AFNI program: @animal_warper
Output of -help
Overview ~1~
This is a script to:
+ align a subject structural dataset to a template
+ save the warp + inverse warps, for any future mapping
+ apply the warps to "follower" datasets in either space (e.g.,
atlases, segmentations, masks, or other anatomicals) to map those
dsets to the other space
- one common use: send a template atlas to native space
+ estimate surfaces of ROIs (with scripts also made to simplify viewing)
+ make automatic QC images to show the outputs, for quick evaluation
This @animal_warper (AW) program uses basic AFNI commands to compute
affine and nonlinear alignments. The program works by first aligning
centers of the subject to that of the template. Affine and nonlinear
alignment follow. The inverse warp is computed to bring the template
and atlas segmentation into the center-shifted grid. Skullstripping is
provided by masking with the template. Finally, the grids are adjusted
back to the original center. Surfaces are made for all the atlas
regions and for a transformed copy of the template dataset.
Throughout this help file and output text files, note that the
following terminology applies:
AP = afni_proc.py
AW = @animal_warper
xform = transform
NL = nonlinear
aff = affine
orig = 'original' (or native) subject data and/or space
base = template and/or template space
osh = original subject data/space shifted to base
pshft = pre-shift (simple center of mass shift) to base
shft = 'full' shift (includes aff align) to base
warp2std = a dset now warped to standard/template space (from original)
Usage Example ~1~
animal_warper \
-input macaque1+orig \
-base ../NMT.nii.gz \
-atlas atlas_dir/D99_atlas_1.2a_al2NMT.nii.gz \
-outdir aligned_data
Note only the input dset and template_dset are *required*. If no
"-atlas .." dset is given, then only the alignment steps are
performed.
Note also that you might want to include the "-ok_to_exist" flag,
in case you need to restart the command at some point, and want to
make use of previously created datasets (to save time).
Options ~1~
-input input_dset :required input dataset to align to base template
(what is called the 'source' in other AFNI
alignment programs).
-base base_dset :required dataset. Can be given with a normal
path-specification, or by just being somewhere
that @FindAfniDsetPath can find it. Note,
this volume will also be used to try to
skullstrip in the input dset (unless an
explicit '-brainmask ..' dset is input; see
that option below).
-template_prefix TP :*no longer an option*. See/use '-base_abbrev',
below.
-outdir outputdir :create new directory and do all processing there.
Minor note: '.' is not allowed; that is, you must
choose a new directory. NB: the input, base and
any atlas followers get copied into that directory
(def = 'aw_results')
-skullstrip brainmask
:one can provide a brainmask that is in the
base template space. This brainmask will be
warped back to native space and used to
skullstrip the original volume. This dataset
should share exactly the same grid as the
base template dataset. (If this opt isn't
used to provide a brainmask, then the '-base
..' volume itself will be used to do so.)
-atlas ATL1 ATL2 ATL3 ...
-atlas_followers ATL1 ATL2 ATL3 ...
:either of these option flags does the exact
same thing-- one or more atlas (int-valued)
dsets in the *base* volume space can be
provided, and each will be mapped to the
input dset's native space. Atlas labeling
will be preserved. Additionally, isosurfaces
of each that can be viewed in SUMA will be
created. Atlas locations can be given with a
normal path-specification, or by just being
somewhere that @FindAfniDsetPath can find it.
-seg_followers S1 S2 S3 ...
:one or more (int-valued) dsets in the *base*
volume space can be provided, and each will
be mapped to the input dset's native space.
Must share the same grid of the base
dataset. Atlas labeling will be preserved.
different than the atlas_followers above (no
surfaces generated for these).
-template_followers T1 T2 T3 ...
:one or more dsets in the *base* volume space
can be provided, and each will be mapped to
the input dset's native space. Not required
to be int-valued here.
-dset_followers D1 D2 D3 ...
:one or more dsets in the *input* volume space
can be provided, and each will be mapped to
the base dset's template space. Not required
to be int-valued here.
-roidset_followers dset1 dset2 ...
:one or more (int-valued) dsets in the *input*
volume space can be provided, and each will
be mapped to the base dset's template space.
-input_abbrev INP_ABBR
:when a dset DSET is warped to a space, it
will like DSET_in_SOMETHING.nii.gz. If that
SOMETHING is the input dset space, then you
can specify that label/abbreviation here.
