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<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;mso-fareast-language:EN-US">Hi Michael,<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;mso-fareast-language:EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;mso-fareast-language:EN-US">I think Jan-Matthijs intended to do the following (which I do currently as well):<o:p></o:p></span></p>
<p class="p1" style="margin-left:36.0pt"> %Calculate the channel covariance matrix for all exposures<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg = [];<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg.covariance =
<span class="s1">'yes'</span>;<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg.covariancewindow =
<span class="s1">'all'</span>;<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg.keeptrials<span class="apple-converted-space">
</span>= <span class="s1">'yes'</span>;<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>timelock_allexp = ft_timelockanalysis(cfg, data_filt);<o:p></o:p></p>
<p class="p3"><span class="apple-converted-space"> </span><o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"><span style="color:black">
</span></span>%Calculate the channel covariance matric for each exposure seperate<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>timelock=cell(length(exposures),1);<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span><span class="s3">for</span> j=1:length(exposures)<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg = [];<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg.covariance =
<span class="s1">'yes'</span>;<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg.covariancewindow =
<span class="s1">'all'</span>;<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg.trials = find(ismember(trialinfo, exposures{j}));<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg.keeptrials<span class="apple-converted-space">
</span>= <span class="s1">'yes'</span>;<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>timelock{j} = ft_timelockanalysis(cfg, data_filt);<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span><span class="s3">end</span><o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span><span class="s3">if</span> ~debug; clear
<span class="s1">data_filt</span>; <span class="s3">end</span><o:p></o:p></p>
<p class="p3"><span class="apple-converted-space"> </span><o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"><span style="color:black">
</span></span>%General source localisation<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg = [];<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg.headmodel = vol;<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg.elec = elec_aligned;<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg.grid = grid;<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg.method =
<span class="s1">'lcmv'</span>;<o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"><span style="color:black">
</span></span>%cfg.lcmv.projectnoise='yes'; %needed for neural activity index<o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"><span style="color:black">
</span></span><span class="s2">cfg.lcmv.fixedori = </span><span class="s1">'yes'</span><span class="s2">;</span><span class="apple-converted-space"><span style="color:black">
</span></span>%Project onto largest variance orientation<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg.lcmv.keepfilter =
<span class="s1">'yes'</span>; <span class="s4">%Keep the beamformer weights</span><o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg.lcmv.lambda =
<span class="s1">'5%'</span>; <span class="s4">%Regularise a little</span><o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"><span style="color:black">
</span></span>%<span class="apple-converted-space"> </span>cfg.rawtrial<span class="apple-converted-space">
</span>= 'yes';<span class="apple-converted-space"> </span>% project each single trial through the filter.<o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"><span style="color:black">
</span></span>%<span class="apple-converted-space"> </span>cfg.keeptrials<span class="apple-converted-space">
</span>= 'yes';<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>source_allexp = ft_sourceanalysis(cfg, timelock_allexp);<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span><span class="s3">if</span> ~debug; clear
<span class="s1">timelock_allexp</span>; <span class="s3">end</span><o:p></o:p></p>
<p class="p3"><span class="apple-converted-space"> </span><o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"><span style="color:black">
</span></span>% source localisation for each exposure seperate using combined filter<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg = [];<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg.headmodel = vol;<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg.elec = elec_aligned;<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg.grid = grid;<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg.grid.filter = source_allexp.avg.filter;
<span class="s4">% use the common filter computed in the previous step!</span><o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg.method =
<span class="s1">'lcmv'</span>;<o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"><span style="color:black">
</span></span>%cfg.lcmv.projectnoise='yes'; %needed for neural activity index<o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"><span style="color:black">
</span></span><span class="s2">cfg.lcmv.fixedori = </span><span class="s1">'yes'</span><span class="s2">;</span><span class="apple-converted-space"><span style="color:black">
</span></span>%Project onto largest variance orientation<o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"><span style="color:black">
</span></span>%cfg.lcmv.keepfilter = 'yes'; %Keep the beamformer weights<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg.lcmv.lambda =
<span class="s1">'5%'</span>; <span class="s4">%Regularise a little</span><o:p></o:p></p>
