Thank you for your help!
I ran the uber_subjects.py with the settings you suggested and I received the following error that I am not sure I understand:
e[7m** FATAL ERROR:e[0m ‘-stim_times 1’ file ‘stimuli/NonRevP_learn_corr_fed.txt’ has 1 auxiliary values per time point [nopt=14]
I am not sure what is meant that my regressors have auxiliary values. I should mention that my regressors are defined as: stimulus onset (seconds): stimulus duration. Is this because I included the duration in my regressors?
I read in another discussion feed that “Timing with auxiliary values should be given via either -stim_times_AM1 or -stim_times_AM2, and likely the latter.” With this in mind, should I change the stim types from “times” to “AM1”?
I have also copied and pasted the resultant uber_subjects script below
Thank you again,
Tamara
execute via :
tcsh -xef proc.s_9444A |& tee output.proc.s_9444A
=========================== auto block: setup ============================
script setup
take note of the AFNI version
afni -ver
check that the current AFNI version is recent enough
afni_history -check_date 23 Mar 2018
if ( $status ) then
echo “** this script requires newer AFNI binaries (than 23 Mar 2018)”
echo " (consider: @update.afni.binaries -defaults)"
exit
endif
the user may specify a single subject to run with
if ( $#argv > 0 ) then
set subj = $argv[1]
else
set subj = s_9444A
endif
assign output directory name
set output_dir = $subj.results
verify that the results directory does not yet exist
if ( -d $output_dir ) then
echo output dir “$subj.results” already exists
exit
endif
set list of runs
set runs = (count -digits 2 1 4)
create results and stimuli directories
mkdir $output_dir
mkdir $output_dir/stimuli
copy stim files into stimulus directory
cp /home/bmiadmin/Tamara/PRR_Analysis/9444_test/NonRevP_learn_corr_fed.txt
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/NonRevP_learn_corr.txt
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/NonRevP_learn_incor_fed.txt
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/NonRevP_learn_incor.txt
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/NonRevP_perf_corr_fed.txt
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/NonRevP_perf_corr.txt
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/NonRevP_perf_incor_fed.txt
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/NonRevP_perf_incor.txt
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/RevP_acq_corr_fed.txt
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/RevP_acq_corr.txt
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/RevP_acq_incor_fed.txt
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/RevP_acq_incor.txt
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/RevP_rev_corr_fed.txt
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/RevP_rev_corr.txt
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/RevP_rev_incor_fed.txt
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/RevP_rev_incor.txt
$output_dir/stimuli
copy anatomy to results dir
3dcopy /home/bmiadmin/Tamara/PRR_Analysis/9444_test/9444.anat+orig
$output_dir/9444.anat