The INP_ABBR is also used for some files as
SOMETHING_*.
Default naming will be to use the prefix of
the input dset, such as would come from:
3dinfo -prefix_noext INPUT_DSET
Created file names can be quite long due to this,
so an INP_ABBR might be useful.
-base_abbrev BASE_ABBR
:used just like the '-input_abbrev ..' value
above, but for the base dset.
Default here is to use the space information
from a dset, namely:
3dinfo -space BASE_DSET
See also the '-use_known_abbrev_*' options
for being able to let this program try to
recognize a commonly known dset from its name.
-atlas_abbrevs AA1 AA2 AA3 ...
:used just like the '-input_abbrev ..' value
above, but for the atlas follower dsets. NB:
you either need to have the same number of
atlas abbreviations as input atlas followers,
or none.
Default abbreviation is:
3dinfo -prefix_noext ATLAS_DSET
See also the '-use_known_abbrev_*' options
for being able to let this program try to
recognize a commonly known dset from its name.
-template_abbrevs TA1 TA2 TA3 ...
:used just like the '-atlas_abbrevs ..' opt
above, but for the template follower dsets.
Default abbreviation is:
3dinfo -prefix_noext TEMPLATE_DSET
Has the same 'known' list as the base abbrevs,
so make sure you don't run into having two files
share the same abbrev!
-seg_abbrevs SA1 SA2 SA3 ...
:used just like the '-atlas_abbrevs ..' opt
above, but for the seg follower dsets.
Default abbreviation is:
3dinfo -prefix_noext SEG_DSET
Has no 'known' abbrevs.
-dset_abbrevs DA1 DA2 DA3 ...
:used just like the '-atlas_abbrevs ..' opt
above, but for the dset follower dsets.
Default abbreviation is:
3dinfo -prefix_noext DSET_DSET
Has no 'known' abbrevs.
-roidset_abbrevs RA1 RA2 RA3 ...
:used just like the '-atlas_abbrevs ..' opt
above, but for the dset follower dsets.
Default abbreviation is:
3dinfo -prefix_noext ROIDSET_DSET
Has no 'known' abbrevs.
-use_known_abbrev_base
:try to 'guess' an appropriate abbreviation
for a base dset as processing proceeds, for
naming created dsets. Shares same list of
knowns as the 'template' followers.
-use_known_abbrev_atlas
:try to 'guess' an appropriate abbreviation
for an atlas dset as processing proceeds, for
naming created dsets.
-use_known_abbrev_template
:try to 'guess' an appropriate abbreviation
for a template follower dset as processing
proceeds, for naming created dsets. Shares
same list of knowns as the 'base'.
-use_known_abbrev_ALL
:like using all the other '-use_known_abbrev*'
opts.
-align_centers_meth ACM
:By default, an early step here is to use
"Align_Centers -grid .." to start the
alignment (align centers of grids). If you
want to, you can enter any of the "Center
options" that @Align_Centers permits by using
the given option in place of "ACM" *without*
the preceding minus (e.g. a useful one might
be: cm).
You can also provide the keyword "OFF" as an
argument, and then @Align_Centers won't be
run at all (the dset is just copied at that
step), which is useful if you have already
centered your dataset nicely.
-aff_move_opt AMO :by default, '-giant_move' is used in the affine
alignment step (via align_epi_anat.py). With this
option, you can change the movement type to be
any of the values allowed by align_epi_anat.py---
note that you should *not* include the hyphen from
the align_epi_anat.py option name, and if the option
takes two terms, then you should put it in quotes,
such as: "cmass cmass" (default: "giant_move").
A special value of "OFF" means that none of these
extra movement options is included (e.g., your
input dset overlaps the base VERY well already).
-cost xxx :choose a cost function for affine and nonlinear
alignment. The same or similar cost function
will be used for both alignments. The cost
functions are listed in the help for
3dAllineate and 3dQwarp. Cost functions,
like lpa+ZZ for 3dAllineate, are not
available in 3dQwarp, so the "+ZZ" part would
removed from the NL part of warping (i.e.,
lpa would then be used for 3dQwarp's NL
warping cost function). The default cost
function is lpa+ZZ for affine warping (via
align_epi_anat.py and 3dAllineate) and a
clipped Pearson correlation for nonlinear
warping (via auto_warp.py and 3dQwarp)
-maxlev nn :maximum level for nonlinear warping. Determines
final neighborhood 'patch' size that is
matched+refined. Allowed values are:
0 <= nn <= 11
See 3dQwarp help for information on maxlev.
