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_data¶

nilearn.datasets.load_fsaverage_data(mesh='fsaverage5', mesh_type='pial', data_type='sulcal', data_dir=None)[source]¶

Return freesurfer data on an fsaverage mesh as a SurfaceImage.

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

mesh_typestr, default=’pial’

Must be one of:

"pial"
"white_matter"
"inflated"
"sphere"
"flat"

data_typestr, default=’sulcal’

Must be one of:

"curvature",
"sulcal",
"thickness",

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:

imgSurfaceImage: SurfaceImage with the freesurfer mesh and data.

Examples using `nilearn.datasets.load_fsaverage_data`¶

Loading and plotting of a cortical surface atlas

Making a surface plot of a 3D statistical map

Seed-based connectivity on the surface

Cortical surface-based searchlight decoding

Example of surface-based first-level analysis

A short demo of the surface images & maskers

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

nilearn.datasets.load_fsaverage_data¶

Examples using nilearn.datasets.load_fsaverage_data¶

Examples using `nilearn.datasets.load_fsaverage_data`¶