Hello,
I am fairly new to AFNI and am having trouble with my alignment between my functional and my anatomical scans.
I have used different cost functions (lpc+Z, lpc, lpa), different outlier limits (0.3, 0.25, 0.2), different motion limits (0.3, 0.25), different volume registrations (minimal outlier, 3rd TR), and different alignment functions (giant move, ginormous move), deobliqued vs non-deobliqued anatomicals, however, my alignment isn’t great. The stats functional (REML) seems to be going outside the final anatomical (activation outside anatomical).
The anatomical (brain.nii) has been motion corrected and manually edited by our collaborators. They said that “the T1 image they collected is a multi-echo image with multiple TEs and a RMS of these TEs. The multiple TEs give slightly different tissue contrast which can be used by freesurfer to better delineate the structure boundaries (although our contact is not sure if they did this or if they just used the RMS with manual corrections for each image).”
The final anatomical does not have a skull, but I am also in possession of the MPRAGE T1 scans that have not been skull stripped (we also ran with these MPRAGE T1 scans and the same issue is occurring).
I also deobliqued the functional scans so that they would be centered.
Before I ran my script, the deobliqued functional scans were near the raw anatomical scan (brain.nii) in alignment, however, the functional scans were a little off from each other.
Final Output: When I look at my Volume Registered (3Dvolreg) outputs, they are poorly aligned with the final_anat even before looking at the statistical functional maps (REML). I’ve included a picture of the run 1 3Dvolreg. The other runs are relatively similar - they are just not 100% aligned with each other. The stats REML does not align with the anat_final. The activation seems to be going outside the anat_final.
What can I do to get the best alignment possible? I have included my script below. Any advice would help!
Thanks!
-Becca
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[size=x-large]Script:[/size]
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
/space/kmsyn03/projects/MGH_alc/Stroop/individual/SUBJ/Afni/SUBJ_stimuli1.txt
/space/kmsyn03/projects/MGH_alc/Stroop/individual/SUBJ/Afni/SUBJ_stimuli2.txt
/space/kmsyn03/projects/MGH_alc/Stroop/individual/SUBJ/Afni/SUBJ_stimuli3.txt
/space/kmsyn03/projects/MGH_alc/Stroop/individual/SUBJ/Afni/SUBJ_stimuli4.txt
$output_dir/stimuli
copy anatomy to results dir
3dcopy
/space/kmsyn03/projects/MGH_alc/Stroop/individual/SUBJ/Afni/brain.nii
$output_dir/brain
============================ 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 \
/space/kmsyn03/projects/MGH_alc/Stroop/individual/SUBJ/Afni/SUBJ_stroop_RUN01_deobl+orig'[0..]’
3dTcat -prefix $output_dir/pb00.subj.r02.tcat \
/space/kmsyn03/projects/MGH_alc/Stroop/individual/SUBJ/Afni/SUBJ_stroop_RUN02_deobl+orig'[0..]’
3dTcat -prefix $output_dir/pb00.subj.r03.tcat \
/space/kmsyn03/projects/MGH_alc/Stroop/individual/SUBJ/Afni/SUBJ_stroop_RUN03_deobl+orig'[0..]’
3dTcat -prefix $output_dir/pb00.subj.r04.tcat \
/space/kmsyn03/projects/MGH_alc/Stroop/individual/SUBJ/Afni/SUBJ_stroop_RUN04_deobl+orig'[0..]’
3dTcat -prefix $output_dir/pb00.subj.r05.tcat \
/space/kmsyn03/projects/MGH_alc/Stroop/individual/SUBJ/Afni/SUBJ_stroop_RUN05_deobl+orig'[0..]’
3dTcat -prefix $output_dir/pb00.subj.r06.tcat \
/space/kmsyn03/projects/MGH_alc/Stroop/individual/SUBJ/Afni/SUBJ_stroop_RUN06_deobl+orig'[0..]’
