For citation:
Grishchenko A. A., Sysoeva M. V., Sysoev I. V. Detecting the primary time scale of evolution of information properties for local field potentials in brain at absence epilepsy. Izvestiya VUZ. Applied Nonlinear Dynamics, 2020, vol. 28, iss. 1, pp. 98-110. DOI: 10.18500/0869-6632-2020-28-1-98-110
Detecting the primary time scale of evolution of information properties for local field potentials in brain at absence epilepsy
The purpose of the current study is to determine how the characteristic time (lag) responsible for keeping information about the previous dynamics in the brain local field potential signals evaluates in time. This time is necessary to know in order to construct forecasting models for coupling estimation and seizure prediction and detection. Methods. Mutual information function calculated between a signal with itself shifted in time is used. The shift varies from 0 to one half of characteristic oscillation period. Mann–Whitney test is used for comparative statistical analysis of distributions of the lag obtained for different animals and recordings. Results. Two records of local field potentials for each of five WAG/Rij rats (genetic models of absence seizures) were analyzed. Four channels were taken into account: frontal, parietal and occipital cotexes and hippocampus. There were 28 investigated seizures for each recording. Six time intervals of length 2 s were considered, starting from baseline, then preictal, begin of seizure, middle of seizure, immediately before the termination and after it. Distributions of lag for different records were compared statistically. Conclusion. The lag distribution unification for different animals was detected at the seizure beginning, with distributions from records of the same animal being usually closer than for different ones. This unification is expressed in the parietal cortex least of all. In the frontal cortex and hippocampus the unification ends with the seizure, while in the occipital cortex it remains even after seizure termination.
1. Marescaux C., Vergnes M., and Depaulis A. Genetic absence epilepsy in rats from Strasbourg – a review. Journal of Neural Transmission (Supplementum), 1992, vol. 35, pp. 37–69.
2. Vergnes M., Marescaux C., Depaulis A., Micheletti G., and Warter J.M. Spontaneous spike and wave discharges in thalamus and cortex in a rat model of genetic petit mal-like seizures. Experimental Neurology, 1987, vol. 96, pp. 127–136.
3. Coenen A. and van Luijtelaar E.L.J.M. Genetic animal models for absence epilepsy: A review of the WAG/Rij strain of rats. Behavior Genetics, 2003, vol. 33, pp. 635–655.
4. Depaulis A. and van Luijtelaar G. Genetic Models of Absence Epilepsy in the Rat in Animal Models of Seizures and Epilepsy. A. Pitkanen, S. Moshe, and P. Schwartzkroin, eds. San Diego: Elsevier Inc., 2006, pp. 223–248.
5. Meeren H., Pijn J., van Luijtelaar E., Coenen A., and da Silva F.L. Cortical focus drives widespread corticothalamic networks during spontaneous absence seizures in rats. J. Neurosci, 2002, vol. 22, pp. 1480–1495.
6. Sitnikova E., Dikanev T.V., Smirnov D.A., Bezruchko B.P., and van Luijtelaar G. Granger causality: Cortico-thalamic interdependencies during absence seizures in WAG/Rij rats. J Neurosci Methods, 2008, vol. 170, no. 2, pp. 245–254.
7. Van Luijtelaar G., Sitnikova E., and Luttjohann A. On the origin and suddenness of absences in ¨ genetic absence models. Clin EEG Neurosci, 2011, vol. 42, no. 2, pp. 83–97.
8. Akman O., Demiralp T., Ates N., and Onat F. Electroencephalographic differences between WAG/Rij and GAERS rat models of absence epilepsy. Epilepsy Research, 2010, vol. 89, pp. 185–193.
9. Timmer J., Haussler S., Lauk M., and Lucking C.-H. Pathological tremor: Deterministic chaos or nonlinear stochastic oscillators. Chaos, 2000, vol. 10, no. 1, pp. 278–288.
10. Takahashi D.Y., Baccala L.A., and Sameshima K. Connectivity inference between neural structures via partial directed coherence. Journal of Applied Statistics, 2007, vol. 34, no. 10, pp. 1255–1269.
11. Brovelli A., Ding M., Ledberg A., Chen Y., Nakamura R., and Bressler S. Beta oscillations in a large-scale sensorimotor cortical network: Directional influences revealed by Granger causality. PNAS, 2004, vol. 101, pp. 9849–9854.
12. Vicente R. Transfer entropy–a model-free measure of effective connectivity for the neurosciences. Journal of Computational Neuroscience, 2011, vol. 30, no. 1, pp. 45–67.
