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

[dgw]

IMO, oct-6 is severely decimated (only 2.75% of the points in the
source space originally). I'm not sure that --loose 0.2 will
accurately capture the range of orientations (especially dangerous if
not using the cps). Plus by morphing on top of this coarse spacing
with 20 smooth steps that ends up smoothing the result a lot. If you
want to keep the analysis in the time domain, I would first explore
the timecourses of the labels within the individuals to see what sort
of pattern emerges (particularly with respect to their individual
anatomy). As I said before it could be anatomical variability, but you
may be able to correct for this if the whole label actually has a
consistent orientation across participants (the current decimation (in
combination with your other parameters), could be causing the sign
flipping you are seeing). In that case simply adding cps "should"
improve the situation.

Of course one "easy fix" is to just pick the peak frequency in the
region/time you are interested in and average the power in that band
(this will save you from the flipping issues, by removing phase).

HTH,
D

Aside:
What would be "explicit" decimation? To me reducing the source space
to oct-6 is decimation (how else could one decimate?).

On Fri, Aug 8, 2014 at 9:06 AM, Denis-Alexander Engemann
<denis.engemann at gmail.com> wrote:
>
>
>
> On Fri, Aug 8, 2014 at 3:01 PM, dgw <dgwakeman at gmail.com> wrote:
>>
>> On Fri, 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.
>>
>> How did you map the full ~300,000 vertices from each participant to
>> fsaverage without decimating?
>>
>
> As I said, not *explcitly*, it's the an oct 6 source space that we usually
> recommend as default.
>
>>
>>
>> >
>> >>
>> >> 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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