.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_06_manipulating_images_plot_extract_rois_smith_atlas.py>`     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

Fetching the smith ICA 10 RSN by importing datasets utilities


.. code-block:: default

    from nilearn import datasets

    smith_atlas = datasets.fetch_atlas_smith_2009()
    atlas_networks = smith_atlas.rsn10








Import region extractor to extract atlas networks


.. 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_





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    /home/nicolas/GitRepos/nilearn-fork/nilearn/image/image.py:1106: FutureWarning: The parameter "sessions" will be removed in 0.9.0 release of Nilearn. Please use the parameter "runs" instead.
      data = signal.clean(




Visualization
Show region extraction results by importing image & plotting utilities


.. 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:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_001.png
          :alt: plot extract rois smith atlas
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_002.png
          :alt: plot extract rois smith atlas
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_003.png
          :alt: plot extract rois smith atlas
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_004.png
          :alt: plot extract rois smith atlas
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_005.png
          :alt: plot extract rois smith atlas
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_006.png
          :alt: plot extract rois smith atlas
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_007.png
          :alt: plot extract rois smith atlas
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/06_manipulating_images/images/sphx_glr_plot_extract_rois_smith_atlas_008.png
          :alt: plot extract rois smith atlas
          :class: sphx-glr-multi-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    /home/nicolas/GitRepos/nilearn-fork/nilearn/plotting/displays.py:101: UserWarning: No contour levels were found within the data range.
      im = getattr(ax, type)(data_2d.copy(),





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 2 minutes  39.428 seconds)


.. _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 <plot_extract_rois_smith_atlas.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_extract_rois_smith_atlas.ipynb <plot_extract_rois_smith_atlas.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_