multi-echo, tedana, block order, alignment

AFNI Message Board
1

Hi everyone,

I’ve been collecting multi-echo data and using meica.py for some time now. I recently decided to switch to afni_proc, and have been having issues. I started by modelling my script from example 12b in the afni_proc documentation. However, tedana failed to reach convergence in about 1 in 3 subjects. This is much higher than if I use meica.py. I checked the usage recommendations for tedana and they suggest that you perform perform distortion correction, spatial normalization, smoothing, and any rescaling or filtering after tedana.

However, the default block order for the afni_proc multi_echo examples is


-blocks tshift align tlrc volreg mask combine blur scale regress

If I switch the block order to


-blocks tshift volreg combine align tlrc mask blur scale regress

tedana converges on all subjects, but because the epi2anat alignment is done in the volreg stage, this order breaks the alignment pipeline in afni_proc.

Side note, I tried passing the epi2anat option to the alignment block, but this results in the following align block:


align_epi_anat.py -anat2epi -anat 034.anat.aparc_ss+orig \
       -suffix _al_keep                                  \
       -epi vr_base_min_outlier+orig -epi_base 0         \
       -epi_strip 3dAutomask                             \
       -anat_has_skull no                                \
       -giant_move -epi2anat -partial_axial -cost lpc+ZZ \
       -volreg off -tshift off

It appears that the -anat2epi flag is added by default even if you try to force the epi2anat.

I guess my question is:

How do I write my afni_proc command so that tedana works consistently and so I end up with my EPI data in MNI space?

thanks,
-nick

P.S. Here is my afni_proc script:


