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Cited by 3Neuroscience and Neural EngineeringNanotechnology research and applicationsNeuroethics, Human Enhancement, Biomedical Innovations

Human Connectome Mapping and Monitoring Using Neuronanorobots

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Martins, Nuno R. B., Erlhagen, Wolfram, Freitas, Jr., Robert A.·2016·Journal of Ethics and Emerging Technologies
DOI 10.55613/jeet.v26i1.49
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Abstract

Neuronanorobotics is the application of medical nanorobots to the human brain. This paper proposes three specific classes of neuronanorobots, named endoneurobots, gliabots and synaptobots, which together can non-destructively map and monitor the structural changes occurring on the 86 x 109 neurons and the 2.42 x 1014 synapses in the human brain, while also recording the synaptic-processed 4.31 x 1015 spikes/sec carrying electrical functional information processed in the neuronal and synaptic network.

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  • The application of nanorobots to the human brain (neuralnanorobotics) may allow for the monitoring, recording, and even manipulation of many types of brain-related information at cellular and organellar levels.

    “The application of nanorobots to the human brain is denoted here as “neuralnanorobotics.” This technology may allow for the monitoring, recording, and even manipulation of many types of brain-related information at cellular and organellar levels”

    From Human Brain/Cloud Interface (2019) · cites this paper as Martins et al., 2016

    The Abstract defines neuronanorobotics as applying medical nanorobots to the human brain and describes mapping, monitoring, and recording brain information. Section 1 extends the proposed monitoring to cellular and subcellular information, and Section 2 mentions monitoring neuronal organelles. Our copy does not say these nanorobots can manipulate brain-related information. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • The most promising long-term future technology for non-destructive, real-time human-brain-computer interfaces and brain-to-brain communications may be neuralnanorobotics.

    “The most promising long-term future technology for non-destructive, real-time human–brain–computer interfaces and brain-to-brain communications may be neuralnanorobotics”

    From Human Brain/Cloud Interface (2019) · cites this paper as Martins et al., 2016

    The Introduction proposes neuronanorobots for in-vivo, real-time whole-brain monitoring, and Section 4 says the approach appears uniquely able to provide non-destructive whole-brain scanning at nanometric resolution. The paper does not establish the further claim that neuronanorobotics is the most promising long-term technology for brain–computer interfaces or brain-to-brain communication. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Neuralnanorobotics is expected to provide a non-destructive, real-time, secure, long-term, and virtually autonomous in vivo system that can realize the first functional human B/CI.

    “Neuralnanorobotics is expected to provide a non-destructive, real-time, secure, long-term, and virtually autonomous in vivo system that can realize the first functional human B/CI (Martins et al., 2012, 2015, 2016).”

    From Human Brain/Cloud Interface (2019) · cites this paper as Martins et al., 2016

    The Introduction and Conclusions propose non-destructive, real-time, in vivo monitoring of the human brain using neuronanorobots. The paper does not establish that the proposed system would be secure, long-term, virtually autonomous, or capable of realizing the first functional human brain–computer interface. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • A 10 μm3 volume of endoneurobots would egress the bloodstream, traverse the BBB, enter the brain parenchyma, navigate within the neuropil, enter the neuron cell soma, and position themselves intracellularly within the AIS.

    “A 10 μm3 volume of endoneurobots (Figure 3) would subsequently egress the bloodstream, traverse the BBB by methods that have been extensively reviewed elsewhere (Freitas, 2016), enter the brain parenchyma, and begin to navigate within the neuropil. Subsequently, they would enter the neuron cell soma and position themselves intracellularly within the AIS (Martins et al., 2016).”

    From Human Brain/Cloud Interface (2019) · cites this paper as Martins et al., 2016

  • Monitoring the intracellular structural and functional connectome may be enabled by three classes of neuralnanorobots: endoneurobots, synaptobots, and gliabots.

