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Dynamic decomposition of spatiotemporal neural signals

Author

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  • Luca Ambrogioni
  • Marcel A J van Gerven
  • Eric Maris

Abstract

Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals.Author summary: In neuroscience, researchers are often interested in the modulations of specific signal components (e.g., oscillations in a particular frequency band), that have to be extracted from a background of both rhythmic and non-rhythmic activity. As the interfering background signals often have higher amplitude than the component of interest, it is crucial to develop methods that are able to perform some sort of signal decomposition. In this paper, we introduce a Bayesian decomposition method that exploits a prior dynamical model of the neural temporal dynamics in order to extract signal components with well-defined dynamic features. The method is based on Gaussian process regression with prior distributions determined by the covariance functions of linear stochastic differential equations. Using simulations and analysis of real MEG data, we show that these informed prior distributions allow for the extraction of interpretable dynamic components and the estimation of relevant signal modulations. We generalize the method to the analysis of spatiotemporal cortical activity and show that the framework is intimately related to well-established source-reconstruction techniques.

Suggested Citation

  • Luca Ambrogioni & Marcel A J van Gerven & Eric Maris, 2017. "Dynamic decomposition of spatiotemporal neural signals," PLOS Computational Biology, Public Library of Science, vol. 13(5), pages 1-37, May.
  • Handle: RePEc:plo:pcbi00:1005540
    DOI: 10.1371/journal.pcbi.1005540
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    References listed on IDEAS

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    1. Michael E. Tipping & Christopher M. Bishop, 1999. "Probabilistic Principal Component Analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 611-622.
    2. Pace, R Kelley & Barry, Ronald & Clapp, John M. & Rodriquez, Mauricio, 1998. "Spatiotemporal Autoregressive Models of Neighborhood Effects," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 15-33, July.
    3. Evgueniy V. Lubenov & Athanassios G. Siapas, 2009. "Hippocampal theta oscillations are travelling waves," Nature, Nature, vol. 459(7246), pages 534-539, May.
    4. Yury Petrov, 2012. "Harmony: EEG/MEG Linear Inverse Source Reconstruction in the Anatomical Basis of Spherical Harmonics," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-15, October.
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    Cited by:

    1. Gowthaman, I. & Singh, Uday & Chandrasekar, V.K. & Senthilkumar, D.V., 2021. "Dynamical robustness in a heterogeneous network of globally coupled nonlinear oscillators," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).

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