[FieldTrip] granger causality on beamformer data

[Julian Keil]

Hi Tyler,

if you follow the link, you get to the "Volumes of Interest" Data base.
You can browse through the volumes by then clicking on "Alphabetic index" (http://neuro.imm.dtu.dk/services/jerne/ninf/voi/index-alphabetic.html)
If you then click on a volume (e.g. "amygdala" http://neuro.imm.dtu.dk/services/jerne/ninf/voi/amygdala.html) you get to a definition of that VOI. Here, you can now download .hdr and .img files. These are ANALYZE volumes with 1s for all voxels inside a VOI and 0s outside.
You can read these with ft_read_mri and normalize to the MNI brain.

tl;dr You don't need any functions, just the .hdr and .img files defining the volumes

Best,

Julian

Am 28.05.2014 um 03:06 schrieb 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> 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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