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_msdl

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

Download and load the MSDL brain Probabilistic atlas.

It can be downloaded at 1, and cited using 2. See also 3 for more information.

data_dirpathlib.Path or str, optional

Path where data should be downloaded. By default, files are downloaded in home directory.

urlstr, optional

URL of file to download. Override download URL. Used for test only (or if you setup a mirror of the data). Default=None.

resumebool, optional

Whether to resume download of a partly-downloaded file. Default=True.

verboseint, optional

Verbosity level (0 means no message). Default=1.


Dictionary-like object, the interest attributes are :

  • ‘maps’: str, path to nifti file containing the Probabilistic atlas image (shape is equal to (40, 48, 35, 39)).

  • ‘labels’: list of str, list containing the labels of the regions. There are 39 labels such that data.labels[i] corresponds to map i.

  • ‘region_coords’: list of length-3 tuple, data.region_coords[i] contains the coordinates (x, y, z) of region i in MNI space.

  • ‘networks’: list of str, list containing the names of the networks. There are 39 network names such that data.networks[i] is the network name of region i.

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



Spatially constrained parcellation. Accessed: 2021-05-19.


Gael Varoquaux, Alexandre Gramfort, Fabian Pedregosa, Vincent Michel, and Bertrand Thirion. Multi-subject dictionary learning to segment an atlas of brain spontaneous activity. In Information Processing in Medical Imaging, 562–573. Berlin, Heidelberg, 2011. Springer Berlin Heidelberg.


Gaël Varoquaux and R. Cameron Craddock. Learning and comparing functional connectomes across subjects. NeuroImage, 80:405–415, 2013. Mapping the Connectome. URL:, doi: