By default, SPM masks the images that contribute to an analysis at the Estimation stage.  If a voxel is masked out because it fails to exceed an arbitrary analysis threshold (set to a defulat of 0.8 in SPM5), then its values are replaced with NaNs, and that voxel does not contribute to the final output.  Incidentally, this masking contributes to the non-analysis of orbitofrontal and frontopolar regions as a consequence of signal dropout.

If you want to include voxels that do not exceed the threshold (useful if you are interesting in analysing data presented in unusual units, e.g. maps of residuals), you can edit the spm_defaults.m file.  Around line 42 should be the following text:

% Mask defaults
%=======================================================================
defaults.mask.thresh    = 0.8;
This can be edited (e.g. replaced with -Inf if you want to remove the threshold altogether), the spm_defaults.m file saved, and the analysis run with a more liberal masking threshold implemented.  This can greatly increase the number of comparison that are made, and can include a lot of computationally expensive junk i.e. comparisons with non-brain tissue.  To get round this issue, it is worthwhile setting an explicit mask in the model specification stage (e.g. the whole brain mask I wrote about here) whenever you lower the implicit masking threshold.

There is a little more on this from the SPM list here.  As with all SPM tweaks, make note of what you have tweaked, and make sure you change it back to its default setting once you have done what you set out to do.

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