dear all
I think that across subjects, one would have to be a bit careful
not to breach (too much !) the equal variance hypothesis when region sizes
are very different.
Obvioulsly, one would also have to be careful to make the
region specification independant of the contrast tested ...
Otherwise, practically speaking, I don't think the work is
masive to hack spm to do this. The only thing to take care
of is the temporal filter specification.
jb
>
> Dear Stan, Darren, Geraint,
>
> I agree, Stan's problem here is:
>
> > 2. Generating some group statistic for activation in such a 'localiser'
> > defined area, where the 'localised' area may be spatially variable across
> > subjects.
>
> I wouldn't claim to be any kind on an expert either, but I would have thought
> the technique has two advantages; the first is as Geraint has said, avoiding
> some of the problems of variability across subjects. The second is using an
> optimal shape for averaging in the area that you are interested in - rather
> than relying on the smoothing kernel being the right shape. Personally I find
> the approach very attractive - it's good to see bags of formal apriori
> specification every now and then...
>
> You could get the data from a region pretty easily; I recently posted a little
> function to do this once you have specified to the region with an image; you
> could do this by editing an spm t image, a results image, or a contrast image;
> see:
>
> http://www.mrc-cbu.cam.ac.uk/Imaging/External/vol_corr.html#DefVVOI
>
> for how to edit the image to get an ROI.
>
> However, doing the stats is a harder problem. You could hack spm_spm.m to do
> it for you, but it would be a reasonable amount of work. A very useful bit of
> work though; perhaps somebody could write an SPM toolbox?
>
> All regards,
>
> Matthew
>
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