Making a surface plot of a 3D statistical map¶

In this example, we will project a 3D statistical map onto a cortical mesh using `vol_to_surf`, display a surface plot of the projected map using `plot_surf_stat_map` with different plotting engines, and add contours of regions of interest using `plot_surf_contours`.

Get a statistical map¶

```from nilearn import datasets

```

Get a cortical mesh¶

```fsaverage = datasets.fetch_surf_fsaverage()
```

Use mesh curvature to display useful anatomical information on inflated meshes

Here, we load the curvature map of the hemisphere under study, and define a surface map whose value for a given vertex is 1 if the curvature is positive, -1 if the curvature is negative.

```import numpy as np

from nilearn import surface

curv_right_sign = np.sign(curv_right)
```

Sample the 3D data around each node of the mesh¶

```texture = surface.vol_to_surf(stat_img, fsaverage.pial_right)
```

Plot the result¶

You can visualize the texture on the surface using the function `plot_surf_stat_map` which uses `matplotlib` as the default plotting engine.

```from nilearn import plotting

fig = plotting.plot_surf_stat_map(
fsaverage.infl_right,
texture,
hemi="right",
title="Surface right hemisphere",
colorbar=True,
threshold=1.0,
bg_map=curv_right_sign,
)
fig.show()
```

Interactive plotting with Plotly¶

If you have a recent version of Nilearn (>=0.8.2), and if you have `plotly` installed, you can easily configure `plot_surf_stat_map` to use `plotly` instead of `matplotlib`:

```engine = "plotly"
# If plotly is not installed, use matplotlib
try:
import plotly.graph_objects as go  # noqa: F401
except ImportError:
engine = "matplotlib"

print(f"Using plotting engine {engine}.")

fig = plotting.plot_surf_stat_map(
fsaverage.infl_right,
texture,
hemi="right",
title="Surface right hemisphere",
colorbar=True,
threshold=1.0,
bg_map=curv_right_sign,
bg_on_data=True,
engine=engine,  # Specify the plotting engine here
)

# Display the figure as with matplotlib figures
# fig.show()
```
```Using plotting engine plotly.
/opt/hostedtoolcache/Python/3.12.6/x64/lib/python3.12/site-packages/nilearn/plotting/surf_plotting.py:980: UserWarning: vmin cannot be chosen when cmap is symmetric
fig = _plot_surf_plotly(
```

When using `matplotlib` as the plotting engine, a standard `matplotlib.figure.Figure` is returned. With `plotly` as the plotting engine, a custom `PlotlySurfaceFigure` is returned which provides a similar API to the `Figure`. For example, you can save a static version of the figure to file (this option requires to have `kaleido` installed):

```# Save the figure as we would do with a matplotlib figure
# Uncomment the following line to save the previous figure to file
# fig.savefig("right_hemisphere.png")
```

Plot 3D image for comparison¶

```plotting.plot_glass_brain(
stat_img,
display_mode="r",
plot_abs=False,
title="Glass brain",
threshold=2.0,
)

plotting.plot_stat_map(
stat_img,
display_mode="x",
threshold=1.0,
cut_coords=range(0, 51, 10),
title="Slices",
)
```
```<nilearn.plotting.displays._slicers.XSlicer object at 0x7f6ee2f63230>
```

Use an atlas and choose regions to outline¶

```destrieux_atlas = datasets.fetch_atlas_surf_destrieux()
parcellation = destrieux_atlas["map_right"]

# these are the regions we want to outline
regions_dict = {
b"G_postcentral": "Postcentral gyrus",
b"G_precentral": "Precentral gyrus",
}

# get indices in atlas for these labels
regions_indices = [
np.where(np.array(destrieux_atlas["labels"]) == region)[0][0]
for region in regions_dict
]

labels = list(regions_dict.values())
```
```[get_dataset_dir] Dataset found in /home/runner/work/nilearn/nilearn/nilearn_data/destrieux_surface
```

Display outlines of the regions of interest on top of a statistical map¶

```figure = plotting.plot_surf_stat_map(
fsaverage.infl_right,
texture,
hemi="right",
title="Surface right hemisphere",
colorbar=True,
threshold=1.0,
bg_map=fsaverage.sulc_right,
)

plotting.plot_surf_contours(
fsaverage.infl_right,
parcellation,
labels=labels,
levels=regions_indices,
figure=figure,
legend=True,
colors=["g", "k"],
)
plotting.show()
```

Plot with higher-resolution mesh¶

`fetch_surf_fsaverage` takes a `mesh` argument which specifies whether to fetch the low-resolution `fsaverage5` mesh, or the high-resolution fsaverage mesh. Using `mesh="fsaverage"` will result in more memory usage and computation time, but finer visualizations.

```big_fsaverage = datasets.fetch_surf_fsaverage("fsaverage")
big_texture = surface.vol_to_surf(stat_img, big_fsaverage.pial_right)

plotting.plot_surf_stat_map(
big_fsaverage.infl_right,
big_texture,
hemi="right",
colorbar=True,
title="Surface right hemisphere: fine mesh",
threshold=1.0,
bg_map=big_fsaverage.sulc_right,
)
```
```[get_dataset_dir] Dataset found in /home/runner/work/nilearn/nilearn/nilearn_data/fsaverage

<Figure size 470x500 with 2 Axes>
```

Plot multiple views of the 3D volume on a surface¶

`plot_img_on_surf` takes a statistical map and projects it onto a surface. It supports multiple choices of orientations, and can plot either one or both hemispheres. If no `surf_mesh` is given, `plot_img_on_surf` projects the images onto FreeSurfer's fsaverage5.

```plotting.plot_img_on_surf(
stat_img,
views=["lateral", "medial"],
hemispheres=["left", "right"],
colorbar=True,
)
plotting.show()
```

3D visualization in a web browser¶

An alternative to `nilearn.plotting.plot_surf_stat_map` is to use `nilearn.plotting.view_surf` or `nilearn.plotting.view_img_on_surf` that give more interactive visualizations in a web browser. See 3D Plots of statistical maps or atlases on the cortical surface for more details.

```view = plotting.view_surf(
fsaverage.infl_right, texture, threshold="90%", bg_map=fsaverage.sulc_right
)

# In a Jupyter notebook, if ``view`` is the output of a cell, it will
# be displayed below the cell
view
```