Hi there,
I am receiving this error message when I try to run my pre-processing script (see below). I don’t see any additional spaces in my script. Any suggestions would be helpful!
Error Message in Output Script
3dTstat -prefix rm.mean_r02 pb03.1423.r02.blur+tlrc
++ 3dTstat: AFNI version=AFNI_20.3.02 (Nov 12 2020) [64-bit]
++ Authored by: KR Hammett & RW Cox
++ Output dataset ./rm.mean_r02+tlrc.BRIK
3dcalc -a pb03.1423.r02.blur+tlrc -b rm.mean_r02+tlrc -c mask_epi_extents+tlrc -expr c * min(200, a/b*100)*step(a)step(b) -prefix pb04.1423.r02.scale
++ 3dcalc: AFNI version=AFNI_20.3.02 (Nov 12 2020) [64-bit]
++ Authored by: A cast of thousands
++ Output dataset ./pb04.1423.r02.scale+tlrc.BRIK
end
3dTstat -prefix rm.mean_r03 pb03.1423.r03.blur+tlrc
++ 3dTstat: AFNI version=AFNI_20.3.02 (Nov 12 2020) [64-bit]
++ Authored by: KR Hammett & RW Cox
++ Output dataset ./rm.mean_r03+tlrc.BRIK
3dcalc -a pb03.1423.r03.blur+tlrc -b rm.mean_r03+tlrc -c mask_epi_extents+tlrc -expr c * min(200, a/b100)*step(a)step(b) -prefix pb04.1423.r03.scale
++ 3dcalc: AFNI version=AFNI_20.3.02 (Nov 12 2020) [64-bit]
++ Authored by: A cast of thousands
++ Output dataset ./pb04.1423.r03.scale+tlrc.BRIK
end
3dTstat -prefix rm.mean_r04 pb03.1423.r04.blur+tlrc
++ 3dTstat: AFNI version=AFNI_20.3.02 (Nov 12 2020) [64-bit]
++ Authored by: KR Hammett & RW Cox
++ Output dataset ./rm.mean_r04+tlrc.BRIK
3dcalc -a pb03.1423.r04.blur+tlrc -b rm.mean_r04+tlrc -c mask_epi_extents+tlrc -expr c * min(200, a/b100)*step(a)step(b) -prefix pb04.1423.r04.scale
++ 3dcalc: AFNI version=AFNI_20.3.02 (Nov 12 2020) [64-bit]
++ Authored by: A cast of thousands
++ Output dataset ./pb04.1423.r04.scale+tlrc.BRIK
end
3dTstat -prefix rm.mean_r05 pb03.1423.r05.blur+tlrc
++ 3dTstat: AFNI version=AFNI_20.3.02 (Nov 12 2020) [64-bit]
++ Authored by: KR Hammett & RW Cox
++ Output dataset ./rm.mean_r05+tlrc.BRIK
3dcalc -a pb03.1423.r05.blur+tlrc -b rm.mean_r05+tlrc -c mask_epi_extents+tlrc -expr c * min(200, a/b100)*step(a)step(b) -prefix pb04.1423.r05.scale
++ 3dcalc: AFNI version=AFNI_20.3.02 (Nov 12 2020) [64-bit]
++ Authored by: A cast of thousands
++ Output dataset ./pb04.1423.r05.scale+tlrc.BRIK
end
3dTstat -prefix rm.mean_r06 pb03.1423.r06.blur+tlrc
++ 3dTstat: AFNI version=AFNI_20.3.02 (Nov 12 2020) [64-bit]
++ Authored by: KR Hammett & RW Cox
++ Output dataset ./rm.mean_r06+tlrc.BRIK
3dcalc -a pb03.1423.r06.blur+tlrc -b rm.mean_r06+tlrc -c mask_epi_extents+tlrc -expr c * min(200, a/b100)*step(a)step(b) -prefix pb04.1423.r06.scale
++ 3dcalc: AFNI version=AFNI_20.3.02 (Nov 12 2020) [64-bit]
++ Authored by: A cast of thousands
++ Output dataset ./pb04.1423.r06.scale+tlrc.BRIK
end
1d_tool.py -infile dfile_rall.1D -set_nruns 6 -demean -write motion_demean.1D
1d_tool.py -infile dfile_rall.1D -set_nruns 6 -derivative -demean -write motion_deriv.1D
1d_tool.py -infile motion_demean.1D -set_nruns 6 -split_into_pad_runs mot_demean
1d_tool.py -infile motion_deriv.1D -set_nruns 6 -split_into_pad_runs mot_deriv
1d_tool.py -infile dfile_rall.1D -set_nruns 6 -show_censor_count -censor_prev_TR -censor_motion 2 motion_1423
total number of censored TRs (simple form) = 4
1deval -a motion_1423_censor.1D -b outcount_1423_censor.1D -expr ab
set ktrs = 1d_tool.py -infile censor_${subj}_combined_2.1D -show_trs_uncensored encoded
1d_tool.py -infile censor_1423_combined_2.1D -show_trs_uncensored encoded
Unmatched ‘’'.