============================ auto block: tcat ============================
apply 3dTcat to copy input dsets to results dir,
while removing the first 0 TRs
3dTcat -prefix $output_dir/pb00.$subj.r01.tcat
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/9444.1+orig’[0…$]’
3dTcat -prefix $output_dir/pb00.$subj.r02.tcat
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/9444.2+orig’[0…$]’
3dTcat -prefix $output_dir/pb00.$subj.r03.tcat
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/9444.3+orig’[0…$]’
3dTcat -prefix $output_dir/pb00.$subj.r04.tcat
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/9444.4+orig’[0…$]’
and make note of repetitions (TRs) per run
set tr_counts = ( 153 153 153 153 )
-------------------------------------------------------
enter the results directory (can begin processing data)
cd $output_dir
========================== auto block: outcount ==========================
data check: compute outlier fraction for each volume
touch out.pre_ss_warn.txt
foreach run ( $runs )
3dToutcount -automask -fraction -polort 3 -legendre
pb00.$subj.r$run.tcat+orig > outcount.r$run.1D
# outliers at TR 0 might suggest pre-steady state TRs
if ( `1deval -a outcount.r$run.1D"{0}" -expr "step(a-0.4)"` ) then
echo "** TR #0 outliers: possible pre-steady state TRs in run $run" \
>> out.pre_ss_warn.txt
endif
end
catenate outlier counts into a single time series
cat outcount.r*.1D > outcount_rall.1D
get run number and TR index for minimum outlier volume
set minindex = 3dTstat -argmin -prefix - outcount_rall.1D\'
set ovals = ( 1d_tool.py -set_run_lengths $tr_counts \ -index_to_run_tr $minindex )
save run and TR indices for extraction of vr_base_min_outlier
set minoutrun = $ovals[1]
set minouttr = $ovals[2]
echo “min outlier: run $minoutrun, TR $minouttr” | tee out.min_outlier.txt
================================= tshift =================================
time shift data so all slice timing is the same
foreach run ( $runs )
3dTshift -tzero 0 -quintic -prefix pb01.$subj.r$run.tshift
pb00.$subj.r$run.tcat+orig
end
--------------------------------
extract volreg registration base
3dbucket -prefix vr_base_min_outlier
pb01.$subj.r$minoutrun.tshift+orig"[$minouttr]"
================================= align ==================================
for e2a: compute anat alignment transformation to EPI registration base
(new anat will be intermediate, stripped, 9444.anat_ns+orig)
align_epi_anat.py -anat2epi -anat 9444.anat+orig
-save_skullstrip -suffix _al_junk
-epi vr_base_min_outlier+orig -epi_base 0
-epi_strip 3dAutomask
-volreg off -tshift off
================================== tlrc ==================================
warp anatomy to standard space
@auto_tlrc -base TT_N27+tlrc -input 9444.anat_ns+orig -no_ss
store forward transformation matrix in a text file
cat_matvec 9444.anat_ns+tlrc::WARP_DATA -I > warp.anat.Xat.1D
================================= volreg =================================
align each dset to base volume, align to anat, warp to tlrc space
verify that we have a +tlrc warp dataset
if ( ! -f 9444.anat_ns+tlrc.HEAD ) then
echo “** missing +tlrc warp dataset: 9444.anat_ns+tlrc.HEAD”
exit
endif
register and warp
foreach run ( $runs )
