prime-re / prime-re.github.io

Open resource exchange platform for non-human primate neuroimaging

Welcome to PRIME-RE

Welcome to PRIME-RE, the primary exchange platform for resources related to non-human primate neuroimaging data.

The preprocessing and analysis of nonhuman primate (NHP) magnetic resonance imaging (MRI) data presents some unique challenges. Over the years, many laboratories and researchers have created their own custom solutions to many of these problems. PRIME-RE aims to provide an overview of the main difficulties and curate a collection of solutions that currently exist within the broader NHP-MRI community for specific processing steps that are commonly performed on NHP MRI data. Since this is a community effort, we strongly encourage you to contribute your workflows and pipelines.

PRIME-RE is maintained by members of the PRIME-DE consortium, and you can join our discussions around the data exchange and the resource exchange any time.

Open science

Our goal is to accelerate reproducible discovery, minimise redundant efforts and maximise efficiency by allowing the community to curate relevant resources, disseminate and encourage open science, and strengthen communication and interactions in the research community. We aim at making NHP resources open and FAIR, as this is essential to enhance reproducibility. Our template for tool submission requires a minimum set of metadata to ensure a consistent and comprehensive index. The data provided by the PRIME-DE initiative complies with the Brain Imaging Data Structure. PRIME-RE supports the use of BIDS and the adoption of code development best practices, such as version control, testing, coding style, and continuous integration.

How to contribute

Send new content by using this issue template. You can also join our communication channel on the Brainhack Community via PRIME-RE Mattermost-channel. If you want to discuss the Primate Data Exchange in general, join on PRIME-DE Mattermost-channel.

Wiki

A wiki has been set up to share knowledge about NHP MRI acquisition and image processing. Anyone can share knowledge and experience (problems with solutions, fixes, tricks...).

prime-re.github.io's Issues

Improve web design

Improving looks, navigation, UI in general.

BrainBox

Resource info table

Name	BrainBox
Authors	Katja Heuer & Roberto Toro
Description	A Web application for visualising, annotating & segmenting 3D brain imaging data in real time, collaboratively.
Documentation	3 min video
Link	https://brainbox.pasteur.fr
Language	JavaScript, HTML, CSS
Publication	Open Neuroimaging Laboratory
Communication	Mattermost or GitHub issues
Restrictions	Developed and tested in Chrome Browser
Category	structural

• Do NOT include this resource in periodic mailings about new resources on PRIME-RE

Instructions

Please fill out the table above as completely as possible, i.e. replace the complete str > .... < with your information.
Then submit it as a new issue and tag the issue with all applicable (yellow) categorical tags to specify what type of resource you are contributing.
We will use this table and these tags to add your resource to the main list.

Name

Provide a name for your resource. Try to make it a bit descriptive, but as long as it as not incredibly offensive, we will allow it.

Authors

Who wrote the resource or deserves to be credited? This would be a good place to list them.

Description

Tell the community what it is that your resource does. Keep it concise (a few lines).

Documentation

Does your resource include instructions on how to use it, and if so, where?
This can be a hyperlink, or you can simply state that it can be found through the main link (e.g., because it's in a GitHub ReadMe.md).
Jupyter Notebooks or richly annotated scripts would be great, but any documentation would be great.

Link

Provide a link to the resource. This can be a link to a GitHub repository, website, or shared file.
If you don't already have your resource hosted somewhere and would like to have it hosted on this GitHub,
tell us and provide us with a link to the resource (Dropbox, Google Drive, WeTransfer, etc)

Language

What language (e.g., python, shell, matlab, etc) is your resource in? This will help people to look at solutions in languages they are familiar with first.
We will accept anything.

Associated publications (if available)

If your resource is a published method, you can link to the paper here.

Communication

How can a potential user get in contact with the submitter/author of the resource?
Is there a Slack or Mattermost channel? Can issues be opened on a GitHub repo? Is there an email adress?
If you want to submit your resource 'as-is' and not offer any means for communication you can also mention that.

Restrictions

Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?

Category

What is the most suitable category to list your resource under?

NeuroElf

Resource info table

Name	NeuroElf
Authors	Jochen Weber
Description	NeuroElf is a powerful Matlab-based toolbox for working with neuroimaging data
Documentation	http://neuroelf.net/
Link	http://neuroelf.net/
Language	MATLAB
Publication	http://neuroelf.net/
Communication	https://github.com/neuroelf/neuroelf-matlab
Restrictions	http://neuroelf.net/wiki/doku.php?id=neuroelf_license

• Do NOT include this resource in periodic mailings about new resources on PRIME-RE

Metadata sharing

Resource info table

Name	Metadata sharing
Authors	Colline Poirier and Andrew Fox
Description	This resource describes recommendations to share metadata to enrich NHP neuroimaging data (what, where and how).
Documentation	In progress
Link
Language
Publication
Communication	email
Restrictions
Category

** Please check the applicable boxes**

• [ x] All required ethical approval (human/animal) for the development of this resource was obtained
• [x ] No ethical approval (human/animal) was required for the development of this resource
• Do NOT include this resource in periodic mailings about new resources on PRIME-RE

MR Monitoring Resource (PRIME-MRM)

Resource info table

Name	PRIME-MRM: MRI monitoring of monkeys in neuroscience
Authors	F Balezeau, J Nacef, Y Kikuchi, F Schneider, F Rocchi, RS Muers, R Fernandez-Palacios O’Connor, C Blau, B Wilson, RC Saunders, M Howard III, A Thiele, TD Griffiths, *CI Petkov & K Murphy ( joint senior authors)
Description	Information from Magnetic Resonance Imaging (MRI) is useful for diagnosis and treatment management of human neurological patients. MRI monitoring might also prove useful for non-human animals involved in neuroscience research provided that MRI is available and feasible and that there are no MRI contra-indications precluding scanning. We establish an MRI Monitoring (PRIME-MRM) resource within the PRIMatE Data Exchange (PRIME-DE) and encourage submissions by the scientific community to grow the resource and the evidence base on MRI monitoring. The community is also encouraged to contribute to PRIME-RE to gow the resource exchange linked to PRIME-MRM.
Documentation	For more information, please see the PRIME-DE contributions linked under PRIME-MRM for the data. The forthcoming paper (Balezeau et al.) provides further information on how to contribute, how MRI monitoring could be useful and how to grow the evidence base including quantification in relation to normative data on PRIME-DE. For the initial cases contributed, we show MRI quantification of internal controls in relation to treatment steps and comparisons with normative data in typical monkeys drawn from PRIME-DE. MRI monitoring can assist in precise and early diagnosis of cerebral events and can be useful for visualising, treating and quantifying treatment response. The scientific community is encouraged to grow the PRIME-MRM resource with other cases and larger samples to further assess and increase the evidence base on the benefits of MRI monitoring of primates, complementing the animals’ clinical monitoring and treatment regime. We also suggest in the paper a set of basic scans that can serve as a basic scan toolkit, as well as a more extensive set of scans as needed. We will be compiling a list of veterinary, radiology or clinicians that could be consulted or provide further guidance. We encourage a brief animal history and any diagnostic information to be included as meta data on PRIME-DE contributions.
Link	https://fcon_1000.projects.nitrc.org/indi/indiPRIME.html
Publication	forthcoming, currently under minor revision ...
Communication	[email protected] or [email protected]; Please feel free to contact us if you have questions.
Restrictions	No restrictions
Category	PRIME-DE resource

** Please check the applicable boxes**

• All required ethical approvals contributing to the development of this resource were obtained (see paper)
• No ethical approval (human/animal) was required for the development of this resource
• Do NOT include this resource in periodic mailings about new resources on PRIME-RE

Instructions

Please fill out the table above as completely as possible, i.e. replace the complete str > .... < with your information.
Then submit it as a new issue and tag the issue with all applicable (yellow) categorical tags to specify what type of resource you are contributing.
We will use this table and these tags to add your resource to the main list.

