Theoretical neuroscience

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art_SOUSA_Theoretical_neuroscience_2019.PDF (1.44 MB)
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0
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bookPart
Data de publicação
2019
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Scopus
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ELSEVIER
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SOUSA, M. V. Pires de
CHACUR, M.
MARTINS, D. O.
Citação
Pires de Sousa, M. V.; Chacur, M.; Martins, D. O.; Rondinoni, C.. Theoretical neuroscience. In: . PHOTOBIOMODULATION IN THE BRAIN: LOW-LEVEL LASER (LIGHT) THERAPY IN NEUROLOGY AND NEUROSCIENCE: ELSEVIER, 2019. p.9-19.
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Resumo
This chapter provides an overview of the complexity of neuroscience research, presenting descriptions of different molecular and cellular neuroscience areas. The main milestones of the historical development of neuroscience are introduced by pointing out the main discoveries over the decades. Highlights regarding the applications of molecular neuroscience research provide a basis to understand how the benchmark techniques can be translated to clinical practice. Important initiatives on computational and mathematical methods for neuroscience are presented, leading to simulations of neural function on different temporal and spatial scales. This overview could not be complete without an introductory part about cognition and behavior. Finally, some examples of neural treatment simulation are presented, giving hints about how one can understand the effects of light on biological neural tissue even before turning on the first laser or LED. This wide overview on theoretical neuroscience will contribute to a better understating of the variety and reach of knowledge to make the remainder of the book more comprehensible. © 2019 Elsevier Inc. All rights reserved.
Palavras-chave
Action potential, Computational neuroscience, Computer simulations, Light transport in tissue, Neuroscience, Signaling
URI
https://observatorio.fm.usp.br/handle/OPI/51168
Referências
  1. Achler, T., Symbolic neural networks for cognitive capacities (2014) Biol. Inspired Cogn. Architect., 9, pp. 71-81
  2. Aizawa, K., Cognition and behavior (2017) Synthese, 194 (11), pp. 4269-4288
  3. Albarracin, D., Gillette, J.C., A test of major assumptions about behavior change: A comprehensive look at the effects of passive and active HIV-prevention interventions since the beginning of the epidemic (2005) Psychol. Bull., 131 (6), pp. 856-897
  4. Anders, J.J., Moges, H., In vitro and in vivo optimization of infrared laser treatment for injured peripheral nerves (2014) Lasers Surg. Med., 46 (1), pp. 34-45
  5. Balch, W.E., Dunphy, W.G., Braell, W.A., Rothman, J.E., Reconstitution of the transport of protein between successive compartments of the Golgi measured by the coupled incorporation of N-acetylglucosamine (1984) Cell, 39, pp. 405-416
  6. Barrett, D.W., Gonzalez-Lima, F., Transcranial infrared laser stimulation produces beneficial cognitive and emotional effects in humans (2013) Neuroscience, 230, pp. 13-23
  7. Bartelle, B.B., Barandov, A., Molecular fMRI (2016) J. Neurosci., 36 (15), pp. 4139-4148
  8. Bechtel, W., Abrahamsen, A., (1991) Connectionism and the Mind, , Blackwell Publishers
  9. Blanco, N.J., Saucedo, C.L., Transcranial infrared laser stimulation improves rule-based, but not information-integration, category learning in humans (2017) Neurobiol. Learn. Mem., 139, pp. 69-75
  10. Blanco, N.J., Maddox, W.T., Improving executive function using transcranial infrared laser stimulation (2017) J. Neuropsychol., 11 (1), pp. 14-25