and make note of repetitions (TRs) per run
set tr_counts = ( 146 146 146 146 146 146 )
-------------------------------------------------------
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 0.3 of automask voxels are outliers
# - step() defines which TRs to remove via censoring
1deval -a outcount.r$run.1D -expr "1-step(a-0.3)" > rm.out.cen.r$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
================================= 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 current brain+orig)
align_epi_anat.py -anat2epi -anat brain+orig
-suffix _al_junk
-epi vr_base_min_outlier+orig -epi_base 0
-epi_strip 3dAutomask
-anat_has_skull no
-cost lpc+ZZ -giant_move
-volreg off -tshift off
================================== tlrc ==================================
warp anatomy to standard space
@auto_tlrc -base MNI_avg152T1+tlrc -input brain+orig -no_ss
store forward transformation matrix in a text file
cat_matvec brain+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 brain+tlrc.HEAD ) then
echo “** missing +tlrc warp dataset: brain+tlrc.HEAD”
exit
endif
register and warp
foreach run ( $runs )
# register each volume to the base
3dvolreg -verbose -zpad 1 -base vr_base_min_outlier+orig
-1Dfile dfile.r$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 \
brain+tlrc::WARP_DATA -I \
brain_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 brain+tlrc \
-input pb01.$subj.r$run.tshift+orig \
-1Dmatrix_apply mat.r$run.warp.aff12.1D \
-mast_dxyz 4 \
-prefix rm.epi.nomask.r$run
# warp the all-1 dataset for extents masking
3dAllineate -base brain+tlrc \
-input rm.epi.all1+orig \
-1Dmatrix_apply mat.r$run.warp.aff12.1D \
-mast_dxyz 4 -final NN -quiet \
-prefix rm.epi.1.r$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
brain+tlrc::WARP_DATA -I
brain_al_junk_mat.aff12.1D -I > mat.basewarp.aff12.1D
3dAllineate -base brain+tlrc
-input vr_base_min_outlier+orig
-1Dmatrix_apply mat.basewarp.aff12.1D
-mast_dxyz 4
-prefix final_epi_vr_base_min_outlier
create an anat_final dataset, aligned with stats
3dcopy brain+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
================================== blur ==================================
blur each volume of each run
foreach run ( $runs )
3dmerge -1blur_fwhm 8.0 -doall -prefix pb03.$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.$subj+tlrc -input brain+tlrc
-prefix rm.resam.anat
convert to binary anat mask; fill gaps and holes
3dmask_tool -dilate_input 5 -5 -fill_holes -input rm.resam.anat+tlrc
-prefix mask_anat.$subj
compute 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, MNI_avg152T1+tlrc)
3dresample -master full_mask.$subj+tlrc -prefix ./rm.resam.group
-input
/usr/pubsw/packages/afni/AFNI_LATEST/linux_xorg7_64/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$run.scale
end
================================ regress =================================
compute de-meaned motion parameters (for use in regression)
1d_tool.py -infile dfile_rall.1D -set_nruns 6
-demean -write motion_demean.1D
compute motion parameter derivatives (for use in regression)
1d_tool.py -infile dfile_rall.1D -set_nruns 6
-derivative -demean -write motion_deriv.1D
create censor file motion_${subj}_censor.1D, for censoring motion
1d_tool.py -infile dfile_rall.1D -set_nruns 6
-show_censor_count -censor_prev_TR
-censor_motion 0.25 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_${subj}_combined_2.1D \ -show_trs_uncensored encoded
------------------------------
run the regression analysis
3dDeconvolve -input pb04.subj.r*.scale+tlrc.HEAD \
-censor censor_{subj}_combined_2.1D
-polort 3
-num_stimts 16
-stim_times 1 stimuli/SUBJ_stimuli1.txt ‘TENT(-4.4,13.2,8)’
-stim_label 1 stimuli1
-stim_times 2 stimuli/SUBJ_stimuli2.txt ‘TENT(-4.4,13.2,8)’
-stim_label 2 stimuli2
-stim_times 3 stimuli/SUBJ_stimuli3.txt ‘TENT(-4.4,13.2,8)’
-stim_label 3 stimuli3