Use smaller values for faster performance and
testing. Increase up to 11 for finer warping.
(def = 09)
-no_surfaces :do not make surfaces for atlas regions in native
space. Default is to create a surface directory
with surfaces of each region in native space.
-feature_size mm :set size in mm for affine alignment. Use about 0.1
for mouse, 0.5 for macaque or rat. (def: 0.5)
-supersize :allow for up to 50% size difference between subject
and template
-init_scale IS :useful if the input dset is much larger or smaller
than the reference template. 'IS' is the approximate
length ratio of the input to the template. So, if
you align a baby shark (doo doo, doo doo doo doo)
brain to an adult shark template brain, you might
use this option with a value of 0.75, for example.
-mode_smooth_size n :modal smoothing kernel size in voxels (not mm)
This determines the size of a spatial regularization
neighborhood for both ROI followers and segmentation
datasets. Voxel values are replaced with the mode
(most common value) in the spherical neighborhood.
The default uses a 1 voxel radius. Use higher values
depending on the irregularities of the edges of the
regions and ROI
Turn off by setting this to 0
-mode_smooth_replacement_off
:the current default behavior for modal
smoothing is to do both 1) modal smoothing
(with 3dLocalstat) and then 2) check if any
ROIs got lost in that process, and 3) if ROIs
got lost, put them back in (those specific
ones won't be smoothed, just re-placed).
Using this opt will mean that steps #2 and #3
do NOT happen -- you just get plain modal
smoothing without replacement.
-center_out CO :center native-space output to native original
space or to center-shifted space over the center
of template. Allowed values of CO are 'native'
(def, leaves center at original location)
and 'center_shift' (shift the input toward base,
and calculate all other warps and outputs to/from
there).
****Note using the center_out native data
transformations might require extra care.
3dNmatrix_apply may require vast amounts of memory
if the center of the original dataset is far from
the center of the template dataset, usually around
an xyz coordinate of 0,0,0.
If datasets are far from a center around 0,0,0,
then consider using
3drefit -oblique_recenter
3drefit -oblique_recenter_raw
or a preprocessing center alignment for all the
native space datasets
@Align_Centers -base template -dset mydset \
-child dset2 dset3 ...
-align_type AT :provide alignment only to specified level, of which
your choices are:
rigid - align using rotation and translation
rigid_equiv - compute alignment with full
affine but apply only the rigid
parameters. This is usually
preferred over the rigid body
alignment because it handles
different sizes better. The
purpose here is to put data
into approximately the same
position as the template
(AC-PC, axialized, ...)
affine - full affine, 12 parameters
rotation, translation, shearing and
scaling
all - go through affine and nonlinear warps
(default)
In each case the full script runs. However, note that
the detail of alignment (and quality of masking) from
less-than-nonlinear warps will necessarily be more
approximate.
-extra_qw_opts "EQO" :specify other options to add on to the
existing options for 3dQwarp either as a group
of options in quotes as in "-nopenalty
-workhard 0:3" or by repeated use of this
option. 3dQwarp is called indirectly using
auto_warp.py.
-keep_temp :keep temporary files including awpy directory (from
auto_warp.py) and other intermediate datasets
-ver :display program version
-ok_to_exist :reuse and do not overwrite existing datasets.
This option is used for faster restarts or with
limited alignment options
-echo :copy all commands being run into the terminal
(like running 'tcsh -x ...')
Outputs (we got plenty of 'em!) ~1~
@animal_warper provides multiple outputs to assist in registering
your anatomicals and associated MRI data to the template. Below,
INP refers to the abbreviation used to refer to the "-input .."
subject dset, and TEM to that of the "-base .." template
(typically in some standard space).
Main datasets ~2~
The following are all contained in the main output directory
("-outdir ..")
