There is a significant interest in understanding how structural/functional changes in the brain explains symptoms caused by neurodegenerative diseases such as Alzheimer’s Disease (AD). Despite clear variations in the brain reported in the literature at the dementia stage of the AD, changes in the preclinical stage of AD still remain poorly characterized. Such preclinical datasets are typically small and the group differences are subtle, and thus makes their analyses challenging. This talk will describe some of my recent work to overcome these difficulties in an effort to elucidate how the human brain varies as a function of risk factors even in asymptomatic individuals. The theory driving these analyses is motivated by the multi-resolution scheme for performing statistical analysis on graph-structured data in neuroimaging. The framework derives a novel representation at each data point which captures its context at multiple resolutions using a spectral approach. Extensive empirical results using statistical group analysis (i.e., diseased vs. healthy) describing how this framework offers improved statistical power in the analyses of cortical thickness on brain meshes and tractography derived brain connectivity using Diffusion Tensor Images (DTI) to identify potentially subtle variation in the brain due to Ad or AD risk factors.
Won Hwa Kim is an Assitant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA). He obtained his Ph.D in Computer Sciences from University of Wisconsin – Madison in 2017, M.S. in Robotics from KAIST in 2010 and B.S. in Information and Communication Engineering from Sungkyunkwan University in 2008. Prior to joining UTA, he briefly worked as a researcher in Data Science team at NEC Labs, America in 2017 and also has experience as a research in Hyundai Motors Company developing power electronic parts for hybrid/electric vehicles from 2010 to 2011. His current research combines Machine Learning, Computer Vision and Neuroscience and is focused on developing novel methods for analyses of data in non-Euclidean spaces and their applications in Image Analysis including Neuroimaging. His work has been published in top-tier AI conferences such as NIPS, CVPR, ICCV, ECCV, MICCI as well as in high impact journals such as NeuroImage.