-stim_times 4 stimuli/SUBJ_stimuli4.txt ‘TENT(-4.4,13.2,8)’
-stim_label 4 stimuli4
-stim_file 5 motion_demean.1D’[0]’ -stim_base 5 -stim_label 5 roll_01
-stim_file 6 motion_demean.1D’[1]’ -stim_base 6 -stim_label 6 pitch_01
-stim_file 7 motion_demean.1D’[2]’ -stim_base 7 -stim_label 7 yaw_01
-stim_file 8 motion_demean.1D’[3]’ -stim_base 8 -stim_label 8 dS_01
-stim_file 9 motion_demean.1D’[4]’ -stim_base 9 -stim_label 9 dL_01
-stim_file 10 motion_demean.1D’[5]’ -stim_base 10 -stim_label 10 dP_01
-stim_file 11 motion_deriv.1D’[0]’ -stim_base 11 -stim_label 11 roll_02
-stim_file 12 motion_deriv.1D’[1]’ -stim_base 12 -stim_label 12 pitch_02
-stim_file 13 motion_deriv.1D’[2]’ -stim_base 13 -stim_label 13 yaw_02
-stim_file 14 motion_deriv.1D’[3]’ -stim_base 14 -stim_label 14 dS_02
-stim_file 15 motion_deriv.1D’[4]’ -stim_base 15 -stim_label 15 dL_02
-stim_file 16 motion_deriv.1D’[5]’ -stim_base 16 -stim_label 16 dP_02
-iresp 1 iresp_stimuli1.$subj
-iresp 2 iresp_stimuli2.$subj
-iresp 3 iresp_stimuli3.$subj
-iresp 4 iresp_stimuli4.$subj
-gltsym ‘SYM: stimuli2 -stimuli1’
-glt_label 1 I-C
-fout -tout -x1D X.xmat.1D -xjpeg X.jpg
-x1D_uncensored X.nocensor.xmat.1D
-fitts fitts.subj \
-errts errts.{subj}
-bucket stats.$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
look for odd timing in files for TENT functions
timing_tool.py -multi_timing stimuli/SUBJ_stimuli1.txt
stimuli/SUBJ_stimuli2.txt
stimuli/SUBJ_stimuli3.txt
stimuli/SUBJ_stimuli4.txt
-tr 2.2 -warn_tr_stats |& tee out.TENT_warn.txt
– execute the 3dREMLfit script, written by 3dDeconvolve –
tcsh -x stats.REML_cmd
if 3dREMLfit fails, terminate the script
if ( $status != 0 ) then
echo ‘---------------------------------------’
echo ‘** 3dREMLfit error, failing…’
exit
endif
create an all_runs dataset to match the fitts, errts, etc.
3dTcat -prefix all_runs.$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}_REML+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}_REML+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 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
– estimate blur for each run in err_reml –
touch blur.err_reml.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.err_reml.r$run.1D
errts.${subj}_REML+tlrc"[$trs]" >> blur.err_reml.1D
end
compute average FWHM blur (from every other row) and append
set blurs = ( 3dTstat -mean -prefix - blur.err_reml.1D'{0..$(2)}'\' )
echo average err_reml FWHM blurs: $blurs
echo “$blurs # err_reml FWHM blur estimates” >> blur_est.$subj.1D
compute average ACF blur (from every other row) and append
set blurs = ( 3dTstat -mean -prefix - blur.err_reml.1D'{1..$(2)}'\' )
echo average err_reml ACF blurs: $blurs
echo “$blurs # err_reml ACF blur estimates” >> blur_est.$subj.1D
add 3dClustSim results as attributes to any stats dset
mkdir files_ClustSim
run Monte Carlo simulations using method ‘ACF’
set params = ( grep ACF blur_est.$subj.1D | tail -n 1 )
3dClustSim -both -mask full_mask.$subj+tlrc -acf $params[1-3]
-cmd 3dClustSim.ACF.cmd -prefix files_ClustSim/ClustSim.ACF
run 3drefit to attach 3dClustSim results to stats
set cmd = ( cat 3dClustSim.ACF.cmd )
$cmd stats.subj+tlrc stats.{subj}_REML+tlrc
================== auto block: generate review scripts ===================
generate a review script for the unprocessed EPI data
gen_epi_review.py -script @epi_review.$subj
-dsets pb00.$subj.r*.tcat+orig.HEAD
generate scripts to review single subject results
(try with defaults, but do not allow bad exit status)
gen_ss_review_scripts.py -mot_limit 0.25 -out_limit 0.3 -exit0
========================== auto block: finalize ==========================
remove temporary files
\rm -f rm.*
if the basic subject review script is here, run it
(want this to be the last text output)
if ( -e @ss_review_basic ) ./@ss_review_basic |& tee out.ss_review.$subj.txt
return to parent directory
cd …
echo “execution finished: date”
