[FieldTrip] granger causality on beamformer data
julian.keil at gmail.com
Tue May 27 18:25:18 CEST 2014
I can't comment on the usefulness of directionality between 1400 sources,
but keep in mind, that you would have to compute something in the range of
connections, so I hope you have a fast computer.
As for the regions, in case you want to use anatomically defined regions,
you an either use the atlases (atlanti? atlae?) that come with fieldtrip or
generate a mask from this website:
The general idea is to build a grid with a gridpoint per voxel of your MRI
using ft_prepare_sourcemodel. Then you can check which of your virtual
channels is closest to the voxel-gridpoints and thus select the virtual
channels that are inside your ROI.
In the first case, you can use ft_volumelookup to find the voxels
corresponding to your ROI. In the latter case you can just use the mask and
check which voxels are 1 (= inside your ROI).
I hope that helps, if you have specific questions, feel free to ask.
On Tue, May 27, 2014 at 7:44 AM, Tyler Grummett <
tyler.grummett at flinders.edu.au> wrote:
> Hello fieldtrippers,
> I was just wondering whether it would be sensible to do granger
> causality on all 1400 virtual channels, as calculated using beamformer.
> Or should you do a PCA reduction of some description beforehand.
> I was also wondering how to create regions of interest. Some of my
> colleagues think that we should use some kind of spatial ICA technique.
> Im open to all suggestions.
> *Tyler Grummett ( BBSc, BSc(Hons I))*
> *PhD Candidate*
> *Brain Signals Laboratory*
> *Flinders University*
> *Rm 5A301*
> *Ext 66124*
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
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