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Implication of Stochastic Resonance on Neurological Disease Quantification

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Interdisciplinary Topics in Applied Mathematics, Modeling and Computational Science

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 117))

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Abstract

This presents an application of stochastic resonance in a data-driven nonlinear bistable system, in which inhibitory and excitatory electrophysiological neuronal activity in the prefrontal cortex (PFC) is quantified in a control and a putative rodent model of schizophrenia brains. An empirical mode decomposition protocol was applied for processing and analyzing the spike data. Within the different experimental conditions, we extracted different asymmetric shapes of bistable model potentials using the Fokker–Planck equation (FPE). Our analyses in control brains suggest that neuronal firing, along with noise (e.g., synaptic activity) before and after amphetamine administration provide asymmetries with phase transition in the bistable model allowing bidirectional information flow. Such transitions appear to be impaired in the disease model.

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References

  1. McNamara, B., Wiesenfeld, K.: Theory of stochastic resonance. Phys. Rev. A 39(9), 4854 (1989)

    Google Scholar 

  2. Wiesenfeld, K., Moss, F.: Stochastic resonance and the benefits of noise: From ice ages to crayfish and SQUIDs. Nature 373(6509), 33–36 (1995)

    Google Scholar 

  3. Gammaitoni, L., Hänggi, P., Jung, P., Marchesoni, F.: Stochastic resonance. Rev. Mod. Phys. 70(1), 223 (1998)

    Google Scholar 

  4. Swain, P. S., Longtin, A.: Noise in genetic and neural networks. Chaos: Interdiscip. J. Nonlinear Sci. 16(2), 026101 (2006)

    Google Scholar 

  5. Wang, Z., Hou, Z., Xin, H.: Internal noise stochastic resonance of synthetic gene network. Chem. Phys. Lett. 401(1), 307–311 (2005)

    Google Scholar 

  6. Zheng, X.D., Yang, X.Q., Tao, Y.: Bistability, probability transition rate and first-passage time in an autoactivating positive-feedback loop. PloS ONE 6(3), e17104 (2011)

    Google Scholar 

  7. McDonnell, M.D., Abbott, D.: What is stochastic resonance? Definitions, misconceptions, debates, and its relevance to biology. PLoS Comput. Biol. 5(5), e1000348 (2009)

    Google Scholar 

  8. Goldman-Rakic, P.S.: Cellular and circuit basis of working memory in prefrontal cortex of nonhuman primates. Prog. Brain Res. 85, 325–336 (1990)

    Google Scholar 

  9. Bunney, W. E., Bunney, B. G.: Evidence for a compromised dorsolateral prefrontal cortical parallel circuit in schizophrenia. Brain Res. Rev. 31(2), 138–146 (2000)

    Google Scholar 

  10. Cowan, W. M., Harter, D. H., Kandel, E. R.: The emergence of modern neuroscience: Some implications for neurology and psychiatry. Ann Rev. Neurosci. 23(1), 343–391 (2000)

    Google Scholar 

  11. Lazar, N. L., Rajakumar, N., Cain, D. P.: Injections of NGF into neonatal frontal cortex decrease social interaction as adults: A rat model of schizophrenia. Schizophr. Bull. 34(1), 127–136 (2008)

    Google Scholar 

  12. Gardiner, C.W.: Handbook of Stochastic Methods, 3rd edn., pp. 342–372. Springer-Verlag, Berlin (2004)

    Google Scholar 

  13. Aur, D., Jog, M. S.: Building spike representation in tetrodes. J. Neurosci. Methods 157(2), 364–373 (2006)

    Google Scholar 

  14. Jog, M. S., Connolly, C. I., Kubota, Y., Iyengar, D. R., Garrido, L., Harlan, R., Graybiel, A. M.: Tetrode technology: Advances in implantable hardware, neuroimaging, and data analysis techniques. J. Neurosci. Methods 117(2), 141–152 (2002)

    Google Scholar 

  15. Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. Ser. A: Math. Phys. Eng. Sci. 454(1971), 903–995 (1998)

    Google Scholar 

  16. Rehman, N., Mandic, D.P.: Multivariate empirical mode decomposition. Proc. R. Soc. A: Math. Phys. Eng. Sci. 466, 1291–1302 (2010)

    Google Scholar 

  17. Marek, G.J., Behl, B., Bespalov, A.Y., Gross, G., Lee, Y., Schoemaker, H.: Glutamatergic (N-methyl-D-aspartate receptor) hypofrontality in schizophrenia: Too little juice or a miswired brain? Mol. Pharmacol. 77(3), 317–326 (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, N6A 5A5, London, ON, Canada

    T. K. Das & M. Jog

  2. Department of Anatomy and Cell Biology, Western University, N6A 5C1, ON, London, Canada

    N. Rajakumar

Authors
  1. T. K. Das
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  2. N. Rajakumar
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  3. M. Jog
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Corresponding author

Correspondence to T. K. Das .

Editor information

Editors and Affiliations

  1. Department of Mathematics & Statistics, University of Guelph, Guelph, Ontario, Canada

    Monica G. Cojocaru

  2. Dept of Physics and Computer Science, Wilfrid Laurier University, Waterloo, Ontario, Canada

    Ilias S. Kotsireas

  3. Department of Mathematics and MS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, Waterloo, Ontario, Canada

    Roman N. Makarov

  4. Department of Mathematics & MS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, Waterloo, Ontario, Canada

    Roderick V. N. Melnik

  5. MS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, Waterloo, Ontario, Canada

    Hasan Shodiev

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© 2015 Springer International Publishing Switzerland

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Das, T., Rajakumar, N., Jog, M. (2015). Implication of Stochastic Resonance on Neurological Disease Quantification. In: Cojocaru, M., Kotsireas, I., Makarov, R., Melnik, R., Shodiev, H. (eds) Interdisciplinary Topics in Applied Mathematics, Modeling and Computational Science. Springer Proceedings in Mathematics & Statistics, vol 117. Springer, Cham. https://doi.org/10.1007/978-3-319-12307-3_24

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