This is a really good question, and I don't think it has been
discussed at the list. I think the FSL people usually does it the
other way around, but I hvae not seen any paper comparing the benefits
of the two approaches. I guess the reason for the current procedure
could in part be historical, spatial normalisation of raw data was
needed for a fixed effect group analysis which was once very popular.
But with the current summary statistics approach it is indeed possible
to do the stats first and normalise the contrast images (the
normalisation parameters should of course not be estimated from the
maps of parameter estimates). From a computational point it would make
sense to only normalise the maps of parameter estimates in stead off
all the raw images. The default template in SPM has a resolution of
2x2x2mm but most people use at least 3x3x3mm voxels so the
normalisation involves a great deal of interpolation. For interleaved
sequences I think it makes just as much sense to interpolate the maps
of parameter estimates as the raw images. Similarly one could argue
for smoothing of beta or contrast images instead of the raw images,
but if AR(1) modelling is turned on the two do not commute. But which
is the more correct?
I think there are goods reasons for normalising the maps of parameter
estimates but I also have the habit of running stats on normalised data.
Torben Ellegaard Lund
Assistant Professor, PhD
The Danish National Research Foundation's Center of Functionally
Integrative Neuroscience (CFIN)
Aarhus University Hospital
8000 Aarhus C
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Den 07/03/2009 kl. 08.17 skrev Michael T Rubens:
> Just out of curiosity, and to help guide my analysis, what makes
> more sense?
> 1) Run stats (i.e., GLM) in native space, then normalizing the
> resulting statistical image
> 2) Running the statistics on normalized data?
> is the answer different for different analyses? what do you do? what
> is your justification?
> Thanks for any advice,
> p.s. I have been in the habit of typically running stats on
> normalized data
> Research Associate
> Gazzaley Lab
> Department of Neurology
> University of California, San Francisco