Skip to main content

Ionic Concentration and Action Potential Differences Between a Healthy and Alzheimer’s Disease Person

  • Conference paper
  • First Online:
Data Science and Analytics (REDSET 2019)

Abstract

Depressed \( Na^{ + } \)/\( K^{ + } \) ATParse amount and diminished glutamate agreement in Alzheimer’s disease brain is coupled through Alzheimer’s disease. Cellular ion disparity might be its outcome. As soon as the cell is energized by some inner or exterior spur then action potential in the shape of very small pulses arises. It overreaches the decisive threshold of the membrane. The action potential initiation observed in cortical neurons is not capable to be explained by HH type models. This paper reviews HH model and shows the difference between a HH model with its sodium and potassium ion currents and the ionic concentrations of sodium and potassium of a person with Alzheimer’s disease in it. Results are generated in form of graphs. This model is implemented in Matlab software to fairly accurate the differential equations. By using HH model, further parameters are created, \( Na^{ + } \) and \( K^{ + } \). The new parameters need to be carefully considered and analyzed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Vitvitsky, V.M., Garg, S.K., Keep, R.F., Albin, R.L., Banerjee, R.: Na+ and K+ ion imbalances in Alzheimer’s disease. Biochim. Biophys. Acta 1822(11), 1671–1681 (2012)

    Article  Google Scholar 

  2. Bagheri, S., Squitti, R., Haertlé, T., Siotto, M., Saboury, A.A.: Role of copper in the onset of Alzheimer’s disease compared to other metals. Front. Aging Neurosci. 9, article 446 (2018)

    Google Scholar 

  3. Emin Tagluk, M., Tekin, R.: The influence of ion concentrations on the dynamic behavior of the Hodgkin–Huxley model-based cortical network. Cogn. Neurodyn. 8(4), 287–298 (2014). https://doi.org/10.1007/s11571-014-9281-5

    Article  Google Scholar 

  4. Kager, H., Wadman, W.J., Somjen, G.G.: Simulated seizures and spreading depression in a neuron model incorporating interstitial space and ion concentrations. Am. Physiol. Soc. 84(1), 495–512 (2000)

    Google Scholar 

  5. Stankevich, N.: A rare and hidden attractor with noise in a biophysical Hodgkin–Huxley-type of model. Nonlinear Sciences, Chaotic Dynamics (2017)

    Google Scholar 

  6. Cisternas, P., et al.: The increased potassium intake improves cognitive performance and attenuates histopathological markers in a model of Alzheimer’s disease. Biochem. Biophys. Acta 1852, 2630–2644 (2015)

    Google Scholar 

  7. Levi, T., Khoyratee, F., Saïghi, S., Ikeuchi, Y.: Digital implementation of Hodgkin–Huxley neuron model for neurological diseases studies. Artif. Life Robot. 23(1), 10–14 (2017). https://doi.org/10.1007/s10015-017-0397-7

    Article  Google Scholar 

  8. Vermeer, S.E., Prins, N.D., den Heijer, T., Hofman, A., Koudstaal, P.J., Breteler, M.M.: Silent brain infarcts and the risk of dementia and cognitive decline. N. Engl. J. Med. 348(13), 1215–1222 (2003)

    Article  Google Scholar 

  9. Ozer, M., Perc, M., Uzuntarla, M.: Controlling the spontaneous spiking regularity via channel blocking on Newman-Watts networks of Hodgkin–Huxley neurons. Front. Phys. 86(4), 40008 (2009)

    Google Scholar 

  10. Brookmeyer, R., Johnson, E., Ziegler-Graham, K., Michael Arrighi, H.: Forecasting the global burden of Alzheimer’s disease. Alzheimer’s Dementia 3, 186–191 (2007)

    Article  Google Scholar 

  11. Ozer, M., Uzuntarla, M., Perc, M., Graham, L.J.: Spike latency and jitter of neuronal membrane patches with stochastic Hodgkin-Huxley channels. J. Theor. Biol. 261(1), 83–92 (2009)

    Article  Google Scholar 

  12. Bazsó, F., Zalányi, L., Csárdi, G.: Channel noise in Hodgkin–Huxley model neurons. Phys. Lett. A 311(1), 13–20 (2003)

    Article  MathSciNet  Google Scholar 

  13. Hill, B., Schubert, E., Nokes, M., Michelson, R.: Laser interferometer measurement of changes in crayfish axon diameter concurrent with action potential. Science 196(4288), 426–428 (1977)

