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.

8.2.2.6. nilearn.datasets.fetch_atlas_difumo

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

Fetch DiFuMo brain atlas.

Dictionaries of Functional Modes, or “DiFuMo”, can serve as probabilistic 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.

legacy_formatbool, optional

If set to True, the fetcher will return recarrays. Otherwise, it will return pandas dataframes. Default=True.

Returns
datasklearn.utils.Bunch

Dictionary-like object, the interest attributes are :

  • ‘maps’: str, path to 4D nifti file containing regions definition. The shape of the image is (104, 123, 104, dimension) where dimension is the requested dimension of the atlas.

  • ‘labels’: numpy.recarray containing the labels of the regions. The length of the label array corresponds to the number of dimensions requested. data.labels[i] is the label corresponding to volume i in the ‘maps’ image. If legacy_format is set to False, this is a pandas.DataFrame.

  • ‘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.

8.2.2.6.1. Examples using nilearn.datasets.fetch_atlas_difumo