Monte Carlo Expectation Maximization with Hidden Markov Models to Detect Functional Networks in Resting-State fMRI

We propose a novel Bayesian framework for partitioning the cortex into distinct functional networks based on resting-state fMRI. Spatial coherence within the network clusters is modeled using a hidden Markov randomfield prior. The normalized time-series data, which lie on a high-dimensional sphere, are modeled with a mixture of von Mises-Fisher distributions. To estimate the parameters of this model, we maximize the posterior using a Monte Carlo expectation maximization (MCEM) algorithm in which the intractable expectation over all possible labelings is approximated using Monte Carlo integration. We show that MCEM solutions on synthetic data are superior to those computed using a mode approximation of the expectation step. Finally, we demonstrate on real fMRI data that ourmethod is able to identify visual, motor, salience, and default mode networks with considerable consistency between subjects.