A Diffeomorphic Aging Model for Adult Human Brain from Cross-Sectional Data
This provides a reference for assessing neurological structural disorders in clinical settings, but it is incremental as it adapts existing methods to cross-sectional data.
The authors tackled the problem of developing a normative aging model for the adult human brain without requiring longitudinal data, by using cross-sectional data from different subjects at different ages, and successfully validated their diffeomorphic deformation model on two public datasets.
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.