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Transformation of neuronal modes associated with low-Mg2 + /high-K +  conditions in an in vitro model of epilepsy

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Abstract

Nonparametric system modeling constitutes a robust method for the analysis of physiological systems as it can be used to identify nonlinear dynamic input–output relationships and facilitate their description. First- and second-order kernels of hippocampal CA3 pyramidal neurons in an in vitro slice preparation were computed using the Volterra–Wiener approach to investigate system changes associated with epileptogenic low-magnesium/high-potassium (low-Mg2 + /high-K + ) conditions. The principal dynamic modes (PDMs) of neurons were calculated from the first- and second-order kernel estimates in order to characterize changes in neural coding functionality. From our analysis, an increase in nonlinear properties was observed in kernels under low-Mg2 + /high-K + . Furthermore, the PDMs revealed that the sampled hippocampal CA3 neurons were primarily of integrating character and that the contribution of a differentiating mode component was enhanced under low-Mg2 + /high-K + .

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References

  1. Bragin, A., Mody, I., Wilson, C.L., Engel, J.J.: Local generation of fast ripples in epileptic brain. J. Neurosci. 22, 2012–2021 (2002)

    Google Scholar 

  2. Karnup, S., Stelzer, A.: Seizure-like activity in the disinhibited CA1 minislice of adult guinea-pigs. J. Physiol. 532, 713–730 (2001)

    Article  Google Scholar 

  3. Walther, H., Lambert, J.D., Jones, R.S., Heinemann, U., Hamon, B.: Epileptiform activity in combined slices of the hippocampus, subiculum and entorhinal cortex during perfusion with low magnesium medium. Neurosci. Lett. 69, 156–161 (1986)

    Article  Google Scholar 

  4. Buckmaster, P.S., Zhang, G.F., Yamawaki, R.: Axon sprouting in a model of temporal lobe epilepsy creates a predominantly excitatory feedback circuit. J. Neurosci. 22, 6650–6658 (2002)

    Google Scholar 

  5. McNamara, J.O.: Cellular and molecular basis of epilepsy. J. Neurosci. 14, 3413–3425 (1994)

    Google Scholar 

  6. French, A.S., Marmarelis, V.Z.: Nonlinear analysis of neuronal systems. In: Windhorst, U., Johansson, H. (eds.) Modern Techniques in Neuroscience Research. Springer, New York (1999)

    Google Scholar 

  7. Dimoka, A., Courellis, S.H., Gholmieh, G.I., Marmarelis, V.Z., Berger, T.W.: Modeling the nonlinear properties of the in vitro hippocampal perforant path-dentate system using multielectrode array technology. IEEE Trans. Biomed. Eng. 55, 693–702 (2008)

    Article  Google Scholar 

  8. Dimoka, A., Courellis, S.H., Marmarelis, V.Z., Berger, T.W.: Modeling the nonlinear dynamic interactions of afferent pathways in the dentate gyrus of the hippocampus. Ann. Biomed. Eng. 36, 852–864 (2008)

    Article  Google Scholar 

  9. Naylor, D.: Changes in nonlinear signal processing in rat hippocampus associated with loss of paired-pulse inhibition or epileptogenesis. Epilepsia 43(Suppl 5), 188–193 (2002)

    Article  Google Scholar 

  10. Zhong, Y., Wang, H., Ju, K.H., Jan, K.M., Chon, K.H.: Nonlinear analysis of the separate contributions of autonomic nervous systems to heart rate variability using principal dynamic modes. IEEE Trans. Biomed. Eng. 51, 255–262 (2004)

    Article  ADS  Google Scholar 

  11. Marmarelis, V.Z.: Signal transformation and coding in neural systems. IEEE Trans. Biomed. Eng. 36, 15–24 (1989)

    Article  Google Scholar 

  12. Marmarelis, V.Z., Orme, M.E.: Modeling of neural systems by use of neuronal modes. IEEE Trans. Biomed. Eng. 40, 1149–1158 (1993)

