Optimal Transport Features for Morphometric Population Analysis
This is an incremental improvement for neuroimaging researchers analyzing brain pathologies like Alzheimer's disease.
The authors tackled the problem of detecting spatially dispersed tissue loss in neuroimaging by augmenting morphometric analysis with an optimal transport feature extraction step, which increased statistical power and identified tissue changes not measurable with existing methods in an Alzheimer's disease study.
Brain pathologies often manifest as partial or complete loss of tissue. The goal of many neuroimaging studies is to capture the location and amount of tissue changes with respect to a clinical variable of interest, such as disease progression. Morphometric analysis approaches capture local differences in the distribution of tissue or other quantities of interest in relation to a clinical variable. We propose to augment morphometric analysis with an additional feature extraction step based on unbalanced optimal transport. The optimal transport feature extraction step increases statistical power for pathologies that cause spatially dispersed tissue loss, minimizes sensitivity to shifts due to spatial misalignment or differences in brain topology, and separates changes due to volume differences from changes due to tissue location. We demonstrate the proposed optimal transport feature extraction step in the context of a volumetric morphometric analysis of the OASIS-1 study for Alzheimer's disease. The results demonstrate that the proposed approach can identify tissue changes and differences that are not otherwise measurable.