My Pre-processing script
#!/bin/tcsh -xef
execute via :
tcsh -xef proc.Nov30 |& tee output.proc_Nov23.txt
=========================== auto block: setup ============================
script setup
the user may specify a single subject to run with
if ( $#argv > 0 ) then
set subj = $argv[1]
else
set subj = 1423
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 6)
create results and stimuli directories
mkdir $output_dir
mkdir $output_dir/stimuli
copy stim files into stimulus directory
cp /imaging/Tamara/Youth/1423/Dfeel_Bad_NoResp.txt
/imaging/Tamara/Youth/1423/Dfeel_Bad_Resp.txt
/imaging/Tamara/Youth/1423/Dfeel_Happy_NoResp.txt
/imaging/Tamara/Youth/1423/Dfeel_Happy_Resp.txt
/imaging/Tamara/Youth/1423/Feel_Bad_NoResp.txt
/imaging/Tamara/Youth/1423/Feel_Bad_Resp.txt
/imaging/Tamara/Youth/1423/Feel_Happy_NoResp.txt
/imaging/Tamara/Youth/1423/Feel_Happy_Resp.txt
/imaging/Tamara/Youth/1423/Instruct.txt
/imaging/Tamara/Youth/1423/Nat_neg_NoResp.txt
/imaging/Tamara/Youth/1423/Nat_neg_Resp.txt
/imaging/Tamara/Youth/1423/Nat_post_NoResp.txt
/imaging/Tamara/Youth/1423/Nat_post_Resp.txt
/imaging/Tamara/Youth/1423/Rating.txt
$output_dir/stimuli
copy anatomy to results dir
3dcopy 1423.anat+orig $output_dir/1423.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 1423.1+orig’[0…$]’
3dTcat -prefix $output_dir/pb00.$subj.r02.tcat 1423.2+orig’[0…$]’
3dTcat -prefix $output_dir/pb00.$subj.r03.tcat 1423.3+orig’[0…$]’
3dTcat -prefix $output_dir/pb00.$subj.r04.tcat 1423.4+orig’[0…$]’
3dTcat -prefix $output_dir/pb00.$subj.r05.tcat 1423.5+orig’[0…$]’
3dTcat -prefix $output_dir/pb00.$subj.r06.tcat 1423.6+orig’[0…$]’
and make note of repetitions (TRs) per run
set tr_counts = ( 366 366 366 366 366 366 )
-------------------------------------------------------
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 ( $runs )
3dToutcount -automask -fraction -polort ‘A’ -legendre
pb00.$subj.r$run.tcat+orig > outcount.r$run.1D
# censor outlier TRs per run, ignoring the first 0 TRs
# - censor when more than 0.1 of automask voxels are outliers
# - step() defines which TRs to remove via censoring
1deval -a outcount.r$run.1D -expr "1-step(a-0.1)" > rm.out.cen.r$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 $run" \
>> out.pre_ss_warn.txt
endif
end
catenate outlier counts into a single time series
cat outcount.r*.1D > outcount_rall.1D
catenate outlier censor files into a single time series
cat rm.out.cen.r*.1D > outcount_${subj}_censor.1D
================================= 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$run.tcat+orig
end
--------------------------------
extract volreg registration base
3dbucket -prefix vr_base pb01.$subj.r01.tshift+orig"[2]"
================================= align ==================================
