In deformation based morphometry (DBM) group-wise differences in brain structure are measured using deformable registration and some form of statistical test. However, it is known that DBM results are sensitive to both the registration method and statistical test used. Given the lack of an objective model of group variation it has been difficult to determine the extent of the influence of registration implementation or contraints on DBM analysis. In this thesis, we use registration methods with varying levels of theoretic similarity to study the influence of registration mechanics on DBM results. We show that because of the extent of the influence of registration mechanics on DBM results, analysis of changes should always be made with a thorough understanding of the registration method used. We also show that minor variations in registration methods can lead to large changes in DBM results. When using DBM, it would be imprudent to use only one registration method to draw any conclusions about the variations being studied. In order to provide a more complete representation of inter-group changes, we propose a method for combining multiple registration methods using Dempster-Shafer evidence theory to produce belief maps of categorical changes between groups. We show that the Dempster-Shafer combination produces a unique and easy to interpret belief map of regional changes between and within groups without the complications associated with hypothesis testing.

Another, often confounding, element of DBM is the parametric hypothesis test used to specify voxels undergoing significant change between the two groups. The accuracy and reliability of these tests are contingent on a number of fundamental assumptions made about the distribution of the data used in the tests. Many DBM studies often overlook these assumptions and fail to verify their validity for the data being tested. This raises many doubts about the credibility of the results from such tests. In this thesis, we propose to perform statistical analysis on DBM data using nonparametric, distribution independent hypothesis tests. With no data distributional assumptions, these tests provide both increased flexibility and reliability of DBM statistical analysis.