# register each volume to the base image
3dvolreg -verbose -zpad 1 -base vr_base_min_outlier+orig
-1Dfile dfile.r$run.1D -prefix rm.epi.volreg.r$run
-cubic
-1Dmatrix_save mat.r$run.vr.aff12.1D
pb01.$subj.r$run.tshift+orig
# create an all-1 dataset to mask the extents of the warp
3dcalc -overwrite -a pb01.$subj.r$run.tshift+orig -expr 1 \
-prefix rm.epi.all1
# catenate volreg/epi2anat/tlrc xforms
cat_matvec -ONELINE \
9444.anat_ns+tlrc::WARP_DATA -I \
9444.anat_al_junk_mat.aff12.1D -I \
mat.r$run.vr.aff12.1D > mat.r$run.warp.aff12.1D
# apply catenated xform: volreg/epi2anat/tlrc
3dAllineate -base 9444.anat_ns+tlrc \
-input pb01.$subj.r$run.tshift+orig \
-1Dmatrix_apply mat.r$run.warp.aff12.1D \
-mast_dxyz 2 \
-prefix rm.epi.nomask.r$run
# warp the all-1 dataset for extents masking
3dAllineate -base 9444.anat_ns+tlrc \
-input rm.epi.all1+orig \
-1Dmatrix_apply mat.r$run.warp.aff12.1D \
-mast_dxyz 2 -final NN -quiet \
-prefix rm.epi.1.r$run
# make an extents intersection mask of this run
3dTstat -min -prefix rm.epi.min.r$run rm.epi.1.r$run+tlrc
end
make a single file of registration params
cat dfile.r*.1D > dfile_rall.1D
----------------------------------------
create the extents mask: mask_epi_extents+tlrc
(this is a mask of voxels that have valid data at every TR)
3dMean -datum short -prefix rm.epi.mean rm.epi.min.r*.HEAD
3dcalc -a rm.epi.mean+tlrc -expr ‘step(a-0.999)’ -prefix mask_epi_extents
and apply the extents mask to the EPI data
(delete any time series with missing data)
foreach run ( $runs )
3dcalc -a rm.epi.nomask.r$run+tlrc -b mask_epi_extents+tlrc
-expr ‘a*b’ -prefix pb02.$subj.r$run.volreg
end
warp the volreg base EPI dataset to make a final version
cat_matvec -ONELINE
9444.anat_ns+tlrc::WARP_DATA -I
9444.anat_al_junk_mat.aff12.1D -I > mat.basewarp.aff12.1D
3dAllineate -base 9444.anat_ns+tlrc
-input vr_base_min_outlier+orig
-1Dmatrix_apply mat.basewarp.aff12.1D
-mast_dxyz 2
-prefix final_epi_vr_base_min_outlier
create an anat_final dataset, aligned with stats
3dcopy 9444.anat_ns+tlrc anat_final.$subj
record final registration costs
3dAllineate -base final_epi_vr_base_min_outlier+tlrc -allcostX
-input anat_final.$subj+tlrc |& tee out.allcostX.txt
-----------------------------------------
warp anat follower datasets (affine)
3dAllineate -source 9444.anat+orig
-master anat_final.$subj+tlrc
-final wsinc5 -1Dmatrix_apply warp.anat.Xat.1D
-prefix anat_w_skull_warped
================================== blur ==================================
blur each volume of each run
foreach run ( $runs )
3dmerge -1blur_fwhm 4.0 -doall -prefix pb03.$subj.r$run.blur
pb02.$subj.r$run.volreg+tlrc
end
================================== mask ==================================
create ‘full_mask’ dataset (union mask)
foreach run ( $runs )
3dAutomask -dilate 1 -prefix rm.mask_r$run pb03.$subj.r$run.blur+tlrc
end
create union of inputs, output type is byte
3dmask_tool -inputs rm.mask_r*+tlrc.HEAD -union -prefix full_mask.$subj
---- create subject anatomy mask, mask_anat.$subj+tlrc ----
(resampled from tlrc anat)
3dresample -master full_mask.$subj+tlrc -input 9444.anat_ns+tlrc
-prefix rm.resam.anat