  11. Bonzon, P., Towards neuro-inspired symbolic models of cognition: Linking neural dynamics to behaviors through asynchronous communications (2017) Cogn. Neurodyn., 11, pp. 327-353. , https://doi.org/10.1007/s11571-017-9435-3
  12. Brazil, V., Translational simulation: Not ‘where?' but ‘why?' A functional view of in situ simulation (2017) Adv. Simul., 2, p. 20. , https://doi.org/10.1186/s41077-017-0052-3
  13. Carpenter, M.J., Jardin, B.F., Clinical strategies to enhance the efficacy of nicotine replacement therapy for smoking cessation: A review of the literature (2013) Drugs, 73 (5), pp. 407-426
  14. Clark, V.P., Parasuraman, R., Neuroenhancement: Enhancing brain and mind in health and in disease (2014) Neuroimage, 85, pp. 889-894
  15. Crick, F.H., On protein synthesis (1958) Symp. Soc. Exp. Biol., 12, pp. 138-163
  16. Deco, G., Tononi, G., Boly, M., Kringelbach, M.L., Rethinking segregation and integration (2015) Nat. Rev. Neurosci., 16 (7), pp. 430-439. , https://doi.org/10.1038/nrn3963
  17. Di Paolo, E.A., Rohde, M., De Jaegher, H., Horizons for the enactive mind: Values, social interaction, and play (2010) Enaction: Toward a New Paradigm for Cognitive Science, pp. 34-87. , J. Stewart., O. Gapenne. E.A. Di Paolo, MAMIT Press Cambridge
  18. Disner, S.G., Beevers, C.G., Transcranial laser stimulation as neuroenhancement for attention bias modification in adults with elevated depression symptoms (2016) Brain Stimul., 9 (5), pp. 780-787
  19. Dombrowski, S.U., Knittle, K., Long term maintenance of weight loss with non-surgical interventions in obese adults: Systematic review and meta-analyses of randomised controlled trials (2014) BMJ, 348, p. g2646
  20. Doronin, A., Meglinski, I., Using peer-to-peer network for on-line Monte Carlo computation of fluence rate distribution (2013) Conference Paper in Proceedings of SPIE-The International Society for Optical Engineering 8699, p. 869909
  21. Eells, J.T., Wong-Riley, M.T., Mitochondrial signal transduction in accelerated wound and retinal healing by near-infrared light therapy (2004) Mitochondrion, 4 (5-6), pp. 559-567
  22. Eilemann, S., Abdellah, M., Antille, N., Bilgili, A., Chevtchenko, G., Dumusc, R., (2017) From Big Data to Big Displays High-Performance Visualization at Blue Brain, , http://doi.org/10.1007/978-3-319-67630-2_47, Springer
  23. Falcon, M.I., Riley, J.D., Jirsa, V., McIntosh, A.R., Sheree, A.D., Chen, E., The virtual brain: Modeling biological correlates of recovery after chronic stroke (2015) Front. Neurol., 6, p. 228. , https://doi.org/10.3389/fneur.2015.00228
  24. Fisk, A.S., Tam, S.K.E., Light and cognition: Roles for circadian rhythms, sleep, and arousal (2018) Front. Neurol., 9, p. 56
  25. Friedman, N.P., Miyake, A., Corley, R.P., Young, S.E., DeFries, J.C., Hewitt, J.K., Not all executive functions are related to intelligence (2006) Psychol. Sci., 17 (2), pp. 172-179
  26. Gonzalez-Lima, F., Barrett, D.W., Augmentation of cognitive brain functions with transcranial lasers (2014) Front. Syst. Neurosci., 8, p. 36
  27. Hata, Y., Slaughter, C.A., Südhof, T.C., Synaptic vesicle fusion complex contains unc-18 homologue bound to syntaxin (1993) Nature, 366, pp. 347-351
  28. Hayworth, C.R., Rojas, J.C., In vivo low-level light therapy increases cytochrome oxidase in skeletal muscle (2010) Photochem. Photobiol., 86 (3), pp. 673-680
  29. Hellmers, J., Wriedt, T., New approaches for a light scattering Internet information portal and categorization schemes for light scattering software (2009) J. Quant. Spectrosc. Radiat. Transf., 110 (14-16), pp. 1511-1517. , https://doi.org/10.1016/j.jqsrt.2009.01.023
  30. Henrich, J., McElreath, R., The evolution of cultural evolution (2003) Evol. Anthropol.: Issues, News, Rev., 12 (3), pp. 123-135
