Hi, Korann-
A few things there.
What is your AFNI version ("afni -ver")? If it is quite recent, then you should get a directory called QC_[something] (in your case, probably QC_INSP004) that would have a full report of quality control (QC) blocks from your afni_proc.py processing. This is described in detail here:
[
afni.nimh.nih.gov]
It will show you individual alignment step results (EPI to anat, and anat to template), and you can get a sense of what piece might be running amok.
For aligning to MNI 152, we have been recommending that people use @SSwarper as a pre-afni_proc.py step generally. Basically, you provide your raw anatomical volume, and this program will do bth the skullstripping *and* estimation of a nonlinear warp to standard space. You then provide the results of its hard work to afni_proc.py. If you read its help file, then you will see how to do both steps:
[
afni.nimh.nih.gov]
@SSwarper \
-input ANAT_VOL_WITH_SKULL \
-base MNI152_2009_template_SSW.nii.gz \
-subid INSP004 \
-odir SOMEWHERE \
There will be some snapshot/JPGs created automatically by that program to show you how the alignment (and skullstripping) both look.
... and then your afni_proc.py command would contain some lines like the following (just grabbed this from @SSwarper's help file, where ${subj} is the subj ID and ${tpath} is the path to the reference MNI vol)
-volreg_tlrc_warp -tlrc_base $tpath/MNI152_2009_template_SSW.nii.gz \
-tlrc_NL_warp \
-tlrc_NL_warped_dsets \
anatQQ.${subj}.nii \
anatQQ.${subj}.aff12.1D \
anatQQ.${subj}_WARP.nii
That is how afni_proc.py gets the results of @SSwarper.
While I see that you are not censoring data, I will note for future reference that, f you do start censoring your data, you should read this thread about how best to calculate RSFC params:
[
afni.nimh.nih.gov]
Basically, censoring complicates things- as does bandpassing, to some degree (and to some degree of freedom, but that's a separate note).
--pt