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<p style="">Hey Julian,<br>
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<p style="">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.<br>
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<p style="">Would you use ft_prepare_sourcemodel instead of ft_prepare_leadfield? or would you run in before running it?<br>
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<p style="">Regards,<br>
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<p style="">Tyler<br>
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<div><font face="Arial"><i>Tyler Grummett ( BBSc, BSc(Hons I))</i></font></div>
<div><font face="Arial"><i>PhD Candidate</i></font></div>
<div><font face="Arial"><i>Brain Signals Laboratory</i></font></div>
<div><font face="Arial"><i>Flinders University</i></font></div>
<div><font face="Arial"><i>Rm 5A301</i></font></div>
<div><font face="Arial"><i>Ext 66124</i></font></div>
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<div id="divRplyFwdMsg" dir="ltr"><font face="Calibri, sans-serif" color="#000000" style="font-size:11pt"><b>From:</b> fieldtrip-bounces@science.ru.nl <fieldtrip-bounces@science.ru.nl> on behalf of Julian Keil <julian.keil@gmail.com><br>
<b>Sent:</b> Wednesday, 28 May 2014 1:55 AM<br>
<b>To:</b> FieldTrip discussion list<br>
<b>Subject:</b> Re: [FieldTrip] granger causality on beamformer data</font>
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<div>Hi Tyler,<br>
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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
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connections, so I hope you have a fast computer.<br>
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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:
<a href="http://neuro.imm.dtu.dk/services/jerne/ninf/voi.html">http://neuro.imm.dtu.dk/services/jerne/ninf/voi.html</a><br>
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<div>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.<br>
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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).<br>
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I hope that helps, if you have specific questions, feel free to ask.<br>
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Best,<br>
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Julian<br>
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<div class="gmail_quote">On Tue, May 27, 2014 at 7:44 AM, Tyler Grummett <span dir="ltr">
<<a href="mailto:tyler.grummett@flinders.edu.au" target="_blank">tyler.grummett@flinders.edu.au</a>></span> wrote:<br>
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<p>Hello fieldtrippers,<br>
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<p>I was just wondering whether it would be sensible to do granger causality on all 1400 virtual channels, as calculated using beamformer.<br>
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<p>Or should you do a PCA reduction of some description beforehand.<br>
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<p>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.<br>
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<p>Im open to all suggestions.<br>
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<p>Tyler<br>
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<div><font face="Arial"><i>Tyler Grummett ( BBSc, BSc(Hons I))</i></font></div>
<div><font face="Arial"><i>PhD Candidate</i></font></div>
<div><font face="Arial"><i>Brain Signals Laboratory</i></font></div>
<div><font face="Arial"><i>Flinders University</i></font></div>
<div><font face="Arial"><i>Rm 5A301</i></font></div>
<div><font face="Arial"><i>Ext 66124</i></font></div>
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