Dear Lynn
> 
> Most of your questions come from fMRI users these days.  However, we have
> FDG PET data on 30 subjects (one condition).  They are assigned to one of
> three groups (0,1,2) and it is the metabolic differences between groups that
> are of interest. There is reason to expect global differences between the
> groups.  We would like to use a confounding covariate.  
> 
> Is SPM appropriate to use with these data?
>

SPM could certainly be used for these data. 

> If so: 
> 1) What is the correct way to set up the design?

When you launch the "Statistical analysis" module you will be prompted
to "Select design type". You should select "Compare groups: 1 scan per
subject". Specify number of groups etc. Select one when asked how many
confounding covariates you want, and specify their values in the same
order as you specified your scans (you will want to check this in the
output to make sure that the right values are entered for each scan).

Since you expect that there may be global differences between the
groups (and I assume these differences are of interest) you should do
no global normalisation. I also assume that some form of model-based
quantification has been performed on your images (otherwise you can't
really assess global differences) in which case you will not want to
perform any scaling of the grand mean either. After that it should be
pretty straightforward.

> 2) Group assignment is not random but is based upon DSM diagnosis, ranked
> from no symptoms (0, normal) to few symptoms (1, indeterminate) to many
> symptoms (2, definite).  Would this be considered a "fixed effects" model?
> 
The "random" in the "random effects" model does not imply that subjects
are randomly assigned to groups, but rather that the specific sample at
hand is a random sample from the entire population of subjects with
this specific condition. e.g. in your case you have picked ten subjects
with symptom severity 2, and have obtained a certain realisation (your
specific ten scans). Had you picked ten other subjects with symptom
severity 2, you would have obtained ten other scans that were different
from your first, hence the randomness. Therefore, one cannot based on a
"non-random" group assignment say that one has a fixed effects model.

However in this specific case, where you have only one condition, there
is no difference between the random and the fixed effects models and
you will effectively be performing a one-way ANCOVA. The recent
interest in the random effects model stems from the realisation that
the between subject variance is different from the within subject
variance. It is typically larger and may well have a different spatial
distribution. Hence, one needs to consider both the within and between
subject variances. Since there is no within subject variance in your
case, this doesn't apply.

You should realise that since the between subject variance, which is
what you will be dealing with, is typically quite large and you will be
left with quite few degrees of freedom your power will not be
tremendeously large. You may want to consider using the non-parametric
methods developed by Andrew Holmes since they will not depend on high
degrees of freedom for their validity and they will allow you to smooth
the variance map quite severly, resulting in higher power.

> 
> Thanks for your help.
> 
>
				Good luck Jesper 


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