<p class="p1"><span class="apple-converted-space"><span style="color:black">
</span></span><span class="s2">cfg.rawtrial</span><span class="apple-converted-space"><span style="color:black">
</span></span><span class="s2">= </span><span class="s1">'yes'</span><span class="s2">;</span><span class="apple-converted-space"><span style="color:black">
</span></span>% project each single trial through the filter.<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span>cfg.keeptrials<span class="apple-converted-space">
</span>= <span class="s1">'yes'</span>;<o:p></o:p></p>
<p class="p2"><span class="apple-converted-space"> </span><span class="s3">if</span> ~debug; clear
<span class="s1">source_allexp</span>; <span class="s3">end</span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;mso-fareast-language:EN-US">This calculates the filter for all exposures and then uses this filter to calculate the mom for each trial separate for each exposure (have both
rawetrial and keeptrials on).<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;mso-fareast-language:EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;mso-fareast-language:EN-US">I had the same problem with running out of memory. I found that Fieldtrip was putting all the trials on 1 very long time axis (based on their previous
time axis), with a lot of zeros in front and behind each sample. This created a huge data matrix, which was mostly empty. That why I realigned all time axes before the data goes into the timelockanalysis function:<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:7.5pt;font-family:Courier;color:black">
</span><span style="font-size:7.5pt;font-family:Courier;color:forestgreen">%align time axis (all time axis start at 0)<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:7.5pt;font-family:Courier"> data_filt.time(:)={0:1/data_filt.fsample:(length(data_filt.time{1})/data_filt.fsample-1/data_filt.fsample)};<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;mso-fareast-language:EN-US">This might be the case for you as well?<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;mso-fareast-language:EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;mso-fareast-language:EN-US">I trialled granger as well and couldn’t get a difference between exposures to various narcotic agents. I am currently working with mutual information,
which seems to work reasonably well. Upside of MI over GC is its non-linear sensitivity.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;mso-fareast-language:EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;mso-fareast-language:EN-US">Regards,<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;mso-fareast-language:EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;mso-fareast-language:EN-US">Xavier<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;mso-fareast-language:EN-US"><o:p> </o:p></span></p>
<div style="border:none;border-top:solid #B5C4DF 1.0pt;padding:3.0pt 0cm 0cm 0cm">
<p class="MsoNormal"><b><span style="color:black">From: </span></b><span style="color:black">fieldtrip <fieldtrip-bounces@science.ru.nl> on behalf of Michael Glassen - Biomedical Engineer <MGlassen@kesslerfoundation.org><br>
<b>Reply to: </b>FieldTrip discussion list <fieldtrip@science.ru.nl><br>
<b>Date: </b>Friday, 8 May 2020 at 3:55 AM<br>
<b>To: </b>FieldTrip discussion list <fieldtrip@science.ru.nl><br>
<b>Subject: </b>[FORGED] Re: [FieldTrip] Computing source-level ROI EEG connectivity<o:p></o:p></span></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
</div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D">Hi Jan,</span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"> </span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D">Thank you so much for your help, I just had a few follow up questions.</span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"> </span><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:11.0pt">-I would check whether the individual trials after source reconstruction yield meaningful values in the ‘mom’-field. Particularly, are the different trials different? Do the time series look meaningful
to begin with (both in terms of signal magnitude and ’shape’)? The reason I ask, is that I am not fully sure whether the ‘rawtrial’ option is behaving according to your expectations.</span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:11.0pt">-Another way for getting the mom would be to use cfg.keepfilter in ft_sourceanalysis, and left-multiply each dipole location’s ’spatial filter’ (in sourceEEG.avg.filter) with a dataEEG.trial matrix.</span></i><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"> </span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"> I went through and plotted a couple different time series from the mom field from different trials and the same dipole, they are not equal and
the shape looks ok to me, the magnitudes range from 10^-7 to 10^-4, is that normal? Source was calculated with everything in meters if that matters. Can you explain a little more the other method for calculating mom? The only option I have to add is keepfilter
to sourceanalysis? And then I multiply each of these filters against the trial data matrix and it will leave me with what rawtrial is giving me but more reliably? Can I then still use the parcellate option on the output here? Or is this not necessary as rawtrial
seems to be working? </span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"> </span><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt">-In order to get a ‘parcellated’ representation of your source level data, I recommend to use ft_sourceparcellate, which should achieve the parcellation you performed by hand (with the binary masks).</span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt"> </span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">
</span></i><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F4E79">I tried using the ft_sourceparcellate function but I was running into a memory issue when ran on the rawtrial source, and when I remove that option and run it on the default
