Hi,

I am preprocessing a resting state dataset using afni_proc. My dataset is fairly short (6 minutes, ~150 time points). The preprocessing steps are fairly straight forward but I have issue preprocessing the majority of my subjects because the way afni_proc applies temporal filtering through regression. I ask it to regress out white matter, CSF, global signals, plus the six motion correction parameters and their derivatives, totaling 15 regressors. But with the temporal filter regressors (sine and cosine waves of the frequencies we are trying to filter out) I end up with 108 regressors, which means for a lot of my subjects, after motion ensoring, there is not enough degrees of freedom to perform the regression and I will have to discard them. I was wondering if there is a way to apply the temporal filter without regressing sine and cosine waves, for example using a Butterworth filter, similar to what MATLAB does. If not, I can perform motion censoring, temporal filtering, and regression of nuisance variables (white matter, CSF, global signal and motion correction parameters) in another software package, but then the question is how do I get afni_proc to calculate white matter, CSF, and global signals plus motion correction parameters for me without regressing them out, so I can load those files in another software package?

Thanks for your help,
Pantea

P.S. Here is my code:
afni_proc.py -subj_id sub-A00000300\
-dsets sub-A00000300_ses-20110101_task-rest_bold.nii\
-copy_anat sub-A00000300_ses-20110101_acq-mprage_run-01_T1w.nii\
-blocks tshift align tlrc volreg mask blur regress\
-tcat_remove_first_trs 5\
-tlrc_base MNI152_T1_2009c+tlrc\
-volreg_align_e2a\
-volreg_tlrc_warp\
-tlrc_NL_warp\
-mask_segment_anat yes\
-mask_segment_erode yes\
-volreg_align_to MIN_OUTLIER\
-regress_ROI WMe CSFe brain\
-regress_apply_mot_types demean deriv\
-regress_bandpass 0.01 0.1\
-blur_size 4\
-regress_censor_motion 0.2\
-regress_censor_outliers 0.05\