Thank you for the recommendation! I began running this analysis when the binaries were updated, so I am a bit worried about updating them now as I am in mid-analysis. Do you think I should still update them? Would it be recommended for me to re-run my analysis again?

I've also pasted a picture of the anatomical scan in case you wanted to look at the movement.

I have pasted my script below.

Thank you for your help!
Tamara



#!/bin/tcsh -xef

echo "auto-generated by afni_proc.py, Wed Jul 18 09:58:21 2018"
echo "(version 6.10, Apr 26, 2018)"
echo "execution started: `date`"

# execute via :
# tcsh -xef proc.July19_Final_9409_test2 |& tee output.proc.July19_Final_9409_test2

# =========================== 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 = July19_Final_9409_test2
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/9409/NonRevP_learn_corr_fed.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9409/NonRevP_learn_corr.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9409/NonRevP_learn_incor_fed.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9409/NonRevP_learn_incor.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9409/NonRevP_perf_corr_fed.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9409/NonRevP_perf_corr.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9409/NonRevP_perf_incor_fed.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9409/NonRevP_perf_incor.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9409/RevP_acq_corr_fed.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9409/RevP_acq_corr.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9409/RevP_acq_incor_fed.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9409/RevP_acq_incor.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9409/RevP_rev_corr_fed.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9409/RevP_rev_corr.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9409/RevP_rev_incor_fed.txt \
/home/bmiadmin/Tamara/PRR_Analysis/9409/RevP_rev_incor.txt \
$output_dir/stimuli

# copy anatomy to results dir
3dcopy 9409.anat+orig $output_dir/9409.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 9409.1+orig'[0..$]'
3dTcat -prefix $output_dir/pb00.$subj.r02.tcat 9409.2+orig'[0..$]'
3dTcat -prefix $output_dir/pb00.$subj.r03.tcat 9409.3+orig'[0..$]'
3dTcat -prefix $output_dir/pb00.$subj.r04.tcat 9409.4+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

# 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.r04.tshift+orig"[152]"

# ================================= align ==================================
# for e2a: compute anat alignment transformation to EPI registration base
# (new anat will be intermediate, stripped, 9409.anat_ns+orig)
align_epi_anat.py -anat2epi -anat 9409.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 9409.anat_ns+orig \
-skull_strip_input no -affine_input_xmat ID

# 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 9409.anat_ns awpy/9409.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 9409.anat_ns+tlrc.HEAD ) then
echo "** missing +tlrc warp dataset: 9409.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 \
9409.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 9409.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 9409.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 \
9409.anat_al_junk_mat.aff12.1D -I > mat.basewarp.aff12.1D

3dNwarpApply -master 9409.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 9409.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 9409.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 9409.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/bmiadmin/abin/MNI152_T1_2009c+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_${subj}_censor.1D, for censoring motion
1d_tool.py -infile dfile_rall.1D -set_nruns 4 \
-show_censor_count -censor_prev_TR \
-censor_motion 2 motion_${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 3 \
-num_stimts 64 \
-stim_times_AM1 1 stimuli/NonRevP_learn_corr_fed.txt 'dmBLOCK' \
-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_AM1 3 stimuli/NonRevP_learn_incor_fed.txt 'dmBLOCK' \
-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_AM1 5 stimuli/NonRevP_perf_corr_fed.txt 'dmBLOCK' \
-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_AM1 7 stimuli/NonRevP_perf_incor_fed.txt 'dmBLOCK' \
-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_AM1 9 stimuli/RevP_acq_corr_fed.txt 'dmBLOCK' \
-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_AM1 11 stimuli/RevP_acq_incor_fed.txt 'dmBLOCK' \
-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_AM1 13 stimuli/RevP_rev_corr_fed.txt 'dmBLOCK' \
-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_AM1 15 stimuli/RevP_rev_incor_fed.txt 'dmBLOCK' \
-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 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`"




# ==========================================================================
# script generated by the command:
#
# afni_proc.py -subj_id July19_Final_9409_test2 -script proc.July19_Final_9409_test2 -scr_overwrite \
# -blocks tshift align tlrc volreg blur mask scale regress -copy_anat \
# /home/bmiadmin/Tamara/PRR_Analysis/9409/9409.anat+orig -dsets \
# /home/bmiadmin/Tamara/PRR_Analysis/9409/9409.1+orig.HEAD \
# /home/bmiadmin/Tamara/PRR_Analysis/9409/9409.2+orig.HEAD \
# /home/bmiadmin/Tamara/PRR_Analysis/9409/9409.3+orig.HEAD \
# /home/bmiadmin/Tamara/PRR_Analysis/9409/9409.4+orig.HEAD \
# -tcat_remove_first_trs 0 -tlrc_base MNI152_T1_2009c+tlrc -tlrc_NL_warp \
# -volreg_align_to last -volreg_align_e2a -volreg_tlrc_warp -blur_size \
# 4.0 -regress_stim_times \
# /home/bmiadmin/Tamara/PRR_Analysis/9409/NonRevP_learn_corr_fed.txt \
# /home/bmiadmin/Tamara/PRR_Analysis/9409/NonRevP_learn_corr.txt \
# /home/bmiadmin/Tamara/PRR_Analysis/9409/NonRevP_learn_incor_fed.txt \
# /home/bmiadmin/Tamara/PRR_Analysis/9409/NonRevP_learn_incor.txt \
# /home/bmiadmin/Tamara/PRR_Analysis/9409/NonRevP_perf_corr_fed.txt \
# /home/bmiadmin/Tamara/PRR_Analysis/9409/NonRevP_perf_corr.txt \
# /home/bmiadmin/Tamara/PRR_Analysis/9409/NonRevP_perf_incor_fed.txt \
# /home/bmiadmin/Tamara/PRR_Analysis/9409/NonRevP_perf_incor.txt \
# /home/bmiadmin/Tamara/PRR_Analysis/9409/RevP_acq_corr_fed.txt \
# /home/bmiadmin/Tamara/PRR_Analysis/9409/RevP_acq_corr.txt \
# /home/bmiadmin/Tamara/PRR_Analysis/9409/RevP_acq_incor_fed.txt \
# /home/bmiadmin/Tamara/PRR_Analysis/9409/RevP_acq_incor.txt \
# /home/bmiadmin/Tamara/PRR_Analysis/9409/RevP_rev_corr_fed.txt \
# /home/bmiadmin/Tamara/PRR_Analysis/9409/RevP_rev_corr.txt \
# /home/bmiadmin/Tamara/PRR_Analysis/9409/RevP_rev_incor_fed.txt \
# /home/bmiadmin/Tamara/PRR_Analysis/9409/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 dmBLOCK -regress_stim_types AM1 -regress_censor_motion \
# 2.0 -regress_censor_outliers 0.1 -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