Note
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Visualizing multiscale functional brain parcellations¶
This example shows how to download and fetch brain parcellations of
multiple networks using
fetch_atlas_basc_multiscale_2015
and visualize them using plotting function 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.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"]
[get_dataset_dir] Dataset created in
/home/runner/nilearn_data/basc_multiscale_2015
[fetch_single_file] Downloading data from
https://ndownloader.figshare.com/files/1861819 ...
[fetch_single_file] ...done. (1 seconds, 0 min)
[uncompress_file] Extracting data from
/home/runner/nilearn_data/basc_multiscale_2015/5fd26f33e54da6efc0bc7eca321ad622/
1861819...
[uncompress_file] .. done.
[get_dataset_dir] Dataset found in
/home/runner/nilearn_data/basc_multiscale_2015
[get_dataset_dir] Dataset found in
/home/runner/nilearn_data/basc_multiscale_2015
Visualizing brain parcellations¶
# import plotting module and use `plot_roi` function, since the maps are in 3D
from nilearn.plotting import cm, 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=cm.bwr, title="64 regions of brain clusters")
plot_roi(networks_197, cmap=cm.bwr, title="197 regions of brain clusters")
plot_roi(networks_444, cmap=cm.bwr_r, title="444 regions of brain clusters")
show()
Total running time of the script: (0 minutes 3.835 seconds)
Estimated memory usage: 108 MB
