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/ 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.

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
    networks_64,, title="64 regions of brain clusters"

    networks_197,, title="197 regions of brain clusters"

    networks_444,, title="444 regions of brain clusters"
  • plot multiscale parcellations
  • plot multiscale parcellations
  • plot multiscale parcellations

Total running time of the script: ( 0 minutes 4.216 seconds)

Estimated memory usage: 8 MB

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