Name

Provide a name for your resource. Try to make it a bit descriptive, but as long as it as not incredibly offensive, we will allow it.

Authors

Who wrote the resource or deserves to be credited? This would be a good place to list them.

Description

Tell the community what it is that your resource does. Keep it concise (a few lines).

Documentation

Does your resource include instructions on how to use it, and if so, where?
This can be a hyperlink, or you can simply state that it can be found through the main link (e.g., because it's in a GitHub ReadMe.md).
Jupyter Notebooks or richly annotated scripts would be great, but any documentation would be great.

Link

Provide a link to the resource. This can be a link to a GitHub repository, website, or shared file.
If you don't already have your resource hosted somewhere and would like to have it hosted on this GitHub,
tell us and provide us with a link to the resource (Dropbox, Google Drive, WeTransfer, etc)

Language

What language (e.g., python, shell, matlab, etc) is your resource in? This will help people to look at solutions in languages they are familiar with first.
We will accept anything.

Associated publications (if available)

If your resource is a published method, you can link to the paper here.

Communication

How can a potential user get in contact with the submitter/author of the resource?
Is there a Slack or Mattermost channel? Can issues be opened on a GitHub repo? Is there an email adress?
If you want to submit your resource 'as-is' and not offer any means for communication you can also mention that.

Restrictions

Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?

Category

What is the most suitable category to list your resource under?

Checkboxes

Please indicate whether ethical approval was acquired for activities related to the development of this resource, or whether this wasn't necessary. Also, indicate if you do not want your resource advertised in sparsely sent updates from the PRIME-RE team to the community.

<NMT v1.3 (NIMH Macaque Template - version 1.3)>

Resource info table

Name	> NMT v1.3 (NIMH Macaque Template - version 1.3) <
Authors	> Adam Messinger, Benjamin Jung, Jakob Seidlitz, Paul Taylor, Daniel Glen <
Description	> A volumetric template of the rhesus macaque created by nonlinear averaging of T1-weighted anatomical MRIs from multiple adult monkeys. Surface files are included. Version 1.3 updates include: an improved mapping of the D99 atlas, improved brain masking and normalization, and files that are defined in a common template space (NMT space), using the short data type for improved storage efficiency. <
Documentation	> https://github.com/jms290/NMT/blob/master/NMT_v1.3/README.md<
Link	> https://github.com/jms290/NMT/tree/master/NMT_v1.3 <
Language	> Volumetric files are in NIFTI (.nii) format, while surface files are in GIFTI (.gii) format. Scripts for processing single subjects rely on bash shell and AFNI. Some scripts also require ANTs. <
Publication	> Seidlitz, J., Sponheim, C., Glen, D., Ye, F.Q., Saleem, K.S., Leopold, D.A., Ungerleider, L., Messinger, A. A population MRI brain template and analysis tools for the macaque. NeuroImage 170:121-131 (2018). https://doi.org/10.1016/j.neuroimage.2017.04.063 <
Communication	> Through the GitHub repo or via email to Dr. Adam Messinger: [email protected] <
Restrictions	>To use the NMT_v1.3 cite the above article: Seidlitz et al., (2018): https://doi.org/10.1016/j.neuroimage.2017.04.063. To use the D99 atlas cite: Reveley et al. (2016). https://doi.org/10.1093/cercor/bhw248. <
Category	> template, atlas <

• Do NOT include this resource in periodic mailings about new resources on PRIME-RE

Instructions

Please fill out the table above as completely as possible, i.e. replace the complete str > .... < with your information.
Then submit it as a new issue and tag the issue with all applicable (yellow) categorical tags to specify what type of resource you are contributing.
We will use this table and these tags to add your resource to the main list.

Name

Provide a name for your resource. Try to make it a bit descriptive, but as long as it as not incredibly offensive, we will allow it.

Authors

Who wrote the resource or deserves to be credited? This would be a good place to list them.

Description

Tell the community what it is that your resource does. Keep it concise (a few lines).

Documentation

Does your resource include instructions on how to use it, and if so, where?
This can be a hyperlink, or you can simply state that it can be found through the main link (e.g., because it's in a GitHub ReadMe.md).
Jupyter Notebooks or richly annotated scripts would be great, but any documentation would be great.

Link

Provide a link to the resource. This can be a link to a GitHub repository, website, or shared file.
If you don't already have your resource hosted somewhere and would like to have it hosted on this GitHub,
tell us and provide us with a link to the resource (Dropbox, Google Drive, WeTransfer, etc)

Language

What language (e.g., python, shell, matlab, etc) is your resource in? This will help people to look at solutions in languages they are familiar with first.
We will accept anything.

Associated publications (if available)

If your resource is a published method, you can link to the paper here.

Communication

How can a potential user get in contact with the submitter/author of the resource?
Is there a Slack or Mattermost channel? Can issues be opened on a GitHub repo? Is there an email adress?
If you want to submit your resource 'as-is' and not offer any means for communication you can also mention that.

Restrictions

Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?

Category

What is the most suitable category to list your resource under?

NMT v2.0 (Entry Update. Not a new resource)

Resource info table

Name	NMT v2.0 Templates (NIMH Macaque Template - version 2.0)
Authors	Benjamin Jung, Paul A. Taylor, Jakob Seidlitz, Caleb Sponheim, Pierce Perkins, Leslie G. Ungerleider, Daniel Glen, Adam Messinger
Description	A collection of volumetric templates of the rhesus macaque created by nonlinear averaging of T1-weighted anatomical MRIs from multiple adult monkeys. Surface files are included. The NMT 2.0 provides increased flexibility in macaque group analysis by providing symmetric and asymmetric templates. Both templates are available with an expanded, full head field of view in Horsley-Clarke stereotaxic orientation. Updates to the templates include: an improved mapping of the D99 atlas, the addition of the CHARM and SARM atlases, improved brain masking and normalization, and files that are defined in a common template space (NMT space), using the short data type for improved storage efficiency.
Documentation	AFNI
Link	AFNI
Language	Volumetric files are in NIFTI (.nii) format, while surface files are in GIFTI (.gii) format. Scripts for processing single subjects rely on bash shell and AFNI.
Publication	biorxiv
Communication	Email to Dr. Adam Messinger
Restrictions	Usage requires citation of publication
Category	template

