Basic Atlas plotting

Plot the regions of reference atlases.

Retrieving the atlas data

from nilearn import datasets

dataset_ho = datasets.fetch_atlas_harvard_oxford("cort-maxprob-thr25-2mm")
atlas_ho_filename = dataset_ho.filename
print(f"Atlas ROIs are located at: {atlas_ho_filename}")

dataset_ju = datasets.fetch_atlas_juelich("maxprob-thr0-1mm")
atlas_ju_filename = dataset_ju.filename
print(f"Atlas ROIs are located at: {atlas_ju_filename}")

Visualizing the Harvard-Oxford atlas

from nilearn.plotting import plot_roi, show

plot_roi(atlas_ho_filename, title="Harvard Oxford atlas")

Visualizing the Juelich atlas

plot_roi(atlas_ju_filename, title="Juelich atlas")

Visualizing the Harvard-Oxford atlas with contours

plot_roi(
    atlas_ho_filename,
    view_type="contours",
    title="Harvard Oxford atlas in contours",
)
show()

Visualizing the Juelich atlas with contours

plot_roi(
    atlas_ju_filename, view_type="contours", title="Juelich atlas in contours"
)
show()

Visualizing an atlas with its own colormap

Some atlases come with a look-up table that determines the color to use to represent each of its regions.

You can pass this look-up table as a pandas dataframe to the cmap argument to use its colormap.

Look-up table format

The look-up table must be formatted according to the BIDS standard. and that the colors must be in color column using hexadecimal values.

If an invalid look-up table is passed, a warning will be thrown and the plot_roi function will fall back to using its default colormap.

Here we are using the Yeo atlas that comes with a predefined colormap.

dataset_yeo = datasets.fetch_atlas_yeo_2011(n_networks=17)

print(dataset_yeo.lut)

Let’s compare the atlas with the default colormap and its own colormap.

plot_roi(
    dataset_yeo.maps,
    title="Yeo atlas",
    colorbar=True,
)

plot_roi(
    dataset_yeo.maps,
    title="Yeo atlas with its own colors",
    cmap=dataset_yeo.lut,
    colorbar=True,
)

show()

Estimated memory usage: 0 MB