+ Text file "dictionary" reference of outputs
o animal_outs.txt - guide to data in main dir and subdirs;
contains version number and history of
command run
+ Subject scans in native space of input
o INP.nii.gz - copy of original input
o INP_ns.nii.gz - same as above, but "no skull" (ns) version
o INP_nsu.nii.gz - same as above, but also unifized (brightness)
o INP_mask.nii.gz - mask of input (should match "ns" version)
o DSET_FOLL - copy(s) of "-dset_followers .." (not abbrev)
o ROIDSET_FOLL - copy(s) of "-roidset_followers .." (not
abbrev)
+ Template scans in native space of input
o TEM_in_INP.nii.gz - template aligned to input
+ Template followers (e.g., atlas ATL, segmentation SEG) in native
space of input; could be several of each, each with own abbreviation
o ATL_in_INP.nii.gz - "-atlas_followers .." aligned to input
o SEG_in_INP.nii.gz - "-seg_followers .." aligned to input
+ Template dsets and followers in template space
o TEMPLATE - copy of "-template .." (not abbrev)
o TEMPLATE_MASK - copy of "-skullstrip .." mask (not abbrev)
o ATL_FOLL - copy(s) of "-atlas_followers .." (not abbrev)
o SEG_FOLL - copy(s) of "-seg_followers .." (not abbrev)
o TEMPLATE_FOLL - copy of "-template_followers .." (not abbrev)
+ Subject scans mapped to the template
o INP_warp2std.nii.gz - input dset nonlinearly warped to TEM
o INP_warp2std_ns.nii.gz - same as above, but "no skull" version
o INP_warp2std_nsu.nii.gz - same as above, but also unifized (brightness)
+ Alignment data (INP->TEM)
o INP_composite_linear_to_template.1D - matrix, full affine part
o INP_shft_WARP.nii.gz - NL warp part (TEM grid)
+ Alignment data (TEM->INP)
o INP_composite_linear_to_template_inv.1D - matrix, full affine part
o INP_shft_WARPINV.nii.gz - NL part of warp (TEM grid)
QC info ~2~
The following are all contained in the "QC/" subdirectory.
The following quality control (QC) images are automatically
generated during processing, to help with speedy checking of
processing. In each case, there are three sets of PNG montages
(one for sag, cor and axi views) and a copy of the colorbar used
(same prefix as file name, *.jpg). Additionally, there is also a
*.txt file of ranges of values related to the ulay and olay, which
might be useful for QC or figure-generation.
+ init_qc_00.input+base*.jpg, [init_qc_00_*_DEOB*]
[ulay] input source dset, original location
[olay] base dset
o single image montage to check initial overlap of source and base,
ignoring any obliquity that might be present (i.e., the way AFNI
GUI does by default, and also how alignment starts)
o if initial overlap is not strong, alignment can fail or
produce weirdness
o *if* either dset has obliquity, then an image of both after
deobliquing with 3dWarp is created (*DEOB.jpg), and a text file
about obliquity is also created (*DEOB.txt).
+ init_qc_01.input_sh+base*.jpg
[ulay] input source dset, center-shifted location
[olay] base dset
+ init_qc_01.input_sh_scale+base*.jpg (only created if using '-init_scale ..')
[ulay] same as previous ulay, but with init_scale value applied
[olay] base dset
+ init_qc_02.input_aff+base*.jpg
[ulay] input source dset, affine-aligned to base
[olay] base dset
+ init_qc_03.input_NL+base*.jpg
[ulay] input source dset, NL-aligned to base
[olay] base dset
+ qc_00.wrpd_input+base* (in base space)
[ulay] edges of the base dset
[olay] warped input dset
+ qc_01.input+wrpd_base* (in input space)
[ulay] edges of the (warped) base dset
[olay] original input dset
+ qc_02.input+mask* (in input space)
[ulay] input dset
[olay] estimated (or input) mask, showing skullstripping
+ qc_03.input+wrpd_{ATL,SEG}* (in input space)
[ulay] 'edge enhanced' original input dset
[olay] warped atlas or seg dset
o NB: if the olay dset has >1 subbrick, each will be snapshotted
separately, because I heard the baying of the crowds for
such.
Additionally, if follower datasets are used (e.g., mapping atlases
from template to subject space), then report*1D text files are
also output, detailing information about ROIs before and after
mapping.
