Mutual information identifies spurious Hurst phenomena in resting state EEG and fMRI data

Frederic von Wegner, Helmut Laufs, and Enzo Tagliazucchi
Phys. Rev. E 97, 022415 – Published 28 February 2018

Abstract

Long-range memory in time series is often quantified by the Hurst exponent H, a measure of the signal's variance across several time scales. We analyze neurophysiological time series from electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) resting state experiments with two standard Hurst exponent estimators and with the time-lagged mutual information function applied to discretized versions of the signals. A confidence interval for the mutual information function is obtained from surrogate Markov processes with equilibrium distribution and transition matrix identical to the underlying signal. For EEG signals, we construct an additional mutual information confidence interval from a short-range correlated, tenth-order autoregressive model. We reproduce the previously described Hurst phenomenon (H>0.5) in the analytical amplitude of alpha frequency band oscillations, in EEG microstate sequences, and in fMRI signals, but we show that the Hurst phenomenon occurs without long-range memory in the information-theoretical sense. We find that the mutual information function of neurophysiological data behaves differently from fractional Gaussian noise (fGn), for which the Hurst phenomenon is a sufficient condition to prove long-range memory. Two other well-characterized, short-range correlated stochastic processes (Ornstein-Uhlenbeck, Cox-Ingersoll-Ross) also yield H>0.5, whereas their mutual information functions lie within the Markovian confidence intervals, similar to neural signals. In these processes, which do not have long-range memory by construction, a spurious Hurst phenomenon occurs due to slow relaxation times and heteroscedasticity (time-varying conditional variance). In summary, we find that mutual information correctly distinguishes long-range from short-range dependence in the theoretical and experimental cases discussed. Our results also suggest that the stationary fGn process is not sufficient to describe neural data, which seem to belong to a more general class of stochastic processes, in which multiscale variance effects produce Hurst phenomena without long-range dependence. In our experimental data, the Hurst phenomenon and long-range memory appear as different system properties that should be estimated and interpreted independently.

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  • Received 9 November 2016
  • Revised 10 January 2018

DOI:https://doi.org/10.1103/PhysRevE.97.022415

©2018 American Physical Society

Physics Subject Headings (PhySH)

Physics of Living Systems

Authors & Affiliations

Frederic von Wegner, Helmut Laufs, and Enzo Tagliazucchi

  • Department of Neurology and Brain Imaging Center, Goethe University, Schleusenweg 2-16, 60528 Frankfurt am Main, Germany
  • Department of Neurology, University Hospital Kiel, Arnold-Heller-Straße 3, 24105 Kiel, Germany

  • *vonWegner@med.uni-frankfurt.de
  • http://www.kgu.de/bic/

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Issue

Vol. 97, Iss. 2 — February 2018

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