trial-averaged sources I get a different error, which is pasted below. mri2 is the broddman area atlas interpolated onto my source model. Also, is there any way to parcellate twice with the function like I am doing by hand? The broddman area atlas I have is
not divided into left and right hemispheres so I have another atlas I am using that is divided into hemispheres.</span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:10.0pt"> </span><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:36.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">mri2 =
</span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt"> </span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt"> struct with fields:</span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt"> </span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt"> pos: [8196×3 double]</span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt"> tri: [16384×3 double]</span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt"> unit: 'm'</span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt"> tissue: [8196×1 double]</span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt"> tissuelabel: {1×70 cell}</span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt"> cfg: [1×1 struct]</span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt"> </span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt"> cfg =
</span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt"> </span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt"> struct with fields:</span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt"> </span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt"> method: 'mean'</span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt"> parcellation: 'tissue'</span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt"> parameter: 'mom'</span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt"> </span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">ft_sourceparcellate(cfg,sourceEEG,mri2)</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">there is no "Dentate" in "tissue"</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">there is no "Hypothalamus" in "tissue"</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">there is no "Substania Nigra" in "tissue"</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">there is no "Caudate Tail" in "tissue"</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">there is no "Putamen" in "tissue"</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">there is no "Medial Geniculum Body" in "tissue"</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">there is no "Lateral Globus Pallidus" in "tissue"</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">there is no "Subthalamic Nucleus" in "tissue"</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">there is no "Medial Globus Pallidus" in "tissue"</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">there is no "Lateral Geniculum Body" in "tissue"</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">there is no "Anterior Commissure" in "tissue"</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">there is no "Ventral Posterior Lateral Nucleus" in "tissue"</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">there is no "Ventral Posterior Medial Nucleus" in "tissue"</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">there is no "Ventral Lateral Nucleus" in "tissue"</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">there is no "Ventral Anterior Nucleus" in "tissue"</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">there is no "Caudate Body" in "tissue"</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">there is no "Lateral Posterior Nucleus" in "tissue"</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:108.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">there is no "Lateral Dorsal Nucleus" in "tissue"</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:36.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">there is no "Midline Nucleus" in "tissue"</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">there are in total 8196 positions, 8190 positions are inside the brain, 2611 positions have a label</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">2609 of the positions inside the brain have a label</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">2609 of the labeled positions are inside the brain</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">5581 of the positions inside the brain do not have a label</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">creating 70 parcels for parameter mom by taking the mean</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">computing parcellation</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">Index exceeds the number of array elements (0).</span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt"> </span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">Error in ft_sourceparcellate>cellmean1 (line 488)</span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">y = x{1};</span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt"> </span></i><o:p></o:p></p>
<p class="MsoNormal" style="margin-left:72.0pt;text-indent:36.0pt"><i><span style="font-size:10.0pt">Error in ft_sourceparcellate (line 226)</span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt"> tmp(j,:,:) = cellmean1(dat(seg==j));</span></i><o:p></o:p></p>
<p class="MsoNormal"> <o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F4E79"> -If I cannot get source parcellate to work, does my method of creating a mask and pulling out the source powers seem sound? I see that I should
use mom instead of pow, so would I then just take all the voxels in an ROI and average the x direction together, the y direction together and the z together? Then find the direction with the largest power and use that as my 1by x time series power(using svd
as described in documentation)? This is what I’m assuming sourceparcellate does if I select ‘eig’ as the method.
