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.

fetch_atlas_harvard_oxford¶

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

Load Harvard-Oxford parcellations from FSL.

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

This function can also load Harvard Oxford atlas from your local directory specified by your FSL installed path given in data_dir argument.

For more information, see the dataset description.

Note

For atlases ‘cort-prob-1mm’, ‘cort-prob-2mm’, ‘cortl-prob-1mm’, ‘cortl-prob-2mm’, ‘sub-prob-1mm’, and ‘sub-prob-2mm’, the function returns a Probabilistic atlas, and the Nifti1Image returned is 4D. For deterministic atlases, the Nifti1Image returned is 3D.

Parameters:

atlas_namestr: Name of atlas to load. Can be: “cort-maxprob-thr0-1mm”, “cort-maxprob-thr0-2mm”, “cort-maxprob-thr25-1mm”, “cort-maxprob-thr25-2mm”, “cort-maxprob-thr50-1mm”, “cort-maxprob-thr50-2mm”, “cort-prob-1mm”, “cort-prob-2mm”, “cortl-maxprob-thr0-1mm”, “cortl-maxprob-thr0-2mm”, “cortl-maxprob-thr25-1mm”, “cortl-maxprob-thr25-2mm”, “cortl-maxprob-thr50-1mm”, “cortl-maxprob-thr50-2mm”, “cortl-prob-1mm”, “cortl-prob-2mm”, “sub-maxprob-thr0-1mm”, “sub-maxprob-thr0-2mm”, “sub-maxprob-thr25-1mm”, “sub-maxprob-thr25-2mm”, “sub-maxprob-thr50-1mm”, “sub-maxprob-thr50-2mm”, “sub-prob-1mm”, “sub-prob-2mm”.
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. 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 the Harvard Oxford atlas from your installed directory. Since we mimic the 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.
verbosebool or int, default=1: Verbosity level (0 or False means no message).

Returns:

datasklearn.utils.Bunch

Dictionary-like object, contains:

atlas_type: str. Type of atlas. See Probabilistic atlas and Deterministic atlas.
labels: list of str. List of the names of the regions.
lut: pandas.DataFrame. Act as a look up table (lut) with at least columns ‘index’ and ‘name’. Formatted according to ‘dseg.tsv’ format from BIDS.
maps: nibabel.nifti1.Nifti1Image. It is a 4D image with shape (182, 218, 182, 48) if a Probabilistic atlas is requested, and a 3D image with shape (182, 218, 182) and 48 regions (+ background) 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.
template: str. The standardized space of analysis in which the data is provided.

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.

Examples using `nilearn.datasets.fetch_atlas_harvard_oxford`¶

Basic Atlas plotting

Using brain templates from TemplateFlow

Extracting signals from a brain parcellation

Extracting signals from brain regions using the NiftiLabelsMasker

fetch_atlas_harvard_oxford¶

Examples using nilearn.datasets.fetch_atlas_harvard_oxford¶

Examples using `nilearn.datasets.fetch_atlas_harvard_oxford`¶