[Mne_analysis] Discrete Source Space source localization

Wed Feb 28 11:30:34 EST 2018

Sure,

Although it'll depend somewhat on what you want the example to illustrate.

Attached is a cleaned up version of the script I was using to investigate
this. It uses sample data so is almost an example now. Give it a run and if
this is the kind of thing you have in mind I'll open a PR and we can
modify/discuss it there.

On Tue, Feb 27, 2018 at 2:53 PM Alexandre Gramfort <
alexandre.gramfort at inria.fr> wrote:

> hi Luke,
>
> thanks for the feedback.
>
> do you think you could make an example from this so we can explain this on
> the website?
>
> thanks
> Alex
>
> On Tue, Feb 27, 2018 at 6:07 PM, Eric Larson <larson.eric.d at gmail.com>
> wrote:
>
>> Great, thanks for letting us know how it worked out.
>>
>> Eric
>>
>>
>> On Tue, Feb 27, 2018 at 11:50 AM, Luke Bloy <luke.bloy at gmail.com> wrote:
>>
>>> Ok.
>>>
>>> While I'm not 100% sure I understand all of the intricacies, I was able
>>> to identify the problem.
>>>
>>> Essentially, it was a problem of regularization. With only a few source
>>> locations and a loose=1 the radial part of the leadfield for each source
>>> was playing a large roll by essentially magnifying noise.
>>>
>>> This doesn't cause issues when dipole fitting because dipole fitting
>>> doesn't invert the leadfield so the small singular value for that source
>>> orientation is ignored.
>>>
>>> I'm still not 100% how/why these source orientations get suppressed when
>>> doing a full cortical sheet fit with loose=1, such as VectorSourceEstimate.
>>> But from looking at the results of estimate_snr it seems like it doesn't.
>>>
>>> So the take home, at least for me, is to always check the lambad2 values
>>> you use, via estimate_snr or plot_snr_estimate, particularly when using
>>> discrete source spaces or even volume source spaces.
>>>
>>> -Luke
>>>
>>>
>>>
>>> On Fri, Feb 23, 2018 at 10:46 AM Eric Larson <larson.eric.d at gmail.com>
>>> wrote:
>>>
>>>> This doesn't make sense to me. Both the dipole and the single source
>>>>> volume source space should have the same lead field and should be trying to
>>>>> explain the same data. granted there is regularization in the dspm, but I'm
>>>>> not sure why that would move the peaks around in time?
>>>>>
>>>>
>>>> To get to the bottom of this I would try simulating evoked data
>>>> <https://martinos.org/mne/stable/generated/mne.simulation.simulate_evoked.html#mne.simulation.simulate_evoked>.
>>>> First I'd use a single source (discrete source space with a single point)
>>>> activated with some pattern (Hann window
>>>> <https://docs.scipy.org/doc/numpy/reference/generated/numpy.hanning.html>?)
>>>> with no noise (nave=np.inf) and a diagonal ad-hoc noise covariance
>>>> <https://martinos.org/mne/stable/generated/mne.make_ad_hoc_cov.html#mne.make_ad_hoc_cov>
>>>> during `fit_dipole` and `make_inverse_operator` and repeat this analysis.
>>>> Hopefully they all agree and give you back your activation. Then you could
>>>> add noise with a realistic spatial pattern (e.g., from your real data
>>>> baseline period) and see what happens. Hopefully this moves toward what you
>>>> see with the real data. But testing through simulation should allow you to
>>>> probe how each choice (covariance, regularization, noise sources, inverse,
>>>> etc.) affect the results.
>>>>
>>>> If you try it, let us know what you find! Eventually we could consider
>>>> turning it into some sort of tutorial.
>>>>
>>>> Eric
>>>>
>>>>
>>>>>
>>>>> On Thu, Feb 22, 2018 at 12:42 PM Eric Larson <larson.eric.d at gmail.com>
>>>>> wrote:
>>>>>
>>>>>> What I am trying to do is to fit auditory data by placing
>>>>>>> anatomically defined sources in left and right auditory cortex. The issue
>>>>>>> I'm having is that the time courses aren't matching up with time courses
>>>>>>> generated using a similar approach in BESA.
>>>>>>>
>>>>>>
>>>>>> Are you doing the equivalent computation in BESA, i.e. a minimum norm
>>>>>> estimate with dSPM noise normalization, regularization parameter = 1. / 9.,
>>>>>> and so forth?
>>>>>>
>>>>>> More concerning, the time courses don't match what I get if I do a
>>>>>>> full cortical sheet source localization and extract the time courses from a
>>>>>>> functional label.
>>>>>>>
>>>>>>
>>>>>> If I understand your source space correctly -- that it is just a
>>>>>> handful of dipoles (or one dipole) in left and right auditory cortices, or
>>>>>> a similar small subset -- this is not too surprising to me (though I have
>>>>>> never tried it). Minimum norm will try to account for all observed sensor
>>>>>> data using activations in exactly the set of source points provided. So if
>>>>>> you only provide auditory cortex sources in the source space, MNE will try
>>>>>> to explain all sensor activity using only these sources. This means that if
>>>>>> there is any activation outside of auditory cortex -- or even sensor or
>>>>>> environmental noise -- it will show up in "auditory cortex" anyway (as it
>>>>>> is the *only* place it can show up). If you have a full cortical
>>>>>> source space on the other hand, activity from other areas can be accounted
>>>>>> for by those other source points, and sensor/environmental noise sources
>>>>>> can be distributed across other sources.
>>>>>>
>>>>>> Thinking about the limiting case of only a few locations, activations
>>>>>> in tiny source space might approach something like the time course of the
>>>>>> global field power (although dSPM normalization might change this a bit).
>>>>>>
>>>>>> There might be other things going on, too -- I'm not sure how nicely
>>>>>> the minimum norm and dSPM code behave if there are *fewer* source
>>>>>> points than sensors, for example, though I'd expect it to be okay -- but
>>>>>> this is at least what first came to mind.
>>>>>>
>>>>>> Eric
>>>>>>
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