Hi Rutger,
Each voxel at first level has a different cope and variance of cope
(varcope), and the higher-level analysis using FLAME is designed to get
the best possible analysis using the low-level varcopes, separately for
each voxel. Therefore I suspect that it is preferable to to a normal
first- and then second-level (and then third if relevant) analysis and
then do roi analysis on the highest-level output.
I hope I've understood your question correctly. Does this make sense?
Cheers, Steve.
On Thu, 18 Sep 2003, Goekoop, R. wrote:
> Dear FSL-users,
>
> In analysing data from a study that examined the effects of medication
> challenge on brain activation patterns, it is possible to calculate most
> COPE images of interest (COIs :-) using basic COPE images at the first level
> and examine the effects of medication on these COIs using a second level
> analysis. It is however also possible to enter basic COPEs as inputs to the
> second level analysis, calculate the COIs on this level, and then use a
> third level analysis to extract the final effect of medication from these
> higher-level COIs. Would there perhaps be a reason why any of these two
> approaches could be considered optimal (despite the carrying up of the
> lower-level variances)? It probably depends a lot on study design and the
> kind of (basic) COPE images that are chosen as inputs to the higher level
> analyses, but do the different statistical approaches used on the various
> levels have any bearing on this issue?
>
> Thanks,
>
> Rutger.
>
> Drs. R. Goekoop, MD.
> Department of Neurology
> Vrije Universiteit Medical Centre
> P.O. Box 7057, 1007 MB
> Amsterdam, the Netherlands
> Phone: +31 20 444 0316
> E-mail: <mailto:[log in to unmask]> [log in to unmask]
>
>
>
Stephen M. Smith MA DPhil CEng MIEE
Associate Director, FMRIB and Analysis Research Coordinator
Oxford University Centre for Functional MRI of the Brain
John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK
+44 (0) 1865 222726 (fax 222717)
[log in to unmask] http://www.fmrib.ox.ac.uk/~steve
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