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_schaefer_2018#
- nilearn.datasets.fetch_atlas_schaefer_2018(n_rois=400, yeo_networks=7, resolution_mm=1, data_dir=None, base_url=None, resume=True, verbose=1)[source]#
Download and return file names for the Schaefer 2018 parcellation.
New in version 0.5.1.
This function returns a Deterministic atlas, and the provided images are in MNI152 space.
For more information on this dataset, see Schaefer 2018 parcellation[1], Schaefer et al.[2], and Thomas Yeo et al.[3].
- Parameters:
- n_rois{100, 200, 300, 400, 500, 600, 700, 800, 900, 1000}, default=400
Number of regions of interest.
- yeo_networks{7, 17}, default=7
ROI annotation according to yeo networks.
- resolution_mm{1, 2}, default=1mm
Spatial resolution of atlas image in mm.
- 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
.- base_url
str
, optional Base URL of files to download (
None
results in defaultbase_url
).- resume
bool
, default=True Whether to resume download of a partly-downloaded file.
- verbose
int
, default=1 Verbosity level (0 means no message).
- Returns:
- data
sklearn.utils.Bunch
Dictionary-like object, contains:
‘maps’:
str
, path to nifti file containing the 3DNifti1Image
(its shape is(182, 218, 182)
). The values are consecutive integers between 0 andn_rois
which can be interpreted as indices in the list of labels.‘labels’:
numpy.ndarray
ofstr
, array containing the ROI labels including Yeo-network annotation.Warning
The list of labels does not contain ‘Background’ by default. To have proper indexing, you should either manually add ‘Background’ to the list of labels:
# Prepend background label data.labels = np.insert(data.labels, 0, "Background")
Or be careful that the indexing should be offset by one:
# Get region ID of label '7Networks_LH_Vis_3' when # 'Background' was not added to the list of labels: # idx should be equal to 3: idx = np.where(data.labels == b"7Networks_LH_Vis_3")[0] + 1
‘description’:
str
, short description of the atlas and some references.
- data
Notes
Release v0.14.3 of the Schaefer 2018 parcellation is used by default. Versions prior to v0.14.3 are known to contain erroneous region label names. For more details, see https://github.com/ThomasYeoLab/CBIG/blob/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal/Parcellations/Updates/Update_20190916_README.md # noqa: E501
Licence: MIT.
References