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"""
=============================
Morph surface source estimate
=============================
This example demonstrates how to morph an individual subject's
:class:`mne.SourceEstimate` to a common reference space. We achieve this using
:class:`mne.SourceMorph`. Pre-computed data will be morphed based on
a spherical representation of the cortex computed using the spherical
registration of
:ref:`FreeSurfer <sphx_glr_auto_tutorials_plot_background_freesurfer.py>`
(https://surfer.nmr.mgh.harvard.edu/fswiki/SurfaceRegAndTemplates) [1]_. This
transform will be used to morph the surface vertices of the subject towards the
reference vertices. Here we will use 'fsaverage' as a reference space (see
https://surfer.nmr.mgh.harvard.edu/fswiki/FsAverage).
The transformation will be applied to the surface source estimate. A plot
depicting the successful morph will be created for the spherical and inflated
surface representation of ``'fsaverage'``, overlaid with the morphed surface
source estimate.
References
----------
.. [1] Greve D. N., Van der Haegen L., Cai Q., Stufflebeam S., Sabuncu M.
R., Fischl B., Brysbaert M.
A Surface-based Analysis of Language Lateralization and Cortical
Asymmetry. Journal of Cognitive Neuroscience 25(9), 1477-1492, 2013.
.. note:: For a tutorial about morphing see:
:ref:`sphx_glr_auto_tutorials_plot_morph_stc.py`.
"""
# Author: Tommy Clausner <tommy.clausner@gmail.com>
#
# License: BSD (3-clause)
import os
import mne
from mne.datasets import sample
print(__doc__)
###############################################################################
# Setup paths
sample_dir_raw = sample.data_path()
sample_dir = os.path.join(sample_dir_raw, 'MEG', 'sample')
subjects_dir = os.path.join(sample_dir_raw, 'subjects')
fname_stc = os.path.join(sample_dir, 'sample_audvis-meg')
###############################################################################
# Load example data
# Read stc from file
stc = mne.read_source_estimate(fname_stc, subject='sample')
###############################################################################
# Setting up SourceMorph for SourceEstimate
# -----------------------------------------
#
# In MNE surface source estimates represent the source space simply as
# lists of vertices (see
# :ref:`sphx_glr_auto_tutorials_plot_object_source_estimate.py`).
# This list can either be obtained from
# :class:`mne.SourceSpaces` (src) or from the ``stc`` itself.
#
# Since the default ``spacing`` (resolution of surface mesh) is ``5`` and
# ``subject_to`` is set to 'fsaverage', :class:`mne.SourceMorph` will use
# default ico-5 ``fsaverage`` vertices to morph, which are the special
# values ``[np.arange(10242)] * 2``.
#
# .. note:: This is not generally true for other subjects! The set of vertices
# used for ``fsaverage`` with ico-5 spacing was designed to be
# special. ico-5 spacings for other subjects (or other spacings
# for fsaverage) must be calculated and will not be consecutive
# integers.
#
# If src was not defined, the morph will actually not be precomputed, because
# we lack the vertices *from* that we want to compute. Instead the morph will
# be set up and when applying it, the actual transformation will be computed on
# the fly.
#
# Initialize SourceMorph for SourceEstimate
morph = mne.compute_source_morph(stc, subject_from='sample',
subject_to='fsaverage',
subjects_dir=subjects_dir)
###############################################################################
# Apply morph to (Vector) SourceEstimate
# --------------------------------------
#
# The morph will be applied to the source estimate data, by giving it as the
# first argument to the morph we computed above.
stc_fsaverage = morph.apply(stc)
###############################################################################
# Plot results
# ------------
# Define plotting parameters
surfer_kwargs = dict(
hemi='lh', subjects_dir=subjects_dir,
clim=dict(kind='value', lims=[8, 12, 15]), views='lateral',
initial_time=0.09, time_unit='s', size=(800, 800),
smoothing_steps=5)
# As spherical surface
brain = stc_fsaverage.plot(surface='sphere', **surfer_kwargs)
# Add title
brain.add_text(0.1, 0.9, 'Morphed to fsaverage (spherical)', 'title',
font_size=16)
###############################################################################
# As inflated surface
brain_inf = stc_fsaverage.plot(surface='inflated', **surfer_kwargs)
# Add title
brain_inf.add_text(0.1, 0.9, 'Morphed to fsaverage (inflated)', 'title',
font_size=16)
###############################################################################
# Reading and writing SourceMorph from and to disk
# ------------------------------------------------
#
# An instance of SourceMorph can be saved, by calling
# :meth:`morph.save <mne.SourceMorph.save>`.
#
# This method allows for specification of a filename under which the ``morph``
# will be save in ".h5" format. If no file extension is provided, "-morph.h5"
# will be appended to the respective defined filename::
#
# >>> morph.save('my-file-name')
#
# Reading a saved source morph can be achieved by using
# :func:`mne.read_source_morph`::
#
# >>> morph = mne.read_source_morph('my-file-name-morph.h5')
#
# Once the environment is set up correctly, no information such as
# ``subject_from`` or ``subjects_dir`` must be provided, since it can be
# inferred from the data and use morph to 'fsaverage' by default. SourceMorph
# can further be used without creating an instance and assigning it to a
# variable. Instead :func:`mne.compute_source_morph` and
# :meth:`mne.SourceMorph.apply` can be
# easily chained into a handy one-liner. Taking this together the shortest
# possible way to morph data directly would be:
stc_fsaverage = mne.compute_source_morph(stc,
subjects_dir=subjects_dir).apply(stc)
|
