MICCAI 2013 - LNCS 8149-8151

Modeling Dynamic Functional Information Flows on Large-Scale Brain Networks

Peili Lv¹, Lei Guo¹, Xintao Hu¹, Xiang Li², Changfeng Jin³, Junwei Han¹, Lingjiang Li³, and Tianming Liu²

¹School of Automation, Northwestern Polytechnical University, Xi’an, China

²Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA

³The Mental Health Institute, The Second Xiangya Hospital, Central South University, Changsha, China

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.

Keywords: structural brain networks, brain state changes, dynamic functional information flow, default mode network, post trauma stress disorder

LNCS 8150, p. 698 ff.

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