Bio:

Michele Guindani, Ph.D., is a Professor in the Department of Biostatistics at the Fielding School of Public Health, University of California, Los Angeles (UCLA).

Professor Guindani’s research spans Biostatistics, Data Science, Machine Learning, Statistical decision-making under Uncertainty, Multiple comparison problems, Statistical Imaging, Clinical Trials, Study Design, Clustering, Bayesian modeling, and Nonparametric Bayesian models. 

Professor Guindani is Fellow of the American Statistical Association (ASA) and the International Society for Bayesian Analysis (ISBA). He is the current President of the International Society for Bayesian Analysis (ISBA).

Title:

Connecting the Dots: Statistical Models in Brain Imaging and Brain Networks Dynamics

Statistical methods play a crucial role in brain imaging, enabling researchers to uncover the complex patterns of brain function and connectivity. In this talk, we will begin by highlighting the critical role that statistical approaches play in the analysis of imaging data, particularly in the context of functional magnetic resonance imaging (fMRI). We will discuss how appropriate statistical methods are necessary to handle the complexity of spatial and temporal correlations typical of brain data.

Building on this foundation, we will then discuss approaches to studying dynamic brain connectivity, which seeks to understand the changing interactions between different brain regions over time. We will present two novel Bayesian approaches designed to capture these dynamic relationships within multivariate time series data.

First, 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.

Next, we will introduce a Bayesian framework for sparse Gaussian graphical modeling, which employs discrete autoregressive switching processes. This method improves the estimation of dynamic connectivity by modeling state-specific precision matrices, using innovative prior structures to account for temporal and spatial dependencies.

Throughout the talk, we will illustrate the power and flexibility of these Bayesian methods with examples from simulation studies and real-world fMRI data. Our discussion will emphasize the importance of these innovative statistical tools in advancing our understanding of brain connectivity and their potential for applications in neuroscience research and clinical practice.