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_msdl

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

Download and load the MSDL brain Probabilistic atlas.

It can be downloaded at Spatially Constrained Parcellation[1], and cited using Varoquaux et al.[2]. See also Varoquaux and Craddock[3] for more information.

Parameters:

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.

urlstr, default=None

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

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, 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.

References

[1]

Spatially constrained parcellation. https://team.inria.fr/parietal/files/2015/01/MSDL_rois.zip. Accessed: 2021-05-19.

[2]

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.

[3]

Gaël Varoquaux and R. Cameron Craddock. Learning and comparing functional connectomes across subjects. NeuroImage, 80:405–415, 2013. Mapping the Connectome. doi:10.1016/j.neuroimage.2013.04.007.

Examples using nilearn.datasets.fetch_atlas_msdl

Visualizing a probabilistic atlas: the default mode in the MSDL atlas

Visualizing a probabilistic atlas: the default mode in the MSDL atlas

Computing a connectome with sparse inverse covariance

Computing a connectome with sparse inverse covariance

Extracting signals of a probabilistic atlas of functional regions

Extracting signals of a probabilistic atlas of functional regions

Group Sparse inverse covariance for multi-subject connectome

Group Sparse inverse covariance for multi-subject connectome

Classification of age groups using functional connectivity

Classification of age groups using functional connectivity

Functional connectivity predicts age group

Functional connectivity predicts age group