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- #!/bin/tcsh -xef
- echo "auto-generated by afni_proc.py, Tue Jun 5 09:14:37 2018"
- echo "(version 6.14, May 25, 2018)"
- echo "execution started: `date`"
- # execute via :
- # tcsh -xef proc.FT.rest |& tee output.proc.FT.rest
- # =========================== 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 3 May 2018
- if ( $status ) then
- echo "** this script requires newer AFNI binaries (than 3 May 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 = FT.rest
- 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 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
- # ========================== 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
- # 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
- # ================================ despike =================================
- # apply 3dDespike to each run
- foreach run ( $runs )
- 3dDespike -NEW -nomask -prefix pb01.$subj.r$run.despike \
- pb00.$subj.r$run.tcat+orig
- end
- # ================================= tshift =================================
- # time shift data so all slice timing is the same
- foreach run ( $runs )
- 3dTshift -tzero 0 -quintic -prefix pb02.$subj.r$run.tshift \
- pb01.$subj.r$run.despike+orig
- end
- # --------------------------------
- # extract volreg registration base
- 3dbucket -prefix vr_base_min_outlier \
- pb02.$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 \
- -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, align 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 \
- pb02.$subj.r$run.tshift+orig
- # create an all-1 dataset to mask the extents of the warp
- 3dcalc -overwrite -a pb02.$subj.r$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 pb02.$subj.r$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 pb03.$subj.r$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
- # ================================== blur ==================================
- # blur each volume of each run
- foreach run ( $runs )
- 3dmerge -1blur_fwhm 4.0 -doall -prefix rm.pb04.$subj.r$run.blur \
- pb03.$subj.r$run.volreg+tlrc
- # and apply extents mask, since no scale block
- 3dcalc -a rm.pb04.$subj.r$run.blur+tlrc -b mask_epi_extents+tlrc \
- -expr 'a*b' -prefix pb04.$subj.r$run.blur
- end
- # ================================== mask ==================================
- # create 'full_mask' dataset (union mask)
- foreach run ( $runs )
- 3dAutomask -prefix rm.mask_r$run pb04.$subj.r$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
- # ---- segment anatomy into classes CSF/GM/WM ----
- 3dSeg -anat anat_final.$subj+tlrc -mask AUTO -classes 'CSF ; GM ; WM'
- # copy resulting Classes dataset to current directory
- 3dcopy Segsy/Classes+tlrc .
- # make individual ROI masks for regression (CSF GM WM and CSFe GMe WMe)
- foreach class ( CSF GM WM )
- # unitize and resample individual class mask from composite
- 3dmask_tool -input Segsy/Classes+tlrc"<$class>" \
- -prefix rm.mask_${class}
- 3dresample -master pb04.$subj.r01.blur+tlrc -rmode NN \
- -input rm.mask_${class}+tlrc -prefix mask_${class}_resam
- # also, generate eroded masks
- 3dmask_tool -input Segsy/Classes+tlrc"<$class>" -dilate_input -1 \
- -prefix rm.mask_${class}e
- 3dresample -master pb04.$subj.r01.blur+tlrc -rmode NN \
- -input rm.mask_${class}e+tlrc -prefix mask_${class}e_resam
- end
- # ================================ regress =================================
- # compute de-meaned motion parameters (for use in regression)
- 1d_tool.py -infile dfile_rall.1D -set_nruns 3 \
- -demean -write motion_demean.1D
- # compute motion parameter derivatives (for use in regression)
- 1d_tool.py -infile dfile_rall.1D -set_nruns 3 \
- -derivative -demean -write motion_deriv.1D
- # convert motion parameters for per-run regression
- 1d_tool.py -infile motion_demean.1D -set_nruns 3 \
- -split_into_pad_runs mot_demean
- 1d_tool.py -infile motion_deriv.1D -set_nruns 3 \
- -split_into_pad_runs mot_deriv
- # create censor file motion_${subj}_censor.1D, for censoring motion
- 1d_tool.py -infile dfile_rall.1D -set_nruns 3 \
- -show_censor_count -censor_prev_TR \
- -censor_motion 0.2 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
- # create bandpass regressors (instead of using 3dBandpass, say)
- # (make separate regressors per run, with all in one file)
- foreach index ( `count -digits 1 1 $#runs` )
- set nt = $tr_counts[$index]
- set run = $runs[$index]
- 1dBport -nodata $nt 2 -band 0.01 0.1 -invert -nozero > rm.bpass.1D
- 1d_tool.py -infile rm.bpass.1D -pad_into_many_runs $run $#runs \
- -set_run_lengths $tr_counts \
- -write bpass.r$run.1D
- end
- 1dcat bpass.r*1D > bandpass_rall.1D
- # create ROI regressor: WMe
- # (get each ROI average time series and remove resulting mean)
- foreach run ( $runs )
- 3dmaskave -quiet -mask mask_WMe_resam+tlrc \
- pb03.$subj.r$run.volreg+tlrc \
- | 1d_tool.py -infile - -demean -write rm.ROI.WMe.r$run.1D
- end
- # and catenate the demeaned ROI averages across runs
- cat rm.ROI.WMe.r*.1D > ROI.WMe_rall.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*.blur+tlrc.HEAD \
- -censor censor_${subj}_combined_2.1D \
- -ortvec bandpass_rall.1D bandpass \
- -ortvec ROI.WMe_rall.1D ROI.WMe \
- -polort 3 \
- -num_stimts 36 \
- -stim_file 1 mot_demean.r01.1D'[0]' -stim_base 1 -stim_label 1 roll_01 \
- -stim_file 2 mot_demean.r01.1D'[1]' -stim_base 2 -stim_label 2 pitch_01 \
- -stim_file 3 mot_demean.r01.1D'[2]' -stim_base 3 -stim_label 3 yaw_01 \
- -stim_file 4 mot_demean.r01.1D'[3]' -stim_base 4 -stim_label 4 dS_01 \
- -stim_file 5 mot_demean.r01.1D'[4]' -stim_base 5 -stim_label 5 dL_01 \
- -stim_file 6 mot_demean.r01.1D'[5]' -stim_base 6 -stim_label 6 dP_01 \
- -stim_file 7 mot_demean.r02.1D'[0]' -stim_base 7 -stim_label 7 roll_02 \
- -stim_file 8 mot_demean.r02.1D'[1]' -stim_base 8 -stim_label 8 pitch_02 \
- -stim_file 9 mot_demean.r02.1D'[2]' -stim_base 9 -stim_label 9 yaw_02 \
- -stim_file 10 mot_demean.r02.1D'[3]' -stim_base 10 -stim_label 10 dS_02 \
- -stim_file 11 mot_demean.r02.1D'[4]' -stim_base 11 -stim_label 11 dL_02 \
- -stim_file 12 mot_demean.r02.1D'[5]' -stim_base 12 -stim_label 12 dP_02 \
- -stim_file 13 mot_demean.r03.1D'[0]' -stim_base 13 -stim_label 13 roll_03 \
- -stim_file 14 mot_demean.r03.1D'[1]' -stim_base 14 -stim_label 14 \
- pitch_03 \
- -stim_file 15 mot_demean.r03.1D'[2]' -stim_base 15 -stim_label 15 yaw_03 \
- -stim_file 16 mot_demean.r03.1D'[3]' -stim_base 16 -stim_label 16 dS_03 \
- -stim_file 17 mot_demean.r03.1D'[4]' -stim_base 17 -stim_label 17 dL_03 \
- -stim_file 18 mot_demean.r03.1D'[5]' -stim_base 18 -stim_label 18 dP_03 \
- -stim_file 19 mot_deriv.r01.1D'[0]' -stim_base 19 -stim_label 19 roll_04 \
- -stim_file 20 mot_deriv.r01.1D'[1]' -stim_base 20 -stim_label 20 pitch_04 \
- -stim_file 21 mot_deriv.r01.1D'[2]' -stim_base 21 -stim_label 21 yaw_04 \
- -stim_file 22 mot_deriv.r01.1D'[3]' -stim_base 22 -stim_label 22 dS_04 \
- -stim_file 23 mot_deriv.r01.1D'[4]' -stim_base 23 -stim_label 23 dL_04 \
- -stim_file 24 mot_deriv.r01.1D'[5]' -stim_base 24 -stim_label 24 dP_04 \
- -stim_file 25 mot_deriv.r02.1D'[0]' -stim_base 25 -stim_label 25 roll_05 \
- -stim_file 26 mot_deriv.r02.1D'[1]' -stim_base 26 -stim_label 26 pitch_05 \
- -stim_file 27 mot_deriv.r02.1D'[2]' -stim_base 27 -stim_label 27 yaw_05 \