** Please check the applicable boxes**

• All required ethical approval (human/animal) for the development of this resource was obtained
• No ethical approval (human/animal) was required for the development of this resource
• Do NOT include this resource in periodic mailings about new resources on PRIME-RE

Neurovault.org <Resource Name>

Resource info table

Name	> Neurovault.org <
Authors	> Fox AS & Gorgolewski KJ <
Description	> The method for optimally sharing voxelwise data from NHP neuroimaging studies remains unclear. To address this issue, Fox and colleagues have adapted the Neurovault.org resource (Gorgolewski et al., 2015), which is commonly used to share human neuroimaging data, to other species, including rhesus monkeys. To complement within-manuscript reporting (e.g. location of peak-activations), researchers can now upload any voxelwise images aligned to NMT-template space, including unthresohlded statistical maps, atlases, and ROIs to Neurovault.org by specifying their species-specific template. Collections of voxelwise analyses can be linked to publication DOIs to appropriately credit researchers, and will ultimately facilitate meta-analyses, and other large-scale data-scientific efforts, such as cross-species comparisons.
<
Documentation	> https://neurovault.org/FAQ <
Link	> https://neurovault.org/ <
Publication	> Gorgolewski KJ, Varoquaux G, Rivera G, Schwartz Y, Ghosh SS, Maumet C, Sochat VV, Nichols TE, Poldrack RA, Poline J-B, Yarkoni T and Margulies DS (2015) NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the brain. Front. Neuroinform. 9:8. doi: 10.3389/fninf.2015.00008 <
Communication	> [email protected] <
Category	> data sharing <

<UNet model for skull stripping and brain masks of PRIME-DE>

Resource info table

Name	UNet model for skull stripping and brain masks of anatomical images from PRIME-DE
Authors	Xindi Wang, Ting Xu
Description	The preprocessed brain masks of T1w images for all macaque monkeys from PRIME-DE. A convolutional network - UNet model was used to generate the brain mask for T1w images. The UNet model was initially trained in a large human sample and upgraded with a few macaque data. With a small macaque training sample (N=1-2), the UNet model achieves a decent performance of brain extraction with a minimal processing time (GPU: ~20s, CPU: 2-10 min).
Documentation	UNet model on PRIME-DE
Link	UNet model, code, preprocessed brain masks
Language	python
Publication	In prepartion
Communication	GitHub repo: email: ting.xu @childmind.org
Restrictions	GNU
Category	Brain extraction tool, preprocessed data

• Do NOT include this resource in periodic mailings about new resources on PRIME-RE

Macapype

Macapype

Pipeline Description

Segmentation Tools For Monkey Brains

Contributors

Kep kee Loh, David Meunier, Bastien Cagna, Julien Sein, Régis Trapeau
@ Institut de Neurosciences de la Timone, Marseille, France

Dependencies

What different types of people could contribute?

Link to your code

GitHub

Communication

Link to the communication channel for your project. You can, for example, create a slack channel for your project inside the Brainhack slack community, and include a slack badge to invite people to Brainhack slack, where they can then find and join your channel.
Or create a community on gitter and link to the chat by including a Gitter badge linking to your community

Main page structure

Thresholdmann

Resource info table

Name	Thresholdmann
Authors	Katja Heuer & Roberto Toro
Description	A Web tool for interactively creating adaptive thresholds to segment nifti images
Documentation	Readme in the GitHub repo
Link	https://neuroanatomy.github.io/thresholdmann
Language	JavaScript, HTML, CSS
Publication	Work in progress
Communication	GitHub issues
Restrictions	Developed and tested in Chrome Browser
Category	structural

• Do NOT include this resource in periodic mailings about new resources on PRIME-RE

Instructions

Please fill out the table above as completely as possible, i.e. replace the complete str > .... < with your information.
Then submit it as a new issue and tag the issue with all applicable (yellow) categorical tags to specify what type of resource you are contributing.
We will use this table and these tags to add your resource to the main list.

Name

Provide a name for your resource. Try to make it a bit descriptive, but as long as it as not incredibly offensive, we will allow it.

Authors

Who wrote the resource or deserves to be credited? This would be a good place to list them.

Description

Tell the community what it is that your resource does. Keep it concise (a few lines).

Documentation

Does your resource include instructions on how to use it, and if so, where?
This can be a hyperlink, or you can simply state that it can be found through the main link (e.g., because it's in a GitHub ReadMe.md).
Jupyter Notebooks or richly annotated scripts would be great, but any documentation would be great.

Link

Provide a link to the resource. This can be a link to a GitHub repository, website, or shared file.
If you don't already have your resource hosted somewhere and would like to have it hosted on this GitHub,
tell us and provide us with a link to the resource (Dropbox, Google Drive, WeTransfer, etc)

Language

What language (e.g., python, shell, matlab, etc) is your resource in? This will help people to look at solutions in languages they are familiar with first.
We will accept anything.

Associated publications (if available)

If your resource is a published method, you can link to the paper here.

Communication

How can a potential user get in contact with the submitter/author of the resource?
Is there a Slack or Mattermost channel? Can issues be opened on a GitHub repo? Is there an email adress?
If you want to submit your resource 'as-is' and not offer any means for communication you can also mention that.

Restrictions

Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?

Category

What is the most suitable category to list your resource under?

MR Comparative Anatomy Toolbox (Mr Cat)

Resource info table

MR Comparative Anatomy Toolbox (MrCat)	> replace this with a name for your resource <
Rogier B. Mars, Lennart Verhagen, and the members and collaborators of the Cognitive Neuroecology Lab	> replace this with author names <
A collection of tools for processing of multi-species neuroimaging data	> replace this with a brief description of your resource <
Documentation	> replace this with a link to more extensive documentation <
www.neuroecologylab.org	> replace this with a link to your resource <
shell, matlab	> replace with language (python, shell, matlab, etc) your resource uses <
http://www.rbmars.dds.nl/pubs/Mars2016NBR.pdf, among others	> replace this with a link to a related publication <
Communication	> replace this with a means for communication <
Restrictions	> replace this with potential usage restrictions <
structural/fMRI/diffusion	> template/atlas/versatile/structural/fMRI/diffusion <

• Do NOT include this resource in periodic mailings about new resources on PRIME-RE

Instructions

Please fill out the table above as completely as possible, i.e. replace the complete str > .... < with your information.
Then submit it as a new issue and tag the issue with all applicable (yellow) categorical tags to specify what type of resource you are contributing.
We will use this table and these tags to add your resource to the main list.

Name

Provide a name for your resource. Try to make it a bit descriptive, but as long as it as not incredibly offensive, we will allow it.

Authors

Who wrote the resource or deserves to be credited? This would be a good place to list them.

Description

Tell the community what it is that your resource does. Keep it concise (a few lines).

Documentation

Does your resource include instructions on how to use it, and if so, where?
This can be a hyperlink, or you can simply state that it can be found through the main link (e.g., because it's in a GitHub ReadMe.md).
Jupyter Notebooks or richly annotated scripts would be great, but any documentation would be great.

