Dear AFNI Experts,

I have inherited SPM-preprocessed EPI images for a resting-state analysis. These images have already undergone bandpass filtering, but NOT the removal of other nuisance regressors. I have the following text files (.txt) for each participant containing the 1) motion parameters, 2) global white matter signal, 3) global CSF signal, 4) global brain signal, and 5) censored TRs.

I would like to remove these nuisance regressors using AFNI’s 3dDeconvolve, but have typically used proc.py for this in the past, and not sure if 3dDeconvolve is the most appropriate program to handle all of these? Below is my sample code, could you please let me know if this looks reasonable, or if there might be a more appropriate method. I plan to use the resulting errts file for the remaining analysis.

3dDeconvolve -input rs.nii \
-mask brainMask_RS.nii.gz \
-censor censor.txt \
-polort 1 \
-num_stimts 9 \
-stim_file 1 motion.txt'[0]' -stim_base 1 -stim_label 1 roll \
-stim_file 2 motion.txt'[1]' -stim_base 2 -stim_label 2 pitch \
-stim_file 3 motion.txt'[2]' -stim_base 3 -stim_label 3 ya \
-stim_file 4 motion.txt'[3]' -stim_base 4 -stim_label 4 ds \
-stim_file 5 motion.txt'[4]' -stim_base 5 -stim_label 5 dl \
-stim_file 6 motion.txt'[5]' -stim_base 6 -stim_label 6 dp \
-stim_file 7 csf.txt'[0]' -stim_base 7 -stim_label 7 csf \
-stim_file 8 wm.txt'[0]' -stim_base 8 -stim_label 8 wm \
-stim_file 9 global.txt'[0]' -stim_base 8 -stim_label 9 gl \
-fitts rs_fitts -errts rs_errts -bucket rs_all \
-jobs 2

Your advice is very much appreciated,
Alfonso



Edited 1 time(s). Last edit at 01/14/2021 07:55AM by Alfonso Alfini.