Shape regression analysis is a powerful tool to study local shape changes as a function of an independent regressor
variable. In this paper, we introduce spherical harmonic(SPHARM) representation to surface manifold learning and shape regression. Here, we use root mean square distance(RMSD) to measure the deformation degree of the surface, and find out that the hippocampus’ deformation degree is increased over age. We also investigate the particular changing area, and discover that the hippocampus have significant changes in the frontal area and tail area, especially in CA1 subfield.
Recent advances in imaging technologies, such as Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) and Diffusion Tensor Imaging (DTI) have accelerated brain research in many aspects. In order to better understand the synergy of the many processes involved in normal brain function, integrated modeling and analysis of MRI, PET, and DTI is highly desirable. Unfortunately, the current state-of-art computational tools fall short in offering a comprehensive computational framework that is accurate and mathematically rigorous. In this paper we present a framework which is based on conformal parameterization of a brain from high-resolution structural MRI data to a canonical spherical domain. This model allows natural integration of information from co-registered PET as well as DTI data and lays the foundation for a quantitative analysis of the relationship between diverse data sets. Consequently, the system can be designed to provide a software environment able to facilitate statistical detection of abnormal functional brain patterns in patients with a large number of neurological disorders.