Inferring functional interaction and transition patterns via dynamic bayesian variable partition models

• Type of Material: Article
• Authors: Jing Zhang, Yale University; Xiang Li, University of Georgia; Cong Li, Yale University; Zhichao Lian, Yale University; Xiu Huang, Yale University; Guocheng Zhong, Yale University; Dajiang Zhu, University of Georgia; Kaiming Li, Emory University; Changfeng Jin, Central South University; Xintao Hu, Northwestern Polytechnic University; Junwei Han, Northwestern Polytechnical University; Lei Guo, Northwestern Polytechnical University; Xiaoping Hu, Emory University; Lingjiang Li, Central South University; Tianming Liu, University of Georgia
• Language: English
• Date: 2014-07-01
• Publisher: Wiley: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2013 Wiley Periodicals, Inc.
• Final Published Version (URL): http://dx.doi.org/10.1002/hbm.22404
• Title of Journal or Parent Work: Human Brain Mapping
• ISSN: 1065-9471
• Volume: 35
• Issue: 7
• Start Page: 3314
• End Page: 3331
• Grant/Funding Information: J Han was supported by the National Science Foundation of China under Grant 61005018 and 91120005; NPU-FFR-JC20120237; and Program for New Century Excellent Talents in University under grant NCET-10-0079.; X Huang and C Li were supported by the China Scholarship Council-Yale World Scholars Program.; X Hu was supported by the National Science Foundation of China under Grant 61103061; China Postdoctoral Science Foundation under Grant 20110490174 and 2012T50819.; J Zhang was supported by start-up funding Seessel Award from Yale University.; The computation was done with the help from the Yale University Biomedical High Performance Computing Center, which was supported by the NIH grant RR19895.; L Guo was supported by the National Science Foundation of China under Grant 61273362.; T Liu was supported by the NIH Career Award (NIH EB 006878); NIH R01 HL087923-03S2; NIH R01 DA033393; NSF CAREER Award (IIS-1149260); and The University of Georgia start-up research funding.; Lingjiang Li was supported by The National Natural Science Foundation of China (30830046) and The National 973 Program of China (2009 CB918303).
• Keywords: Neurosciences & Neurology; DEFAULT-MODE; CAUSAL CONNECTIVITY; BRAIN NETWORKS; EFFECTIVE CONNECTIVITY; dynamics; Neurosciences; DISCRIMINATION; Neuroimaging; functional interaction; Bayesian graphic models; MATLAB TOOLBOX; FLUCTUATIONS; Life Sciences & Biomedicine; Radiology, Nuclear Medicine & Medical Imaging; MRI; Science & Technology; LOCALIZATION
• Research Categories: Health Sciences, Radiology; Engineering, Biomedical

Abstract

Multivariate connectivity and functional dynamics have been of wide interest in the neuroimaging field, and a variety of methods have been developed to study functional interactions and dynamics. In contrast, the temporal dynamic transitions of multivariate functional interactions among brain networks, in particular, in resting state, have been much less explored. This article presents a novel dynamic Bayesian variable partition model (DBVPM) that simultaneously considers and models multivariate functional interactions and their dynamics via a unified Bayesian framework. The basic idea is to detect the temporal boundaries of piecewise quasi-stable functional interaction patterns, which are then modeled by representative signature patterns and whose temporal transitions are characterized by finite-state transition machines. Results on both simulated and experimental datasets demonstrated the effectiveness and accuracy of the DBVPM in dividing temporally transiting functional interaction patterns. The application of DBVPM on a post-traumatic stress disorder (PTSD) dataset revealed substantially different multivariate functional interaction signatures and temporal transitions in the default mode and emotion networks of PTSD patients, in comparison with those in healthy controls. This result demonstrated the utility of DBVPM in elucidating salient features that cannot be revealed by static pair-wise functional connectivity analysis.