Fatal error using 3dNwarpApply

AFNI Message Board
1

Hello,

Recently, I’ve attempted to add non-linear warping via auto_warp.py to our single-subject preprocessing. Given the results below, I’ve clearly done something very wrong. I’m at a loss for what even happened with this error, so any help would be appreciated!

Below is, in order:

  1. The output containing the error text
  2. The code that was run - the script also contains blurring, masking, etc after the section posted below, but this is where I’ve seen the error.
  3. The command executed to run the code.

Thanks!

3dNwarpApply -source anat_mprage_unif+orig -master anat_final.ERwD004+orig -ainterp wsinc5 -nwarp anat.aff.qw_WARP.nii anat.aff.Xat.1D anat_mprage_unif_al_keep_mat.aff12.1D -prefix anat_w_skull_warped
++ 3dNwarpApply: AFNI version=AFNI_17.0.06 (Feb 1 2017) [64-bit]
++ Authored by: Zhark the Warped
*+ WARNING: If you are performing spatial transformations on an oblique dset,
such as ./anat_mprage_unif+orig.BRIK,
or viewing/combining it with volumes of differing obliquity,
you should consider running:
3dWarp -deoblique
on this and other oblique datasets in the same session.
See 3dWarp -help for details.
++ Oblique dataset:./anat_mprage_unif+orig.BRIK is 1.489115 degrees from plumb.
++ opened source dataset ‘anat_mprage_unif+orig’
++ -master dataset is ‘anat_final.ERwD004+orig’
++ Processing -nwarp *** Error in `3dNwarpApply’: free(): invalid next size (fast): 0x0000000001c993d0 ***
======= Backtrace: =========
/usr/lib64/libc.so.6(+0x7d023)[0x7fc7b6f74023]
/usr/lib64/libz.so.1(gzclose_r+0x44)[0x7fc7b77cbc84]
3dNwarpApply[0x7e91f6]
3dNwarpApply[0x7d540e]
3dNwarpApply[0x7da4af]
3dNwarpApply[0x7e82c9]
3dNwarpApply[0x615fb2]
3dNwarpApply[0x5605b5]
3dNwarpApply[0x57194d]
3dNwarpApply[0x476940]
3dNwarpApply[0x47e85a]
3dNwarpApply[0x4801ca]
/usr/lib64/libc.so.6(__libc_start_main+0xf5)[0x7fc7b6f18b15]
3dNwarpApply[0x408399]
======= Memory map: ========
00400000-009a9000 r-xp 00000000 fd:01 10226500 /opt/afni/3dNwarpApply
00ba8000-00bf3000 rw-p 005a8000 fd:01 10226500 /opt/afni/3dNwarpApply
00bf3000-00c38000 rw-p 00000000 00:00 0
01c85000-01ca6000 rw-p 00000000 00:00 0 [heap]
7fc7b0000000-7fc7b0021000 rw-p 00000000 00:00 0
7fc7b0021000-7fc7b4000000 —p 00000000 00:00 0
7fc7b596d000-7fc7b5982000 r-xp 00000000 fd:01 6462588 /usr/lib64/libgcc_s-4.8.5-20150702.so.1
7fc7b5982000-7fc7b5b81000 —p 00015000 fd:01 6462588 /usr/lib64/libgcc_s-4.8.5-20150702.so.1
7fc7b5b81000-7fc7b5b82000 r–p 00014000 fd:01 6462588 /usr/lib64/libgcc_s-4.8.5-20150702.so.1
