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.

  • Review Article
  • Published:

What does fMRI tell us about neuronal activity?

Key Points

  • Functional magnetic resonance imaging (fMRI) is now widely used in cognitive neuroscience to look for changes in neural activity that correlate with particular cognitive processes. But what does fMRI tell us? Until we know exactly what the fMRI signal represents, we will not know how to interpret the results of these studies.

  • It is generally assumed that the fMRI signal is roughly proportional to a measure of local neural activity, averaged over several millimetres and several seconds — the linear transform model of the fMRI signal. The relationship between the signal and the underlying neural activity depends on the fMRI acquisition technique, the behavioural and stimulation protocol, and how neural activity is measured and quantified (for example, by firing rate, local field potential or synchronous firing).

  • The linear transform model predicts that the fMRI response should sum over time. For example, the fMRI response to a 12-s stimulus should be the same as that to two 6-s stimuli. The frequently observed failure of this prediction could be due to a ceiling effect, or to the disproportionately large responses to very short stimuli that are seen in primary sensory cortex.

  • It should be possible to predict the fMRI response from the underlying neural activity. Studies that have compared neural activity and fMRI responses indirectly have supported this relationship. A direct comparison of simultaneously recorded fMRI responses and neuronal signals in monkey visual cortex found that local field potentials predicted fMRI responses better than multi-unit activity, but that both were variable in accuracy.

  • The linear transform model also predicts that fMRI responses and neural activity should be colocalized. There is increasing evidence that this is the case, although the analysis methods used influence the localization of the fMRI signal.

  • It will be important to improve our understanding of the relationships between neural activity, blood flow and metabolism, and fMRI signals, if we are correctly to interpret fMRI studies. It will also be necessary to optimize protocols and analysis techniques if this powerful tool is to live up to its potential.

Abstract

In recent years, cognitive neuroscientists have taken great advantage of functional magnetic resonance imaging (fMRI) as a non-invasive method of measuring neuronal activity in the human brain. But what exactly does fMRI tell us? We know that its signals arise from changes in local haemodynamics that, in turn, result from alterations in neuronal activity, but exactly how neuronal activity, haemodynamics and fMRI signals are related is unclear. It has been assumed that the fMRI signal is proportional to the local average neuronal activity, but many factors can influence the relationship between the two. A clearer understanding of how neuronal activity influences the fMRI signal is needed if we are correctly to interpret functional imaging data.

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

Access options

Buy this article

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

Figure 1: The linear transform model of fMRI responses.
Figure 2: Temporal summation of fMRI responses.
Figure 3: Neuronal versus fMRI responses.

Similar content being viewed by others

References

  1. Ogawa, S., Lee, T. M., Kay, A. R. & Tank, D. W. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc. Natl Acad. Sci. USA 87, 9868–9872 (1990).The first demonstration of BOLD MRI. This paper was followed two years later by several concurrent reports (references 2–5 ) of the use of BOLD fMRI to measure activity non-invasively in the human brain.

    Article  CAS  PubMed  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  PubMed  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  PubMed  Google Scholar 

  4. 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  PubMed  Google Scholar 

  5. Blamire, A. M. et al. Dynamic mapping of the human visual cortex by high-speed magnetic resonance imaging. Proc. Natl Acad. Sci. USA 89, 11069–11073 (1992).

    Article  CAS  PubMed  Google Scholar 

  6. Kanwisher, N. & Wojciulik, E. Visual attention: insights from brain imaging. Nature Rev. Neurosci. 1, 91–100 (2000).

    Article  CAS  Google Scholar 

  7. Luck, S. J., Chelazzi, L., Hillyard, S. A. & Desimone, R. Neural mechanisms of spatial selective attention in areas V1, V2, and V4 of macaque visual cortex. J. Neurophysiol. 77, 24–42 (1997).

    Article  CAS  PubMed  Google Scholar 

  8. McAdams, C. J. & Maunsell, J. H. R. Effects of attention on orientation-tuning functions of single neurons in macaque cortical area V4. J. Neurosci. 19, 431–441 (1999).

    Article  CAS  PubMed  Google Scholar 

  9. Kastner, S., Pinsk, M. A., De Weerd, P., Desimone, R. & Ungerleider, L. G. Increased activity in human visual cortex during directed attention in the absence of visual stimulation. Neuron 22, 751–761 (1999).

