title: Characterizing and Differentiating Brain State Dynamics via Hidden Markov Models.
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.
authors.affiliation: School of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
authors.forename: Jinli
authors.initials: J
authors.lastname: Ou
authors.affiliation: School of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
authors.forename: Li
authors.initials: L
authors.lastname: Xie
authors.affiliation: The Mental Health Institute, The Second Xiangya Hospital, Central South University, Changsha, China.
authors.forename: Changfeng
authors.initials: C
authors.lastname: Jin
authors.affiliation: Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
authors.forename: Xiang
authors.initials: X
authors.lastname: Li
authors.affiliation: Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
authors.forename: Dajiang
authors.initials: D
authors.lastname: Zhu
authors.affiliation: School of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
authors.forename: Rongxin
authors.initials: R
authors.lastname: Jiang
authors.affiliation: School of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
authors.forename: Yaowu
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Department of Mathematics and Statistics, Georgia State University, Atlanta, 30303, GA, USA. jing.maria.zhang@gmail.com.
authors.forename: Jing
authors.initials: J
authors.lastname: Zhang
authors.affiliation: The Mental Health Institute, The Second Xiangya Hospital, Central South University, Changsha, China. llj2920@163.com.
authors.forename: Lingjiang
authors.initials: L
authors.lastname: Li
authors.affiliation: Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA. tliu@cs.uga.edu.
authors.forename: Tianming
authors.initials: T
authors.lastname: Liu
mesh_terms: D000328:Adult; D001921:Brain / Q000503:physiopathology*; D016022:Case-Control Studies; D063132:Connectome; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008390:Markov Chains; D008959:Models, Neurological; D009434:Neural Pathways / Q000503:physiopathology; D013313:Stress Disorders, Post-Traumatic / Q000503:physiopathology*