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View Code? Open in Web Editor NEWOpen resource exchange platform for non-human primate neuroimaging
Home Page: https://prime-re.github.io
License: MIT License
Open resource exchange platform for non-human primate neuroimaging
Home Page: https://prime-re.github.io
License: MIT License
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 < |
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**
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...
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 |
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 < |
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.
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.
Who wrote the resource or deserves to be credited? This would be a good place to list them.
Tell the community what it is that your resource does. Keep it concise (a few lines).
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.
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)
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.
If your resource is a published method, you can link to the paper here.
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.
Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?
What is the most suitable category to list your resource under?
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 | |
Restrictions | |
Category |
** Please check the applicable boxes**
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 |
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.
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.
Who wrote the resource or deserves to be credited? This would be a good place to list them.
Tell the community what it is that your resource does. Keep it concise (a few lines).
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.
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)
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.
If your resource is a published method, you can link to the paper here.
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.
Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?
What is the most suitable category to list your resource under?
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 < |
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.
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.
Who wrote the resource or deserves to be credited? This would be a good place to list them.
Tell the community what it is that your resource does. Keep it concise (a few lines).
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.
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)
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.
If your resource is a published method, you can link to the paper here.
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.
Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?
What is the most suitable category to list your resource under?
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).
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):
3drefit -orient SAR your_image_to_correct.nii.gz
fslreorient2std your_image_to_correct.nii.gz your_image_corrected.nii.gz
Do you think it is a sound procedure?
Do you think I should add this in the wiki?
Thank you for your answer!
Segmentation Tools For Monkey Brains
Kep kee Loh, David Meunier, Bastien Cagna, Julien Sein, Régis Trapeau
@ Institut de Neurosciences de la Timone, Marseille, France
What different types of people could contribute?
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
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**
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.
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.
Who wrote the resource or deserves to be credited? This would be a good place to list them.
Tell the community what it is that your resource does. Keep it concise (a few lines).
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.
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)
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.
If your resource is a published method, you can link to the paper here.
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.
Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?
What is the most suitable category to list your resource under?
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.
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 < |
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.
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.
Who wrote the resource or deserves to be credited? This would be a good place to list them.
Tell the community what it is that your resource does. Keep it concise (a few lines).
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.
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)
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.
If your resource is a published method, you can link to the paper here.
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.
Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?
What is the most suitable category to list your resource under?
When do tools fail, how easy are they to use. Could be useful section for potential users.
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**
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 |
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 < |
** Please check the applicable boxes**
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.
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.
Who wrote the resource or deserves to be credited? This would be a good place to list them.
Tell the community what it is that your resource does. Keep it concise (a few lines).
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.
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)
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.
If your resource is a published method, you can link to the paper here.
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.
Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?
What is the most suitable category to list your resource under?
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.
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 < |
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.
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.
Who wrote the resource or deserves to be credited? This would be a good place to list them.
Tell the community what it is that your resource does. Keep it concise (a few lines).
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.
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)
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.
If your resource is a published method, you can link to the paper here.
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.
Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?
What is the most suitable category to list your resource under?
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
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.
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.
Who wrote the resource or deserves to be credited? This would be a good place to list them.
Tell the community what it is that your resource does. Keep it concise (a few lines).
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.
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,
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We will accept anything.
If your resource is a published method, you can link to the paper here.
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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.
Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?
What is the most suitable category to list your resource under?
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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Tell the community what it is that your resource does. Keep it concise (a few lines).
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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.
Provide a link to the resource. This can be a link to a GitHub repository, website, or shared file.
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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.
If your resource is a published method, you can link to the paper here.
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Is there a Slack or Mattermost channel? Can issues be opened on a GitHub repo? Is there an email adress?
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Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?
What is the most suitable category to list your resource under?
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?
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**
Please fill out the table above as completely as possible, i.e. replace the complete str > .... <
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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.
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Tell the community what it is that your resource does. Keep it concise (a few lines).
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.
Provide a link to the resource. This can be a link to a GitHub repository, website, or shared file.
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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.
If your resource is a published method, you can link to the paper here.
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Is there a Slack or Mattermost channel? Can issues be opened on a GitHub repo? Is there an email adress?
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Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?
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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.
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?
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
Create a submission template to add resources. Include a filled out example.
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.
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
Please fill out the table above as completely as possible, i.e. replace the complete str > .... <
with your information.
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We will use this table and these tags to add your resource to the main list.
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.
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Tell the community what it is that your resource does. Keep it concise (a few lines).
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.
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,
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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.
If your resource is a published method, you can link to the paper here.
How can a potential user get in contact with the submitter/author of the resource?
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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**
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**
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 |
** Please check the applicable boxes**
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 |
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.
Having a rating system (like GitHub's stars) for resources could be useful.
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) |
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.
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.
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Tell the community what it is that your resource does. Keep it concise (a few lines).
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.
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,
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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.
If your resource is a published method, you can link to the paper here.
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.
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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 |
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.
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Tell the community what it is that your resource does. Keep it concise (a few lines).
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.
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,
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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.
If your resource is a published method, you can link to the paper here.
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?
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Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?
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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 < |
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.
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.
Who wrote the resource or deserves to be credited? This would be a good place to list them.
Tell the community what it is that your resource does. Keep it concise (a few lines).
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.
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)
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.
If your resource is a published method, you can link to the paper here.
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?
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Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?
What is the most suitable category to list your resource under?
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 |
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**
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
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**
Please fill out the table above as completely as possible, i.e. replace the complete str > .... <
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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.
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.
Who wrote the resource or deserves to be credited? This would be a good place to list them.
Tell the community what it is that your resource does. Keep it concise (a few lines).
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.
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)
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.
If your resource is a published method, you can link to the paper here.
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.
Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?
What is the most suitable category to list your resource under?
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.
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 |
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.
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.
Who wrote the resource or deserves to be credited? This would be a good place to list them.
Tell the community what it is that your resource does. Keep it concise (a few lines).
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.
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)
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.
If your resource is a published method, you can link to the paper here.
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.
Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?
What is the most suitable category to list your resource under?
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 < |
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.
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.
Who wrote the resource or deserves to be credited? This would be a good place to list them.
Tell the community what it is that your resource does. Keep it concise (a few lines).
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.
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)
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.
If your resource is a published method, you can link to the paper here.
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.
Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?
What is the most suitable category to list your resource under?
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**
Improving looks, navigation, UI in general.
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.
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 < |
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.
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.
Who wrote the resource or deserves to be credited? This would be a good place to list them.
Tell the community what it is that your resource does. Keep it concise (a few lines).
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.
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)
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.
If your resource is a published method, you can link to the paper here.
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.
Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?
What is the most suitable category to list your resource under?
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 |
** Please check the applicable boxes**
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.
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.
Who wrote the resource or deserves to be credited? This would be a good place to list them.
Tell the community what it is that your resource does. Keep it concise (a few lines).
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.
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)
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.
If your resource is a published method, you can link to the paper here.
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.
Are there any limitations for how the analysis method can be used (e.g., citation or acknowledgement required, etc)?
What is the most suitable category to list your resource under?
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.
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.
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