convert to binary anat mask; fill gaps and holes
3dmask_tool -dilate_input 5 -5 -fill_holes -input rm.resam.anat+tlrc
-prefix mask_anat.$subj
compute tighter EPI mask by intersecting with anat mask
3dmask_tool -input full_mask.$subj+tlrc mask_anat.$subj+tlrc
-inter -prefix mask_epi_anat.$subj
compute overlaps between anat and EPI masks
3dABoverlap -no_automask full_mask.$subj+tlrc mask_anat.$subj+tlrc
|& tee out.mask_ae_overlap.txt
note Dice coefficient of masks, as well
3ddot -dodice full_mask.$subj+tlrc mask_anat.$subj+tlrc
|& tee out.mask_ae_dice.txt
---- create group anatomy mask, mask_group+tlrc ----
(resampled from tlrc base anat, TT_N27+tlrc)
3dresample -master full_mask.$subj+tlrc -prefix ./rm.resam.group
-input /home/bmiadmin/abin/TT_N27+tlrc
convert to binary group mask; fill gaps and holes
3dmask_tool -dilate_input 5 -5 -fill_holes -input rm.resam.group+tlrc
-prefix mask_group
================================= scale ==================================
scale each voxel time series to have a mean of 100
(be sure no negatives creep in)
(subject to a range of [0,200])
foreach run ( $runs )
3dTstat -prefix rm.mean_r$run pb03.$subj.r$run.blur+tlrc
3dcalc -a pb03.$subj.r$run.blur+tlrc -b rm.mean_r$run+tlrc
-c mask_epi_extents+tlrc
-expr ‘c * min(200, a/b*100)*step(a)*step(b)’
-prefix pb04.$subj.r$run.scale
end
================================ regress =================================
compute de-meaned motion parameters (for use in regression)
1d_tool.py -infile dfile_rall.1D -set_nruns 4
-demean -write motion_demean.1D
compute motion parameter derivatives (for use in regression)
1d_tool.py -infile dfile_rall.1D -set_nruns 4
-derivative -demean -write motion_deriv.1D
convert motion parameters for per-run regression
1d_tool.py -infile motion_demean.1D -set_nruns 4
-split_into_pad_runs mot_demean
1d_tool.py -infile motion_deriv.1D -set_nruns 4
-split_into_pad_runs mot_deriv
create censor file motion_${subj}_censor.1D, for censoring motion
1d_tool.py -infile dfile_rall.1D -set_nruns 4
-show_censor_count -censor_prev_TR
-censor_motion 0.3 motion_${subj}
note TRs that were not censored
set ktrs = 1d_tool.py -infile motion_${subj}_censor.1D \ -show_trs_uncensored encoded
------------------------------
run the regression analysis
3dDeconvolve -input pb04.$subj.r*.scale+tlrc.HEAD
-censor motion_${subj}_censor.1D
-polort 3
-num_stimts 64
-stim_times 1 stimuli/NonRevP_learn_corr_fed.txt ‘BLOCK(1.4)’
-stim_label 1 NonRevP_learn_corr_fed
-stim_times_AM1 2 stimuli/NonRevP_learn_corr.txt ‘dmBLOCK’
-stim_label 2 NonRevP_learn_corr
-stim_times 3 stimuli/NonRevP_learn_incor_fed.txt ‘BLOCK(1.4)’
-stim_label 3 NonRevP_learn_incor_fed
-stim_times_AM1 4 stimuli/NonRevP_learn_incor.txt ‘dmBLOCK’
-stim_label 4 NonRevP_learn_incor
-stim_times 5 stimuli/NonRevP_perf_corr_fed.txt ‘BLOCK(1.4)’
-stim_label 5 NonRevP_perf_corr_fed
-stim_times_AM1 6 stimuli/NonRevP_perf_corr.txt ‘dmBLOCK’
-stim_label 6 NonRevP_perf_corr
-stim_times 7 stimuli/NonRevP_perf_incor_fed.txt ‘BLOCK(1.4)’
-stim_label 7 NonRevP_perf_incor_fed
-stim_times_AM1 8 stimuli/NonRevP_perf_incor.txt ‘dmBLOCK’
-stim_label 8 NonRevP_perf_incor