for e2a: compute anat alignment transformation to EPI registration base
(new anat will be intermediate, stripped, 1423.anat_ns+orig)
align_epi_anat.py -anat2epi -anat 1423.anat+orig
-save_skullstrip -suffix _al_junk
-epi vr_base+orig -epi_base 0
-epi_strip 3dAutomask
-volreg off -tshift off
================================== tlrc ==================================
warp anatomy to standard space (non-linear warp)
auto_warp.py -base MNI152_T1_2009c+tlrc -input 1423.anat_ns+orig
-skull_strip_input no
move results up out of the awpy directory
(NL-warped anat, affine warp, NL warp)
(use typical standard space name for anat)
(wildcard is a cheap way to go after any .gz)
3dbucket -prefix 1423.anat_ns awpy/1423.anat_ns.aw.nii*
mv awpy/anat.un.aff.Xat.1D .
mv awpy/anat.un.aff.qw_WARP.nii .
================================= volreg =================================
align each dset to base volume, align to anat, warp to tlrc space
verify that we have a +tlrc warp dataset
if ( ! -f 1423.anat_ns+tlrc.HEAD ) then
echo “** missing +tlrc warp dataset: 1423.anat_ns+tlrc.HEAD”
exit
endif
register and warp
foreach run ( $runs )
# register each volume to the base image
3dvolreg -verbose -zpad 1 -base vr_base+orig
-1Dfile dfile.r$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 \
anat.un.aff.Xat.1D \
1423.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/NLtlrc
# then apply non-linear standard-space warp
3dNwarpApply -master 1423.anat_ns+tlrc -dxyz 2 \
-source pb01.$subj.r$run.tshift+orig \
-nwarp "anat.un.aff.qw_WARP.nii mat.r$run.warp.aff12.1D" \
-prefix rm.epi.nomask.r$run
# warp the all-1 dataset for extents masking
3dNwarpApply -master 1423.anat_ns+tlrc -dxyz 2 \
-source rm.epi.all1+orig \
-nwarp "anat.un.aff.qw_WARP.nii mat.r$run.warp.aff12.1D" \
-interp cubic \
-ainterp NN -quiet \
-prefix rm.epi.1.r$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
anat.un.aff.Xat.1D
1423.anat_al_junk_mat.aff12.1D -I > mat.basewarp.aff12.1D
3dNwarpApply -master 1423.anat_ns+tlrc -dxyz 2
-source vr_base+orig
-nwarp “anat.un.aff.qw_WARP.nii mat.basewarp.aff12.1D”
-prefix final_epi_vr_base
create an anat_final dataset, aligned with stats
3dcopy 1423.anat_ns+tlrc anat_final.$subj
record final registration costs
3dAllineate -base final_epi_vr_base+tlrc -allcostX
-input anat_final.$subj+tlrc |& tee out.allcostX.txt
-----------------------------------------
warp anat follower datasets (non-linear)
3dNwarpApply -source 1423.anat+orig
-master anat_final.$subj+tlrc
-ainterp wsinc5 -nwarp anat.un.aff.qw_WARP.nii anat.un.aff.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.$subj+tlrc -input 1423.anat_ns+tlrc
-prefix rm.resam.anat
convert to binary anat mask; fill gaps and holes
3dmask_tool -dilate_input 5 -5 -fill_holes -input rm.resam.anat+tlrc
