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A novel neural computational model of generalized periodic discharges in acute hepatic encephalopathy

  • Jiang-Ling Song
  • Luis Paixao
  • Qiang Li
  • Si-Hui Li
  • Rui Zhang
  • M. Brandon WestoverEmail author
Article

Abstract

Acute hepatic encephalopathy (AHE) due to acute liver failure is a common form of delirium, a state of confusion, impaired attention, and decreased arousal. The electroencephalogram (EEG) in AHE often exhibits a striking abnormal pattern of brain activity, which epileptiform discharges repeat in a regular repeating pattern. This pattern is known as generalized periodic discharges, or triphasic-waves (TPWs). While much is known about the neurophysiological mechanisms underlying AHE, how these mechanisms relate to TPWs is poorly understood. In order to develop hypotheses how TPWs arise, our work builds a computational model of AHE (AHE-CM), based on three modifications of the well-studied Liley model which emulate mechanisms believed central to brain dysfunction in AHE: increased neuronal excitability, impaired synaptic transmission, and enhanced postsynaptic inhibition. To relate our AHE-CM to clinical EEG data from patients with AHE, we design a model parameter optimization method based on particle filtering (PF-POM). Based on results from 7 AHE patients, we find that the proposed AHE-CM not only performs well in reproducing important aspects of the EEG, namely the periodicity of triphasic waves (TPWs), but is also helpful in suggesting mechanisms underlying variation in EEG patterns seen in AHE. In particular, our model helps explain what conditions lead to increased frequency of TPWs. In this way, our model represents a starting point for exploring the underlying mechanisms of brain dynamics in delirium by relating microscopic mechanisms to EEG patterns.

Keywords

Acute hepatic encephalopathy (AHE) Neural computational model Liley model Generalized periodic discharges Electroencephalogram (EEG) Particle filtering 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 61473223, the Innovative Talents Promotion Plan of Shaanxi Province under Grant 2018TD-016, and the Foundation for the National Institutes of Health of United States under Grants 1K23NS090900, 1R01NS102190, 1R01NS102574, 1R01NS107291.

Compliance with Ethical Standards

Conflict of interest

We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Supplementary material

10827_2019_727_MOESM1_ESM.pdf (63.4 mb)
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References