-stim_times 9 stimuli/RevP_acq_corr_fed.txt ‘BLOCK(1.4)’
-stim_label 9 RevP_acq_corr_fed
-stim_times_AM1 10 stimuli/RevP_acq_corr.txt ‘dmBLOCK’
-stim_label 10 RevP_acq_corr
-stim_times 11 stimuli/RevP_acq_incor_fed.txt ‘BLOCK(1.4)’
-stim_label 11 RevP_acq_incor_fed
-stim_times_AM1 12 stimuli/RevP_acq_incor.txt ‘dmBLOCK’
-stim_label 12 RevP_acq_incor
-stim_times 13 stimuli/RevP_rev_corr_fed.txt ‘BLOCK(1.4)’
-stim_label 13 RevP_rev_corr_fed
-stim_times_AM1 14 stimuli/RevP_rev_corr.txt ‘dmBLOCK’
-stim_label 14 RevP_rev_corr
-stim_times 15 stimuli/RevP_rev_incor_fed.txt ‘BLOCK(1.4)’
-stim_label 15 RevP_rev_incor_fed
-stim_times_AM1 16 stimuli/RevP_rev_incor.txt ‘dmBLOCK’
-stim_label 16 RevP_rev_incor
-stim_file 17 mot_demean.r01.1D’[0]’ -stim_base 17 -stim_label 17 roll_01
-stim_file 18 mot_demean.r01.1D’[1]’ -stim_base 18 -stim_label 18
pitch_01
-stim_file 19 mot_demean.r01.1D’[2]’ -stim_base 19 -stim_label 19 yaw_01
-stim_file 20 mot_demean.r01.1D’[3]’ -stim_base 20 -stim_label 20 dS_01
-stim_file 21 mot_demean.r01.1D’[4]’ -stim_base 21 -stim_label 21 dL_01
-stim_file 22 mot_demean.r01.1D’[5]’ -stim_base 22 -stim_label 22 dP_01
-stim_file 23 mot_demean.r02.1D’[0]’ -stim_base 23 -stim_label 23 roll_02
-stim_file 24 mot_demean.r02.1D’[1]’ -stim_base 24 -stim_label 24
pitch_02
-stim_file 25 mot_demean.r02.1D’[2]’ -stim_base 25 -stim_label 25 yaw_02
-stim_file 26 mot_demean.r02.1D’[3]’ -stim_base 26 -stim_label 26 dS_02
-stim_file 27 mot_demean.r02.1D’[4]’ -stim_base 27 -stim_label 27 dL_02
-stim_file 28 mot_demean.r02.1D’[5]’ -stim_base 28 -stim_label 28 dP_02
-stim_file 29 mot_demean.r03.1D’[0]’ -stim_base 29 -stim_label 29 roll_03
-stim_file 30 mot_demean.r03.1D’[1]’ -stim_base 30 -stim_label 30
pitch_03
-stim_file 31 mot_demean.r03.1D’[2]’ -stim_base 31 -stim_label 31 yaw_03
-stim_file 32 mot_demean.r03.1D’[3]’ -stim_base 32 -stim_label 32 dS_03
-stim_file 33 mot_demean.r03.1D’[4]’ -stim_base 33 -stim_label 33 dL_03
-stim_file 34 mot_demean.r03.1D’[5]’ -stim_base 34 -stim_label 34 dP_03
-stim_file 35 mot_demean.r04.1D’[0]’ -stim_base 35 -stim_label 35 roll_04
-stim_file 36 mot_demean.r04.1D’[1]’ -stim_base 36 -stim_label 36
pitch_04
-stim_file 37 mot_demean.r04.1D’[2]’ -stim_base 37 -stim_label 37 yaw_04
-stim_file 38 mot_demean.r04.1D’[3]’ -stim_base 38 -stim_label 38 dS_04
-stim_file 39 mot_demean.r04.1D’[4]’ -stim_base 39 -stim_label 39 dL_04
-stim_file 40 mot_demean.r04.1D’[5]’ -stim_base 40 -stim_label 40 dP_04
-stim_file 41 mot_deriv.r01.1D’[0]’ -stim_base 41 -stim_label 41 roll_05
-stim_file 42 mot_deriv.r01.1D’[1]’ -stim_base 42 -stim_label 42 pitch_05
-stim_file 43 mot_deriv.r01.1D’[2]’ -stim_base 43 -stim_label 43 yaw_05
-stim_file 44 mot_deriv.r01.1D’[3]’ -stim_base 44 -stim_label 44 dS_05
-stim_file 45 mot_deriv.r01.1D’[4]’ -stim_base 45 -stim_label 45 dL_05
-stim_file 46 mot_deriv.r01.1D’[5]’ -stim_base 46 -stim_label 46 dP_05
-stim_file 47 mot_deriv.r02.1D’[0]’ -stim_base 47 -stim_label 47 roll_06
-stim_file 48 mot_deriv.r02.1D’[1]’ -stim_base 48 -stim_label 48 pitch_06
-stim_file 49 mot_deriv.r02.1D’[2]’ -stim_base 49 -stim_label 49 yaw_06
-stim_file 50 mot_deriv.r02.1D’[3]’ -stim_base 50 -stim_label 50 dS_06
