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.experimental.surface.SurfaceImage¶
- class nilearn.experimental.surface.SurfaceImage(mesh, data)[source]¶
Surface image, usually containing meshes & data for both hemispheres.
- Parameters:
- meshPolyMesh | dict[str, Mesh | str | Path]
- dataPolyData | dict[str, Mesh | str | Path]
- Attributes:
- shape(int, int)
shape of the surface data array
- classmethod from_volume(mesh, volume_img, inner_mesh=None, **vol_to_surf_kwargs)[source]¶
Create surface image from volume image.
- Parameters:
- meshPolyMesh or dict[str, Mesh | str | Path]
Surface mesh.
- volume_imgNiimg-like object
3D or 4D volume image to project to the surface mesh.
- inner_mesh: PolyMesh or dict[str, Mesh | str | Path], optional
Inner mesh to pass to
nilearn.surface.vol_to_surf
.- vol_to_surf_kwargs: dict[str, Any]
Dictionary of extra key-words arguments to pass to
nilearn.surface.vol_to_surf
.
Examples
>>> from nilearn.experimental.surface import ( ... SurfaceImage, ... 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,)>
Examples using nilearn.experimental.surface.SurfaceImage
¶
Seed-based connectivity on the surface
Example of surface-based first-level analysis
Surface-based dataset first and second level analysis of a dataset
Loading and plotting of a cortical surface atlas
A short demo of the surface images & maskers
Making a surface plot of a 3D statistical map