-prefix mask_anat.$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.$subj+tlrc
|& tee out.mask_ae_dice.txt
---- create group anatomy mask, mask_group+tlrc ----
(resampled from tlrc base anat, MNI152_T1_2009c+tlrc)
3dresample -master full_mask.$subj+tlrc -prefix ./rm.resam.group
-input /home/ttavare/abin/MNI_avg152T1+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$run.scale
end
================================ regress =================================
compute de-meaned motion parameters (for use in regression)
1d_tool.py -infile dfile_rall.1D -set_nruns 6
-demean -write motion_demean.1D
compute motion parameter derivatives (for use in regression)
1d_tool.py -infile dfile_rall.1D -set_nruns 6
-derivative -demean -write motion_deriv.1D
convert motion parameters for per-run regression
1d_tool.py -infile motion_demean.1D -set_nruns 6
-split_into_pad_runs mot_demean
1d_tool.py -infile motion_deriv.1D -set_nruns 6
-split_into_pad_runs mot_deriv
create censor file motion_${subj}_censor.1D, for censoring motion
1d_tool.py -infile dfile_rall.1D -set_nruns 6
-show_censor_count -censor_prev_TR
-censor_motion 2 motion_${subj}
combine multiple censor files
1deval -a motion_${subj}censor.1D -b outcount${subj}censor.1D
-expr “a*b” > censor${subj}_combined_2.1D
note TRs that were not censored
set ktrs = 1d_tool.py -infile censor_${subj}_combined_2.1D \ -show_trs_uncensored encoded
------------------------------
run the regression analysis
3dDeconvolve -input pb04.$subj.r*.scale+tlrc.HEAD
-censor censor_${subj}_combined_2.1D
-polort ‘A’
-num_stimts 62
-stim_times 1 stimuli/Dfeel_Bad_NoResp.txt ‘GAM’
-stim_label 1 Dfeel_Bad_NoResp
-stim_times 2 stimuli/Dfeel_Bad_Resp.txt ‘GAM ’
-stim_label 2 Dfeel_Bad_Resp
-stim_times 3 stimuli/Dfeel_Happy_NoResp.txt ‘GAM ’
-stim_label 3 Dfeel_Happy_NoResp
-stim_times 4 stimuli/Dfeel_Happy_Resp.txt ‘GAM’
-stim_label 4 Dfeel_Happy_Resp
-stim_times 5 stimuliFeel_Bad_NoResp.txt ’ GAM’
-stim_label 5 Feel_Bad_NoResp
-stim_times 6 stimuli/Feel_Bad_Resp.txt ‘GAM ’
-stim_label 6 Feel_Bad_Resp
-stim_times 7 stimuli/Feel_Happy_NoResp.txt ‘GAM ’
-stim_label 7 Feel_Happy_NoResp
-stim_times 8 stimuli/Feel_Happy_Resp.txt ‘GAM ’
-stim_label 8 Feel_Happy_Resp
-stim_times 9 stimuli/Instruct.txt ‘GAM ’
-stim_label 9 Instruct
-stim_times 10 stimuli/Nat_neg_NoResp.txt ‘GAM ’
-stim_label 10 Nat_neg_NoResp
-stim_times 11 stimuli/Nat_neg_Resp.txt ‘GAM ’
-stim_label 11 Nat_neg_Resp
-stim_times 12 stimuli/Nat_post_NoResp.txt ‘GAM ’
-stim_label 12 Nat_post_NoResp
-stim_times 13 stimuli/Nat_post_Resp.txt ‘GAM ’
-stim_label 13 Nat_post_Resp
-stim_times 14 stimuli/Rating.txt ‘GAM ’
-stim_label 14 Rating
-stim_file 15 mot_demean.r01.1D’[0]’ -stim_base 15 -stim_label 15 roll_01
-stim_file 16 mot_demean.r01.1D’[1]’ -stim_base 16 -stim_label 16
pitch_01
