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7.2.3. nilearn.datasets.fetch_atlas_harvard_oxford

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7.2.5. nilearn.datasets.fetch_coords_power_2011

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.

7.2.4. nilearn.datasets.fetch_atlas_msdl

nilearn.datasets.fetch_atlas_msdl(data_dir=None, url=None, resume=True, verbose=1)

Download and load the MSDL brain atlas.

Parameters:

data_dir: string, optional

Path of the data directory. Used to force data storage in a specified location. Default: None

url: string, optional

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

Returns:

data: sklearn.datasets.base.Bunch

Dictionary-like object, the interest attributes are :

  • ‘maps’: str, path to nifti file containing regions definition.
  • ‘labels’: string list containing the labels of the regions.
  • ‘region_coords’: tuple list (x, y, z) containing coordinates of each region in MNI space.
  • ‘networks’: string list containing names of the networks.
  • ‘description’: description about the atlas.

References

Download:https://team.inria.fr/parietal/files/2015/01/MSDL_rois.zip
Paper to cite:Multi-subject dictionary learning to segment an atlas of brain spontaneous activity Gael Varoquaux, Alexandre Gramfort, Fabian Pedregosa, Vincent Michel, Bertrand Thirion. Information Processing in Medical Imaging, 2011, pp. 562-573, Lecture Notes in Computer Science.
Other references:
 Learning and comparing functional connectomes across subjects. Gael Varoquaux, R.C. Craddock NeuroImage, 2013.