nilearn.datasets: Automatic Dataset Fetching

Helper functions to download NeuroImaging datasets.

User guide: See the Fetching open datasets from Internet section for further details.

Templates

Functions

fetch_icbm152_2009([data_dir, url, resume, ...])	Download and load the ICBM152 template (dated 2009).
fetch_icbm152_brain_gm_mask([data_dir, ...])	Download ICBM152 template first, then loads the 'gm' mask.
fetch_surf_fsaverage([mesh, data_dir])	Download a Freesurfer fsaverage surface.
load_fsaverage([mesh, data_dir])	Load fsaverage for both hemispheres as PolyMesh objects.
load_fsaverage_data([mesh, mesh_type, ...])	Return freesurfer data on an fsaverage mesh as a SurfaceImage.
load_mni152_brain_mask([resolution, threshold])	Load the MNI152 whole-brain mask.
load_mni152_gm_mask([resolution, threshold, ...])	Load the MNI152 grey-matter mask.
load_mni152_gm_template([resolution])	Load the MNI152 grey-matter template.
load_mni152_template([resolution])	Load the MNI152 skullstripped T1 template.
load_mni152_wm_mask([resolution, threshold, ...])	Load the MNI152 white-matter mask.
load_mni152_wm_template([resolution])	Load the MNI152 white-matter template.

Templates descriptions

• ICBM 152 template
• fsaverage template
• fsaverage3 template
• fsaverage4 template
• fsaverage5 template
• fsaverage6 template

Atlases

Deterministic atlases

Warning

The atlases shipped with Nilearn do not necessarily use the same MNI template as the default MNI template used by Nilearn for plotting.

This may lead to atlas being poorly coregistered to the underlay image used as underlay: the atlas can appear smaller or bigger than the brain.

This can seen clearly on some of the images below.

You also should not use maskers with an atlas that is not coregistered properly coregistered with the images you want to extract data from as this may lead to invalid results.

name	image
AAL template: MNI number of regions: [117, 167] description
BASC multiscale (2015) template: MNI152sym number of regions: [7, 12, 20, 36, 64, 122, 197, 325, 444] description
Destrieux (2009; surface) template: MNI152NLin6Asym number of regions: 75 description
Destrieux (2009; volume) template: fsaverage number of regions: 148 description
Harvard-Oxford (subcortical) template: MNI152NLin6Asym number of regions: 21 description
Harvard-Oxford template: MNI152NLin6Asym number of regions: 48 description
Juelich template: MNI? number of regions: 62 description
Pauli (2017) template: MNI? number of regions: 16 description
Schaefer (2018) template: MNI152NLin6Asym number of regions: [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000] description
Talairach template: Talairach number of regions: [3, 7, 12, 55, 71] description
Yeo (2011) template: MNI152NLin6Asym number of regions: [7, 17] description

fetch_atlas_aal([version, data_dir, url, ...])	Download and returns the AAL template for SPM 12.
fetch_atlas_basc_multiscale_2015([data_dir, ...])	Download and load multiscale functional brain parcellations.
fetch_atlas_destrieux_2009([lateralized, ...])	Download and load the Destrieux cortical deterministic atlas (dated 2009).
fetch_atlas_harvard_oxford(atlas_name[, ...])	Load Harvard-Oxford parcellations from FSL.
fetch_atlas_juelich(atlas_name[, data_dir, ...])	Load Juelich parcellations from FSL.
fetch_atlas_pauli_2017([atlas_type, ...])	Download the Pauli et al. (2017) atlas.
fetch_atlas_schaefer_2018([n_rois, ...])	Download and return file names for the Schaefer 2018 parcellation.
fetch_atlas_surf_destrieux([data_dir, url, ...])	Download and load Destrieux et al, 2010 cortical Deterministic atlas.
fetch_atlas_talairach(level_name[, ...])	Download the Talairach Deterministic atlas.
fetch_atlas_yeo_2011([data_dir, url, ...])	Download and return file names for the Yeo 2011 parcellation.
fetch_coords_dosenbach_2010([ordered_regions])	Load the Dosenbach et al 160 ROIs.
fetch_coords_power_2011()	Download and load the Power et al. brain atlas composed of 264 ROIs.
fetch_coords_seitzman_2018([ordered_regions])	Load the Seitzman et al. 300 ROIs.

Probabilistic atlases

Warning

The atlases shipped with Nilearn do not necessarily use the same MNI template as the default MNI template used by Nilearn for plotting.

This may lead to atlas being poorly coregistered to the underlay image used as underlay: the atlas can appear smaller or bigger than the brain.

This can seen clearly on some of the images below.