</span><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt;font-family:"Calibri",sans-serif;color:#1F4E79"> </span></i><o:p></o:p></p>
<p class="MsoNormal"><i><span style="font-size:10.0pt">ALSO IMPORTANT: if you go for a granger-like measure (granger/dtf/pdc), in the spectral analysis you should probably use a decent amount of zero padding (cfg.pad) and sufficient tapsmofrq in order to get
spectral data that stands a chance to get a stable factorization.</span></i><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"> </span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"> </span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"> -Finally, if I choose to go for granger, what kind of cfg.pad and tapsmofrq would you recommend? I am mostly interested in the beta band(12-30Hz)
and my trial lengths are 357 samples(1.3 seconds)</span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"> </span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D">Thank you again so much for all your help!</span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"> </span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D">Best,</span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D">Michael Glassen</span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"> </span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"> </span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"> </span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"> </span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"> </span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D">-</span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"> </span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"> </span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"> </span><o:p></o:p></p>
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<div style="border:none;border-top:solid #E1E1E1 1.0pt;padding:3.0pt 0cm 0cm 0cm">
<p class="MsoNormal"><b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">From:</span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> fieldtrip <fieldtrip-bounces@science.ru.nl>
<b>On Behalf Of </b>Schoffelen, J.M. (Jan Mathijs)<br>
<b>Sent:</b> Thursday, May 7, 2020 3:17 AM<br>
<b>To:</b> FieldTrip discussion list <fieldtrip@science.ru.nl><br>
<b>Subject:</b> Re: [FieldTrip] Computing source-level ROI EEG connectivity</span><o:p></o:p></p>
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<p class="MsoNormal"> <o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:13.5pt">Dear Michael, </span> <o:p></o:p></p>
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<p class="MsoNormal"> <o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:13.5pt">It looks that you already got quite far! I agree that the DTF values do not make much sense.</span><o:p></o:p></p>
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<p class="MsoNormal"> <o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:13.5pt">a few pointers:</span><o:p></o:p></p>
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<p class="MsoNormal"> <o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:13.5pt">-I would use some regularisation for the MNE reconstruction.</span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:13.5pt">-I would check whether the individual trials after source reconstruction yield meaningful values in the ‘mom’-field. Particularly, are the different trials different? Do the time series look meaningful to
begin with (both in terms of signal magnitude and ’shape’)? The reason I ask, is that I am not fully sure whether the ‘rawtrial’ option is behaving according to your expectations.</span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:13.5pt">-Another way for getting the mom would be to use cfg.keepfilter in ft_sourceanalysis, and left-multiply each dipole location’s ’spatial filter’ (in sourceEEG.avg.filter) with a dataEEG.trial matrix.</span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:13.5pt">-IMPORTANT: don’t use the ‘pow’ for the subsequent spectral analysis and connectivity estimation. You should work with the mom.</span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:13.5pt">-In order to get a ‘parcellated’ representation of your source level data, I recommend to use ft_sourceparcellate, which should achieve the parcellation you performed by hand (with the binary masks).</span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:13.5pt">-The parcellation operation can be an average of the mom across the dipoles that belong to a parcel, but an alternative might be the the 1st principal component. The advantage of the latter would be that the
per-parcel signal will be reduced to a single time series (rather then 3 x/y/z orientation time courses), which is convenient (and required) for the interpretation of the subsequent computations.</span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:13.5pt">-You may want to think a bit about the depth weighting, since the MNE favors superficial locations in terms of amplitude, and this will also percolate to the way the parcellated time series are computed.</span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:13.5pt">-IMPORTANT: using ‘dtf’ without any additional specification of parameters is probably not what you want here. The reason is that under the hood a multivariate factorization of the spectral matrix is performed
(at least: an attempt is made), which will miserably fail in your case. The reason for this failure (thinking a bit more about this, I am quite surprised that it worked to begin with) is the fact that you ask for a factorization of a size NparcelxNparcel(xfrequency
bins) matrix, where the effective rank of the matrix is less than the number of channels in your EEG array. Even if Nparcel is less than the number of EEG electrodes, this factorization will be very poorly estimated and nonsensical. I’d recommend using ‘granger’
as a method, in combination with cfg.granger.sfmethod = ‘bivariate’. This results in a parcel-pairwise factorization (which is hopefully stable most of the time). I am not a fan of dtf/pdc anyway given that the measures are claimed to provide estimates unproblematic
estimates that are interpretable without any caveats.</span><o:p></o:p></p>
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<div>
<p class="MsoNormal"><span style="font-size:13.5pt">-ALSO IMPORTANT: if you go for a granger-like measure (granger/dtf/pdc), in the spectral analysis you should probably use a decent amount of zero padding (cfg.pad) and sufficient tapsmofrq in order to get
spectral data that stands a chance to get a stable factorization.</span><o:p></o:p></p>
