. That would make the workflow a little more complicated, since it would involve something like running sMRIPrep (of fMRIPrep with --anat-only) first, then making the synthesized image, and then doing the rest of fMRIPrep (passing in the preprocessed anatomical derivatives with the --anat-derivatives flag). I might give this a shot since I have a 7T dataset with multiple T1 images.