"""
.. _tut-lcmv-beamformer:
==============================================
Source reconstruction using an LCMV beamformer
==============================================
This tutorial gives an overview of the beamformer method and shows how to
reconstruct source activity using an LCMV beamformer.
"""
# Authors: Britta Westner
# Eric Larson
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import matplotlib.pyplot as plt
import mne
from mne.beamformer import apply_lcmv, make_lcmv
from mne.datasets import fetch_fsaverage, sample
# %%
# Introduction to beamformers
# ---------------------------
# A beamformer is a spatial filter that reconstructs source activity by
# scanning through a grid of pre-defined source points and estimating activity
# at each of those source points independently. A set of weights is
# constructed for each defined source location which defines the contribution
# of each sensor to this source.
#
# Beamformers are often used for their focal reconstructions and their ability
# to reconstruct deeper sources. They can also suppress external noise sources.
# The beamforming method applied in this tutorial is the linearly constrained
# minimum variance (LCMV) beamformer :footcite:`VanVeenEtAl1997` operates on
# time series.
#
# Frequency-resolved data can be reconstructed with the dynamic imaging of
# coherent sources (DICS) beamforming method :footcite:`GrossEtAl2001`.
# As we will see in the following, the spatial filter is computed from two
# ingredients: the forward model solution and the covariance matrix of the
# data.
# %%
# Data processing
# ---------------
# We will use the sample data set for this tutorial and reconstruct source
# activity on the trials with left auditory stimulation.
data_path = sample.data_path()
subjects_dir = data_path / "subjects"
meg_path = data_path / "MEG" / "sample"
raw_fname = meg_path / "sample_audvis_filt-0-40_raw.fif"
# Read the raw data
raw = mne.io.read_raw_fif(raw_fname)
raw.info["bads"] = ["MEG 2443"] # bad MEG channel
# Set up epoching
event_id = 1 # those are the trials with left-ear auditory stimuli
tmin, tmax = -0.2, 0.5
events = mne.find_events(raw)
# pick relevant channels
raw.pick(["meg", "eog"]) # pick channels of interest
# Create epochs
proj = False # already applied
epochs = mne.Epochs(
raw,
events,
event_id,
tmin,
tmax,
baseline=(None, 0),
preload=True,
proj=proj,
reject=dict(grad=4000e-13, mag=4e-12, eog=150e-6),
)
# for speed purposes, cut to a window of interest
evoked = epochs.average().crop(0.05, 0.15)
# Visualize averaged sensor space data
evoked.plot_joint()
del raw # save memory
# %%
# Computing the covariance matrices
# ---------------------------------
# Spatial filters use the data covariance to estimate the filter
# weights. The data covariance matrix will be `inverted`_ during the spatial
# filter computation, so it is valuable to plot the covariance matrix and its
# eigenvalues to gauge whether matrix inversion will be possible.
# Also, because we want to combine different channel types (magnetometers and
# gradiometers), we need to account for the different amplitude scales of these
# channel types. To do this we will supply a noise covariance matrix to the
# beamformer, which will be used for whitening.
# The data covariance matrix should be estimated from a time window that
# includes the brain signal of interest,
# and incorporate enough samples for a stable estimate. A rule of thumb is to
# use more samples than there are channels in the data set; see
# :footcite:`BrookesEtAl2008,WestnerEtAl2022` for more detailed advice on
# covariance estimation for beamformers. Here, we use a time
# window incorporating the expected auditory response at around 100 ms post
# stimulus and extend the period to account for a low number of trials (72) and
# low sampling rate of 150 Hz.
data_cov = mne.compute_covariance(epochs, tmin=0.01, tmax=0.25, method="empirical")
noise_cov = mne.compute_covariance(epochs, tmin=tmin, tmax=0, method="empirical")
data_cov.plot(epochs.info)
