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_msdl

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

Download and load the MSDL brain Probabilistic atlas.

For more information, see the dataset description.

Parameters:
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.

urlstr or None, 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.

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. There are 39 labels such that data.labels[i] corresponds to map i.

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

  • 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.

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

  • template: str. The standardized space of analysis in which the data is provided.

Notes

If the dataset files are already present in the user’s Nilearn data directory, this fetcher will not re-download them. To force a fresh download, you can remove the existing dataset folder from your local Nilearn data directory.

For more details on how Nilearn stores datasets.

Examples using nilearn.datasets.fetch_atlas_msdl

Visualizing a probabilistic atlas

Visualizing a probabilistic atlas

Classification of age groups using functional connectivity

Classification of age groups using functional connectivity

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

Functional connectivity predicts age group

Functional connectivity predicts age group