Link

Provide a link to the resource. This can be a link to a GitHub repository, website, or shared file.
If you don't already have your resource hosted somewhere and would like to have it hosted on this GitHub,
tell us and provide us with a link to the resource (Dropbox, Google Drive, WeTransfer, etc)

Language

What language (e.g., python, shell, matlab, etc) is your resource in? This will help people to look at solutions in languages they are familiar with first.
We will accept anything.

Associated publications (if available)

If your resource is a published method, you can link to the paper here.

Communication

How can a potential user get in contact with the submitter/author of the resource?
Is there a Slack or Mattermost channel? Can issues be opened on a GitHub repo? Is there an email adress?
If you want to submit your resource 'as-is' and not offer any means for communication you can also mention that.

Restrictions

Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?

Category

What is the most suitable category to list your resource under?

[Idea] addition to the wiki about wrong labels on images

Hi @pcklink @niksirbi ,

I would like to report a procedure that we use now in our lab to correct for the wrong label issue in some NHP acquisitions (like when the animal is in sphinx position, or that there is any kind of mismatch between the registration at the subject on the scanner and the actual position of the animal in the scanner).

Here is an example:

The solution we use now lies in in two lines using AFNI and FSL: The labels are wrong so don't look at them. First you need to look at the data storage (in FSLeyes for example) (you have to determine toward each direction each dimension is increasing):

• in x, the cursor is going toward the neck when x increases: I (Inferior)
• in y, the cursor is going posterior when y increases: P (Posterior)
• in z, the cursor is going left (opposite to the pastille), when z increases: L (Left)
  => The data storage is 'IPL' in FSL convention, and 'SAR' in AFNI convention (opposite convention between AFNI and FSL, why keep things simple? ;-) )
  So the first command is from AFNI:
  3drefit -orient SAR your_image_to_correct.nii.gz
  => the label are now correct but the display is not standard in FSLeye
  So to have a correct display, we now use FSL:
  fslreorient2std your_image_to_correct.nii.gz your_image_corrected.nii.gz
  => the image is now ready to work with!

Do you think it is a sound procedure?
Do you think I should add this in the wiki?

Thank you for your answer!

Electrical stimulation and neuroimaging: humans and macaques

Resource info table

Name	Electrical stimulation and neuroimaging: humans and macaques
Authors	F Rocchi*, H Oya*, F Balezeau, AJ Billig, Z Kocsis, RL Jenison, KV Nourski, CK Kovach, M Steinschneider, Y Kikuchi, AE Rhone, BJ Dlouhy, H Kawasaki, R Adolphs, JDW Greenlee, TD Griffiths, MA Howard III, CI Petkov
Description	Comparative human and monkey combined electrical stimulation and fMRI
Documentation	Common Fronto-temporal Effective Connectivity in Humans and Monkeys: This paper and resource establishes a comparative es-fMRI resource in human neurosurgery patients and monkeys. The work also establishes considerable correspondence between fronto-temporal auditory cognitive systems involving ventro-lateral prefrontal cortex and the hippocampus. The data in monkeys are shared via PRIME-DE and the human data via OpenNeuro. The data are also available in the Open Science Framework. Human datasets also include the electrical tractography data.
Link	https://osf.io/arqp8; https://fcon_1000.projects.nitrc.org/indi/indiPRIME.html; https://openneuro.org/
Publication	Pre-accepted in Neuron
Communication	[email protected] or [email protected]
Restrictions	No restrictions
Category	Electrical stimulation and neuroimaging

** Please check the applicable boxes**

• All required ethical approval (human/animal) for the development of this resource was obtained
• No ethical approval (human/animal) was required for the development of this resource
• Do NOT include this resource in periodic mailings about new resources on PRIME-RE

Instructions

Please fill out the table above as completely as possible, i.e. replace the complete str > .... < with your information.
Then submit it as a new issue and tag the issue with all applicable (yellow) categorical tags to specify what type of resource you are contributing.
We will use this table and these tags to add your resource to the main list.

Name

Provide a name for your resource. Try to make it a bit descriptive, but as long as it as not incredibly offensive, we will allow it.

Authors

Who wrote the resource or deserves to be credited? This would be a good place to list them.

Description

Tell the community what it is that your resource does. Keep it concise (a few lines).

Documentation

Does your resource include instructions on how to use it, and if so, where?
This can be a hyperlink, or you can simply state that it can be found through the main link (e.g., because it's in a GitHub ReadMe.md).
Jupyter Notebooks or richly annotated scripts would be great, but any documentation would be great.

Link

Provide a link to the resource. This can be a link to a GitHub repository, website, or shared file.
If you don't already have your resource hosted somewhere and would like to have it hosted on this GitHub,
tell us and provide us with a link to the resource (Dropbox, Google Drive, WeTransfer, etc)

Language

What language (e.g., python, shell, matlab, etc) is your resource in? This will help people to look at solutions in languages they are familiar with first.
We will accept anything.

Associated publications (if available)

If your resource is a published method, you can link to the paper here.

Communication

How can a potential user get in contact with the submitter/author of the resource?
Is there a Slack or Mattermost channel? Can issues be opened on a GitHub repo? Is there an email adress?
If you want to submit your resource 'as-is' and not offer any means for communication you can also mention that.

Restrictions

Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?

Category

What is the most suitable category to list your resource under?

Checkboxes

Please indicate whether ethical approval was acquired for activities related to the development of this resource, or whether this wasn't necessary. Also, indicate if you do not want your resource advertised in sparsely sent updates from the PRIME-RE team to the community.

<precon_all>

Resource info table

Name	precon_all
Authors	R. Austin Benn, Ting Xu
Description	precon_all is an anatomical surface reconstruction pipeline that can be used with Non-Human Primates, and other large animals including pigs, dogs, and potentially many more. The pipeline can be easily modified to work on most species with a reasonable T1 image by simply providing 5 masks. The pipeline provides both free surfer and HCP compatible outputs in native image space. Group average surfaces and spherical registration templates can also be created within the precon_all framework.
Documentation	https://github.com/recoveringyank/precon_all
Link	https://github.com/recoveringyank/precon_all
Language	shell
Publication	In Preparation
Communication	[email protected]
Restrictions	None
Category	Structural MRI

• Do NOT include this resource in periodic mailings about new resources on PRIME-RE

[Idea] Investigate discussion options

It would be nice to have a more forum-like communication platform on PRIME-RE.

We now point people to Neurostars which is ok but may be a bit aspecific?
The GitHub Discussion function might only be for people sith repo edit rights?
Issues could work but is perhaps also not ideal?

Opinions?