    “Monitoring the intracellular structural and functional connectome may be enabled by three classes of neuralnanorobots, introduced here as endoneurobots, synaptobots, and gliabots (Martins et al., 2016).”

    From Human Brain/Cloud Interface (2019) · cites this paper as Martins et al., 2016

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Research works printed but not linked: 90

  1. Alivisatos, A.P., M. Chun, G.M. Church, R.J. Greenspan, M.L. Roukes, and R. Yuste. 2012. The Brain Activity Map Project and the challenge of functional connectomics. Neuron 74(6): 970-74.
  2. Amunts, K., C. Lepage, L. Borgeat, H. Mohlberg, T. Dickscheid, M. Rousseau, S. Bludau, P. Bazin, L.B. Lewis, A. Oros-Peusquens, N.J. Shah, T. Lippert, K. Zilles, and A.C. Evans. 2013. BigBrain: An ultrahigh- resolution 3D human brain model. Science 21 June. Vol. 340 no. 6139: 1472-1475.
  3. Anderson, J., and C.M.V. Itallie. 2009. Physiology and function of the tight junction. Cold Spring Harbor Perspectives in Biology 1(2): a002584.
  4. Arking, R. 2006. Biology of aging: Observations and principles. 3rd ed. New York: Oxford University Press.
  5. Ascoli. G. 2009. Coming of age of the hippocampome. Front. Neur. Conference Abstract: Neuroinformatics 2009.
  6. Astier, Y., H. Bayley, and S. Howorka. 2005. Protein components for nanodevices. Current Opinion in Chemical Biology 9(6): 576-84.
  7. Azevedo, F.A., L.R. Carvalho, L.T. Grinberg, J.M. Farfel, R.E. Ferretti, R.E. Leite, W.J. Filho, R. Lent, and S. Herculano-Houzel. 2009. Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. Journal of Comparative Neurology 513(5): 532-41.
  8. Baude, A., Z. Nusser, J.D. Roberts, E. Mulvihill, R.A. McIlhinney, and P. Somogyi. 1993. The metabotropic glutamate receptor (mGluR1 alpha) is concentrated at perisynaptic membrane of neuronal subpopulations as detected by immunogold reaction. Neuron 11(4): 771-87.
  9. Bayés, A. 2010. Characterization of the proteome, diseases and evolution of the human postsynaptic density. Nature Neuroscience 14(1): 19-21.
  10. Bertoni-Freddari, C., P Fattoretti, T. Casoli, G. Di Stefano, M. Solazzi, E. Perna, and C. Angelis. 2006. Reactive structural dynamics of synaptic mitochondria in ischemic delayed neuronal death. Annals of the New York Academy of Sciences 1090(1): 26-34.
  11. Bhatt, D.H., S. Zhang, and W-B. Gan. 2009. Dendritic spine dynamics. Annual Review of Physiology 71: 261- 82.
  12. Biswal, B.B., M. Mennes, X-N. Zuo, S. Gohel, C. Kelly, S.M. Smith, C.F. Beckmann, J.S. Adelstein, R.L. Buckner, S. Colcombe, A-M. Dogonowski, M. Ernst, D. Fair, M. Hampson, M.J. Hoptman, J.S. Hyde, V.J. Kiviniemi, R. Kötter, S-J. Li, C-P-Lin, M.J. Lowe, C. Mackay, D.J. Madden, K.H. Madsen, D.S. Margulies, H.S. Mayberg, K. McMahon, C.S. Monk, S.H. Mostofsky, B.J. Nagel, J.J. Pekar, S.J. Peltier, S.E. Petersen, V. Riedl , S.A.R.B. Rombouts, B. Rypma, B.L. Schlaggar, S. Schmidt, R.D. Seidler, G.J. Siegle, C. Sorg, G-J. Teng, J. Veijola, A. Villringer, M. Walter, L. Wang, X-C. Weng, S. Whitfield-Gabrieli, P. Williamson, C. Windischberger, Y-F. Zang, H-Y. Zhang, F.X. Castellanos, and M.P. Milham. 2010. Toward discovery science of human brain function. Proceedings of the National Academy of Sciences 107(10): 4734-4740.
