[Mne_analysis] equivalence of averaged single trial STCs and evoked STCs

[Hari Bharadwaj]

Hi Denis,

Just some thoughts..

Your observations about post hoc normalization make sense:
When looking at a single vertex over time or frequency, MNE, dSPM,
sLORETA, etc. are just scaled versions of each other (i.e., by a constant
scalar factor).. The effect of changing 'nave' (which is what is different
between your apply_inverse and apply_inverse_epochs cases) would also only
affect the scaling factor with the time course itself intact...Thus, when
you normalize within vertex post hoc, they become the same once again..

*However*, when looking *across sources*, this constant scale factor for
each source would be different (which of course is the basis for the
improved resolution of dSPM and sLORETA over MNE).. So if you are looking
at spatial maps AND projecting single trials, it may be useful to
explicitly set nave to n_epochs even in the single trial case..

Thoughts?

Hari

On Mon, March 10, 2014 4:36 pm, Denis-Alexander Engemann wrote:
> Hi Hari,
>
> the 'nave' parameter is supposed to be 'n_epochs' in the evoked case and 1
> in the single trial case.
>
> It seems, post-hoc normalizing to the baseline does the trick, as
> suspected:
>
> https://www.dropbox.com/s/kugvns9klfqhu2z/methods_comparison_evoked_single_trial.pdf
>
> once more, gist updated: https://gist.github.com/dengemann/9470121
>
> What do people think?
>
> Best,
> Denis
>
>
>
> On Mon, Mar 10, 2014 at 8:42 PM, Hari Bharadwaj
> <hari at nmr.mgh.harvard.edu>wrote:
>
>> Also, it might be interesting to see what the default 'nave's are
>> apply_inverse() and apply_inverse_epochs() and what implications that
>> might have..
>>
>>
>> On Mon, March 10, 2014 3:26 pm, Denis-Alexander Engemann wrote:
>> > [reposted, attachment failure]
>> >
>> > Hi Dan,
>> >
>> > that was indeed a very good hint.
>> >
>> > I've updated the gist to do some more systematic comparisons between
>> > orientations and methods:
>> >
>> > https://gist.github.com/dengemann/9470121
>> >
>> > As to you clarification questions, the lines referred to in the legend
>>  as
>> > 'single*' are related to averaging in source space.
>> >
>> > It seems using MNE and with a post-hoc normalization, e.g. using
>> z-scores
>> > might be the way to go.
>> >>From my ad-hoc parameter experiment I cannot exclude the possibility,
>> >> that
>> > 'dSPM' and 'sLORETA' with 'normal' orientation might work as well.
>> > This would require a mean-flip method though which is not implemented
>> in
>> > my
>> > script due to the manual extraction used.
>> > My extraction function, here, was `lambda x: np.abs(x).mean(0)`
>> >
>> > This needs more investigation + discussion
>> >
>> > Denis
>> >
>> >
>> > Images:
>> >
>> > https://www.dropbox.com/s/e4ykrm6xdh6ntio/fig-None-MNE.png
>> >
>> > https://www.dropbox.com/s/qwlt5ugxxo0owo2/fig-normal-MNE.png
>> >
>> > https://www.dropbox.com/s/mijmz9pvffgt5ms/fig-None-sLORETA.png
>> >
>> > https://www.dropbox.com/s/8dxf0czptuhyybs/fig-normal-sLORETA.png
>> >
>> > https://www.dropbox.com/s/hxcjla8x9euwzd3/fig-None-dSPM.png
>> >
>> > https://www.dropbox.com/s/e5pbr7iyday3za4/fig-normal-dSPM.png
>> >
>> >
>> >
>> >
>> > On Mon, Mar 10, 2014 at 7:13 PM, dgw <dgwakeman at gmail.com> wrote:
>> >
>> >> Hi Denis,
>> >>
>> >> Which line represents "averaging in source space" and which
>> represents
>> >> "projecting evokeds"?
>> >>
>> >> Regardless. I think you are referring to why are the results
>> different
>> >> when I average single trial data on the source space as opposed to
>> >> averaging that same data in sensor space and then source localizing?
>> >>
>> >> The first reason I can think of is that you are using the dSPM here
>> and
>> >> I
>> >> believe that that can lead to some non-linearity between these
>> results.
>> >> Do
>> >> you get the same problem with the L2?
>> >>
>> >>  HTH,
>> >> D
>> >>
>> >>
>> >> On Mon, Mar 10, 2014 at 1:56 PM, Denis-Alexander Engemann <
>> >> denis.engemann at gmail.com> wrote:
>> >>
>> >>> Hi folks,
>> >>>
>> >>> in one of my recent analyses I ran into some problems with regard to
>> >>> morphing and single trial
>> >>> label time series extraction which lead me to set up some
>> comparisons:
>> >>>
>> >>> https://gist.github.com/dengemann/9470121
>> >>>
>> >>> (gist based on sample data, should run with a proper MNE-Python
>> >>> install)
>> >>>
>> >>> [image: Inline image 1]
>> >>>
>> >>> It turned out morphing was unproblematic, but,
>> >>> I'm wondering whether the differences between averaging in source
>> space
>> >>> and
>> >>> projecting evokeds is expected, and if so, how it can be avoided?
>> >>>
>> >>> The background is that I'd like to be confident about the relating
>> >>> single
>> >>> trial analyses at the evoked level.
>> >>>
>> >>> Any thoughts?
>> >>>
>> >>> Cheers,
>> >>> Denis
>> >>>
>> >>>
>> >>>
>> >>>
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>>
>> --
>> Hari Bharadwaj
>> PhD Candidate, Biomedical Engineering,
>> 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
>>
>>
>>
>


-- 
Hari Bharadwaj
PhD Candidate, Biomedical Engineering,
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