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Mathematical Modeling and the Quantification of Brain Dynamics

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Molecular Imaging in the Clinical Neurosciences

Part of the book series: Neuromethods ((NM,volume 71))

Abstract

Neuroimaging greatly expanded the fundamental understanding of brain functions, and it has revealed novel treatment options in disciplines such as neurology, neurosurgery, and neuropsychiatry. The last 30 years have witnessed a flourish of approaches that include novel opportunities to image not only structure in ever-increasing resolution but also, and perhaps more importantly, the basic mechanisms of brain work that include the roles of regional cerebral blood flow and energy metabolism, neuronal network and neurotransmitter system activity, and most recently the abnormal deposition of amyloid-beta in brain tissue and the abnormalities of second messenger cascades that likely underlie important neuropathology.

The quantification of brain images is vital to the appropriate understanding and interpretation of these experimental and clinical findings. While many brain imaging agents, such as markers of amyloid-beta in dementia, are used with the ultimate goal of application to clinical prognostication and differential diagnosis, others will be fundamental research tools for understanding new drugs, such as antipsychotics, antidepressants, and anxiolytics, as well as drugs for relief of devastating neurological disorders such as multiple sclerosis, stroke, and dementia.

This chapter provides a brief introduction to some of the quantitative methods of understanding brain work and brain functions that neuroscientists developed in the last 30 years, and it highlights their importance to future tests of treatment. Here, an overall description of the basic elements of quantification, and, in particular, mathematical modeling of dynamic brain images, is presented both to justify the role of such modeling in initial study development, and to validate specifications for use in clinical settings. Quantification and kinetic modeling are just as important as image reconstruction and structural identification of regions of interest, and they are fundamental components of all new brain imaging tools. The quantitative methods presented in this brief introduction continue to underpin the routine approaches and hence matter to most clinicians and clinician scientists involved in brain imaging.

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References

  1. Gjedde A, Bauer WR, Wong DF (2011) Neurokinetics: The dynamics of neurobiology in vivo. Springer, New York

    Google Scholar 

  2. Kuikka JT et al (1991) Mathematical modelling in nuclear medicine. Eur J Nucl Med 18(5):351–362

    Article  PubMed  CAS  Google Scholar 

  3. Sheppard CW (1948) The theory of the study of transfers within a multi-compartment system. J Appl Phys 19(70)

    Google Scholar 

  4. Rescigno A, Beck J (1972) Compartments. In: Rosen R (ed) Foundations of mathematical biology, 1st edn. Academic, New York, pp 255–322

    Google Scholar 

  5. Rescigno A, Beck JS (1987) The use and abuse of models. J Pharmacokinet Biopharm 15(3):327–344

    PubMed  CAS  Google Scholar 

  6. Gjedde A (1980) Rapid steady-state analysis of blood-brain glucose transfer in rat. Acta Physiol Scand 108(4):331–339

    Article  PubMed  CAS  Google Scholar 

  7. Gjedde A (2008) Functional brain imaging celebrates 30th anniversary. Acta Neurol Scand 117(4):219–223

    Article  PubMed  CAS  Google Scholar 

  8. Kety SS, Schmidt CF (1948) The nitrous oxide method for the quantitative determination of cerebral blood flow in man; theory, procedure and normal values. J Clin Invest 27(4):476–483

    Article  Google Scholar 

  9. Raichle ME et al (1983) Brain blood flow measured with intravenous H2(15)O. II. Implementation and validation. J Nucl Med 24(9):790–798

    PubMed  CAS  Google Scholar 

  10. Ohta S et al (1996) Cerebral [15O]water clearance in humans determined by PET: I. Theory and normal values. J Cereb Blood Flow Metab 16(5):765–780

    Article  PubMed  CAS  Google Scholar 

  11. Gjedde A (1981) High- and low-affinity transport of d-glucose from blood to brain. J Neurochem 36(4):1463–1471

    Article  PubMed  CAS  Google Scholar 

  12. Ter-Pogossian MM et al (1970) The measure in vivo of regional cerebral oxygen utilization by means of oxyhemoglobin labeled with radioactive oxygen-15. J Clin Invest 49(2):381–391

    Article  PubMed  CAS  Google Scholar 

  13. Ohta S et al (1992) Oxygen consumption of the living human brain measured after a single inhalation of positron emitting oxygen. J Cereb Blood Flow Metab 12(2):179–192

