Note
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Visualizing multiscale functional brain parcellations#
This example shows how to download and fetch brain parcellations of
multiple networks using
nilearn.datasets.fetch_atlas_basc_multiscale_2015
and visualize them using plotting function nilearn.plotting.plot_roi.
We show here only three different networks of ‘symmetric’ version. For more details about different versions and different networks, please refer to its documentation.
Retrieving multiscale group brain parcellations#
# import datasets module and use `fetch_atlas_basc_multiscale_2015` function
from nilearn import datasets
parcellations = datasets.fetch_atlas_basc_multiscale_2015(version="sym")
# We show here networks of 64, 197, 444
networks_64 = parcellations["scale064"]
networks_197 = parcellations["scale197"]
networks_444 = parcellations["scale444"]
/usr/share/miniconda3/envs/testenv/lib/python3.9/site-packages/nilearn/datasets/atlas.py:1318: FutureWarning: The default behavior of the function will be deprecated and replaced in release 0.13 to use the new parameters resolution and and version.
warnings.warn(category=FutureWarning,
Visualizing brain parcellations#
# import plotting module and use `plot_roi` function, since the maps are in 3D
from nilearn import plotting
# The coordinates of all plots are selected automatically by itself
# We manually change the colormap of our choice
plotting.plot_roi(
networks_64, cmap=plotting.cm.bwr, title="64 regions of brain clusters"
)
plotting.plot_roi(
networks_197, cmap=plotting.cm.bwr, title="197 regions of brain clusters"
)
plotting.plot_roi(
networks_444, cmap=plotting.cm.bwr_r, title="444 regions of brain clusters"
)
plotting.show()
Total running time of the script: ( 0 minutes 4.216 seconds)
Estimated memory usage: 8 MB
