Magnetic resonance imaging (MRI) of transgenic mice has the potential to provide valuable insight into the complex mechanisms underlying Alzheimer's disease (AD). Quantification of pathological changes is typically performed using manual segmentation methods, and requires a priori hypotheses about anatomical structures for volumetric measurement. Alternatively, deformation-based morphometry (DBM) has been shown to be a powerful, automated technique for detecting anatomical differences between populations by examining the deformation fields used to nonlinearly warp MR images. In this multiple timepoint, in vivo study, we have applied an automated, unbiased technique for the creation of a nonlinear, population-specific reference space from which robust DBM analysis can be performed. A general, linear mixed-effects model framework was developed to follow the evolution of structural changes in mouse brain from 2.5 to 9 months of age, and to examine neuroanatomical differences between a transgenic (TG) APP/PS1 murine model of AD and wild-type (WT) littermates. Morphometric abnormalities in the TG group were localized to regions of the hippocampus, cortex, olfactory bulbs, stria terminalis, brain stem, cerebellum, and ventricles. Although volumetric reductions were detected in TG mice, no general brain atrophy was found, suggesting a developmental, rather than a degenerative, pathological process. Finally, we established a strong correlation between a DBM summary measure and manually segmented volumes for each image in the dataset. These results support the utility of DBM to study longitudinal morphological changes in mouse models of central nervous system diseases in an automated and exploratory fashion.