After the segmentation steps you have to smooth your data. For the first trial I would start with a FWHM of 12 mm. The filter width depends on what you want to see according to the Matched Filter Theorem. You can increase signal-to-noise ratio of your signal that is in the order of your filter size. For more focal effects you can decrease the filter width and if you expect larger effects try a larger filter size.
Keep in mind that modulation will additionally smooth your data and to obtain approximately the same resulting smoothness you should lower the FWHM to around 70 % if you have applied the default cutoff for spation normalization (25 mm).
For the following statistical analysis you can use for almost all possibly designs the functions of this toolbox. In contrast to the “Basic models” some non-neccessary options are removed. If you miss something you can still use the models provided with SPM2.
If you have only one group and 1 ore more covariates this is a (multiple) regression model. If you have more groups this corresponds either to an AnCova (with nuisance parameters) or Anova (without nuisance parameters). Additionally you can model group interactions with a covariate and at least two groups.
For longitudinal data the simplest model is a paired t-test for one group and 2 conditions (time points). However, you can also model more complex designs with varying number of conditions and more groups and/or covariates and nuisance parameters.
Constraining your analysis to one tissue type
You should use an absolute threshold for masking around 0.1..0.15 (and finally up to 0.2). This prevents that you get (opposite) results in white matter although you only made an analysis for gray matter. This effect is due to the negative correlation of gray and white matter intensity values around the border between gray and white matter. If you choose an absolute threshold masking you will only compute your statistic in those areas that are above this threshold. Areas below this threshold will be excluded. Be careful if you get any result in white matter although you made an analysis of gray matter. These results are suspected to show an opposite effect.
A nice side-effect of using a higher absolute threshold (if you not cut off your clusters) is that the search area will be smaller which positively affects your p-values corrected for multiple comparisons.