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Detecting Change Points in fMRI Data via Bayesian Inference and Genetic Algorithm Model

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Bioinformatics Research and Applications (ISBRA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10330))

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Abstract

Dynamic functional connectivity detection in fMRI has been recently proved to be powerful for exploring brain conditions, and a variety of methods have been proposed. This paper mainly investigates the field of change point detection based on Bayesian inference and genetic algorithm. We define different indicator vectors as different individuals, which represent some possible change point distributions, and use Bayesian posterior probability to evaluate their fitness. Accordingly, we also present an improved genetic algorithm, which is applied to evolve the individuals toward the best one. Then, the most possible change points distribution could be resolved. The method has been applied to several synthesized data and simulation results reveal that the proposed method can detect change points in fMRI datasets with higher precision and lower time consumption.

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Acknowledgements

The authors are grateful for support from Georgia State University Brains-Behavior Seed grant. This research was supported by the Molecular Basis of Disease(MBD) at Georgia State University.

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Authors and Affiliations

  1. College of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang, 524025, China

    Xiuchun Xiao

  2. Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, 30303, USA

    Bing Liu & Jing Zhang

  3. Department of Computer Science, Georgia State University, Atlanta, GA, 30303, USA

    Xiuchun Xiao, Xueli Xiao & Yi Pan

  4. Department of Biology, Georgia State University, Atlanta, GA, 30303, USA

    Yi Pan

Authors
  1. Xiuchun Xiao
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  2. Bing Liu
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  3. Jing Zhang
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  4. Xueli Xiao
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  5. Yi Pan
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Corresponding authors

Correspondence to Jing Zhang or Yi Pan .

Editor information

Editors and Affiliations

  1. Georgia State University, Atlanta, Georgia, USA

    Zhipeng Cai

  2. University of Texas at Dallas, Richardson, Texas, USA

    Ovidiu Daescu

  3. Central South University, Changsha, China

    Min Li

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Xiao, X., Liu, B., Zhang, J., Xiao, X., Pan, Y. (2017). Detecting Change Points in fMRI Data via Bayesian Inference and Genetic Algorithm Model. In: Cai, Z., Daescu, O., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science(), vol 10330. Springer, Cham. https://doi.org/10.1007/978-3-319-59575-7_28

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  • DOI: https://doi.org/10.1007/978-3-319-59575-7_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59574-0

  • Online ISBN: 978-3-319-59575-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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