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<p class="MsoNormal"> <o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:13.5pt">So, no definitive answers unfortunately, but perhaps some food for thought that might get you closer to a good result.</span><o:p></o:p></p>
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<p class="MsoNormal"> <o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:13.5pt">Good luck and keep up the good work,</span><o:p></o:p></p>
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<p class="MsoNormal"> <o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:13.5pt">Jan-Mathijs</span><o:p></o:p></p>
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<p class="MsoNormal"> <o:p></o:p></p>
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<p class="MsoNormal"> <o:p></o:p></p>
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<p class="MsoNormal" style="margin-bottom:12.0pt"> <o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:13.5pt;color:black">J.M.Schoffelen, MD PhD<br>
Associate PI, VIDI-fellow - PI, language in interaction<br>
Telephone: +31-24-3614793</span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:13.5pt;color:black">Physical location: room 00.028</span><o:p></o:p></p>
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<p class="MsoNormal" style="margin-bottom:12.0pt"><span style="font-size:13.5pt;color:black">Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands<br>
<br>
<br>
</span><o:p></o:p></p>
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<p class="MsoNormal"> <o:p></o:p></p>
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<p class="MsoNormal"><br>
<br>
<br>
<o:p></o:p></p>
<blockquote style="margin-top:5.0pt;margin-bottom:5.0pt">
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<p class="MsoNormal">On 6 May 2020, at 22:34, Michael Glassen - Biomedical Engineer <<a href="mailto:MGlassen@kesslerfoundation.org">MGlassen@kesslerfoundation.org</a>> wrote:<o:p></o:p></p>
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<p class="MsoNormal"> <o:p></o:p></p>
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<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">Hi all,</span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> </span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">I am having issues using fieldtrip for source reconstruction and connectivity analysis with preprocessed EEG data. I believe
the issue is occurring when I try to project my sources to specific ROIs before getting the connectivity. I have outlined my pipeline below:</span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> </span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">-Because I do not have subject MRIs, I am using a template headmodel and sourcemodel that come included with fieldtrip</span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> - headmodel = standard_bem.mat</span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> -sourcemodel = cortex_8196.surf.gii</span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> </span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">-After importing EEG data, I project the EEG electrodes to the scalp using the code below:</span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> <span class="apple-converted-space"> </span></span><span style="font-size:9.0pt;font-family:"Courier New";color:blue">if</span><span class="apple-converted-space"><span style="font-size:9.0pt;font-family:"Courier New""> </span></span><span style="font-size:9.0pt;font-family:"Courier New"">strcmp(headmodel.type,<span class="apple-converted-space"> </span><span style="color:#A020F0">'bemcp'</span>)</span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:9.0pt;font-family:"Courier New""> scalp_index = 3;</span><o:p></o:p></p>
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<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New";color:blue">elseif</span><span class="apple-converted-space"><span style="font-size:9.0pt;font-family:"Courier New""> </span></span><span style="font-size:9.0pt;font-family:"Courier New"">strcmp(headmodel.type,<span class="apple-converted-space"> </span><span style="color:#A020F0">'dipoli'</span>)</span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:9.0pt;font-family:"Courier New""> scalp_index = 1;</span><o:p></o:p></p>
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<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New";color:blue">end</span><o:p></o:p></p>
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<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg = [];</span><o:p></o:p></p>
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<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg.method =<span class="apple-converted-space"> </span><span style="color:#A020F0">'project'</span>;</span><o:p></o:p></p>
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<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg.headshape = headmodel.bnd(scalp_index);</span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:9.0pt;font-family:"Courier New";color:forestgreen"> </span><o:p></o:p></p>
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<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">eegData.elec = ft_electroderealign(cfg, eegData.elec);</span><o:p></o:p></p>
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<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New""> </span><o:p></o:p></p>
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<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New""> </span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">-I then calculate covariance on a trial basis and calculate the leadfield matrix:</span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> </span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> <span class="apple-converted-space"> </span></span><span style="font-size:9.0pt;font-family:"Courier New"">cfg = [];</span><o:p></o:p></p>
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<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg.covariance =<span class="apple-converted-space"> </span><span style="color:#A020F0">'yes'</span>;</span><o:p></o:p></p>
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<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg.covariancewindow = [-inf 0];</span><o:p></o:p></p>
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<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg.keeptrials =<span class="apple-converted-space"> </span><span style="color:#A020F0">'yes'</span>;</span><o:p></o:p></p>
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<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">tlckEeg = ft_timelockanalysis(cfg, eegData);</span><o:p></o:p></p>
