Abstract:

Understanding the inner workings of human brains, as well as their connections with neurological disorders, is one of the most intriguing scientific questions. Studies in neuroscience are greatly facilitated by a variety of 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 size and complexity of medical imaging data, however, pose numerous challenges, and call for constant development of new statistical methods. In this talk, I give 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 also illustrate with a number of specific case studies, including time-varying brain connectivity analysis, kernel ordinary differential equation for brain effective connectivity analysis, and inference for multivariate mediation analysis.

 

Speaker Bio:

Lexin Li, Ph.D., is a Professor of Biostatistics at the Department of Biostatistics and Epidemiology, and Helen Wills Neuroscience Institute, of the University of California, Berkeley. His research interests include neuroimaging analysis, deep brain stimulation, brain-computer-interface, reinforcement learning, ordinary differential equations, tensor data analysis, and network data analysis. He is a Fellow of the American Statistical Association (ASA), a Fellow of the Institute of Mathematical Statistics (IMS), 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, and an Associate Editor for Journal of the American Statistical Association, Journal of Royal Statistical Society, Series, B., among others.