Hi, I still have questions about this command.

After I used uber_subject.py GUI to generate the required files to do fMRI statistical analysis, there are two script generated. One is 'cmd.ap.Subject' script and another one is 'proc.Subject' script.

Now, I want to add new regressors and make a new GLM. However, I don't want to do the whole process again since it is very time consuming. I only want to run the regression part and the following steps to save some time.

The options -write_3dD_script and -write_3dD_prefix were recommended to add to the command script in the subject directory. So my question is,

1. Is the command script in the subject directory refers to the script 'cmd.ap.Subject' generated after uber_subject.py GUI?

2. Where should I place these two options in the command script?

3. What should be add as the parameters after these two options?

4. After successfully adding these two options, is it using command 'tcsh -xef cmd.ap.Subject' to run the afni_proc.py again?

Very appreciated if someone can answer me these questions. Below is the cmd.ap.Subject script generated after uber_subject.py GUI in my project.


#!/usr/bin/env tcsh

# created by uber_subject.py: version 0.40 (March 30, 2017)
# creation date: Thu Aug 30 17:15:54 2018

# set data directories
set top_dir = /data/ffang3/fMRI_analysis/Subj15_du_jia_peng
set anat_dir = $top_dir/Raw_Data/
set epi_dir = $top_dir/Raw_Data
set stim_dir = $top_dir/Time

# set subject and group identifiers
set subj = Subj15_DuJiaPeng
set group_id = EmtionRegulationStudy

# run afni_proc.py to create a single subject processing script
afni_proc.py
-subj_id $subj \
-script proc.$subj -scr_overwrite \
-blocks tshift align tlrc volreg blur mask scale regress \
-copy_anat $anat_dir/du_jia_peng_T1.nii \
-tcat_remove_first_trs 0 \
-dsets \
$epi_dir/du_jia_peng_sess1.nii \
$epi_dir/du_jia_peng_sess2.nii \
$epi_dir/du_jia_peng_sess3.nii \
$epi_dir/du_jia_peng_sess4.nii \
-volreg_align_to third \
-volreg_align_e2a \
-volreg_tlrc_warp \
-blur_size 3.0 \
-regress_stim_times \
$stim_dir/stim.du_jia_peng.negEmoReg.1D \
$stim_dir/stim.du_jia_peng.negative.1D \
$stim_dir/stim.du_jia_peng.neutral.1D \
-regress_stim_labels \
gEmoReg gative utral \
-regress_basis 'BLOCK(5,1)' \
-regress_censor_motion 0.3 \
-regress_apply_mot_types demean deriv \
-regress_opts_3dD \
-jobs 4 \
-gltsym 'SYM: gEmoReg -gative' -glt_label 1 gEmoReg-gative \
-gltsym 'SYM: gEmoReg -utral' -glt_label 2 gEmoReg-utral \
-gltsym 'SYM: gative -utral' -glt_label 3 gative-utral \
-gltsym 'SYM: 0.333*gEmoReg +0.333*gative +0.333*utral' \
-glt_label 4 mean.GGU \
-gltsym 'SYM: gEmoReg -0.5*gative -0.5*utral' -glt_label 5 G-GU \
-regress_compute_fitts \
-regress_make_ideal_sum sum_ideal.1D \
-regress_est_blur_epits \
-regress_est_blur_errts