.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/06_manipulating_images/plot_extract_rois_smith_atlas.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_06_manipulating_images_plot_extract_rois_smith_atlas.py: Regions Extraction of Default Mode Networks using Smith Atlas ============================================================= This simple example shows how to extract regions from Smith atlas resting state networks. In particular, we show how Default Mode Network regions are extracted using :class:`nilearn.regions.RegionExtractor` from regions module .. GENERATED FROM PYTHON SOURCE LINES 13-14 Fetching the smith ICA 10 RSN by importing datasets utilities .. GENERATED FROM PYTHON SOURCE LINES 14-19 .. code-block:: default from nilearn import datasets smith_atlas = datasets.fetch_atlas_smith_2009() atlas_networks = smith_atlas.rsn10 .. GENERATED FROM PYTHON SOURCE LINES 20-21 Import region extractor to extract atlas networks .. GENERATED FROM PYTHON SOURCE LINES 21-31 .. code-block:: default from nilearn.regions import RegionExtractor # min_region_size in voxel volume mm^3 extraction = RegionExtractor(atlas_networks, min_region_size=800, threshold=98, thresholding_strategy='percentile') # Just call fit() to execute region extraction procedure extraction.fit() regions_img = extraction.regions_img_ .. GENERATED FROM PYTHON SOURCE LINES 32-34 Visualization Show region extraction results by importing image & plotting utilities .. GENERATED FROM PYTHON SOURCE LINES 34-58 .. code-block:: default 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, 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) plotting.show() .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_001.png :alt: plot extract rois smith atlas :srcset: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_001.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_002.png :alt: plot extract rois smith atlas :srcset: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_002.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_003.png :alt: plot extract rois smith atlas :srcset: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_003.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_004.png :alt: plot extract rois smith atlas :srcset: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_004.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_005.png :alt: plot extract rois smith atlas :srcset: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_005.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_006.png :alt: plot extract rois smith atlas :srcset: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_006.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_007.png :alt: plot extract rois smith atlas :srcset: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_007.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_008.png :alt: plot extract rois smith atlas :srcset: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_008.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none /home/nicolas/GitRepos/nilearn-fork/nilearn/plotting/displays/_axes.py:71: UserWarning: No contour levels were found within the data range. .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 2 minutes 30.155 seconds) **Estimated memory usage:** 391 MB .. _sphx_glr_download_auto_examples_06_manipulating_images_plot_extract_rois_smith_atlas.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/nilearn/nilearn.github.io/main?filepath=examples/auto_examples/06_manipulating_images/plot_extract_rois_smith_atlas.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_extract_rois_smith_atlas.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_extract_rois_smith_atlas.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_