Encyclopedia of Computational Neuroscience

Living Edition
| Editors: Dieter Jaeger, Ranu Jung

Biophysical Models: Neurovascular Coupling, Cortical Microcircuits, and Metabolism

  • Jorge Riera
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7320-6_522-1



Forward-generative models are considered crucial to interpret and integrate data obtained with different functional neuroimaging modalities (Riera 2014). Typically, these models are formulated to represent biological principles at the mesoscopic scale, which basically stand for an average voxel (1 mm3) in any brain imaging technique. To this end, biophysical models for neuronal activity, which are mostly available from the neuronal computationcommunity, have been modified to incorporate general physiological mechanisms governing glial cell activity, vascular dynamics, and metabolism. For the cerebral cortex, the most relevant brain structure in functional neuroimaging, a variety of such extended biophysical models has been consolidating around three main research topics over the last decade: (a) the principles for neurovascular coupling, (b) the organization of cortical microcircuits, and (c) the cellular...


GABAergic Interneuron Apical Dendrite Biophysical Model Axon Initial Segment Tonic Signaling 
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  1. Attwell D, Laughlin SB (2001) An energy budget for signaling in the grey matter of the brain. J Cereb Blood Flow Metab 21(10):1133–1145PubMedCrossRefGoogle Scholar
  2. Buxton RB, Frank LR (1997) A model of the coupling between cerebral blood flow and oxygen metabolism during neural stimulation. J Cereb Blood Flow Metab 17:64–72PubMedCrossRefGoogle Scholar
  3. Daunizeau J, David O, Stephan KE (2011) Dynamic causal modelling: a critical review of the biophysical and statistical foundations. Neuroimage 58:312–322PubMedCrossRefGoogle Scholar
  4. Deco G, Jirsa VK, Robinson PA, Breakspear M, Friston K (2008) The dynamic brain: From spiking neurons to neural masses and cortical fields. PLoS Comput Biol 4(8): e1000092Google Scholar
  5. Deco G (2014) Multi-Scale brain connectivity. Encyclopedia of computational neuroscience, SpringerGoogle Scholar
  6. DeFelipe J, Fariñas I (1992) The pyramidal neuron of the cerebral cortex: morphological and chemical characteristics of the synaptic inputs. Prog Neurobiol 39(6):563–607PubMedCrossRefGoogle Scholar
  7. DeFelipe J, Alonso-Nanclares L, Arellano JI (2002) Microstructure of the neocortex: comparative aspects. J Neurocytol 31:299–316Google Scholar
  8. Friston KJ, Harrison L, Penny W (2003) Dynamic causal modelling. Neuroimage 19:1273–1302PubMedCrossRefGoogle Scholar
  9. Gjedde A (1997) The relation between brain function and cerebral blood flow and metabolism. In: Batjer HH (ed) Cerebrovascular disease. Lippincott-Raven, Philadelphia, pp 23–40Google Scholar
  10. Gratiy SL, Pettersen KH, Einevoll GT, Dale AM (2013) Pitfalls in the interpretation of multielectrode data: on the infeasibility of the neuronal current-source monopoles. J Neurophysiol 109:1681–1682PubMedCentralPubMedCrossRefGoogle Scholar
  11. Grieb P, Forster RE, Strome D, Goodwin CW, Pape PC (1985) O2 exchange between blood and brain tissues studied with 18O2 indicator-dilution technique. J Appl Physiol 58:1929–1941PubMedGoogle Scholar
  12. Herman P, Trübel HK, Hyder F (2006) A multi-parametric assessment of oxygen efflux from the brain. J Cereb Blood Flow Metab 26:79–91PubMedCrossRefGoogle Scholar
  13. Herman P, Sanganahalli BG, Blumenfeld H, Rothman DL, Hyder F (2013) Quantitative basis for neuroimaging of cortical laminae with calibrated fMRI. Proc Natl Acad Sci USA 110:15115–15120Google Scholar
  14. Hertz L, Peng L, Dienel GA (2007) Energy metabolism in astrocytes: high rate of oxidative metabolism and spatiotemporal dependence on glycolysis/glycogenolysis. J Cereb Blood Flow Metab 27:219–249PubMedCrossRefGoogle Scholar
  15. Hirano Y, Stefanovic B, Silva AC (2011) Spatiotemporal evolution of the functional magnetic resonance imaging response to ultrashort stimuli. J Neurosci 31:1440–1447PubMedCentralPubMedCrossRefGoogle Scholar
  16. Hyder F, Shulman RG, Rothman DL (1998) A model for the regulation of cerebral oxygen delivery. J Appl Physiol 85:554–564PubMedGoogle Scholar
  17. Hyder F, Kennan RP, Kida I, Mason GF, Behar KL, Rothman DL (2000) Dependence of oxygen delivery on blood flow in rat brain: a 7 tesla nuclear magnetic resonance study. J Cereb Blood Flow Metab 20:485–498PubMedCrossRefGoogle Scholar
  18. Hyder F, Patel AB, Gjedde A, Rothman DL, Behar KL, Shulman RG (2006) Neuronal-glial glucose oxidation and glutamatergic-GABAergic function. J Cereb Blood Flow Metab 26:865–877PubMedCrossRefGoogle Scholar
