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On Complexity and Phase Effects in Reconstructing the Directionality of Coupling in Non-linear Systems

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Directed Information Measures in Neuroscience

Abstract

From the theoretical point of view, brain signals measured with electroencephalogram (EEG), or magnetoencephalogram (MEG) can be described as the manifestation of coupled nonlinear systems with time delays in coupling. From the empirical point of view, to understand how the information is processed in the brain, there is a need to characterize the information flow in a network of spatially distinct brain areas. Tools for reconstructing the directionality of coupling, which can be formalized as Granger causality, provide a framework for gaining the insight into the functional organization of the brain networks. In turn, it is not completely understood what kind of effects are captured by causal statistics. Under the context of coupled non-linear oscillating systems with time delay in coupling, we consider two effects that can contribute to the estimation of causality. First, we explore the problem of ambiguity of phase delays observed between the dynamics of the driver and the response, and its effect on the linear, spectral and information-theoretic statistics. Second, we show that the directionality of coupling can be understood as the differences in signal complexity between the driver and response.

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References

  1. Arnhold, J., Grassberger, P., Lehnertz, K., Elger, C.E.: A robust method for detecting interdependences: application to intracranially recorded EEG. Physica D: Nonlinear Phenomena 134(4), 419–430 (1999)

    Article  MATH  Google Scholar 

  2. Buzsaki, G.: Rhythms of the brain. Oxford University Press, New York (2006)

    Book  MATH  Google Scholar 

  3. Chavez, M., Martinerie, J., Le Van Quyen, M.: Statistical assessment of nonlinear causality: application to epileptic eeg signals. J. Neurosci. Methods 124(2), 113–128 (2003)

    Article  Google Scholar 

  4. Costa, M., Goldberger, A.L., Peng, C.K.: Multiscale entropy analysis of physiologic time series. Phys. Rev. Lett. 89, 062102 (2002)

    Google Scholar 

  5. Deco, G., Jirsa, V., McIntosh, A.R., Sporns, O., Ktter, R.: Key role of coupling, delay, and noise in resting brain fluctuations. Proceedings of the National Academy of Sciences 106(25), 10302–10307 (2009)

    Article  Google Scholar 

  6. Florin, E., Gross, J., Pfeifer, J., Fink, G.R., Timmermann, L.: The effect of filtering on Granger causality based multivariate causality measures. Neuroimage 50(2), 577–578 (2010)

    Article  Google Scholar 

  7. Geweke, J.: Measurement of linear dependence and feedback between multiple time series. Journal of the American Statistical Association 7, 304–313 (1982)

    Article  MathSciNet  Google Scholar 

  8. Ghosh, A., Rho, Y., McIntosh, A.R., Ktter, R., Jirsa, V.: Cortical network dynamics with time delays reveals functional connectivity in the resting brain. Cognitive Neurodynamics 2(2), 115–120 (2008)

    Article  Google Scholar 

  9. Gotman, J.: Measurement of small time differences between EEG channels: method and application to epileptic seizure propagation. Electroenceph. Clin. Neurophysiol. 56, 501–514 (1983)

    Article  Google Scholar 

  10. Gourévitch, B., Le Bouquin-Jeannès, R., Faucon, G.: Linear and nonlinear causality between signals: methods, examples and neurophysiological applications. Biological Cybernetics 95(4), 349–369 (2007)

    Article  Google Scholar 

  11. Granger, C.W.J.: Investigating causal relations by econometric models and cross spectral methods. Econometrica 37, 428–438 (1969)

    Google Scholar 

  12. Grassberger, P., Procaccia, I.: Estimation of the Kolmogorov entropy from a chaotic signal. Phys. Rev. A 28, 2591–2593 (1983)

    Article  Google Scholar 

  13. Hadjipapas, A., Casagrande, E., Nevado, A., Barnes, G.R., Green, G., Holliday, I.E.: Can we observe collective neuronal activity from macroscopic aggregate signals? NeuroImage 44(4), 1290–1303 (2009)

    Article  Google Scholar 

  14. Haken, H.: Principles of brain functioning. Springer (1996)

    Google Scholar 

  15. Kamiński, M., Ding, M., Truccolo, W.A., Bressler, S.L.: Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance. Biological Cybernetics 85, 145–157 (2001)

    Article  MATH  Google Scholar 

  16. Mišić, B., Vakorin, V., Paus, T., McIntosh, A.R.: Functional embedding predicts the variability of neural activity. Frontiers in Systems Neuroscience 5, 90 (2011)

    Google Scholar 

  17. Nolte, G., Ziehe, A., Nikulin, V.V., Brismar, T., Müller, K.R., Schlögl, A., Krämer, N.: Robustly estimating the flow direction of information in complex physical systems. Phys. Rev. Lett. 100(23), 234101 (2008)

