Theme: Brain Regions Identified as Being Associated with Verbal Reasoning through the Use of Imaging Regression via Internal Variation

Lecturer: Professor ZHANG Heping, Yale University

Host: Professor LIN Huazhen, School of Statistics

Date: 10:00-11:00, June 22, 2020

Conference ID: ZOOM, 980 5888 4717

Organizers: Center of Statistical Research, Joint Laboratory for Data Science and Business Intelligence, School of Statistics and Office of Research Affairs

Introduction to the Lecturer:

Heping Zhang is Susan Dwight Bliss Professor of Biostatistics, Professor of Statistics and Data Science, and Professor of Child Study at Yale University. Dr. Zhang published over 300 research articles and monographs in theory and applications of statistical methods and in several areas of biomedical research including epidemiology, genetics, mental health, and reproductive health. He directs the Collaborative Center for Statistics in Science that coordinates major national research networks to understand the etiology of pregnancy outcomes and to evaluate treatment effectiveness for infertility. He is a fellow of the American Statistical Association and a fellow of the Institute of Mathematical Statistics. He was named the 2008 Myrto Lefokopoulou distinguished lecturer by Harvard School of Public Health and a 2011 Medallion Lecturer by the Institute of Mathematical Statistics. Professor Zhang is the Editor of the Journal of the American Statistical Association – Applications and Case Studies.

Content Summary:

Brain-imaging data have been increasingly used to understand intellectual disabilities. Despite significant progress in biomedical research, the mechanisms for most of the intellectual disabilities remain unknown. Finding the underlying neurological mechanisms has been proved difficult, especially in children due to the rapid development of their brains. We investigate verbal reasoning, which is a reliable measure of individuals’ general intellectual abilities, and develop a class of high-order imaging regression models to identify brain sub-regions which might be associated with this specific intellectual ability. A key novelty of our method is to take advantage of spatial brain structures, and specifically the piecewise smooth nature of most imaging coefficients in the form of high-order tensors. Our approach provides an effective and urgently needed method for identifying brain sub-regions potentially underlying certain intellectual disabilities. The idea behind our approach is a carefully constructed concept called Internal Variation (IV). The IV employs tensor decomposition and provides a computationally feasible substitution for Total Variation (TV), which has been considered in the literature to deal with similar problems but is problematic in high order tensor regression. Before applying our method to analyze the real data, we conduct comprehensive simulation studies to demonstrate the validity of our method in imaging signal identification. Then, we present our results from the analysis of a dataset based on the Philadelphia Neurodevelopmental Cohort for which we preprocessed the data including re-orienting, bias-field correcting, extracting, normalizing and registering the magnetic resonance images from 978 individuals. Our analysis identified a sub-region across the cingulate cortex and the corpus callosum as being associated with individuals’ verbal reasoning ability, which, to the best of our knowledge, is a novel region that has not been reported in the literature. This finding is useful in further investigation of functional mechanisms for verbal reasoning. This is a joint work with Long Feng and Xuan Bi.