.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/07_advanced/plot_ica_resting_state.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <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 :term:`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 :term:`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).

.. include:: ../../../examples/masker_note.rst

.. GENERATED FROM PYTHON SOURCE LINES 25-26

Load movie watching dataset

.. GENERATED FROM PYTHON SOURCE LINES 26-35

.. code-block:: Python

    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(f"First subject functional nifti image (4D) is at: {dataset.func[0]}")





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

 .. code-block:: none

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




.. GENERATED FROM PYTHON SOURCE LINES 36-37

Preprocess

.. GENERATED FROM PYTHON SOURCE LINES 37-52

.. code-block:: Python

    from nilearn.maskers 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="zscore_sample",
    )
    data_masked = masker.fit_transform(func_filename)








.. GENERATED FROM PYTHON SOURCE LINES 53-54

Apply ICA

.. GENERATED FROM PYTHON SOURCE LINES 54-73

.. code-block:: Python

    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) < 0.8] = 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

 .. code-block:: none

    /home/himanshu/.local/miniconda3/envs/nilearnpy/lib/python3.12/site-packages/sklearn/decomposition/_fastica.py:128: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.
      warnings.warn(




.. GENERATED FROM PYTHON SOURCE LINES 74-75

Visualize the results

.. GENERATED FROM PYTHON SOURCE LINES 75-88

.. code-block:: Python

    from nilearn import image
    from nilearn.plotting import plot_stat_map, show

    # Show some interesting components

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

    *

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






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

   **Total running time of the script:** (0 minutes 12.777 seconds)

**Estimated memory usage:**  533 MB


.. _sphx_glr_download_auto_examples_07_advanced_plot_ica_resting_state.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/nilearn/nilearn/0.10.4?urlpath=lab/tree/notebooks/auto_examples/07_advanced/plot_ica_resting_state.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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