ANALYZING THE IMPACT OF TEMPERATURE ON AXOPLASMIC FLUID PROPERTIES DEFINING NEURONAL EXCITATION

Abstract

Axoplasmic fluid properties for neuronal excitation have been investigated with respect to temperature. Density, the mass fraction of ions and rate of addition of ions are the parameters considered for characterizing axoplasmic fluid properties. The behavior of these parameters has been analyzed with respect to the changes in temperature ranging from -5 degree Celsius to 35 degree Celsius. The temperature has been defined using Q10of3 coefficient as done in the Hodgkin-Huxley model. The trend of these parameters at different temperatures has been depicted along the axonal length represented through x-axis of the graphs. The conduction velocities of the above said parameters have also been recorded at different temperatures. The range [-5,35] degree Celsius has been increased by 20 degrees, 10 degree on the lower side and 10 degree on the upper side of the range [-5,25] degree Celsius and it is found that temperature dependency using Q10of3 coefficient for said parameters is valid only in the temperature ranging from 5 degree Celsius to 25 degree Celsius as it is for membrane voltage in the Hodgkin-Huxley model. These findings strongly support the obtained results and also suggest obtaining the temperature coefficient value which is applicable for a wider range of temperatures impacting neuronal excitation.

Keywords

• Axoplasmic fluid properties,Temperature Dependency,Neuronal Excitation

References

1. [1] Lodish H, Berk A, Zipursky SL, Matsudaira P, Baltimore D, Darnell J. Overview of Neuron Structure and Function. Molecular Cell Biology 4th edition. 2000.
2. [2] Bhatia S, Singh P, Sharma P. Hodgkin-Huxley model based on ionic transport in axoplasmic fluid. J Integr Neurosci. 2017;16(4):401–17. https://doi.org/10.3233/JIN-170029.
3. [3] Cook ND. The neuron-level phenomena underlying cognition and consciousness: synaptic activity and the action potential. Neuroscience. 2008 May 15; 153(3):556–70. https://doi.org/ 10.1016/j.neuroscience.2008.02.042.
4. [4] Yu Y, Hill AP, McCormick DA. Warm Body Temperature Facilitates Energy Efficient Cortical Action Potentials. PLOS Computational Biology. 2012 Apr 12; 8(4):e1002456. https://doi.org/ 10.1371/journal.pcbi.1002456.
5. [5] Hodgkin AL, Huxley AF. A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol. 1952 Aug 28;117(4):500–44.
6. [6] Guttman R. Temperature Dependence of Accommodation and Excitation in Space-Clamped Axons. J Gen Physiol. 1968 Jun 1; 51(6):759–69.
7. [7] Fitzhugh R. Theoretical Effect of Temperature on Threshold in the Hodgkin-Huxley Nerve Model. J Gen Physiol. 1966 May 1;49(5):989–1005. https://doi.org/ 10.1085/jgp.49.5.989.
8. [8] Chapman RA. Dependence on temperature of the conduction velocity of the action potential of the squid giant axon. Nature. 1967 Mar 18;213(5081):1143–4. https://doi.org/ 10.1038/2131143a0.

9. [9] Lv Y, Liu J. Interpretation on thermal comfort mechanisms of human bodies by combining Hodgkin-Huxley neuron model and Pennes bioheat equation. Forsch Ingenieurwes. 2005 Mar 1;69(2):101-14. https://doi.org/ 10.1007/s10010-004-0145-8.
10. [10] Nielsen N, Wetterslev J, Cronberg T, Erlinge D, Gasche Y, Hassager C, et al. Targeted temperature management at 33°C versus 36°C after cardiac arrest. N Engl J Med. 2013 Dec 5;369(23):2197–206. https://doi.org/ 10.1056/NEJMoa1310519.
11. [11] Peters C, Rosch RE, Hughes E, Ruben PC. Temperature-dependent changes in neuronal dynamics in a patient with an SCN1A mutation and hyperthermia induced seizures. Sci Rep. 2016 01;6:31879. https://doi.org/10.1038/srep31879.
12. [12] Walter EJ, Carraretto M. The neurological and cognitive consequences of hyperthermia. Crit Care. 2016;20. https://doi.org/ 10.1186/s13054-016-1376-4.
13. [13] Forrest MD. Can the Thermodynamic Hodgkin-Huxley Model of Voltage-Dependent Conductance Extrapolate for Temperature? Computation. 2014 Jun;2(2):47–60. https://doi.org/ 10.3390/computation2020047.
14. [14] Bhatia, S., Singh, P., Sharma, P. Hodgkin-Huxley Model Revisited to Incorporate the Physical Parameters Affected by Anesthesia. In: Pant M, Ray K, Sharma TK, Rawat S, Bandyopadhyay A, editors. Soft Computing: Theories and Applications: Proceedings of SoCTA 2016, Volume 1. Springer Singapore; 2018. (Advances in Intelligent Systems and Computing).
15. [15] Dayan, P., Abbott, L. F. Theoretical Neuroscience. The MIT Press.
16. [16] Gerstner, W., Werner M. Kistler. Spiking Neuron Models: Single Neurons, Populations, Plasticity. 1 edition. Cambridge University Press. 2002. https://doi.org/ 10.1017/CBO9780511815706.
17. [17] Daghighi Y. Microfluidic technology and its biomedical applications. Journal of Thermal Engineering. 2015;1(7):621–6.
18. [18] Fillafer C, Schneider MF. Temperature and excitable cells. Commun Integr Biol. 2013 Nov 1; 6(6). https://doi.org/10.4161/cib.26730.
19. [19] Wang H, Wang B, Normoyle KP, Jackson K, Spitler K, Sharrock MF, et al. Brain temperature and its fundamental properties: a review for clinical neuroscientists. Front Neurosci. 2014;8:307. https://doi.org/ 10.3389/fnins.2014.00307.
20. [20] Billioux BJ, Nath A, Stavale EJ, Dorbor J, Fallah MP, Sneller MC, et al. Cerebrospinal Fluid Examination in Survivors of Ebola Virus Disease. JAMA Neurol. 2017 01; 74(9):1141–3. https://doi.org/ 10.1001/jamaneurol.2017.1460.
21. [21] Estellé P, Halelfadl S, Maré T. Thermal conductivity of cnt water based nanofluids: experimental trends and models overview. Journal of Thermal Engineering. 2015 Apr; 1 (2):381–90.