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.

load_fsaverage

nilearn.datasets.load_fsaverage(mesh='fsaverage5', data_dir=None)[source]

Load fsaverage for both hemispheres as PolyMesh objects.

Added in Nilearn 0.11.0.

Parameters:
meshstr, 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_dirpathlib.Path or str or None, 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 also nilearn.datasets.utils.get_data_dirs.

Returns:
datasklearn.utils.Bunch

Dictionary-like object, contains:

Examples using nilearn.datasets.load_fsaverage

Loading and plotting of a cortical surface atlas

Loading and plotting of a cortical surface atlas

Making a surface plot of a 3D statistical map

Making a surface plot of a 3D statistical map

Seed-based connectivity on the surface

Seed-based connectivity on the surface

Cortical surface-based searchlight decoding

Cortical surface-based searchlight decoding

Example of surface-based first-level analysis

Example of surface-based first-level analysis

Smoothing an image

Smoothing an image

A short demo of the surface images & maskers

A short demo of the surface images & maskers

Surface-based dataset first and second level analysis of a dataset

Surface-based dataset first and second level analysis of a dataset