-stim_file 17 mot_demean.r01.1D’[2]’ -stim_base 17 -stim_label 17 yaw_01
-stim_file 18 mot_demean.r01.1D’[3]’ -stim_base 18 -stim_label 18 dS_01
-stim_file 19 mot_demean.r01.1D’[4]’ -stim_base 19 -stim_label 19 dL_01
-stim_file 20 mot_demean.r01.1D’[5]’ -stim_base 20 -stim_label 20 dP_01
-stim_file 21 mot_demean.r02.1D’[0]’ -stim_base 21 -stim_label 21 roll_02
-stim_file 22 mot_demean.r02.1D’[1]’ -stim_base 22 -stim_label 22
pitch_02
-stim_file 23 mot_demean.r02.1D’[2]’ -stim_base 23 -stim_label 23 yaw_02
-stim_file 24 mot_demean.r02.1D’[3]’ -stim_base 24 -stim_label 24 dS_02
-stim_file 25 mot_demean.r02.1D’[4]’ -stim_base 25 -stim_label 25 dL_02
-stim_file 26 mot_demean.r02.1D’[5]’ -stim_base 26 -stim_label 26 dP_02
-stim_file 27 mot_demean.r03.1D’[0]’ -stim_base 27 -stim_label 27 roll_03
-stim_file 28 mot_demean.r03.1D’[1]’ -stim_base 28 -stim_label 28
pitch_03
-stim_file 29 mot_demean.r03.1D’[2]’ -stim_base 29 -stim_label 29 yaw_03
-stim_file 30 mot_demean.r03.1D’[3]’ -stim_base 30 -stim_label 30 dS_03
-stim_file 31 mot_demean.r03.1D’[4]’ -stim_base 31 -stim_label 31 dL_03
-stim_file 32 mot_demean.r03.1D’[5]’ -stim_base 32 -stim_label 32 dP_03
-stim_file 33 mot_demean.r04.1D’[0]’ -stim_base 33 -stim_label 33 roll_04
-stim_file 34 mot_demean.r04.1D’[1]’ -stim_base 34 -stim_label 34
pitch_04
-stim_file 35 mot_demean.r04.1D’[2]’ -stim_base 35 -stim_label 35 yaw_04
-stim_file 36 mot_demean.r04.1D’[3]’ -stim_base 36 -stim_label 36 dS_04
-stim_file 37 mot_demean.r04.1D’[4]’ -stim_base 37 -stim_label 37 dL_04
-stim_file 38 mot_demean.r04.1D’[5]’ -stim_base 38 -stim_label 38 dP_04
-stim_file 39 mot_deriv.r01.1D’[0]’ -stim_base 39 -stim_label 39 roll_05
-stim_file 40 mot_deriv.r01.1D’[1]’ -stim_base 40 -stim_label 40 pitch_05
-stim_file 41 mot_deriv.r01.1D’[2]’ -stim_base 41 -stim_label 41 yaw_05
-stim_file 42 mot_deriv.r01.1D’[3]’ -stim_base 42 -stim_label 42 dS_05
-stim_file 43 mot_deriv.r01.1D’[4]’ -stim_base 43 -stim_label 43 dL_05
-stim_file 44 mot_deriv.r01.1D’[5]’ -stim_base 44 -stim_label 44 dP_05
-stim_file 45 mot_deriv.r02.1D’[0]’ -stim_base 45 -stim_label 45 roll_06
-stim_file 46 mot_deriv.r02.1D’[1]’ -stim_base 46 -stim_label 46 pitch_06
-stim_file 47 mot_deriv.r02.1D’[2]’ -stim_base 47 -stim_label 47 yaw_06
-stim_file 48 mot_deriv.r02.1D’[3]’ -stim_base 48 -stim_label 48 dS_06
-stim_file 49 mot_deriv.r02.1D’[4]’ -stim_base 49 -stim_label 49 dL_06
-stim_file 50 mot_deriv.r02.1D’[5]’ -stim_base 50 -stim_label 50 dP_06
-stim_file 51 mot_deriv.r03.1D’[0]’ -stim_base 51 -stim_label 51 roll_07
-stim_file 52 mot_deriv.r03.1D’[1]’ -stim_base 52 -stim_label 52 pitch_07
-stim_file 53 mot_deriv.r03.1D’[2]’ -stim_base 53 -stim_label 53 yaw_07
-stim_file 54 mot_deriv.r03.1D’[3]’ -stim_base 54 -stim_label 54 dS_07