+ report_{ATL,SEG}*.1D
o this text file includes both absolute and relative volumes, as
well as ratios of volumes. Additionally, one can see if any
ROIs got lost in the mapping process (e.g., were too small or
narrow, got squeezed too much or fell outside the mask).
o this text file can be viewed in a regular text editor and also
used for calculations with AFNI programs
o each report calculated separately for each subbrick of an
input ATL or SEG
Surfaces generated ~2~
(Unless you turn off surface estimate) there will be a "surfaces/"
directory with full sets of ROI surfaces calculated from the
'-atlas_follower ..' and '-seg_follower ..' dsets.
+ surfaces_{ATL,SEG}*/
o full set of surfaces of each region in the respective dset
o if the atlas has >1 subbrick (e.g., the CHARM), then each
subbrick will have its own subdir
+ do_view_surfaces_{ATL,SEG}*.tcsh
o automatically generated script to view the contents of each
surfaces_{ATL,SEG}*/ directory in SUMA
+ TEM_in_INP.gii
o slightly polished surface of the warped template in input
space
+ do_view_isosurf_TEM_in_INP.tcsh
o automatically generated script to view TEM_in_INP.gii in SUMA
Intermediate results directory ~2~
There is an "intermediate/" directory with lots of intermediate
warps, affine transforms and datasets.
*If* you are supremely confident about your outputs, you can
remove this directory to save space. **But** you should probably
only do so if you really need to, because invariably once you
delete it you will need to check something from it. That's just
life.
This directory is useful to keep around for asking questions,
checking alignment (esp. checking if something went wrong),
potentially debugging (not my fault!), etc.
Comments ~2~
All atlas_points and labeltables on all input dsets should be
passed along to their warped versions, preserving those useful
functionalities and information.
Integrating AW with afni_proc.py (AP) ~1~
Let's say that you plan to run AW as a prelude to processing FMRI
data with AP (a good idea, by the way!).
This might be an example AW command (written with variables in ye
olde 'tcsh' style):
set anat_subj = sub-001_anat.nii.gz # input anat
set refvol = NMT_*_SS.nii.gz # ref: template
set refatl = CHARM*.nii.gz # ref: atlas
set odir_aw = dir_aw/sub-001 # output dir
@animal_warper \
-input ${anat_subj} \
-base ${refvol} \
-atlas ${refatl} \
-outdir ${odir_aw} \
-ok_to_exist
If you are mapping your FMRI data to standard space and using the
"tlrc" block in your AP command, then there are probably 4 main
outputs from there that you would then put into every successive AP
command, as well as using the same "refvol" and noting that your
anatomical dset has already been skullstripped. We highlight these
in the following AP skeleton command (where the '...' means some
other entries/options would likely be included; order doesn't matter
for the AP command, but we are following the style in which most
afni_proc.py help examples are written):
| # root of AW output dsets
| set anat_base = `3dinfo -prefix_noext ${anat_subj}`
|
| afni_proc.py \
| ... \
| -blocks ... align tlrc volreg ... \
| ... \
| -copy_anat ${odir_aw}/${anat_base}_ns.nii.gz \
| -anat_has_skull no \
| ... \
| -tlrc_base ${refvol} \
| -tlrc_NL_warp \
| -tlrc_NL_warped_dsets \
| ${odir_aw}/${anat_base}_warp2std_nsu.nii.gz \
| ${odir_aw}/${anat_base}_composite_linear_to_template.1D \
| ${odir_aw}/${anat_base}_shft_WARP.nii.gz \
| ...
In the preceding, please note the naming conventions in the *.1D
affine matrix and *WARP.nii.gz nonlinear warp dset which are
provided to the '-tlrc_NL_warped_dsets ..' option.