7fc7b5b82000-7fc7b5b83000 rw-p 00015000 fd:01 6462588 /usr/lib64/libgcc_s-4.8.5-20150702.so.1
7fc7b5b83000-7fc7b5baa000 r-xp 00000000 fd:01 6426976 /usr/lib64/libexpat.so.1.6.0
7fc7b5baa000-7fc7b5daa000 —p 00027000 fd:01 6426976 /usr/lib64/libexpat.so.1.6.0
7fc7b5daa000-7fc7b5dac000 r–p 00027000 fd:01 6426976 /usr/lib64/libexpat.so.1.6.0
7fc7b5dac000-7fc7b5dad000 rw-p 00029000 fd:01 6426976 /usr/lib64/libexpat.so.1.6.0
7fc7b5dad000-7fc7b5dce000 r-xp 00000000 fd:01 6427036 /usr/lib64/libxcb.so.1.1.0
7fc7b5dce000-7fc7b5fcd000 —p 00021000 fd:01 6427036 /usr/lib64/libxcb.so.1.1.0
7fc7b5fcd000-7fc7b5fce000 r–p 00020000 fd:01 6427036 /usr/lib64/libxcb.so.1.1.0
7fc7b5fce000-7fc7b5fcf000 rw-p 00021000 fd:01 6427036 /usr/lib64/libxcb.so.1.1.0
7fc7b5fcf000-7fc7b5fd3000 r-xp 00000000 fd:01 6426692 /usr/lib64/libuuid.so.1.3.0
7fc7b5fd3000-7fc7b61d2000 —p 00004000 fd:01 6426692 /usr/lib64/libuuid.so.1.3.0
7fc7b61d2000-7fc7b61d3000 r–p 00003000 fd:01 6426692 /usr/lib64/libuuid.so.1.3.0
7fc7b61d3000-7fc7b61d4000 rw-p 00004000 fd:01 6426692 /usr/lib64/libuuid.so.1.3.0
7fc7b61d4000-7fc7b61d6000 r-xp 00000000 fd:01 6427006 /usr/lib64/libXau.so.6.0.0
7fc7b61d6000-7fc7b63d6000 —p 00002000 fd:01 6427006 /usr/lib64/libXau.so.6.0.0
7fc7b63d6000-7fc7b63d7000 r–p 00002000 fd:01 6427006 /usr/lib64/libXau.so.6.0.0
7fc7b63d7000-7fc7b63d8000 rw-p 00003000 fd:01 6427006 /usr/lib64/libXau.so.6.0.0
7fc7b63d8000-7fc7b63e1000 r-xp 00000000 fd:01 6450711 /usr/lib64/libXrender.so.1.3.0
7fc7b63e1000-7fc7b65e0000 —p 00009000 fd:01 6450711 /usr/lib64/libXrender.so.1.3.0
7fc7b65e0000-7fc7b65e1000 r–p 00008000 fd:01 6450711 /usr/lib64/libXrender.so.1.3.0
7fc7b65e1000-7fc7b65e2000 rw-p 00009000 fd:01 6450711 /usr/lib64/libXrender.so.1.3.0
7fc7b65e2000-7fc7b6682000 r-xp 00000000 fd:01 6426603 /usr/lib64/libfreetype.so.6.10.0
7fc7b6682000-7fc7b6881000 —p 000a0000 fd:01 6426603 /usr/lib64/libfreetype.so.6.10.0
7fc7b6881000-7fc7b6887000 r–p 0009f000 fd:01 6426603 /usr/lib64/libfreetype.so.6.10.0
7fc7b6887000-7fc7b6888000 rw-p 000a5000 fd:01 6426603 /usr/lib64/libfreetype.so.6.10.0
7fc7b6888000-7fc7b68c2000 r-xp 00000000 fd:01 6438009 /usr/lib64/libfontconfig.so.1.7.0
7fc7b68c2000-7fc7b6ac1000 —p 0003a000 fd:01 6438009 /usr/lib64/libfontconfig.so.1.7.0
7fc7b6ac1000-7fc7b6ac3000 r–p 00039000 fd:01 6438009 /usr/lib64/libfontconfig.so.1.7.0
7fc7b6ac3000-7fc7b6ac4000 rw-p 0003b000 fd:01 6438009 /usr/lib64/libfontconfig.so.1.7.0
7fc7b6ac4000-7fc7b6ada000 r-xp 00000000 fd:01 6425165 /usr/lib64/libpthread-2.17.so