    Article  Google Scholar 

  13. Marmarelis, V.Z.: Identification of nonlinear biological systems using Laguerre expansions of kernels. Ann. Biomed. Eng. 21, 573–589 (1993)

    Article  Google Scholar 

  14. Wiener, N.: Nonlinear Problems in Random Theory. Wiley, New York (1958)

    MATH  Google Scholar 

  15. Hung, G., Stark, L.: The kernel identification method (1910–1977) – review of theory, calculation, application, and interpretation. Math. Biosci. 37, 135–190 (1977)

    Article  MATH  MathSciNet  Google Scholar 

  16. Hong, S., Aguera y Arcas, B., Fairhall, A.L.: Single neuron computation: from dynamical system to feature detector. Neural Comput. 19, 3133–3172 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  17. Bardakjian, B.L., Courville, A., Vigmond, E.J.: Memory of neuronal networks: the white noise approach. In: Proceedings of the IEEE Engineering in Medicine & Biology 18th Annual Conference, Chicago, October 1997

  18. Bardakjian, B.L., Wright, W.N., Valiante, T., Carlen, P.L.: Nonlinear system identification of hippocampal neurons. In: Marmarelis, V.Z. (ed.) Advanced Methods of Physiological System Modeling. Plenum Press, New York (1994)

    Google Scholar 

  19. Vigmond, E.J., Perez Velazquez, J.L., Valiante, T.A., Bardakjian, B.L., Carlen, P.L.: Mechanisms of electrical coupling between pyramidal cells. J. Neurophysiol. 78, 3107–3116 (1997)

    Google Scholar 

  20. Musallam, S., Tomlinson, R.D.: Asymmetric integration recorded from vestibular-only cells in response to position transients. J. Neurophysiol. 88, 2104–2113 (2002)

    Google Scholar 

  21. Jones, L.M., Depireux, D.A., Simons, D.J., Keller, A.: Robust temporal coding in the trigeminal system. Science 304, 1986–1989 (2004)

    Article  ADS  Google Scholar 

  22. Bedingham, W., Tatton, W.G.: Kinematic representation of imposed forearm movements by pericruciate neurons (areas 4 and 3a) in the awake cat. J. Neurophysiol. 53, 886–909 (1985)

    Google Scholar 

  23. Nakazawa, K., McHugh, T.J., Wilson, M.A., Tonegawa, S.: NMDA receptors, place cells and hippocampal spatial memory. Nat. Rev. Neurosci. 5, 361–372 (2004)

    Article  Google Scholar 

  24. Urban, N.N., Barrionuevo, G.: Induction of hebbian and non-hebbian mossy fiber long-term potentiation by distinct patterns of high-frequency stimulation. J. Neurosci. 16, 4293–4299 (1996)

    Google Scholar 

  25. Jacobs, J., LeVan, P., Chander, R., Hall, J., Dubeau, F., Gotman, J.: Interictal high-frequency oscillations (80–500 Hz) are an indicator of seizure onset areas independent of spikes in the human epileptic brain. Epilepsia 49, 1893–1907 (2008)

    Article  Google Scholar 

  26. Moschovos, C., Kostopoulos, G., Papatheodoropoulos, C.: Long-term potentiation of high-frequency oscillation and synaptic transmission characterize in vitro NMDA receptor-dependent epileptogenesis in the hippocampus. Neurobiol. Dis. 29, 368–380 (2008)

    Article  Google Scholar 

  27. Mohajer, M.: Nonlinear time series analysis of electrical activity in a slice model of epilepsy. M.Sc. thesis, University of Toronto (1999)

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Correspondence to Berj L. Bardakjian.

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Kang, E.E., Zalay, O.C., Cotic, M. et al. Transformation of neuronal modes associated with low-Mg2 + /high-K +  conditions in an in vitro model of epilepsy. J Biol Phys 36, 95–107 (2010). https://doi.org/10.1007/s10867-009-9144-1

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  • DOI: https://doi.org/10.1007/s10867-009-9144-1

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