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Network configurations of pain: an efficiency characterization of information transmission

  • Regular Article - Statistical and Nonlinear Physics
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Abstract

Recent studies have shown that gamma-band oscillations are directly related to pain intensity. Pain can be exacerbated or diminished via deactivation or activation of inhibitory interneurons in the dorsal horn. We consider a biologically plausible network model with different proportion of inhibitory neurons to emulate gamma elicited activity during pain processes. We perform an analysis using graph theory to gain further insight in the functional state of the circuitry underlying nociceptive process, considering all the possible gamma elicited configurations of pain when changing the number of inhibitory neurons. The probability distribution of the signal associated with each node or neuron is estimated through the Bandt and Pompe approach. We evaluate the Jensen–Shannon distance between all the possible pairs of nodes/neurons, characterizing the different network configurations by calculating the closeness centrality. Thus, by building the graph properties through the node strength distributions and using an information theoretical approach, we characterize the dynamics of the network configurations of pain. This allows us to identify the nonlinear dynamical structure underlying the nociceptive process. Importantly, our findings show that a network configuration with a \(20\%\) of inhibitory neurons boosts information transmission of the complex network circuitry associated with the pain processing.

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Data Availability Statement

This manuscript has no associated data or the data will not be deposited. [Authors’ comment: This is a theoretical study and no experimental data has been listed.]

References

  1. G. Castellani, N. Intrator, D. Remondini, Front. Genet. 5 (2014)

  2. M. Rubinov, O. Sporns, Neuroimage 52, 1059 (2010)

    Article  Google Scholar 

  3. M.P. Van Den Heuvel, H.E. Hulshoff Pol, Eur. Neuropsychopharmacol. 20, 519 (2010)

    Article  Google Scholar 

  4. E.W. Lang, A. Tomé, I.R. Keck, J. Górriz-Sáez, C. Puntonet, Comput. Intell. Neurosci. 2012, 8 (2012)

    Article  Google Scholar 

  5. F. Montani, A. Oliynyk, L. Fadiga, Int. J. Neural Syst. 27, 1650009 (2017)

    Article  Google Scholar 

  6. J. Wang, X. Zuo, Y. He, Front. Syst. Neurosci. 4, 1 (2010)

    Google Scholar 

  7. Ja Braz, C. Solorzano, X. Wang, A.I. Basbaum, Neuron 82, 522 (2014)

    Article  Google Scholar 

  8. A. Franois, S.A. Low, E.I. Sypek, A.J. Christensen, C. Sotoudeh, K.T. Beier, C. Ramakrishnan, K.D. Ritola, R. Sharif-Naeini, K. Deisseroth et al., Neuron 93, 822 (2017)

