Hi Rick,
Thanks for your reply. I am not censoring but you're right that it's a lot of regressors. I wanted to verify with you that the steps I'm taking are correct to create regression matrices and run nuisance regression. For each participant, I created an ortvec matrix consisting of the 22 regressors of interest (10 CompCors and 6 realignment parameters and their first derivatives). This matrix was then fed into 3dDeconvolve to create a xmat file for regression:
3dDeconvolve -input ${sid}_task-rest_run-1_space-MNI152NLin2009cAsym_res-2_desc-preproc_bold_scale.nii.gz \
-ortvec ${basedir}/aCompCor_RegMatrix/${sid}_${regtype}_confounds.txt ${regtype}_confounds \
-polort 1 -num_stimts 0 -fout -tout -x1D X.scale.xmat.1D -xjpeg X.scale.jpg \
-x1D_uncensored X.scale.nocensor.xmat.1D -fitts fitts.scale.nomask.${sid} \
-errts errts.scale.${sid}.nii.gz -x1D_stop -bucket stats.scale.${sid}
Then I fed the resulting xmat into 3dTproject and used the -passband argument to perform bandpass filtering:
3dTproject -polort 0 -input ${sid}_task-rest_run-1_space-MNI152NLin2009cAsym_res-2_desc-preproc_bold_scale.nii.gz \
-ort X.scale.nocensor.xmat.1D -prefix errts.${sid}.scale.${regtype}.tproject.nii.gz \
-passband 0.008 0.09
Please let me know if you have any comments on the above steps. The goal is to regress the first 5 CompCors from WM/CSF, 12 motion parameters, and bandpass filter the data (0.008-0.09 Hz). Noted on bandpass filtering. I am trying a few variations of preprocessing on this data but am seeing that it's eating up many DOF.
Thank you,
Jenna