-stim_file 55 mot_deriv.r03.1D’[4]’ -stim_base 55 -stim_label 55 dL_07
-stim_file 56 mot_deriv.r03.1D’[5]’ -stim_base 56 -stim_label 56 dP_07
-stim_file 57 mot_deriv.r04.1D’[0]’ -stim_base 57 -stim_label 57 roll_08
-stim_file 58 mot_deriv.r04.1D’[1]’ -stim_base 58 -stim_label 58 pitch_08
-stim_file 59 mot_deriv.r04.1D’[2]’ -stim_base 59 -stim_label 59 yaw_08
-stim_file 60 mot_deriv.r04.1D’[3]’ -stim_base 60 -stim_label 60 dS_08
-stim_file 61 mot_deriv.r04.1D’[4]’ -stim_base 61 -stim_label 61 dL_08
-stim_file 62 mot_deriv.r04.1D’[5]’ -stim_base 62 -stim_label 62 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.$subj+tlrc rm.errts.unit+tlrc
> gmean.errts.unit.1D
3dTstat -sos -prefix - gmean.errts.unit.1D' > out.gcor.1D
echo “-- GCOR = cat out.gcor.1D”
---------------------------------------------------
compute correlation volume
(per voxel: average correlation across masked brain)
(now just dot product with average unit time series)
3dcalc -a rm.errts.unit+tlrc -b gmean.errts.unit.1D -expr ‘a*b’ -prefix rm.DP
3dTstat -sum -prefix corr_brain rm.DP+tlrc
create ideal files for fixed response stim types
1dcat X.nocensor.xmat.1D’[16]’ > ideal_ Dfeel_Bad_NoResp.1D
1dcat X.nocensor.xmat.1D’[17]’ > ideal_ Dfeel_Bad_Resp.1D
1dcat X.nocensor.xmat.1D’[18]’ > ideal_ Dfeel_Happy_NoResp.1D
1dcat X.nocensor.xmat.1D’[19]’ > ideal_ Dfeel_Happy_Resp.1D
1dcat X.nocensor.xmat.1D’[20]’ > ideal_ Feel_Bad_NoResp.1D
1dcat X.nocensor.xmat.1D’[21]’ > ideal_ Feel_Bad_Resp.1D
1dcat X.nocensor.xmat.1D’[22]’ > ideal_ Feel_Happy_NoResp.1D
1dcat X.nocensor.xmat.1D’[23]’ > ideal_ Feel_Happy_Resp.1D
1dcat X.nocensor.xmat.1D’[24]’ > ideal_ Instruct.1D
1dcat X.nocensor.xmat.1D’[25]’ > ideal_ Nat_neg_NoResp.1D
1dcat X.nocensor.xmat.1D’[26]’ > ideal_ Nat_neg_Resp.1D
1dcat X.nocensor.xmat.1D’[27]’ > ideal_ Nat_post_NoResp.1D
1dcat X.nocensor.xmat.1D’[28]’ > ideal_ Nat_post_Resp.1D
1dcat X.nocensor.xmat.1D’[29]’ > ideal_Rating.1D
--------------------------------------------------------
compute sum of non-baseline regressors from the X-matrix
(use 1d_tool.py to get list of regressor colums)
set reg_cols = 1d_tool.py -infile X.nocensor.xmat.1D -show_indices_interest
3dTstat -sum -prefix sum_ideal.1D X.nocensor.xmat.1D"[$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.$subj.r*.tcat+orig.HEAD
generate scripts to review single subject results
(try with defaults, but do not allow bad exit status)
gen_ss_review_scripts.py -mot_limit 2.0 -out_limit 0.1 -exit0
========================== auto block: finalize ==========================
remove temporary files
\rm -fr rm.* awpy
if the basic subject review script is here, run it
(want this to be the last text output)
if ( -e @ss_review_basic ) ./@ss_review_basic |& tee out.ss_review.$subj.txt
return to parent directory
cd …
echo “execution finished: date”