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)[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.

Parameters

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.

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. URL: https://www.sciencedirect.com/science/article/pii/S1053811913003340, doi:https://doi.org/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 4D probabilistic atlas maps

Extracting signals of a probabilistic atlas of functional regions

Computing a connectome with sparse inverse covariance

Group Sparse inverse covariance for multi-subject connectome

Classification of age groups using functional connectivity

Functional connectivity predicts age group

nilearn.datasets.fetch_atlas_msdl#

Examples using nilearn.datasets.fetch_atlas_msdl#

Examples using `nilearn.datasets.fetch_atlas_msdl`#