Bio: Andrew Brown holds a BS in Applied Mathematics from Georgia Tech and earned his MS and PhD in Statistics from the University of Georgia under the guidance of Gauri Datta and Nicole Lazar. He subsequently took a faculty position in the School of Mathematical and Statistical Sciences at Clemson University, where he is now an Associate Professor. His primary research interests are in uncertainty quantification / computer experiments, Bayesian computation, and neuroimaging data analysis. This is in addition to some interdisciplinary work he has been involved with, including seroprevalence mapping in parasitology, group testing, engineering design, and risk assessment. He was a visiting research fellow at SAMSI for the program on Challenges in Computational Neuroscience, and has served as elected treasurer of the Industrial Statistics section of ISBA, secretary for the UQ interest group of the ASA, and President of the South Carolina chapter of the ASA. His work has been supported by the National Science Foundation and the Department of Education.

Abstract: Alzheimer's disease is a neurodegenerative condition that accelerates cognitive decline relative to normal aging. It is of critical scientific importance to gain a better understanding of early disease mechanisms in the brain to facilitate effective, targeted therapies. The volume of the hippocampus is often used in diagnosis and monitoring of the disease. Measuring this volume via neuroimaging is difficult since each hippocampus must either be manually identified or automatically delineated, a task referred to as segmentation. Automatic hippocampal segmentation often involves mapping a previously manually segmented image to a new brain image and propagating the labels to obtain an estimate of where each hippocampus is located in the new image. A more recent approach to this problem is to propagate labels from multiple manually segmented atlases and combine the results using a process known as label fusion. To date, most label fusion algorithms employ voting procedures with voting weights assigned directly or estimated via optimization. We propose using a fully Bayesian spatial regression model for label fusion that facilitates direct incorporation of covariate information while making accessible the entire posterior distribution. Our results suggest that incorporating tissue classification (e.g, gray matter) into the label fusion procedure can greatly improve segmentation when relatively homogeneous, healthy brains are used as atlases for diseased brains. The fully Bayesian approach also produces meaningful uncertainty measures about hippocampal volumes, information which can be leveraged to detect significant, scientifically meaningful differences between healthy and diseased populations, improving the potential for early detection and tracking of the disease.