  13. Black, J.E., K.R. Isaacs, B.J. Anderson, A.A. Alcantara, and W.T. Greenough. 1990. Learning causes synaptogenesis, whereas motor activity causes angiogenesis, in cerebellar cortex of adult rats. Proceedings of the National Academy of Sciences 87: 5568-5572.
  14. Bourne, J.N., and K.M. Harris. 2008. Balancing structure and function at hippocampal dendritic spines. Annual Review of Neuroscience 31: 47-67.
  15. Bumbarger, D. J., M. Riebesell, C. Rödelsperger, and R.J. Sommer. 2013. System-wide rewiring underlies behavioral differences in predatory and bacterial-feeding mematodes. Cell 152(1): 109-119.
  16. Calabrese B., M.S. Wilsos, and S. Halpain. 2006. Development and regulation of dendritic spine synapses. Physiology 21(1): 38-47.
  17. Chen, X., L. Vinade, R.D. Leapman, J.D. Petersen, T. Nakagawa, T.M. Phillips, M. Sheng, and T.S. Reese. 2005. Mass of the postsynaptic density and enumeration of three key molecules. Proceedings of the National Academy of Sciences 102: 11551-11556.
  18. Chen, B.L., D.H. Hall, and D.B. Chklovskii. 2006. Wiring optimization can relate neuronal structure and function. Proceedings of the National Academy of Sciences 103: 4723-4728.
  19. Chung, K., and K. Deisseroth. 2013. CLARITY for mapping the nervous system. Nature Methods 10(6): 508- 13.
  20. Contreras, D. 2004. Electrophysiological classes of neocortical neurons. Neural Networks 17(5-6): 633-46.
  21. Dekosky, S.T., and S. Scheff. 1990. Synapse loss in frontal cortex biopsies in Alzheimer's disease: correlation with cognitive severity. Annals of Neurology 27(5): 457-64.
  22. Donohue, D.E., and G.A. Ascoli. 2008. A comparative computer simulation of dendritic morphology. PLoS Computational Biology 4(6): e1000089.
  23. Fiala, J.C., M. Feinberg, V. Popov, and K.M. Harris. 1998. Synaptogenesis via dendritic filopodia in developing hippocampal area CA1. Journal of Neuroscience 18: 8900-8911.
  24. Fiala, J.C., and K.M. Harris. 2001. Extending unbiased stereology of brain ultrastructure to three-dimensional volumes. Journal of the American Medical Informatics Association 8(1): 1-16.
  25. Freitas, R.A., Jr. 2000. Nanodentistry. Journal of the American Dental Association 131: 1559-1566.
  26. Freitas, R.A., Jr. 2005b. Current status of nanomedicine and medical nanorobotics. Journal of Computational and Theoretical Nanoscience 2: 1-25.
  27. Freitas, R.A., Jr. 2005c. Microbivores: Artificial mechanical phagocytes using digest and discharge protocol. Journal of Evolution and Technology 14 (April): 1-52. Available online http://jetpress.org/volume14/freitas.pdf (accessed December 23, 2015).
  28. Freitas, R.A., Jr. 2006a. Pharmacytes: An ideal vehicle for targeted drug delivery. Journal of Nanoscience and Nanotechnology 6: 2769-2775.
  29. Freitas, R.A., Jr. 2007. The ideal gene delivery vector: Chromallocytes, cell repair nanorobots for chromosome replacement therapy. Journal of Evolution and Technology 16 (June): 1-97. Available online http://jetpress.org/v16/freitas.pdf (accessed December 23, 2015).
  30. Freitas, R.A., Jr. 2010. Comprehensive nanorobotic control of human morbidity and aging. In The future of aging: Pathways to human life extension, ed. G.M. Fahy, M.D. West, L.S. Coles, and S.B. Harris, 685-805. New York: Springer.