afni_proc.py \
-script proc.${subject}.me \
-scr_overwrite \
-subj_id ${subject} \
-out_dir ${deldir}/${subject}.results.me \
-blocks despike tshift volreg mask combine tlrc align blur scale regress \
-align_opts_aea \
-giant_move \
-partial_axial \
-cost lpc+ZZ \
-copy_anat ${t1} \
-anat_has_skull no \
-tlrc_base MNI152_T1_2009c+tlrc \
-tlrc_NL_warp \
-blip_forward_dset $forwardepi \
-blip_reverse_dset $reversedepi \
-volreg_align_to MIN_OUTLIER \
-dsets_me_run $run1epi \
-dsets_me_run $run2epi \
-dsets_me_run $run3epi \
-dsets_me_run $run4epi \
-echo_times ${e[1]} ${e[2]} ${e[3]} \
-combine_method tedana \
-combine_opts_tedana --sourceTEs=0 --kdaw=10 --rdaw=1 \
-reg_echo 2 \
-blur_in_mask yes \
-regress_opts_3dD \
-regress_motion_per_run \
-regress_apply_mot_types demean deriv \
-regress_censor_motion 0.5 \
-regress_censor_outliers 0.15 \
-regress_censor_first_trs 4 \
-regress_est_blur_errts \
-regress_stim_times \
${stmdir}/${subject}.button_presses.1d \
${stmdir}/${subject}.shocks.1d \
${stmdir}/${subject}.cue.saf.hgh.hgh.l.1d \
${stmdir}/${subject}.cue.saf.hgh.hgh.r.1d \
${stmdir}/${subject}.cue.saf.hgh.low.l.1d \
${stmdir}/${subject}.cue.saf.hgh.low.r.1d \
${stmdir}/${subject}.cue.saf.low.hgh.l.1d \
${stmdir}/${subject}.cue.saf.low.hgh.r.1d \
${stmdir}/${subject}.cue.saf.low.low.l.1d \
${stmdir}/${subject}.cue.saf.low.low.r.1d \
${stmdir}/${subject}.cue.thr.hgh.hgh.l.1d \
${stmdir}/${subject}.cue.thr.hgh.hgh.r.1d \
${stmdir}/${subject}.cue.thr.hgh.low.l.1d \
${stmdir}/${subject}.cue.thr.hgh.low.r.1d \
${stmdir}/${subject}.cue.thr.low.hgh.l.1d \
${stmdir}/${subject}.cue.thr.low.hgh.r.1d \
${stmdir}/${subject}.cue.thr.low.low.l.1d \
${stmdir}/${subject}.cue.thr.low.low.r.1d \
${stmdir}/${subject}.square.saf.hgh.hgh.l.1d \
${stmdir}/${subject}.square.saf.hgh.hgh.r.1d \
${stmdir}/${subject}.square.saf.hgh.low.l.1d \
${stmdir}/${subject}.square.saf.hgh.low.r.1d \
${stmdir}/${subject}.square.saf.low.hgh.l.1d \
${stmdir}/${subject}.square.saf.low.hgh.r.1d \
${stmdir}/${subject}.square.saf.low.low.l.1d \
${stmdir}/${subject}.square.saf.low.low.r.1d \
${stmdir}/${subject}.square.thr.hgh.hgh.l.1d \
${stmdir}/${subject}.square.thr.hgh.hgh.r.1d \
${stmdir}/${subject}.square.thr.hgh.low.l.1d \
${stmdir}/${subject}.square.thr.hgh.low.r.1d \
${stmdir}/${subject}.square.thr.low.hgh.l.1d \
${stmdir}/${subject}.square.thr.low.hgh.r.1d \
${stmdir}/${subject}.square.thr.low.low.l.1d \
${stmdir}/${subject}.square.thr.low.low.r.1d \
${stmdir}/${subject}.target.saf.hgh.hgh.l.1d \
${stmdir}/${subject}.target.saf.hgh.hgh.r.1d \
${stmdir}/${subject}.target.saf.hgh.low.l.1d \
${stmdir}/${subject}.target.saf.hgh.low.r.1d \
${stmdir}/${subject}.target.saf.low.hgh.l.1d \
${stmdir}/${subject}.target.saf.low.hgh.r.1d \
${stmdir}/${subject}.target.saf.low.low.l.1d \
${stmdir}/${subject}.target.saf.low.low.r.1d \
${stmdir}/${subject}.target.thr.hgh.hgh.l.1d \
${stmdir}/${subject}.target.thr.hgh.hgh.r.1d \
${stmdir}/${subject}.target.thr.hgh.low.l.1d \
${stmdir}/${subject}.target.thr.hgh.low.r.1d \
${stmdir}/${subject}.target.thr.low.hgh.l.1d \
${stmdir}/${subject}.target.thr.low.hgh.r.1d \
${stmdir}/${subject}.target.thr.low.low.l.1d \
${stmdir}/${subject}.target.thr.low.low.r.1d \
-regress_stim_types \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
-regress_basis_multi \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
-regress_stim_labels \
"button_presses" \
"shocks" \
"cue.saf.hgh.hgh.l" \
"cue.saf.hgh.hgh.r" \
"cue.saf.hgh.low.l" \
"cue.saf.hgh.low.r" \
"cue.saf.low.hgh.l" \
"cue.saf.low.hgh.r" \
"cue.saf.low.low.l" \
"cue.saf.low.low.r" \
"cue.thr.hgh.hgh.l" \
"cue.thr.hgh.hgh.r" \
"cue.thr.hgh.low.l" \
"cue.thr.hgh.low.r" \
"cue.thr.low.hgh.l" \
"cue.thr.low.hgh.r" \
"cue.thr.low.low.l" \
"cue.thr.low.low.r" \
"square.saf.hgh.hgh.l" \
"square.saf.hgh.hgh.r" \
"square.saf.hgh.low.l" \
"square.saf.hgh.low.r" \
"square.saf.low.hgh.l" \
"square.saf.low.hgh.r" \
"square.saf.low.low.l" \
"square.saf.low.low.r" \
"square.thr.hgh.hgh.l" \
"square.thr.hgh.hgh.r" \
"square.thr.hgh.low.l" \
"square.thr.hgh.low.r" \
"square.thr.low.hgh.l" \
"square.thr.low.hgh.r" \
"square.thr.low.low.l" \
"square.thr.low.low.r" \
"target.saf.hgh.hgh.l" \
"target.saf.hgh.hgh.r" \
"target.saf.hgh.low.l" \
"target.saf.hgh.low.r" \
"target.saf.low.hgh.l" \
"target.saf.low.hgh.r" \
"target.saf.low.low.l" \
"target.saf.low.low.r" \
"target.thr.hgh.hgh.l" \
"target.thr.hgh.hgh.r" \
"target.thr.hgh.low.l" \
"target.thr.hgh.low.r" \
"target.thr.low.hgh.l" \
"target.thr.low.hgh.r" \
"target.thr.low.low.l" \
"target.thr.low.low.r" \
-test_stim_files no \
-remove_preproc_files \
-execute
2

