Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Neurometabolic coupling in cerebral cortex reflects synaptic more than spiking activity

Nature Neuroscience volume 10, pages 1308–1312 (2007)Cite this article

Abstract

In noninvasive neuroimaging, neural activity is inferred from local fluctuations in deoxyhemoglobin. A fundamental question of functional magnetic resonance imaging (fMRI) is whether the inferred neural activity is driven primarily by synaptic or spiking activity. The answer is critical for the interpretation of the blood oxygen level–dependent (BOLD) signal in fMRI. Here, we have used well-established visual-system circuitry to create a stimulus that elicits synaptic activity without associated spike discharge. In colocalized recordings of neural and metabolic activity in cat primary visual cortex, we observed strong coupling between local field potentials (LFPs) and changes in tissue oxygen concentration in the absence of spikes. These results imply that the BOLD signal is more closely coupled to synaptic activity.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Temporal frequency tuning of tissue oxygen, MUA and LFP responses.
Figure 2: Example recording site of responses to large (60°) stimulus.
Figure 3: Response to small stimulus at high spatial frequency.
Figure 4: Population data: mean LFP responses across sites.

References

  1. Bandettini, P.A., Wong, E.C., Hinks, R.S., Tikofsky, R.S. & Hyde, J.S. Time course EPI of human brain function during task activation. Magn. Reson. Med. 25, 390–397 (1992).

    Article  CAS  Google Scholar 

  2. Ogawa, S. et al. Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc. Natl. Acad. Sci. USA 89, 5951–5955 (1992).

    Article  CAS  Google Scholar 

  3. Kwong, K.K. et al. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc. Natl. Acad. Sci. USA 89, 5675–5679 (1992).

    Article  CAS  Google Scholar 

  4. Kim, S.G. & Ugurbil, K. Functional magnetic resonance imaging of the human brain. J. Neurosci. Methods 74, 229–243 (1997).

    Article  CAS  Google Scholar 

  5. Logothetis, N.K. & Wandell, B.A. Interpreting the BOLD signal. Annu. Rev. Physiol. 66, 735–769 (2004).

    Article  CAS  Google Scholar 

  6. Duong, T.Q., Kim, D.S., Ugurbil, K. & Kim, S.G. Localized cerebral blood flow response at submillimeter columnar resolution. Proc. Natl. Acad. Sci. USA 98, 10904–10909 (2001).

    Article  CAS  Google Scholar 

  7. Kim, D.S. et al. Spatial relationship between neuronal activity and BOLD functional MRI. Neuroimage 21, 876–885 (2004).

    Article  Google Scholar 

  8. Zheng, Y. et al. A model of the hemodynamic response and oxygen delivery to the brain. Neuroimage 16, 617–637 (2002).

    Article  Google Scholar 

  9. Thompson, J.K., Peterson, M.R. & Freeman, R.D. Separate spatial scales determine neural activity–dependent changes in tissue oxygen within central visual pathways. J. Neurosci. 25, 9046–9058 (2005).

    Article  CAS  Google Scholar 

  10. Offenhauser, N., Thomsen, K., Caesar, K. & Lauritzen, M. Activity-induced tissue oxygenation changes in rat cerebellar cortex: interplay of postsynaptic activation and blood flow. J. Physiol. (Lond.) 565, 279–294 (2005).

    Article  CAS  Google Scholar 

  11. Thompson, J.K., Peterson, M.R. & Freeman, R.D. Single-neuron activity and tissue oxygenation in the cerebral cortex. Science 299, 1070–1072 (2003).

    Article  CAS  Google Scholar 

  12. Fatt, I. Polarographic Oxygen Sensors 197–218 (CRC Press, Cleveland, Ohio, 1976).

    Google Scholar 

  13. Mathiesen, C., Caesar, K., Akgoren, N. & Lauritzen, M. Modification of activity-dependent increases of cerebral blood flow by excitatory synaptic activity and spikes in rat cerebellar cortex. J. Physiol. (Lond.) 512, 555–566 (1998).

    Article  CAS  Google Scholar 

  14. Logothetis, N.K., Pauls, J., Augath, M., Trinath, T. & Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157 (2001).

    Article  CAS  Google Scholar 

  15. Caesar, K., Thomsen, K. & Lauritzen, M. Dissociation of spikes, synaptic activity, and activity-dependent increments in rat cerebellar blood flow by tonic synaptic inhibition. Proc Natl Acad Sci. USA 100, 16000–16005 (2003).

    Article  CAS  Google Scholar 

  16. Devor, A. et al. Coupling of total hemoglobin concentration, oxygenation, and neural activity in rat somatosensory cortex. Neuron 39, 353–359 (2003).

