title: A functional network estimation method of resting-state fMRI using a hierarchical Markov random field.
abstract: We propose a hierarchical Markov random field model for estimating both group and subject functional networks simultaneously. The model takes into account the within-subject spatial coherence as well as the between-subject consistency of the network label maps. The statistical dependency between group and subject networks acts as a regularization, which helps the network estimation on both layers. We use Gibbs sampling to approximate the posterior density of the network labels and Monte Carlo expectation maximization to estimate the model parameters. We compare our method with two alternative segmentation methods based on K-Means and normalized cuts, using synthetic and real fMRI data. The experimental results show that our proposed model is able to identify both group and subject functional networks with higher accuracy on synthetic data, more robustness, and inter-session consistency on the real data.
authors: Scientific Computing and Imaging Institute, University of UT, USA. Electronic address: weiliu@sci.utah.edu.
authors: Wei
authors: W
authors: Liu
authors: Scientific Computing and Imaging Institute, University of UT, USA. Electronic address: suyash@sci.utah.edu.
authors: Suyash P
authors: SP
authors: Awate
authors: Department of Radiology, University of UT, USA. Electronic address: andersonjeffs@gmail.com.
authors: Jeffrey S
authors: JS
authors: Anderson
authors: Scientific Computing and Imaging Institute, University of UT, USA. Electronic address: fletcher@sci.utah.edu.
authors: P Thomas
authors: PT
authors: Fletcher
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D001931:Brain Mapping / Q000379:methods*; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D008390:Markov Chains; D055815:Young Adult
keywords: Bayesian; Functional connectivity; Hierarchical Markov random field; Resting-state functional MRI; Segmentation