    Article  Google Scholar 

  9. A.J. Todd, Nat. Rev. Neurosci. 11, 823 (2010)

    Article  Google Scholar 

  10. H.C. Johannssen, F. Helmchen, J. Physiol. 588, 3397 (2010)

    Article  Google Scholar 

  11. T. Takazawa, A.B. MacDermott, Ann. N. Y. Acad. Sci. 1198, 153 (2010)

    Article  ADS  Google Scholar 

  12. W. Ren, V. Neugebauer, Mol. Pain 6, 93 (2010)

    Article  Google Scholar 

  13. F. Montani, E.B. Deleglise, O.A. Rosso, Phys. A Stat. Mech. Appl. 401, 58 (2014)

    Article  Google Scholar 

  14. R. Bardoni, K.F. Shen, H. Li, J. Jeffry, D.M. Barry, A. Comitato, Y.Q. Li, Z.F. Chen, Sci. Rep. 9, 15804 (2019)

    Article  ADS  Google Scholar 

  15. L.L. Tan, M.J. Oswald, C. Heinl, O.A.R. Romero, S.K. Kaushalya, H. Monyer, R. Kuner, Sci. Rep. 10, 983 (2019)

    Google Scholar 

  16. G. Buzsaki, Rhythms of the Brain (Oxford University Press, Oxford, 2009)

    MATH  Google Scholar 

  17. M. Hauck, J. Lorenz, A.K. Engel, J. Neurosci. 27, 9270 (2007)

    Article  Google Scholar 

  18. M.N. Baliki, A.T. Baria, A.V. Apkarian, J. Neurosci. 31, 13981 (2011)

    Article  Google Scholar 

  19. M. Ploner, C. Sorg, J. Gross, Trends Cogn. Sci. 21, 100 (2017)

    Article  Google Scholar 

  20. J. Gross, A. Schnitzler, L. Timmermann, M. Ploner, PLoS Biol. 5, e133 (2007)

    Article  Google Scholar 

  21. Z. Zhang, L. Hu, Y. Hung, A. Mouraux, G. Iannetti, J. Neurosci. 32, 7429 (2012)

    Article  Google Scholar 

  22. E.S. May, M.M. Nickel, S.T. Dinh, L. Tiemann, H. Heitmann, I. Voth, T.R. Tölle, J. Gross, M. Ploner, Hum. Brain Mapp. 40, 293 (2019)

    Article  Google Scholar 

  23. E.M. Izhikevich, IEEE Trans. Neural Netw. 14, 1569 (2003)

    Article  Google Scholar 

  24. E.M. Izhikevich, Neural Comput. 18, 245 (2006)

    Article  MathSciNet  Google Scholar 

  25. R. Baravalle, N. Guisande, M. Granado, O.A. Rosso, F. Montani, Front. Phys. 7, 115 (2019)

    Article  Google Scholar 

  26. O. Rosso, C. Masoller, Phys. Rev. E 79, 040106(R) (2009)

    Article  ADS  Google Scholar 

  27. O. Rosso, C. Masoller, Eur. Phys. J. B 69, 37 (2009)

    Article  ADS  Google Scholar 

  28. O. Rosso, F. Olivares, A. Plastino, Paper Phys. 7, 070006 (2015)

    Article  Google Scholar 

  29. F. Montani, O.A. Rosso, Entropy 16, 4677 (2014)

    Article  ADS  Google Scholar 

  30. E.M. Izhikevich, Dynamical Systems in Neuroscience (MIT Press, Cambridge, 2007)

    Google Scholar 

  31. L. Risinger, K. Kaikhah, Innovations in Applied Artificial Intelligence (Springer, Berlin, 2004), pp. 1033–1042

    Book  Google Scholar 

  32. T. Schwalger, L. Schimansky-Geier, Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 77, 031914 (2008)

    Article  ADS  Google Scholar 

  33. K.S. Kravtsov, M.P. Fok, P.R. Prucnal, D. Rosenbluth, Opt. Express 19, 2133 (2011)

    Article  ADS  Google Scholar 

  34. C. Bandt, B. Pompe, Phys. Rev. Lett. 88, 174102 (2002)

    Article  ADS  Google Scholar 

  35. F. Olivares, A. Plastino, O. Rosso, Phys. A 391, 2518 (2012)

    Article  Google Scholar 

  36. F. Olivares, A. Plastino, O. Rosso, Phys. Lett. A 376, 1577 (2012)

    Article  ADS  Google Scholar 

  37. F. Montani, O.A. Rosso, F.S. Matias, S.L. Bressler, C.R. Mirasso, Philos. Trans. R. Soc. Lond. Ser. A 373, 20150110 (2015)

    ADS  Google Scholar 

  38. F. Montani, R. Baravalle, L. Montangie, O.A. Rosso, Philos. Trans. R. Soc. Lond. Ser. A 373, 20150109 (2015)

    ADS  Google Scholar 

  39. C. Shannon, W. Weaver, The Mathematical Theory of Communication (University of Illinois Press, Champaign, 1949)

    MATH  Google Scholar 

  40. T.M. Cover, J.A. Thomas, Elements of Information Theory (Wiley-Interscience, New York, 2012)

    MATH  Google Scholar 

  41. B. Frieden, Science from Fisher Information: A Unification (Cambridge University Press, Cambridge, 2004)

    Book  MATH  Google Scholar 

  42. R. Lopez-Ruiz, H.L. Mancini, X. Calbet, Phys. Lett. A 209, 321 (1995)

    Article  ADS  Google Scholar 

  43. C. Tsallis, Introduction to Nonextensive Statistical Mechanics (Springer, Berlin, 2009)

    MATH  Google Scholar 

  44. A. Renyi, On measures of entropy and information, in Fourth Berkeley Symposium on Mathematical Statistics and Probability, pp. 547–561 (1961)

