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}")

[get_dataset_dir] Dataset created in /home/runner/nilearn_data/fsl
[fetch_single_file] Downloading data from
https://www.nitrc.org/frs/download.php/9902/HarvardOxford.tgz ...
[fetch_single_file]  ...done. (0 seconds, 0 min)

[uncompress_file] Extracting data from
/home/runner/nilearn_data/fsl/5c734f16e50cc772ef593cab9bb3137b/HarvardOxford.tgz
...
[uncompress_file] .. done.

Atlas ROIs are located at: /home/runner/nilearn_data/fsl/data/atlases/HarvardOxford/HarvardOxford-cort-maxprob-thr25-2mm.nii.gz
[get_dataset_dir] Dataset found in /home/runner/nilearn_data/fsl
[fetch_single_file] Downloading data from
https://www.nitrc.org/frs/download.php/12096/Juelich.tgz ...
[fetch_single_file]  ...done. (0 seconds, 0 min)

[uncompress_file] Extracting data from
/home/runner/nilearn_data/fsl/7e62e7e7fcc4d6e1428b6b2cb48f7a7c/Juelich.tgz...
[uncompress_file] .. done.

Atlas ROIs are located at: /home/runner/nilearn_data/fsl/data/atlases/Juelich/Juelich-maxprob-thr0-1mm.nii.gz

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 0x7f5ba43acd30>

Visualizing the Juelich atlas

plot_roi(atlas_ju_filename, title="Juelich atlas")

<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7f5ba40adee0>

Visualizing the Harvard-Oxford atlas with contours

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

/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/site-packages/nilearn/plotting/displays/_axes.py:74: UserWarning: No contour levels were found within the data range.
  im = getattr(ax, type)(

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)

[get_dataset_dir] Dataset found in /home/runner/nilearn_data/yeo_2011
    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: 325 MB