- -stim_file 28 mot_deriv.r02.1D'[3]' -stim_base 28 -stim_label 28 dS_05 \
- -stim_file 29 mot_deriv.r02.1D'[4]' -stim_base 29 -stim_label 29 dL_05 \
- -stim_file 30 mot_deriv.r02.1D'[5]' -stim_base 30 -stim_label 30 dP_05 \
- -stim_file 31 mot_deriv.r03.1D'[0]' -stim_base 31 -stim_label 31 roll_06 \
- -stim_file 32 mot_deriv.r03.1D'[1]' -stim_base 32 -stim_label 32 pitch_06 \
- -stim_file 33 mot_deriv.r03.1D'[2]' -stim_base 33 -stim_label 33 yaw_06 \
- -stim_file 34 mot_deriv.r03.1D'[3]' -stim_base 34 -stim_label 34 dS_06 \
- -stim_file 35 mot_deriv.r03.1D'[4]' -stim_base 35 -stim_label 35 dL_06 \
- -stim_file 36 mot_deriv.r03.1D'[5]' -stim_base 36 -stim_label 36 dP_06 \
- -fout -tout -x1D X.xmat.1D -xjpeg X.jpg \
- -x1D_uncensored X.nocensor.xmat.1D \
- -fitts fitts.$subj \
- -errts errts.${subj} \
- -x1D_stop \
- -bucket stats.$subj
- # -- use 3dTproject to project out regression matrix --
- 3dTproject -polort 0 -input pb04.$subj.r*.blur+tlrc.HEAD \
- -censor censor_${subj}_combined_2.1D -cenmode ZERO \
- -ort X.nocensor.xmat.1D -prefix errts.${subj}.tproject
- # 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.$subj.r*.blur+tlrc.HEAD
- # --------------------------------------------------
- # create a temporal signal to noise ratio dataset
- # signal: if 'scale' block, mean should be 100
- # noise : compute standard deviation of errts
- 3dTstat -mean -prefix rm.signal.all all_runs.$subj+tlrc"[$ktrs]"
- 3dTstat -stdev -prefix rm.noise.all errts.${subj}.tproject+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}.tproject+tlrc
- 3dmaskave -quiet -mask full_mask.$subj+tlrc rm.errts.unit+tlrc \
- > gmean.errts.unit.1D
- 3dTstat -sos -prefix - gmean.errts.unit.1D\' > out.gcor.1D
- echo "-- GCOR = `cat out.gcor.1D`"
- # ---------------------------------------------------
- # compute correlation volume
- # (per voxel: average correlation across masked brain)
- # (now just dot product with average unit time series)
- 3dcalc -a rm.errts.unit+tlrc -b gmean.errts.unit.1D -expr 'a*b' -prefix rm.DP
- 3dTstat -sum -prefix corr_brain rm.DP+tlrc
- # --------------------------------------------------------
- # compute sum of non-baseline regressors from the X-matrix
- # (use 1d_tool.py to get list of regressor colums)
- set reg_cols = `1d_tool.py -infile X.nocensor.xmat.1D -show_indices_interest`
- 3dTstat -sum -prefix sum_ideal.1D X.nocensor.xmat.1D"[$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 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}.tproject+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.$subj.r*.tcat+orig.HEAD
- # generate scripts to review single subject results
- # (try with defaults, but do not allow bad exit status)
- gen_ss_review_scripts.py -mot_limit 0.2 -out_limit 0.1 \
- -errts_dset errts.${subj}.tproject+tlrc.HEAD -exit0
- # ========================== auto block: finalize ==========================
- # remove temporary files
- \rm -fr rm.* Segsy
- # 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 FT.rest -script proc.FT.rest -scr_overwrite -blocks \
- # despike tshift align tlrc volreg blur mask regress -copy_anat \
- # FT/FT_anat+orig -tcat_remove_first_trs 2 -dsets FT/FT_epi_r1+orig.HEAD \
- # FT/FT_epi_r2+orig.HEAD FT/FT_epi_r3+orig.HEAD -volreg_align_to \
- # MIN_OUTLIER -volreg_align_e2a -volreg_tlrc_warp -blur_size 4.0 \
- # -mask_segment_anat yes -mask_segment_erode yes -regress_motion_per_run \
- # -regress_censor_motion 0.2 -regress_censor_outliers 0.1 \
- # -regress_bandpass 0.01 0.1 -regress_apply_mot_types demean deriv \
- # -regress_ROI WMe -regress_run_clustsim no -regress_est_blur_errts
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