[Mne_analysis] Discrete Source Space source localization

[Luke Bloy]

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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