123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406407408409410411412413414415416417418419420421422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451452453454455456457458459460461462463464465466467468469470471472473474475476477478479480481482483484485486487488489490491492493494495496497498 |
- #!/bin/tcsh -xef
- echo "auto-generated by afni_proc.py, Thu May 23 13:05:05 2019"
- echo "(version 6.37, May 23, 2019)"
- echo "execution started: `date`"
- # to execute via tcsh:
- # tcsh -xef proc.FT |& tee output.proc.FT
- # to execute via bash:
- # tcsh -xef proc.FT 2>&1 | tee output.proc.FT
- # =========================== 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 10 May 2019
- if ( $status ) then
- echo "** this script requires newer AFNI binaries (than 10 May 2019)"
- 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 = FT
- 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 3`)
- # create results and stimuli directories
- mkdir $output_dir
- mkdir $output_dir/stimuli
- # copy stim files into stimulus directory
- cp FT/AV1_vis.txt FT/AV2_aud.txt $output_dir/stimuli
- # copy anatomy to results dir
- 3dcopy FT/FT_anat+orig $output_dir/FT_anat
- # ============================ auto block: tcat ============================
- # apply 3dTcat to copy input dsets to results dir,
- # while removing the first 2 TRs
- 3dTcat -prefix $output_dir/pb00.$subj.r01.tcat FT/FT_epi_r1+orig'[2..$]'
- 3dTcat -prefix $output_dir/pb00.$subj.r02.tcat FT/FT_epi_r2+orig'[2..$]'
- 3dTcat -prefix $output_dir/pb00.$subj.r03.tcat FT/FT_epi_r3+orig'[2..$]'
- # and make note of repetitions (TRs) per run
- set tr_counts = ( 150 150 150 )
- # -------------------------------------------------------
- # enter the results directory (can begin processing data)
- cd $output_dir
- # ---------------------------------------------------------
- # data check: compute correlations with spherical ~averages
- @radial_correlate -nfirst 0 -do_clean yes -rdir radcor.pb00.tcat \
- pb00.$subj.r*.tcat+orig.HEAD
- # ========================== 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, FT_anat_ns+orig)
- align_epi_anat.py -anat2epi -anat FT_anat+orig \
- -save_skullstrip -suffix _al_junk \
- -epi vr_base_min_outlier+orig -epi_base 0 \
- -epi_strip 3dAutomask \
- -check_flip \
- -volreg off -tshift off
- # ================================== tlrc ==================================
- # warp anatomy to standard space
- @auto_tlrc -base TT_N27+tlrc -input FT_anat_ns+orig -no_ss
- # store forward transformation matrix in a text file
- cat_matvec FT_anat_ns+tlrc::WARP_DATA -I > warp.anat.Xat.1D
- # ================================= volreg =================================
- # align each dset to base volume, to anat, warp to tlrc space
- # verify that we have a +tlrc warp dataset
- if ( ! -f FT_anat_ns+tlrc.HEAD ) then
- echo "** missing +tlrc warp dataset: FT_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 \
- FT_anat_ns+tlrc::WARP_DATA -I \
- FT_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 FT_anat_ns+tlrc \
- -input pb01.$subj.r$run.tshift+orig \
- -1Dmatrix_apply mat.r$run.warp.aff12.1D \
- -mast_dxyz 2.5 \
- -prefix rm.epi.nomask.r$run
- # warp the all-1 dataset for extents masking
- 3dAllineate -base FT_anat_ns+tlrc \
- -input rm.epi.all1+orig \
- -1Dmatrix_apply mat.r$run.warp.aff12.1D \
- -mast_dxyz 2.5 -final NN -quiet \
- -prefix rm.epi.1.r$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 \
- FT_anat_ns+tlrc::WARP_DATA -I \
- FT_anat_al_junk_mat.aff12.1D -I > mat.basewarp.aff12.1D
- 3dAllineate -base FT_anat_ns+tlrc \
- -input vr_base_min_outlier+orig \
- -1Dmatrix_apply mat.basewarp.aff12.1D \
- -mast_dxyz 2.5 \
- -prefix final_epi_vr_base_min_outlier
- # create an anat_final dataset, aligned with stats
- 3dcopy FT_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 FT_anat+orig \
- -master anat_final.$subj+tlrc \
- -final wsinc5 -1Dmatrix_apply warp.anat.Xat.1D \
- -prefix anat_w_skull_warped
- # ---------------------------------------------------------
- # data check: compute correlations with spherical ~averages
- @radial_correlate -nfirst 0 -do_clean yes -rdir radcor.pb02.volreg \
- pb02.$subj.r*.volreg+tlrc.HEAD
- # ================================== 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 ( $runs )
- 3dAutomask -prefix rm.mask_r$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.$subj+tlrc -input FT_anat_ns+tlrc \
- -prefix rm.resam.anat
- # convert to binary anat mask; fill gaps and holes
- 3dmask_tool -dilate_input 5 -5 -fill_holes -input rm.resam.anat+tlrc \
- -prefix mask_anat.$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/rickr/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$run.scale
- end
- # ================================ regress =================================
- # compute de-meaned motion parameters (for use in regression)
- 1d_tool.py -infile dfile_rall.1D -set_nruns 3 \
- -demean -write motion_demean.1D
- # compute motion parameter derivatives (just to have)
- 1d_tool.py -infile dfile_rall.1D -set_nruns 3 \
- -derivative -demean -write motion_deriv.1D
- # convert motion parameters for per-run regression
- 1d_tool.py -infile motion_demean.1D -set_nruns 3 \
- -split_into_pad_runs mot_demean
- # create censor file motion_${subj}_censor.1D, for censoring motion
- 1d_tool.py -infile dfile_rall.1D -set_nruns 3 \
- -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 \
- -ortvec mot_demean.r01.1D mot_demean_r01 \
- -ortvec mot_demean.r02.1D mot_demean_r02 \
- -ortvec mot_demean.r03.1D mot_demean_r03 \
- -polort 3 \
- -num_stimts 2 \
- -stim_times 1 stimuli/AV1_vis.txt 'BLOCK(20,1)' \
- -stim_label 1 vis \
- -stim_times 2 stimuli/AV2_aud.txt 'BLOCK(20,1)' \
- -stim_label 2 aud \
- -jobs 2 \
- -gltsym 'SYM: vis -aud' \
- -glt_label 1 V-A \
- -gltsym 'SYM: 0.5*vis +0.5*aud' \
- -glt_label 2 mean.VA \
- -fout -tout -x1D X.xmat.1D -xjpeg X.jpg \
- -x1D_uncensored X.nocensor.xmat.1D \
- -errts errts.${subj} \
- -bucket stats.$subj
- # if 3dDeconvolve fails, terminate the script
- if ( $status != 0 ) then
- echo '---------------------------------------'
- echo '** 3dDeconvolve error, failing...'
