.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_07_advanced_plot_ica_resting_state.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_07_advanced_plot_ica_resting_state.py:


Multivariate decompositions: Independent component analysis of fMRI
===================================================================


This example is meant to demonstrate nilearn as a low-level tools used to
combine feature extraction with a multivariate decomposition algorithm
for movie-watching.

This example is a toy. To apply ICA to fmri timeseries data, it is advised
to look at the example
:ref:`sphx_glr_auto_examples_03_connectivity_plot_compare_decomposition.py`.

The example here applies the scikit-learn ICA to movie watching timeseries data.
Note that following the code in the example, any unsupervised
decomposition model, or other latent-factor models, can be applied to
the data, as the scikit-learn API enables to exchange them as almost
black box (though the relevant parameter for brain maps might no longer
be given by a call to fit_transform).

Load movie watching dataset


.. code-block:: default

    from nilearn import datasets
    # Here we use only single subject to get faster-running code.
    dataset = datasets.fetch_development_fmri(n_subjects=1)
    func_filename = dataset.func[0]

    # print basic information on the dataset
    print('First subject functional nifti image (4D) is at: %s' %
          dataset.func[0])  # 4D data






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

 Out:

 .. code-block:: none

    First subject functional nifti image (4D) is at: /home/varoquau/nilearn_data/development_fmri/development_fmri/sub-pixar123_task-pixar_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz




Preprocess


.. code-block:: default

    from nilearn.input_data import NiftiMasker

    # This is fmri timeseries data: the background has not been removed yet,
    # thus we need to use mask_strategy='epi' to compute the mask from the
    # EPI images
    masker = NiftiMasker(smoothing_fwhm=8, memory='nilearn_cache', memory_level=1,
                         mask_strategy='epi', standardize=True)
    data_masked = masker.fit_transform(func_filename)









Apply ICA


.. code-block:: default


    from sklearn.decomposition import FastICA
    n_components = 10
    ica = FastICA(n_components=n_components, random_state=42)
    components_masked = ica.fit_transform(data_masked.T).T

    # Normalize estimated components, for thresholding to make sense
    components_masked -= components_masked.mean(axis=0)
    components_masked /= components_masked.std(axis=0)
    # Threshold
    import numpy as np
    components_masked[np.abs(components_masked) < .8] = 0

    # Now invert the masking operation, going back to a full 3D
    # representation
    component_img = masker.inverse_transform(components_masked)





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

 Out:

 .. code-block:: none

    /home/varoquau/dev/scikit-learn/sklearn/decomposition/_fastica.py:118: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.
      warnings.warn('FastICA did not converge. Consider increasing '




Visualize the results


.. code-block:: default


    # Show some interesting components
    from nilearn import image
    from nilearn.plotting import plot_stat_map, show

    # Use the mean as a background
    mean_img = image.mean_img(func_filename)

    plot_stat_map(image.index_img(component_img, 0), mean_img)

    plot_stat_map(image.index_img(component_img, 1), mean_img)

    show()



.. rst-class:: sphx-glr-horizontal


    *

      .. image:: /auto_examples/07_advanced/images/sphx_glr_plot_ica_resting_state_001.png
          :alt: plot ica resting state
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/07_advanced/images/sphx_glr_plot_ica_resting_state_002.png
          :alt: plot ica resting state
          :class: sphx-glr-multi-img






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

   **Total running time of the script:** ( 0 minutes  8.707 seconds)


.. _sphx_glr_download_auto_examples_07_advanced_plot_ica_resting_state.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example


  .. container:: binder-badge

    .. image:: https://mybinder.org/badge_logo.svg
      :target: https://mybinder.org/v2/gh/nilearn/nilearn.github.io/master?filepath=examples/auto_examples/07_advanced/plot_ica_resting_state.ipynb
      :width: 150 px


  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_ica_resting_state.py <plot_ica_resting_state.py>`



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

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


.. only:: html

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

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