  45. W.K. Wootters, Phys. Rev. D 23, 357 (1981)

    Article  ADS  MathSciNet  Google Scholar 

  46. S. Kullback, R.A. Leibler, Ann. Math. Stat. 22, 79–86 (1951)

    Article  Google Scholar 

  47. I. Grosse, P. Bernaola-Galván, P. Carpena, R. Román-Roldán, J. Oliver, H.E. Stanley, Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 65, 041905 (2002)

    Article  ADS  Google Scholar 

  48. F. Montani, O.A. Rosso, S.R. Schultz, AIP Conf. Proc. 913, 184 (2007)

    Article  ADS  Google Scholar 

  49. M. Martín, A. Plastino, O. Rosso, Phys. A 369, 439 (2006)

    Article  Google Scholar 

  50. O. Rosso, H. Larrondo, M. Martín, A. Plastino, M. Fuentes, Phys. Rev. Lett. 99, 154102 (2007)

    Article  ADS  Google Scholar 

  51. L.C. Freeman, Soc. Netw. 1, 215 (1979)

    Article  Google Scholar 

  52. L.C. Freeman, D. Roeder, R.R. Mulholland, Soc. Netw. 2, 119 (1979/1980)

  53. D.S. Bassett, E.T. Bullmore, Curr. Opin. Neurol. 22, 340 (2009)

    Article  Google Scholar 

  54. E.T. Bullmore, O. Sporns, Nat. Rev. Neurosci. 10, 186 (2009)

    Article  Google Scholar 

  55. C.J. Stam, J.C. Reijneveld, Nonlinear Biomed. Phys. 1, 3 (2007)

    Article  Google Scholar 

  56. M. Newman, Networks: An Introduction (Oxford University Press, Oxford, 2010)

    Book  MATH  Google Scholar 

  57. E. Brigham, R. Morrow, Spectr. IEEE 4, 63 (1967)

    Article  Google Scholar 

  58. S. Ross, Introduction to Probability Models (Academic Press, New York, 2009)

    Google Scholar 

  59. R. Baravalle, O. Rosso, F. Montani, Chaos Interdiscip. J. Nonlinear Sci. 28, 075513 (2018)

    Article  Google Scholar 

  60. I. Nemenman, W. Bialek, RdR van Steveninck, Phys. Rev. E 69, 056111 (2004)

    Article  ADS  Google Scholar 

  61. M. Prokopenko, J.T. Lizier, O. Obst, X.R. Wang, Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 84, 041116 (2011)

    Article  Google Scholar 

  62. M. Prokopenko, J.T. Lizier, Sci. Rep. 4 (2014)

  63. M. Prokopenko, L. Barnett, M. Harr, J.T. Lizier, O. Obst, X.R. Wang, Proc. R. Soc. A 471, 20150610 (2015)

    Article  ADS  Google Scholar 

  64. O. Rosso, F. Olivares, L. Zunino, L. De Micco, A. Aquino, A. Plastino, H. Larrondo, Eur. Phys. J. B 86 (2012)

  65. H. Paugam-Moisya, R. Martineza, S. Bengio, Nat. Rev. Neurosci. 71, 1143 (2008)

    Google Scholar 

  66. R. Wang, G. Cohen, K. Stiefel, T. Hamilton, J. Tapson, A. van Schaik, Front. Neurosci. 7, 14 (2013)

    Article  Google Scholar 

  67. A. Eguchi, J.B. Isbister, N. Ahmad, S. Stringer, Psychol. Rev. 125, 545 (2018)

    Article  Google Scholar 

  68. M.N. Economo, J.A. White, PLoS Comput. Biol. 8, e1002354 (2018)

    Article  Google Scholar 

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Acknowledgements

We gratefully acknowledge funding from PUE 22920170100066CO IFLP-CONICET Argentina, PIP 11220130100327CO (2014/2016) CONICET, Argentina (F.M.), and project 80120190100127LP Universidad Nacional de La Plata, Argentina.

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All the authors were involved in the preparation of the manuscript. All the authors have read and approved the final manuscript.

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Correspondence to Fernando Montani.

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De Luise, R., Baravalle, R., Rosso, O.A. et al. Network configurations of pain: an efficiency characterization of information transmission. Eur. Phys. J. B 94, 34 (2021). https://doi.org/10.1140/epjb/s10051-021-00046-6

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