Note

This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the user guide for the big picture.

nilearn.surface.SurfaceImage¶

class nilearn.surface.SurfaceImage(mesh, data)[source]¶

Surface image containing meshes & data for both hemispheres.

Added in version 0.11.0.

Parameters:

meshnilearn.surface.PolyMesh, or dict of nilearn.surface.SurfaceMesh, str, pathlib.Path: Meshes for the both hemispheres.
datanilearn.surface.PolyData, or dict of numpy.ndarray, str, pathlib.Path: Data for the both hemispheres.
squeeze_on_savebool or None, default=None: If True axes of length one from the data will be removed before saving them to file. If None is passed, then the value will be set to True if any of the data parts is one dimensional.

Attributes:

shape(int, int): Shape of the data.

__init__(mesh, data)[source]¶: Create a SurfaceImage instance.

classmethod from_volume(mesh, volume_img, inner_mesh=None, **vol_to_surf_kwargs)[source]¶

Create surface image from volume image.

Parameters:

meshnilearn.surface.PolyMesh or dict of nilearn.surface.SurfaceMesh, str, pathlib.Path: Surface mesh.
volume_imgNiimg-like object: 3D or 4D volume image to project to the surface mesh.
inner_meshnilearn.surface.PolyMesh or dict of nilearn.surface.SurfaceMesh, str, pathlib.Path, default=None: Inner mesh to pass to nilearn.surface.vol_to_surf.
vol_to_surf_kwargsdict [ str , Any]: Dictionary of extra key-words arguments to pass to nilearn.surface.vol_to_surf.

Examples

>>> from nilearn.surface import SurfaceImage
>>> from nilearn.datasets import load_fsaverage
>>> from nilearn.datasets import load_sample_motor_activation_image

>>> fsavg = load_fsaverage()
>>> vol_img = load_sample_motor_activation_image()
>>> img = SurfaceImage.from_volume(fsavg["white_matter"], vol_img)
>>> img
<SurfaceImage (20484,)>
>>> img = SurfaceImage.from_volume(
...     fsavg["white_matter"], vol_img, inner_mesh=fsavg["pial"]
... )
>>> img
<SurfaceImage (20484,)>

property shape¶: Shape of the data.

Examples using `nilearn.surface.SurfaceImage`¶

Working with Surface images

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.surface.SurfaceImage¶

Examples using nilearn.surface.SurfaceImage¶

Examples using `nilearn.surface.SurfaceImage`¶