    Article  PubMed  CAS  Google Scholar 

  14. Sokoloff L et al (1977) The [14C]deoxyglucose method for the measurement of local cerebral glucose utilization: theory, procedure, and normal values in the conscious and anesthetized albino rat. J Neurochem 28(5):897–916

    Article  PubMed  CAS  Google Scholar 

  15. Gjedde A (1982) Calculation of cerebral glucose phosphorylation from brain uptake of glucose analogs in vivo: a re-examination. Brain Res 257(2):237–274

    PubMed  CAS  Google Scholar 

  16. Gjedde A et al (1985) Comparative regional analysis of 2-fluorodeoxyglucose and methylglucose uptake in brain of four stroke patients. With special reference to the regional estimation of the lumped constant. J Cereb Blood Flow Metab 5(2):163–178

    Article  PubMed  CAS  Google Scholar 

  17. Reivich M et al (1979) The [18F]fluorodeoxyglucose method for the measurement of local cerebral glucose utilization in man. Circ Res 44(1):127–137

    Article  PubMed  CAS  Google Scholar 

  18. Bass L et al (2011) Analogue tracers and lumped constant in capillary beds. J Theor Biol 285(1):177–181

    Article  PubMed  Google Scholar 

  19. Hasselbalch SG et al (2001) The [18F]fluorodeoxyglucose lumped constant determined in human brain from extraction fractions of [18F]F-fluorodeoxyglucose and glucose. J Cereb Blood Flow Metab 21(8):995–1002

    Article  PubMed  CAS  Google Scholar 

  20. Kuwabara H, Evans AC, Gjedde A (1990) Michaelis-Menten constraints improved cerebral glucose metabolism and regional lumped constant measurements with [18F]fluorodeoxyglucose. J Cereb Blood Flow Metab 10(2):180–189

    Article  PubMed  CAS  Google Scholar 

  21. Garnett ES, Firnau G, Nahmias C (1983) Dopamine visualized in the basal ganglia of living man. Nature 305(5930):137–138

    Article  PubMed  CAS  Google Scholar 

  22. Gjedde A et al (1991) Dopa decarboxylase activity of the living human brain. Proc Natl Acad Sci U S A 88(7):2721–2725

    Article  PubMed  CAS  Google Scholar 

  23. Kumakura Y et al (2005) PET studies of net blood-brain clearance of FDOPA to human brain: age-dependent decline of [18F]fluorodopamine storage capacity. J Cereb Blood Flow Metab 25(7):807–819

    Article  PubMed  CAS  Google Scholar 

  24. Wagner HN Jr et al (1983) Imaging dopamine receptors in the human brain by positron tomography. Science 221(4617):1264–1266

    Article  PubMed  CAS  Google Scholar 

  25. Wong DF et al (1984) Effects of age on dopamine and serotonin receptors measured by positron tomography in the living human brain. Science 226(4681):1393–1396

    Article  PubMed  CAS  Google Scholar 

  26. Wong DF et al (1997) Quantification of neuroreceptors in the living human brain: III. D2-like dopamine receptors: theory, validation, and changes during normal aging. J Cereb Blood Flow Metab 17(3):316–330

    Article  PubMed  CAS  Google Scholar 

  27. Wong DF, Gjedde A, Wagner HN Jr (1986) Quantification of neuroreceptors in the living human brain. I. Irreversible binding of ligands. J Cereb Blood Flow Metab 6(2):137–146

    Article  PubMed  CAS  Google Scholar 

  28. Wong DF et al (1986) Quantification of Neuroreceptors in the living human brain. II. Inhibition studies of receptor density and affinity. J Cereb Blood Flow Metab 6(2):147–153

    Article  PubMed  CAS  Google Scholar 

  29. Gjedde A, Wong DF (2001) Quantification of neuroreceptors in living human brain. v. endogenous neurotransmitter inhibition of haloperidol binding in psychosis. J Cereb Blood Flow Metab 21(8):982–994

    Article  PubMed  CAS  Google Scholar 

  30. Mintun MA et al (1984) A quantitative model for the in vivo assessment of drug binding sites with positron emission tomography. Ann Neurol 15(3):217–227

    Article  PubMed  CAS  Google Scholar 

  31. Farde L et al (1986) Quantitative analysis of D2 dopamine receptor binding in the living human brain by PET. Science 231(4735):258–261

    Article  PubMed  CAS  Google Scholar 

  32. Gjedde A et al (2005) Mapping neuroreceptors at work: on the definition and interpretation of binding potentials after 20 years of progress. Int Rev Neurobiol 63(1):1–20

