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.

7.2.3. nilearn.datasets.fetch_atlas_harvard_oxford

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

Load Harvard-Oxford parcellation from FSL if installed or download it.

This function looks up for Harvard Oxford atlas in the system and load it if present. If not, it downloads it and stores it in NILEARN_DATA directory.


atlas_name: string

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, sub-maxprob-thr0-1mm, sub-maxprob-thr0-2mm, sub-maxprob-thr25-1mm, sub-maxprob-thr25-2mm, sub-maxprob-thr50-1mm, sub-maxprob-thr50-2mm, cort-prob-1mm, cort-prob-2mm, sub-prob-1mm, sub-prob-2mm

data_dir: string, optional

Path of data directory. It can be FSL installation directory (which is dependent on your installation).

symmetric_split: bool, optional

If True, split every symmetric region in left and right parts. Effectively doubles the number of regions. Default: False. Not implemented for probabilistic atlas (-prob- atlases)


data: sklearn.datasets.base.Bunch

dictionary-like object, keys are:

  • “maps”: nibabel.Nifti1Image, 4D maps if a probabilistic atlas is requested and 3D labels if a maximum probabilistic atlas was requested.
  • “labels”: string list, labels of the regions in the atlas. Examples using nilearn.datasets.fetch_atlas_harvard_oxford