7fc7b6ada000-7fc7b6cda000 —p 00016000 fd:01 6425165 /usr/lib64/libpthread-2.17.so
7fc7b6cda000-7fc7b6cdb000 r–p 00016000 fd:01 6425165 /usr/lib64/libpthread-2.17.so
7fc7b6cdb000-7fc7b6cdc000 rw-p 00017000 fd:01 6425165 /usr/lib64/libpthread-2.17.so
7fc7b6cdc000-7fc7b6ce0000 rw-p 00000000 00:00 0
7fc7b6ce0000-7fc7b6cf6000 r-xp 00000000 fd:01 6427209 /usr/lib64/libgomp.so.1.0.0
7fc7b6cf6000-7fc7b6ef5000 —p 00016000 fd:01 6427209 /usr/lib64/libgomp.so.1.0.0
7fc7b6ef5000-7fc7b6ef6000 r–p 00015000 fd:01 6427209 /usr/lib64/libgomp.so.1.0.0
7fc7b6ef6000-7fc7b6ef7000 rw-p 00016000 fd:01 6427209 /usr/lib64/libgomp.so.1.0.0
7fc7b6ef7000-7fc7b70ad000 r-xp 00000000 fd:01 6425139 /usr/lib64/libc-2.17.so
7fc7b70ad000-7fc7b72ad000 —p 001b6000 fd:01 6425139 /usr/lib64/libc-2.17.so
7fc7b72ad000-7fc7b72b1000 r–p 001b6000 fd:01 6425139 /usr/lib64/libc-2.17.so
7fc7b72b1000-7fc7b72b3000 rw-p 001ba000 fd:01 6425139 /usr/lib64/libc-2.17.so
7fc7b72b3000-7fc7b72b8000 rw-p 00000000 00:00 0
7fc7b72b8000-7fc7b72bb000 r-xp 00000000 fd:01 6425145 /usr/lib64/libdl-2.17.so
7fc7b72bb000-7fc7b74ba000 —p 00003000 fd:01 6425145 /usr/lib64/libdl-2.17.so
7fc7b74ba000-7fc7b74bb000 r–p 00002000 fd:01 6425145 /usr/lib64/libdl-2.17.so
7fc7b74bb000-7fc7b74bc000 rw-p 00003000 fd:01 6425145 /usr/lib64/libdl-2.17.so
7fc7b74bc000-7fc7b75bd000 r-xp 00000000 fd:01 6425147 /usr/lib64/libm-2.17.so
7fc7b75bd000-7fc7b77bc000 —p 00101000 fd:01 6425147 /usr/lib64/libm-2.17.so
7fc7b77bc000-7fc7b77bd000 r–p 00100000 fd:01 6425147 /usr/lib64/libm-2.17.so
7fc7b77bd000-7fc7b77be000 rw-p 00101000 fd:01 6425147 /usr/lib64/libm-2.17.so
7fc7b77be000-7fc7b77d3000 r-xp 00000000 fd:01 6426593 /usr/lib64/libz.so.1.2.7
Fatal Signal 6 (SIGABRT) received
THD_open_nifti
THD_open_one_dataset
THD_open_dataset
CW_read_dataset_warp
IW3D_read_nwarp_catlist
3dNwarpApply
Bottom of Debug Stack
** Command line was:
3dNwarpApply -source anat_mprage_unif+orig -master anat_final.ERwD004+orig -ainterp wsinc5 -nwarp anat.aff.qw_WARP.nii anat.aff.Xat.1D anat_mprage_unif_al_keep_mat.aff12.1D -prefix anat_w_skull_warped
** AFNI version = AFNI_17.0.06 Compile date = Feb 1 2017
** [[Precompiled binary linux_openmp_64: Feb 1 2017]]
** Program Death **
** If you report this crash to the AFNI message board,
** please copy the error messages EXACTLY, and give
** the command line you used to run the program, and
** any other information needed to repeat the problem.
** You may later be asked to upload data to help debug.
** Crash log is appended to file /home/usr/andersonn/.afni.crashlog


