This is a fork of the data of the Neuroparc repository to be used with datalad and get around the GIT LFS issues of that repo.
Install datalad
pip install datalad
Get the dataset and the annexed content of a given atlas.
datalad clone git@gin.g-node.org:/cpp-lln-lab/neuroparc_data.git
cd neuroparc_data
datalad get label/Human/Yeo-7_space-MNI152NLin6_res-4x4x4.nii.gz
below follows a copy of the original README
This repository contains a number of useful parcellations, templates, masks, and transforms to (and from) MNI152NLin6 space. The files are named according to the BIDs specification
Atlas Name | # Regions | Symmetrical? | Hierarchical? | Labelled? | Generation Method | Average Vol/Region | Native coordinate space | Description | Reference Publication | Year of Origin | File Source/Download URL |
---|---|---|---|---|---|---|---|---|---|---|---|
AAL | 116 | No | No | Yes | Delineated with respect to anatomical landmarks (following sulci course in brain) | 12758.353 | MNI | Automated anatomical labelling based on sulci. | https://www.ncbi.nlm.nih.gov/pubmed/11771995 | 2002 | https://www.gin.cnrs.fr/en/tools/aal/ http://www.gin.cnrs.fr/wp-content/uploads/aal2_for_SPM12.tar.gz |
AICHA | 384 | No | No | Yes | Built by estimation of resting-state networks, k-means clustering, homotopic regional grouping based on maximal inter-hemispheric functional correlation, and ROI labeling. | 3004.333 | N/A | Adaptation of AAL focused on the idea that each region in one hemisphere has a homologue in the other hemisphere | https://www.ncbi.nlm.nih.gov/pubmed/26213217 | 2015 | Included in mricron: https://people.cas.sc.edu/rorden/mricron/index.html |
Brodmann | 40 | Yes | Yes | No | Corticall parcellation separating regions based on cellular morphology and organization | 32978.512 | N/A | Brodman areas separated by gyri | http://digital.zbmed.de/zbmed/id/554966 | 1909 | https://surfer.nmr.mgh.harvard.edu/fswiki/BrodmannAreaMaps |
CAPRSC | 333 | Yes | No | Yes | Automatic using resting-state functional connectivity (RSFC) boundary maps | 1389.09 | N/A | Created using RSFC-boundary maps to define parcels that represent putative cortical areas. Focuses on the cortical surface and was created using functional MRI scans. | https://www.ncbi.nlm.nih.gov/pubmed/25316338 | 2016 | Obtained from Freesurfer: https://sites.wustl.edu/petersenschlaggarlab/parcels-19cwpgu/ |
CPAC200 | 200 | No | No | No | Created by parcellating whole brain resting-state fMRI data into spatially coherent regions of homogeneous functional connectivity. | 5860.755 | N/A | ROIs with anatomic homology | https://pubmed.ncbi.nlm.nih.gov/21769991/ | 2018 | https://fcp-indi.s3.amazonaws.com/data/Projects/ABIDE_Initiative/Resources/cc200_roi_atlas.nii.gz |
Desikan | 70 | Yes | No | No | Anatomical Landmarks based on gyri. Averaged based on majority voting | 24786.857 | N/A | Surface parcellation | https://www.sciencedirect.com/science/article/pii/S1053811906000437?via%3Dihub | 2006 | Obtained from FreeSurfer: https://surfer.nmr.mgh.harvard.edu/fswiki/CorticalParcellation |
DesikanKlein | 97 | No | No | No | Automated labeling system that subdivided the human cerebral cortex into gyral based regions of interest. | 74443.62 | N/A | Gyral based parcellations | https://www.sciencedirect.com/science/article/pii/S1053811906000437?via%3Dihub | 2006 | Obtained from FreeSurfer: https://surfer.nmr.mgh.harvard.edu/fswiki/CorticalParcellation |
Destrieux | 75 | Yes | No | Yes | Automatically assigned a neuroanatomical label to each location on a cortical surface model based on probabilistic information estimated from a manually labeled training set. | 96280.43 | MNI152 | Cortical surface probabilitstic atlas | https://academic.oup.com/cercor/article/14/1/11/433466 | 2004 | Obtained from FreeSurfer: https://surfer.nmr.mgh.harvard.edu/fswiki/CorticalParcellation |
DKT | 84 | Yes | No | No | Automatic | 85964.66 | N/A | Created by using a modified Desikan protocol in order to improve segmentation and make it more suited for FreeSurfer’s classifier algorithm. | https://www.frontiersin.org/articles/10.3389/fnins.2012.00171/full | 2012 | Obtained from FreeSurfer: https://surfer.nmr.mgh.harvard.edu/fswiki/CorticalParcellation |
Glasser | 360 | Yes | Yes | Yes | Semi-automated. Separated based on function, connectivity, cortical architecture, topography, and expert analysis | 521.994 | MNI | Cortical parcellation from multi-modal images of 210 adults in HCP | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4990127/ | 2016 | https://balsa.wustl.edu/file/show/3VLx |
