Bayesian Modeling for Functional Neuroimaging Data

Functional neuroimaging data, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), often exhibit rich temporal, spatial, and spectral structure, posing unique challenges for statistical modeling. This talk presents Bayesian modeling approaches for functional neuroimaging data, focusing on time-frequency representations of EEG signals from multi-condition experiments. In such experiments, brain activity is recorded as subjects engage in various tasks or are exposed to different stimuli. The resulting data often exhibit smooth variation across time and frequency and can be naturally represented as two-way functional data, with conditions nested within subjects. To jointly account for the data’s multilevel structure, functional nature, and subject-level covariates, we propose a Bayesian mixed-effects model incorporating covariate-dependent fixed effects and multilevel random effects. For interpretability and parsimony, we introduce a novel decomposition of the fixed effects with marginally interpretable time and frequency patterns, along with a sparsity-inducing prior for rank selection. The proposed method is evaluated through extensive simulations and applied to EEG data collected to investigate the effects of alcoholism on cognitive processing in response to visual stimuli.  Extensions to modeling dynamic functional connectivity and other Bayesian methods developed for fMRI data will also be discussed. 

To join this seminar virtually, please request Zoom connection details from ea@stat.ubc.ca.