A Diffeomorphic Aging Model for Adult Human Brain from Cross-Sectional Data

TL;DR

This paper introduces a novel diffeomorphic aging model for adult human brains that leverages cross-sectional data instead of longitudinal data, enabling the study of brain aging when follow-up data is unavailable.

Contribution

It presents a topology-preserving aging model based on diffeomorphic deformation, developed from cross-sectional datasets, which is a significant advancement over traditional longitudinal approaches.

Findings

Successfully validated on two public datasets

Model preserves brain topology during aging simulation

Effective in capturing natural aging trends

Abstract

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.