Markov random field modelling of fMRI data using a mean field EM-algorithm

This paper considers the use of the EM-algorithm, combined with mean field theory, for parameter estimation in Markov random field models from unlabelled data. Special attention is given to the theoretical justification for this procedure, based on recent results from the machine learning literature. With these results established, an example is given of the application of this technique for analysis of single trial functional magnetic resonance (fMR) imaging data of the human brain. The resulting model segments fMR images into regions with different 'brain response' characteristics