Statistical Neuroimaging Analysis: An Overview

Abstract: Understanding the inner workings of human brains, and their connections to both neurological disorders and normal development, is one of the most intriguing scientific questions. Advances in neuroscience have been greatly facilitated by various neuroimaging technologies, including anatomical magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), electroencephalography (EEG), diffusion tensor imaging, positron emission tomography (PET), among many others. The sheer size and complexity of medical imaging data, however, present significant challenges and call for continual development of new statistical methodologies. In this talk, I will provide an overview of a range of neuroimaging topics our group has been investigating, including imaging tensor analysis, brain connectivity network analysis, multimodality analysis, and imaging causal analysis. I will also illustrate with a number of specific case studies.

Host: Ran Chen

Lexin Li, Ph.D., is Professor of Biostatistics at the Department of Biostatistics and Epidemiology, Department of Statistics, and Helen Wills Neuroscience Institute, of the University of California, Berkeley. His research interests include neuroimaging analysis, brain-computer-interface, reinforcement learning, differential equations, point process, tensor and network data analysis. He is a Fellow of the American Association for the Advancement of Science (AAAS), the Institute of Mathematical Statistics (IMS), the American Statistical Association (ASA), the Asia-Pacific Artificial Intelligence Association (AAIA), and an Elected Member of the International Statistical Institute (ISI). He is the Editor-in-Chief of the Annals of Applied Statistics for 2025-27.