title: Modeling dynamic functional information flows on large-scale brain networks.
abstract: Growing evidence from the functional neuroimaging field suggests that human brain functions are realized via dynamic functional interactions on large-scale structural networks. Even in resting state, functional brain networks exhibit remarkable temporal dynamics. However, it has been rarely explored to computationally model such dynamic functional information flows on large-scale brain networks. In this paper, we present a novel computational framework to explore this problem using multimodal resting state fMRI (R-fMRI) and diffusion tensor imaging (DTI) data. Basically, recent literature reports including our own studies have demonstrated that the resting state brain networks dynamically undergo a set of distinct brain states. Within each quasi-stable state, functional information flows from one set of structural brain nodes to other sets of nodes, which is analogous to the message package routing on the Internet from the source node to the destination. Therefore, based on the large-scale structural brain networks constructed from DTI data, we employ a dynamic programming strategy to infer functional information transition routines on structural networks, based on which hub routers that most frequently participate in these routines are identified. It is interesting that a majority of those hub routers are located within the default mode network (DMN), revealing a possible mechanism of the critical functional hub roles played by the DMN in resting state. Also, application of this framework on a post trauma stress disorder (PTSD) dataset demonstrated interesting difference in hub router distributions between PTSD patients and healthy controls.
authors.affiliation: School of Automation, Northwestern Polytechnical University, Xi'an, China.
authors.forename: Peili
authors.initials: P
authors.lastname: Lv
authors.affiliation: School of Automation, Northwestern Polytechnical University, Xi'an, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Guo
authors.affiliation: School of Automation, Northwestern Polytechnical University, Xi'an, China.
authors.forename: Xintao
authors.initials: X
authors.lastname: Hu
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: The Mental Health Institute, The Second Xiangya Hospital, Central South University, Changsha, China.
authors.forename: Changfeng
authors.initials: C
authors.lastname: Jin
authors.affiliation: School of Automation, Northwestern Polytechnical University, Xi'an, China.
authors.forename: Junwei
authors.initials: J
authors.lastname: Han
authors.affiliation: The Mental Health Institute, The Second Xiangya Hospital, Central South University, Changsha, China.
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.
authors.forename: Tianming
authors.initials: T
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D009415:Nerve Net / Q000503:physiopathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013313:Stress Disorders, Post-Traumatic / Q000175:diagnosis / Q000503:physiopathology*