tpl-NMT31Sym

The NIMH Macaque Template v2 (NMT v2)

In depth information on the NMT v2 can be read in our paper:

Jung, B., Taylor, P.A., Seidlitz, J., Sponheim, C., Perkins P.,
    Ungerleider, L.G., Glen, D., Messinger, A.
    A comprehensive macaque fMRI pipeline and hierarchical atlas.
    bioRxiv 2020.08.05.237818; doi: https://doi.org/10.1101/2020.08.05.237818

For any issues or questions, contact Adam Messinger at messinga@nih.gov

If you have any resources that you have made for the NMT v2 that you think would be a useful addition, please send us an email and we'd be happy to integrate them in the NMT v2 package.

Description

The National Institute of Mental Health Macaque Template (NMT) v2 is a set of anatomical MRI templates of the macaque brain that serves as a standardized space for macaque neuroimaging analysis. It includes a fully-symmetric template and an asymmetric themplate in stereotaxic orientation (i.e., with the horizontal defined by the Horsley-Clarke plane) (Horsley and Clarke, 1908). Coordinates in this stereotaxic space are measured from ear bar zero (EBZ, i.e., the intersection of the midsagittal plane and a line through the interaural meatus). The adoption of stereotaxic orientation and coordinates will assist users in conducting surgical planning and reporting coordinates commensurate with those used with other techniques (e.g. electrophysiology, intracerebral injection).

Briefly, the NMT was created by iteratively nonlinearly registering the T1-weighted MDEFT (Modified Driven Equilibrium Fourier Transform) scans of 31 adult rhesus macaque brains to a working template, averaging the nonlinearly registered scans and then using the inverse transformations to bring the working template closer to the group average (Seidlitz, Sponheim et al., 2018). The symmetric NMT was generated through the same process except that each subject's anatomical was input twice, once in its true orientation and once mirrored about the midline. Modifications to the scan averaging and postprocessing have improved template contrast and spatial resolution compared to the NMT v1.2. Brainmasks, a 5-class tissue segmentation, and various other masks have been provided with the NMT v2. We provide both symmetric and asymmetric variants of the NMT v2 to allow users to choose the version best suited to their analysis. The default NMT v2 is provided at 0.25 mm isotropic resolution, with a field of view (FOV) that covers the entire brain and some surrounding CSF. We additionally, provide a "full-head" version with an expanded FOV, to improve alignment of scans with FOVs different than the NMT v2, and a "low-res" 0.5 mm isotropic version for fast and efficient alignment. Surfaces for NMT v2, generated using the new CIVET-Macaque platform (Lepage et al., submitted), are provided to facilitate surface analysis and data visualization.

Citation

If you use the NMT v2 or the CHARM in your work, please cite the paper below:

Jung, B., Taylor, P.A., Seidlitz, J., Sponheim, C., Perkins P.,
    Ungerleider, L.G., Glen, D., Messinger, A.
    A comprehensive macaque fMRI pipeline and hierarchical atlas.
    bioRxiv 2020.08.05.237818; doi: https://doi.org/10.1101/2020.08.05.237818

Use of the SARM atlas should be accompanied with the following citation:

Hartig, R., Glen, D., Jung, B., Logothetis, N.K., Paxinos G.,
    Garza-Villarreal, E.A., Messinger A., Evrard H.
    Subcortical Atlas of the Rhesus Macaque (SARM) for Magnetic Resonance Imaging
    bioRxiv 2020.09.16.300053; doi: https://doi.org/10.1101/2020.09.16.300053

Use of the D99 atlas (warped to the NMT v2 in this repository) should be accompanied with the following citation:

Reveley, C., Gruslys, A., Ye, F.Q., Glen, D., Samaha, J., E. Russ, B.,
    Saad, Z., K. Seth, A., Leopold, D.A., Saleem, K.S., 2017.
    Three-Dimensional Digital Template Atlas of the Macaque Brain.
    Cereb. Cortex N. Y. NY 27, 4463–4477. https://doi.org/10.1093/cercor/bhw248

Atlases

We provide multiple anatomical atlases that have been manually refined to match the morphology of the NMT v2: including the D99 atlas (Reveley et al., 2017) and two new atlases specifically designed for the NMT v2: the CHARM and SARM atlases.

