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

[Hari Bharadwaj]

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..

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