    Article  CAS  PubMed  Google Scholar 

  10. Ress, D., Backus, B. T. & Heeger, D. J. Activity in primary visual cortex predicts performance in a visual detection task. Nature Neurosci. 3, 940–945 (2000).

    Article  CAS  PubMed  Google Scholar 

  11. Fries, P., Reynolds, J. H., Rorie, A. E. & Desimone, R. Modulation of oscillatory neuronal synchronization by selective visual attention. Science 291, 1560–1563 (2001).

    Article  CAS  PubMed  Google Scholar 

  12. Scannell, J. W. & Young, M. P. Neuronal population activity and functional imaging. Proc. R. Soc. Lond. B 266, 875–881 (1999).

    Article  CAS  Google Scholar 

  13. Boynton, G. M., Engel, S. A., Glover, G. H. & Heeger, D. J. Linear systems analysis of functional magnetic resonance imaging in human V1. J. Neurosci. 16, 4207–4221 (1996).Framed the linear transform hypothesis and reported the first measurements of temporal summation.

    Article  CAS  PubMed  Google Scholar 

  14. Friston, K. J., Jezzard, P. & Turner, R. Analysis of functional MRI time-series. Hum. Brain Mapp. 1, 153–171 (1994).Developed methods, widely used to analyse fMRI data, that rely on the linear transform hypothesis.

    Article  Google Scholar 

  15. Boxerman, J. L. et al. The intravascular contribution to fMRI signal change: Monte Carlo modeling and diffusion-weighted studies in vivo. Magn. Reson. Med. 34, 4–10 (1995).

    Article  CAS  PubMed  Google Scholar 

  16. Zhong, J., Kennan, R. P., Fulbright, R. K. & Gore, J. C. Quantification of intravascular and extravascular contributions to BOLD effects induced by alteration in oxygenation or intravascular contrast agents. Magn. Reson. Med. 40, 526–536 (1998).

    Article  CAS  PubMed  Google Scholar 

  17. Hoogenraad, F. G. et al. Quantitative differentiation between BOLD models in fMRI. Magn. Reson. Med. 45, 233–246 (2001).

    Article  CAS  PubMed  Google Scholar 

  18. Song, A. W., Wong, E. C., Tan, S. G. & Hyde, J. S. Diffusion weighted fMRI at 1.5 T. Magn. Reson. Med. 35, 155–158 (1996).

    Article  CAS  PubMed  Google Scholar 

  19. Kim, S. G. Quantification of relative cerebral blood flow change by flow-sensitive alternating inversion recovery (FAIR) technique: application to functional mapping. Magn. Reson. Med. 34, 293–301 (1995).

    Article  CAS  PubMed  Google Scholar 

  20. Miller, K. L. et al. Nonlinear temporal dynamics of the cerebral blood flow response. Hum. Brain Mapp. 13, 1–12 (2001).

    Article  CAS  PubMed  Google Scholar 

  21. Mandeville, J. B. et al. Dynamic functional imaging of relative cerebral blood volume during rat forepaw stimulation. Magn. Reson. Med. 39, 615–624 (1998).

    Article  CAS  PubMed  Google Scholar 

  22. Darquie, A., Poline, J. B., Poupon, C., Saint-Jalmes, H. & Le Bihan, D. Transient decrease in water diffusion observed in human occipital cortex during visual stimulation. Proc. Natl Acad. Sci. USA 98, 9391–9395 (2001).

    Article  CAS  PubMed  Google Scholar 

  23. Luh, W. M., Wong, E. C., Bandettini, P. A., Ward, B. D. & Hyde, J. S. Comparison of simultaneously measured perfusion and BOLD signal increases during brain activation with T1-based tissue identification. Magn. Reson. Med. 44, 137–143 (2000).

    Article  CAS  PubMed  Google Scholar 

  24. Hoge, R. D. et al. Stimulus-dependent BOLD and perfusion dynamics in human V1. Neuroimage 9, 573–585 (1999).

    Article  CAS  PubMed  Google Scholar 

  25. Engel, S. A. et al. fMRI of human visual cortex. Nature 369, 525 (1994).Introduced the travelling-wave stimulation protocol for measuring retinotopic maps in human visual cortex, and resolved the 'brain–vein' debate by showing that one could reliably distinguish activity separated by <1.5 mm in cortex.