-stim_file 51 mot_deriv.r02.1D’[4]’ -stim_base 51 -stim_label 51 dL_06
-stim_file 52 mot_deriv.r02.1D’[5]’ -stim_base 52 -stim_label 52 dP_06
-stim_file 53 mot_deriv.r03.1D’[0]’ -stim_base 53 -stim_label 53 roll_07
-stim_file 54 mot_deriv.r03.1D’[1]’ -stim_base 54 -stim_label 54 pitch_07
-stim_file 55 mot_deriv.r03.1D’[2]’ -stim_base 55 -stim_label 55 yaw_07
-stim_file 56 mot_deriv.r03.1D’[3]’ -stim_base 56 -stim_label 56 dS_07
-stim_file 57 mot_deriv.r03.1D’[4]’ -stim_base 57 -stim_label 57 dL_07
-stim_file 58 mot_deriv.r03.1D’[5]’ -stim_base 58 -stim_label 58 dP_07
-stim_file 59 mot_deriv.r04.1D’[0]’ -stim_base 59 -stim_label 59 roll_08
-stim_file 60 mot_deriv.r04.1D’[1]’ -stim_base 60 -stim_label 60 pitch_08
-stim_file 61 mot_deriv.r04.1D’[2]’ -stim_base 61 -stim_label 61 yaw_08
-stim_file 62 mot_deriv.r04.1D’[3]’ -stim_base 62 -stim_label 62 dS_08
-stim_file 63 mot_deriv.r04.1D’[4]’ -stim_base 63 -stim_label 63 dL_08
-stim_file 64 mot_deriv.r04.1D’[5]’ -stim_base 64 -stim_label 64 dP_08
-fout -tout -x1D X.xmat.1D -xjpeg X.jpg
-x1D_uncensored X.nocensor.xmat.1D
-fitts fitts.$subj
-errts errts.${subj}
-bucket stats.$subj
if 3dDeconvolve fails, terminate the script
if ( $status != 0 ) then
echo ‘---------------------------------------’
echo ‘** 3dDeconvolve error, failing…’
echo ’ (consider the file 3dDeconvolve.err)’
exit
endif
display any large pairwise correlations from the X-matrix
1d_tool.py -show_cormat_warnings -infile X.xmat.1D |& tee out.cormat_warn.txt
create an all_runs dataset to match the fitts, errts, etc.
3dTcat -prefix all_runs.$subj pb04.$subj.r*.scale+tlrc.HEAD
--------------------------------------------------
create a temporal signal to noise ratio dataset
signal: if ‘scale’ block, mean should be 100
noise : compute standard deviation of errts
3dTstat -mean -prefix rm.signal.all all_runs.$subj+tlrc"[$ktrs]"
3dTstat -stdev -prefix rm.noise.all errts.${subj}+tlrc"[$ktrs]"
3dcalc -a rm.signal.all+tlrc
-b rm.noise.all+tlrc
-c full_mask.$subj+tlrc
-expr ‘c*a/b’ -prefix TSNR.$subj
---------------------------------------------------
compute and store GCOR (global correlation average)
(sum of squares of global mean of unit errts)
3dTnorm -norm2 -prefix rm.errts.unit errts.${subj}+tlrc
3dmaskave -quiet -mask full_mask.$subj+tlrc rm.errts.unit+tlrc
> gmean.errts.unit.1D
3dTstat -sos -prefix - gmean.errts.unit.1D' > out.gcor.1D
echo “-- GCOR = cat out.gcor.1D”
---------------------------------------------------
compute correlation volume
(per voxel: average correlation across masked brain)
(now just dot product with average unit time series)
3dcalc -a rm.errts.unit+tlrc -b gmean.errts.unit.1D -expr ‘a*b’ -prefix rm.DP
3dTstat -sum -prefix corr_brain rm.DP+tlrc
create ideal files for fixed response stim types
1dcat X.nocensor.xmat.1D’[16]’ > ideal_NonRevP_learn_corr_fed.1D
1dcat X.nocensor.xmat.1D’[17]’ > ideal_NonRevP_learn_corr.1D
1dcat X.nocensor.xmat.1D’[18]’ > ideal_NonRevP_learn_incor_fed.1D
1dcat X.nocensor.xmat.1D’[19]’ > ideal_NonRevP_learn_incor.1D
1dcat X.nocensor.xmat.1D’[20]’ > ideal_NonRevP_perf_corr_fed.1D
1dcat X.nocensor.xmat.1D’[21]’ > ideal_NonRevP_perf_corr.1D
