[Mne_analysis] head position information when building forward model for different runs

[Ellen Lau]

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Hi Lin,

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? 

Ellen

> 
> Message: 1
> Date: Thu, 21 Mar 2019 13:05:37 -0400
> From: Lin Wang <wanglinsisi at gmail.com>
> Subject: [Mne_analysis] head position information when building
> 	forward	model for different runs
> To: Discussion and support forum for the users of MNE Software
> 	<mne_analysis at nmr.mgh.harvard.edu>
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> 
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> 
> Hi MNE experts,
> 
> I have a question about what head position information to use when building
> a forward model for each run in one participant.
> 
> We have eight runs of MEG data. At the beginning, we used the head position
> of the first run to build just one forward model for one participant. Then
> we thought it might be more accurate to use the run-specific head position
> to build forward models separately for different runs. In both analyses,
> the forward models were used to calculate the inverse operators, which were
> applied to the evoked response for each run. We then averaged the
> activation across runs within each participant. Finally, we compared the
> activation difference between two conditions at the group level.
> 
> Although the group-averaged activation looks very similar from the two
> analyses, the use of run-specific head position reduced the statistical
> power at the group level. Do you know why?
> Is there a better way to account for the head movement within each run? We
> didn't apply the MaxFilter to the data because the head movement was not
> considered to be serious during the data acquisition. Is there a way to
> incorporate the head movement within each run or the whole experiment
> without running the MaxFilter?
> 
> Thanks a lot for your input!
> 
> Best,
> Lin
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> ------------------------------
> 
> Message: 2
> Date: Thu, 21 Mar 2019 21:11:44 +0100
> From: Alexandre Gramfort <alexandre.gramfort at inria.fr>
> Subject: Re: [Mne_analysis] head position information when building
> 	forward model for different runs
> To: Discussion and support forum for the users of MNE Software
> 	<mne_analysis at nmr.mgh.harvard.edu>
> Message-ID:
> 	<CADeotZogU6vbv85Se-oQH3odoEbv34bEVLK2TfOQcU8JF9JjSw at mail.gmail.com>
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> 
> hi Lin,
> 
> did you average dSPM values? as given the noise normalization the average
> of the dSPM is not the dSPM of the average. If could make a big difference
> unless all runs have the same number of epochs in all conditions.
> 
> Alex
> 
> 
> On Thu, Mar 21, 2019 at 6:07 PM Lin Wang <wanglinsisi at gmail.com> wrote:
> 
>>        External Email - Use Caution
>> 
>> Hi MNE experts,
>> 
>> I have a question about what head position information to use when
>> building a forward model for each run in one participant.
>> 
>> We have eight runs of MEG data. At the beginning, we used the head
>> position of the first run to build just one forward model for one
>> participant. Then we thought it might be more accurate to use the
>> run-specific head position to build forward models separately for different
>> runs. In both analyses, the forward models were used to calculate the
>> inverse operators, which were applied to the evoked response for each run.
>> We then averaged the activation across runs within each participant.
>> Finally, we compared the activation difference between two conditions at
>> the group level.
>> 
>> Although the group-averaged activation looks very similar from the two
>> analyses, the use of run-specific head position reduced the statistical
>> power at the group level. Do you know why?
>> Is there a better way to account for the head movement within each run? We
>> didn't apply the MaxFilter to the data because the head movement was not
>> considered to be serious during the data acquisition. Is there a way to
>> incorporate the head movement within each run or the whole experiment
>> without running the MaxFilter?
>> 
>> Thanks a lot for your input!
>> 
>> Best,
>> Lin
>> _______________________________________________
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>> Mne_analysis at nmr.mgh.harvard.edu
>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
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