[Mne_analysis] GSoC Update: dSPM source reconstruction for the right amygdala in the SPM dataset

Alexandre Gramfort alexandre.gramfort at telecom-paristech.fr
Tue May 27 02:47:53 EDT 2014
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hi everyone,

I agree with that have been said. We're at the beginning
of the GSOC and our first objective is to have the machinery
working. Then we'll do the science right.

@sheraz : When I said we need the cortical surface in the source
space to not just estimate the sources on the amygdala it
is precisely because I had in mind the issue you raised.

Alan please continue to assemble the machinery, maybe next time
avoid the stats at the end ( :-) ) as it is certainly not a good
conclusion.

cheers,
Alex

PS : with all this interest on this topic, it looks like we're going
to have a lot of early adopters to test :)

On Tue, May 27, 2014 at 12:30 AM, Hari Bharadwaj
<hari at nmr.mgh.harvard.edu> wrote:
> I would add that the issue of having the software machinery to potentially
> try localizing deep sources is a separate issue from trusting the obtained
> solution..Thus, for the purposes of demos and code development, localizing
> simulated deep source activity might be the best scenario where the ground
> truth is known..
>
> If real data demos are really desired, something like the ABR might be a
> potentially safer candidate...
>
>
>
> "Ghuman, Avniel" <ghumana at upmc.edu> wrote:
>>
>> I should say though, things like Hippocampus and cerebellum might be more
>> feasible.
>>
>>> On May 26, 2014, at 5:33 PM, "Ghuman, Avniel" <ghumana at upmc.edu> wrote:
>>>
>>> It also might be a reasonable time to discuss a bit of the physics and
>>> physiology of what MEG is and is not sensitive to. MEG is not particularly
>>> sensitive to four things:
>>>
>>> 1. Radial sources
>>> 2. Deep sources
>>> 3. Sources where the neurons are not aligned
>>> 4. Even worse are sources with randomly aligned neurons that are
>>> relatively spherical (and small)
>>>
>>> The amygdala strikes out on the bottom three. Thus, in my opinion, it is
>>> fairly unlikely that MEG can pick up activity from the amygdala. I would
>>> love to hear other opinions.
>>>
>>> Best wishes,
>>> Avniel
>>>
>>> Avniel Ghuman, Ph.D.
>>> Director of MEG Research
>>> Assistant Professor of Neurological Surgery, Neurobiology, and Psychiatry
>>> Faculty in the Center for the Neural Basis of Cognition
>>> University of Pittsburgh
>>>
>>> ________________________________
>>>
>>> From: mne_analysis-bounces at nmr.mgh.harvard.edu
>>> [mne_analysis-bounces at nmr.mgh.harvard.edu] on behalf of
>>> sheraz at nmr.mgh.harvard.edu [sheraz at nmr.mgh.harvard.edu]
>>> Sent: Monday, May 26, 2014 5:13 PM
>>> To: Discussion and support forum for the users of MNE Software
>>> Subject: Re: [Mne_analysis] GSoC Update: dSPM source reconstruction for
>>> the right amygdala in the SPM dataset
>>>
>>> hi Alex,
>>>
>>> my 2 cents, if you look at the waveform carefully, the responses follows
>>> the same time course as the cortical responses. Putting this example will
>>> give very wrong impression about MEG and deep sources.
>>>
>>> Putting premature examples might not be great idea :)
>>>
>>> Sheraz
>>>
>>> hi Alan,
>>>
>>> if the demo aims to demonstrate inverse modeling with deep structures
>>> I would put it in examples/inverse
>>>
>>> Alex
>>>
>>>
>>>> On Mon, May 26, 2014 at 9:00 PM, Alan <leggitta3 at gmail.com> wrote:
>>>> Hi Alex,
>>>>
>>>> Thanks. I will make that my next step. Where in the mne code structure
>>>> should I put this? I was thinking in under examples/datasets.
>>>>
>>>> Alan
>>>>
>>>>
>>>> On Sat, May 24, 2014 at 5:43 AM, Alexandre Gramfort
>>>> <alexandre.gramfort at telecom-paristech.fr> wrote:
>>>>
>>>>> hi Alan,
>>>>>
>>>>> nice work !
>>>>>
>>>>> I have a few comments to make on the code so a PR
>>>>>  on
>>>>> github would allow
>>>>> me to comment inline.
>>>>>
>>>>> Regarding the approach, it does what it is supposed to do in a pretty
>>>>> elegant way, but
>>>>> it is dangerous to only run the inverse method on such a tiny
>>>>> source space. An activation from the cortex would necessarily be
>>>>> projected
>>>>> to the amygdala. My hunch, is that an LCMV beamformer would suffer
>>>>> less from this.
>>>>>
>>>>> I think the next step would be to be able to combine a surface source
>>>>> space
>>>>> and the deep volume source space you create.
>>>>>
>>>>> best,
>>>>> Alex
>>>>>
>>>>>
>>>>>> On Sat, May 24, 2014 at 1:10 AM, Alan <leggitta3 at gmail.com> wrote:
>>>>>> Hello,
>>>>>>
>>>>>> I worked through creating a discrete source space of the right
>>>>>
>>>>> amygdala
>>>>>>
>>>>>> and
>>>>>> performing dSPM source reconstruction in the SPM dataset. My code is
>>>>>> available on my GSoC blog.
>>>>>>
>>>>>>
>>>>>>
>>>>>> http://misterbrainley.blogspot.com/2014/05/day-5-putting-it-together.html
>>>>>>
>>>>>> I've attached a copy of the results for data filtered between 1-45
>>>>>
>>>>> hz.
>>>>>>
>>>>>> The
>>>>>> grey region indicates statistical significance using an independent
>>>>>> t-test
>>>>>> with fdr correction.
>>>>>>
>>>>>> I would greatly appreciate any feedback you could offer.
>>>>>>
>>>>>> Thanks!
>>>>>> Alan
>>>>>>
>>>>>>
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