Visualizing multiscale functional brain parcellations

This example shows how to visualize brain parcellations using plotting function plot_roi.

dataset

This example uses the parcellations of multiple networks from the BASC multiscale atlas.

We show here only three different networks of ‘symmetric’ version.

Retrieving multiscale group brain parcellations

# import datasets module and use `fetch_atlas_basc_multiscale_2015` function
from nilearn.datasets import fetch_atlas_basc_multiscale_2015

parcellations = [
    fetch_atlas_basc_multiscale_2015(version="sym", resolution=64),
    fetch_atlas_basc_multiscale_2015(version="sym", resolution=197),
    fetch_atlas_basc_multiscale_2015(version="sym", resolution=444),
]

# We show here networks of 64, 197, 444
networks_64 = parcellations[0]["maps"]
networks_197 = parcellations[1]["maps"]
networks_444 = parcellations[2]["maps"]

[fetch_atlas_basc_multiscale_2015] Dataset directory found: /home/runner/work/nilearn/nilearn/nilearn_data/basc_multiscale_2015_atlas
[fetch_atlas_basc_multiscale_2015] Dataset directory found: /home/runner/work/nilearn/nilearn/nilearn_data/basc_multiscale_2015_atlas
[fetch_atlas_basc_multiscale_2015] Dataset directory found: /home/runner/work/nilearn/nilearn/nilearn_data/basc_multiscale_2015_atlas

Visualizing brain parcellations

# import plotting module and use `plot_roi` function, since the maps are in 3D
from nilearn.plotting import plot_roi, show

# The coordinates of all plots are selected automatically by itself
# We manually change the colormap of our choice
plot_roi(networks_64, cmap="bwr", title="64 regions of brain clusters")

plot_roi(networks_197, cmap="bwr", title="197 regions of brain clusters")

plot_roi(networks_444, cmap="bwr_r", title="444 regions of brain clusters")

show()

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Estimated memory usage: 276 MB