Hi, thanks for the quick reply!
I am still a bit confused, though, because I do not understand how I can get separate time-courses for the "new" subcomponents.
For FSLNets, I would need files as provided by dr_stage1, where each column contains a time-course. For my purpose, I would need those not for the original ICA output, but for the subregions I created from the components.
Is that even possible, considering the way the components were created?
Thanks again for your help.
Helen
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From: FSL - FMRIB's Software Library [[log in to unmask]] On Behalf Of Stephen Smith [[log in to unmask]]
Sent: 23 June 2013 22:24
To: [log in to unmask]
Subject: Re: [FSL] Splitting components from ICA output into relevant subregions for further analysis
Hi
On 23 Jun 2013, at 15:54, Lückmann, Helen (Stud. FPN) <[log in to unmask]<mailto:[log in to unmask]>> wrote:
Dear experts,
I ran MELODIC and dual regression on my resting state dataset and am now interested in the connectivity between three components that MELODIC produced. I'd like to investigate this using FSLNets.
If I understood correctly, FSLNets provides information about (partial) correlations between the components entered into the analysis. While this always refers to the relation between the complete components, I am rather interested in the relation between subparts (e.g. posterior part of the DMN or the PCC) of the two networks (within and between network connectivity) as well. I then want to compare correlations between groups.
I think this would mean I would have to split my component in two or more parts. What would be the best way to do this? Should I do this before Dual regression, on the stage2 component file (concatenated for the whole group),
Yes - that would be best. Split them and recombine into a new 4D file that will have an increased number of "time" points - that solves your question below too.
Cheers.
or for the individual stage 2 files? Most importantly: How do I get different time-courses for these components (as the ICA and DR seem to only provide component-wise, not voxel-wise, time-series)? Could I just use parcellations from the Harvard-Oxford atlas or is there a more precise way to do this?
Thanks for your help.
Helen
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Stephen M. Smith, Professor of Biomedical Engineering
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