[Mne_analysis] DICS beamformer

[Eric Larson]

        External Email - Use Caution        

Have you checked some basics? For example, do you have at some points
sensor-space patterns that look like bilateral auditory activation? In your
source reconstruction do you get a reasonable auditory N100 localization to
the stimulus onset? I would check these first (if possible for your
paradigm) if you haven't. Knowing that they are correct rules out things
like coregistration or epoching errors and helps narrow it down to being
related to the method of source localization (DICS).

Eric


On Thu, Feb 13, 2020 at 3:17 PM Seung Goo Kim, Ph.D. <seunggoo.kim at duke.edu>
wrote:

>         External Email - Use Caution
>
> 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:
>
>
>
> 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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