.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_07_advanced_plot_localizer_mass_univariate_methods.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_localizer_mass_univariate_methods.py:


Massively univariate analysis of a motor task from the Localizer dataset
========================================================================

This example shows the results obtained in a massively univariate
analysis performed at the inter-subject level with various methods.
We use the [left button press (auditory cue)] task from the Localizer
dataset and seek association between the contrast values and a variate
that measures the speed of pseudo-word reading. No confounding variate
is included in the model.

1. A standard Anova is performed. Data smoothed at 5 voxels FWHM are used.

2. A permuted Ordinary Least Squares algorithm is run at each voxel. Data
   smoothed at 5 voxels FWHM are used.


.. code-block:: default

    # Author: Virgile Fritsch, <virgile.fritsch@inria.fr>, May. 2014
    import numpy as np
    import matplotlib.pyplot as plt
    from nilearn import datasets
    from nilearn.input_data import NiftiMasker
    from nilearn.mass_univariate import permuted_ols
    from nilearn.image import get_data








Load Localizer contrast


.. code-block:: default

    n_samples = 94
    localizer_dataset = datasets.fetch_localizer_contrasts(
        ['left button press (auditory cue)'], n_subjects=n_samples)

    # print basic information on the dataset
    print('First contrast nifti image (3D) is located at: %s' %
          localizer_dataset.cmaps[0])

    tested_var = localizer_dataset.ext_vars['pseudo']
    # Quality check / Remove subjects with bad tested variate
    mask_quality_check = np.where(tested_var != b'n/a')[0]
    n_samples = mask_quality_check.size
    contrast_map_filenames = [localizer_dataset.cmaps[i]
                              for i in mask_quality_check]
    tested_var = tested_var[mask_quality_check].astype(float).reshape((-1, 1))
    print("Actual number of subjects after quality check: %d" % n_samples)






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

 Out:

 .. code-block:: none

    /usr/lib/python3/dist-packages/numpy/lib/npyio.py:2358: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.
      output = genfromtxt(fname, **kwargs)
    First contrast nifti image (3D) is located at: /home/varoquau/nilearn_data/brainomics_localizer/brainomics_data/S01/cmaps_LeftAuditoryClick.nii.gz
    Actual number of subjects after quality check: 89




Mask data


.. code-block:: default

    nifti_masker = NiftiMasker(
        smoothing_fwhm=5,
        memory='nilearn_cache', memory_level=1)  # cache options
    fmri_masked = nifti_masker.fit_transform(contrast_map_filenames)









Anova (parametric F-scores)


.. code-block:: default

    from sklearn.feature_selection import f_regression
    _, pvals_anova = f_regression(fmri_masked, tested_var, center=True)
    pvals_anova *= fmri_masked.shape[1]
    pvals_anova[np.isnan(pvals_anova)] = 1
    pvals_anova[pvals_anova > 1] = 1
    neg_log_pvals_anova = - np.log10(pvals_anova)
    neg_log_pvals_anova_unmasked = nifti_masker.inverse_transform(
        neg_log_pvals_anova)






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

 Out:

 .. code-block:: none

    /home/varoquau/dev/scikit-learn/sklearn/utils/validation.py:73: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
      return f(**kwargs)




Perform massively univariate analysis with permuted OLS


.. code-block:: default

    neg_log_pvals_permuted_ols, _, _ = permuted_ols(
        tested_var, fmri_masked,
        model_intercept=True,
        n_perm=5000,  # 5,000 for the sake of time. Idealy, this should be 10,000
        n_jobs=1)  # can be changed to use more CPUs
    neg_log_pvals_permuted_ols_unmasked = nifti_masker.inverse_transform(
        np.ravel(neg_log_pvals_permuted_ols))









Visualization


.. code-block:: default

    from nilearn.plotting import plot_stat_map, show

    # Various plotting parameters
    z_slice = 12  # plotted slice

    threshold = - np.log10(0.1)  # 10% corrected
    vmax = min(np.amax(neg_log_pvals_permuted_ols),
               np.amax(neg_log_pvals_anova))

    # Plot Anova p-values
    fig = plt.figure(figsize=(5, 7), facecolor='k')

    display = plot_stat_map(neg_log_pvals_anova_unmasked,
                            threshold=threshold,
                            display_mode='z', cut_coords=[z_slice],
                            figure=fig, vmax=vmax, black_bg=True)

    n_detections = (get_data(neg_log_pvals_anova_unmasked) > threshold).sum()
    title = ('Negative $\\log_{10}$ p-values'
             '\n(Parametric + Bonferroni correction)'
             '\n%d detections') % n_detections

    display.title(title, y=1.2)

    # Plot permuted OLS p-values
    fig = plt.figure(figsize=(5, 7), facecolor='k')

    display = plot_stat_map(neg_log_pvals_permuted_ols_unmasked,
                            threshold=threshold,
                            display_mode='z', cut_coords=[z_slice],
                            figure=fig, vmax=vmax, black_bg=True)

    n_detections = (get_data(neg_log_pvals_permuted_ols_unmasked)
                    > threshold).sum()
    title = ('Negative $\\log_{10}$ p-values'
             '\n(Non-parametric + max-type correction)'
             '\n%d detections') % n_detections

    display.title(title, y=1.2)

    show()



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


    *

      .. image:: /auto_examples/07_advanced/images/sphx_glr_plot_localizer_mass_univariate_methods_001.png
          :alt: plot localizer mass univariate methods
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/07_advanced/images/sphx_glr_plot_localizer_mass_univariate_methods_002.png
          :alt: plot localizer mass univariate methods
          :class: sphx-glr-multi-img


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

 Out:

 .. code-block:: none

    /home/varoquau/dev/nilearn/nilearn/plotting/displays.py:1608: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
      ax = fh.add_axes([fraction * index * (x1 - x0) + x0, y0,





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

   **Total running time of the script:** ( 1 minutes  2.105 seconds)


.. _sphx_glr_download_auto_examples_07_advanced_plot_localizer_mass_univariate_methods.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_localizer_mass_univariate_methods.ipynb
      :width: 150 px


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

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



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

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


.. only:: html

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

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