[Mne_analysis] pick orientation, MNE, dSPM and group analyses

Denis-Alexander Engemann denis.engemann at gmail.com
Fri Aug 8 14:53:22 EDT 2014
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Hari,

good you mention it. I had something similar in mind.

Denis

On Fri, Aug 8, 2014 at 8:49 PM, Hari Bharadwaj <hari at nmr.mgh.harvard.edu>
wrote:

> Hi Denis,
>    If you are interested in the grand average (across subjects) in a
> label, then perhaps you could get a label average for each subject
> separately and use those time-courses (1 per subejct) to then do a
> "mean flip" like procedure when averaging across subjects?
>
> This was the procedure I used for one of my studies and it worked
> reasonably well.. Of course, if you did the loose and used unsigned
> values, you don't run into the polarity issue..
>
>
Indeed, but the you also loose comparibility with sensor space results and
for example TFR analyses are prohibitive with free orientation estimates,
etc., and so on.

Denis


> Hari
>
> On Fri, August 8, 2014 9:03 am, Denis-Alexander Engemann wrote:
> > Hi Hari,
> >
> >
> > On Fri, Aug 8, 2014 at 2:58 PM, Hari Bharadwaj <hari at nmr.mgh.harvard.edu
> >
> > wrote:
> >
> >> Hi Denis,
> >>    With the orientation fixed,
> >>
> >
> > should try with fixed ...
> >
> >
> >> is it the across vertex (within a parcel for a give subject) or the
> >> across
> >> subject averaging that is reducing the dSPMs?
> >>
> >
> > to me it seem it's the cross-subject reduction, cross-vertex looks
> > sane-ish
> > thanks to flipping.
> >
> >
> >> Also, is the data some kind of event related response where you'd expect
> >> the peaks across subjects to along in time?
> >>
> >
> > yes, roughtly so. I cannot say too much here since I'm not looking at
> > standard components.
> >
> >
> >> I ask because the signed estimates when not aligned across subjects (in
> >> time) could go down quite a bit when you average across subjects...the
> >> "mis-alignment" could also simply come from the possibility the polarity
> >> is
> >> not the same across subjects,
> >>
> >
> > I also thought about this.
> >
> >
> >> i.e., though the peaks are around the same time, for some subjects the
> >> peaks are negative and for some positive (both in vertex by vertex
> >> grand-averaging case ..or the label wise average for each subject
> >> separately case)
> >>
> >>
> > the question would then be how to deal with it.
> >
> >
> >> Hari
> >>
> >> On Aug 8, 2014, at 8:26 AM, Denis-Alexander Engemann <
> >> denis.engemann at gmail.com> wrote:
> >>
> >> Hi Dan,
> >>
> >>
> >> On Thu, Aug 7, 2014 at 9:45 PM, dgw <dgwakeman at gmail.com> wrote:
> >>
> >>> Hi Denis,
> >>>
> >>> This effect can be influenced by a lot of variables. I would say
> >>> anatomical variability is a huge one but there are tons of factors
> >>> which affect just that:
> >>>
> >>> Did you decimate? (I guess you must have to morph, but how severely
> >>> did you decimate?)
> >>>
> >>
> >> I think I did not explicitlly decimate. Simply a 20 steps morpch from
> >> subject to fsaverage.
> >>
> >>
> >>> Are you using --loose, or --loosevar
> >>> What parameters with those
> >>>
> >>
> >> our default, loose=0.2
> >>
> >>
> >>> Did you use cps?
> >>>
> >>>
> >> I'm actually not sure whether Python takes the cps into account / where
> >> /
> >> when /
> >>
> >>
> >>> I suspect the morphing will also influence this, but that is easy to
> >>> check (and wise to do see how the labels morph back on the
> >>> individual's surface?).
> >>
> >>
> >> Yeah, or compute the grand ave time series based on time courses
> >> extracted
> >> from unorphed stcs.
> >>
> >>
> >>> Though as long as you have FreeSurfer quality
> >>> scans, I don't expect the segmentation to be an issue. What if any
> >>> smoothing did you do (at each stage)?
> >>>
> >>>
> >> see above
> >>
> >>
> >>> HTH,
> >>> D
> >>>
> >>>
> >> more imporatanlty, does all this actually matter at all if the SNR seems
> >> to be ok.
> >>
> >> Denis
> >>
> >>
> >>> On Thu, Aug 7, 2014 at 11:25 AM, Denis-Alexander Engemann
> >>> <denis.engemann at gmail.com> wrote:
> >>> > Dear list,
> >>> >
> >>> > I'm currently comparing group grand averages in a set of functional
> >>> labels
> >>> > which are derived from the PALS B12 Brodmann parcellation. These were
> >>> then
> >>> > used with subjects' stcs after morphing to fsaverage.
> >>> > Now I'm really struck that with surface orientation AND mean flipping
> >>> the
> >>> > minima and maxima, even for dSPM shrink to values below 1 while the
> >>> expected
> >>> > temporal dynamics are preserved. In the 'wild', that is, *before*
> >>> averaging,
> >>> > the signed dSPMS are between -7 and 8, just as the free-orientation
> >>> dSPM
> >>> > maxima are around 8 --- *after*  --- averaging.
> >>> >
> >>> > I'm wondering whether this could be a result of the morphing, the
> >>> anatomical
> >>> > variability, or even the segmentation quality.
> >>> >
> >>> > Any hint would be appreciated.
> >>> >
> >>> > Denis
> >>> >
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>
> --
> Hari Bharadwaj
> Post-doctoral Associate,
> Center for Computational Neuroscience
>   and Neural Technology (CompNet),
> Boston University
> 677 Beacon St.,
> Boston, MA 02215
>
> Martinos Center for Biomedical Imaging,
> Massachusetts General Hospital
> 149 Thirteenth Street,
> Charlestown, MA 02129
>
> hari at nmr.mgh.harvard.edu
> Ph: 734-883-5954
> www.haribharadwaj.com
>
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