UCLA

We present a collection of methods that model and interpret information represented in structural magnetic resonance imaging (MRI) and diffusion MRI images of the living human brain. Our solution to the problem of brain segmentation in structural MRI combines artificial life and deformable models to develop a customizable plan for segmentation realized as cooperative deformable organisms. We also present work to represent and register white matter pathways as described in diffusion MRI. Our method represents these pathways as maximum density paths (MDPs), which compactly represent information and are compared using shape based registration for population studies. In addition, we present a group of methods focused on connectivity in the brain. These include an optimization for a global probabilistic tractography algorithm that computes fibers representing connectivity pathways in tissue, a novel maximum-flow based measure of connectivity, a classification framework identifying Alzheimer's disease based on connectivity measures, and a statistical framework to find the optimal partition of the brain for connectivity analysis. These methods seek to advance our understanding and analysis of neuroimaging data from crucial pre-processing steps to our fundamental understanding of connectivity in the brain.