Hammersmith | 83 | No | No | Yes | Algorithm using prior information from 30 normal adult brain MR images, which had been manually segmented to create 30 atlases, each labeling 83 anatomical structures. | 19947.72 | MNI152 | Automatic segmentation of young children's brains into 83 regions of interest. | https://www.sciencedirect.com/science/article/pii/S1053811907010634?via%3Dihub | 2003 | http://brain-development.org/brain-atlases/adult-brain-atlases/adult-brain-maximum-probability-map-hammers-mith-atlas-n30r83-in-mni-space/ |
HarvardOxford | 48 | No | Yes | Yes | Created by subdividing neocortex by topographic criteria into 48 parcellation units corresponding to the principal cerebral gyri. | 21966.104 | N/A | Neuroanatomic subdivisions delineated by this general segmentation generaly corresponding to natural gray matter boundaries. | https://www.sciencedirect.com/science/article/pii/S0920996405004998?via%3Dihub | 2005 | Included in FSL: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases |
JHU | 48 | Yes | No | Yes | One subject manually labelled and warped to 29 other adult atlases (Large Deformation Diffeomorphic Metric Mapping) | 3541.792 | N/A | A small version of a larger (289 ROI) atlas composed based on parcellation of deep white matter. Split into 4 groups: Tracts in the brainstem, projection fibers, association fibers, and commisural fibers | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2724595/ | 2004 | https://neurovault.org/collections/264/ |
Juelich | 103 | No | No | Yes | Probabilistic atlas created by averaging multi-subject post-mortem cyto- and myelo-architectonic segmentations. | 69433 | N/A | N/A | https://linkinghub.elsevier.com/retrieve/pii/S1053-8119(04)00792-X | 2005 | https://interactive-viewer.apps.hbp.eu/?templateSelected=MNI+Colin+27&parcellationSelected=JuBrain+Cytoarchitectonic+Atlas |
MICCAI | 136 | No | No | No | N/A | 52708.26 | N/A | N/A | MICCAI 2012 Workshop: https://my.vanderbilt.edu/masi/workshops/ | 2012 | http://www.neuromorphometrics.com/2012_MICCAI_Challenge_Data.html |
Princeton | 49 | Yes | Yes | Yes | Identified 25 topographic maps in a large population of individual subjects and transformed them into either surface- or volume-based standardized space. | 1217.388 | N/A | Atlas exclusively containing parcellations of the visual cortex. | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4585523/ | 2015 | https://scholar.princeton.edu/napl/resources |
Schaefer1000 | 1000 | No | No | No | Automatic using gwMRF | 1055.685 | N/A | Gradient-weighted Markov Random Fields (gwMRF) to group similar fMRI regions (dependent on # of regions specified) | http://people.csail.mit.edu/ythomas/publications/2018LocalGlobal-CerebCor.pdf | 2017 | https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal/Parcellations |
Schaefer200 | 200 | No | No | No | Automatic using gwMRF | 5278.425 | N/A | Gradient-weighted Markov Random Fields (gwMRF) to group similar fMRI regions (dependent on # of regions specified) | http://people.csail.mit.edu/ythomas/publications/2018LocalGlobal-CerebCor.pdf | 2017 | https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal/Parcellations |
Schaefer300 | 300 | No | No | No | Automatic using gwMRF | 3518.95 | N/A | Gradient-weighted Markov Random Fields (gwMRF) to group similar fMRI regions (dependent on # of regions specified) | http://people.csail.mit.edu/ythomas/publications/2018LocalGlobal-CerebCor.pdf | 2017 | https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal/Parcellations |
Schaefer400 | 400 | No | No | No | Automatic using gwMRF | 2639.213 | N/A | Gradient-weighted Markov Random Fields (gwMRF) to group similar fMRI regions (dependent on # of regions specified) | http://people.csail.mit.edu/ythomas/publications/2018LocalGlobal-CerebCor.pdf | 2017 | https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal/Parcellations |
Slab1068 | 1068 | No | No | No | Calculated spatially averaged time series for each of 1068 regions of interest placed in a regular 12-mm grid throughout the brain | 493.719 | N/A | Grid of ROI points spanning entire MNI brain volume. | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5507181/ | 2017 | https://umich.app.box.com/s/w46icx4bng1mw1nc3sg72t13ug5ecyib https://www.nitrc.org/projects/kessler_jama16/ |
Slab907 | 907 | No | No | No | Placed 907 ROIs at regular intervals throughout the cortex | 7952.68 | N/A | Grid of ROI points spanning entire MNI brain volume | https://pubmed.ncbi.nlm.nih.gov/25225387/ | 2014 | https://umich.app.box.com/s/jowv4pogzhbfevt301n8 |