The Cortical Hierarchy Atlas of the Rhesus Macaque (CHARM; Jung et al., submitted) is a novel six-level anatomical parcellation of the macaque cerebral cortex, where the cortical sheet is subdivided into finer and finer parcellations at each successive level. The broadest level consists of the four cortical lobes and the finest level is based on the D99 atlas with modifications that make the regions more robust when applied to low resolution (e.g. fMRI) data.

The Subcortical Atlas of the Rhesus Macaque (SARM; Hartig et al., submitted) was designed based on updated regions originally defined in Paxinos et al. (2008). These regions were drawn onto an ex-vivo MRI template and warped to the symmetric NMT v2, where they were manually refined to correct for warping inaccuracies. The atlas was then converted into a six-level hierarchy, in which the original labels were combined into composite regions to form successively larger structures.

Different scales of the CHARM or the SARM can be combined together so that, for example, a tracer injection or the seed region for a resting state analysis can be described using a fine scale while the anatomical or functional connectivity can be succinctly described using a broader scale. In this way, whole brain data can be characterized on a spatial scale that respects one’s findings. Users can also use these hierarchies select a spatial scale a priori based on how many regions it contains and thus what degree of multiple comparison correction is required.

Download

The NMT v2 is available directly from the AFNI website. The asymmetric and symmetric NMT v2 templates are packaged in separate tar files. Each tar file contains the standard NMT v2 template, as well as the "full-head" and "low-res" variants, and the associated surfaces. These files are provided in the standard NIFTI/GIFTI format, allowing them to be used with most neuroimaging programs.

The @Install_NMT command is provided in AFNI to allow for downloading of the NMT v2 with a single command.

To download the symmetric NMT v2, type the following into your terminal:

@Install_NMT -set_env -NMT_ver 2.0 -sym sym

To download the asymmetric NMT v2, type the following into your terminal:

@Install_NMT -set_env -NMT_ver 2.0 -sym asym

NMT v2 Files

After downloading the NMT v2 and extracting its contents, you will see 7 directories:

• Volumetric templates
  • NMTv2.0[sym]/
  • NMTv2.0[sym]_05mm/
  • NMTv2.0[sym]_fh/
• Surfaces
  • NMTv2.0[sym]_surfaces/
• Atlas Information
  • tables_CHARM/
  • tables_SARM/
  • tables_D99/

where [sym] is either "sym" or "asym" depending on the selected template. The contents of these directories are described in detail below.

Volumetric Templates

There are 3 volumetric template directories, which all contain the NMT v2 template with associated masks and atlases.

• NMTv2.0[sym]/ is the default NMT v2, with a limited FOV and 0.25 mm isotropic resolution
• NMTv2.0[sym]_05mm/ is the "low-res" NMT v2, with the same FOV and 0.50 mm isotropic resolution
• NMTv2.0[sym]_fh/ is the "full-head" NMT v2, with an expanded FOV and 0.25 mm isotropic resolution

Each directory will have the following files:

• NMTv2.0[suf].nii.gz : NMT v2 template
• NMTv2.0[suf]_SS.nii.gz : skullstripped template
• NMTv2.0[suf]_brainmask.nii.gz : brain mask
• NMTv2.0[suf]_segmentation.nii.gz : 5-class tissue segmentation
• NMTv2.0[suf]_GM_cortical_mask.nii.gz : cortical sheet mask
• CHARM_in_NMTv2.0[suf].nii.gz : A 4D volume of all 6 CHARM levels
• supplemental_CHARM/
  • CHARM_1_in_NMTv2.0[suf].nii.gz : Level 1 of the CHARM
  • CHARM_2_in_NMTv2.0[suf].nii.gz : Level 2 of the CHARM
  • CHARM_3_in_NMTv2.0[suf].nii.gz : Level 3 of the CHARM
  • CHARM_4_in_NMTv2.0[suf].nii.gz : Level 4 of the CHARM
  • CHARM_5_in_NMTv2.0[suf].nii.gz : Level 5 of the CHARM
  • CHARM_6_in_NMTv2.0[suf].nii.gz : Level 6 of the CHARM
• SARM_in_NMTv2.0[suf].nii.gz : A 4D volume of all 6 SARM levels
• supplemental_SARM/
  • SARM_1_in_NMTv2.0[suf].nii.gz : Level 1 of the SARM atlas
  • SARM_2_in_NMTv2.0[suf].nii.gz : Level 2 of the SARM atlas
  • SARM_3_in_NMTv2.0[suf].nii.gz : Level 3 of the SARM atlas
  • SARM_4_in_NMTv2.0[suf].nii.gz : Level 4 of the SARM atlas
  • SARM_5_in_NMTv2.0[suf].nii.gz : Level 5 of the SARM atlas
  • SARM_6_in_NMTv2.0[suf].nii.gz : Level 6 of the SARM atlas
• D99_atlas_in_NMTv2.0[suf].nii.gz : The D99 atlas mapped to the NMT
• supplemental_masks/
  • NMTv2.0[suf]_LR_brainmask.nii.gz : Hemisphere-specific brain mask
  • NMTv2.0[suf]_cerebellum_mask.nii.gz : Mask of the cerebellum
  • NMTv2.0[suf]_ventricles.nii.gz : Mask of some of the ventricles