    Article  Google Scholar 

  14. Kalnay, E.: Atmospheric Modeling, Data Assimilation, and Predictability. Cambridge University Press, New York (2003)

    Google Scholar 

  15. Roper, S., Obenaus, A., Dudek, F.: Osmolality and nonsynaptic epileptiform bursts in rat CA1 and dentate gyrus. Ann. Neurol. 31(1), 81–85 (1992)

    Article  Google Scholar 

  16. Rosen, A., Andrew, R.: Osmotic effects upon excitability in rat neocortical slices. Neuroscience 38(3), 579–590 (1990)

    Article  Google Scholar 

  17. Rudolph, M., Piwkowska, Z., Badoual, M., Bal, T., Destexhe, A.: A method to estimate synaptic conductances from membrane potential fluctuations. J. Neurophysiol. 91(6), 2884–2896 (2004)

    Article  Google Scholar 

  18. Ullah, G., Schiff, S.: Assimilating seizure dynamics. PLoS Comput. Biol. 6(5), e1000776 (2010)

    Article  Google Scholar 

  19. Sun, X., Perc, M., Lu, Q., Kurths, J.: Spatial coherence resonance on diffusive and small-world networks of Hodgkin–Huxley neurons. Interdiscip. J. Nonlinear Sci. 18(2), 023102 (2008)

    Article  MathSciNet  Google Scholar 

  20. Roberts, B.R., et al.: Rubidium and potassium levels are altered in Alzheimer’s disease brain and blood but not in cerebrospinal fluid. Acta Neuropathol. Commun. 4(1), 119 (2016)

    Article  Google Scholar 

  21. Gupta, S., Singh, J.: Investigating correlation properties of Hodgkin–Huxley model with leaky ıntegrate – and-fire model. In: Proceedings of International Conference on Advances in Computer Science, AETACS (2013)

    Google Scholar 

  22. Fuller, S., Steele, M., Münch, G.: Activated astroglia during chronic inflammation in Alzheimer’s disease—do they neglect their neurosupportive roles? Mutat. Res. 690, 40–49 (2010)

    Article  Google Scholar 

  23. Gupta, S., Singh, J.: Spiking patterns of Hodgkin-Huxley model in Alzheimer’s disease: effects caused by noise current. Int. J. Comput. Appl. (0975-8887) 94(11), 1–6 (2014)

    Google Scholar 

  24. Kneller, J., Ramirez, R.J., Chartier, D., Courtemanche, M., Nattel, S.: Time dependent transients in an ionically based mathematical model of the canine atrial action potential. Am. J. Physiol. Heart Circ. Physiol. 282, H1437–H1451 (2002)

    Article  Google Scholar 

  25. Ziburkus, J., Cressman, J., Barreto, E., Schiff, S.: Interneuron and pyramidal cell interplay during in vitro seizure-like events. J. Neurophysiol. 95(6), 3948–3954 (2006)

    Article  Google Scholar 

  26. Allaman, I., et al.: Amyloid-β aggregates cause alterations of astrocytic metabolic phenotype: impact on neuronal viability. J. Neurosci. 30(9), 3326–3338 (2010). https://doi.org/10.1523/jneurosci.5098-09.2010

    Article  Google Scholar 

  27. Ullah, G., Schiff, S.J.: Personality and Alzheimer’s disease: an integrative review. Pers. Disord. 10(1), 4–12 (2019)

    Article  Google Scholar 

  28. Scheffer, M., et al.: Anticipating critical transition. Science 338, 344–348 (2012)

    Article  Google Scholar 

  29. Barreto, E., Cressman, J.R.: Ion concentration dynamics as a mechanism for neuronal bursting. J. Biol. Phys. 37, 361–373 (2011)

    Article  Google Scholar 

  30. Supnet, C., Bezprozvanny, I.: The dysregulation of intracellular calcium in Alzheimer disease. Cell Calcium 47, 183–189 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Shruti Gupta , Jyotsna Singh or Kaushal Kumar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gupta, S., Singh, J., Kumar, K. (2020). Ionic Concentration and Action Potential Differences Between a Healthy and Alzheimer’s Disease Person. In: Batra, U., Roy, N., Panda, B. (eds) Data Science and Analytics. REDSET 2019. Communications in Computer and Information Science, vol 1229. Springer, Singapore. https://doi.org/10.1007/978-981-15-5827-6_23

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-5827-6_23

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5826-9

  • Online ISBN: 978-981-15-5827-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics