Compute seed-based time-frequency connectivity in sensor space

Computes the connectivity between a seed-gradiometer close to the visual cortex and all other gradiometers. The connectivity is computed in the time-frequency domain using Morlet wavelets and the debiased squared weighted phase lag index [1] is used as connectivity metric.

# Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
#
# License: BSD (3-clause)

import os.path as op

import mne
import numpy as np
from mne import io
from mne.datasets import sample
from mne.time_frequency import AverageTFRArray

from mne_connectivity import seed_target_indices, spectral_connectivity_epochs

print(__doc__)

Set parameters

data_path = sample.data_path()
raw_fname = op.join(data_path, "MEG", "sample", "sample_audvis_filt-0-40_raw.fif")
event_fname = op.join(data_path, "MEG", "sample", "sample_audvis_filt-0-40_raw-eve.fif")

# Setup for reading the raw data
raw = io.read_raw_fif(raw_fname)
events = mne.read_events(event_fname)

# Add a bad channel
raw.info["bads"] += ["MEG 2443"]

# Pick MEG gradiometers
picks = mne.pick_types(
    raw.info, meg="grad", eeg=False, stim=False, eog=True, exclude="bads"
)

# Create epochs for left-visual condition
event_id, tmin, tmax = 3, -0.2, 0.5
epochs = mne.Epochs(
    raw,
    events,
    event_id,
    tmin,
    tmax,
    picks=picks,
    baseline=(None, 0),
    reject=dict(grad=4000e-13, eog=150e-6),
    preload=True,
)

# Use 'MEG 2343' as seed
seed_ch = "MEG 2343"
picks_ch_names = [raw.ch_names[i] for i in picks]

# Create seed-target indices for connectivity computation
seed = picks_ch_names.index(seed_ch)
targets = np.arange(len(picks))
indices = seed_target_indices(seed, targets)

# Define wavelet frequencies and number of cycles
cwt_freqs = np.arange(7, 30, 2)
cwt_n_cycles = cwt_freqs / 7.0

# Run the connectivity analysis using 2 parallel jobs
sfreq = raw.info["sfreq"]  # the sampling frequency
con = spectral_connectivity_epochs(
    epochs,
    indices=indices,
    method="wpli2_debiased",
    mode="cwt_morlet",
    sfreq=sfreq,
    cwt_freqs=cwt_freqs,
    cwt_n_cycles=cwt_n_cycles,
    n_jobs=1,
)
times = con.times
freqs = con.freqs

# Mark the seed channel with a value of 1.0, so we can see it in the plot
con.get_data()[np.where(indices[1] == seed)] = 1.0

# Show topography of connectivity from seed
title = "WPLI2 - Visual - Seed %s" % seed_ch

layout = mne.find_layout(epochs.info, "meg")  # use full layout

# Note that users of mne < 1.7 should use the `AverageTFR` class
tfr = AverageTFRArray(epochs.info, con.get_data(), times, freqs, nave=len(epochs))
tfr.plot_topo(fig_facecolor="w", font_color="k", border="k")

Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
        Average EEG reference (1 x 60)  idle
    Range : 6450 ... 48149 =     42.956 ...   320.665 secs
Ready.
Not setting metadata
73 matching events found
Setting baseline interval to [-0.19979521315838786, 0.0] s
Applying baseline correction (mode: mean)
0 projection items activated
Loading data for 73 events and 106 original time points ...
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
6 bad epochs dropped
Adding metadata with 3 columns
Connectivity computation...
    using t=-0.200s..0.499s for estimation (106 points)
    frequencies: 9.0Hz..29.0Hz (11 points)
    computing connectivity for 204 connections
    using CWT with Morlet wavelets to estimate spectra
    the following metrics will be computed: Debiased WPLI Square
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[Connectivity computation done]
No baseline correction applied

<Figure size 640x480 with 2 Axes>

References

[1]

Martin Vinck, Robert Oostenveld, Marijn van Wingerden, Franscesco Battaglia, and Cyriel M.A. Pennartz. An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. NeuroImage, 55(4):1548–1565, 2011. doi:10.1016/j.neuroimage.2011.01.055.