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# -*- coding: utf-8 -*-
"""
.. _ex-mixed-source-space-inverse:
=====================================================================
Compute MNE inverse solution on evoked data with a mixed source space
=====================================================================
Create a mixed source space and compute an MNE inverse solution on an
evoked dataset.
"""
# Author: Annalisa Pascarella <a.pascarella@iac.cnr.it>
#
# License: BSD-3-Clause
# %%
import matplotlib.pyplot as plt
from nilearn import plotting
import mne
from mne.minimum_norm import make_inverse_operator, apply_inverse
# Set dir
data_path = mne.datasets.sample.data_path()
subject = 'sample'
data_dir = data_path / 'MEG' / subject
subjects_dir = data_path / 'subjects'
bem_dir = subjects_dir / subject / 'bem'
# Set file names
fname_mixed_src = bem_dir / f'{subject}-oct-6-mixed-src.fif'
fname_aseg = subjects_dir / subject / 'mri' / 'aseg.mgz'
fname_model = bem_dir / f'{subject}-5120-bem.fif'
fname_bem = bem_dir / f'{subject}-5120-bem-sol.fif'
fname_evoked = data_dir / f'{subject}_audvis-ave.fif'
fname_trans = data_dir / f'{subject}_audvis_raw-trans.fif'
fname_fwd = data_dir / f'{subject}_audvis-meg-oct-6-mixed-fwd.fif'
fname_cov = data_dir / f'{subject}_audvis-shrunk-cov.fif'
# %%
# Set up our source space
# -----------------------
# List substructures we are interested in. We select only the
# sub structures we want to include in the source space:
labels_vol = ['Left-Amygdala',
'Left-Thalamus-Proper',
'Left-Cerebellum-Cortex',
'Brain-Stem',
'Right-Amygdala',
'Right-Thalamus-Proper',
'Right-Cerebellum-Cortex']
# %%
# Get a surface-based source space, here with few source points for speed
# in this demonstration, in general you should use oct6 spacing!
src = mne.setup_source_space(subject, spacing='oct5',
add_dist=False, subjects_dir=subjects_dir)
# %%
# Now we create a mixed src space by adding the volume regions specified in the
# list labels_vol. First, read the aseg file and the source space bounds
# using the inner skull surface (here using 10mm spacing to save time,
# we recommend something smaller like 5.0 in actual analyses):
vol_src = mne.setup_volume_source_space(
subject, mri=fname_aseg, pos=10.0, bem=fname_model,
volume_label=labels_vol, subjects_dir=subjects_dir,
add_interpolator=False, # just for speed, usually this should be True
verbose=True)
# Generate the mixed source space
src += vol_src
print(f"The source space contains {len(src)} spaces and "
f"{sum(s['nuse'] for s in src)} vertices")
# %%
# View the source space
# ---------------------
src.plot(subjects_dir=subjects_dir)
# %%
# We could write the mixed source space with::
#
# >>> write_source_spaces(fname_mixed_src, src, overwrite=True)
#
# We can also export source positions to NIfTI file and visualize it again:
nii_fname = bem_dir / f'{subject}-mixed-src.nii'
src.export_volume(nii_fname, mri_resolution=True, overwrite=True)
plotting.plot_img(str(nii_fname), cmap='nipy_spectral')
# %%
# Compute the fwd matrix
# ----------------------
fwd = mne.make_forward_solution(
fname_evoked, fname_trans, src, fname_bem,
mindist=5.0, # ignore sources<=5mm from innerskull
meg=True, eeg=False, n_jobs=None)
del src # save memory
leadfield = fwd['sol']['data']
print("Leadfield size : %d sensors x %d dipoles" % leadfield.shape)
print(f"The fwd source space contains {len(fwd['src'])} spaces and "
f"{sum(s['nuse'] for s in fwd['src'])} vertices")
# Load data
condition = 'Left Auditory'
evoked = mne.read_evokeds(fname_evoked, condition=condition,
baseline=(None, 0))
noise_cov = mne.read_cov(fname_cov)
# %%
# Compute inverse solution
# ------------------------
snr = 3.0 # use smaller SNR for raw data
inv_method = 'dSPM' # sLORETA, MNE, dSPM
parc = 'aparc' # the parcellation to use, e.g., 'aparc' 'aparc.a2009s'
loose = dict(surface=0.2, volume=1.)
lambda2 = 1.0 / snr ** 2
inverse_operator = make_inverse_operator(
evoked.info, fwd, noise_cov, depth=None, loose=loose, verbose=True)
del fwd
stc = apply_inverse(evoked, inverse_operator, lambda2, inv_method,
pick_ori=None)
src = inverse_operator['src']
# %%
# Plot the mixed source estimate
# ------------------------------
# sphinx_gallery_thumbnail_number = 3
initial_time = 0.1
stc_vec = apply_inverse(evoked, inverse_operator, lambda2, inv_method,
pick_ori='vector')
brain = stc_vec.plot(
hemi='both', src=inverse_operator['src'], views='coronal',
initial_time=initial_time, subjects_dir=subjects_dir,
brain_kwargs=dict(silhouette=True), smoothing_steps=7)
# %%
# Plot the surface
# ----------------
brain = stc.surface().plot(initial_time=initial_time,
subjects_dir=subjects_dir, smoothing_steps=7)
# %%
# Plot the volume
# ---------------
fig = stc.volume().plot(initial_time=initial_time, src=src,
subjects_dir=subjects_dir)
# %%
# Process labels
# --------------
# Average the source estimates within each label of the cortical parcellation
# and each sub structure contained in the src space
# Get labels for FreeSurfer 'aparc' cortical parcellation with 34 labels/hemi
labels_parc = mne.read_labels_from_annot(
subject, parc=parc, subjects_dir=subjects_dir)
label_ts = mne.extract_label_time_course(
[stc], labels_parc, src, mode='mean', allow_empty=True)
# plot the times series of 2 labels
fig, axes = plt.subplots(1)
axes.plot(1e3 * stc.times, label_ts[0][0, :], 'k', label='bankssts-lh')
axes.plot(1e3 * stc.times, label_ts[0][-1, :].T, 'r', label='Brain-stem')
axes.set(xlabel='Time (ms)', ylabel='MNE current (nAm)')
axes.legend()
mne.viz.tight_layout()
|