#!/bin/tcsh -xef

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

echo "(version 4.67, June 16, 2016)"
echo "execution started: `date`"

####################################################################################################
# There are two primary blocks to change in this script.
# The first is a pair of lines, just below this note.
# The second is a large block, farther down and also marked.
####################################################################################################


##################################################
# ***CHANGE***
# This is the list of subjects (VCnumbers, etc) you'd like to loop through
foreach subj_number (ERwD004 ERwD005 ERwD006 ERwD007 ERwD008 ERwD009 ERwD010 ERwD011 ERwD012 ERwD013 ERwD014 ERwD015 ERwD017 ERwD018)
# foreach subj_number (vc12345, vc12346, vc12347, vc12348, vc12349, vc12350)
##################################################

echo $subj_number

##################################################
# ***CHANGE***
# This is is the initial directory where all analyses will be saved.
cd /data/nil-external/mcl/Anderson/All_Script_Testing
##################################################

# mkdir $subj_number
#
# cd $subj_number
if ( -d subject_results ) then
	echo "subject_results directory already exists!"
else
	mkdir subject_results
endif

cd subject_results

# mkdir group.ERwD
#
# cd group.ERwD

mkdir subj.$subj_number

cd subj.$subj_number








# =========================== 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 28 Oct 2015
if ( $status ) then
	echo "** this script requires newer AFNI binaries (than 28 Oct 2015)"
	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 = $subj_number
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

# create results and stimuli directories
mkdir $output_dir
mkdir $output_dir/stimuli


echo "$output_dir"


##################################################
# ***CHANGE***

# This is the directory where each individual subject's data is kept (updates for each participant loop)
set input_dir='/data/nil-external/mcl/Anderson/All_Script_Testing/'"$subj"

# This is the MNI template file (does not update for each participant loop)
# Note: there is no need to change this unless you are not running on the Icarus server.
set mask_dir='/data/nil-external/mcl/tools/afni/MNI152_T1_2009c+tlrc'

# set list of runs
set runs = (`count -digits 2 1 9`) # the final digit is your number of runs (2 1 6 reads: 2 digit numbers, 1 through 6)

# and make note of repetitions (TRs) per run
set tr_counts = ( 314 314 314 314 314 314 314 314 314 )

# You only need to change the file extension after #$input_dir
# This is where each individual subject's T1 is located (updates for each participant loop)
3dcopy $input_dir/anat/coT1_"$subj".nii \
	$output_dir/anat_mprage


# ============================ auto block: tcat ============================
# apply 3dTcat to copy input dsets to results dir, while
# removing the first 0 TRs

# Change only the second part of each line (the absolute path to each individual subject's run data (updates for each participant loop)
# Make sure the number of runs match your run number. If you copy/paste, make sure to increment the run number (e.g.pb00.$subj.r08) in the first line
3dTcat -prefix+orig $output_dir/pb00.$subj.r01.tcat \
    $input_dir/func/Run01_"$subj"Encoding1.nii'[0..$]'
3dTcat -prefix+orig $output_dir/pb00.$subj.r02.tcat \
    $input_dir/func/Run02_"$subj"Retrieval1a.nii'[0..$]'
3dTcat -prefix+orig $output_dir/pb00.$subj.r03.tcat \
    $input_dir/func/Run03_"$subj"Retrieval1b.nii'[0..$]'
3dTcat -prefix+orig $output_dir/pb00.$subj.r04.tcat \
    $input_dir/func/Run04_"$subj"Encoding2.nii'[0..$]'
3dTcat -prefix+orig $output_dir/pb00.$subj.r05.tcat \
    $input_dir/func/Run05_"$subj"Retrieval2a.nii'[0..$]'
3dTcat -prefix+orig $output_dir/pb00.$subj.r06.tcat \
    $input_dir/func/Run06_"$subj"Retrieval2b.nii'[0..$]'
3dTcat -prefix+orig $output_dir/pb00.$subj.r07.tcat \
    $input_dir/func/Run07_"$subj"Encoding3.nii'[0..$]'
3dTcat -prefix+orig $output_dir/pb00.$subj.r08.tcat \
    $input_dir/func/Run08_"$subj"Retrieval3a.nii'[0..$]'
3dTcat -prefix+orig $output_dir/pb00.$subj.r09.tcat \
    $input_dir/func/Run09_"$subj"Retrieval3b.nii'[0..$]'


