Button to scroll to the top of the page.

Events Calendar

Yearly View
View as List
Monthly View
By Month
Weekly View
By Week
Daily View
Download as iCal file
SDS Seminar Series: Dr. Michele Guindani (Friday 11/5/21, 2pm)
Friday, November 05, 2021, 02:00pm - 03:00pm

The Fall 2021 SDS Seminar Series concludes on Friday, November 5 from 2:00 p.m. to 3:00 p.m. via Zoom with Dr. Michele Guindani (Professor at the Department of Statistics and the Donald Bren School of Information and Computer Sciences at the University of California, Irvine)

Please contact stat.admin@austin.utexas.edu for the Zoom link.

SDS Logo
Title: Bayesian methods for studying heterogeneity in brain imaging experiments

Abstract: An improved understanding of the heterogeneity of brain mechanisms is considered key for enabling the development of interventions based on imaging features. In this talk, we will discuss some examples of heterogeneity in animals’ and humans’ experiments. More specifically, we will first discuss the analysis of neuronal responses to external stimuli in awake behaving animals through the analysis of intra-cellular calcium signals. We propose a nested Bayesian finite mixture specification that allows for the estimation of spiking activity and, simultaneously, reconstructs the distributions of the calcium transient spikes' amplitudes under different experimental conditions. The proposed model borrows information between experiments and discovers similarities in the distributional patterns of neuronal responses to different stimuli. In the second part of the talk, we will discuss a computationally efficient time-varying Bayesian VAR approach for studying dynamic effective connectivity in functional magnetic resonance imaging (fMRI). The proposed framework employs a tensor decomposition for the VAR coefficient matrices at different lags. Dynamically varying connectivity patterns are captured by assuming that at any given time the VAR coefficient matrices are obtained as a mixture of only an active subset of components in the tensor decomposition. We show the performances of our model formulation via simulation studies and data from  real fluorescence microscopy and fMRI studies.