1dcat X.nocensor.xmat.1D’[22]’ > ideal_NonRevP_perf_incor_fed.1D
1dcat X.nocensor.xmat.1D’[23]’ > ideal_NonRevP_perf_incor.1D
1dcat X.nocensor.xmat.1D’[24]’ > ideal_RevP_acq_corr_fed.1D
1dcat X.nocensor.xmat.1D’[25]’ > ideal_RevP_acq_corr.1D
1dcat X.nocensor.xmat.1D’[26]’ > ideal_RevP_acq_incor_fed.1D
1dcat X.nocensor.xmat.1D’[27]’ > ideal_RevP_acq_incor.1D
1dcat X.nocensor.xmat.1D’[28]’ > ideal_RevP_rev_corr_fed.1D
1dcat X.nocensor.xmat.1D’[29]’ > ideal_RevP_rev_corr.1D
1dcat X.nocensor.xmat.1D’[30]’ > ideal_RevP_rev_incor_fed.1D
1dcat X.nocensor.xmat.1D’[31]’ > ideal_RevP_rev_incor.1D
--------------------------------------------------------
compute sum of non-baseline regressors from the X-matrix
(use 1d_tool.py to get list of regressor colums)
set reg_cols = 1d_tool.py -infile X.nocensor.xmat.1D -show_indices_interest
3dTstat -sum -prefix sum_ideal.1D X.nocensor.xmat.1D"[$reg_cols]"
also, create a stimulus-only X-matrix, for easy review
1dcat X.nocensor.xmat.1D"[$reg_cols]" > X.stim.xmat.1D
============================ blur estimation =============================
compute blur estimates
touch blur_est.$subj.1D # start with empty file
create directory for ACF curve files
mkdir files_ACF
– estimate blur for each run in epits –
touch blur.epits.1D
restrict to uncensored TRs, per run
foreach run ( $runs )
set trs = 1d_tool.py -infile X.xmat.1D -show_trs_uncensored encoded \ -show_trs_run $run
if ( $trs == “” ) continue
3dFWHMx -detrend -mask full_mask.$subj+tlrc
-ACF files_ACF/out.3dFWHMx.ACF.epits.r$run.1D
all_runs.$subj+tlrc"[$trs]" >> blur.epits.1D
end
compute average FWHM blur (from every other row) and append
set blurs = ( 3dTstat -mean -prefix - blur.epits.1D'{0..$(2)}'\' )
echo average epits FWHM blurs: $blurs
echo “$blurs # epits FWHM blur estimates” >> blur_est.$subj.1D
compute average ACF blur (from every other row) and append
set blurs = ( 3dTstat -mean -prefix - blur.epits.1D'{1..$(2)}'\' )
echo average epits ACF blurs: $blurs
echo “$blurs # epits ACF blur estimates” >> blur_est.$subj.1D
– estimate blur for each run in errts –
touch blur.errts.1D
restrict to uncensored TRs, per run
foreach run ( $runs )
set trs = 1d_tool.py -infile X.xmat.1D -show_trs_uncensored encoded \ -show_trs_run $run
if ( $trs == “” ) continue
3dFWHMx -detrend -mask full_mask.$subj+tlrc
-ACF files_ACF/out.3dFWHMx.ACF.errts.r$run.1D
errts.${subj}+tlrc"[$trs]" >> blur.errts.1D
end
compute average FWHM blur (from every other row) and append
set blurs = ( 3dTstat -mean -prefix - blur.errts.1D'{0..$(2)}'\' )
echo average errts FWHM blurs: $blurs
echo “$blurs # errts FWHM blur estimates” >> blur_est.$subj.1D
compute average ACF blur (from every other row) and append
set blurs = ( 3dTstat -mean -prefix - blur.errts.1D'{1..$(2)}'\' )
echo average errts ACF blurs: $blurs
echo “$blurs # errts ACF blur estimates” >> blur_est.$subj.1D
================== auto block: generate review scripts ===================
generate a review script for the unprocessed EPI data
gen_epi_review.py -script @epi_review.$subj
-dsets pb00.$subj.r*.tcat+orig.HEAD
generate scripts to review single subject results
(try with defaults, but do not allow bad exit status)
