05. An MCMC-based method for dynamic causal modeling of effective connectivity in functional MRI (fMRI)

Effective connectivity analysis in functional magnetic resonance imaging (fMRI) studies the directional interactions among brain regions and experimental stimuli. A widely used method to estimate effective connectivity is dynamic causal modeling (DCM), which uses a state space model representation; this consists of a latent neural signal model and an observation model which transforms this signal into the observed blood-oxygen–level-dependent (BOLD) signal in fMRI data. A standard DCM model involves a complex neural-hemodynamic model system with a variational Bayes method for parameter estimation. While physically sound, this approach can lead to various practical challenges such as inexact solutions and underestimated uncertainty in parameter estimates. In our work, we introduce a Markov chain Monte Carlo (MCMC)-based DCM method that adopts a simpler observation model and the No U-Turn Sampler for posterior distribution sampling of network parameters. Preliminary results indicate that this approach maintains robustness against misspecification, allows accurate uncertainty quantification of inferred parameters, and consistent estimation of parameters related to the experimental inputs for both simulated and real data.

Mid-Level

Knowledge

Women in Statistics and Data Science 2024