  31. Hobbs, N., Godfrey, A., Are behavioral interventions effective in increasing physical activity at 12 to 36 months in adults aged 55 to 70 years? A systematic review and meta-analysis (2013) BMC Med., 11, p. 75
  32. Hwang, J., Castelli, D.M., Cognitive enhancement by transcranial laser stimulation and acute aerobic exercise (2016) Lasers Med. Sci., 31 (6), pp. 1151-1160
  33. Jirsa, V.K., Proix, T., Perdikis, D., Woodman, M.M., Wang, H., Gonzalez-Martinez, J., The virtual epileptic patient: Individualized whole-brain models of epilepsy spread (2017) Neuroimage, 145, pp. 377-388
  34. Kaiser, C.A., Schekman, R., Distinct sets of SEC genes govern transport vesicle formation and fusion early in the secretory pathway (1990) Cell, 61, pp. 723-733
  35. Kaiser, M., The potential of the human connectome as a biomarker of brain disease (2013) Front. Hum. Neurosci., 7, pp. 1-4. , https://doi.org/10.3389/fnhum.2013.00484
  36. Kandel, E.R., (1976) Cellular Basis of Behavior: An Introduction to Behavioral Neurobiology, , W.H. Freeman San Francisco, CA
  37. Kandel, E.R., The molecular biology of memory storage: A dialogue between genes and synapses (2001) Science, 294, pp. 1030-1038. , https://doi.org/10.1126/science.1067020
  38. Kienle, A., Hibst, R., Light guiding in biological tissue due to scattering (2006) Phys. Rev. Lett., 97 (1), p. 018104
  39. Kirillin, M., Meglinski, I., Sergeeva, E., Kuzmin, V.L., Myllyla, R., Simulation of optical coherence tomography images by Monte Carlo modeling based on polarization vector approach (2010) Opt. Express, 18 (21), pp. 21714-21724
  40. Kochunov, P., Thompson, P.M., The common genetic influence over processing speed and white matter microstructure: Evidence from the Old Order Amish and Human Connectome Projects (2016) Neuroimage, 125, pp. 189-197
  41. Landriscina, F., (2013) Simulation and Learning. A Model-Centered Approach, , https://doi.org/10.1007/978-1-4614-1954-9, Springer New York, NY
  42. Le Bihan, D., Johansen-Berg, H., Diffusion MRI at 25: Exploring brain tissue structure and function (2012) Neuroimage, 61 (2), pp. 324-341. , https://doi.org/10.1016/j.neuroimage.2011.11.006
  43. Li, L., Men, W.W., Acute aerobic exercise increases cortical activity during working memory: A functional MRI study in female college students (2014) PLoS One, 9 (6), p. e99222
  44. Li, T., Xue, C., Wang, P., Li, Y., Wu, L., Photon penetration depth in human brain for light stimulation and treatment: A realistic Monte Carlo simulation study (2017) J. Innov. Opt. Health Sci., 10 (5), p. 1743002. , https://doi.org/10.1142/S1793545817430027
  45. Markram, H., The blue brain project (2006) Nat. Rev. Neurosci., 7, pp. 153-160
  46. Markram, H., Reconstruction and simulation of neocortical microcircuitry (2015) Cell, 163 (2), pp. 456-492
  47. Marshall, D.A., Applying dynamic simulation modeling methods in health care delivery research-The SIMULATE Checklist: Report of the ISPOR simulation modeling emerging good practices task force (2015) Value Health, 18 (1), pp. 5-16
  48. Maturana, H.V.F., (1980) Autopoiesis and Cognition: The Realization of the Living, , Springer Dordrecht
  49. McGaghie, W.C., Evaluating the impact of simulation on translational patient outcomes (2011) Simul. Healthc., 6, pp. S42-S47. , https://doi.org/10.1097/SIH.0b013e318222fde9
  50. Michalikova, S., Ennaceur, A., Emotional responses and memory performance of middle-aged CD1 mice in a 3D maze: Effects of low infrared light (2008) Neurobiol. Learn. Mem., 89 (4), pp. 480-488
  51. Mitchell, P., (2016) From Concept to Classroom. What Is Translational Research?, , Australian Council for Educational Research Camberwell
  52. Naeser, M.A., Martin, P.I., Transcranial, red/near-infrared light-emitting diode therapy to improve cognition in chronic traumatic brain injury (2016) Photomed. Laser Surg., 34 (12), pp. 610-626
