Bayesian spatiotemporal model of fMRI data using transfer functions

Abstract

This research describes a new Bayesian spatiotemporal model to analyse BOLD fMRI studies. In the temporal dimension, we describe the shape of the hemodynamic response function (HRF) with a transfer function model. In the spatial dimension, we use a Gaussian Markov random field prior on the parameter indicating activations that embody our prior knowledge that evoked responses are spatially contiguous. The proposal constitutes an extension of the spatiotemporal model presented in a previous approach [Quir'os, A., Montes Diez, R. and Gamerman, D. (2010). Bayesian spatiotemporal model of fMRI data, Neuroimage, 49: 442-456.], o?ering more flexibility in the estimation of the HRF and computational advantages in the resulting MCMC algorithm. 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 a real data set from a block-design fMRI experiment, showing good performance in the detection of activity and significant flexibility in the estimation of the