Hi everyone,

I haven’t gotten a reply, so I might ping someone from the AFNI team. In the meantime, I think I have found a workaround that works for me. At its core, I follow 3 basic steps.

[ol]
[li] run afni_proc on each run with minimal preprocessing using tedana to combine the echos.
[/li][li] extract the “bad” component timeseries using @extract_meica_ortvec
[/li][li] run the full afni_proc command using the combine OC option, adding the “bad” component timeseries to the 3ddeconvolve command using the -ortvec flag.
[/li][/ol]

Although the default tedana parameters worked for most files, I had to run several afni_proc commands with different tedana options to get tedana to process all the datasets ~383 total (96 subs x 4 runs -1 missing dataset). I ran the commands successively, so that once one afni_proc command completed successfully, the others did not run for that file. Here’s a breakdown of the different tedana options and the number of files processed for each.

tedana options, number of datasets completed
[ol]
[li] kdaw=10, rdaw=1 (default), n=320
[/li][li] kdaw=5, rdaw=1, n=36
[/li][li] kdaw=15, rdaw=1, n=2
[/li][li] kdaw=-1, rdaw=-1, n=21
[/li][li] kdaw=10, rdaw=1, stabilize, n=0
[/li][li] kdaw=-1, rdaw=-1, stabilize, n=3
[/li][li] fail and move on n = 1
[/li][/ol]

I’m not sure if this is the best approach, but it worked for all but one file, so it seems pretty robust. I realize that the basic approach is similar to using the tedana_OC_tedort flag; however, I think it has 2 added benefits.

[ol]
[li] It allows you to use different tedana parameters for each run if needed.
[/li][li] Given that the final afni_proc command does not rely on tedana for the denoising, it will not fail even if tedana fails on one run. Instead, the “bad” component timeseries for the the failed run are just not passed to the second afni_proc command, which defaults to the OC combine method.
[/li][/ol]

Here is the code for each of the 3 steps.

#########################
Initial afni_proc commands
#########################


afni_proc.py \
-script proc.${subject}.${run}.me \
-scr_overwrite \
-subj_id ${subject} \
-out_dir ${deldir}/${subject}.results.${run}.me \
-blocks despike tshift volreg mask combine  \
-volreg_align_to MIN_OUTLIER \
-dsets_me_run $d \
-echo_times ${e[1]} ${e[2]} ${e[3]} \
-combine_method tedana \
-reg_echo 2 \
-remove_preproc_files \
-execute

######################################kdaw5 
if (-e ${deldir}/${subject}.results.${run}.me/tedana_r01/TED.r01/dn_ts_OC.nii ) then 
echo "######################################reg worked"
else

rm -rf ${deldir}/${subject}.results.${run}.me

afni_proc.py \
-script proc.${subject}.${run}.me \
-scr_overwrite \
-subj_id ${subject} \
-out_dir ${deldir}/${subject}.results.${run}.me \
-blocks despike tshift volreg mask combine  \
-volreg_align_to MIN_OUTLIER \
-dsets_me_run $d \
-echo_times ${e[1]} ${e[2]} ${e[3]} \
-combine_method tedana \
-combine_opts_tedana --kdaw=5 \
-reg_echo 2 \
-remove_preproc_files \
-execute

