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

[Lin Wang]

        External Email - Use Caution        

Hi Alex,

Thanks for your response.

Yeah, after applying the inverse operator to the ERF within each run for
each condition, I averaged the dSPM values across runs for each condition.
The number of epochs for the ERFs was not equalized between runs because
there were only a few epochs per run (ranging between 1 - 4 epochs).

I see that the noise normalization takes into the number of epochs into
account, so that the average of the dSPM is not the dSPM of the average. In
our case, do you suggest to first average the epochs across runs and then
calculate the dSPM values? But then how can we account for the different
head positions (and different bad channels) across runs?

Or should we calculate the MNE values instead of dSPM values for each run
separately and then average them across runs?

A related but slightly different question is what head position information
should we use when calculating the forward model. If we calculate the
inverse operator separately for each run, should we also use the head
position within that run for the forward model? Is there a way to get the
head position within the whole run instead of only the head position at the
beginning of the run?

Best,
Lin



On Thu, 21 Mar 2019 at 16:12, Alexandre Gramfort <
alexandre.gramfort at inria.fr> wrote:

>         External Email - Use Caution
>
> 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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