  31. Friston, K.J. 2011. Functional and effective connectivity: A review. Brain Connectivity 1: 13-36.
  32. Fuhrmann, G., I. Segev, H. Markram, and M. Tsodyks. 2002. Coding of temporal information by activity- dependent synapses. Journal of Neurophysiology 87(1): 140-48.
  33. Gould, E., C.S. Woolley, M. Frankfurt, and B.S. McEwen. 1990. Gonadal steroids regulate dendritic spine density in hippocampal pyramidal cells in adulthood. Journal of Neuroscience 10: 1286-1291.
  34. Hayworth, K.J. (2012) Electron imaging technology for whole brain neural circuit mapping. International Journal of Machine Consciousness 4(1) 87-108.
  35. IBM. 2008. IBM seeks to build the computer of the future based on insights from the brain -IBM awarded DARPA funding for cognitive computing collaboration. https://www-03.ibm.com/press/us/en/pressrelease/26123.wss (accessed December 23, 2015).
  36. Ingber, D.E. 2006. Cellular mechanotransduction: Putting all the pieces together again. Journal of the Federation of American Societies for Experimental Biologies (FASEB) 20(7): 811-27.
  37. Johnson, G.A. 2010. An image-based reference for coordinating mouse brain research. NeuroImage 53(2): 365.
  38. Johnson, M.H., Y. Munakata, and R. Gilmore. 2002. Brain development and cognition: A reader. 2nd Edition. Oxford: Blackwell.
  39. Jones, E.G., and L.M. Mendell. 1999. Assessing the decade of the brain. Science 284(5415): 739.
  40. Kandel, E.R., J. Schwartz, and T. Jessell. 1991. Principles of neural science. 3rd ed. New York: McGraw- Hill.
  41. Kandel, E.R. 2001. The molecular biology of memory storage: A dialogue between gene and synapses. Science 294 (5544): 1030-1038.
  42. Kandel, E.R., J. Schwartz, and T. Jessell. 2000. Principles of neural science. 4th ed. New York: McGraw Hill.
  43. Kasthuri, N., and J.W. Lichtman. 2007. The rise of the "projectome." Nature Methods 4(4): 307-308.
  44. Kleinfeld, D., A. Bharioke, P. Blinder, D.D. Bock, K.L. Briggman, D.B. Chklovskii, W. Denk, M. Helmstaedter, J.P. Kaufhold, W-C. A. Lee, H.S. Meyer, K.D. Micheva, M. Oberlaender, S. Prohaska, R.C. Reid, S.J. Smith, S. Takemura, P.S. Tsai, and B. Sakmann. 2011. Large-scale automated histology in the pursuit of connectomes. Journal of Neuroscience 31(45): 16, 125-16, 138.
  45. Kole, M.H. P., J.J. Letzkus, and G.J. Stuart. 2007. Axon initial segment Kv1 channels control axonal action potential waveform and synaptic efficacy. Neuron 55(4): 633-47.
  46. Kostarelos K. 2010. Nanorobots for medicine: How close are we? Nanomedicine 5(3): 341-42.
  47. Kreutzberg, G. W. 1995. The first line of defense in brain pathologies. Drug Research 45(1): 357-60.
  48. Lee, S.H., J.H. Choi, N. Lee, H.R. Lee, J.L. Kim, N.K. Yu, S.L. Choi, S.H. Lee, H. Kim, and B.K. Kaang. 2008. Synaptic protein degradation underlies destabilization of retrieved fear memory. Science 319(5867): 1253-1256.
