STATISTICS COLLOQUIUM
In-Person talk
Hongtu Zhu
Professor, Department of Biostatistics
University of North Carolina at Chapel Hill
Gillings School of Global Public Health
Statistical Learning Methods for Neuroimaging Data Analysis with Applications
Abstract
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.
Bio: Dr. Hongtu Zhu is a professor in the Department of Biostatistics. He has a broad background in statistics, biostatistics, medical imaging, genetics and computational neuroscience, with specific training and expertise in neuroimaging data analysis and big data integration as well as secondary data analysis on neurodegenerative and neuropsychiatric diseases. As a graduate student and postdoctoral fellow, he developed various new statistical methods for analyzing genetic, behavioral and clinical data from cross-sectional, longitudinal and family studies, and solved associated statistical issues (e.g., estimation, hypothesis testing). As a faculty member at Columbia University and New York State Psychiatric Institute, he expanded his research to develop new statistical methods for the analysis of medical imaging data including magnetic resonance images (MRI), DTI, EEG/MEG, Ultra Sound, fMRI and PET.
Friday, November 18, 2022
AUST 344
10:00 am ET, 1-hour duration
Coffee @ 9:30 in AUST 326
For more information, contact: Tracy Burke at tracy.burke@uconn.edu