A Bayesian Time-Varying Psychophysiological Interaction Model by Schetzsle Brian & Lee Jaylen & Bornstein Aaron & Shahbaba Babak & Guindani Michele instant download

A Bayesian Time-Varying Psychophysiological Interaction Model by Schetzsle Brian & Lee Jaylen & Bornstein Aaron & Shahbaba Babak & Guindani Michele instant download

ABSTRACT ARTICLE HISTORY Functional connectivity, the study of coordination between distinct brain regions, is a key focus in Received 2 December 2024 Revised 11 April 2025 neuroscience. The Psychophysiological Interaction (PPI) model, commonly used to infer taskAccepted 29 May 2025 dependent functional connectivity, is limited by its susceptibility to confounding effects. We propose using partial correlations, instead of PPI regression coefficients, as they correct for confoundKEYWORDS ing. We show how the PPI model can be used to estimate the precision matrix of a Gaussian Psychophysiological interGraphical Model (GGM), from which partial correlations are easily derived. We then propose a action; time-varying Bayesian extension to the PPI model that allows this measure of functional connectivity to vary parameter; dynamic over time. We enforce sparsity in the GGM precision matrix through scale-mixture shrinkage priors, covariance; functional mitigating overfitting. Additionally, we identify structural zeros in the precision matrix using a connectivity Bayesian multicomparison decision-theoretic framework. We demonstrate the efficacy of our model over the standard PPI model using simulated data and we further apply it to human fMRI data from a serial reaction time experiment. Our framework offers a more robust and dynamic approach to functional connectivity analysis.