del epochs
# %%
# When looking at the covariance matrix plots, we can see that our data is
# slightly rank-deficient as the rank is not equal to the number of channels.
# Thus, we choose to regularize the covariance matrix before inverting it
# in the beamformer calculation. This can be achieved by setting the parameter
# ``reg=0.05`` when calculating the spatial filter with
# :func:`~mne.beamformer.make_lcmv`. This corresponds to loading the diagonal
# of the covariance matrix with 5% of the sensor power. Other ways to deal with
# rank-deficient covariance matrices are discussed in
# :footcite:`WestnerEtAl2022`.
# %%
# The forward model
# -----------------
# The forward model is the other important ingredient for the computation of a
# spatial filter. Here, we will load the forward model from disk; more
# information on how to create a forward model can be found in this tutorial:
# :ref:`tut-forward`.
# Note that beamformers are usually computed in a :class:`volume source space
# `, because estimating only cortical surface
# activation can misrepresent the data.
# Read forward model
fwd_fname = meg_path / "sample_audvis-meg-vol-7-fwd.fif"
forward = mne.read_forward_solution(fwd_fname)
# %%
# Handling depth bias
# -------------------
#
# The forward model solution is inherently biased toward superficial sources.
# When analyzing single conditions it is best to mitigate the depth bias
# somehow. There are several ways to do this:
#
# - :func:`mne.beamformer.make_lcmv` has a ``depth`` parameter that normalizes
# the forward model prior to computing the spatial filters. See the docstring
# for details.
# - Unit-noise gain beamformers handle depth bias by normalizing the
# weights of the spatial filter. Choose this by setting
# ``weight_norm='unit-noise-gain'``.
# - When computing the Neural activity index, the depth bias is handled by
# normalizing both the weights and the estimated noise (see
# :footcite:`VanVeenEtAl1997`). Choose this by setting ``weight_norm='nai'``.
#
# Note that when comparing conditions, the depth bias will cancel out and it is
# possible to set both parameters to ``None``.
#
#
# Compute the spatial filter
# --------------------------
# Now we can compute the spatial filter. We'll use a unit-noise gain beamformer
# to deal with depth bias, and will also optimize the orientation of the
# sources such that output power is maximized.
# This is achieved by setting ``pick_ori='max-power'``.
# This gives us one source estimate per source (i.e., voxel), which is known
# as a scalar beamformer.
filters = make_lcmv(
evoked.info,
forward,
data_cov,
reg=0.05,
noise_cov=noise_cov,
pick_ori="max-power",
weight_norm="unit-noise-gain",
rank=None,
)
# You can save the filter for later use with:
# filters.save('filters-lcmv.h5')
# %%
# It is also possible to compute a vector beamformer, which gives back three
# estimates per voxel, corresponding to the three direction components of the
# source. This can be achieved by setting
# ``pick_ori='vector'`` and will yield a :class:`volume vector source estimate
# `. Note that we switch the ``weight_norm``
# parameter to ``'unit-noise-gain-invariant'``, which is only necessary for the
# vector unit-noise-gain beamformer. For more in-depth detail, see
# :footcite:`WestnerEtAl2022`.
# We will compute another set of filters using the vector beamformer approach:
filters_vec = make_lcmv(
evoked.info,
forward,
data_cov,
reg=0.05,
noise_cov=noise_cov,
pick_ori="vector",
weight_norm="unit-noise-gain-invariant",
rank=None,
)
# save a bit of memory
src = forward["src"]
del forward
# %%
# Apply the spatial filter
# ------------------------
# The spatial filter can be applied to different data types: raw, epochs,
# evoked data or the data covariance matrix to gain a static image of power.
# The function to apply the spatial filter to :class:`~mne.Evoked` data is
# :func:`~mne.beamformer.apply_lcmv` which is
# what we will use here. The other functions are
# :func:`~mne.beamformer.apply_lcmv_raw`,
# :func:`~mne.beamformer.apply_lcmv_epochs`, and
# :func:`~mne.beamformer.apply_lcmv_cov`.
stc = apply_lcmv(evoked, filters)
stc_vec = apply_lcmv(evoked, filters_vec)