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<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg = [];</span><o:p></o:p></p>
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<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg.elec = tlckEeg.elec; <span class="apple-converted-space"> </span><span style="color:forestgreen">% sensor information</span></span><o:p></o:p></p>
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<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg.channel = tlckEeg.label; <span class="apple-converted-space"> </span><span style="color:forestgreen">% the used channels</span></span><o:p></o:p></p>
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<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg.grid = sourcemodel; <span class="apple-converted-space"> </span><span style="color:forestgreen">% source points</span></span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg.headmodel = headmodel; <span class="apple-converted-space"> </span><span style="color:forestgreen">% volume conduction model</span></span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg.singleshell.batchsize = 5000;<span class="apple-converted-space"> </span><span style="color:forestgreen">% speeds up the computation</span></span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">leadfield = ft_prepare_leadfield(cfg);</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> <span class="apple-converted-space"> </span></span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">-Now with the leadfield calculated, I calculate sources on a trial basis here:</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg = [];</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg.method =<span class="apple-converted-space"> </span><span style="color:#A020F0">'mne'</span>;</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg.sourcemodel = leadfield;</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg.headmodel = headmodel;</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg.mne.prewhiten =<span class="apple-converted-space"> </span><span style="color:#A020F0">'yes'</span>;</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg.mne.lambda = 0;</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg.mne.scalesourcecov =<span class="apple-converted-space"> </span><span style="color:#A020F0">'yes'</span>;</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg.rawtrial =<span class="apple-converted-space"> </span><span style="color:#A020F0">'yes'</span>;</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">sourceEEG = ft_sourceanalysis(cfg,tlckEeg);</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> sourceEEG =<span class="apple-converted-space"> </span></span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> struct with fields:</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> time: [1×357 double]</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> inside: [8196×1 logical]</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> pos: [8196×3 double]</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> tri: [16384×3 double]</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> method: 'rawtrial'</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> trial: [1×400 struct]</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> df: 400</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> cfg: [1×1 struct]</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> sourceEEG.trial =<span class="apple-converted-space"> </span></span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> 1×400 struct array with fields:</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> mom</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> pow</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> noisecov</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> <span class="apple-converted-space"> </span></span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">size(sourceEEG.trial(1).pow)</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> </span><o:p></o:p></p>
</div>
<div style="margin-left:36.0pt">
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">ans =</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> 8196 357</span><o:p></o:p></p>
</div>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> </span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">-So at this point I have 400 trials, and for each of these trials I have a time course of source power at 8196 voxels and 357
time points for each of those voxels.</span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">-I then import two atlases, one that includes broddman areas and one that includes left and right hemispheres. I interpolate
these atlases onto my source model and get the following:</span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> <span class="apple-converted-space"> </span></span><span style="font-size:9.0pt;font-family:"Calibri Light",sans-serif">broddman
=</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:36.0pt;text-indent:36.0pt;line-height:11.65pt">
<span style="font-size:9.0pt;font-family:"Calibri Light",sans-serif">struct with fields:</span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:9.0pt;font-family:"Calibri Light",sans-serif"> </span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:9.0pt;font-family:"Calibri Light",sans-serif"> pos: [8196×3 double]</span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:9.0pt;font-family:"Calibri Light",sans-serif"> tri: [16384×3 double]</span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:9.0pt;font-family:"Calibri Light",sans-serif"> unit: 'm'</span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:9.0pt;font-family:"Calibri Light",sans-serif"> tissue: [8196×1 double] (<b>each of these is a value 1-70, corresponding to which
atlas entry the voxel is located in)</b></span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:9.0pt;font-family:"Calibri Light",sans-serif"> tissuelabel: {1×70 cell}<b>(ROI labels for the atlas, includes broddman areas not
separated by hemisphere)</b></span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:9.0pt;font-family:"Calibri Light",sans-serif"> cfg: [1×1 struct]</span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;text-indent:36.0pt;line-height:11.65pt">
<span style="font-size:9.0pt;font-family:"Calibri Light",sans-serif">hemis2 =</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:36.0pt;text-indent:36.0pt;line-height:11.65pt">
<span style="font-size:9.0pt;font-family:"Calibri Light",sans-serif">struct with fields:</span><o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:8.0pt;margin-left:36.0pt;text-indent:36.0pt;line-height:11.65pt">
<span style="font-size:9.0pt;font-family:"Calibri Light",sans-serif">pos: [8196×3 double]</span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:9.0pt;font-family:"Calibri Light",sans-serif"> tri: [16384×3 double]</span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:9.0pt;font-family:"Calibri Light",sans-serif"> unit: 'm'</span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:9.0pt;font-family:"Calibri Light",sans-serif"> tissue: [8196×1 double] (<b>each of these is a value 1-7, corresponding to which
atlas entry the voxel is located in)</b></span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:9.0pt;font-family:"Calibri Light",sans-serif"> </span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:9.0pt;font-family:"Calibri Light",sans-serif"> tissuelabel: {1×7 cell}<span class="apple-converted-space"> </span><b>(ROI Labels
for hemisphere atlas, contains 3 Left areas, 3 Right Areas and one inter-hemispheric)</b></span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:9.0pt;font-family:"Calibri Light",sans-serif"> cfg: [1×1 struct]</span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:9.0pt;font-family:"Calibri Light",sans-serif"> </span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:9.0pt;font-family:"Calibri Light",sans-serif"> </span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">Now I create a binary mask for each left and right broddman area. For each ROI in the broddman area atlas(1-70), I find all
voxels that are located within. This gives me a list of voxels for each broddman area. I then take each of these lists and find which are in the left hemisphere and which are in the right to get left and right broddman areas.</span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">After these steps I am left with 140 binary masks, (size 1x8196), which equal 1 if the voxel is contained in the specific ROI
and 0 if it is not. Using these masks, I extract the time course power for each ROI by taking the mean power of every voxel in the roi at each time point:</span><o:p></o:p></p>
<div>
<p class="MsoNormal"><span style="font-size:9.0pt;font-family:"Calibri",sans-serif"> <span class="apple-converted-space"> </span></span><span style="font-size:9.0pt;font-family:"Courier New"">roiData(i,i3,i2) = mean(sourceEEG.trial(i2).pow(Masks{i},i3));</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:9.0pt;font-family:"Courier New""> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">where i = roi number,</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:9.0pt;font-family:"Courier New""> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:9.0pt;font-family:"Courier New""> i2 = trial number,</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:9.0pt;font-family:"Courier New""> <span class="apple-converted-space"> </span></span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:9.0pt;font-family:"Courier New""> i3 = time point</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:9.0pt;font-family:"Courier New""> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">In order to get this data into fieldtrip format, I put ROI data into an EEGLAB structure along with 6 emg channels that were recorded at the same time and use eeglab2fieldtrip
function to have a fieldtrip structure containing the broddman area sources as channels. (contained in<span class="apple-converted-space"> </span><i>header</i>)</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">After this I perform the final step of computing dtf connectivity between each of the source rois and the emg channels with the code below:</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> <span class="apple-converted-space"> </span></span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg = [];</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg.method =<span class="apple-converted-space"> </span><span style="color:#A020F0">'mtmfft'</span>;</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg.taper =<span class="apple-converted-space"> </span><span style="color:#A020F0">'dpss'</span>;</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg.output =<span class="apple-converted-space"> </span><span style="color:#A020F0">'fourier'</span>;</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg.tapsmofrq = 2;</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">freq = ft_freqanalysis(cfg, header);</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg = [];</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">cfg.method =<span class="apple-converted-space"> </span><span style="color:#A020F0">'dtf'</span>;</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New"">dtf = ft_connectivityanalysis(cfg, freq);</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New""> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal" style="text-indent:36.0pt"><span style="font-size:9.0pt;font-family:"Courier New""> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">The values I get in DTF are extremely low, 10^-20. I am not sure why but I believe it has to do with the way I am projecting source activations onto specific ROIs. Any help
or advice on how to change my pipeline is appreciated.</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:9.0pt;font-family:"Calibri",sans-serif"> </span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">Best,</span><o:p></o:p></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">Michael Glassen</span><o:p></o:p></p>
</div>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:9.0pt;font-family:"Calibri",sans-serif"> </span><o:p></o:p></p>
<p class="MsoNormal" style="margin-bottom:8.0pt;line-height:11.65pt"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> </span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:10.5pt;font-family:Helvetica"><br>
_______________________________________________<br>
fieldtrip mailing list<br>
</span><a href="https://mailman.science.ru.nl/mailman/listinfo/fieldtrip"><span style="font-size:10.5pt;font-family:Helvetica;color:#954F72">https://mailman.science.ru.nl/mailman/listinfo/fieldtrip</span></a><span style="font-size:10.5pt;font-family:Helvetica"><br>
</span><a href="https://doi.org/10.1371/journal.pcbi.1002202"><span style="font-size:10.5pt;font-family:Helvetica;color:#954F72">https://doi.org/10.1371/journal.pcbi.1002202</span></a><o:p></o:p></p>
</div>
</blockquote>
</div>
<p class="MsoNormal"> <o:p></o:p></p>
</div>
<p class="MsoNormal"> <o:p></o:p></p>
</div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><br>
<o:p></o:p></span></p>
</div>
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