Probabilistic modeling of single-trial fMRI data

This paper describes a probabilistic framework for modeling single-trial fu nctional magnetic resonance (fMR) images based on a parametric model for th e hemodynamic response and Markov random field (MRP) image models. The mode l is fitted to image data by maximizing a lower bound on the log likelihood . The result is an approximate maximum a posteriori estimate of the joint d istribution over the model parameters and pixel labels. Examples show how t his technique can used to segment two-dimensional (2-D) fMR images, or part s thereof, into regions with different characteristics of their hemodynamic response.