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

[Denis-Alexander Engemann]

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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