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

Lin Wang wanglinsisi at gmail.com
Thu Mar 21 16:48:34 EDT 2019
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Hi Ellen,

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.

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.

Best,
Lin



On Thu, 21 Mar 2019 at 16:36, Ellen Lau <ellenlau at umd.edu> wrote:

>         External Email - Use Caution
>
> 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>
> > Message-ID:
> >       <CADsMai1N2JQzovOAj7rt8aUT_gS6C=
> SKyW1j40D9HtQMHr-6wQ at mail.gmail.com>
> > Content-Type: text/plain; charset="utf-8"
> >
> >        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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> > ------------------------------
> >
> > 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>
> > Content-Type: text/plain; charset="utf-8"
> >
> >        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
> >> _______________________________________________
> >> Mne_analysis mailing list
> >> Mne_analysis at nmr.mgh.harvard.edu
> >> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
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