.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_04_glm_first_level_plot_adhd_dmn.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_04_glm_first_level_plot_adhd_dmn.py:

Default Mode Network extraction of AHDH dataset
===============================================

This example shows a full step-by-step workflow of fitting a GLM to data
extracted from a seed on the Posterior Cingulate Cortex and saving the results.

More specifically:

1. A sequence of fMRI volumes are loaded.
2. A design matrix with the Posterior Cingulate Cortex seed is defined.
3. A GLM is applied to the dataset (effect/covariance, then contrast estimation).
4. The Default Mode Network is displayed.


.. code-block:: default

    import numpy as np

    from nilearn import datasets, plotting
    from nilearn.input_data import NiftiSpheresMasker

    from nilearn.glm.first_level import FirstLevelModel
    from nilearn.glm.first_level import make_first_level_design_matrix








Prepare data and analysis parameters
-------------------------------------
Prepare the data.


.. code-block:: default

    adhd_dataset = datasets.fetch_adhd(n_subjects=1)

    # Prepare timing
    t_r = 2.
    slice_time_ref = 0.
    n_scans = 176

    # Prepare seed
    pcc_coords = (0, -53, 26)





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

 Out:

 .. code-block:: none

    /home/varoquau/dev/nilearn/nilearn/datasets/func.py:438: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.
      phenotypic = np.genfromtxt(phenotypic, names=True, delimiter=',',




Estimate contrasts
------------------
Specify the contrasts.


.. code-block:: default

    seed_masker = NiftiSpheresMasker([pcc_coords], radius=10, detrend=True,
                                     standardize=True, low_pass=0.1,
                                     high_pass=0.01, t_r=2.,
                                     memory='nilearn_cache',
                                     memory_level=1, verbose=0)
    seed_time_series = seed_masker.fit_transform(adhd_dataset.func[0])
    frametimes = np.linspace(0, (n_scans - 1) * t_r, n_scans)
    design_matrix = make_first_level_design_matrix(frametimes, hrf_model='spm',
                                                   add_regs=seed_time_series,
                                                   add_reg_names=["pcc_seed"])
    dmn_contrast = np.array([1] + [0] * (design_matrix.shape[1] - 1))
    contrasts = {'seed_based_glm': dmn_contrast}








Perform first level analysis
----------------------------
Setup and fit GLM.


.. code-block:: default

    first_level_model = FirstLevelModel(t_r=t_r, slice_time_ref=slice_time_ref)
    first_level_model = first_level_model.fit(run_imgs=adhd_dataset.func[0],
                                        design_matrices=design_matrix)





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

 Out:

 .. code-block:: none

    /home/varoquau/dev/nilearn/nilearn/glm/first_level/first_level.py:61: UserWarning: Mean values of 0 observed.The data have probably been centered.Scaling might not work as expected
      warn('Mean values of 0 observed.'




Estimate the contrast.


.. code-block:: default

    print('Contrast seed_based_glm computed.')
    z_map = first_level_model.compute_contrast(contrasts['seed_based_glm'],
                                               output_type='z_score')

    # Saving snapshots of the contrasts
    filename = 'dmn_z_map.png'
    display = plotting.plot_stat_map(z_map, threshold=3.0, title='Seed based GLM',
                                     cut_coords=pcc_coords)
    display.add_markers(marker_coords=[pcc_coords], marker_color='g',
                        marker_size=300)
    display.savefig(filename)
    print("Save z-map in '{0}'.".format(filename))




.. image:: /auto_examples/04_glm_first_level/images/sphx_glr_plot_adhd_dmn_001.png
    :alt: plot adhd dmn
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none

    Contrast seed_based_glm computed.
    Save z-map in 'dmn_z_map.png'.




Generating a report
-------------------
It can be useful to quickly generate a
portable, ready-to-view report with most of the pertinent information.
This is easy to do if you have a fitted model and the list of contrasts,
which we do here.


.. code-block:: default


    from nilearn.reporting import make_glm_report

    report = make_glm_report(first_level_model,
                             contrasts=contrasts,
                             title='ADHD DMN Report',
                             cluster_threshold=15,
                             min_distance=8.,
                             plot_type='glass',
                             )








We have several ways to access the report:


.. code-block:: default


    # report  # This report can be viewed in a notebook
    # report.save_as_html('report.html')
    # report.open_in_browser()








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

   **Total running time of the script:** ( 0 minutes  19.524 seconds)


.. _sphx_glr_download_auto_examples_04_glm_first_level_plot_adhd_dmn.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/04_glm_first_level/plot_adhd_dmn.ipynb
      :width: 150 px


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

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



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

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


.. only:: html

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

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