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Complete Dielectric Resonator Model of Human Brain from MRI Data: A Journey from Connectome Neural Branching to Single Protein

  • Pushpendra Singh
  • Kanad Ray
  • D. Fujita
  • Anirban Bandyopadhyay
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 478)

Abstract

Using freely available MRI data of structural components mapping of human brain from different universities (primarily Rajat Jain, 25; 38-years-old lady from UK), we have built actual structural database of the human brain components, e.g., neural network connectome data, blood vessel map, ventricles, cavities for cerebral-spinal fluid, hippocampus regions of midbrain, etc. In previous studies, we have argued that every single element in the brain behaves as dielectric resonator. Here, we run rigorous dielectric resonance simulation to verify the hypothesis that the scale-free resonance does exist in the material architecture of the brain. From MRI-derived structures, we simulate the resonance frequencies, distribution of electric, and magnetic field of the brain components in CST and detect the phase response behavior, specially phase transition and symmetry breaking as a function of resonance frequency. We find that electric and magnetic fields distribute inhomogeneously in the dielectric structure, not just in the neural branches but also in the blood vessels and proteins like axon and microtubule bundles. The resonance frequencies show a characteristic topological pattern, specially, every single brain component is splitting electromagnetic field in such a way that at certain frequencies magnetic field dominates and at certain resonance frequency, electric field dominates. This distinct behavior of splitting fields at all spatial and time scale was never reported before. We speculate that there may exist a unified geometric pattern hidden in the vibrational frequencies of the brain components, which hold important information for the brain’s information processing.

Keywords

Connectome Brain Dielectric resonator Neuron Microtubule Axon 

Notes

Acknowledgements

We thank UCSD and other unknown universities for making MRI data free and sincerely thank numerous unknown researchers who worked relentlessly to produce structural data used in producing all seven figures in this paper. Authors acknowledge the Asian office of Aerospace R&D (AOARD), a part of United States Air Force (USAF) for the Grant no. FA2386-16-1-0003 (2016–2019) on the electromagnetic resonance based communication and intelligence of biomaterials.

