Analysis of Metric Distances and Volumes of Hippocampi Indicates Different Morphometric Changes over Time in Dementia of Alzheimer Type and Nondemented Subjects

In this article, we analyze the morphometry (shape and size) of hippocampus in subjects with very mild dementia of Alzheimer’s type (DAT) and nondemented controls and how the morphometry changes over a two-year period. Morphometric differences with respect to a template hippocampus were measured by the metric distance obtained from the Large Deformation Diffeomorphic Metric Mapping (LDDMM) algorithm which was previously used to calculate dense one-to-one correspondence vector fields between the shapes. LDDMM assigns metric distances on the space of anatomical images thereby allowing for the direct comparison and quantization of morphometric changes. We characterize what additional information the metric distances provide in terms of size and shape given the volume measurements of the hippocampi. Moreover, we demonstrate how metric distances can be used in cross-sectional, longitudinal, and left-right asymmetry comparisons. We perform a principal component analysis on metric distances and hippocampus, brain, and intracranial volumes. We use repeated measures ANOVA models to test the main effects of and interaction between the diagnosis, duration, and hemisphere factors to see which factors significantly explain the differences in metric distances. When a factor is found to be significant, we use classical parametric and non-parametric tests to compare the metric distances for that factor. The analysis of metric distances is then used to compare the effects of aging in the hippocampus. At baseline, the metric distances for demented subjects are found not to be significantly different from those for nondemented subjects. At follow-up, the metric distances for demented subjects were significantly larger compared to nondemented subjects. The metric distances for demented subjects increased significantly from baseline to follow-up but not for nondemented subjects. We also demonstrate that metric distances can be used in a logistic regression model for diagnostic discrimination of subjects. We compare metric distances with the volumes and obtain similar results in cross-sectional and longitudinal comparisons. In classification, the model that uses volume, metric distance, and volume loss over time together performs better in detecting DAT. Thus, metric distances with respect to a template computed via LDDMM can be a powerful tool in detecting differences in shape in cross-sectional as well as longitudinal studies.