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Discovering Change-Point Patterns in Dynamic Functional Brain Connectivity of a Population

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Information Processing in Medical Imaging (IPMI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10265))

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Abstract

This paper seeks to discover common change-point patterns, associated with functional connectivity (FC) in human brain, across multiple subjects. FC, represented as a covariance or a correlation matrix, relates to the similarity of fMRI responses across different brain regions, when a brain is simply resting or performing a task under an external stimulus. While the dynamical nature of FC is well accepted, this paper develops a formal statistical test for finding change-points in times series associated with FC observed over time. It represents instantaneous connectivity by a symmetric positive-definite matrix, and uses a Riemannian metric on this space to develop a graphical method for detecting change-points in a time series of such matrices. It also provides a graphical representation of estimated FC for stationary subintervals in between detected change-points. Furthermore, it uses a temporal alignment of the test statistic, viewed as a real-valued function over time, to remove temporal variability and to discover common change-point patterns across subjects, tasks, and regions. This method is illustrated using HCP database for multiple subjects and tasks.

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Acknowledgments

This research was supported in part by NSF grants DMS 1621787 and CCF 1617397 to AS. ZZ was partially supported by NSF grant DMS-1127914 to SAMSI. Data were provided in part by the HCP, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657).

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Authors and Affiliations

  1. Department of Statistics, Florida State University, Tallahassee, FL, USA

    Mengyu Dai & Anuj Srivastava

  2. Statistical and Applied Mathematical Sciences Institute, Research Triangle Park, NC, USA

    Zhengwu Zhang

  3. Department of Statistical Science, Duke University, Durham, NC, USA

    Zhengwu Zhang

Authors
  1. Mengyu Dai
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  2. Zhengwu Zhang
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  3. Anuj Srivastava
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Corresponding author

Correspondence to Mengyu Dai .

Editor information

Editors and Affiliations

  1. University of North Carolina, Chapel Hill, North Carolina, USA

    Marc Niethammer

  2. University of North Carolina, Chapel Hill, North Carolina, USA

    Martin Styner

  3. Kitware Inc., Carrboro, North Carolina, USA

    Stephen Aylward

  4. University of North Carolina, Chapel Hill, North Carolina, USA

    Hongtu Zhu

  5. University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Ipek Oguz

  6. University of North Carolina, Chapel Hill, North Carolina, USA

    Pew-Thian Yap

  7. University of North Carolina, Chapel Hill, North Carolina, USA

    Dinggang Shen

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Dai, M., Zhang, Z., Srivastava, A. (2017). Discovering Change-Point Patterns in Dynamic Functional Brain Connectivity of a Population. In: Niethammer, M., et al. Information Processing in Medical Imaging. IPMI 2017. Lecture Notes in Computer Science(), vol 10265. Springer, Cham. https://doi.org/10.1007/978-3-319-59050-9_29

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  • DOI: https://doi.org/10.1007/978-3-319-59050-9_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59049-3

  • Online ISBN: 978-3-319-59050-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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