13. Vakorin V.A., Misic B., Krakovska O., Bezgin G., and M.A.R. Confounding Effects of Phase Delays on Causality Estimation. PLoS ONE, 2013, vol. 8, no. 1, e53588.
14. Dikanev T., Smirnov D., Wennberg R., Velazquez J.P., and B.B. EEG nonstationarity during intracranially recorded seizures: Statistical and dynamical analysis. Clinical Neurophysiology, 2005, vol. 116, pp. 1796–1807.
15. Bezruchko B.P., Ponomarenko V.I., Prokhorov M.D., Smirnov D.A., and Tass P.A. Modeling nonlinear oscillatory systems and diagnostics of coupling between them using chaotic time series analysis: Applications in neurophysiology. Physics-Uspekhi, 2008, vol. 51, pp. 304–310.
16. Paxinos G., Watson C. The Rat Brain in Stereotaxic Coordinates. 6th ed. San Diego: Academic Press, 2006.
17. Sysoeva M.V., Luttjohann A., van Luijtelaar G., and Sysoev I.V. Dynamics of directional coupling ¨ underlying spike-wave discharges. Neuroscience, 2016, vol. 314, pp. 75–89.
18. Sysoeva M.V., Sitnikova E., Sysoev I.V., Bezruchko B.P., and van Luijtelaar G. Application of adaptive nonlinear Granger causality: Disclosing network changes before and after absence seizure onset in a genetic rat model. J. Neurosci. Methods, 2014, vol. 226, pp. 33–41.
19. Packard N., Crutchfield J., Farmer J., and Shaw R. Geometry from a Time Series. Phys. Rev. Lett., 1980, vol. 45, pp. 712–716.
20. Legendre A.M. Appendice sur la methodes des moindres quarr ´ es. Nouvelles m ´ ethodes pour la ´ determination des orbites des com ´ etes. ` Firmin-Didot, 1805.
21. Schwarz G. Estimating the dimension of a model. Annals of Statistics. Annals of Statistics, 1978, vol. 6, no. 2, pp. 461–464.
22. Kornilov M.V., Medvedeva T.M., Bezruchko B.P., and Sysoev I.V. Choosing the optimal model parameters for Granger causality in application to time series with main timescale. Chaos, Solitons and Fractals, 2016, vol. 82, pp. 11–21.
23. Kraskov A., Stogbauer H., and Grassberger P. Estimating mutual information. ¨ Physical Review E, 2004, vol. 69, 066138.
24. Zemljannikov A.S. and Sysoev I.V. Diagnostics and correction of systematic error while estimating transfer entropy with k-nearest neighbours method. Izvestiya VUZ. Applied Nonlinear Dynamics, 2015, vol. 23, no. 4, pp. 24–31 (in Russian).
25. Sysoeva M.V., Dikanev T.V., and Sysoev I.V. Selecting time scales for empirical model construction. Izvestiya VUZ. Applied Nonlinear Dynamics, 2012, vol. 20, no. 2, pp. 54–62 (in Russian).
26. Sysoeva M.V., Vinogradova L.V., Kuznetsova G.D., Sysoev I.V. Clementina M. van Rijn. Changes in corticocortical and corticohippocampal network during absence seizures in WAG/Rij rats revealed with time varying Granger causality. Epilepsy & Behavior, 2016, vol. 64, pp. 44–50.
27. Sysoeva M.V., Sitnikova E., Sysoev I.V. Thalamo-cortical mechanisms of initiation, maintenance and termination of spike-wave discharges at WAG/Rij rats. Zhurnal Vysshei Nervnoi Deyatelnosti Imeni I.P. Pavlova, 2016, vol. 66, no. 1, pp. 103–112.
28. Sysoev I.V., Perescis M.F.J., Vinogradova L.V., Sysoeva M.V., van Rijn C.M. Directional functional coupling during limbic seizures in rats revealed by nonlinear Granger causality. Russian Open Medical Journal, 2018, vol. 7, no. 4, e0404.
29. Sysoeva M.V., Vinogradova L.V., Perescis M., van Rijn C.M., and Sysoev I.V. Revealing changes in directed interstructural couplings at limbic seizures, induced by injection of CB1 receptor antagonist using nonlinear Granger causality method. Zhurnal Vysshei Nervnoi Deyatelnosti Imeni I.P. Pavlova, 2019, vol. 69, no. 6, pp. 752–767.
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