[FieldTrip] calculate DICS Beamformer on a group level?

[Stephen Whitmarsh]

Dear Christian,

Here are my 2 cents;

If I understand you correctly (*) you are asking whether you can beamform
your PCA components? That does seem a redundant mix of source-localization
techniques to me (of which PCA is one). Technically I would be at a loss
explaining why I wouldn't expect this, but conceptually I would say that
PCA already took care of the source-extraction, in this case a
time-course + sensor-topography. That time-course is one 'side' of your
source (the temporal part), and belongs to that topography - the other
(spatial) side of that coin. Which is to say, you already did a spatial
source analysis by manually selecting the spatial topography.

You could, for reasons of validation perhaps, beamform the power in a
particular frequency / time period (the 'traditional way'), and see if it
is also able to separate your source spatio-temporally in a similar way,
but that would be a qualitative comparison between source-localization
techniques.

Anyway, that's probably what you were thinking about anyway :)

Concerning the beamformer specifically; you do not beamform an average. The
whole point is to use subject-specific headmodels (based on individual
MRIs) and individual subject's CSDs to be able to estimate power on a
gridpoint-by-gridpoint basis. This then also allows you to better 'average'
or 'normalize' those individual brains and their activity-topographies,
namely based on their individual headshape and headmodel, than a
sensor-level averaging would do, given the undetermined relationship
between brain and MEG helmet in the latter. However, your PCA method
already got rid of this problem by removing the spatial part (the manual
selection of the component).

So, you *could* do a 'traditional' DICS beamformer, but because you already
done your statistics on your sources, that might be only for some
qualitative source-location validation that might not be worth the effort?

Anyway, that's all the change I have in my pocket :)

Cheers,
Stephen

*) If you are doing your statistical test on the same effect as the one you
used to select the PCA component, you are of course double-dipping.













Op ma 23 nov. 2020 om 13:48 schreef christian.merkel at med.ovgu.de <
christian.merkel at med.ovgu.de>:

> Hello,
>
>
> I hope some of you can give me some advice regarding a more conceptual
> problem with localizing specific oscillations within MEG data:
>
> I wanted to use the DICS-approach to localize a specific spectral effect
> in my MEG-data within the source space. Now I don't know whether this can
> be done on an averaged dataset across multiple subjects, since in this case
> the cross-spectral-density matrices of all the subjects would have to be
> averaged as well and I am not sure whether such an averaged CSD-matrix
> still makes sense.
>
> In more detail, I have spectral timeseries (complex) of several seconds
> within one specific frequency-band (sens x time). I calculate the grand
> average across time to get the evolution of the topography of this effect
> over time. Additionally, I ran a PCA on that averaged timecourse to extract
> the specific source (component) in sensorspace that I am interested in.
> Then I used the resulting unmixing-matrix to multiply that components
> weights with each subjects timecourses (sens x time). Thus I have one
> overall topography of that effect in sensorspace and its timecourse +
> variance (which I can run statistics on). Now I thought I can also look for
> the same effect in sourcespace but I actually just have the topography
> without the CSD-Matrix. And even if one could average the single-subjects
> CSDs, how would I retain the CSD for a specific component after a pca?
>
>
> Thanks for any help,
>
>
> Christian M.
>
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> https://doi.org/10.1371/journal.pcbi.1002202
>
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