You also should not use maskers with an atlas that is not coregistered properly coregistered with the images you want to extract data from as this may lead to invalid results.

name	image
Allen (2011) template: MNI152 number of regions: 75 description
Craddock (2012) template: MNI? number of regions: 43 description
Difumo template: MNI152NLin6Asym number of regions: [64, 128, 256, 512, 1024] description
Harvard-Oxford (subcortical) template: MNI152NLin6Asym number of regions: 21 description
Harvard-Oxford template: MNI152NLin6Asym number of regions: 48 description
Juelich template: MNI? number of regions: 62 description
MSDL template: MNI? number of regions: 39 description
Smith (2009) template: MNI? number of regions: [10, 20, 70] description

fetch_atlas_allen_2011([data_dir, url, ...])	Download and return file names for the Allen and MIALAB ICA Probabilistic atlas (dated 2011).
fetch_atlas_craddock_2012([data_dir, url, ...])	Download and return file names for the Craddock 2012 parcellation.
fetch_atlas_difumo([dimension, ...])	Fetch DiFuMo brain atlas.
fetch_atlas_harvard_oxford(atlas_name[, ...])	Load Harvard-Oxford parcellations from FSL.
fetch_atlas_juelich(atlas_name[, data_dir, ...])	Load Juelich parcellations from FSL.
fetch_atlas_msdl([data_dir, url, resume, ...])	Download and load the MSDL brain Probabilistic atlas.
fetch_atlas_pauli_2017([atlas_type, ...])	Download the Pauli et al. (2017) atlas.
fetch_atlas_smith_2009([data_dir, url, ...])	Download and load the Smith ICA and BrainMap Probabilistic atlas (2009).

Preprocessed datasets

Functions

fetch_abide_pcp([data_dir, n_subjects, ...])	Fetch ABIDE dataset.
fetch_adhd([n_subjects, data_dir, url, ...])	Download and load the ADHD resting-state dataset.
fetch_bids_langloc_dataset([data_dir, verbose])	Download language localizer example bids dataset.
fetch_development_fmri([n_subjects, ...])	Fetch movie watching based brain development dataset (fMRI).
fetch_ds000030_urls([data_dir, verbose])	Fetch URLs for files from the ds000030 BIDS dataset.
fetch_fiac_first_level([data_dir, verbose])	Download a first-level fiac fMRI dataset (2 runs).
fetch_haxby([data_dir, subjects, ...])	Download and loads complete haxby dataset.
fetch_language_localizer_demo_dataset([...])	Download language localizer demo dataset.
fetch_localizer_first_level([data_dir, verbose])	Download a first-level localizer fMRI dataset.
fetch_miyawaki2008([data_dir, url, resume, ...])	Download and loads Miyawaki et al. 2008 dataset (153MB).
fetch_spm_auditory([data_dir, data_name, ...])	Fetch SPM auditory single-subject data.
fetch_spm_multimodal_fmri([data_dir, ...])	Fetcher for Multi-modal Face Dataset.
fetch_surf_nki_enhanced([n_subjects, ...])	Download and load the NKI enhanced resting-state dataset, preprocessed and projected to the fsaverage5 space surface.
load_nki([mesh, mesh_type, n_subjects, ...])	Load NKI enhanced surface data into a surface object.

Datasets descriptions

• ABIDE PCP dataset
• ADHD dataset
• BIDS language localizer
• development fMRI dataset
• fiac first level dataset
• Haxby dataset
• language localizer demo dataset
• localizer first level dataset
• Miyawaki 2008 dataset
• SPM auditory dataset
• SPM multimodal dataset
• NKI enhanced surface dataset
• Brainomics Localizer

Statistical maps/derivatives

Functions

fetch_localizer_button_task([data_dir, verbose])	Fetch left vs right button press contrast maps from the localizer.
fetch_localizer_calculation_task([...])	Fetch calculation task contrast maps from the localizer.
fetch_localizer_contrasts(contrasts[, ...])	Download and load Brainomics/Localizer dataset (94 subjects).
fetch_megatrawls_netmats([dimensionality, ...])	Download and return Network Matrices data from MegaTrawls release in HCP.
fetch_mixed_gambles([n_subjects, data_dir, ...])	Fetch Jimura "mixed gambles" dataset.
fetch_oasis_vbm([n_subjects, ...])	Download and load Oasis "cross-sectional MRI" dataset (416 subjects).
fetch_neurovault_auditory_computation_task([...])	Fetch a contrast map from Neurovault showing the effect of mental subtraction upon auditory instructions.
fetch_neurovault_motor_task([data_dir, ...])	Fetch left vs right button press group contrast map from Neurovault.

Statistical maps/derivatives descriptions

• MegaTrawls Network Matrices HCP
• Mixed gambles statistical maps
• OASIS volume based morphometry maps

General functions

Functions

fetch_neurovault([max_images, ...])	Download data from neurovault.org that match certain criteria.
fetch_neurovault_ids([collection_ids, ...])	Download specific images and collections from neurovault.org.
fetch_openneuro_dataset([urls, data_dir, ...])	Download OpenNeuro BIDS dataset.
get_data_dirs([data_dir])	Return the directories in which nilearn looks for data.
patch_openneuro_dataset(file_list)	Add symlinks for files not named according to BIDS conventions.
select_from_index(urls[, inclusion_filters, ...])	Select subset of urls with given filters.
load_sample_motor_activation_image()	Load a single functional image showing motor activations.

General functions descriptions

• Neurovault statistical maps