[Mne_analysis] Discrete Source Space source localization

[Eric Larson]

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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