  53. Perin, M.S., Fried, V.A., Mignery, G.A., Jahn, R., Südhof, T.C., Phospholipid binding by a synaptic vesicle protein homologous to the regulatory region of protein kinase C (1990) Nature, 345, pp. 260-263
  54. Proix, T., Jirsa, V.K., Bartolomei, F., Guye, M., Truccolo, W., Predicting the spatiotemporal diversity of seizure propagation and termination in human focal epilepsy (2018) Nat. Commun., 9, p. 1088. , https://doi.org/10.1038/s41467-018-02973-y
  55. Quirk, B.J., Torbey, M., Near-infrared photobiomodulation in an animal model of traumatic brain injury: Improvements at the behavioral and biochemical levels (2012) Photomed. Laser Surg., 30 (9), pp. 523-529
  56. Reimann, M.W., Nolte, M., Scolamiero, M., Turner, K., Perin, R., Chindemi, G., Cliques of neurons bound into cavities provide a missing link between structure and function (2017) Front. Comput. Neurosci., 11, p. 48. , https://doi.org/10.3389/fncom.2017.00048
  57. Ritter, P., Schirner, M., McIntosh, A.R., Jirsa, V.K., The virtual brain integrates computational modeling and multimodal neuroimaging (2012) Brain Connect., 3 (2), pp. 121-145. , https://doi.org/10.1089/brain.2012.0120
  58. Roberts, R.P., Wiebels, K., An fMRI investigation of the relationship between future imagination and cognitive flexibility (2017) Neuropsychologia, 95, pp. 156-172
  59. Rojas, J.C., Lee, J., Neuroprotective effects of near-infrared light in an in vivo model of mitochondrial optic neuropathy (2008) J. Neurosci., 28 (50), pp. 13511-13521
  60. Schirner, M., Inferring multi-scale neural mechanisms with brain network modelling (2018) eLife, 7, p. e28927. , https://doi.org/10.7554/eLife.28927
  61. Shapiro, L., Embodied cognition (2011) Philos. Top., 39 (1)
  62. Sollner, T., Whiteheart, W., Brunner, M., Erdjument-Bromage, H., Geromanos, S., Tempst, P., SNAP receptor implicated in vesicle targeting and fusion (1993) Nature, 362, pp. 318-324
  63. Vargas, E., Barrett, D.W., Beneficial neurocognitive effects of transcranial laser in older adults (2017) Lasers. Med. Sci., 32 (5), pp. 1153-1162
  64. Westfall, J.M., Mold, J., Fagnan, L., Practice-based research-“blue highways” on the NIH roadmap (2007) JAMA, 297, pp. 403-406
  65. Woolf, S.H., The meaning of translational research and why it matters (2008) JAMA, 299, pp. 211-213
  66. Wu, X., Alberico, S.L., Pulsed light irradiation improves behavioral outcome in a rat model of chronic mild stress (2012) Lasers Surg. Med., 44 (3), pp. 227-232
  67. Yanagisawa, H., Dan, I., Acute moderate exercise elicits increased dorsolateral prefrontal activation and improves cognitive performance with Stroop test (2010) Neuroimage, 50 (4), pp. 1702-1710
  68. Yeh, F.C., Vettel, J.M., Quantifying differences and similarities in whole-brain white matter architecture using local connectome fingerprints (2016) PLoS Comput. Biol., 12 (11), p. e1005203
  69. Yue, L., Monge, M., Ozgur, M.H., Murphy, K., Louie, S., Miller, C.A., Simulation and measurement of transcranial near infrared light penetration (2015) Proc. SPIE 9321, Optical Interactions with Tissue and Cells XXVI, p. 93210. , https://doi.org/10.1117/12.2077019
  70. Watson, J.D., Crick, F.H.C., A structure for deoxyribose nucleic acid (1953) Nature, 171, pp. 737-738
  71. Willshaw, D., Dennett, D., Partridge, D., Non-symbolic approaches to artificial intelligence and the mind [and discussion] (1994) Phil. Trans.: Phys. Sci. Eng., 349 (1689), pp. 87-102. , http://www.jstor.org/stable/54378
  72. Woodman, M.M., Integrating neuroinformatics tools in TheVirtualBrain (2014) Front. Neuroinform., 8, p. 36. , https://doi.org/10.3389/fninf.2014.00036

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Livros e Capítulos de Livros - FM/Outros
Livros e Capítulos de Livros - LIM/44