<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><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).<div><div><br></div><div>Eric</div><div><br></div></div></div><br><div class="gmail_quote"><div dir="ltr" class="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></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">
<div dir="ltr"><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><p></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="http://epochs.info" target="_blank">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%;" 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%;" 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://mne.tools/dev/auto_examples/inverse/plot_dics_source_power.html" id="gmail-m_-2099193686266710740LPlnk867471" style="font-family:Calibri,Arial,Helvetica,sans-serif;font-size:12pt" target="_blank">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="gmail-m_-2099193686266710740LPUrlAnchor158293" href="https://mne.tools/dev/auto_examples/inverse/plot_dics_source_power.html" style="text-decoration:none" target="_blank">Compute source power using DICS beamfomer — MNE 0.20.dev0 documentation</a></div>
<div id="gmail-m_-2099193686266710740LPDescription158293" style="font-size:14px;max-height:100px;font-family:wf_segoe-ui_normal,"Segoe UI","Segoe WP",Tahoma,Arial,sans-serif;margin-bottom:12px;margin-right:8px;overflow:hidden;color:rgb(102,102,102)">
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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