References

  1. 1.
    Abbott, L.F.: Lapique’s introduction of the integrate-and-fire model neuron. Brain Res. Bull. 50, 303–304 (1999)CrossRefGoogle Scholar
  2. 2.
    Hodgkin, A.L., Huxley, A.F., Katz, B.: Measurements of current-voltage relations in the membrane of the giant axon of Loligo. J. Phys. 116, 424–448 (1952)Google Scholar
  3. 3.
    Hamill, O.P., Marty, A., Neher, E., Sakmann, B., Sigworth, F.J.: Improved patch-clamp techniques for high-resolution current recording from cells and cell-free membrane patches. Pflügers Archiv Eur. J. Phys. 391, 85–100 (1981)CrossRefGoogle Scholar
  4. 4.
    Sporns, O., Tononi, G., Kotter, R.: The human Connectome: a structural description of human brain. PLoS Comput. Biol. 1(4), 245–251 (2005)CrossRefGoogle Scholar
  5. 5.
    Sanes, S.R., Lichtman, J.W.: Can molecules explain long term potentiation? Nat. Neurosci. Rev. 2, 597–605 (1999)CrossRefGoogle Scholar
  6. 6.
    McCormick, D.A., Shu, Y., Yu, Y.: Hodgkin and Huxley model—still standing? Nature 445, E1–E2, References on challenging the Hodgkin Huxley Action Potential Initiation in the Hodgkin-Huxley Model, Lucy J. Colwell mail, Michael P. Brenner Published (2009).  https://doi.org/10.1371/journal.pcbi.1000265 (2007)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Ghosh, S., Sahu, S., Agrawal, L., Shiga, T., Bandyopadhyay, A.: Inventing a co-axial atomic resolution patch clamp to study a single resonating protein complex and ultra-low power communication deep inside a living neuron cell. J. Integr. Neurosci. 15(4), 403–433 (2016)CrossRefGoogle Scholar
  8. 8.
    Striegel, D.A., Hurdal, M.K.: Chemically based mathematical model for development of cerebral cortical folding patterns. PLoS Comput. Biol. (2009).  https://doi.org/10.1371/journal.pcbi.1000524MathSciNetCrossRefGoogle Scholar
  9. 9.
    Hurdal, M.K., Bowers, P.L., Stephenson, K., Sumners, D.W.L., Rehm, K., Schaper, K., Rottenberg, D.A.: Quasi-conformally flat mapping the human cerebellum. In: Taylor, C., Colchester, A. (eds.) Medical Image Computing and Computer-Assisted Intervention—MICCAI’99. Lecture Notes in Computer Science, pp. 279–286. Springer, Berlin (1999)CrossRefGoogle Scholar
  10. 10.
    Van Essen, D.C.: Cause and effect in cortical folding. Nat. Rev. Neurosci. 8, 12 (2007)Google Scholar
  11. 11.
    Çukur, T., Nishimoto, S., Huth, A.G., Gallant, J.L.: Attention during natural vision warps semantic representation across the human brain. Nat. Neurosci. 16, 763–770 (2013)CrossRefGoogle Scholar
  12. 12.
    Van Essen, D.C.: A tension-based theory of morphogenesis and compact wiring in the central nervous system. Nature 385, 313–318 (1997)CrossRefGoogle Scholar
  13. 13.
    Noctor, S., Martinez-Cerdeno, V., Ivic, L., Kriegstein, A.: Cortical neurons arise in symmetric and asymmetric division zones and migrate through specific phases. Nat. Rev. Neurosci. 7, 136–144 (2004)CrossRefGoogle Scholar
  14. 14.
    Basar, E.: Chaotic dynamics and resonance phenomena in brain function: progress, perspectives and thoughts. In: Basar, E. (ed.) Chaos in Brain Function, pp. 1–30. Springer-Verlag, Heidelberg (1990)CrossRefGoogle Scholar
  15. 15.
    Hoke, M., Lehnertz, K., Pantev, C., Lütkenhöner, B.: Spatiotemporal aspects of synergetic processes in the auditory cortex as revealed by the magnetoencephalogram. In: Basar, E., Bullock, T.H. (eds.) Brain Dynamics, pp. 84–108. Springer-Verlag (1989)Google Scholar
  16. 16.
    Liebovitch, L.S., Fischbarg, J., Konairek, J.P., Todorova, I., Mei, W.: Fractal model of ion-channel kinetics. Biochim. Biophys. Acta 896, 173–180 (1987)CrossRefGoogle Scholar
  17. 17.
    Ghosh, S., Sahu, S., Fujita, D., Bandyopadhyay, A.: Design and operation of a brain like computer: a new class of frequency-fractal computing using wireless communication in a supramolecular organic, inorganic systems. Information 5, 28–99 (2014)CrossRefGoogle Scholar
  18. 18.
    Sahu, S., Ghosh, S., Hirata, K., Fujita, D., Bandyopadhyay, A.: Multi-level memory-switching properties of a single brain microtubule. Appl. Phys. Lett. 102, 123701.1–123701.4 (2013)CrossRefGoogle Scholar
  19. 19.
    Sahu, S., Ghosh, S., Fujita, D., Bandyopadhyay, A.: Live visualizations of single isolated tubulin protein self-assembly via tunneling current: effect of electromagnetic pumping during spontaneous growth of microtubule. Sci. Rep. 4, 7303 (2014)CrossRefGoogle Scholar
  20. 20.
    Stahl, S.M.: Structure and Function of Neurons, 3rd edn. Cambridge University Press. http://assets.cambridge.org/97805218/57024/excerpt/9780521857024_excerpt.pdf
  21. 21.
    Xu, K., Zhong, G., Zhuang, X.: Actin, spectrin and associated proteins form a periodic cytoskeleton structure in axons. Science 339, 452–456 (2013)CrossRefGoogle Scholar
  22. 22.
    Sporns, O., Tononi, G., Kotter, R.: The human connectome: a structural description of the human brain. PLoS Comput. Biol. 1(4), 245–251 (2005)CrossRefGoogle Scholar
  23. 23.
    Hagmann, P.: From diffusion MRI to brain connectomeics. Ph.D. Thesis, Ecole Polytechnique Federale de Lausanne (2005)Google Scholar
  24. 24.
    Sporns, O.: The human connectome: a complex network. http://dx.doi.org/10.1016/S0920-9964(12)70100-7
  25. 25.
    Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10(3), 186–198 (2009)CrossRefGoogle Scholar
  26. 26.
  27. 27.
    Yau, K.W.: Receptive fields, geometry and conduction block of sensory neurones in the central nervous system of the leech. J. Physiol. 263(3), 513–538 (1976)CrossRefGoogle Scholar
  28. 28.
    Debanne, D.: Information processing in the axon. Nat. Rev. Neurosci. 5, 304–316 (2004)CrossRefGoogle Scholar
  29. 29.
  30. 30.
    McKay, J.C., Prato, F.S., Thomas, A.W.: A literature review: the effects of magnetic field exposure on blood flow and blood vessels in the microvasculature. Bioelectromagnetics 28(2), 81–98 (2007)CrossRefGoogle Scholar
  31. 31.
    Sahu, S., Ghosh, S., Ghosh, B., Aswani, K., Hirata, K., Fujita, D., Bandyopadhyay, A.: Atomic water channel controlling remarkable properties of a single brain microtubule: Correlating single protein to its supramolecular assembly. Biosens. Bioelectron. 47, 141–148 (2013)CrossRefGoogle Scholar
  32. 32.
    Agrawal, L., Sahu, S., Ghosh, S., Shiga, T., Fujita, D., Bandyopadhyay, A.: Inventing atomic resolution scanning dielectric microscopy to see a single protein complex operation live at resonance in a neuron without touching or adulterating the cell. J. Integrat. Neurosci. 15(4), 435–462 (2016)CrossRefGoogle Scholar
  33. 33.
  34. 34.
    Miranda, P.C., Mekonnen, A., Salvador, R., Basser, P.J.: Predicting the electric field distribution in the brain for the treatment of Glioblastoma. Phys. Med. Biol. 59(15), 4137–4147 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Pushpendra Singh
    • 1
    • 2
  • Kanad Ray
    • 3
  • D. Fujita
    • 1
  • Anirban Bandyopadhyay
    • 1
  1. 1.Advanced Key Technologies DivisionNational Institute for Materials ScienceTsukubaJapan
  2. 2.Amity University Rajasthan Kant KalwarJaipurIndia
  3. 3.Amity School of Applied SciencesAmity University, JaipurJaipurIndia

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