Summary of what happened:
Hi everyone!
I have a question directly related to AFNI use.
I am working on calculating fALFF and ReHo values for a large dataset. My data was preprocessed with fMRIprep (23.0.2). My plan is to use the 3dRSFC function to extract all the LFF related measures, using the -ort fllag for counfound regression, and then 3dReHo. I was wondering which would be the recommended confounds to include for regression (i.e. what to include in the 1D file provided for -ort). I’ve been searching but found out benchmarking papers are usually focused directly on how to pre-process data for functional connectivity analysis (for example Ciric et al 2017), but there is not so much information about how these decisions affect LFF metrics. I was thinking of adding just the 6 basic motion parameters, CSF and WM.
Additionally, I’d like to know if it would be advisable to use the LFF 4D volume for later connectivity analyses or if it would be more advisable to reprocess it with it’s own confounds and only highpass filtering (instead of the bandpass filtering used for ALFF/fALFF).
Here is how my code looks like for now:
Command used (and if a helper script was used, a link to the helper script or the command generated):
## Remove the first 5 volumes from the preprocessed BOLD data
preproc_bold=${func_dir}${sub}_${ses}_task-rest_space-MNI152NLin2009cAsym_res-2_desc-preproc_bold.nii.gz
3dTcat -prefix ${tmpsubdir}/${sub}_bold_tcat $preproc_bold'[5..$]'
## Run 3dRSFC to get fALFF, ALFF, mALFF, RSFA
3dRSFC -mask ${brain_mask} \
-prefix ${tmpsubdir}/${sub}_${ses} \
-ort ${afni_confounds} \
-despike \
-quiet \
0.01 0.08 ${tmpsubdir}/${sub}_bold_tcat+tlrc
3dReHo -prefix ${tmpsubdir}/${sub}_${ses}_ReHo \
-inset ${tmpsubdir}/${sub}_${ses}_LFF+tlrc \
-mask ${brain_mask}
Version:
(afni -ver): Version AFNI_99.99.99 ‘Alexios V Doukas the Bushy-eyebrowed’
Environment (Docker, Singularity / Apptainer, custom installation):
Precompiled binary
Thank you