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,
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Is the command script in the subject directory refers to the script ‘cmd.ap.Subject’ generated after uber_subject.py GUI?
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Where should I place these two options in the command script?
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What should be add as the parameters after these two options?
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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: 0.333gEmoReg +0.333gative +0.333utral’
-glt_label 4 mean.GGU
-gltsym 'SYM: gEmoReg -0.5gative -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