.. _sphx_glr_auto_tutorials_plot_compute_forward.py:


.. _tut_forward:

=====================
Compute forward model
=====================

Here we'll first compute a source space, then the bem model
and finally the forward solution.



.. code-block:: python

    # Author: Denis A. Enegemann
    # License: BSD 3 clause

    import os.path as op
    import mne
    import hcp







we assume our data is inside a designated folder under $HOME



.. code-block:: python

    storage_dir = op.expanduser('~/mne-hcp-data')
    hcp_path = op.join(storage_dir, 'HCP')
    recordings_path = op.join(storage_dir, 'hcp-meg')
    subjects_dir = op.join(storage_dir, 'hcp-subjects')
    subject = '105923'  # our test subject







and we assume to have the downloaded data, the MNE/freesurfer style
anatomy directory, and the MNE style MEG directory.
these can be obtained from :func:`make_mne_anatomy`.
See also :ref:`tut_make_anatomy`.


first we read the coregistration.



.. code-block:: python


    head_mri_t = mne.read_trans(
        op.join(recordings_path, subject, '{}-head_mri-trans.fif'.format(
                subject)))







Now we can setup our source model.
Note that spacing has to be set to 'all' since no common MNE resampling
scheme has been employed in the HCP pipelines.
Since this will take very long time to compute and at this point no other
decimation scheme is available inside MNE, we will compute the source
space on fsaverage, the freesurfer average brain, and morph it onto
the subject's native space. With `oct6` we have ~8000 dipole locations.



.. code-block:: python


    src_fsaverage = mne.setup_source_space(
        subject='fsaverage', subjects_dir=subjects_dir, add_dist=False,
        spacing='oct6', overwrite=True)

    # now we morph it onto the subject.

    src_subject = mne.morph_source_spaces(
        src_fsaverage, subject, subjects_dir=subjects_dir)







For the same reason `ico` has to be set to `None` when computing the bem.
The headshape is not computed with MNE and has a none standard configuration.



.. code-block:: python


    bems = mne.make_bem_model(subject, conductivity=(0.3,),
                              subjects_dir=subjects_dir,
                              ico=None)  # ico = None for morphed SP.
    bem_sol = mne.make_bem_solution(bems)
    bem_sol['surfs'][0]['coord_frame'] = 5







Now we can read the channels that we want to map to the cortical locations.
Then we can compute the forward solution.



.. code-block:: python


    info = hcp.read_info(subject=subject, hcp_path=hcp_path, data_type='rest',
                         run_index=0)

    picks = mne.pick_types(info, meg=True, ref_meg=False)
    info = mne.pick_info(info, picks)

    fwd = mne.make_forward_solution(info, trans=head_mri_t, bem=bem_sol,
                                    src=src_subject)
    mag_map = mne.sensitivity_map(
        fwd, projs=None, ch_type='mag', mode='fixed', exclude=[], verbose=None)







we display sensitivity map on the original surface with little smoothing
and admire the expected curvature-driven sensitivity pattern.



.. code-block:: python


    mag_map = mag_map.to_original_src(src_fsaverage, subjects_dir=subjects_dir)
    mag_map.plot(subject='fsaverage', subjects_dir=subjects_dir,
                 clim=dict(kind='percent', lims=[0, 50, 99]),
                 smoothing_steps=2)



.. image:: /auto_tutorials/images/sphx_glr_plot_compute_forward_001.png
    :align: center


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

 Out::

    Updating smoothing matrix, be patient..
    Smoothing matrix creation, step 1
    Smoothing matrix creation, step 2
    colormap: fmin=1.99e-02 fmid=4.15e-01 fmax=9.01e-01 transparent=1


**Total running time of the script:** ( 1 minutes  9.762 seconds)



.. container:: sphx-glr-download

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



.. container:: sphx-glr-download

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

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

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