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.

8.2.6. nilearn.datasets.fetch_atlas_difumo

nilearn.datasets.fetch_atlas_difumo(dimension=64, resolution_mm=2, data_dir=None, resume=True, verbose=1)[source]

Fetch DiFuMo brain atlas

Dictionaries of Functional Modes, or “DiFuMo”, can serve as atlases to extract functional signals with different dimensionalities (64, 128, 256, 512, and 1024). These modes are optimized to represent well raw BOLD timeseries, over a with range of experimental conditions. See 1.

New in version 0.7.1.

Parameters
dimensionint, optional

Number of dimensions in the dictionary. Valid resolutions available are {64, 128, 256, 512, 1024}. Default=64.

resolution_mmint, optional

The resolution in mm of the atlas to fetch. Valid options available are {2, 3}. Default=2mm.

data_dirpathlib.Path or str, optional

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

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.datasets.base.Bunch

Dictionary-like object, the interest attributes are :

  • ‘maps’: str, 4D path to nifti file containing regions definition.

  • ‘labels’: Numpy recarray containing the labels of the regions.

  • ‘description’: str, general description of the dataset.

Notes

Direct download links from OSF:

References

1

Kamalaker Dadi, Gaël Varoquaux, Antonia Machlouzarides-Shalit, Krzysztof J. Gorgolewski, Demian Wassermann, Bertrand Thirion, and Arthur Mensch. Fine-grain atlases of functional modes for fmri analysis. NeuroImage, 221:117126, 2020. URL: https://www.sciencedirect.com/science/article/pii/S1053811920306121, doi:https://doi.org/10.1016/j.neuroimage.2020.117126.