  1. Agrawal, S., Umapathy, S., Dhiman, R.K. (2015). . Journal of Clinical and Experimental Hepatology, 5, S42.CrossRefGoogle Scholar
  2. Amodio, P., Del Piccolo, F., Pettenò, E., Mapelli, D., Angeli, P., Iemmolo, R., Muraca, M., Musto, C., Gerunda, G., Rizzo, C., et al. (2001). . Journal of Hepatology, 35(1), 37.CrossRefGoogle Scholar
  3. Babajani-Feremi, A., & Soltanian-zadeh, H. (2010). . NeuroImage, 52, 793–811.CrossRefGoogle Scholar
  4. Back, T., Nedergaard, M., Ginsberg, M. (1998). Cerebrovascular disease: pathophysiology, Diagnosis and Management, (pp. 276–286). Malden, Mass: Blackwell Science.Google Scholar
  5. Barbaro, G., Di Lorenzo, G., Soldini, M., Giancaspro, G., Bellomo, G., Belloni, G., Grisorio, B., Annese, M., Bacca, D., Francavilla, R., et al. (1998). . Hepatology, 28(2), 374.CrossRefGoogle Scholar
  6. Beurle, R.L. (1956). . Trans. Roy Soc. (Lond) B, 240, 55.CrossRefGoogle Scholar
  7. Bojak, I., & Liley, D. (2005). . Physical Review E, 71(4), 041902.CrossRefGoogle Scholar
  8. Bojak, I., Stoyanov, Z.V., Liley, D.T. (2015). . Frontiers in Systems Neuroscience, 9, 18.CrossRefGoogle Scholar
  9. Butterworth, R.F. (2016). . The Journal of Steroid Biochemistry and Molecular Biology, 160, 94.CrossRefGoogle Scholar
  10. D’amico, G., Morabito, A., Pagliaro, L., Marubini, E., et al. (1986). . Digestive Diseases and Sciences, 31(5), 468.CrossRefGoogle Scholar
  11. Dhanda, S., Sunkaria, A., Halder, A., Sandhir, R. (2018). . Metabolic Brain Disease, 33(1), 209.CrossRefGoogle Scholar
  12. Ermentrout, B. (1994). . Neural Computation, 6(4), 679.CrossRefGoogle Scholar
  13. Fauci, A.S., & et al. (1998). Harrison’s principles of internal medicine Vol. 2. New York: Mcgraw-hill.Google Scholar
  14. Ferenci, P. (1987). In Assessment and Management of Hepatobiliary Disease (pp. 431–435). Springer.Google Scholar
  15. Freeman, W.J. (1987). . Biological Cybernetics, 56(2-3), 139.CrossRefGoogle Scholar
  16. Foreman, B., Mahulikar, A., Tadi, P., Claassen, J., Szaflarski, J., Halford, J.J., Dean, B.C., Kaplan, P.W., Hirsch, L.J., LaRoche, S., et al. (2016). . Clinical Neurophysiology, 127(2), 1073.CrossRefGoogle Scholar
  17. Fröhlich, F., & Jezernik, S. (2004). . Journal of Computational Neuroscience, 17(2), 165.CrossRefGoogle Scholar
  18. Hirsch, L., LaRoche, S., Gaspard, N., Gerard, E., Svoronos, A., Herman, S., Mani, R., Arif, H., Jette, N., Minazad, Y., et al. (2013). . Journal of Clinical Neurophysiology, 30(1), 1.CrossRefGoogle Scholar
  19. Hutt, A., & Buhry, L. (2014). . Journal of Computational Neuroscience, 37(3), 417.CrossRefGoogle Scholar
  20. Izumi, Y., Svrakic, N., O’Dell, K., Zorumski, C.F. (2013). . Neuroscience, 233, 166.CrossRefGoogle Scholar
  21. Jansen, B.H., Zouridakis, G., Brandt, M.E. (1993). . Biological Cybernetics, 68, 275.CrossRefGoogle Scholar
  22. Jansen, B.H., & Rit, V.G. (1995). . Biological Cybernetics, 73, 357.CrossRefGoogle Scholar
  23. Jing, J., Dauwels, J., Rakthanmanon, T., Keogh, E., Cash, S., Westover, M. (2016). . Journal of Neuroscience Methods, 274, 179.CrossRefGoogle Scholar
  24. Kailath, T. (1967). . IEEE Transactions on Communication Technology, 15(1), 52.CrossRefGoogle Scholar
  25. Kaplan, P.W., & Sutter, R. (2015). . Journal of Clinical Neurophysiology, 32(5), 401.CrossRefGoogle Scholar
  26. Khazipov, R., Congar, P., Ben-Ari, Y. (1995). . Journal of Neurophysiology, 74(5), 2138.CrossRefGoogle Scholar