Pypreclin

Name:
Pypreclin, automatic Python preclinical pipeline for monkey fMRI preprocessing
Authors:
Jordy TASSERIE, Antoine GRIGIS, Lynn Uhrig, Morgan Dupont, Alexis Amadon and Béchir JARRAYA.
Description & language:
Pypreclin is a Python code available on GitHub, on the Python Package Index (PyPI) for easy installation and upgrading, and has been made available as a Singularity container.
Documentation & links:
Github
Python Package Index (PyPI)
Singularity container
Publication:
Please cite the related publication,
Tasserie J, Grigis A, Uhrig L, Dupont M, Amadon A, Jarraya B.Neuroimage. 2019 Nov 16:116353. doi: 10.1016/j.neuroimage.2019.116353. PMID:31743789
PubMed
Journal website

Reorient

Resource info table

Name	Reorient
Authors	Katja Heuer & Roberto Toro
Description	A Web tool for reorienting and cropping MRI data
Documentation	Readme in the GitHub repo
Link	https://neuroanatomy.github.io/reorient
Language	JavaScript, HTML, CSS
Publication	Work in progress
Communication	GitHub issues
Restrictions	Developed and tested in Chrome Browser
Category	structural

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Enrich content of spatial preprocessing subpart

Create a space for sharing hardware related resources

NHP neuroimaging is not only challenging after data collection. Collecting the data may also require a large number of custom solutions. PRIME-RE should probably have a place to exchange information about such issues and solutions as well. Could be in the wiki, could be a resource/hardware page.

C-PAC: The Configurable Pipeline for the Analysis of Connectomes

Resource info table

Name	C-PAC: The Configurable Pipeline for the Analysis of Connectomes
Authors	Steven Giavasis, Cameron Craddock, Michael Milham
Description	The Configurable Pipeline for the Analysis of Connectomes (C-PAC) is a configurable, open-source, Nipype-based, automated processing pipeline for resting state functional MRI (R-fMRI) data, for use by both novice and expert users. It is designed and tested for use with human data (all ages), as well as with non-human primate and rodent data.
Documentation	http://fcp-indi.github.io/
Link	Quick-Start - pull a container
Language	Python
Publication	Towards Automated Analysis of Connectomes: The Configurable Pipeline for the Analysis of Connectomes (C-PAC)
Communication	C-PAC Forum
Restrictions	None
Category	versatile, structural, fMRI

Nencki–Monash Template

Resource info table

Name	Nencki-Monash Template
Authors	The template has been created in collaboration between the Laboratory of Neuroinformatics at the Nencki Institute of Experimental Biology (Poland) and prof. Marcello Rosa Laboratory at Monash University (Australia).
Description	The Nencki-Monash (NM) template represents a morphological average of 20 brains of young adult individuals, obtained by 3D reconstructions generated from Nissl-stained serial sections. The template combines combines the main advantages of histology-based atlases with features associated with MRI-based templates. It is also accompanied with spatial transformations to other popular marmoset brain templates, thus enabling integration with magnetic resonance imaging (MRI) and tracer-based connectivity data.
Documentation	The documentation and examples are available within individual datasets available for download via http://www.marmosetbrain.org/nencki_monash_template
Link	http://www.marmosetbrain.org/nencki_monash_template
Language	N/A
Publication	Histology-based average template of the marmoset cortex with probabilistic localization of cytoarchitectural areas
Communication	Piotr Majka ([email protected])
Restrictions	Nencki-Monash Template is licensed under Creative Commons Attribution-ShareAlike 4.0 (CC-BY-SA) License. You are free to share (copy and redistribute) and adapt (remix, transform, and build upon) the marmoset-related material in any medium or format as long as you attribute the Nencki-Monash Template and cite the relevant publication. If you adapt the material, you must distribute your contributions under the same license as the original.
Category	Templates and Atlases > Marmoset

Table entry
Template: Nencki-Monash
Species: C. jacchus
Resolution (mm3): 0.05
With atlas: Cortical areas of the Paxinos et al., (2012)
Volume format: NIFTI
Surface format: N/A
Links: http://www.marmosetbrain.org/nencki_monash_template

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Refer to the forthcoming paper

If/when the paper comes out, the website should mention it somewhere and ask people to refer to it if this website was helpful. This could increase the number of people that are aware that the site exists.

NMT v2.0 Templates (Updated Listing)

Resource info table

Name	NMT v2.0 Templates (NIMH Macaque Template - version 2.0)
Authors	Benjamin Jung, Paul A. Taylor, Jakob Seidlitz, Caleb Sponheim, Pierce Perkins, Leslie G. Ungerleider, Daniel Glen, Adam Messinger
Description	A collection of volumetric templates of the rhesus macaque created by nonlinear averaging of T1-weighted anatomical MRIs from multiple adult monkeys. Surface files are included. The NMT 2.0 provides increased flexibility in macaque group analysis by providing symmetric and asymmetric templates. Both templates are in Horsley-Clarke stereotaxic orientation and available with an expanded, full head field of view. Updates to the templates include: an improved mapping of the D99 atlas, the addition of the CHARM and SARM atlases, improved brain masking and normalization, and files that are defined in a common template space (NMT space), using the short data type for improved storage efficiency.
Documentation	AFNI
Link	AFNI
Language	Volumetric files are in NIFTI (.nii) format, while surface files are in GIFTI (.gii) format. Scripts for processing single subjects rely on bash shell and AFNI.
Publication	Jung et al. (submitted)
Communication	Email to Dr. Adam Messinger
Restrictions	Please cite the above publication
Category	template

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Subcortical Atlas of the Rhesus Macaque (CHARM) - NEW LISTING

Resource info table

Name	Subcortical Atlas of the Rhesus Macaque (SARM)
Authors	Renée Hartig, Daniel Glen, Benjamin Jung, Nikos K. Logothetis, George Paxinos, Eduardo A. Garza-Villarreal, Adam Messinger, Henry C. Evrard
Description	An anatomical parcellation of the entire macaque subcortex tailored for magnetic resonance imaging (MRI). The regions-of-interest (ROIs) are hierarchically organized, with grouping levels suited for both fine structural and spatially broader functional analyses. SARM aims to facilitate the identification, localization, and study of neural interactions involving subcortical regions of the brain.
Documentation	AFNI
Link	AFNI
Language	Volumetric files are in NIFTI (.nii) format, while surface files are in GIFTI (.gii) format.
Publication	Hartig et al. (submitted)
Communication	Email to Dr. Henry Evrard or Dr. Adam Messinger
Restrictions	Please cite the above publication
Category	atlas

** Please check the applicable boxes**

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Templates/Atlases & warps between them

<afni_proc.py>

Resource info table

Name	> afni_proc.py <
Authors	> Rick Reynolds, Paul Taylor, Daniel Glen, Gang Chen, Bob Cox <
Description	> FMRI analysis pipeline tool in AFNI. This widely used, general purpose and flexible tool for creating a full, single subject FMRI processing stream can be used for macaque analyses. This program creates fully commented, single subject processing scripts for all FMRI study designs (task, resting state, naturalistic, etc.) and for either volumetric- or surface-based analyses. The typical goal is to create volumes of aligned response magnitudes (stimulus beta weights from a GLM) to use as input for a group analysis. <
Documentation	> https://afni.nimh.nih.gov/pub/dist/doc/program_help/afni_proc.py.html <
Link	>https://afni.nimh.nih.gov/ <
Language	> python, tcsh, C, AFNI <
Publication	> TBD <
Communication	> AFNI message board - https://afni.nimh.nih.gov/afni/community/board/list.php?1 <
Restrictions	> no usage restrictions <
Category	> fMRI <

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[Contact]: ribbon.mgz

I have followed the pipeline on https://prime-re.github.io/structural/surfaces_and_flatmaps_notebook/Surfaces_and_Flatmaps.html. It was extremely helpful, thanks.

