Abstract
Functional connectivity measured from resting state fMRI (R-fMRI) data has been widely used to examine the brain’s functional activities and has been recently used to characterize and differentiate brain conditions. However, the dynamical transition patterns of the brain’s functional states have been less explored. In this work, we propose a novel computational framework to quantitatively characterize the brain state dynamics via hidden Markov models (HMMs) learned from the observations of temporally dynamic functional connectomics, denoted as functional connectome states. The framework has been applied to the R-fMRI dataset including 44 post-traumatic stress disorder (PTSD) patients and 51 normal control (NC) subjects. Experimental results show that both PTSD and NC brains were undergoing remarkable changes in resting state and mainly transiting amongst a few brain states. Interestingly, further prediction with the best-matched HMM demonstrates that PTSD would enter into, but could not disengage from, a negative mood state. Importantly, 84 % of PTSD patients and 86 % of NC subjects are successfully classified via multiple HMMs using majority voting.
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Acknowledgments
T Liu was supported by NIH R01 DA033393, NIH R01 AG-042599, NSF CBET-1302089 and NSF CAREER Award IIS-1149260. Lingjiang Li was supported by The National Natural Science Foundation of China (30830046) and The National 973 Program of China (2009 CB918303). J Zhang was supported by start-up funding and Sesseel Award from Yale University. The authors would like to thank the anonymous reviewers for their constructive comments and thank Elliott Chung for his proofreading.
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Ou, J., Xie, L., Jin, C. et al. Characterizing and Differentiating Brain State Dynamics via Hidden Markov Models. Brain Topogr 28, 666–679 (2015). https://doi.org/10.1007/s10548-014-0406-2
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DOI: https://doi.org/10.1007/s10548-014-0406-2