Detecting Brain State Changes via Fiber-Centered Functional Connectivity Analysis
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ORIGINAL ARTICLE
Detecting Brain State Changes via Fiber-Centered Functional Connectivity Analysis Xiang Li & Chulwoo Lim & Kaiming Li & Lei Guo & Tianming Liu
Published online: 2 September 2012 # Springer Science+Business Media, LLC 2012
Abstract Diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) have been widely used to study structural and functional brain connectivity in recent years. A common assumption used in many previous functional brain connectivity studies is the temporal stationarity. However, accumulating literature evidence has suggested that functional brain connectivity is under temporal dynamic changes in different time scales. In this paper, a novel and intuitive approach is proposed to model and detect dynamic changes of functional brain states based on multimodal fMRI/DTI data. The basic idea is that functional connectivity patterns of all fiber-connected cortical voxels are concatenated into a descriptive functional feature vector to represent the brain’s state, and the temporal change points of brain states are decided by detecting the abrupt changes of the functional vector patterns via the sliding window approach. Our extensive experimental results have shown that meaningful brain state change points can be detected in task-based fMRI/DTI, resting state fMRI/DTI, and natural stimulus fMRI/DTI data sets. Particularly, the detected change points of functional brain states in task-based fMRI corresponded well to the external stimulus paradigm administered to the participating subjects, thus partially validating the proposed brain state change detection approach. The work in this paper provides novel perspective on the dynamic behaviors of functional brain connectivity and offers Xiang Li and Chulwoo Lim are joint first authors. X. Li : C. Lim : K. Li : T. Liu (*) Department of Computer Science and Bioimaging Research Center, The University of Georgia, Boyd GSRC 420, Athens, GA 30602, USA e-mail: [email protected] URL: http://www.cs.uga.edu/~tliu K. Li : L. Guo School of Automation, Northwestern Polytechnic University, Xi’an, China
a starting point for future elucidation of the complex patterns of functional brain interactions and dynamics. Keywords Brain connectivity . Diffusion tensor imaging . Functional MRI . Brain state change
Introduction Studying structural and functional connectivity in brain networks has received increasingly strong interest recently due to its significant importance in basic and clinical neurosciences (e.g., Friston et al. 2003; Sporns et al. 2005; Biswal et al. 2010; Van Dijk et al. 2010; Lynall et al. 2010; Kennedy 2010; Hagmann et al. 2010; Li et al. 2012). A common assumption used in many previous functional brain connectivity studies (e.g., Wang et al. 2006; Dickerson and Sperling 2009; Lynall et al. 2010; Liu 2011) is the temporal stationarity; that is, functional connectivity are computed over the entire fMRI scan and used to characterize the strengths of connections across regions. However, accumulating literature evidence (e.g., L
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