.. only:: html

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        Click :ref:`here <sphx_glr_download_auto_examples_01_plotting_plot_surf_stat_map.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_01_plotting_plot_surf_stat_map.py:


Seed-based connectivity on the surface
=======================================

The dataset that is a subset of the enhanced NKI Rockland sample
(http://fcon_1000.projects.nitrc.org/indi/enhanced/, Nooner et al, 2012)

Resting state fMRI scans (TR=645ms) of 102 subjects were preprocessed
(https://github.com/fliem/nki_nilearn) and projected onto the Freesurfer
fsaverage5 template (Dale et al, 1999, Fischl et al, 1999). For this example
we use the time series of a single subject's left hemisphere.

The Destrieux parcellation (Destrieux et al, 2010) in fsaverage5 space as
distributed with Freesurfer is used to select a seed region in the posterior
cingulate cortex.

Functional connectivity of the seed region to all other cortical nodes in the
same hemisphere is calculated using Pearson product-moment correlation
coefficient.

The :func:`nilearn.plotting.plot_surf_stat_map` function is used
to plot the resulting statistical map on the (inflated) pial surface.

See also :ref:`for a similar example but using volumetric input data
<sphx_glr_auto_examples_03_connectivity_plot_seed_to_voxel_correlation.py>`.

See :ref:`plotting` for more details on plotting tools.

References
----------

Nooner et al, (2012). The NKI-Rockland Sample: A model for accelerating the
pace of discovery science in psychiatry. Frontiers in neuroscience 6, 152.
URL http://dx.doi.org/10.3389/fnins.2012.00152

Dale et al, (1999). Cortical surface-based analysis.I. Segmentation and
surface reconstruction. Neuroimage 9.
URL http://dx.doi.org/10.1006/nimg.1998.0395

Fischl et al, (1999). Cortical surface-based analysis. II: Inflation,
flattening, and a surface-based coordinate system. Neuroimage 9.
http://dx.doi.org/10.1006/nimg.1998.0396

Destrieux et al, (2010). Automatic parcellation of human cortical gyri and
sulci using standard anatomical nomenclature. NeuroImage, 53, 1.
URL http://dx.doi.org/10.1016/j.neuroimage.2010.06.010.

Retrieving the data
-------------------


.. code-block:: default


    # NKI resting state data from nilearn
    from nilearn import datasets

    nki_dataset = datasets.fetch_surf_nki_enhanced(n_subjects=1)

    # The nki dictionary contains file names for the data
    # of all downloaded subjects.
    print(('Resting state data of the first subjects on the '
           'fsaverag5 surface left hemisphere is at: %s' %
          nki_dataset['func_left'][0]))

    # Destrieux parcellation for left hemisphere in fsaverage5 space
    destrieux_atlas = datasets.fetch_atlas_surf_destrieux()
    parcellation = destrieux_atlas['map_left']
    labels = destrieux_atlas['labels']

    # Fsaverage5 surface template
    fsaverage = datasets.fetch_surf_fsaverage()

    # The fsaverage dataset contains file names pointing to
    # the file locations
    print('Fsaverage5 pial surface of left hemisphere is at: %s' %
          fsaverage['pial_left'])
    print('Fsaverage5 inflated surface of left hemisphere is at: %s' %
          fsaverage['infl_left'])
    print('Fsaverage5 sulcal depth map of left hemisphere is at: %s' %
          fsaverage['sulc_left'])





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

 Out:

 .. code-block:: none

    Resting state data of the first subjects on the fsaverag5 surface left hemisphere is at: /home/nicolas/nilearn_data/nki_enhanced_surface/A00028185/A00028185_left_preprocessed_fwhm6.gii
    Fsaverage5 pial surface of left hemisphere is at: /home/nicolas/GitRepos/nilearn-fork/nilearn/datasets/data/fsaverage5/pial_left.gii.gz
    Fsaverage5 inflated surface of left hemisphere is at: /home/nicolas/GitRepos/nilearn-fork/nilearn/datasets/data/fsaverage5/infl_left.gii.gz
    Fsaverage5 sulcal depth map of left hemisphere is at: /home/nicolas/GitRepos/nilearn-fork/nilearn/datasets/data/fsaverage5/sulc_left.gii.gz