    Article  PubMed  CAS  Google Scholar 

  33. Kuhar MJ et al (1978) Dopamine receptor binding in vivo: the feasibility of autoradiographic studies. Life Sci 22(2):203–210

    Article  PubMed  CAS  Google Scholar 

  34. Innis RB et al (2007) Consensus nomenclature for in vivo imaging of reversibly binding radioligands. J Cereb Blood Flow Metab 27(9):1533–1539

    Article  PubMed  CAS  Google Scholar 

  35. Wong DF et al (1997) Quantification of neuroreceptors in the living human brain: IV Effect of aging and elevations of D2-like receptors in schizophrenia and bipolar illness. J Cereb Blood Flow Metab 17(3):331–342

    Article  PubMed  CAS  Google Scholar 

  36. Wong DF et al (1998) Quantification of extracellular dopamine release in schizophrenia and cocaine use by means of TREMBLE. In: Carson RE, Herscovitch P, Daube-Witherspoon ME (eds) Quantitative functional brain imaging with positron emission tomography, 1st edn. Academic, San Diego, pp 463–468

    Chapter  Google Scholar 

  37. Gjedde A et al (2010) Inverted-U-shaped correlation between dopamine receptor availability in striatum and sensation seeking. Proc Natl Acad Sci U S A 107(8):3870–3875

    Article  PubMed  CAS  Google Scholar 

  38. Koepp MJ et al (1998) Evidence for striatal dopamine release during a video game. Nature 393(6682):266–268

    Article  PubMed  CAS  Google Scholar 

  39. Wong DF et al (2006) Increased occupancy of dopamine receptors in human striatum during cue-elicited cocaine craving. Neuropsychopharmacology 31(12):2716–2727

    Article  PubMed  CAS  Google Scholar 

  40. Wong DF et al (2008) Mechanisms of dopaminergic and serotonergic neurotransmission in Tourette syndrome: clues from an in vivo neurochemistry study with PET. Neuropsychopharmacology 33(6):1239–1251

    Article  PubMed  CAS  Google Scholar 

  41. Laruelle M, Abi-Dargham A (1999) Dopamine as the wind of the psychotic fire: new evidence from brain imaging studies. J Psychopharmacol 13(4):358–371

    Article  PubMed  CAS  Google Scholar 

  42. McConathy J, Kilts CD, Goodman MM (2001) Radioligands for PET and SPECT imaging of the central noradrenergic system. CNS Spectr 6(8):704–709

    PubMed  CAS  Google Scholar 

  43. Scott DJ et al (2007) Time-course of change in [11C]carfentanil and [11C]raclopride binding potential after a nonpharmacological challenge. Synapse 61(9):707–714

    Article  PubMed  CAS  Google Scholar 

  44. Maarrawi J et al (2007) Motor cortex stimulation for pain control induces changes in the endogenous opioid system. Neurology 69(9):827–834

    Article  PubMed  CAS  Google Scholar 

  45. Laruelle M et al (1997) Imaging D2 receptor occupancy by endogenous dopamine in humans. Neuropsychopharmacology 17(3):162–174

    Article  PubMed  CAS  Google Scholar 

  46. Yokoi F et al (2002) Dopamine D2 and D3 receptor occupancy in normal humans treated with the antipsychotic drug aripiprazole (OPC 14597): a study using positron emission tomography and [11C]raclopride. Neuropsychopharmacology 27(2):248–259

    Article  PubMed  CAS  Google Scholar 

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Acknowledgements

Global Excellence Award 2010, Capital Region, Denmark (Gjedde). NIH-NIDA midcareer award K24 DA000412 (Wong). Special thanks for technical assistance to Ayon Nandi, MS; and Rebecca Mellinger-Pilgram, BS, Johns Hopkins University.

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Correspondence to Albert Gjedde .

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© 2012 Springer Science+Business Media New York

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Gjedde, A., Wong, D.F. (2012). Mathematical Modeling and the Quantification of Brain Dynamics. In: Gründer, G. (eds) Molecular Imaging in the Clinical Neurosciences. Neuromethods, vol 71. Humana Press, Totowa, NJ. https://doi.org/10.1007/7657_2012_55

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  • DOI: https://doi.org/10.1007/7657_2012_55

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-61779-988-4

  • Online ISBN: 978-1-61779-989-1

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