.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/05_glm_second_level/plot_proportion_activated_voxels.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_05_glm_second_level_plot_proportion_activated_voxels.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_05_glm_second_level_plot_proportion_activated_voxels.py:


Second-level fMRI model: true positive proportion in clusters
=============================================================

This script showcases
the so-called "All resolution inference" procedure
(:footcite:t:`Rosenblatt2018`),
in which the proportion of true discoveries in arbitrary clusters is estimated.
The clusters can be defined from the input image, i.e. in a circular way, as
the error control accounts for arbitrary cluster selection.

.. GENERATED FROM PYTHON SOURCE LINES 14-20

Fetch dataset
--------------
We download a list of left vs right button press contrasts from a
localizer dataset. Note that we fetch individual t-maps that represent the
:term:`BOLD` activity estimate divided
by the uncertainty about this estimate.

.. GENERATED FROM PYTHON SOURCE LINES 20-28

.. code-block:: Python

    from nilearn.datasets import fetch_localizer_contrasts

    n_subjects = 16
    data = fetch_localizer_contrasts(
        ["left vs right button press"],
        n_subjects,
        legacy_format=False,
    )







.. GENERATED FROM PYTHON SOURCE LINES 29-33

Estimate second level model
---------------------------
We define the input maps and the design matrix for the second level model
and fit it.

.. GENERATED FROM PYTHON SOURCE LINES 33-40

.. code-block:: Python

    import pandas as pd

    second_level_input = data["cmaps"]
    design_matrix = pd.DataFrame(
        [1] * len(second_level_input), columns=["intercept"]
    )








.. GENERATED FROM PYTHON SOURCE LINES 41-42

Model specification and fit

.. GENERATED FROM PYTHON SOURCE LINES 42-49

.. code-block:: Python

    from nilearn.glm.second_level import SecondLevelModel

    second_level_model = SecondLevelModel(smoothing_fwhm=8.0, n_jobs=2)
    second_level_model = second_level_model.fit(
        second_level_input, design_matrix=design_matrix
    )








.. GENERATED FROM PYTHON SOURCE LINES 50-52

To estimate the :term:`contrast` is very simple.
We can just provide the column name of the design matrix.

.. GENERATED FROM PYTHON SOURCE LINES 52-54

.. code-block:: Python

    z_map = second_level_model.compute_contrast(output_type="z_score")








.. GENERATED FROM PYTHON SOURCE LINES 55-57

We threshold the second level :term:`contrast`
at uncorrected p < 0.001 and plot

.. GENERATED FROM PYTHON SOURCE LINES 57-89

.. code-block:: Python

    from scipy.stats import norm

    p_val = 0.001
    p001_uncorrected = norm.isf(p_val)

    from nilearn.glm import cluster_level_inference

    proportion_true_discoveries_img = cluster_level_inference(
        z_map, threshold=[3, 4, 5], alpha=0.05
    )

    from nilearn import plotting

    plotting.plot_stat_map(
        proportion_true_discoveries_img,
        threshold=0.0,
        display_mode="z",
        vmax=1,
        colorbar=True,
        title="group left-right button press, proportion true positives",
    )

    plotting.plot_stat_map(
        z_map,
        threshold=p001_uncorrected,
        colorbar=True,
        display_mode="z",
        title="group left-right button press (uncorrected p < 0.001)",
    )

    plotting.show()




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


    *

      .. image-sg:: /auto_examples/05_glm_second_level/images/sphx_glr_plot_proportion_activated_voxels_001.png
         :alt: plot proportion activated voxels
         :srcset: /auto_examples/05_glm_second_level/images/sphx_glr_plot_proportion_activated_voxels_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/05_glm_second_level/images/sphx_glr_plot_proportion_activated_voxels_002.png
         :alt: plot proportion activated voxels
         :srcset: /auto_examples/05_glm_second_level/images/sphx_glr_plot_proportion_activated_voxels_002.png
         :class: sphx-glr-multi-img





.. GENERATED FROM PYTHON SOURCE LINES 90-94

References
----------

 .. footbibliography::


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

   **Total running time of the script:** (0 minutes 6.859 seconds)

**Estimated memory usage:**  9 MB


.. _sphx_glr_download_auto_examples_05_glm_second_level_plot_proportion_activated_voxels.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/05_glm_second_level/plot_proportion_activated_voxels.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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