A Bayesian Time-varying Psychophysiological Interaction Model for fMRI Data

Oregon Convention Center 

Functional connectivity, the study of coordination between functionally distinct brain regions, is a recent focus in neuroscientific research. The Psychophysiological Interaction (PPI) model is commonly used to infer functional connectivity in a task-dependent context, but its main limitation is its susceptibility to confounding effects. We argue that partial correlations, rather than the regression coefficients of the PPI model, are a better measure of functional connectivity because they correct for confounding. We show that the PPI model entails a Gaussian Graphical Model (GGM) from which partial correlations are easily derived. We fit our model efficiently with a set of independent Bayesian linear regressions performed in parallel. We allow the regression coefficients to vary over time which accommodates dynamic background connectivity, overcoming another limitation of the PPI model. A thoughtful choice of scale-mixture shrinkage priors enforces sparsity in the GGM precision matrix and discourages overfitting. We demonstrate the efficacy of our model over the PPI model using simulated data and apply it to human fMRI data from a serial reaction time experiment.

Phsychophysiological Interaction

Bayesian

Time-varying coefficient

fMRI 

Section on Statistics in Imaging