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Cross-covariance isolate detect: a new change-point method for estimating dynamic functional connectivity

Author

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  • Anastasiou, Andreas
  • Cribben, Ivor
  • Fryzlewicz, Piotr

Abstract

Evidence of the non stationary behavior of functional connectivity (FC) networks has been observed in task based functional magnetic resonance imaging (fMRI) experiments and even prominently in resting state fMRI data. This has led to the development of several new statistical methods for estimating this time-varying connectivity, with the majority of the methods utilizing a sliding window approach. While computationally feasible, the sliding window approach has several limitations. In this paper, we circumvent the sliding window, by introducing a statistical method that finds change-points in FC networks where the number and location of change-points are unknown a priori. The new method, called cross-covariance isolate detect (CCID), detects multiple change-points in the second-order (cross-covariance or network) structure of multivariate, possibly high-dimensional time series. CCID allows for change-point detection in the presence of frequent changes of possibly small magnitudes, can assign change-points to one or multiple brain regions, and is computationally fast. In addition, CCID is particularly suited to task based data, where the subject alternates between task and rest, as it firstly attempts isolation of each of the change-points within subintervals, and secondly their detection therein. Furthermore, we also propose a new information criterion for CCID to identify the change-points. We apply CCID to several simulated data sets and to task based and resting state fMRI data and compare it to recent change-point methods. CCID may also be applicable to electroencephalography (EEG), magentoencephalography (MEG) and electrocorticography (ECoG) data. Similar to other biological networks, understanding the complex network organization and functional dynamics of the brain can lead to profound clinical implications. Finally, the R package ccid implementing the method from the paper is available from CRAN.

Suggested Citation

  • Anastasiou, Andreas & Cribben, Ivor & Fryzlewicz, Piotr, 2022. "Cross-covariance isolate detect: a new change-point method for estimating dynamic functional connectivity," LSE Research Online Documents on Economics 112148, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:112148
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    File URL: http://eprints.lse.ac.uk/112148/
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    References listed on IDEAS

    as
    1. Raanju R. Sundararajan & Mohsen Pourahmadi, 2018. "Nonparametric change point detection in multivariate piecewise stationary time series," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(4), pages 926-956, October.
    2. Barigozzi, Matteo & Cho, Haeran & Fryzlewicz, Piotr, 2018. "Simultaneous multiple change-point and factor analysis for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 206(1), pages 187-225.
    3. Claudia Kirch & Birte Muhsal & Hernando Ombao, 2015. "Detection of Changes in Multivariate Time Series With Application to EEG Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1197-1216, September.
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    5. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    6. Haeran Cho & Piotr Fryzlewicz, 2015. "Multiple-change-point detection for high dimensional time series via sparsified binary segmentation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(2), pages 475-507, March.
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    More about this item

    Keywords

    fMRI; dynamic functional connectivity; change-point analysis; networks; time varying connectivity;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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