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Detecting changes in the covariance structure of functional time series with application to fMRI data

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  • Stoehr, Christina
  • Aston, John A D
  • Kirch, Claudia

Abstract

Functional magnetic resonance imaging (fMRI) data provides information concerning activity in the brain and in particular the interactions between brain regions. Resting state fMRI data is widely used for inferring connectivities in the brain which are not due to external factors. As such analyzes strongly rely on stationarity, change point procedures can be applied in order to detect possible deviations from this crucial assumption. FMRI data is modeled as functional time series and tools for the detection of deviations from covariance stationarity via change point alternatives are developed. A nonparametric procedure which is based on dimension reduction techniques is proposed. However, as the projection of the functional time series on a finite and rather low-dimensional subspace involves the risk of missing changes which are orthogonal to the projection space, two test statistics which take the full functional structure into account are considered. The proposed methods are compared in a simulation study and applied to more than 100 resting state fMRI data sets.

Suggested Citation

  • Stoehr, Christina & Aston, John A D & Kirch, Claudia, 2021. "Detecting changes in the covariance structure of functional time series with application to fMRI data," Econometrics and Statistics, Elsevier, vol. 18(C), pages 44-62.
    • Handle: RePEc:eee:ecosta:v:18:y:2021:i:c:p:44-62
    DOI: 10.1016/j.ecosta.2020.04.004
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    References listed on IDEAS

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    Cited by:

    1. Rituparna Sen & Anandamayee Majumdar & Shubhangi Sikaria, 2022. "Bayesian Testing of Granger Causality in Functional Time Series," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(1), pages 191-210, September.
    2. Degras, David & Ting, Chee-Ming & Ombao, Hernando, 2022. "Markov-switching state-space models with applications to neuroimaging," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    3. Soham Sarkar & Victor M. Panaretos, 2022. "CovNet: Covariance networks for functional data on multidimensional domains," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1785-1820, November.
    4. Rituparna Sen & Anandamayee Majumdar & Shubhangi Sikaria, 2021. "Bayesian Testing Of Granger Causality In Functional Time Series," Papers 2112.15315, arXiv.org.

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