# ***END OF CHANGES***
##################################################




# -------------------------------------------------------
# enter the results directory (can begin processing data)
cd $output_dir


# ========================= uniformity correction ==========================
# perform 'unifize' uniformity correction on anatomical dataset
3dUnifize -prefix anat_mprage_unif anat_mprage+orig


# ========================== 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


# ================================ 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
# end

### If above slice timing correction is unnecessary
### Simply create pb02 as an identical copy of pb01
### NOTE: DO NOT use slice timing correction if your scans use multiband sequences
foreach run ( $runs )
	3dcopy pb01.$subj.r$run.despike pb02.$subj.r$run.tshift
end



# --------------------------------
# extract volreg registration base
3dbucket -prefix vr_base pb02.$subj.r01.tshift+orig"[2]"


# ================================= align ==================================
# for e2a: compute anat alignment transformation to EPI registration base
# (new anat will be intermediate, stripped, anat_mprage_ns+orig)
align_epi_anat.py -anat2epi -anat anat_mprage_unif+orig  \
       -suffix _al_keep                             \
	   -epi vr_base+orig -epi_base 0                \
	   -epi_strip 3dAutomask                        \
	   -volreg off -tshift off						\
	   -cost lpc+zz


# ================================== tlrc ==================================
# warp anatomy to standard space
auto_warp.py -base MNI152_T1_2009c+tlrc -input anat_mprage_unif_al_keep+orig \
             -skull_strip_input no -unifize_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 anat_mprage_unif_al_keep                           \
    awpy/anat_mprage_unif_al_keep.aw.nii*
mv awpy/anat.aff.Xat.1D .
mv awpy/anat.aff.qw_WARP.nii .


# ================================= volreg =================================
# align each dset to base volume
foreach run ( $runs )
    # register each volume to the base
    3dvolreg -verbose -zpad 1 -base vr_base+orig                         \
             -1Dfile dfile.r$run.1D -prefix pb03.$subj.r$run.volreg      \
             -cubic                                                      \
             pb02.$subj.r$run.tshift+orig
end

# make a single file of registration params
cat dfile.r*.1D > dfile_rall.1D

# compute motion magnitude time series: the Euclidean norm
# (sqrt(sum squares)) of the motion parameter derivatives
1d_tool.py -infile dfile_rall.1D -set_nruns 9                            \
           -derivative  -collapse_cols euclidean_norm                    \
           -write motion_${subj}_enorm.1D

# create an anat_final dataset, aligned with stats
3dcopy anat_mprage_unif_al_keep+orig anat_final.$subj

# -----------------------------------------
# warp anat follower datasets (non-liner)
3dNwarpApply -source anat_mprage_unif+orig                              \
             -master anat_final.$subj+orig                               \
             -ainterp wsinc5 -nwarp anat.aff.qw_WARP.nii anat.aff.Xat.1D \
             anat_mprage_unif_al_keep_mat.aff12.1D                      \
             -prefix anat_w_skull_warped



tcsh -xef proc.ERwD_Preprocessing_Sequential | & tee output.proc.ERwD_Preprocessing_Sequential
2

Consider the advice here (basically @Align_Centers to prealign the centers of your datasets with the center of the MNI template)

https://afni.nimh.nih.gov/afni/community/board/read.php?1,153114,153494#msg-153494

3

Thanks for the advice. I’ve tried the following, and maybe I’m just having trouble getting this right conceptually. I’ve added this @Align_Centers block but it doesn’t seem to help.


