Hierarchical Statistical Shape Analysis and Prediction of Sub-Cortical Brain Structures

In this paper, we present the application of two multivariate statistical techniques to investigate how different structures within the brain vary statistically relative to each other. The first of these techniques is canonical correlation analysis which extracts and quantifies correlated behaviour between two sets of vector variables. The second technique is partial least squares regression which determines the best factors within a first set of vector variables for predicting a vector variable from a second set. We describe how these techniques can be used to quantify and predict correlated behaviour in sub-cortical structures within the brain using 3D MR images.