Canonical correlation analysis of sub-cortical brain structures using non-rigid registration

Abstract

In this paper, we present the application of canonical correlation analysis to investigate how the shapes of different structures within the brain vary statistically relative to each other. Canonical correlation analysis is a multivariate statistical technique which extracts and quantifies correlated behaviour between two sets of vector variables. Firstly, we perform non-rigid image registration of 93 sets of 3D MR images to build sets of surfaces and correspondences for sub-cortical structures in the brain. Canonical correlation analysis is then used to extract and quantify correlated behaviour in the shapes of each pair of surfaces. The results show that correlations are strongest between neighbouring structures and reveal symmetry in the correlation strengths for the left and right sides of the brain. © Springer-Verlag Berlin Heidelberg 2006.