Bayesian Modeling in Neuroimaging: Brain Networks Dynamics

The critical role that statistical approaches play in analyzing brain imaging data will be first highlighted, particularly for functional magnetic resonance imaging (fMRI) data. Appropriate statistical methods are necessary to handle the complexity of spatial and temporal correlations typical of brain data. More specifically, we will discuss approaches to studying dynamic brain connectivity, which seeks to understand the changing interactions between different brain regions over time. We will present novel Bayesian approaches to capture these dynamic relationships within multivariate time series data.  In particular, we will present a scalable Bayesian time-varying tensor vector autoregressive (TV-VAR) model, aimed at efficiently capturing evolving connectivity patterns.

This model leverages a tensor decomposition of the VAR coefficient matrices at different lags and sparsity-inducing priors to capture dynamic connectivity patterns. If time allows, we will then discuss generalizations of the widely adopted psychophysiological interaction (PPI) models in the neuroscience,  which estimates task-modulated time-varying background functional connectivity from an fMRI experiment. Throughout the talk, we will illustrate the performance of these Bayesian methods with examples from simulation studies and real-world fMRI data.

About Michele Guindani

Dr. Michele Guindani is a Professor in the Department of Biostatistics at the University of California, Los Angeles (UCLA). He earned his Ph.D. in Statistics from Università Bocconi in Milan, Italy, under the guidance of Sonia Petrone and Alan E. Gelfand. Following his doctorate, he completed a postdoctoral fellowship at the University of Texas MD Anderson Cancer Center under the guidance of Gary Rosner and Peter Mueller. Before joining UCLA, Dr. Guindani held academic positions at the University of New Mexico, the University of Texas MD Anderson Cancer Center, and the University of California, Irvine.

His research focuses on Bayesian analysis, particularly Bayesian nonparametrics, with applications in neuroimaging, integrative microbiome analysis, and radiomics. Dr. Guindani is a Fellow of both the American Statistical Association and the International Society for Bayesian Analysis, an elected member of the International Statistical Institute, and a member of the Institute of Mathematical Statistics. He currently serves as the 2025 President of the International Society for Bayesian Analysis. He is also past chair of the Section of Bayesian Statistical Sciences and current chair of the Section on Statistical Imaging of the ASA.

In addition, Dr. Guindani has served as Editor-in-Chief of the journal "Bayesian Analysis" from 2019 to 2021. He is a founding of co-Editor of the new ASA journal on "Statistics and Data Science in Imaging". He is also an Associate Editor for Biometrics, Econometrics and Statistics, and the Journal of the American Statistical Association, Theory and Methods. Dr. Guindani also serves as one of the the Chief Statistical Advisor for Nature Medicine.