  27. Knecht, K., Michalak, A., Rose, C., Rothstein, J.D., Butterworth, R.F. (1997). . Neuroscience Letters, 229(3), 201.CrossRefGoogle Scholar
  28. Kosenko, E., Kaminsky, Y., Grau, E., Miñana, M. D., Marcaida, G., Grisolía, S., Felipo, V. (1994). . Journal of Neurochemistry, 63(6), 2172.CrossRefGoogle Scholar
  29. Liley, D.T. (1997). Spatiotemporal models in biological and artificial systems, (pp. 89–96). Amsterdam: IOS Press.Google Scholar
  30. Liley, D.T., Cadusch, P.J., Wright, J.J. (1999). . Neurocomputing, 26, 795.CrossRefGoogle Scholar
  31. Liley, D.T., & Bojak, I. (2005). . Journal of Clinical Neurophysiology, 22(5), 300.Google Scholar
  32. Marcaida, G., Felipo, V., Hermenegildo, C., Mañana, M.D., Grisolia, S. (1992). . FEBS Letters, 296(1), 67.CrossRefGoogle Scholar
  33. Monfort, P., Kosenko, E., Erceg, S., Canales, J.J., Felipo, V. (2002). . Neurochemistry International, 41(2-3), 95.CrossRefGoogle Scholar
  34. Nunez, P.L. (1974). . Mathematical Biosciences, 21(3-4), 279.CrossRefGoogle Scholar
  35. O’Rourke, D., Chen, P.M., Gaspard, N., Foreman, B., McClain, L., Karakis, I., Mahulikar, A., Westover, M.B. (2016). . Neurocritical Care, 24(2), 233.CrossRefGoogle Scholar
  36. RJ, M., SJ, K., KE, S., RB, R., J, D., KJ, F. (2007). . NeuroImage, 37, 706.CrossRefGoogle Scholar
  37. Rotterdam, A.V., Silva, F.H.L.D., Ende, J.V.D., Viergever, M.A., Hermans, A.J. (1982). . Bulletin of Mathematical Biology, 44(2), 283.CrossRefGoogle Scholar
  38. Ruijter, B.J., Hofmeijer, J., Meijer, H.G.E., van Putten, M.J.A.M. (2017). . Clinical Neurophysiology, 128(9), 1682.CrossRefGoogle Scholar
  39. Saija, A., Princi, P., Lanza, M., Scalese, M., Aramnejad, E., De Sarro, A. (1995). . Life Sciences, 56(10), 775.CrossRefGoogle Scholar
  40. Salmond, D., & Birch, H. (2001). .. In 2001 Proceedings of the 2001 American Control Conference, (Vol. 5 pp. 3755–3760): IEEE.Google Scholar
  41. Shayegh, F., Bellanger, J.J., Sadri, S., Amirfattahi, R., Ansari-Asl, K., Senhadji, L. (2013). . Journal of Medical Signals and Sensors, 3(1), 2.Google Scholar
  42. Tsodyks, M.V., & Markram, H. (1997). . Proceedings of the National Academy of Sciences, 94(2), 719.CrossRefGoogle Scholar
  43. Weiss, N., Saint Hilaire, P.B., Colsch, B., Isnard, F., Attala, S., Schaefer, A., del Mar Amador, M., Rudler, M., Lamari, F., Sedel, F., et al. (2016). . Journal of hepatology, 65(6), 1120.CrossRefGoogle Scholar
  44. Wendling, F., Bartolomei, F., Chauvel, P. (2000). . Biological Cybernetics, 83, 367.CrossRefGoogle Scholar
  45. Wijdicks, E.F. (2016). . New England Journal of Medicine, 375(17), 1660.CrossRefGoogle Scholar
  46. Wilson, H.R., & Cowan, J.D. (1972). . Biophysical Journal, 12(1), 1.CrossRefGoogle Scholar
  47. Zandt, B.J., Visser, S., van Putten, M.J., ten Haken, B. (2014). . Journal of Computational Neuroscience, 37(3), 549.CrossRefGoogle Scholar
  48. Zavaglia, M., Astolfi, L., Babiloni, F., Ursino, M. (2006). . Journal of Neuroscience Methods, 157, 317–329.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Jiang-Ling Song
    • 1
    • 2
  • Luis Paixao
    • 2
  • Qiang Li
    • 1
  • Si-Hui Li
    • 1
  • Rui Zhang
    • 1
  • M. Brandon Westover
    • 2
    Email author
  1. 1.The Medical Big Data Research CenterNorthwest UniversityXi’anChina
  2. 2.The Department of NeurologyMassachusetts General HospitalBostonUSA

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