Statistical Learning Methods for Neuroimaging Data Analysis with Applications

Presented by Hongtu Zhu, Professor, Department of Biostatistics, University of North Carolina at Chapel Hill, Gillings School of Global Public Health

Friday, November 18
10:00 a.m. ET
AUST 344

The aim of this talk is to provide a comprehensive review of statistical challenges in neuroimaging data analysis from neuroimaging techniques to large-scale neuroimaging studies to statistical learning methods. We briefly review eight popular neuroimaging techniques and their potential applications in neuroscience research and clinical translation. We delineate the four common themes of neuroimaging data and review major image processing analysis methods for processing neuroimaging data at the individual level. We briefly review four large-scale neuroimaging-related studies and a consortium on imaging genomics and discuss four common themes of neuroimaging data analysis at the population level. We review nine major population-based statistical analysis methods and their associated statistical challenges and present recent progress in statistical methodology to address these challenges.