Abstract
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 random field 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 our method is able to identify visual, motor, salience, and default mode networks with considerable consistency between subjects.
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Liu, W., Awate, S.P., Anderson, J.S., Yurgelun-Todd, D., Fletcher, P.T. (2011). Monte Carlo Expectation Maximization with Hidden Markov Models to Detect Functional Networks in Resting-State fMRI. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2011. Lecture Notes in Computer Science, vol 7009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24319-6_8
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DOI: https://doi.org/10.1007/978-3-642-24319-6_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24318-9
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