Talairach | 1105 | No | Yes | Yes | Semi-automated? | 1698.114 | Talairach coordinates | A hierarchical atlas split into 5 leves: Hemisphere, Lobe, Gyrus, Tissue Type, and Cell Type | https://www.ncbi.nlm.nih.gov/pubmed/7008525 | 1980 | http://www.talairach.org/ |
Tissue | 3 | No | No | N/A | 609031.667 | N/A | (Tissue-based segmentation: WM, GM, CSF) | 2018 | |||
Yeo 17 | 17 | Yes | No | Yes | Clustered to identify networks of functionally coupled regions | 31040.294 | FreeSurfer surface space | Local networks confined to sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3174820/ | 2011 | https://ftp.nmr.mgh.harvard.edu/pub/data/Yeo_JNeurophysiol11_MNI152.zip http://www.freesurfer.net/fswiki/CorticalParcellation_Yeo2011 |
Yeo 17 Liberal | 17 | Yes | No | Yes | Clustered to identify networks of functionally coupled regions | 62043.118 | FreeSurfer surface space | Local networks confined to sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3174820/ | 2011 | https://ftp.nmr.mgh.harvard.edu/pub/data/Yeo_JNeurophysiol11_MNI152.zip http://www.freesurfer.net/fswiki/CorticalParcellation_Yeo2011 |
Yeo 7 | 7 | Yes | No | Yes | Clustered to identify networks of functionally coupled regions | 75383.571 | FreeSurfer surface space | Local networks confined to sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3174820/ | 2011 | https://ftp.nmr.mgh.harvard.edu/pub/data/Yeo_JNeurophysiol11_MNI152.zip http://www.freesurfer.net/fswiki/CorticalParcellation_Yeo2011 |
Yeo 7 Liberal | 7 | Yes | No | Yes | Clustered to identify networks of functionally coupled regions | 150676.143 | FreeSurfer surface space | Local networks confined to sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3174820/ | 2011 | https://ftp.nmr.mgh.harvard.edu/pub/data/Yeo_JNeurophysiol11_MNI152.zip http://www.freesurfer.net/fswiki/CorticalParcellation_Yeo2011 |
DS Family | 71 - 72784 | No | No | No | Grid segmentation of entire MNI brain | Variable | N/A | Grid segmentation of entire MNI brain | https://ieeexplore.ieee.org/document/6736874 | 2013 | N/A |
below follows a copy of the suggested README for a datalad dataset
This repository is a DataLad dataset. It provides fine-grained data access down to the level of individual files, and allows for tracking future updates. In order to use this repository for data retrieval, DataLad is required. It is a free and open source command line tool, available for all major operating systems, and builds up on Git and git-annex to allow sharing, synchronizing, and version controlling collections of large files. You can find information on how to install DataLad at handbook.datalad.org/en/latest/intro/installation.html.
A DataLad dataset can be cloned
by running
datalad clone <url>
Once a dataset is cloned, it is a light-weight directory on your local machine. At this point, it contains only small metadata and information on the identity of the files in the dataset, but not actual content of the (sometimes large) data files.
After cloning a dataset, you can retrieve file contents by running
datalad get <path/to/directory/or/file>`
This command will trigger a download of the files, directories, or subdatasets you have specified.
DataLad datasets can contain other datasets, so called subdatasets. If you clone the top-level dataset, subdatasets do not yet contain metadata and information on the identity of files, but appear to be empty directories. In order to retrieve file availability metadata in subdatasets, run
datalad get -n <path/to/subdataset>
Afterwards, you can browse the retrieved metadata to find out about subdataset
contents, and retrieve individual files with datalad get
. If you use
datalad get <path/to/subdataset>
, all contents of the subdataset will be
downloaded at once.
DataLad datasets can be updated. The command datalad update
will fetch
updates and store them on a different branch (by default
remotes/origin/master
). Running
datalad update --merge
will pull available updates and integrate them in one go.
DataLad datasets contain their history in the git log
. By running git log
(or a tool that displays Git history) in the dataset or on specific files, you
can find out what has been done to the dataset or to individual files by whom,
and when.
More information on DataLad and how to use it can be found in the DataLad Handbook at handbook.datalad.org. The chapter "DataLad datasets" can help you to familiarize yourself with the concept of a dataset.