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_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
- dimension
int
, optional Number of dimensions in the dictionary. Valid resolutions available are {64, 128, 256, 512, 1024}. Default=64.
- resolution_mm
int
, optional The resolution in mm of the atlas to fetch. Valid options available are {2, 3}. Default=2mm.
- data_dir
pathlib.Path
orstr
, optional Path where data should be downloaded. By default, files are downloaded in home directory.
- resume
bool
, optional Whether to resume download of a partly-downloaded file. Default=True.
- verbose
int
, optional Verbosity level (0 means no message). Default=1.
- legacy_format
bool
, optional If set to
True
, the fetcher will return recarrays. Otherwise, it will return pandas dataframes. Default=True.
- dimension
- Returns
- data
sklearn.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)
wheredimension
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 volumei
in the ‘maps’ image. Iflegacy_format
is set toFalse
, this is apandas.DataFrame
.‘description’:
str
, general description of the dataset.
- data
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.
Examples using nilearn.datasets.fetch_atlas_difumo
#
Visualizing 4D probabilistic atlas maps
Comparing connectomes on different reference atlases