Constrained data decomposition and regression for analyzing healthy aging from fiber tract diffusion properties.

It has been shown that brain structures in normal aging undergo significant changes attributed to neurodevelopmental and neurodegeneration processes as a lifelong, dynamic process. Modeling changes in healthy aging will be necessary to explain differences to neurodegenerative patterns observed in mental illness and neurological disease. Driving application is the analysis of brain white matter properties as a function of age, given a database of diffusion tensor images (DTI) of 86 subjects well-balanced across adulthood. We present a methodology based on constrained PCA (CPCA) for fitting age-related changes of white matter diffusion of fiber tracts. It is shown that CPCA applied to tract functions of diffusion isolates population noise and retains age as a smooth change over time, well represented by the first principal mode. CPCA is therefore applied to a functional data analysis (FDA) problem. Age regression on tract functions reveals a nonlinear trajectory but also age-related changes varying locally along tracts. Four tracts with four different tensor-derived scalar diffusion measures were analyzed, and leave-one-out validation of data compression is shown.