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Group Analysis of Resting-state fMRI by Hierarchical Markov Random Fields

Institution:
Scientific Computing and Imaging Institute, University of Utah, UT, USA. weiliu@sci.utah.edu
Publisher:
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2012
Publication Date:
Oct-2012
Journal:
Med Image Comput Comput Assist Interv
Volume Number:
15
Issue Number:
Pt 3
Pages:
189-96
Citation:
Int Conf Med Image Comput Comput Assist Interv. 2012 Oct;15(Pt 3):189-96.
PubMed ID:
23286130
PMCID:
PMC3749875
Appears in Collections:
NA-MIC
Sponsors:
P41 RR012553/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Liu W., Awate S.P., Fletcher P.T. Group Analysis of Resting-state fMRI by Hierarchical Markov Random Fields. Int Conf Med Image Comput Comput Assist Interv. 2012 Oct;15(Pt 3):189-96. PMID: 23286130. PMCID: PMC3749875.
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Identifying functional networks from resting-state functional MRI is a challenging task, especially for multiple subjects. Most current studies estimate the networks in a sequential approach, i.e., they identify each individual subject's network independently to other subjects, and then estimate the group network from the subjects networks. This one-way flow of information prevents one subject's network estimation benefiting from other subjects. We propose a hierarchical Markov random field model, which takes into account both the within-subject spatial coherence and between-subject consistency of the network label map. Both population and subject network maps are estimated simultaneously using a Gibbs sampling approach in a Monte Carlo expectation maximization framework. We compare our approach to two alternative groupwise fMRI clustering methods, based on K-means and normalized Cuts, using both synthetic and real fMRI data. We show that our method is able to estimate more consistent subject label maps, as well as a stable group label map.

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