.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/02_decoding/plot_haxby_searchlight_surface.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_02_decoding_plot_haxby_searchlight_surface.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_02_decoding_plot_haxby_searchlight_surface.py:


Cortical surface-based searchlight decoding
===========================================

This is a demo for surface-based searchlight decoding, as described in:
Chen, Y., Namburi, P., Elliott, L.T., Heinzle, J., Soon, C.S.,
Chee, M.W.L., and Haynes, J.-D. (2011). Cortical surface-based
searchlight decoding. NeuroImage 56, 582–592.

.. GENERATED FROM PYTHON SOURCE LINES 13-15

Load Haxby dataset
-------------------

.. GENERATED FROM PYTHON SOURCE LINES 15-26

.. code-block:: default

    import pandas as pd
    from nilearn import datasets

    # We fetch 2nd subject from haxby datasets (which is default)
    haxby_dataset = datasets.fetch_haxby()

    fmri_filename = haxby_dataset.func[0]
    labels = pd.read_csv(haxby_dataset.session_target[0], sep=" ")
    y = labels['labels']
    session = labels['chunks']








.. GENERATED FROM PYTHON SOURCE LINES 27-29

Restrict to faces and houses
------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 29-35

.. code-block:: default

    from nilearn.image import index_img
    condition_mask = y.isin(['face', 'house'])

    fmri_img = index_img(fmri_filename, condition_mask)
    y, session = y[condition_mask], session[condition_mask]








.. GENERATED FROM PYTHON SOURCE LINES 36-38

Surface bold response
----------------------

.. GENERATED FROM PYTHON SOURCE LINES 38-60

.. code-block:: default

    from nilearn import datasets, surface
    from sklearn import neighbors

    # Fetch a coarse surface of the left hemisphere only for speed
    fsaverage = datasets.fetch_surf_fsaverage(mesh='fsaverage5')
    hemi = 'left'

    # Average voxels 5 mm close to the 3d pial surface
    radius = 5.
    pial_mesh = fsaverage['pial_' + hemi]
    X = surface.vol_to_surf(fmri_img, pial_mesh, radius=radius).T

    # To define the :term:`BOLD` responses to be included within each searchlight "sphere"
    # we define an adjacency matrix based on the inflated surface vertices such
    # that nearby surfaces are concatenated within the same searchlight.

    infl_mesh = fsaverage['infl_' + hemi]
    coords, _ = surface.load_surf_mesh(infl_mesh)
    radius = 3.
    nn = neighbors.NearestNeighbors(radius=radius)
    adjacency = nn.fit(coords).radius_neighbors_graph(coords).tolil()








.. GENERATED FROM PYTHON SOURCE LINES 61-63

Searchlight computation
-----------------------

.. GENERATED FROM PYTHON SOURCE LINES 63-79

.. code-block:: default

    from sklearn.model_selection import KFold
    from sklearn.pipeline import make_pipeline
    from sklearn.preprocessing import StandardScaler
    from sklearn.linear_model import RidgeClassifier
    from nilearn.decoding.searchlight import search_light

    # Simple linear estimator preceded by a normalization step
    estimator = make_pipeline(StandardScaler(),
                              RidgeClassifier(alpha=10.))

    # Define cross-validation scheme
    cv = KFold(n_splits=3, shuffle=False)

    # Cross-validated search light
    scores = search_light(X, y, estimator, adjacency, cv=cv, n_jobs=1)








.. GENERATED FROM PYTHON SOURCE LINES 80-82

Visualization
-------------

.. GENERATED FROM PYTHON SOURCE LINES 82-89

.. code-block:: default

    from nilearn import plotting
    chance = .5
    plotting.plot_surf_stat_map(infl_mesh, scores - chance,
                                view='medial', colorbar=True, threshold=0.1,
                                bg_map=fsaverage['sulc_' + hemi],
                                title='Accuracy map, left hemisphere')
    plotting.show()



.. image-sg:: /auto_examples/02_decoding/images/sphx_glr_plot_haxby_searchlight_surface_001.png
   :alt: Accuracy map, left hemisphere
   :srcset: /auto_examples/02_decoding/images/sphx_glr_plot_haxby_searchlight_surface_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** ( 1 minutes  21.307 seconds)

**Estimated memory usage:**  916 MB


.. _sphx_glr_download_auto_examples_02_decoding_plot_haxby_searchlight_surface.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/02_decoding/plot_haxby_searchlight_surface.ipynb
      :alt: Launch binder
      :width: 150 px


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

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



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

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


.. only:: html

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

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