where [suf] describes the symmetry of the template ("sym" or "asym") and the variant of the template ("_fh" for full-head or "_05mm" for low-res)

Surfaces

We provide 3 surface types generated using the CIVET-macaque pipeline (Lepage et al., submitted). These surface may be used to project data aligned to any of the previously described volumetric surfaces:

• gray/pial surfaces
  • lh.gray_surface.rsl.gii
  • rh.gray_surface.rsl.gii
• mid-cortical surfaces
  • lh.mid_surface.rsl.gii
  • rh.mid_surface.rsl.gii
• white surfaces (white matter - gray matter boundary)
  • lh.white_surface.rsl.gii
  • rh.white_surface.rsl.gii Additionally, semi-inflated versions of each of the above surfaces are provided. These inflated surfaces may be useful for visualization of activity within sulci.

Surfaces of individual regions from the SARM and CHARM atlases are also provided. These surfaces were generated using AFNI's IsoSurface command.

Atlas Information

These directories contain information about the various atlases packaged with the NMT v2. Each supplemental directory contains a label table, listing the index, abbreviation and long-form name (when available) for each region in the given atlas.

Additionally, we provide hierarchy tables for the CHARM and the SARM. These CSV files show which ROIs are related hierarchically across the multiple levels of these atlases.

AFNI

While the NMT v2 works with any neuroimaging platform that accepts NIFTI/GIFTI format, the NMT v2 templates and the CHARM have been designed to integrate especially well into AFNI (Cox, 1996).

Visualization in AFNI/SUMA

Opening and visualizing the NMT v2 is simple using AFNI and SUMA! This section will use the full-head symmetric NMT v2 as an example, but these steps are applicable to any version of the NMT v2.

Navigate to the directory where you have stored the NMT repository. Then follow these steps:

cd NMT_v2.0_sym/NMT_v2.0_sym_fh/
afni -niml &

This will start AFNI and tell AFNI that a connection with SUMA is imminent. Load in NMT_v2.0_sym_fh.nii.gz as the underlay if it is not loaded automatically. Then, back in the terminal, run:

suma -spec ../NMT_v2.0_sym_surface/NMT_v2.0_sym_both.spec \
         -sv NMT_v2.0_sym_fh.nii.gz &

This should start SUMA, and you should see the left and right WM surfaces. To switch to a different set of surfaces, move your cursor into the SUMA window and toggle the "." key. For other navigational shortcuts and tools, see the SUMA documentation.

Toggle the "t" key to open the connection between AFNI and SUMA. You should see various outlines on the NMT volume in AFNI that correspond with the surfaces loaded into SUMA. To edit or remove the outlines in AFNI, toggle the "Control Surface" button in the AFNI GUI.

Now that AFNI and SUMA are linked, this will allow you to visualize any data (i.e. overlay) from the NMT volume on the surface. Note that only the voxels which intersect the surface outlines will be plotted on the surface. As such, we suggest using the "mid" surface for the visualization of any functional MRI data. For more information about using AFNI interactively, see this slide show.

MRI Alignment and fMRI Analysis

The NMT v2 is an important tool for group and ROI-based analyses. Aligning data to the NMT v2 allows all of your data to be compared in a common space, and all of your results to be reported in a reproducible and transparent manner. Here we describe how AFNI and the NMT v2 can be used together for MRI alignment and fMRI analysis. This is just a cursory explanation of processing macaque data in AFNI. For a more complete picture, we have created downloadable demos showing step-by-step how to perform structural and functional (both task-based and resting state) analyses using AFNI and the NMT v2. These demos can be downloaded using the AFNI commands @Install_MACAQUE_DEMO and @Install_MACAQUE_DEMO_REST.

