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

Matti Hamalainen msh at nmr.mgh.harvard.edu
Mon Mar 10 18:32:57 EDT 2014
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Note: MNE does not change with nave, the others do - Matti

> On Mar 10, 2014, at 17:30, "Hari Bharadwaj" <hari at nmr.mgh.harvard.edu> wrote:
> 
> 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
> 
> 
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