  49. Lein, E.S., M.J. Hawrylycz, N. Ao, M. Ayres, A. Bensinger, A. Bernard, A.F. Boe, M.S. Boguski, K.S. Brockway, E.J. Byrnes, L. Chen, L. Chen, T-M. Chen, M.C. Chin, J. Chong, B.E. Crook, A. Czaplinska, C.N. Dang, S. Datta, N.R. Dee, A.L. Desaki, T. Desta, E. Diep, T.A. Dolbeare, M.J. Donelan, H-W. Dong, J.G. Dougherty, B.J. Duncan, A.J. Ebbert, G. Eichele, L.K. Estin, C. Faber, B.A. Facer, R. Fields, S.R. Fischer, T.P. Fliss, C. Frensley, S.N. Gates, K.J. Glattfelder, K.R. Halverson, M.R. Hart, J.G. Hohmann, M.P. Howell, D.P. Jeung, R.A. Johnson, P.T. Karr, R. Kawal, J.M. Kidney, R.H. Knapik, C.L. Kuan, J.H. Lake, A.R. Laramee, K.D. Larsen, C. Lau, T.A. Lemon, A.J. Liang, Y. Liu, L.T. Luong, J. Michaels, J.J. Morgan, R.J. Morgan, M.T. Mortrud, N.F. Mosqueda, L.L. Ng, R. Ng, G.J. Orta, C.C. Overly, T.H. Pak, S.E. Parry, S.D. Pathak, O.C. Pearson, R.B. Puchalski, Z.L. Riley, H.R. Rockett, S.A. Rowland, J.J. Royall, M.J. Ruiz, N.R. Sarno, K. Schaffnit, N.V. Shapovalova, T. Sivisay, C.R. Slaughterbeck, S.C. Smith, K.A. Smith, B.I. Smith, A.J. Sodt, N.N. Stewart, K-R. Stumpf, S.M. Sunkin, M. Sutram, A. Tam, C.D. Teemer, C. Thaller, C.L. Thompson, L.R. Varnam, A. Visel, R.M. Whitlock, P.E. Wohnoutka, C.K. Wolkey, V.Y. Wong, M. Wood, M.B. Yaylaoglu, R.C. Young, B.L. Youngstrom, X.F. Yuan, B. Zhang, T.A. Zwingman, and A.R. Jones. 2007. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445: 168-76.
  50. Lelièvre, S. A. 2009. Contributions of extracellular matrix signaling and tissue architecture to nuclear mechanisms and spatial organization of gene expression control. Biochimica et Biophysica Acta 1790(9): 925-35.
  51. Lichtman, J., J. Livet, and J.R. Sanes. 2008. A technicolour approach to the connectome. Nature Reviews Neuroscience 9(6): 417-22.
  52. Lu, J.J.C. Tapia, O.L. White, and J.W. Lichtman. 2009. The interscutularis muscle connectome. PLoS Biology 7(4): e1000108.
  53. Martel, S., M. Mohammadi, O. Felfoul, Z. Lu, and P. Pouponneau. 2009. Flagellated magnetotactic bacteria as controlled MRI-trackable propulsion and steering systems for medical nanorobots operating in the human microvasculature. International Journal of Robotics Research 28(4): 571-82.
  54. Martins, N.R.B., W. Erlhagen, and R.A. Freitas, Jr. 2012. Non-destructive whole-brain monitoring using nanorobots: Neural electrical data rate requirements. International Journal of Machine Consciousness 4: 109- 140.
  55. Martins, N.R.B., W. Erlhagen, and R.A. Freitas, Jr. 2015. Action potential monitoring using neuronanorobots: Neuroelectric nanosensors. International Journal of Nanomaterials and Nanostructures 1: 20-41.
  56. Mavroides, D., and A. Ferreira, eds. 2011. NanoRobotics: Current approaches and techniques. New York: Springer.
  57. McAllister, A.K. 2007. Dynamic aspects of CNS synapse formation. Annual Review of Neuroscience 30: 425- 50.
  58. Miyamoto, K. 1986. Numerical density estimation of mitochondria in thick slice by transmission emission microscopy. Science on form: Proceedings of the First International Symposium for Science on Form, 517- 25. Tokyo: KTK Scientific Publishers.
  59. Morris, K. 2001. Macrodoctor, come meet the nanodoctors. Lancet. 357(9258): 778.
  60. Morris, R.L., and P.J. Hollenbeck. 1995. Axonal transport of mitochondria along microtubules and F-actin in living vertebrate neurons. Journal of Cell Biology 131: 1315-1326.
