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.load_fsaverage¶
- nilearn.datasets.load_fsaverage(mesh='fsaverage5', data_dir=None)[source]¶
Load fsaverage for both hemispheres as PolyMesh objects.
Added in version 0.11.0.
- Parameters:
- mesh
str
, default=’fsaverage5’ Which mesh to fetch. Should be one of the following values:
"fsaverage3"
: the low-resolution fsaverage3 mesh (642 nodes)"fsaverage4"
: the low-resolution fsaverage4 mesh (2562 nodes)"fsaverage5"
: the low-resolution fsaverage5 mesh (10242 nodes)"fsaverage6"
: the medium-resolution fsaverage6 mesh (40962 nodes)"fsaverage7"
: same as “fsaverage”"fsaverage"
: the high-resolution fsaverage mesh (163842 nodes)
Note
The high-resolution fsaverage will result in more computation time and memory usage
- 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
.
- mesh
- Returns:
- data
sklearn.utils.Bunch
- Dictionary-like object, the interest attributes are :
'description'
: description of the dataset'pial'
: Polymesh for pial surface for left and right hemispheres'white_matter'
: Polymesh for white matter surfacefor left and right hemispheres
'inflated'
: Polymesh for inglated surfacefor left and right hemispheres
'sphere'
: Polymesh for spherical surfacefor left and right hemispheres
'flat'
: Polymesh for flattened surfacefor left and right hemispheres
- data
Examples using nilearn.datasets.load_fsaverage
¶
Loading and plotting of a cortical surface atlas
Seed-based connectivity on the surface
Making a surface plot of a 3D statistical map
Cortical surface-based searchlight decoding
Example of surface-based first-level analysis
Surface-based dataset first and second level analysis of a dataset
A short demo of the surface images & maskers