Basic Atlas plotting

Plot the regions of reference atlases.

Check the list of atlases to know which ones are shipped with Nilearn.

dataset

This example uses:

• the Harvard-Oxford atlas

• the Juelich atlas

• the Yeo 2011 atlas

Retrieving the atlas data

from nilearn import datasets

dataset_ho = datasets.fetch_atlas_harvard_oxford("cort-maxprob-thr25-2mm")

[fetch_atlas_harvard_oxford] Dataset directory found: /home/runner/work/nilearn/nilearn/nilearn_data/fsl

print(dataset_ho.description.content.maps)
atlas_ho_filename = dataset_ho.maps

{'type': <class 'nibabel.nifti1.Nifti1Image'>, 'desc': 'It is a 4D image with shape ``(182, 218, 182, 48)`` if a :term:`Probabilistic atlas` is requested, and a 3D image with shape ``(182, 218, 182)`` and 48 regions (+ background) if a :term:`maximum probability atlas<Deterministic atlas>` is requested. In the latter case, the image contains integer values which can be interpreted as the indices in the list of labels.'}

dataset_ju = datasets.fetch_atlas_juelich("maxprob-thr0-1mm")

[fetch_atlas_juelich] Dataset directory found: /home/runner/work/nilearn/nilearn/nilearn_data/fsl

print(dataset_ju.description.content.maps)
atlas_ju_filename = dataset_ju.maps

{'type': <class 'nibabel.nifti1.Nifti1Image'>, 'desc': 'It is a 4D image with shape ``(182, 218, 182, 62)`` if a :term:`Probabilistic atlas` is requested, and a 3D image with shape ``(182, 218, 182)`` and 62 regions (+ background) if a :term:`maximum probability atlas<Deterministic atlas>` is requested. In the latter case, the image contains integer values which can be interpreted as the indices in the list of labels.'}

Visualizing the Harvard-Oxford atlas

from nilearn.plotting import plot_roi, show

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

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x720359c09550>

Visualizing the Juelich atlas

plot_roi(atlas_ju_filename, title="Juelich atlas")

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x72034a73c250>

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)

[fetch_atlas_yeo_2011] Dataset directory found: /home/runner/work/nilearn/nilearn/nilearn_data/yeo_2011

print(dataset_yeo.description.content.lut)
print(dataset_yeo.lut)

{'type': <class 'pandas.core.frame.DataFrame'>, 'desc': "\n        Act as a look up table (lut)\n        with at least columns 'index' and 'name'.\n        Formatted according to 'dseg.tsv' format from\n        `BIDS <https://bids-specification.readthedocs.io/en/latest/derivatives/imaging.html#common-image-derived-labels>`_.\n"}
    index           name    color
0       0     Background  #000000
1       1   17Networks_1  #781286
2       2   17Networks_2  #ff0000
3       3   17Networks_3  #4682b4
4       4   17Networks_4  #2acca4
5       5   17Networks_5  #4a9b3c
6       6   17Networks_6  #00760e
7       7   17Networks_7  #c43afa
8       8   17Networks_8  #ff98d5
9       9   17Networks_9  #dcf8a4
10     10  17Networks_10  #7a8732
11     11  17Networks_11  #778cb0
12     12  17Networks_12  #e69422
13     13  17Networks_13  #87324a
14     14  17Networks_14  #0c30ff
15     15  17Networks_15  #000082
16     16  17Networks_16  #ffff00
17     17  17Networks_17  #cd3e4e

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()

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Estimated memory usage: 732 MB