    Article  Google Scholar 

  18. Nunez, P.L.: Neocortical dynamics and human brain rhythms. Oxford University Press (1995)

    Google Scholar 

  19. Paluš, M., Vejmelka, M.: Directionality of coupling from bivariate time series: How to avoid false causalities and missed connections. Phys. Rev. E 75, 056211 (2007)

    Google Scholar 

  20. Paluš, M., Komárek, V., Hrnčíř, Z., Štěrbová, K.: Synchronization as adjustment of infomation rates: Detection from bivariate time series. Phys. Rev. E 63, 046211 (2001)

    Google Scholar 

  21. Pincus, S.M.: Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. USA 88, 2297–2301 (1991)

    Article  MATH  MathSciNet  Google Scholar 

  22. Prichard, D., Theiler, J.: Generralized redundancies for time series analysis. Physica D 84, 476–493 (1995)

    Article  Google Scholar 

  23. Prokhorov, M.D., Ponomarenko, V.I.: Estimation of coupling between time-delay systems from time series. Physical Review E 72(1), 016210 (2005)

    Google Scholar 

  24. Quiroga, R.Q., Arnhold, J., Grassberger, P.: Learning driver-response relationships from synchronization patterns. Phys. Rev. E 61, 5142–5148 (2000)

    Article  Google Scholar 

  25. Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart. Circ. Physiol. 278(6), H2039–H2049 (2000)

    Google Scholar 

  26. Schreiber, T.: Measuring information transfer. Phys. Rev. Letters 85(2), 461–464 (2000)

    Article  Google Scholar 

  27. Schwarz, G.: Estimating the dimension of a model. The Annals of Statistics 6(2), 461–464 (1978)

    Article  MATH  MathSciNet  Google Scholar 

  28. Silchenko, A.N., Adamchic, I., Pawelczyk, N., Hauptmann, C., Maarouf, M., Sturm, V., Tass, P.A.: Data-driven approach to the estimation of connectivity and time delays in the coupling of interacting neuronal subsystems. Journal of Neuroscience Methods 191(1), 32–44 (2010)

    Article  Google Scholar 

  29. Singer, W.: Neuronal synchrony: A versatile code for the definition of relations? Neuron 24, 49–65 (1999)

    Article  Google Scholar 

  30. Small, M., Tse, C.K.: Applying the method of surrogate data to cyclic time series. Physica D 164, 187–201 (2002)

    Article  MATH  Google Scholar 

  31. Takens, F.: Detecting strange attractors in turbulence. In: Dynamical Systems and Turbulence. Lecture Notes in Mathematics, vol. 898. Springer (1981)

    Google Scholar 

  32. Vakorin, V.A., McIntosh, A.R.: Mapping the multi-scale information content of complex brain signals. In: Brinciples of Brain Dynamics: Global State Interactions, pp. 183–208. The MIT Press (2012)

    Google Scholar 

  33. Vakorin, V.A., Krakovska, O.A., McIntosh, A.R.: Confounding effects of indirect connections on causality estimation. Journal of Neuroscience Methods 184(1), 152–160 (2009)

    Article  Google Scholar 

  34. Vakorin, V.A., Mišić, B., Krakovska, O., McIntosh, A.R.: Empirical and theoretical aspects of generation and transfer of information in a neuromagnetic source network. Frontiers in Systems Neuroscience 5(96), 00096 (2012)

    Google Scholar 

  35. Vakorin, V.A., Mišić, B., Krakovska, O., Bezgin, G., McIntosh, A.R.: Confounding effects of phase delays on causality estimation. PLoS One 8(1), e5358 (2013)

    Google Scholar 

  36. Varela, F., Lachaux, J.P., Rodriguez, E., Martinerie, J.: The brainweb: phase synchronization and large-scale integration. Nature Reviews Neuroscience 2(4), 229–239 (2001)

    Article  Google Scholar 

  37. Vicente, R., Wibral, R., Lindner, M., Pipa, G.: Transfer entropy a model-free measure of effective connectivity for the neurosciences 30(1), 45–67 (2011)

    Google Scholar 

  38. Zhang, Y.-C.: Complexity and 1/f noise. A phase space approach. J. Phys. I France 1 (1991)

    Google Scholar 

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Correspondence to Vasily A. Vakorin .

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Vakorin, V.A., Krakovska, O., McIntosh, A.R. (2014). On Complexity and Phase Effects in Reconstructing the Directionality of Coupling in Non-linear Systems. In: Wibral, M., Vicente, R., Lizier, J. (eds) Directed Information Measures in Neuroscience. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54474-3_6

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  • DOI: https://doi.org/10.1007/978-3-642-54474-3_6

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