Compute source power using DICS beamformer

Compute a Dynamic Imaging of Coherent Sources (DICS) [1] filter from single-trial activity to estimate source power across a frequency band. This example demonstrates how to source localize the event-related synchronization (ERS) of beta band activity in the somato dataset.

# Author: Marijn van Vliet <w.m.vanvliet@gmail.com>
#         Roman Goj <roman.goj@gmail.com>
#         Denis Engemann <denis.engemann@gmail.com>
#         Stefan Appelhoff <stefan.appelhoff@mailbox.org>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

import numpy as np

import mne
from mne.beamformer import apply_dics_csd, make_dics
from mne.datasets import somato
from mne.time_frequency import csd_morlet

print(__doc__)

Reading the raw data and creating epochs:

data_path = somato.data_path()
subject = "01"
task = "somato"
raw_fname = data_path / f"sub-{subject}" / "meg" / f"sub-{subject}_task-{task}_meg.fif"

# Use a shorter segment of raw just for speed here
raw = mne.io.read_raw_fif(raw_fname)
raw.crop(0, 120)  # one minute for speed (looks similar to using all ~800 s)

# Read epochs
events = mne.find_events(raw)

epochs = mne.Epochs(raw, events, event_id=1, tmin=-1.5, tmax=2, preload=True)
del raw

# Paths to forward operator and FreeSurfer subject directory
fname_fwd = (
    data_path / "derivatives" / f"sub-{subject}" / f"sub-{subject}_task-{task}-fwd.fif"
)

subjects_dir = data_path / "derivatives" / "freesurfer" / "subjects"

We are interested in the beta band. Define a range of frequencies, using a log scale, from 12 to 30 Hz.

freqs = np.logspace(np.log10(12), np.log10(30), 9)

Computing the cross-spectral density matrix for the beta frequency band, for different time intervals. We use a decim value of 20 to speed up the computation in this example at the loss of accuracy.

csd = csd_morlet(epochs, freqs, tmin=-1, tmax=1.5, decim=20)
csd_baseline = csd_morlet(epochs, freqs, tmin=-1, tmax=0, decim=20)
# ERS activity starts at 0.5 seconds after stimulus onset
csd_ers = csd_morlet(epochs, freqs, tmin=0.5, tmax=1.5, decim=20)
info = epochs.info
del epochs

To compute the source power for a frequency band, rather than each frequency separately, we average the CSD objects across frequencies.

csd = csd.mean()
csd_baseline = csd_baseline.mean()
csd_ers = csd_ers.mean()

Computing DICS spatial filters using the CSD that was computed on the entire timecourse.

fwd = mne.read_forward_solution(fname_fwd)
filters = make_dics(
    info,
    fwd,
    csd,
    noise_csd=csd_baseline,
    pick_ori="max-power",
    reduce_rank=True,
    real_filter=True,
)
del fwd

Applying DICS spatial filters separately to the CSD computed using the baseline and the CSD computed during the ERS activity.

baseline_source_power, freqs = apply_dics_csd(csd_baseline, filters)
beta_source_power, freqs = apply_dics_csd(csd_ers, filters)

Visualizing source power during ERS activity relative to the baseline power.

stc = beta_source_power / baseline_source_power
message = "DICS source power in the 12-30 Hz frequency band"
brain = stc.plot(
    hemi="both",
    views="axial",
    subjects_dir=subjects_dir,
    subject=subject,
    time_label=message,
)

References

[1]

Joachim Groß, Jan Kujala, Matti S. Hämäläinen, Lars Timmermann, Alfons Schnitzler, and Riitta Salmelin. Dynamic imaging of coherent sources: studying neural interactions in the human brain. Proceedings of the National Academy of Sciences, 98(2):694–699, 2001. doi:10.1073/pnas.98.2.694.

Estimated memory usage: 0 MB