Examples ~1~
1) Align a subject anatomical to the NMT template. Use some
'follower' datasets that are defined in the template space, so that
they will be warped to subject space (there are other followers that
can start in the native space and be warped to the standard space,
too). Use abbreviations with the followers to simplify life:
@animal_warper \
-input ${dir_anat}/anat-sub-000.nii.gz \
-input_abbrev ${subj}_anat \
-base ${dir_ref}/NMT_*_SS.nii.gz \
-base_abbrev NMT2 \
-atlas_followers ${dir_ref}/CHARM_*.nii.gz \
-atlas_abbrevs CHARM \
-seg_followers ${dir_ref}/NMT_*_segmentation.nii.gz \
-seg_abbrevs SEG \
-skullstrip ${dir_ref}/NMT_*_brainmask.nii.gz \
-outdir odir_aw \
-ok_to_exist
2) Just like the previous example, but include more followers and
abbrevs:
@animal_warper \
-input ${dir_anat}/anat-sub-000.nii.gz \
-input_abbrev ${subj}_anat \
-base ${dir_ref}/NMT_*_SS.nii.gz \
-base_abbrev NMT2 \
-atlas_followers ${dir_ref}/CHARM_*.nii.gz \
${dir_ref}/D99_*.nii.gz \
-atlas_abbrevs CHARM D99 \
-seg_followers ${dir_ref}/NMT_*_segmentation.nii.gz \
-seg_abbrevs SEG \
-skullstrip ${dir_ref}/NMT_*_brainmask.nii.gz \
-outdir odir_aw \
-ok_to_exist
3) Just like the previous example, but include followers (dset and
roidset) from subject space:
@animal_warper \
-input ${dir_anat}/anat-sub-000.nii.gz \
-input_abbrev ${subj}_anat \
-base ${dir_ref}/NMT_*_SS.nii.gz \
-base_abbrev NMT2 \
-atlas_followers ${dir_ref}/CHARM_*.nii.gz \
${dir_ref}/D99_*.nii.gz \
-atlas_abbrevs CHARM D99 \
-seg_followers ${dir_ref}/NMT_*_segmentation.nii.gz \
-seg_abbrevs SEG \
-skullstrip ${dir_ref}/NMT_*_brainmask.nii.gz \
-dset_followers ${dir_anat}/anat-t2w-sub-000.nii.gz \
-dset_abbrevs T2W \
-roidset_followers ${dir_anat}/parcels-sub-000.nii.gz \
-roidset_abbrevs ROIS \
-outdir odir_aw \
-ok_to_exist
Demos, Tutorials and Online Docs ~1~
+ See the MACAQUE_DEMO_* demos for examples in using the program, as
well as integrating its outputs with afni_proc.py. To download
the demos for task-based FMRI and resting state FMRI analysis,
respectively:
@Install_MACAQUE_DEMO
@Install_MACAQUE_DEMO_REST
... with accompanying webpages here:
https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/nonhuman/macaque_demos/main_toc.html
+ For (growing) documentation on non-human dataset processing in
AFNI, see:
https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/nonhuman/main_toc.html
+ For information on accompanying templates and atlases in the
animal kingdon (such as NMT, CHARM and SARM), as well as how to
download them, please see here:
https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/nonhuman/macaque_tempatl/main_toc.html
References ~1~
If you use this program and/or the NMTv2, CHARM or ARM in your work,
please cite the following:
+ Jung B, Taylor PA, Seidlitz PA, Sponheim C, Perkins P,
Ungerleider LG, Glen DR, Messinger A (2021).
A Comprehensive Macaque FMRI Pipeline and Hierarchical Atlas.
NeuroImage 235:117997.
https://doi.org/10.1016/j.neuroimage.2021.117997
https://www.biorxiv.org/content/10.1101/2020.08.05.237818v1
+ Saad ZS, Glen DR, Chen G, Beauchamp MS, Desai R, Cox RW (2009). A
new method for improving functional-to-structural MRI alignment
using local Pearson correlation. Neuroimage 44 839–848. doi:
10.1016/j.neuroimage.2008.09.037
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2649831/
If you use the SARM or ARM atlas, please cite the following:
R. Hartig, D. Glen, B. Jung, N.K. Logothetis, G. Paxinos,
E.A. Garza-Villarreal, A. Messinger, H. Evrard (2021). The
Subcortical Atlas of the Rhesus Macaque (SARM) for
Neuroimaging. NeuroImage 235:117996.
https://doi.org/10.1016/j.neuroimage.2021.117996.
If you use the D99 atlas (warped to the NMT v2 in this
repository), please cite the following:
C. Reveley, A. Gruslys, F.Q. Ye, D. Glen, J. Samaha, B.E. Russ,
Z. Saad, A.K. Seth, D.A. Leopold, K.S. Saleem (2017).
Three-Dimensional Digital Template Atlas of the Macaque
Brain. Cereb. Cortex 27:4463-4477.
https://doi.org/10.1093/cercor/bhw248.
This program has been written (and rewritten!) by D Glen and PA Taylor
(SSCC, NIMH, NIH, USA), with many helpful contributions and
suggestions from B Jung and A Messinger.
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