Note

This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.

nilearn.datasets.fetch_atlas_yeo_2011

nilearn.datasets.fetch_atlas_yeo_2011(data_dir=None, url=None, resume=True, verbose=1, n_networks=None, thickness=None)[source]

Download and return file names for the Yeo 2011 parcellation.

This function retrieves the so-called yeo deterministic atlases. The provided images are in MNI152 space and have shapes equal to (256, 256, 256, 1). They contain consecutive integers values from 0 (background) to either 7 or 17 depending on the atlas version considered.

For more information on this dataset’s structure, see Cortical Parcellation Estimated by Intrinsic Functional Connectivity[1], and Thomas Yeo et al.[2].

Parameters:
data_dirpathlib.Path or str or None, optional

Path where data should be downloaded. By default, files are downloaded in a nilearn_data folder in the home directory of the user. See also nilearn.datasets.utils.get_data_dirs.

urlstr or None, default=None

URL of file to download. Override download URL. Used for test only (or if you setup a mirror of the data).

resumebool, default=True

Whether to resume download of a partly-downloaded file.

verboseint, default=1

Verbosity level (0 means no message).

n_networks{7, 17, None}, default = None

If not None, then only specific version of the atlas is returned:

  • 7 networks parcellation,

  • 17 networks parcellation.

If thickness is not None, this will default to 7. The default will be set to 7 in version 0.13.0.

Added in version 0.12.0.

thickness{“thin”, “thick”, None}, default = None

If not None, then only specific version of the atlas is returned:

  • "thick": parcellation fitted to thick cortex segmentations,

  • "thin": parcellation fitted to thin cortex segmentations.

If n_networks is not None, this will default to "thick". The default will be set to "thick" in version 0.13.0.

Added in version 0.12.0.

Returns:
datasklearn.utils.Bunch

Dictionary-like object.

If n_networks and thickness are None, keys are:

  • ‘thin_7’: str

    Path to nifti file containing the 7 networks parcellation fitted to thin template cortex segmentations. The image contains integer values which can be interpreted as the indices in colors_7.

  • ‘thick_7’: str

    Path to nifti file containing the 7 networks parcellation fitted to thick template cortex segmentations. The image contains integer values which can be interpreted as the indices in colors_7.

  • ‘thin_17’: str

    Path to nifti file containing the 17 networks parcellation fitted to thin template cortex segmentations. The image contains integer values which can be interpreted as the indices in colors_17.

  • ‘thick_17’: str

    Path to nifti file containing the 17 networks parcellation fitted to thick template cortex segmentations. The image contains integer values which can be interpreted as the indices in colors_17.

  • ‘colors_7’: str

    Path to colormaps text file for 7 networks parcellation. This file maps voxel integer values from data.thin_7 and data.tick_7 to network names.

  • ‘colors_17’: str

    Path to colormaps text file for 17 networks parcellation. This file maps voxel integer values from data.thin_17 and data.tick_17 to network names.

  • ‘anat’: str

    Path to nifti file containing the anatomy image.

  • ‘description’str

    Description of the dataset.

  • ‘template’str

    The standardized space of analysis in which the atlas results are provided. When known it should be a valid template name taken from the spaces described in the BIDS specification.

  • ‘atlas_type’str

    Type of atlas. See Probabilistic atlas and Deterministic atlas.

otherwise the keys are:

  • ‘anat’: str

    Path to nifti file containing the anatomy image.

  • ‘maps’: 3D Nifti1Image. The image contains integer values for each network.

  • ‘labels’list of str

    List of the names of the regions.

  • lutpandas.DataFrame

    Act as a look up table (lut) with at least columns ‘index’ and ‘name’. Formatted according to ‘dseg.tsv’ format from BIDS.

  • ‘description’str

    Description of the dataset.

  • ‘template’str

    The standardized space of analysis in which the atlas results are provided. When known it should be a valid template name taken from the spaces described in the BIDS specification.

  • ‘atlas_type’str

    Type of atlas. See Probabilistic atlas and Deterministic atlas.

Notes

License: unknown.

References

Examples using nilearn.datasets.fetch_atlas_yeo_2011

Basic Atlas plotting

Basic Atlas plotting

Comparing connectomes on different reference atlases

Comparing connectomes on different reference atlases

Breaking an atlas of labels in separated regions

Breaking an atlas of labels in separated regions