title: Group analysis of resting-state fMRI by hierarchical Markov random fields.
abstract: 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.
authors.affiliation: Scientific Computing and Imaging Institute, University of Utah, USA. weiliu@sci.utah.edu
authors.forename: Wei
authors.initials: W
authors.lastname: Liu
authors.forename: Suyash P
authors.initials: SP
authors.lastname: Awate
authors.forename: P Thomas
authors.initials: PT
authors.lastname: Fletcher
mesh_terms: D000293:Adolescent; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008390:Markov Chains; D009415:Nerve Net / Q000502:physiology*; D015203:Reproducibility of Results; D012146:Rest / Q000502:physiology*; D012680:Sensitivity and Specificity