  61. Newman, E.A. 2001. Propagation of intercellular calcium waves in retinal astrocytes and Müller cells. Journal of Neuroscience 21(7): 2215-2223.
  62. NIH News. 2009. NIH Launches the Human Connectome Project to unravel the brain's connections. Press release, July 16.
  63. Nopoulos, P., M. Flaum, D. O'Leary, and N.C. Andreasen. 2000. Sexual dimorphism in the human brain: Evaluation of tissue volume, tissue composition and surface anatomy using magnetic resonance imaging. Psychiatry Research: Neuroimaging 98(1): 1-13.
  64. Okabe S. 2007. Molecular anatomy of the postsynaptic density. Molecular and Cellular Neuroscience 34(4): 503-518.
  65. Pakkenberg, B., and H.J.G. Gundersen. 1997. Neocortical neuron number in humans: Effect of sex and age. Journal of Comparative Neurology 384: 312-320.
  66. Palmer, L. M., and G. J. Stuart. 2006. Site of action potential initiation in Layer 5 pyramidal neurons. Journal of Neuroscience 26(6): 1854-1863.
  67. Park, H.H., A.C. Jamison, and T.R. Lee. 2007. Rise of the nanomachine: The evolution of a revolution in medicine. Nanomedicine 2(4): 425-39.
  68. Patel, G.M., G.C. Patel, R.B. Patel, J.K. Patel, and M. Patel. 2006. Nanorobot: A versatile tool in nanomedicine. Journal of Drug Targeting 14(2): 63-67.
  69. Perrot, R., R. Berges, A. Bocquet, and J. Eyer. 2008. Review of the multiple aspects of neurofilament functions, and their possible contribution to neurodegeneration. Molecular Neurobiology 38(1): 27-65.
  70. Popov, A.M., Y. E Lozovik, S. Fiorito, and L'H. Yahia. 2007. Biocompatibility and applications of carbon nanotubes in medical nanorobots. International Journal of Nanomedicine 2(3): 361-72.
  71. Qu, L., Y. Akbergenova, Y. Hu, and T. Schikorski. 2009. Synapse-to-synapse variation in mean synaptic vesicle size and its relationship with synaptic morphology and function. Journal of Comparative Neurology 514(4): 343-52.
  72. Rengachary, S.S., and R.G., Ellenbogen, eds. 2005. Principles of neurosurgery. Amsterdam: Mosby-Elsevier.
  73. Sala, C., I. Cambianica, and F. Rossi. 2008. Molecular mechanisms of dendritic spine development and maintenance. Acta Neurobiologiae Experimentalis 68(2): 289-304.
  74. Sandberg, A., and N. Bostrom. 2008. Whole brain emulation: A roadmap. Technical report. Future of Humanity Institute, Oxford University.
  75. Schwarz, T.L. 2013. Mitochondrial trafficking in neurons. Cold Spring Harbor Perspectives in Biology 5(6): 5:a011304.
  76. Seung, H.S. 2010. TED Talks -Sebastian Seung: I am my connectome. July, 2010. Oxford, England.
  77. Sheng, Z. H. 2014. Mitochondrial trafficking and anchoring in neurons: New insight and implications. Journal of Cell Biology 204( 7): 1087-1098.
  78. Shepherd, G.M.G., and K.M. Harris. 2010. Three-dimensional structure and composition of CA33CA1 axons in rat hippocampal slices: Implications for presynaptic connectivity and compartmentalization. Journal of Neuroscience 18(20): 8300-8310.
  79. Shin, S.S., T. Verstynen, S. Pathak, K. Jarbo, A.J. Hricik, M. Maserati, S.R. Beers, A.M. Puccio, F.E. Boada, D.O. Okonkwo, and W. Schneider. 2012. High-definition fiber tracking for assessment of neurological deficit in a case of traumatic brain injury: Finding, visualizing, and interpreting small sites of damage: Case report video. Journal of Neurosurgery 116(5): 1062-1069.
