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Detecting Brain State Changes via Fiber-Centered Functional Connectivity Analysis

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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 a starting point for future elucidation of the complex patterns of functional brain interactions and dynamics.

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Acknowledgements

T Liu was supported by the NIH Career Award EB 006878, NIH R01 HL087923-03 S2, NIH R01 DA033393 and The University of Georgia start-up research funding. Parts of the OSPAN working memory fMRI data sets were provided by Carlos Faraco and L. Stephen Miller. The authors would like to thank the anonymous reviewers for their constructive comments and suggestions.

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Authors and Affiliations

  1. Department of Computer Science and Bioimaging Research Center, The University of Georgia, Boyd GSRC 420, Athens, GA, 30602, USA

    Xiang Li, Chulwoo Lim, Kaiming Li & Tianming Liu

  2. School of Automation, Northwestern Polytechnic University, Xi’an, China

    Kaiming Li & Lei Guo

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  1. Xiang Li
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Correspondence to Tianming Liu.

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Xiang Li and Chulwoo Lim are joint first authors.

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Li, X., Lim, C., Li, K. et al. Detecting Brain State Changes via Fiber-Centered Functional Connectivity Analysis. Neuroinform 11, 193–210 (2013). https://doi.org/10.1007/s12021-012-9157-y

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