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<body dir="ltr"><p><span style="padding: 3px 10px; border-radius: 5px; color: #ffffff; font-weight: bold; display: inline-block; background-color: #ff0000;"> External Email - Use Caution </span></p><p></p>
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Dear Eric,</div>
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Thank you for your email. Yes, I have checked the coregistration and MEG-MRI coregistration seems fine given the overlay figure, and the dSPM for the evoked response of sound onsets (although it looks like the PT activity is spreading toward posterior STS;
is this something common in MEG recon? Or does it mean the coreg is actually poor?).</div>
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Best regards,</div>
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-SG</div>
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<div id="divRplyFwdMsg" dir="ltr"><font face="Calibri, sans-serif" style="font-size:11pt" color="#000000"><b>From:</b> mne_analysis-bounces@nmr.mgh.harvard.edu <mne_analysis-bounces@nmr.mgh.harvard.edu> on behalf of Eric Larson <larson.eric.d@gmail.com><br>
<b>Sent:</b> Friday, February 14, 2020 9:46 AM<br>
<b>To:</b> Discussion and support forum for the users of MNE Software <mne_analysis@nmr.mgh.harvard.edu><br>
<b>Subject:</b> Re: [Mne_analysis] DICS beamformer</font>
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<p><span style="padding:3px 10px; border-radius:5px; color:#ffffff; font-weight:bold; display:inline-block; background-color:#ff0000"> External Email - Use Caution </span></p>
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<div dir="ltr">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).
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<div>Eric</div>
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<div dir="ltr" class="x_gmail_attr">On Thu, Feb 13, 2020 at 3:17 PM Seung Goo Kim, Ph.D. <<a href="mailto:seunggoo.kim@duke.edu">seunggoo.kim@duke.edu</a>> wrote:<br>
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<p><span style="padding:3px 10px; border-radius:5px; color:rgb(255,255,255); font-weight:bold; display:inline-block; background-color:rgb(255,0,0)"> External Email - Use Caution </span></p>
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Dear list,</div>
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I am having difficulties in applying the DICS beamformer on induced responses by an amplitude modulation in auditory signals. The script I used was:</div>
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<pre style="font-family:"Ubuntu Mono"; font-size:12pt; background-color:rgb(27,27,27); color:rgb(225,225,225)"><span style="color:rgb(204,120,50)">import </span>mne<span style="color:rgb(204,120,50)">, </span>os<span style="color:rgb(204,120,50)">, </span>pandas <span style="color:rgb(204,120,50)">as </span>pd<span style="color:rgb(204,120,50)">, </span>numpy <span style="color:rgb(204,120,50)">as </span>np<span style="color:rgb(204,120,50)">, </span>matplotlib.pyplot <span style="color:rgb(204,120,50)">as </span>plt<br><span style="color:rgb(204,120,50)">from </span>mne.time_frequency <span style="color:rgb(204,120,50)">import </span>csd_morlet<br><span style="color:rgb(204,120,50)">from </span>mne.beamformer <span style="color:rgb(204,120,50)">import </span>make_dics<span style="color:rgb(204,120,50)">, </span>apply_dics_csd<br>mne.set_config(<span style="color:rgb(140,206,53)">"SUBJECTS_DIR"</span><span style="color:rgb(204,120,50)">, </span><span style="color:rgb(140,206,53)">"/mnt/data/MEG_pitch/data/fs+mne"</span>)<br>df = pd.read_excel(<br> <span style="color:rgb(140,206,53)">"/mnt/data/MEG_pitch/data/rawdata/MEGStudy/Subjects.xlsx"</span><span style="color:rgb(204,120,50)">,<br></span><span style="color:rgb(204,120,50)"> </span><span style="color:rgb(203,97,66)">sheet_name</span>=<span style="color:rgb(140,206,53)">"rawdata"</span>)<br>subjects = df.SubjectID[(df.goodmeg_for_tsss * df.goodmri_for_coreg) == <span style="color:rgb(204,120,50)">True</span>]<br><br>subj=subjects[<span style="color:rgb(104,151,187)">1</span>]<br>dname_meg = <span style="color:rgb(140,206,53)">'/mnt/data/MEG_pitch/data/fs+mne/' </span>+ subj + <span style="color:rgb(140,206,53)">'/meg/'<br></span>epochs = mne.read_epochs(dname_meg + <span style="color:rgb(140,206,53)">'all_tsss_hp0.5Hz_notch50sHz_cor_epo.fif'</span>)<br>fname_fwd = <span style="color:rgb(140,206,53)">"/mnt/data/MEG_pitch/data/fs+mne/" </span>+ subj + <span style="color:rgb(140,206,53)">"/bem/" </span>+ subj + <span style="color:rgb(140,206,53)">"-4098-fwd.fif"<br></span>fwd = mne.read_forward_solution(fname_fwd)<br>freq = [<span style="color:rgb(104,151,187)">40</span>]<br>n_cycles = <span style="color:rgb(104,151,187)">4<br></span>cond = <span style="color:rgb(140,206,53)">'CT20RNR'<br></span>epochs_cond = epochs[cond]<br><br><span style="color:rgb(128,128,128)"># compute cross-spectral density over the whole epoch<br></span><pre style="font-family:"Ubuntu Mono"; font-size:12pt; background-color:rgb(27,27,27); color:rgb(225,225,225)">csd = csd_morlet(epochs_cond<span style="color:rgb(204,120,50)">, </span><span style="color:rgb(203,97,66)">tmin</span>=-<span style="color:rgb(104,151,187)">0.5</span><span style="color:rgb(204,120,50)">, </span><span style="color:rgb(203,97,66)">tmax</span>=<span style="color:rgb(104,151,187)">2.3</span><span style="color:rgb(204,120,50)">, </span><span style="color:rgb(203,97,66)">frequencies</span>=freq<span style="color:rgb(204,120,50)">,<br></span><span style="color:rgb(204,120,50)"> </span><span style="color:rgb(203,97,66)">decim</span>=<span style="color:rgb(104,151,187)">20</span><span style="color:rgb(204,120,50)">, </span><span style="color:rgb(203,97,66)">n_cycles</span>=n_cycles)<br>filters = make_dics(<a href="https://urldefense.proofpoint.com/v2/url?u=http-3A__epochs.info&d=DwMGaQ&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=Oo6kXvV9N4AHFS-wWXXAKKDl4dUe6z92y4XtIzLkZJY&m=03nUvJsvDMigS5h-APMr-YTrVTN8dzxfUVgR1MXBUkU&s=3YrE589SYu_GBEuvYVeamwSDpiNWdMNLZSRsyH4Nrvs&e=" target="_blank"><font color="red"><b>MailScanner has detected a possible fraud attempt from "urldefense.proofpoint.com" claiming to be</b></font> epochs.info</a><span style="color:rgb(204,120,50)">, </span>fwd<span style="color:rgb(204,120,50)">, </span>csd.mean()<span style="color:rgb(204,120,50)">, </span><span style="color:rgb(203,97,66)">pick_ori</span>=<span style="color:rgb(140,206,53)">'max-power'</span>)<br><br>csd_base = csd_morlet(epochs_cond<span style="color:rgb(204,120,50)">, </span><span style="color:rgb(203,97,66)">tmin</span>=-<span style="color:rgb(104,151,187)">0.5</span><span style="color:rgb(204,120,50)">, </span><span style="color:rgb(203,97,66)">tmax</span>=<span style="color:rgb(104,151,187)">0</span><span style="color:rgb(204,120,50)">, </span><span style="color:rgb(203,97,66)">frequencies</span>=freq<span style="color:rgb(204,120,50)">,<br></span><span style="color:rgb(204,120,50)"> </span><span style="color:rgb(203,97,66)">decim</span>=<span style="color:rgb(104,151,187)">20</span><span style="color:rgb(204,120,50)">, </span><span style="color:rgb(203,97,66)">n_cycles</span>=n_cycles)<br>baseline_power = apply_dics_csd(csd_base.mean()<span style="color:rgb(204,120,50)">, </span>filters)<br>csd_R = csd_morlet(epochs_cond<span style="color:rgb(204,120,50)">, </span><span style="color:rgb(203,97,66)">tmin</span>=<span style="color:rgb(104,151,187)">1.7</span><span style="color:rgb(204,120,50)">, </span><span style="color:rgb(203,97,66)">tmax</span>=<span style="color:rgb(104,151,187)">2.2</span><span style="color:rgb(204,120,50)">, </span><span style="color:rgb(203,97,66)">frequencies</span>=freq<span style="color:rgb(204,120,50)">,<br></span><span style="color:rgb(204,120,50)"> </span><span style="color:rgb(203,97,66)">decim</span>=<span style="color:rgb(104,151,187)">20</span><span style="color:rgb(204,120,50)">, </span><span style="color:rgb(203,97,66)">n_cycles</span>=n_cycles)<br>R_power = apply_dics_csd(csd_R.mean()<span style="color:rgb(204,120,50)">, </span>filters)<br>stc = R_power[<span style="color:rgb(104,151,187)">0</span>] / baseline_power[<span style="color:rgb(104,151,187)">0</span>]<br></pre><pre style="font-family:"Ubuntu Mono"; font-size:12pt; background-color:rgb(27,27,27); color:rgb(225,225,225)">stc.plot(<span style="color:rgb(203,97,66)">subject</span>=subj<span style="color:rgb(204,120,50)">, </span><span style="color:rgb(203,97,66)">surface</span>=<span style="color:rgb(140,206,53)">"inflated"</span><span