Extracting the seed time series
--------------------------------


.. code-block:: default


    # Load resting state time series from nilearn
    from nilearn import surface

    timeseries = surface.load_surf_data(nki_dataset['func_left'][0])

    # Extract seed region via label
    pcc_region = b'G_cingul-Post-dorsal'

    import numpy as np
    pcc_labels = np.where(parcellation == labels.index(pcc_region))[0]

    # Extract time series from seed region
    seed_timeseries = np.mean(timeseries[pcc_labels], axis=0)








Calculating seed-based functional connectivity
----------------------------------------------


.. code-block:: default


    # Calculate Pearson product-moment correlation coefficient between seed
    # time series and timeseries of all cortical nodes of the hemisphere
    from scipy import stats

    stat_map = np.zeros(timeseries.shape[0])
    for i in range(timeseries.shape[0]):
        stat_map[i] = stats.pearsonr(seed_timeseries, timeseries[i])[0]

    # Re-mask previously masked nodes (medial wall)
    stat_map[np.where(np.mean(timeseries, axis=1) == 0)] = 0





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

 Out:

 .. code-block:: none

    /home/nicolas/anaconda3/envs/nilearn/lib/python3.8/site-packages/scipy/stats/stats.py:3913: PearsonRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.
      warnings.warn(PearsonRConstantInputWarning())




Display ROI on surface


.. code-block:: default


    # Transform ROI indices in ROI map
    pcc_map = np.zeros(parcellation.shape[0], dtype=int)
    pcc_map[pcc_labels] = 1

    from nilearn import plotting

    plotting.plot_surf_roi(fsaverage['pial_left'], roi_map=pcc_map,
                           hemi='left', view='medial',
                           bg_map=fsaverage['sulc_left'], bg_on_data=True,
                           title='PCC Seed')




.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_surf_stat_map_001.png
    :alt: PCC Seed
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none


    <Figure size 400x400 with 1 Axes>



Display unthresholded stat map with a slightly dimmed background


.. code-block:: default

    plotting.plot_surf_stat_map(fsaverage['pial_left'], stat_map=stat_map,
                                hemi='left', view='medial', colorbar=True,
                                bg_map=fsaverage['sulc_left'], bg_on_data=True,
                                darkness=.3, title='Correlation map')




.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_surf_stat_map_002.png
    :alt: Correlation map
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none


    <Figure size 470x400 with 2 Axes>



Many different options are available for plotting, for example thresholding,
or using custom colormaps


.. code-block:: default

    plotting.plot_surf_stat_map(fsaverage['pial_left'], stat_map=stat_map,
                                hemi='left', view='medial', colorbar=True,
                                bg_map=fsaverage['sulc_left'], bg_on_data=True,
                                cmap='Spectral', threshold=.5,
                                title='Threshold and colormap')




.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_surf_stat_map_003.png
    :alt: Threshold and colormap
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none


    <Figure size 470x400 with 2 Axes>



Here the surface is plotted in a lateral view without a background map.
To capture 3D structure without depth information, the default is to plot a
half transparent surface.
Note that you can also control the transparency with a background map using
the alpha parameter.


.. code-block:: default

    plotting.plot_surf_stat_map(fsaverage['pial_left'], stat_map=stat_map,
                                hemi='left', view='lateral', colorbar=True,
                                cmap='Spectral', threshold=.5,
                                title='Plotting without background')




.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_surf_stat_map_004.png
    :alt: Plotting without background
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none


    <Figure size 470x400 with 2 Axes>



The plots can be saved to file, in which case the display is closed after
creating the figure


.. code-block:: default

    plotting.plot_surf_stat_map(fsaverage['infl_left'], stat_map=stat_map,
                                hemi='left', bg_map=fsaverage['sulc_left'],
                                bg_on_data=True, threshold=.5, colorbar=True,
                                output_file='plot_surf_stat_map.png')

    plotting.show()








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

   **Total running time of the script:** ( 0 minutes  5.441 seconds)


.. _sphx_glr_download_auto_examples_01_plotting_plot_surf_stat_map.py:


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