neuroparc_data

Neuroparc data

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.

How to get the data [Short version]

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

Content desctription

below follows a copy of the original README

• Atlas Spec
• Table

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 Info Summary

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

How to get the data [Long version]

DataLad datasets and how to use them

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.

Get the dataset

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.

Retrieve dataset content

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.

Stay up-to-date

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.

Find out what has been done

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

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.