I am trying to create the gm ribbon on a subject, and the thickness. I used the makesurf to vol, and it asks for the ribbon.mgz. However, the ribbon was not created in the pipeline. Is there a "simple" way to create the ribbon? The code is below

Thanks, John

mri_surf2vol --subject fsSurf --o thickness-in-volume.nii.gz --so lh.white lh.thickness --so rh.white rh.thickness
Loading /work/jerlab/JERLab_MRI/S16001//fsSurf/mri/ribbon.mgz
error: mghRead(/work/jerlab/JERLab_MRI/S16001//fsSurf/mri/ribbon.mgz, -1): could not open file

<AFNI's @animal_warper>

Resource info table

Name	> @animal_warper AFNI command <
Authors	> Daniel Glen, Paul Taylor, Adam Messinger, Benjamin Jung, Jakob Seidlitz <
Description	> Nonlinearly aligns an MRI dataset to a template. The reverse transformation can be used to produce a skullstripped (brain-only) version of the native scan, segmentation/atlas info in the native space, and surfaces for each atlas region. The computed transformations between the anatomical scan and the template is provided for use with FMRI pipeline tools like afni_proc.py. <
Documentation	> https://afni.nimh.nih.gov/pub/dist/doc/program_help/@animal_warper.html <
Link	> https://afni.nimh.nih.gov/ <
Language	> tcsh, python, C, AFNI <
Publication	> TBD <
Communication	> AFNI message board - https://afni.nimh.nih.gov/afni/community/board/list.php?1 <
Restrictions	> no usage restrictions <
Category	> structural <

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<Cross-species alignment (macaque-human)>

Resource info table

Name	Cross-species alignment (macaque-human)
Authors	Ting Xu, Karl-Heinz Nenning
Description	This repo includes 1) landmarks for the macaque-human alignment, 2) functional phylogenetic map (functional connectivity homology index) between macaque and human, 3) evolutional surface area expansion (macaque-to-human), 4) cross-species parcellations (human-to-macaque: Glasser2016, Yeo2011, Broadmann parcellations on the macaque surfaces; macaque-to-human: Markov2014 and Broadmann parcellations on human HCP surface, and 5) human (Human Connectome Project [HCP]) and macaque (MacaqueYerkes19) surfaces
Documentation	replace this with a link to more extensive documentation
Link	https://github.com/TingsterX/alignment_macaque-human
Language	shell, workbench
Publication	Xu, Nenning et al., 2020
Communication	GitHub profile or email
Restrictions	Please cite the above publication and the github repo https://github.com/TingsterX/alignment_macaque-human
Category	template/atlas/cross-species-comparison <

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<Marmoset Brain Mapping Atlas and Template>

Resource info table

Name	> Marmoset Brain Mapping Atlas and Template <
Authors	> Cirong Liu, Daniel Glen, Frank Ye, John Newman, Cecil Yen, Diego Szczupak, Xiaoguang Tian, Piotr Majka, Marcello Rosa, David Leopold, Afonso Silva <
Description	> The Marmoset Brain Mapping Atlas (previously NIH Marmoset Brain Atlas) aims at building comprehensive MRI-based marmoset brain atlases and tools to facilitate neuroimaging and connectome studies of marmosets. <
Documentation	> https://marmosetbrainmapping.org/atlas.html<
Link	>https://marmosetbrainmapping.org/atlas.html <
Language	> shell, matlab, etc. <
Publication	> https://doi.org/10.1016/j.neuroimage.2017.12.004
https://doi.org/10.1038/s41593-019-0575-0
<
Communication	> [email protected] <
Restrictions	>Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA 4.0) <
Category	> template,atlas <

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Logos for contributed resources

Should we include an image option in the contributed template? We could ask for a logo of a specific format/resolution to make its integration on our website easier. It's only a cosmetic issue, but I think it will make the site look much better.

@animal_warper (Updated Listing)

Resource info table

Name	AFNI @animal_warper
Authors	Daniel Glen, Paul Taylor, Adam Messinger, Benjamin Jung, Jakob Seidlitz
Description	Nonlinearly aligns an MRI dataset to a template. The reverse transformation can be used to produce a skullstripped (brain-only) version of the native scan, segmentation/atlas info in the native space, and surfaces for each atlas region. The computed transformations between the anatomical scan and the template is provided for use with FMRI pipeline tools like afni_proc.py.
Documentation	documentation tutorials
Link	https://afni.nimh.nih.gov/
Language	tcsh, python, C, AFNI
Publication	Cox (1996) Jung et al. (submitted)
Communication	AFNI message board
Restrictions	Please cite the above publications
Category	structural

** Please check the applicable boxes**

• All required ethical approval (human/animal) for the development of this resource was obtained
• No ethical approval (human/animal) was required for the development of this resource
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[Idea] Password protected section?

The idea of PRIME-RE to be open, but there might be topics that are better discussed solely with people with the appropriate background to avoid misinterpretation or abuse of information. For example, it could be valuable to share diagnostic and treatment experiences for animal care. People might be more willing to share if this wouldn't be completely open.

Is this possible on the GitHub pages platform?

Create a place for hardware resources

NHP neuroimaging is not only challenging after data collection. Collecting the data may also require a large number of custom solutions. PRIME-RE should probably have a place to exchange information about such issues and solutions as well. Could be in the wiki, could be a resource/hardware page.

Juna.Chimp

Resource info table

Name	Juna.Chimp Templates & Davi130 parcellation
Authors	Sam Vickery, William D Hopkins, Chet C Sherwood, Steven J Schapiro, Jona Fischer, Robert D Latzman, Svenja Caspers, Christian Gaser, Simon B Eickhoff, Robert Dahnke , Felix Hoffstaedter
Description	We provide a chimpanzee structural T1w template, chimpanzee tissue probability maps (TPM), and a manual macroanatomical parcellation of the chimpanzee brain which can be interactively viewed through a web viewer. Additionally, we supply an exemplar preprocessing pipeline using CAT12 with the required templates including a chimpanzee geodesic shooting template.
Documentation	https://github.com/viko18/JunaChimp
Link	http://junachimp.inm7.de/
Language	Volumetric files are in NIFTI (.nii) format, matlab
Publication	https://elifesciences.org/articles/60136
Communication	email address: [email protected] and/or open an issue at https://github.com/viko18/JunaChimp
Restrictions	Please cite our publication if you use this resource
Category	structural template, preprocessing templates, and macroanatomical parcellation

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afni_proc.py (Updated Listing)