style="color:rgb(204,120,50)">, </span><span style="color:rgb(203,97,66)">hemi</span>=<span style="color:rgb(140,206,53)">"both"</span><span style="color:rgb(204,120,50)">, </span><span style="color:rgb(203,97,66)">time_viewer</span>=<span style="color:rgb(204,120,50)">False,<br></span><span style="color:rgb(204,120,50)"> </span><span style="color:rgb(203,97,66)">initial_time</span>=<span style="color:rgb(104,151,187)">0</span><span style="color:rgb(204,120,50)">, </span><span style="color:rgb(203,97,66)">transparent</span>=<span style="color:rgb(204,120,50)">True, </span><span style="color:rgb(203,97,66)">backend</span>=<span style="color:rgb(140,206,53)">'mayavi'</span><span style="color:rgb(204,120,50)">, </span><span style="color:rgb(203,97,66)">views</span>=<span style="color:rgb(140,206,53)">'lat'</span><span style="color:rgb(204,120,50)">, </span><span style="color:rgb(203,97,66)">size</span>=<span style="color:rgb(104,151,187)">400</span>)</pre><br></pre>
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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). </div>
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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), <span style="font-family:Calibri,Arial,Helvetica,sans-serif; font-size:12pt">(2) computing CSD for the baseline (from -0.5 sec to 0 sec), </span><span style="font-family:Calibri,Arial,Helvetica,sans-serif; font-size:12pt">(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.</span></div>
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<span style="font-family:Calibri,Arial,Helvetica,sans-serif; font-size:12pt">Since this was an auditory experiment, I expected this should be localized in the auditory cortices (bilaterally), but the result looks completely unexpected:</span></div>
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<span style="font-family:Calibri,Arial,Helvetica,sans-serif; font-size:12pt"><img size="122489" style="max-width:100%" data-outlook-trace="F:1|T:1" src="cid:17044277e7dcb971f161"><br>
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<span style="font-family:Calibri,Arial,Helvetica,sans-serif; font-size:12pt"><img size="93833" style="max-width:100%" data-outlook-trace="F:1|T:1" src="cid:17044277e7dcb971f162"><br>
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<span style="font-family:Calibri,Arial,Helvetica,sans-serif; font-size:12pt">The code is simply a modification of the tutorial (</span><a href="https://urldefense.proofpoint.com/v2/url?u=https-3A__mne.tools_dev_auto-5Fexamples_inverse_plot-5Fdics-5Fsource-5Fpower.html&d=DwMGaQ&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=Oo6kXvV9N4AHFS-wWXXAKKDl4dUe6z92y4XtIzLkZJY&m=03nUvJsvDMigS5h-APMr-YTrVTN8dzxfUVgR1MXBUkU&s=UJEk1ej1vp7FarL5-twGTh9sv8H0P3YAAbTbokjGRHw&e=" id="x_gmail-m_-2099193686266710740LPlnk867471" target="_blank" style="font-family:Calibri,Arial,Helvetica,sans-serif; font-size:12pt"><font color="red"><b>MailScanner has detected a possible fraud attempt from "urldefense.proofpoint.com" claiming to be</b></font> https://mne.tools/dev/auto_examples/inverse/plot_dics_source_power.html</a><span style="font-family:Calibri,Arial,Helvetica,sans-serif; font-size:12pt">),
so I am very puzzled.</span><br>
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<a id="x_gmail-m_-2099193686266710740LPUrlAnchor158293" href="https://urldefense.proofpoint.com/v2/url?u=https-3A__mne.tools_dev_auto-5Fexamples_inverse_plot-5Fdics-5Fsource-5Fpower.html&d=DwMGaQ&c=imBPVzF25OnBgGmVOlcsiEgHoG1i6YHLR0Sj_gZ4adc&r=Oo6kXvV9N4AHFS-wWXXAKKDl4dUe6z92y4XtIzLkZJY&m=03nUvJsvDMigS5h-APMr-YTrVTN8dzxfUVgR1MXBUkU&s=UJEk1ej1vp7FarL5-twGTh9sv8H0P3YAAbTbokjGRHw&e=" target="_blank" style="text-decoration:none">Compute
source power using DICS beamfomer — MNE 0.20.dev0 documentation</a></div>
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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</div>
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mne.tools</div>
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Any suggestions on where to look/check would be greatly appreciated!<br>
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Best,</div>
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Seung-Goo Kim</div>
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