Hi Mirko,

> I would really like to get help. I have two groups and subjects had two 
> scans before and after treatment.

With this kind of repeated-measures data, you probably want to model 
subject effects in the design matrix, together with a time effect, a 
time-group interaction (probably the treatment effect you are 
interested in) and your covariate(s). So your design matrix would have 
many more than 4 regressors/columns, at least:
(number of subjects) + 2 + (number of covariates)

SPM5's "flexible factorial" model should allow you to set this kind of 
design up; either with subject as a factor, or, perhaps simpler, 
without it but with dependence between levels of the time factor. See 
the following thread, but note that your two-time case won't have the 
complications with the dependencies.

http://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind0701&L=SPM&P=R47197

In the latter case, I think two columns for group, one for time-effect 
and one for time-group interaction, plus the covariate(s) will be needed.

I'm afraid I'm not sure how to set either design up in SPM2.

As an alternative, which is probably simpler, and which I think is 
equivalent, you could create time2-time1 difference images for each 
subject, e.g. with:

http://www.cs.ucl.ac.uk/staff/gridgway/vbm/make_diffs.m

and then just enter these in a two-group t-test with your covariates. 
I think this only allows you to test the time-group interaction (and 
any interactions between time and your covariates, and the three-way 
time-group-covariates interactions), but that's probably what you 
need, I guess...

> In one group subjects were treated 
> with an active drug while in another were treated with placebo. All 
> subject have also behavioral variable measure before and after treatment 
> which are used as covariates. I have problem n evaluating interactions 
> with covariates

It's not quite clear which interactions you are interested in, but I 
think you'd probably need these entered explicitly as product terms in 
the design matrix. E.g. separate grp1*cov1 grp2*cov1 regressors.

> because the SPM permits me to enter only four numbers 
> for contrasts (e.g. 1 -1 -1 1), which in my interpretation could not 
> include covariates interactions.

Just to clarify this, t-contrasts must have the same number of 
elements as there are regressors/columns in the design matrix. For 
some simple interactions, e.g. time-group, it is often possible to get 
interactions with a kind of difference-of-differences contrast, but as 
far as I understand it (probably not far enough) in general, 
interactions are products of regressors.

I hope this is of help, please post to the list again if you want more 
details, or if I have misunderstood your data/aims.

Ged.