[FieldTrip] granger causality on beamformer data

[Tyler Grummett]

?Hey Julian,


Having trouble making sense of that link. Am I correct in saying that I should be downloading the brede toolbox? because it is taking a long time, plus the functions in the brede toolbox dont make a lot of sense.


Would you use ft_prepare_sourcemodel instead of ft_prepare_leadfield? or would you run in before running it?


Regards,


Tyler


*************************

Tyler Grummett ( BBSc, BSc(Hons I))
PhD Candidate
Brain Signals Laboratory
Flinders University
Rm 5A301
Ext 66124
________________________________
From: fieldtrip-bounces at science.ru.nl <fieldtrip-bounces at science.ru.nl> on behalf of Julian Keil <julian.keil at gmail.com>
Sent: Wednesday, 28 May 2014 1:55 AM
To: FieldTrip discussion list
Subject: Re: [FieldTrip] granger causality on beamformer data

Hi Tyler,

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 1400*1400
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: http://neuro.imm.dtu.dk/services/jerne/ninf/voi.html

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.

Best,

Julian


On Tue, May 27, 2014 at 7:44 AM, Tyler Grummett <tyler.grummett at flinders.edu.au<mailto: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


*************************

Tyler Grummett ( BBSc, BSc(Hons I))
PhD Candidate
Brain Signals Laboratory
Flinders University
Rm 5A301
Ext 66124

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