gen_ss_review_scripts.py -mot_limit 0.3 -exit0
========================== auto block: finalize ==========================
remove temporary files
\rm -f rm.*
if the basic subject review script is here, run it
(want this to be the last text output)
if ( -e @ss_review_basic ) ./@ss_review_basic |& tee out.ss_review.$subj.txt
return to parent directory
cd …
echo “execution finished: date”
==========================================================================
script generated by the command:
afni_proc.py -subj_id s_9444A -script proc.s_9444A -scr_overwrite -blocks \
tshift align tlrc volreg blur mask scale regress -copy_anat \
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/9444.anat+orig -dsets \
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/9444.1+orig.HEAD \
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/9444.2+orig.HEAD \
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/9444.3+orig.HEAD \
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/9444.4+orig.HEAD \
-tcat_remove_first_trs 0 -volreg_align_to MIN_OUTLIER -volreg_align_e2a \
-volreg_tlrc_warp -blur_size 4.0 -regress_stim_times \
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/NonRevP_learn_corr_fed.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/NonRevP_learn_corr.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/NonRevP_learn_incor_fed.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/NonRevP_learn_incor.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/NonRevP_perf_corr_fed.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/NonRevP_perf_corr.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/NonRevP_perf_incor_fed.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/NonRevP_perf_incor.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/RevP_acq_corr_fed.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/RevP_acq_corr.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/RevP_acq_incor_fed.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/RevP_acq_incor.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/RevP_rev_corr_fed.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/RevP_rev_corr.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/RevP_rev_incor_fed.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9444_test/RevP_rev_incor.txt \
-regress_stim_labels NonRevP_learn_corr_fed NonRevP_learn_corr \
NonRevP_learn_incor_fed NonRevP_learn_incor NonRevP_perf_corr_fed \
NonRevP_perf_corr NonRevP_perf_incor_fed NonRevP_perf_incor \
RevP_acq_corr_fed RevP_acq_corr RevP_acq_incor_fed RevP_acq_incor \
RevP_rev_corr_fed RevP_rev_corr RevP_rev_incor_fed RevP_rev_incor \
-regress_basis_multi ‘BLOCK(1.4)’ dmBLOCK ‘BLOCK(1.4)’ dmBLOCK \
‘BLOCK(1.4)’ dmBLOCK ‘BLOCK(1.4)’ dmBLOCK ‘BLOCK(1.4)’ dmBLOCK \
‘BLOCK(1.4)’ dmBLOCK ‘BLOCK(1.4)’ dmBLOCK ‘BLOCK(1.4)’ dmBLOCK \
-regress_stim_types times AM1 times AM1 times AM1 times AM1 times AM1 \
times AM1 times AM1 times AM1 -regress_censor_motion 0.3 \
-regress_apply_mot_types demean deriv -regress_motion_per_run \
-regress_make_ideal_sum sum_ideal.1D -regress_est_blur_epits \
-regress_est_blur_errts -regress_run_clustsim no