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# -*- coding: utf-8 -*-
"""
.. _ex-inverse-volume:
=======================================================================
Compute MNE-dSPM inverse solution on evoked data in volume source space
=======================================================================
Compute dSPM inverse solution on MNE evoked dataset in a volume source
space and stores the solution in a nifti file for visualisation.
"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
# %%
from nilearn.plotting import plot_stat_map
from nilearn.image import index_img
from mne.datasets import sample
from mne import read_evokeds
from mne.minimum_norm import apply_inverse, read_inverse_operator
print(__doc__)
data_path = sample.data_path()
meg_path = data_path / 'MEG' / 'sample'
fname_inv = meg_path / 'sample_audvis-meg-vol-7-meg-inv.fif'
fname_evoked = meg_path / 'sample_audvis-ave.fif'
snr = 3.0
lambda2 = 1.0 / snr ** 2
method = "dSPM" # use dSPM method (could also be MNE or sLORETA)
# Load data
evoked = read_evokeds(fname_evoked, condition=0, baseline=(None, 0))
inverse_operator = read_inverse_operator(fname_inv)
src = inverse_operator['src']
# Compute inverse solution
stc = apply_inverse(evoked, inverse_operator, lambda2, method)
stc.crop(0.0, 0.2)
# Export result as a 4D nifti object
img = stc.as_volume(src,
mri_resolution=False) # set True for full MRI resolution
# Save it as a nifti file
# nib.save(img, 'mne_%s_inverse.nii.gz' % method)
t1_fname = data_path / 'subjects' / 'sample' / 'mri' / 'T1.mgz'
# %%
# Plot with nilearn:
plot_stat_map(index_img(img, 61), str(t1_fname), threshold=8.,
title='%s (t=%.1f s.)' % (method, stc.times[61]))
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