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_juelich#

nilearn.datasets.fetch_atlas_juelich(atlas_name, data_dir=None, symmetric_split=False, resume=True, verbose=1)[source]#

Load Juelich parcellations from FSL.

This function downloads Juelich atlas packaged from FSL 5.0 and stores atlases in NILEARN_DATA folder in home directory.

This function can also load Juelich atlas from your local directory specified by your FSL installed path given in data_dir argument. See documentation for details.

New in version 0.8.1.

Note

For atlases ‘prob-1mm’, and ‘prob-2mm’, the function returns a Probabilistic atlas, and the Nifti1Image returned is 4D, with shape (182, 218, 182, 62). For deterministic atlases, the Nifti1Image returned is 3D, with shape (182, 218, 182) and 62 regions (+ background).

Parameters:
atlas_namestr

Name of atlas to load. Can be: “maxprob-thr0-1mm”, “maxprob-thr0-2mm”, “maxprob-thr25-1mm”, “maxprob-thr25-2mm”, “maxprob-thr50-1mm”, “maxprob-thr50-2mm”, “prob-1mm”, “prob-2mm”.

data_dirpathlib.Path or str, 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. Optionally, it can also be a FSL installation directory (which is dependent on your installation). Example, if FSL is installed in /usr/share/fsl/, then specifying as ‘/usr/share/’ can get you Juelich atlas from your installed directory. Since we mimic same root directory as FSL to load it easily from your installation.

symmetric_splitbool, default=False

If True, lateralized atlases of cort or sub with maxprob will be returned. For subcortical types (sub-maxprob), we split every symmetric region in left and right parts. Effectively doubles the number of regions.

Note

Not implemented for full Probabilistic atlas (*-prob-* atlases).

resumebool, default=True

Whether to resume download of a partly-downloaded file.

verboseint, default=1

Verbosity level (0 means no message).

Returns:
datasklearn.utils.Bunch

Dictionary-like object, keys are:

  • ‘maps’: Nifti1Image. It is a 4D image if a Probabilistic atlas is requested, and a 3D image if a maximum probability atlas is requested. In the latter case, the image contains integer values which can be interpreted as the indices in the list of labels.

    Note

    For some atlases, it can be the case that some regions are empty. In this case, no voxels in the map are assigned to these regions. So the number of unique values in the map can be strictly smaller than the number of region names in labels.

  • ‘labels’: list of str, list of labels for the regions in the atlas.

  • ‘filename’: Same as ‘maps’, kept for backward compatibility only.

  • ‘description’: str, description of the atlas.

See also

nilearn.datasets.fetch_atlas_harvard_oxford