endif

######################################kdaw15 
if (-e ${deldir}/${subject}.results.${run}.me/tedana_r01/TED.r01/dn_ts_OC.nii ) then 
echo "######################################kdaw5 worked"
else

rm -rf ${deldir}/${subject}.results.${run}.me

afni_proc.py \
-script proc.${subject}.${run}.me \
-scr_overwrite \
-subj_id ${subject} \
-out_dir ${deldir}/${subject}.results.${run}.me \
-blocks despike tshift volreg mask combine  \
-volreg_align_to MIN_OUTLIER \
-dsets_me_run $d \
-echo_times ${e[1]} ${e[2]} ${e[3]} \
-combine_method tedana \
-combine_opts_tedana --kdaw=15 \
-reg_echo 2 \
-remove_preproc_files \
-execute

endif

######################################kdaw-1 
if (-e ${deldir}/${subject}.results.${run}.me/tedana_r01/TED.r01/dn_ts_OC.nii ) then 
echo "######################################kdaw15 worked"
else

rm -rf ${deldir}/${subject}.results.${run}.me

afni_proc.py \
-script proc.${subject}.${run}.me \
-scr_overwrite \
-subj_id ${subject} \
-out_dir ${deldir}/${subject}.results.${run}.me \
-blocks despike tshift volreg mask combine  \
-volreg_align_to MIN_OUTLIER \
-dsets_me_run $d \
-echo_times ${e[1]} ${e[2]} ${e[3]} \
-combine_method tedana \
-combine_opts_tedana --kdaw=-1 --rdaw=-1 \
-reg_echo 2 \
-remove_preproc_files \
-execute

endif

######################################stabilize 
if (-e ${deldir}/${subject}.results.${run}.me/tedana_r01/TED.r01/dn_ts_OC.nii ) then 
echo "######################################kdaw-1 worked"
else

rm -rf ${deldir}/${subject}.results.${run}.me

afni_proc.py \
-script proc.${subject}.${run}.me \
-scr_overwrite \
-subj_id ${subject} \
-out_dir ${deldir}/${subject}.results.${run}.me \
-blocks despike tshift volreg mask combine  \
-volreg_align_to MIN_OUTLIER \
-dsets_me_run $d \
-echo_times ${e[1]} ${e[2]} ${e[3]} \
-combine_method tedana \
-combine_opts_tedana --stabilize \
-reg_echo 2 \
-remove_preproc_files \
-execute

endif

######################################stabilize kdaw-1
if (-e ${deldir}/${subject}.results.${run}.me/tedana_r01/TED.r01/dn_ts_OC.nii ) then 
echo "######################################stabilize worked"
else

rm -rf ${deldir}/${subject}.results.${run}.me

afni_proc.py \
-script proc.${subject}.${run}.me \
-scr_overwrite \
-subj_id ${subject} \
-out_dir ${deldir}/${subject}.results.${run}.me \
-blocks despike tshift volreg mask combine  \
-volreg_align_to MIN_OUTLIER \
-dsets_me_run $d \
-echo_times ${e[1]} ${e[2]} ${e[3]} \
-combine_method tedana \
-combine_opts_tedana --kdaw=-1 --rdaw=-1 --stabilize \
-reg_echo 2 \
-remove_preproc_files \
-execute

endif

#########################
@extract_meica_ortvec step
#########################



foreach subject (${subs})


cd ${subdir}/${subject}


set runs=`ls -d ${subject}.results.run*.me | cut -d. -f3 | uniq`
set nruns=`ls -d ${subject}.results.run*.me | cut -d. -f3 | uniq | wc -l`
set trs=`3dinfo -nt ${subject}.results.run?.me/tedana_r01/tedprep_Zcat_all.nii.gz`
#TED.r01/dn_ts_OC.nii`
set r=1

foreach run ($runs)


echo "#######################################extract "
@extract_meica_ortvec \
-meica_dir ${subject}.results.${run}.me/tedana_r01/TED.r01 \
-work_dir ${subject}.results.${run}.me/tedana_r01/regwork.r01 \
-prefix ${subject}.tedana.badorts.$run.1d \


echo "################################################################1dtool $r"
1d_tool.py \
-infile ${subject}.tedana.badorts.$run.1d \
-pad_into_many_runs ${r} ${nruns} \
-set_run_lengths ${trs} \
-write ${subject}.tedana.badorts.pad.$run.1d