    Article  CAS  Google Scholar 

  17. Mukamel, R. et al. Coupling between neuronal firing, field potentials, and fMRI in human auditory cortex. Science 309, 951–954 (2005).

    Article  CAS  Google Scholar 

  18. Niessing, J. et al. Hemodynamic signals correlate tightly with synchronized gamma oscillations. Science 309, 948–951 (2005).

    Article  CAS  Google Scholar 

  19. Logothetis, N.K. The underpinnings of the BOLD functional magnetic resonance imaging signal. J. Neurosci. 23, 3963–3971 (2003).

    Article  CAS  Google Scholar 

  20. Henrie, J.A. & Shapley, R. LFP power spectra in V1 cortex: the graded effect of stimulus contrast. J. Neurophysiol. 94, 479–490 (2005).

    Article  Google Scholar 

  21. Malonek, D. & Grinvald, A. Interactions between electrical activity and cortical microcirculation revealed by imaging spectroscopy: implications for functional brain mapping. Science 272, 551–554 (1996).

    Article  CAS  Google Scholar 

  22. Kreiman, G. et al. Object selectivity of local field potentials and spikes in the macaque inferior temporal cortex. Neuron 49, 433–445 (2006).

    Article  CAS  Google Scholar 

  23. Liu, J. & Newsome, W.T. Local field potential in cortical area MT: stimulus tuning and behavioral correlations. J. Neurosci. 26, 7779–7790 (2006).

    Article  CAS  Google Scholar 

  24. Rager, G. & Singer, W. The response of cat visual cortex to flicker stimuli of variable frequency. Eur. J. Neurosci. 10, 1856–1877 (1998).

    Article  CAS  Google Scholar 

  25. Hawken, M.J., Shapley, R.M. & Grosof, D.H. Temporal-frequency selectivity in monkey visual cortex. Vis. Neurosci. 13, 477–492 (1996).

    Article  CAS  Google Scholar 

  26. Gilbert, C.D. Laminar differences in receptive field properties of cells in cat primary visual cortex. J. Physiol. (Lond.) 268, 391–421 (1977).

    Article  CAS  Google Scholar 

  27. Vanzetta, I. & Grinvald, A. Increased cortical oxidative metabolism due to sensory stimulation: implications for functional brain imaging. Science 286, 1555–1558 (1999).

    Article  CAS  Google Scholar 

  28. Issa, N.P., Trepel, C. & Stryker, M.P. Spatial frequency maps in cat visual cortex. J. Neurosci. 20, 8504–8514 (2000).

    Article  CAS  Google Scholar 

  29. Wilke, M., Logothetis, N.K. & Leopold, D.A. Local field potential reflects perceptual suppression in monkey visual cortex. Proc Natl Acad Sci. USA 103, 17507–17512 (2006).

    Article  CAS  Google Scholar 

  30. von Stein, A., Chiang, C. & Konig, P. Top-down processing mediated by interareal synchronization. Proc Natl Acad Sci. USA 97, 14748–14753 (2000).

    Article  CAS  Google Scholar 

  31. Jokisch, D. & Jensen, O. Modulation of gamma and alpha activity during a working memory task engaging the dorsal or ventral stream. J. Neurosci. 27, 3244–3251 (2007).

    Article  CAS  Google Scholar 

  32. Maex, R. & Orban, G.A. Model circuit of spiking neurons generating directional selectivity in simple cells. J. Neurophysiol. 75, 1515–1545 (1996).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank Unisense A/S for continued collaboration in developing the combined sensor, J. Thompson for helpful comments during the conception of the project, P. Mitra and H. Bokil for assistance with LFP analysis, E. Allen and B. Pasley for helpful discussions, and B. Li for help with surgical preparation. This work was supported by research and CORE grants from the US National Eye Institute (EY01175 and EY03716) and a US National Science Foundation Graduate Research Fellowship (A.V.).

Author information

Authors and Affiliations

  1. Group in Vision Science, School of Optometry, Helen Wills Neurosciences Institute, University of California, Berkeley, 94720-2020, California, USA

    Ahalya Viswanathan & Ralph D Freeman

Authors
  1. Ahalya Viswanathan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ralph D Freeman
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.V. conducted the experiments and data analysis. Both A.V. and R.D.F. discussed the results and wrote portions of the manuscript.

Corresponding author

Correspondence to Ralph D Freeman.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Viswanathan, A., Freeman, R. Neurometabolic coupling in cerebral cortex reflects synaptic more than spiking activity. Nat Neurosci 10, 1308–1312 (2007). https://doi.org/10.1038/nn1977

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn1977

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing