  Normative aging trends of the brain can serve as an important reference in
the assessment of neurological structural disorders. Such models are typically
developed from longitudinal brain image data -- follow-up data of the same
subject over different time points. In practice, obtaining such longitudinal
data is difficult. We propose a method to develop an aging model for a given
population, in the absence of longitudinal data, by using images from different
subjects at different time points, the so-called cross-sectional data. We
define an aging model as a diffeomorphic deformation on a structural template
derived from the data and propose a method that develops topology preserving
aging model close to natural aging. The proposed model is successfully
validated on two public cross-sectional datasets which provide templates
constructed from different sets of subjects at different age points.