Anatomical and functional MRI data can be easily and efficiently aligned to the NMT v2 using AFNI. AFNI has an integrated command called @animal_warper that allows for aligning animal (and specifically nonhuman primate) data to the NMT v2. Affine and nonlinear alignments can be calculated with a single command, and it additionally provides QC metrics and images that allow you to easily evaluate the accuracy of your alignment. Additionally, @animal_warper can take atlases and the segmentation from NMT v2 and warp them to native space, allowing for ROI-based analyses in native space. Likewise, defined regions in native space can be warped to the NMT v2 for group analyses. The following is an example command using the NMT v2 with @animal_warper:

@animal_warper                                                  \
    -input  anat-sub-000.nii.gz                                 \
    -base   NMT_v2.0_sym_05mm/NMT_v2.0_sym_05mm.nii.gz          \
    -atlas  NMT_v2.0_sym_05mm/CHARM_in_NMT_v2.0_sym_05mm.nii.gz \
    -outdir AW_out_sub-000/                                     \
    -ok_to_exist

Warp files generated from @animal_warper can be directly used for fMRI analysis in NMT v2 space. AFNI's processing pipeline generation program (afni_proc.py) will take these warps and combine them with volume registration to prevent repeated interpolation of fMRI data.

When afni_proc.py detects that data has been aligned to the NMT v2, it will provide additional automated QC metrics by generating images of resting state connectivity at defined seed locations in the NMT v2. These QC correlations maps allow users to quickly compare their connectivity profiles across subjects, and visually inspect their results for abnormalities (such as high correlations between gray matter and white matter voxels).

For more information on @animal_warper and afni_proc.py, see the relevant commands in AFNI, download the macaque analysis demos (@Install_MACAQUE_DEMO and @Install_MACAQUE_DEMO_REST), or read our paper (Jung et al., submitted)

References

Cox, R.W., 1996. AFNI: software for analysis and visualization of functional
    magnetic resonance neuroimages. Comput. Biomed. Res. Int. J. 29, 162–173.
    https://doi.org/10.1006/cbmr.1996.0014

Hartig, R., Glen, D., Jung, B., Logothetis, N.K., Paxinos G.,
    Garza-Villarreal, E.A., Messinger A., Evrard H.
    Subcortical Atlas of the Rhesus Macaque (SARM) for Magnetic Resonance Imaging
    bioRxiv 2020.09.16.300053; doi: https://doi.org/10.1101/2020.09.16.300053

Horsley, V., Clarke, R.H., 1908. THE STRUCTURE AND FUNCTIONS OF THE
    CEREBELLUM EXAMINED BY A NEW METHOD. Brain 31, 45–124.
    https://doi.org/10.1093/brain/31.1.45

Lepage, C., Wagstyl, K., Jung, B., Seidlitz, J., Sponheim, C., Ungerleider,
    L., Wang, X., Evans, A.C., Messinger, A., submitted. CIVET-macaque: an
    automated pipeline for MRI-based cortical surface generation and cortical
    thickness in macaques. NeuroImage.

Paxinos, G., Petrides, M., Huang, X.F., Toga, A.W., 2008. The Rhesus Monkey
    Brain in Stereotaxic Coordinates, 2 edition. ed. Academic Press, Amsterdam.

Reveley, C., Gruslys, A., Ye, F.Q., Glen, D., Samaha, J., E. Russ, B.,
    Saad, Z., K. Seth, A., Leopold, D.A., Saleem, K.S., 2017. Three-Dimensional
    Digital Template Atlas of the Macaque Brain. Cereb. Cortex N. Y. NY 27,
    4463–4477. https://doi.org/10.1093/cercor/bhw248

Seidlitz, J., Sponheim, C., Glen, D., Ye, F.Q., Saleem, K.S., Leopold, D.A.,
    Ungerleider, L., Messinger, A., 2018. A population MRI brain template and
    analysis tools for the macaque. NeuroImage, Segmenting the Brain 170, 121–131.
    https://doi.org/10.1016/j.neuroimage.2017.04.063