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_smith_2009¶
- nilearn.datasets.fetch_atlas_smith_2009(data_dir=None, url=None, resume=True, verbose=1, mirror='origin', dimension=None, resting=True)[source]¶
Download and load the Smith ICA and BrainMap Probabilistic atlas (2009).
See Smith et al.[1] and Laird et al.[2].
- Parameters:
- data_dir
pathlib.Path
orstr
, 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 alsonilearn.datasets.utils.get_data_dirs
.- url
str
, default=None URL of file to download. Override download URL. Used for test only (or if you setup a mirror of the data).
- resume
bool
, default=True Whether to resume download of a partly-downloaded file.
- verbose
int
, default=1 Verbosity level (0 means no message).
- mirror
str
, default=’origin’ By default, the dataset is downloaded from the original website of the atlas. Specifying “nitrc” will force download from a mirror, with potentially higher bandwidth.
- dimension
int
, optional Number of dimensions in the dictionary. Valid resolutions available are {10, 20, 70}.
- resting
bool
, default=True Either to fetch the resting-fMRI or BrainMap components
- data_dir
- Returns:
- data
sklearn.utils.Bunch
Dictionary-like object, contains:
'description'
:str
, description of the atlas.
- data
- Warns:
- DeprecationWarning
If a dimension input is provided, the current behavior (returning multiple maps) is deprecated. Starting in version 0.13, one map will be returned in a ‘maps’ dict key depending on the dimension and resting value.
Notes
For more information about this dataset’s structure: https://www.fmrib.ox.ac.uk/datasets/brainmap+rsns/
References
Examples using nilearn.datasets.fetch_atlas_smith_2009
¶
3D and 4D niimgs: handling and visualizing
Visualizing 4D probabilistic atlas maps
Regions Extraction of Default Mode Networks using Smith Atlas