Liu W, Awate SP, Anderson JS, Fletcher PT. A functional network estimation method of resting-state fMRI using a
hierarchical Markov random field.
Neuroimage 2014;
100:520-34. [PMID:
24954282 DOI:
10.1016/j.neuroimage.2014.06.001]
[Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2013] [Revised: 05/28/2014] [Accepted: 06/01/2014] [Indexed: 12/14/2022] Open
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.
Collapse