del filters, filters_vec
# %%
# Visualize the reconstructed source activity
# -------------------------------------------
# We can visualize the source estimate in different ways, e.g. as a volume
# rendering, an overlay onto the MRI, or as an overlay onto a glass brain.
#
# The plots for the scalar beamformer show brain activity in the right temporal
# lobe around 100 ms post stimulus. This is expected given the left-ear
# auditory stimulation of the experiment.
lims = [0.3, 0.45, 0.6]
kwargs = dict(
src=src,
subject="sample",
subjects_dir=subjects_dir,
initial_time=0.087,
verbose=True,
)
# %%
# On MRI slices (orthoview; 2D)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
stc.plot(mode="stat_map", clim=dict(kind="value", pos_lims=lims), **kwargs)
# %%
# On MNI glass brain (orthoview; 2D)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
stc.plot(mode="glass_brain", clim=dict(kind="value", lims=lims), **kwargs)
# %%
# Volumetric rendering (3D) with vectors
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# These plots can also be shown using a volumetric rendering via
# :meth:`~mne.VolVectorSourceEstimate.plot_3d`. Let's try visualizing the
# vector beamformer case. Here we get three source time courses out per voxel
# (one for each component of the dipole moment: x, y, and z), which appear
# as small vectors in the visualization (in the 2D plotters, only the
# magnitude can be shown):
# sphinx_gallery_thumbnail_number = 7
brain = stc_vec.plot_3d(
clim=dict(kind="value", lims=lims),
hemi="both",
size=(600, 600),
views=["sagittal"],
# Could do this for a 3-panel figure:
# view_layout='horizontal', views=['coronal', 'sagittal', 'axial'],
brain_kwargs=dict(silhouette=True),
**kwargs,
)
# %%
# Visualize the activity of the maximum voxel with all three components
# ---------------------------------------------------------------------
# We can also visualize all three components in the peak voxel. For this, we
# will first find the peak voxel and then plot the time courses of this voxel.
peak_vox, _ = stc_vec.get_peak(tmin=0.08, tmax=0.1, vert_as_index=True)
ori_labels = ["x", "y", "z"]
fig, ax = plt.subplots(1)
for ori, label in zip(stc_vec.data[peak_vox, :, :], ori_labels):
ax.plot(stc_vec.times, ori, label=f"{label} component")
ax.legend(loc="lower right")
ax.set(
title="Activity per orientation in the peak voxel",
xlabel="Time (s)",
ylabel="Amplitude (a. u.)",
)
mne.viz.utils.plt_show()
del stc_vec
# %%
# Morph the output to fsaverage
# -----------------------------
#
# We can also use volumetric morphing to get the data to fsaverage space. This
# is for example necessary when comparing activity across subjects. Here, we
# will use the scalar beamformer example.
# We pass a :class:`mne.SourceMorph` as the ``src`` argument to
# `mne.VolSourceEstimate.plot`. To save some computational load when applying
# the morph, we will crop the ``stc``:
fetch_fsaverage(subjects_dir) # ensure fsaverage src exists
fname_fs_src = subjects_dir / "fsaverage" / "bem" / "fsaverage-vol-5-src.fif"
src_fs = mne.read_source_spaces(fname_fs_src)
morph = mne.compute_source_morph(
src,
subject_from="sample",
src_to=src_fs,
subjects_dir=subjects_dir,
niter_sdr=[5, 5, 2],
niter_affine=[5, 5, 2],
zooms=7, # just for speed
verbose=True,
)
stc_fs = morph.apply(stc)
del stc
stc_fs.plot(
src=src_fs,
mode="stat_map",
initial_time=0.085,
subjects_dir=subjects_dir,
clim=dict(kind="value", pos_lims=lims),
verbose=True,
)
# %%
# References
# ----------
#
# .. footbibliography::
#
#
# .. LINKS
#
# .. _`inverted`: https://en.wikipedia.org/wiki/Invertible_matrix