# --------------------------------
# Align centers of datasets to template
@Align_Centers -base MNI152_T1_2009c+tlrc            \
    -dset anat_mprage_unif+orig                      \
    -child pb02.$subj.r01.tshift+orig.HEAD           \
    pb02.$subj.r02.tshift+orig.HEAD                  \
    pb02.$subj.r03.tshift+orig.HEAD                  \
    pb02.$subj.r04.tshift+orig.HEAD                  \
    pb02.$subj.r05.tshift+orig.HEAD                  \
    pb02.$subj.r06.tshift+orig.HEAD                  \
    pb02.$subj.r07.tshift+orig.HEAD                  \
    pb02.$subj.r08.tshift+orig.HEAD                  \
    pb02.$subj.r09.tshift+orig.HEAD                  \

# --------------------------------
# extract volreg registration base
3dbucket -prefix vr_base pb02.$subj.r01.tshift_shft+orig"[2]"


# ================================= align ==================================
# for e2a: compute anat alignment transformation to EPI registration base
# (new anat will be intermediate, stripped, anat_mprage_ns+orig)
align_epi_anat.py -anat2epi -anat anat_mprage_unif_shft+orig  \
       -suffix _al_keep                             \
	   -epi vr_base+orig -epi_base 0                \
	   -epi_strip 3dAutomask                        \
	   -volreg off -tshift off						\
	   -cost lpc+zz


# ================================== tlrc ==================================
# warp anatomy to standard space
auto_warp.py -base MNI152_T1_2009c+tlrc -input anat_mprage_unif_shft_al_keep+orig \
             -skull_strip_input no -unifize_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 anat_mprage_unif_shft_al_keep                           \
    awpy/anat_mprage_unif_shft_al_keep.aw.nii*
mv awpy/anat.aff.Xat.1D .
mv awpy/anat.aff.qw_WARP.nii .


# ================================= volreg =================================
# align each dset to base volume
foreach run ( $runs )
    # register each volume to the base
    3dvolreg -verbose -zpad 1 -base vr_base+orig                         \
             -1Dfile dfile.r$run.1D -prefix pb03.$subj.r$run.volreg      \
             -cubic                                                      \
             pb02.$subj.r$run.tshift_shft+orig
end

# make a single file of registration params
cat dfile.r*.1D > dfile_rall.1D

# compute motion magnitude time series: the Euclidean norm
# (sqrt(sum squares)) of the motion parameter derivatives
1d_tool.py -infile dfile_rall.1D -set_nruns 9                            \
           -derivative  -collapse_cols euclidean_norm                    \
           -write motion_${subj}_enorm.1D

# create an anat_final dataset, aligned with stats
3dcopy anat_mprage_unif_shft_al_keep+orig anat_final.$subj

# -----------------------------------------
# warp anat follower datasets (non-liner)
3dNwarpApply -source anat_mprage_unif_shft+orig                              \
             -master anat_final.$subj+orig                               \
             -ainterp wsinc5 -nwarp anat.aff.qw_WARP.nii anat.aff.Xat.1D \
             anat_mprage_unif_shft_al_keep_mat.aff12.1D                      \
             -prefix anat_w_skull_warped
4

So I think I’ve figured out part of the problem. The EPI datasets are from two different sessions with the same individual, 48 hours apart (session 1: runs 1-4, session 2: runs 5-9), and they are being aligned to the same anatomical file (collected at session 1).

I tried switching align_epi_anat.py to align the epi files to the anatomical file instead, but that may have just made it worse.

After using 3dCM, the EPI files are off by anywhere from 15-35mm from the anatomy (before any centering or alignment), and 55-75mm from the template (the anatomical’s center is initially ~40mm from the template).

Unfortunately, this still doesn’t solve my issue - if anyone has any thoughts, I’d appreciate it!

5

You may need to just add “-giant_move” to account for the large shift between sessions. Also consider the approach here:

https://afni.nimh.nih.gov/sscc/dglen/alignmentacross2sessions

6

That was super helpful, it looks like it’s working now! Thanks so much!