Note

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Regions Extraction of Default Mode Networks¶

This simple example shows how to extract regions with a probabilistic atlas.

In particular, we show how default mode network regions are extracted using RegionExtractor from the regions module.

dataset

This example uses the Smith 2009 atlas.

Fetching the Smith atlas 10 resting state networks.

from nilearn import datasets

atlas_networks = datasets.fetch_atlas_smith_2009(resting=True, dimension=10)

[fetch_atlas_smith_2009] Dataset directory found: /home/runner/work/nilearn/nilearn/nilearn_data/smith_2009_atlas

print(atlas_networks.description.content.maps)

{'type': <class 'str'>, 'desc': 'Full path to 4D nifti file containing the requested resting fMRI or BrainMap components (depending on the value passed to ``resting``) image with the number of requested dimensions. The shape of the image is ``(91, 109, 91, dimension)`` where dimension is the value passed to ``dimension``.'}

Import region extractor to extract atlas networks

from nilearn.regions import RegionExtractor

# min_region_size in voxel volume mm^3
extraction = RegionExtractor(
    atlas_networks.maps,
    min_region_size=800,
    threshold=98,
    thresholding_strategy="percentile",
    verbose=1,
)

# Just call fit() to execute region extraction procedure
extraction.fit()
regions_img = extraction.regions_img_

[RegionExtractor.fit] Loading regions from <nibabel.nifti1.Nifti1Image object at 0x720371679850>
[RegionExtractor.fit] Finished fit

Visualization Show region extraction results by importing image & plotting utilities

from nilearn import plotting
from nilearn.image import index_img
from nilearn.plotting import find_xyz_cut_coords

# Showing region extraction results using 4D maps visualization tool
plotting.plot_prob_atlas(
    regions_img,
    display_mode="z",
    cut_coords=1,
    view_type="contours",
    title="Regions extracted.",
)

# To reduce the complexity, we choose to display all the regions
# extracted from network 3
import numpy as np

DMN_network = index_img(atlas_networks.maps, 3)
plotting.plot_stat_map(
    DMN_network,
    display_mode="z",
    cut_coords=1,
    title="Network 3",
    colorbar=False,
)

regions_indices_network3 = np.where(np.array(extraction.index_) == 3)
for index in regions_indices_network3[0]:
    cur_img = index_img(extraction.regions_img_, index)
    coords = find_xyz_cut_coords(cur_img)
    plotting.plot_stat_map(
        cur_img,
        display_mode="z",
        cut_coords=coords[2:3],
        title="Blob of network3",
        colorbar=False,
        cmap="inferno",
        vmax=15,
    )

plotting.show()