[FieldTrip] Can the cortical source of any sensor-level metric be localized?

[Ingmar de Vries]

Dear Fieldtrip community,

I have a conceptual question that potentially applies to many possible
situations. If it helps, here is my situation:

I am currently analyzing MEG data (Neuromag, 306 sensors), using
representational similarity analysis (RSA). I have applied a spatial
searchlight at the sensor level, which results in RSA (i.e. correlation)
values at each of the 306 sensors. This results in an interesting spatial
peak of correlation above areas that are expected to be involved.

Is it possible to localize the cortical source of this sensor-level peak in
correlation?
This question can be generalized to any metric, i.e.: Is it possible
to localize the cortical source of any sensor-level metric?

I can't find any literature doing something similar, and the main problem I
foresee is in the definition of a head model. That is, standard head models
either define electrical conductivity (EEG), or spread of the magnetic
field (MEG), but not the spread of an information-based metric (here
correlation, but could be decoding accuracy as well). Is there a solution
for this?

The alternative approach (plan B) would be to first apply source
reconstruction on the raw MEG signal with a distributed source model (e.g.
MNE), and subsequently apply the RSA analyses on the source-reconstructed
signal.

Thanks for the input and I hope you are all healthy and safe, and able to
continue your research in these strange times!

cheers,

-- 
*~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~*
*Ingmar de Vries, PhD.*
*Postdoc @ CIMeC, University of Trento*
*i.e.j.de.vries at gmail.com <i.e.j.de.vries at gmail.com>*
*~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~*
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20210329/a827aedf/attachment.htm>