Document Type : Original Article

Authors

1 Biomedical Engineering Branch, Department of Electrical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

2 School of Physics, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran

Abstract

According to the electrical-chemical structure of nerve cells, it is expected that applying electrical stimulation affects the dynamics of neural networks, and strengthens or weakens brain activity. On this basis, in the last two decades, the use of electrical stimulation to treat neurological disorders such as depression, epilepsy, Parkinson's, etc. has gained wide acceptance.But the response of nerve tissue to external stimulation is not linear, that is, empirical studies on animal models as well as computational modeling show that changes in the amplitude and pattern of changes in electrical stimulation can lead to completely different results. However, in most stimulation methods, the electric field resulting from direct injection of current to the head (and brain) is relatively low in intensity. But the incomplete understanding of the mechanism and complexities of electrical stimulation sometimes forces physicians to adopt a trial-and-error approach. It means that it actually puts the patient at risk. Using computational modeling, we have analytically calculated the response of the single neuron membrane to the extracellular current stimulation that oscillates in space and time, and investigated the effect of different characteristics of the extracellular stimulation on the neuronal response. In particular, we have for the first time fully considered the spatial non-homogeneity of the electric field, and its effect on neuron activity, using the extended point model, which is simple but contains the geometrical information of the neuron. The obtained results show that the time-frequency-dependent response of neurons strongly depends on the spatial frequency and phase of stimulation. In fact, the intensity of field non-homogeneity in space can affect the frequency behavior of the neuron. The results of this study help to design optimal methods for nerve tissue stimulation, as well as to estimate the amount of risks caused by unwanted exposure to electric fields.

Keywords

Main Subjects

  1. G Galli, et al., Social cognitive and affective neuroscience 17 (2022) 4.
  2. A M Lozano, et al., Nature Reviews. Neurology15 (2019) 148.
  3. M Sabé, et al., Neuroscience & Biobehavioral Reviews 152 (2023)
  4. J Frey, et al., Frontiers in Neurology 13 (2022) 825178.
  5. R. Cajal, NobelPrize.org (1906).
  6. Alcohol health and research world 21, 2 (1997) 107.
  7. A L Hodgkin, and A F Huxely, The Journal of physiology117 (1952) 500.
  8. H H Dale, et al., Journal of Pharmacology and Experimental Therapeutics 6 (1914) 147.
  9. O Loewi, et al., Pflügers Archiv European Journal of Physiology 189 (1921) 239.
  10. A E. Hady and B. B. Machta, Nature communications 6 (2015)
  11. E Kandel, et al., “Principles of Neural Science”, fifth edition, McGraw-Hill Education / Medical ( 2014).
  12. Z Esmaeilpour, et al., Hum. Neurosci 11 (2017) 71.
  13. V Sreekumar, et al., Front Neurosci 11 (2017) 650.
  14. J Lian, et al., J Physiol 574 (2003) 427.
  15. L Marshall, et al., Nature 444 (2006) 610.
  16. M A Nitsche, et al., Brain Stimul 1 (2008) 206.
  17. A L Hewitt, et al., Clin. Pract. 10 (2020) 324.
  18. J L Ostrem and P. A Starr, Neurotherapeutics 5 (2008) 320.
  19. N Zangiabadi, et al., Neurol 10 (2019) 1.
  20. C J Hartmann, et al., Adv. Neurol. Disord 12 (2019) 1.
  21. P J Karas, et al., Frontiers in Neuroscience 12 (2019) 998.
  22. U R Mohan , et al., Brain Stimul. 13 (2020) 1183.
  23. F Aspart, et al., PLOS Comput. Biol. (2016) 1.
  24. F Aspart, et al., PLOS Comput. Biol. (2018)1.
  25. Z Gilbert, et al., Clinical Neurophysiology 152 (2023).
  26. E H S Toloza, et al., Neurophysiol. 119 (2018) 1029.
  27. C Cakan and K. Obermayer, PLOS Comput. Biol. (2020) 1.
  28. J Ladenbauer and K Obermayer, PLOS Comput. Biol. (2019) 1.
  29. R D Saunders and J. G. R Jefferys, Health Phys. 83 (2002) 366.
  30. A Liu , et al., Commun. 9 (2018).
  31. M Bikson, et al., J Physiol. 1 (2004) 175.
  32. T Radman, et al., Brain Stimul. 2 (2009) 215.
  33. M Vöröslakos, et al., Commun. 9 (2018) 483.
  34. J K Deans, et al., J Physiol. 2 (2007) 555.
  35. J T Francis, et al., Neurosci. 23 (2003) 7255.
  36. S Ozen, et al., Neurosci. 30 (2010) 11476.
  37. D Reato, et al., Neurosci. 30 (2010) 15067.
  38. T Radman, et al., Neurosci. 27 (2007) 3030.
  39. M R Krause, et al., Natl. Acad. Sci. 116 (2019) 5747.
  40. L Johnson, et al., Adv. (2020) 1.
  41. S Ronchi, et al., Hum. Neurosci. 13 (2019).
  42. C A Anastassiou, et al., Publ. Gr. 14 (2011) 217.
  43. D Reato, et al., Hum. Neurosci. 7 (2013) 1.
  44. F Frohlich and D. A Mccormick, Neuron 67 (2010) 129.
  45. C S Herrmann, et al., Hum. Neurosci. 7 (2013) 1.
  46. L Marshall and S Binder, Hum. Neurosci. 7 (2013) 614.
  47. R F Helfrich, et al., Biol. 24 (2014) 333.
  48. M M Ali, et al., Neurosci. 33 (2013) 11262.
  49. E Negahbani, et al., Neuroimage (2019) 3.
  50. M Schellenberger Costa, et al., PLoS Comput. Biol. 12 (2016) e1005022.
  51. D Tranchina and C Nicholsont, J. 50 (1977) 1139.
  52. C A Anastassiou, et al., Neurosci. 30 (2010) 1925.
  53. B Howell and C. C Mcintyre, Neuromdulation 24 (2020) 843.
  54. C Koch, “BIOPHYSICS OF COMPUTATION Information Processing in Single Neurons” Oxford University Press (1999).
  55. H C Tuckwell, “Introduction to theoretical neurobiology”, Volume 1. Cambridge University Press (1988).

تحت نظارت وف ایرانی