<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">Hi Ellen,<div><br></div><div>Thanks for your response. Yeah, as Alex also pointed out, the averaging of the dSPM values is not linear because of the noise normalization, which takes the number of epochs into account.</div><div><br></div><div>But if we average the MNE values along the 'normal' orientation for the loose orientation constraints, the operation should be linear, i.e. the average of MNE values across runs is the same as the MNE of the averaged ERF across runs.</div><div><br></div><div>Best,</div><div>Lin</div><div><br></div><div><br></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Thu, 21 Mar 2019 at 16:36, Ellen Lau <<a href="mailto:ellenlau@umd.edu">ellenlau@umd.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"> External Email - Use Caution <br>
<br>
Hi Lin,<br>
<br>
In a related conversation I was recently reminded that the inverse solution may not be linear when the orientation is loose rather than fixed, maybe that is contributing to the difference? <br>
<br>
Ellen<br>
<br>
> <br>
> Message: 1<br>
> Date: Thu, 21 Mar 2019 13:05:37 -0400<br>
> From: Lin Wang <<a href="mailto:wanglinsisi@gmail.com" target="_blank">wanglinsisi@gmail.com</a>><br>
> Subject: [Mne_analysis] head position information when building<br>
> forward model for different runs<br>
> To: Discussion and support forum for the users of MNE Software<br>
> <<a href="mailto:mne_analysis@nmr.mgh.harvard.edu" target="_blank">mne_analysis@nmr.mgh.harvard.edu</a>><br>
> Message-ID:<br>
> <CADsMai1N2JQzovOAj7rt8aUT_gS6C=<a href="mailto:SKyW1j40D9HtQMHr-6wQ@mail.gmail.com" target="_blank">SKyW1j40D9HtQMHr-6wQ@mail.gmail.com</a>><br>
> Content-Type: text/plain; charset="utf-8"<br>
> <br>
> External Email - Use Caution <br>
> <br>
> Hi MNE experts,<br>
> <br>
> I have a question about what head position information to use when building<br>
> a forward model for each run in one participant.<br>
> <br>
> We have eight runs of MEG data. At the beginning, we used the head position<br>
> of the first run to build just one forward model for one participant. Then<br>
> we thought it might be more accurate to use the run-specific head position<br>
> to build forward models separately for different runs. In both analyses,<br>
> the forward models were used to calculate the inverse operators, which were<br>
> applied to the evoked response for each run. We then averaged the<br>
> activation across runs within each participant. Finally, we compared the<br>
> activation difference between two conditions at the group level.<br>
> <br>
> Although the group-averaged activation looks very similar from the two<br>
> analyses, the use of run-specific head position reduced the statistical<br>
> power at the group level. Do you know why?<br>
> Is there a better way to account for the head movement within each run? We<br>
> didn't apply the MaxFilter to the data because the head movement was not<br>
> considered to be serious during the data acquisition. Is there a way to<br>
> incorporate the head movement within each run or the whole experiment<br>
> without running the MaxFilter?<br>
> <br>
> Thanks a lot for your input!<br>
> <br>
> Best,<br>
> Lin<br>
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> ------------------------------<br>
> <br>
> Message: 2<br>
> Date: Thu, 21 Mar 2019 21:11:44 +0100<br>
> From: Alexandre Gramfort <<a href="mailto:alexandre.gramfort@inria.fr" target="_blank">alexandre.gramfort@inria.fr</a>><br>
> Subject: Re: [Mne_analysis] head position information when building<br>
> forward model for different runs<br>
> To: Discussion and support forum for the users of MNE Software<br>
> <<a href="mailto:mne_analysis@nmr.mgh.harvard.edu" target="_blank">mne_analysis@nmr.mgh.harvard.edu</a>><br>
> Message-ID:<br>
> <<a href="mailto:CADeotZogU6vbv85Se-oQH3odoEbv34bEVLK2TfOQcU8JF9JjSw@mail.gmail.com" target="_blank">CADeotZogU6vbv85Se-oQH3odoEbv34bEVLK2TfOQcU8JF9JjSw@mail.gmail.com</a>><br>
> Content-Type: text/plain; charset="utf-8"<br>
> <br>
> External Email - Use Caution <br>
> <br>
> hi Lin,<br>
> <br>
> did you average dSPM values? as given the noise normalization the average<br>
> of the dSPM is not the dSPM of the average. If could make a big difference<br>
> unless all runs have the same number of epochs in all conditions.<br>
> <br>
> Alex<br>
> <br>
> <br>
> On Thu, Mar 21, 2019 at 6:07 PM Lin Wang <<a href="mailto:wanglinsisi@gmail.com" target="_blank">wanglinsisi@gmail.com</a>> wrote:<br>
> <br>
>> External Email - Use Caution<br>
>> <br>
>> Hi MNE experts,<br>
>> <br>
>> I have a question about what head position information to use when<br>
>> building a forward model for each run in one participant.<br>
>> <br>
>> We have eight runs of MEG data. At the beginning, we used the head<br>
>> position of the first run to build just one forward model for one<br>
>> participant. Then we thought it might be more accurate to use the<br>
>> run-specific head position to build forward models separately for different<br>
>> runs. In both analyses, the forward models were used to calculate the<br>
>> inverse operators, which were applied to the evoked response for each run.<br>
>> We then averaged the activation across runs within each participant.<br>
>> Finally, we compared the activation difference between two conditions at<br>
>> the group level.<br>
>> <br>
>> Although the group-averaged activation looks very similar from the two<br>
>> analyses, the use of run-specific head position reduced the statistical<br>
>> power at the group level. Do you know why?<br>
>> Is there a better way to account for the head movement within each run? We<br>
>> didn't apply the MaxFilter to the data because the head movement was not<br>
>> considered to be serious during the data acquisition. Is there a way to<br>
>> incorporate the head movement within each run or the whole experiment<br>
>> without running the MaxFilter?<br>
>> <br>
>> Thanks a lot for your input!<br>
>> <br>
>> Best,<br>
>> Lin<br>
>> _______________________________________________<br>
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</blockquote></div>