Resource info table

Name	AFNI afni_proc.py
Authors	Rick Reynolds, Paul Taylor, Daniel Glen, Gang Chen, Bob Cox
Description	FMRI analysis pipeline tool in AFNI. This widely used, general purpose and flexible tool for creating a full, single subject FMRI processing stream can be used for macaque analyses. This program creates fully commented, single subject processing scripts for all FMRI study designs (task, resting state, naturalistic, etc.) and for either volumetric- or surface-based analyses. The typical goal is to create volumes of aligned response magnitudes (stimulus beta weights from a GLM) to use as input for a group analysis.
Documentation	documentation tutorials
Link	https://afni.nimh.nih.gov/
Language	python, tcsh, C, AFNI
Publication	Cox (1996) Jung et al. (submitted)
Communication	AFNI message board
Restrictions	Please cite the above publications
Category	functional

** Please check the applicable boxes**

• All required ethical approval (human/animal) for the development of this resource was obtained
• No ethical approval (human/animal) was required for the development of this resource
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CIVET-macaque (Updated Listing)

Resource info table

Name	CIVET-macaque
Authors	Claude Lepage, Konrad Wagstyl, Ben Jung, Jakob Seidlitz, Caleb Sponheim, Leslie Ungerleider, Xindi Wang, Alan Evans, Adam Messinger
Description	Fully automated structural MRI pipeline using the NIH Macaque Template (NMT). Performs registration, segmentation, and surface reconstruction of T1-weighted anatomical scans. Provides quality control images, white matter, pial and mid cortical surfaces, and surface morphometrics including cortical thickness maps.
Documentation	Github
Link	Github
Language	C, minc, NIFTI/GIFTI. Binaries available on GitHub.
Publication	Lepage et al. (submitted)
Communication	Email to Dr. Adam Messinger or Github
Restrictions	Please cite the above publication
Category	structural

** Please check the applicable boxes**

• All required ethical approval (human/animal) for the development of this resource was obtained
• No ethical approval (human/animal) was required for the development of this resource
• Do NOT include this resource in periodic mailings about new resources on PRIME-RE

Move general knowledge section to wiki section of GitHub

What do you think about putting the description part of the main page to the wiki section of the GitHub?

Things like spatial preprocessing, functional preprocessing, etc...

Issue Template for contributors

Create a submission template to add resources. Include a filled out example.

National Chimpanzee Brain Resource

Resource info table

National Chimpanzee Brain Resource	> replace this with a name for your resource <
Authors	> replace this with author names <
The National Chimpanzee Brain Resource (NCBR) has the aim of facilitating research advancement through the collection and distribution of chimpanzee neuroimaging data and postmortem brain tissue. The NCBR also serves as a portal to access chimpanzee brain atlas tools, data repository, bibliography of publications, educational information, and links to other chimpanzee brain resources and datasets on the Internet.	> replace this with a brief description of your resource <
Documentation	> replace this with a link to more extensive documentation <
https://www.chimpanzeebrain.org	> replace this with a link to your resource <
Language	> replace with language (python, shell, matlab, etc) your resource uses <
Publication	> replace this with a link to a related publication <
Communication	> replace this with a means for communication <
Restrictions	> replace this with potential usage restrictions <
template/atlas/structural/diffusion	> template/atlas/versatile/structural/fMRI/diffusion <

** Please check the applicable boxes**

• [X ] All required ethical approval (human/animal) for the development of this resource was obtained
• No ethical approval (human/animal) was required for the development of this resource
• Do NOT include this resource in periodic mailings about new resources on PRIME-RE

Instructions

Please fill out the table above as completely as possible, i.e. replace the complete str > .... < with your information.
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Name

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Description

Tell the community what it is that your resource does. Keep it concise (a few lines).

Documentation

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What is the most suitable category to list your resource under?

Checkboxes

Please indicate whether ethical approval was acquired for activities related to the development of this resource, or whether this wasn't necessary. Also, indicate if you do not want your resource advertised in sparsely sent updates from the PRIME-RE team to the community.

PREEMACS

Resource info table

Name	PREEMACS (pipeline for PREprocessing and Extraction of the MACaque brain Surface)
Authors	Pamela Garcia-Saldivar, Arun Garimella, Eduardo A. Garza-Villarreal, Felipe Mendez, Luis Concha and Hugo Merchant
Description	PREEMACS is a pipeline to process raw structural images in order to obtain brain surfaces and cortical thickness, without requiring manual editing. PREEMACS has a modular design, with three modules running independently: Preprocessing, Quality Control and Brain Surface estimation based on FreeSurfer. To evaluate the generalizability of our method, we tested PREEMACS on three different datasets of NHP images: PRIME-DE, UNC-Wisconsin Database and INB-UNAM. Results showed accurate and robust automatic brain surface extraction in our INB-UNAM database and precise extraction in the UNC-Wisconsin and PRIME-DE databases for images that passed the quality control segment of our pipeline.
Documentation	PREEMACS (https://github.com/pGarciaS/PREEMACS/wiki)
Link	GitHub Link (https://github.com/pGarciaS/PREEMACS)
Language	python, shell and matlab
Publication	-
Communication	GitHub Profile (https://github.com/pGarciaS)

• Do NOT include this resource in periodic mailings about new resources on PRIME-RE

Instructions

Please fill out the table above as completely as possible, i.e. replace the complete str > .... < with your information.
Then submit it as a new issue and tag the issue with all applicable (yellow) categorical tags to specify what type of resource you are contributing.
We will use this table and these tags to add your resource to the main list.

Name

Provide a name for your resource. Try to make it a bit descriptive, but as long as it as not incredibly offensive, we will allow it.

Authors

Who wrote the resource or deserves to be credited? This would be a good place to list them.

Description

Tell the community what it is that your resource does. Keep it concise (a few lines).

Documentation

Does your resource include instructions on how to use it, and if so, where?
This can be a hyperlink, or you can simply state that it can be found through the main link (e.g., because it's in a GitHub ReadMe.md).
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Language

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We will accept anything.

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Restrictions

Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?

Category

What is the most suitable category to list your resource under?

Rating system for resources

Having a rating system (like GitHub's stars) for resources could be useful.

<NMT 2.0 Templates (NIMH Macaque Template - version 2.0)>

Resource info table

Name	> NMT 2.0 Templates (NIMH Macaque Template - version 2.0) <
Authors	> Adam Messinger, Benjamin Jung, Jakob Seidlitz, Paul Taylor, Daniel Glen <
Description	> A collection of volumetric templates of the rhesus macaque created by nonlinear averaging of T1-weighted anatomical MRIs from multiple adult monkeys. Surface files are included. The NMT 2.0 provides increased flexibility in macaque group analysis by providing symmetric and asymmetric templates. Both templates are available with an expanded, full head field of view. The templates are available in both AC-PC orientation and in Horsley-Clarke stereotaxic orientation. Updates to the templates include: an improved mapping of the D99 atlas, improved brain masking and normalization, and files that are defined in a common template space (NMT space), using the short data type for improved storage efficiency. <
Documentation	> TBD<
Link	> TBD <
Language	> Volumetric files are in NIFTI (.nii) format, while surface files are in GIFTI (.gii) format. Alignment and brainmasking scripts (@animal_warper) are packaged with AFNI. Processing script rely on ANTs and shell scripting. <
Publication	> TBD <
Communication	> Through the GitHub repo or via email to Dr. Adam Messinger: [email protected] <
Restrictions	> Usage requires citation of this article: TBD <
Category	> template, atlas <

• Do NOT include this resource in periodic mailings about new resources on PRIME-RE

Instructions

Please fill out the table above as completely as possible, i.e. replace the complete str > .... < with your information.
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Documentation

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Restrictions

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Category

What is the most suitable category to list your resource under?