  19. Hyder F, Fulbright RK, Shulman RG, Rothman DL (2013a) Glutamatergic function in the resting awake human brain is supported by uniformly high oxidative energy. J Cereb Blood Flow Metab 33:339–347PubMedCentralPubMedCrossRefGoogle Scholar
  20. Hyder F, Rothman DL, Bennett MR (2013b) Cortical energy demands of signaling and nonsignaling components in brain are conserved across mammalian species and activity levels. Proc Natl Acad Sci USA 110(9):3549–3554Google Scholar
  21. Kassissia IG, Goresky CA, Rose CP, Schwab AJ, Simard A, Huet PM, Bach GG (1995) Tracer oxygen distribution is barrier-limited in the cerebral microcirculation. Circ Res 77:1201–11PubMedCrossRefGoogle Scholar
  22. Kloeden PE, Platen E (1999) Numerical solution of stochastic differential equations, 3rd edn. Springer, BerlinGoogle Scholar
  23. Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A (2001) Neurophysiological investigation of the basis of the fMRI signal. Nature 412:150–157PubMedCrossRefGoogle Scholar
  24. Maandag NJ, Coman D, Sanganahalli BG, Herman P, Smith AJ, Blumenfeld H, Shulman RG, Hyder F (2007) Energetics of neuronal signaling and fMRI activity. Proc Natl Acad Sci USA 104:20546–20551PubMedCentralPubMedCrossRefGoogle Scholar
  25. Markram H, Toledo-Rodriguez M, Wang Y, Gupta A, Silberberg G, Wu C (2004) Interneurons of the neocortical inhibitory system. Nat Rev Neurosci 5:793–807PubMedCrossRefGoogle Scholar
  26. Marreiros AC, Kiebel S, Daunizeau J, Harrison L, Friston KJ (2008) Population dynamics under the Laplace assumption. Neuroimage 44:701–714PubMedCrossRefGoogle Scholar
  27. Mukamel R, Gelbard H, Arieli A, Hasson U, Fried I, Malach R (2005) Coupling between neuronal firing, field potentials, and fMRI in human auditory cortex. Science 309:951–954PubMedCrossRefGoogle Scholar
  28. Ozaki T (2012) Time series modeling of neuroscience data. Chapman & Hall/CRC Interdisciplinary Statistics, LondonCrossRefGoogle Scholar
  29. Ozaki T (2014) Statistical analysis of neuroimaging data. Encyclopedia of computational neuroscience, SpringerGoogle Scholar
  30. Pellerin L, Magistretti PJ (2004) Neuroenergetics: calling upon astrocytes to satisfy hungry neurons. Neuroscientist 10(1):53–62PubMedCrossRefGoogle Scholar
  31. Riera J, Aubert E, Iwata K, Kawashima R, Wan X, Ozaki T (2005) Fusing EEG and fMRI based on a bottom-up model: inferring activation and effective connectivity in neural masses. Phil Trans R Soc B 360, 1025–1041Google Scholar
  32. Riera J, Sumiyoshi A (2010) Brain oscillations: ideal scenery to understand the neurovascular coupling. Curr Opin Neurol 23(4):374–381PubMedGoogle Scholar
  33. Riera J (2014) Brain Imaging: Overview. Encyclopedia of Computational Neuroscience, SpringerGoogle Scholar
  34. Riera J, Schousboe A, Waagepetersen HS, Howarth C, Hyder F (2008) The micro-architecture of the cerebral cortex: functional neuroimaging models and metabolism. Neuroimage 40:1436–1459PubMedCrossRefGoogle Scholar
  35. Riera J, Ogawa T, Goto T, Sumiyoshi A, Nonaka H, Evans A, Miyakawa H, Kawashima R (2012) Pitfalls in the dipolar model for the neocortical EEG sources. J Neurophysiol 108(4):956–975PubMedCrossRefGoogle Scholar
  36. Shulman RG, Hyder F, Rothman DL (2001a) Lactate efflux and the neuroenergetic basis of brain function. NMR Biomed 14:389–396PubMedCrossRefGoogle Scholar
  37. Shulman RG, Hyder F, Rothman DL (2001b) Cerebral energetics and the glycogen shunt: neurochemical basis of functional imaging. Proc Natl Acad Sci U S A 98:6417–6422PubMedCentralPubMedCrossRefGoogle Scholar
  38. Smith AJ, Blumenfeld H, Behar KL, Rothman DL, Shulman RG, Hyder F (2002) Cerebral energetics and spiking frequency: the neurophysiological basis of fMRI. Proc Natl Acad Sci U S A 99:10765–10770PubMedCentralPubMedCrossRefGoogle Scholar
  39. Stephan KE, Roebroeck A (2012) A short history of causal modeling of fMRI data. Neuroimage 62:856–863PubMedCrossRefGoogle Scholar
  40. Stephan KE, Mattout J, David O, Friston KJ (2006) Models of functional neuroimaging data. Curr Med Imaging Rev 2(1):15–34PubMedCentralPubMedCrossRefGoogle Scholar
  41. Stephan KE, Harrison LM, Kiebel SJ, David O, Penny WD, Friston KJ (2007) Dynamic causal models of neural system dynamics: current state and future extensions. J Biosci 32:129–144PubMedCentralPubMedCrossRefGoogle Scholar
  42. Toga AW, Mazziotta JC (2002) Brain mapping: the methods, 2nd edn. Academic, San DiegoGoogle Scholar
  43. Valdes-Sosa PA, Roebroeck A, Daunizeau J, Friston K (2011) Effective connectivity: influence, causality and biophysical modeling. Neuroimage 58:339–361PubMedCentralPubMedCrossRefGoogle Scholar

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© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringFlorida International UniversityMiamiUSA