  80. Smith, S.M., R. Renden, and H. von Gersdorff. 2008. Synaptic vesicle endocytosis: Fast and slow modes of membrane retrieval. Trends in Neurosciences 31(11): 559-68.
  81. Sporns, O., G. Tononi, and R. Kötter. 2005. The human connectome: A structural description of the human brain. PLoS Computational Biology 1(4): e42.
  82. Südhof, T.C. 2004. The synaptic vesicle cycle. Annual Review of Neuroscience 27: 509-547.
  83. Terry, R.D., E. Masliah, D.P. Salmon, N. Butters, R. DeTeresa, R. Hill, L.A. Hansen, and R. Katzman. 1991. Physical basis of cognitive alterations in Alzheimer's disease: Synapse loss is the major correlate of cognitive impairment. Annals of Neurology 30(4): 572-80.
  84. Tkachuk, A., F. Duewer, H. Cui, M. Feser, S. Wang, and W. Yun. 2007. X-ray computed tomography in Zernike phase contrast mode at 8 keV with 50-nm resolution using Cu rotation anode X-ray source. Zeitschrift für Kristallographie 222(11): 650-55.
  85. Travis E. R., and R. M. Wightman. 1998. Spatio-temporal resolution of exocytosis from individual cells. Annual Review of Biophysics and Biomolecular Structure 27: 77-103.
  86. Trushina, E., E. Nemutlu, S. Zhang, T. Christensen, J. Camp, J. Mesa, A. Siddiqui, Y. Tamura, H. Sesaki, T.M. Wengenack, P.P. Dzeja, and J.F. Poduslo. 2012. Defects in mitochondrial dynamics and metabolomic signatures of evolving energetic stress in mouse models of familial Alzheimer's disease. PLoS ONE 7(2): e32737.
  87. Vaidyanathana, M., L.P. Clarke, C. Heidtman, R.P. Velthuizen, and L.O. Hall. 1997. Normal brain volume measurements using multispectral MRI segmentation. Magnetic Resonance Imaging 15(1): 87-97.
  88. Varshney, C., B.L. Chen, E. Paniagua, D.H. Hall, and D.B. Chklovskii. 2011. Structural properties of the C. elegans neuronal network. PLoS Computational Biology 3(7): e1001066.
  89. Zador, A., J. Dubnau, H.K. Oyibo, H. Zhan, G. Cao, and I.D. Peikon. 2012. Sequencing the connectome. PLoS Biology 10(10): e1001411.
  90. Zuo, Y., A. Lin, P. Chang, and W-B. Gan. 2005. Development of long-term dendritic spine stability in diverse regions of cerebral cortex. Neuron 46(2): 181-89.

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Authors
Martins, N. R. B., Erlhagen, W., & Freitas, Jr.
Affiliations
University of Minho (PT)
ORCID
Venue
Journal of Ethics and Emerging Technologies
Publisher
Institute for Ethics and Emerging Technologies
Published
2016-01-01
Volume
26
Issue
1
Pages
1-25
ISSN
2767-6951 (ISSN-L)
DOI
10.55613/jeet.v26i1.49
OpenAlex
W2565459263
Cited by
3
References
14
Citations by year
2026: 1 · 2024: 2
FWCI
0.00
Type
journal article
Language
en
Open access
Licence
Locations
Journal of Ethics and Emerging Technologies (publishedVersion, cc-by)
Funders
Fundação para a Ciência e a Tecnologia (SFRH/BD/69660/2010)
Topics
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
  • Physical Sciences › Engineering › Biomedical Engineering › Nanotechnology research and applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroethics, Human Enhancement, Biomedical Innovations
Keywords
neuromonitoring, medical nanorobots, neuronal networks

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