[Mne_analysis] Discrete Source Space source localization

Eric Larson larson.eric.d at gmail.com
Thu Feb 22 12:41:33 EST 2018
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>
> 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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