@ r++

end
paste \
${subject}.tedana.badorts.pad.run?.1d \
> ${subject}.tedana.badorts.pad.1d


end

#########################
full afni_proc command
#########################



afni_proc.py \
-script proc.${subject}.me \
-scr_overwrite \
-subj_id ${subject} \
-out_dir ${deldir}/${subject}.results.me \
-blocks despike tshift align tlrc volreg mask combine blur scale regress \
-copy_anat ${t1} \
-align_opts_aea \
-giant_move \
-cost lpc+ZZ \
-partial_axial \
-volreg_align_to MIN_OUTLIER \
-volreg_align_e2a \
-volreg_tlrc_warp \
-anat_has_skull no \
-tlrc_base MNI152_T1_2009c+tlrc \
-tlrc_NL_warp \
-blip_forward_dset $forwardepi \
-blip_reverse_dset $reversedepi \
-volreg_align_to MIN_OUTLIER \
$epi \
-echo_times ${e[1]} ${e[2]} ${e[3]} \
-combine_method OC \
-reg_echo 2 \
-blur_in_mask yes \
-regress_opts_3dD \
-ortvec ${subdir}/${subject}/${subject}.tedana.badorts.pad.1d tedana \
-regress_motion_per_run \
-regress_apply_mot_types demean deriv \
-regress_censor_motion 0.5 \
-regress_censor_outliers 0.15 \
-regress_censor_first_trs 4 \
-regress_est_blur_errts \
-regress_stim_times \
${stmdir}/${subject}.button_presses.1d \
${stmdir}/${subject}.shocks.1d \
${stmdir}/${subject}.cue.saf.hgh.hgh.l.1d \
${stmdir}/${subject}.cue.saf.hgh.hgh.r.1d \
${stmdir}/${subject}.cue.saf.hgh.low.l.1d \
${stmdir}/${subject}.cue.saf.hgh.low.r.1d \
${stmdir}/${subject}.cue.saf.low.hgh.l.1d \
${stmdir}/${subject}.cue.saf.low.hgh.r.1d \
${stmdir}/${subject}.cue.saf.low.low.l.1d \
${stmdir}/${subject}.cue.saf.low.low.r.1d \
${stmdir}/${subject}.cue.thr.hgh.hgh.l.1d \
${stmdir}/${subject}.cue.thr.hgh.hgh.r.1d \
${stmdir}/${subject}.cue.thr.hgh.low.l.1d \
${stmdir}/${subject}.cue.thr.hgh.low.r.1d \
${stmdir}/${subject}.cue.thr.low.hgh.l.1d \
${stmdir}/${subject}.cue.thr.low.hgh.r.1d \
${stmdir}/${subject}.cue.thr.low.low.l.1d \
${stmdir}/${subject}.cue.thr.low.low.r.1d \
${stmdir}/${subject}.square.saf.hgh.hgh.l.1d \
${stmdir}/${subject}.square.saf.hgh.hgh.r.1d \
${stmdir}/${subject}.square.saf.hgh.low.l.1d \
${stmdir}/${subject}.square.saf.hgh.low.r.1d \
${stmdir}/${subject}.square.saf.low.hgh.l.1d \
${stmdir}/${subject}.square.saf.low.hgh.r.1d \
${stmdir}/${subject}.square.saf.low.low.l.1d \
${stmdir}/${subject}.square.saf.low.low.r.1d \
${stmdir}/${subject}.square.thr.hgh.hgh.l.1d \
${stmdir}/${subject}.square.thr.hgh.hgh.r.1d \
${stmdir}/${subject}.square.thr.hgh.low.l.1d \
