Hi - if you have the same timeseries model for all subjects then the
easiest thing to do is to use Tensor-ICA (not concatenation) and enter
the timeseries model in the MELODIC GUI in the same way that you do
for single-subjects PICA analysis.
Cheers, Steve.
On 11 Mar 2008, at 09:51, Andreas Pedroni wrote:
> Hi
>
> We are currently working on a study, where we try to identify neural
> structures responding to tooth pain. We already analyzed data with a
> classical GLM. Now we try to run MELODIC to possibly find other neural
> responses to administerd tooth pain. Our idea is, to rund a group PICA
> (concatenated subjects). In a second step we try to identify pain
> related
> ICs by timelocking IC timecourses and testing for each IC
> timecourse, if
> the time locked timecourse follows a regular run (e.g. IC
> timecourses which
> are not due to the pain stimulus should distribute around zero, when
> time
> locked to events, like a ERP). I already tried this with single
> subject PICA
> of data of an event related design and my results seemed to make
> sense,
> regarding the timecourses and the structures found. Now, my
> questions are:
> Can we do the same thing with the concatenated version of PICA? Is
> it true,
> that in the t**.txt files each column represents the timecourse of a
> subject? Finally, do we get many more ICs with 20 subjects than with
> 10? Or
> does it make sense to constrain the analysis to a certain number of
> ICs?
>
> Thanks for any hints!
>
> Cheers
>
> Andreas Pedroni
> Instiute of Psychology Zurich, Neuropsychology
>
>
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Stephen M. Smith, Professor of Biomedical Engineering
Associate Director, Oxford University FMRIB Centre
FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK
+44 (0) 1865 222726 (fax 222717)
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