[Mne_analysis] DICS beamformer

[Seung Goo Kim, Ph.D.]

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Dear list,

I am having difficulties in applying the DICS beamformer on induced responses by an amplitude modulation in auditory signals. The script I used was:

import mne, os, pandas as pd, numpy as np, matplotlib.pyplot as plt
from mne.time_frequency import csd_morlet
from mne.beamformer import make_dics, apply_dics_csd
mne.set_config("SUBJECTS_DIR", "/mnt/data/MEG_pitch/data/fs+mne")
df = pd.read_excel(
    "/mnt/data/MEG_pitch/data/rawdata/MEGStudy/Subjects.xlsx",
    sheet_name="rawdata")
subjects = df.SubjectID[(df.goodmeg_for_tsss * df.goodmri_for_coreg) == True]

subj=subjects[1]
dname_meg = '/mnt/data/MEG_pitch/data/fs+mne/' + subj + '/meg/'
epochs = mne.read_epochs(dname_meg + 'all_tsss_hp0.5Hz_notch50sHz_cor_epo.fif')
fname_fwd = "/mnt/data/MEG_pitch/data/fs+mne/" + subj + "/bem/" + subj + "-4098-fwd.fif"
fwd = mne.read_forward_solution(fname_fwd)
freq = [40]
n_cycles = 4
cond = 'CT20RNR'
epochs_cond = epochs[cond]

# compute cross-spectral density over the whole epoch

csd = csd_morlet(epochs_cond, tmin=-0.5, tmax=2.3, frequencies=freq,
                 decim=20, n_cycles=n_cycles)
filters = make_dics(epochs.info, fwd, csd.mean(), pick_ori='max-power')

csd_base = csd_morlet(epochs_cond, tmin=-0.5, tmax=0, frequencies=freq,
                      decim=20, n_cycles=n_cycles)
baseline_power = apply_dics_csd(csd_base.mean(), filters)
csd_R = csd_morlet(epochs_cond, tmin=1.7, tmax=2.2, frequencies=freq,
                   decim=20, n_cycles=n_cycles)
R_power = apply_dics_csd(csd_R.mean(), filters)
stc = R_power[0] / baseline_power[0]

stc.plot(subject=subj, surface="inflated", hemi="both", time_viewer=False,
        initial_time=0, transparent=True, backend='mayavi', views='lat', size=400)


In the stimulus, only a specific part (from 1.4 sec to 2.3 sec after stimulus onset) has a regular amplitude modulation, so I wanted to compare this part vs. baseline (from -0.5 sec to 0 sec).

So what I did was (1) computing a common filter for the entire epoch (from -0.5 sec to 2.3 sec after stimulus onset), (2) computing CSD for the baseline (from -0.5 sec to 0 sec), (3) computing CSD for the "regular" part (from 1.7 sec to 2.2 sec; same 500 ms as the baseline), and (4) found the ratio of them.

Since this was an auditory experiment, I expected this should be localized in the auditory cortices (bilaterally), but the result looks completely unexpected:
[cid:9e37d2fb-7002-4de2-8d56-ab2f81d6ffc8]
[cid:bb29cee6-72d2-45e5-ae99-be9522979b20]

The code is simply a modification of the tutorial (https://mne.tools/dev/auto_examples/inverse/plot_dics_source_power.html), so I am very puzzled.
Compute source power using DICS beamfomer — MNE 0.20.dev0 documentation<https://mne.tools/dev/auto_examples/inverse/plot_dics_source_power.html>
Compute source power using DICS beamfomer¶. 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 this dataset: Somatosensory
mne.tools

Any suggestions on where to look/check would be greatly appreciated!

Best,
Seung-Goo Kim
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