A current limitation for imaging of brain function is the potential confound of anatomical differences or registration error, which may manifest via apparent functional "activation" for between-subject analyses. With respect to functional activations, underlying tissue mismatches can be regarded as a nuisance variable. We propose adding the probability of gray matter at a given voxel as a covariate (nuisance variable) in the analysis of voxelwise multisubject functional data using standard statistical techniques. A method is presented to assess the extent to which a functional activation can reliably be explained by underlying anatomical differences, and simultaneously, to assess the component of the functional activation which cannot be attributed to anatomical difference and thus is likely due to functional difference alone. Extension of the method to other intermodal imaging applications is discussed. Two exemplary data sets, one PET and one fMRI, are used to demonstrate the implementation and utility of this method, which apportions the relative contributions of anatomy and function for an apparent functional activation. The examples show two distinct types of results. First, a so-called functional activation may actually be caused by a systematic anatomical difference which, when modeled, diminishes the functional effect. In the second result type, including the anatomical differences in the model can account for a large component of otherwise unmodeled variance, yielding an increase in the functional effect cluster size and/or magnitude. In either case, ignoring the readily available structural information can lead to misinterpretation of functional results.