[Idea] Add a 'Experiences with a tool' section

When do tools fail, how easy are they to use. Could be useful section for potential users.

Marmoset Brain Connectivity Atlas

Resource info table

Name	Marmoset Brain Connectivity Atlas
Authors	The project is lead by Piotr Majka of the Laboratory of Neuroinformatics at the Nencki Institute of Experimental Biology (Poland) and prof. Marcello Rosa at Monash University (Australia). For full list of contributors, please visit http://www.marmosetbrain.org/about.
Description	The Marmoset Brain Connectivity Atlas allows for the exploration of a growing collection of retrograde tracer injections in the marmoset neocortex. Data obtained in different animals are registered to a common stereotaxic space of Paxinos et al. (2012) atlas, and the resource incorporates tools for quantitative analyses. In particular, the results can be downloaded in 3D volume format in a template space which allows for ready comparisons to topologies acquired by MRI.
Documentation	Portal’s application programming interface is available at http://analytics.marmosetbrain.org/wiki/api and http://analytics.marmosetbrain.org/wiki/database.
Link	http://www.marmosetbrain.org/
Language	The portal is implemented in JavaScript, Python, and CSS / HTML. The source code of both components of the http://marmosetbrain.org is released under the GPL license (see: https://github.com/Neuroinflab/marmosetbrain.org and https://github.com/Neuroinflab/analysis.Marmosetbrain.org).
Publication	Open access resource for cellular-resolution analyses of corticocortical connectivity in the marmoset monkey. and Towards a comprehensive atlas of cortical connections in a primate brain: Mapping tracer injection studies of the common marmoset into a reference digital template.
Communication	Piotr Majka ([email protected])
Restrictions	Material made public on the Marmoset Brain Connectivity Atlas is licensed under Creative Commons Attribution-ShareAlike 4.0 (CC-BY-SA) License. You are free to share (copy and redistribute) and adapt (remix, transform, and build upon) the marmoset-related material in any medium or format as long as you attribute the Marmoset Brain Connectivity Atlas and provide a link to the two URLs the Marmoset Brain Connectivity Atlas and the CC license). If you adapt the material, you must distribute your contributions under the same license as the original.
Category	Templates and Atlases > Marmoset

Table entry
Template: 3D Paxinos et al. (2012)
Species: C. jacchus
Resolution (mm3): 0.04×0.5×0.04 mm3
With atlas: Cortical areas of the Paxinos et al., (2012)
Volume format: NIFTI
Surface format: N/A
Links: http://www.marmosetbrain.org/reference

• Do NOT include this resource in periodic mailings about new resources on PRIME-RE

Instructions

Please fill out the table above as completely as possible, i.e. replace the complete str > .... < with your information.
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Name

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Who wrote the resource or deserves to be credited? This would be a good place to list them.

Description

Tell the community what it is that your resource does. Keep it concise (a few lines).

Documentation

Does your resource include instructions on how to use it, and if so, where?
This can be a hyperlink, or you can simply state that it can be found through the main link (e.g., because it's in a GitHub ReadMe.md).
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Link

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Language

What language (e.g., python, shell, matlab, etc) is your resource in? This will help people to look at solutions in languages they are familiar with first.
We will accept anything.

Associated publications (if available)

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Restrictions

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Category

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<CIVET-macaque>

Resource info table

Name	> CIVET-macaque structural MRI pipeline <
Authors	> Claude Lepage, Konrad Wagstyl, Ben Jung, Jakob Seidlitz, Caleb Sponheim, Leslie Ungerleider, Xindi Wang, Alan Evans, Adam Messinger <
Description	> Fully automated structural MRI pipeline using the NIH Macaque Template (NMT). Performs registration, segmentation, and surface reconstruction of T1-weighted anatomical scans. Provides quality control images and results. <
Documentation	> https://github.com/aces/CIVET_Full_Project <
Link	> https://github.com/aces/CIVET_Full_Project <
Language	> C, minc, NIFTI/GIFTI. Binaries available on GitHub. <
Publication	> forthcoming <
Communication	> TBD <
Restrictions	> Citation of this article: TBD <
Category	> structural <

• Do NOT include this resource in periodic mailings about new resources on PRIME-RE

Instructions

Please fill out the table above as completely as possible, i.e. replace the complete str > .... < with your information.
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Name

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Documentation

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<D99 Template and Atlas>

Resource info table

Name	> D99 Template and Atlas <
Authors	> Saleem KS, Reveley C, Gruslys A, Ye FQ, Glen D, Samaha J, E Russ B, Saad Z, K Seth A, Leopold DA<
Description	> Digital version of the Saleem and Logothetis atlas with a newer, higher quality D99 surrogate MRI template. Integrated into AFNI/SUMA software <
Documentation	> https://afni.nimh.nih.gov/Macaque <
Link	> https://afni.nimh.nih.gov/pub/dist/atlases/macaque/<
Language	> NIFTI format <
Publication	> https://doi.org/10.1093/cercor/bhw248<
Communication	> [email protected], AFNI message board - https://afni.nimh.nih.gov/afni/community/board/list.php?1 <
Restrictions	> no usage restrictions <
Category	> template, atlas <

• Do NOT include this resource in periodic mailings about new resources on PRIME-RE

Instructions

Please fill out the table above as completely as possible, i.e. replace the complete str > .... < with your information.
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Name

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Description

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Documentation

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Cortical Hierarchy Atlas of the Rhesus Macaque (CHARM) - NEW LISTING

Resource info table

Name	Cortical Hierarchy Atlas of the Rhesus Macaque (CHARM)
Authors	Benjamin Jung, Paul A. Taylor, Jakob Seidlitz, Caleb Sponheim, Pierce Perkins, Leslie G. Ungerleider, Daniel Glen, Adam Messinger
Description	A novel anatomical parcellation of the macaque cerebral cortex, where the cortical sheet is subdivided into six-levels of increasingly fine-grained parcellation. 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.
Documentation	AFNI
Link	AFNI
Language	Volumetric files are in NIFTI (.nii) format, while surface files are in GIFTI (.gii) format.
Publication	Jung et al. (submitted)
Communication	Email to Dr. Adam Messinger
Restrictions	Please cite the above publication
Category	atlas

** Please check the applicable boxes**

• All required ethical approval (human/animal) for the development of this resource was obtained
• No ethical approval (human/animal) was required for the development of this resource
• Do NOT include this resource in periodic mailings about new resources on PRIME-RE