Bayesian spatiotemporal model of fMRI data.

ABSTRACT

This research describes a new Bayesian spatiotemporal model to analyse block-design BOLD fMRI studies. In the temporal dimension, we parameterise the hemodynamic response function's (HRF) shape with a potential increase of signal and a subsequent exponential decay. In the spatial dimension, we use Gaussian Markov random fields (GMRF) priors on activation characteristics parameters (location and magnitude) that embody our prior knowledge that evoked responses are spatially contiguous and locally homogeneous. The result is a spatiotemporal model with a small number of parameters, all of them interpretable. Simulations from the model are performed in order to ascertain the performance of the sampling scheme and the ability of the posterior to estimate model parameters, as well as to check the model sensitivity to signal to noise ratio. Results are shown on synthetic data and on real data from a block-design fMRI experiment.