- echo ' (consider the file 3dDeconvolve.err)'
- exit
- endif
- # display any large pairwise correlations from the X-matrix
- 1d_tool.py -show_cormat_warnings -infile X.xmat.1D |& tee out.cormat_warn.txt
- # display degrees of freedom info from X-matrix
- 1d_tool.py -show_df_info -infile X.xmat.1D |& tee out.df_info.txt
- # create an all_runs dataset to match the fitts, errts, etc.
- 3dTcat -prefix all_runs.$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"[$ktrs]"
- 3dcalc -a rm.signal.all+tlrc \
- -b rm.noise.all+tlrc \
- -c mask_epi_anat.$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.$subj+tlrc rm.errts.unit+tlrc \
- > gmean.errts.unit.1D
- 3dTstat -sos -prefix - gmean.errts.unit.1D\' > out.gcor.1D
- echo "-- GCOR = `cat out.gcor.1D`"
- # ---------------------------------------------------
- # compute correlation volume
- # (per voxel: average correlation across masked brain)
- # (now just dot product with average unit time series)
- 3dcalc -a rm.errts.unit+tlrc -b gmean.errts.unit.1D -expr 'a*b' -prefix rm.DP
- 3dTstat -sum -prefix corr_brain rm.DP+tlrc
- # create fitts dataset from all_runs and errts
- 3dcalc -a all_runs.$subj+tlrc -b errts.${subj}+tlrc -expr a-b \
- -prefix fitts.$subj
- # create ideal files for fixed response stim types
- 1dcat X.nocensor.xmat.1D'[12]' > ideal_vis.1D
- 1dcat X.nocensor.xmat.1D'[13]' > ideal_aud.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 ( $trs == "" ) continue
- 3dFWHMx -detrend -mask mask_epi_anat.$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 ( $trs == "" ) continue
- 3dFWHMx -detrend -mask mask_epi_anat.$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.$subj.r*.tcat+orig.HEAD
- # generate scripts to review single subject results
- # (try with defaults, but do not allow bad exit status)
- gen_ss_review_scripts.py -mot_limit 0.3 -exit0 \
- -ss_review_dset out.ss_review.$subj.txt \
- -write_uvars_json out.ss_review_uvars.json
- # ========================== 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 ) then
- ./@ss_review_basic |& tee out.ss_review.$subj.txt
- # generate html ss review pages
- # (akin to static images from running @ss_review_driver)
- apqc_make_tcsh.py -review_style basic -subj_dir . \
- -uvar_json out.ss_review_uvars.json
- tcsh @ss_review_html |& tee out.review_html
- apqc_make_html.py -qc_dir QC_$subj
- echo "\nconsider running: \n\n afni_open -b $subj.results/QC_$subj/index.html\n"
- endif
- # return to parent directory (just in case...)
- cd ..
- echo "execution finished: `date`"
- # ==========================================================================
- # script generated by the command:
- #
- # afni_proc.py -subj_id FT -blocks tshift align tlrc volreg blur mask scale \
- # regress -radial_correlate_blocks tcat volreg -copy_anat FT/FT_anat+orig \
- # -dsets FT/FT_epi_r1+orig.HEAD FT/FT_epi_r2+orig.HEAD \
- # FT/FT_epi_r3+orig.HEAD -tcat_remove_first_trs 2 -align_opts_aea \
- # -check_flip -volreg_align_to MIN_OUTLIER -volreg_align_e2a \
- # -volreg_tlrc_warp -blur_size 4.0 -mask_epi_anat yes -regress_stim_times \
- # FT/AV1_vis.txt FT/AV2_aud.txt -regress_stim_labels vis aud \
- # -regress_basis 'BLOCK(20,1)' -regress_censor_motion 0.3 \
- # -regress_motion_per_run -regress_opts_3dD -jobs 2 -gltsym 'SYM: vis \
- # -aud' -glt_label 1 V-A -gltsym 'SYM: 0.5*vis +0.5*aud' -glt_label 2 \
- # mean.VA -regress_compute_fitts -regress_make_ideal_sum sum_ideal.1D \
- # -regress_est_blur_epits -regress_est_blur_errts -regress_run_clustsim \
- # no -execute
|