${stmdir}/${subject}.square.thr.hgh.low.r.1d \
${stmdir}/${subject}.square.thr.low.hgh.l.1d \
${stmdir}/${subject}.square.thr.low.hgh.r.1d \
${stmdir}/${subject}.square.thr.low.low.l.1d \
${stmdir}/${subject}.square.thr.low.low.r.1d \
${stmdir}/${subject}.target.saf.hgh.hgh.l.1d \
${stmdir}/${subject}.target.saf.hgh.hgh.r.1d \
${stmdir}/${subject}.target.saf.hgh.low.l.1d \
${stmdir}/${subject}.target.saf.hgh.low.r.1d \
${stmdir}/${subject}.target.saf.low.hgh.l.1d \
${stmdir}/${subject}.target.saf.low.hgh.r.1d \
${stmdir}/${subject}.target.saf.low.low.l.1d \
${stmdir}/${subject}.target.saf.low.low.r.1d \
${stmdir}/${subject}.target.thr.hgh.hgh.l.1d \
${stmdir}/${subject}.target.thr.hgh.hgh.r.1d \
${stmdir}/${subject}.target.thr.hgh.low.l.1d \
${stmdir}/${subject}.target.thr.hgh.low.r.1d \
${stmdir}/${subject}.target.thr.low.hgh.l.1d \
${stmdir}/${subject}.target.thr.low.hgh.r.1d \
${stmdir}/${subject}.target.thr.low.low.l.1d \
${stmdir}/${subject}.target.thr.low.low.r.1d \
-regress_stim_types \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
times \
-regress_basis_multi \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
'BLOCK(1,1)' \
-regress_stim_labels \
"button_presses" \
"shocks" \
"cue.saf.hgh.hgh.l" \
"cue.saf.hgh.hgh.r" \
"cue.saf.hgh.low.l" \
"cue.saf.hgh.low.r" \
"cue.saf.low.hgh.l" \
"cue.saf.low.hgh.r" \
"cue.saf.low.low.l" \
"cue.saf.low.low.r" \
"cue.thr.hgh.hgh.l" \
"cue.thr.hgh.hgh.r" \
"cue.thr.hgh.low.l" \
"cue.thr.hgh.low.r" \
"cue.thr.low.hgh.l" \
"cue.thr.low.hgh.r" \
"cue.thr.low.low.l" \
"cue.thr.low.low.r" \
"square.saf.hgh.hgh.l" \
"square.saf.hgh.hgh.r" \
"square.saf.hgh.low.l" \
"square.saf.hgh.low.r" \
"square.saf.low.hgh.l" \
"square.saf.low.hgh.r" \
"square.saf.low.low.l" \
"square.saf.low.low.r" \
"square.thr.hgh.hgh.l" \
"square.thr.hgh.hgh.r" \
"square.thr.hgh.low.l" \
"square.thr.hgh.low.r" \
"square.thr.low.hgh.l" \
"square.thr.low.hgh.r" \
"square.thr.low.low.l" \
"square.thr.low.low.r" \
"target.saf.hgh.hgh.l" \
"target.saf.hgh.hgh.r" \
"target.saf.hgh.low.l" \
"target.saf.hgh.low.r" \
"target.saf.low.hgh.l" \
"target.saf.low.hgh.r" \
"target.saf.low.low.l" \
"target.saf.low.low.r" \
"target.thr.hgh.hgh.l" \
"target.thr.hgh.hgh.r" \
"target.thr.hgh.low.l" \
"target.thr.hgh.low.r" \
"target.thr.low.hgh.l" \
"target.thr.low.hgh.r" \
"target.thr.low.low.l" \
"target.thr.low.low.r" \
-test_stim_files no \
-remove_preproc_files \
-execute