    Article  CAS  PubMed  Google Scholar 

  26. Glover, G. H. Deconvolution of impulse response in event-related BOLD fMRI. Neuroimage 9, 416–429 (1999).

    Article  CAS  PubMed  Google Scholar 

  27. Ogawa, S. et al. An approach to probe some neural systems interaction by functional MRI at neural time scale down to milliseconds. Proc. Natl Acad. Sci. USA 97, 11026–11031 (2000).

    Article  CAS  PubMed  Google Scholar 

  28. Kershaw, J., Kashikura, K., Zhang, X., Abe, S. & Kanno, I. Bayesian technique for investigating linearity in event-related BOLD fMRI. Magn. Reson. Med. 45, 1081–1094 (2001).

    Article  CAS  PubMed  Google Scholar 

  29. Friston, K. J., Josephs, O., Rees, G. & Turner, R. Nonlinear event-related responses in fMRI. Magn. Reson. Med. 39, 41–52 (1998).

    Article  CAS  PubMed  Google Scholar 

  30. Dale, A. M. & Buckner, R. L. Selective averaging of rapidly presented individual trials using fMRI. Hum. Brain Mapp. 5, 329–340 (1997).

    Article  CAS  PubMed  Google Scholar 

  31. Robson, M. D., Dorosz, J. L. & Gore, J. C. Measurements of the temporal fMRI response of the human auditory cortex to trains of tones. Neuroimage 7, 185–198 (1998).

    Article  CAS  PubMed  Google Scholar 

  32. Vazquez, A. L. & Noll, D. C. Nonlinear aspects of the BOLD response in functional MRI. Neuroimage 7, 108–118 (1998).

    Article  CAS  PubMed  Google Scholar 

  33. Birn, R. M., Saad, Z. S. & Bandettini, P. A. Spatial heterogeneity of the nonlinear dynamics in the fMRI bold response. Neuroimage 14, 817–826 (2001).

    Article  CAS  PubMed  Google Scholar 

  34. Mechelli, A., Price, C. J. & Friston, K. J. Nonlinear coupling between evoked rCBF and BOLD signals: a simulation study of hemodynamic responses. Neuroimage 14, 862–872 (2001).

    Article  CAS  PubMed  Google Scholar 

  35. Rees, G. et al. Characterizing the relationship between BOLD contrast and regional cerebral blood flow measurements by varying the stimulus presentation rate. Neuroimage 6, 270–278 (1997).

    Article  CAS  PubMed  Google Scholar 

  36. Davis, T. L., Kwong, K. K., Weisskoff, R. M. & Rosen, B. R. Calibrated functional MRI: mapping the dynamics of oxidative metabolism. Proc. Natl Acad. Sci. USA 95, 1834–1839 (1998).

    Article  CAS  PubMed  Google Scholar 

  37. Muller, J. R., Metha, A. B., Krauskopf, J. & Lennie, P. Information conveyed by onset transients in responses of striate cortical neurons. J. Neurosci. 21, 6978–6990 (2001).

    Article  CAS  PubMed  Google Scholar 

  38. Ohzawa, I., Sclar, G. & Freeman, R. D. Contrast gain control in the cat visual cortex. Nature 298, 266–268 (1982).

    Article  CAS  PubMed  Google Scholar 

  39. Rees, G., Friston, K. & Koch, C. A direct quantitative relationship between the functional properties of human and macaque V5. Nature Neurosci. 3, 716–723 (2000).

    Article  CAS  PubMed  Google Scholar 

  40. Heeger, D. J., Huk, A. C., Geisler, W. S. & Albrecht, D. G. Spikes versus BOLD: what does neuroimaging tell us about neuronal activity? Nature Neurosci. 3, 631–633 (2000).

    Article  CAS  PubMed  Google Scholar 

  41. Zeki, S. et al. A direct demonstration of functional specialization in human visual cortex. J. Neurosci. 11, 641–649 (1991).

    Article  CAS  PubMed  Google Scholar 

  42. Huk, A. C., Ress, D. & Heeger, D. J. Neuronal basis of the motion aftereffect reconsidered. Neuron 32, 161–172 (2001).

    Article  CAS  PubMed  Google Scholar 

  43. Britten, K. H. & Newsome, W. T. Tuning bandwidths for near-threshold stimuli in area MT. J. Neurophysiol. 80, 762–770 (1998).

    Article  CAS  PubMed  Google Scholar 

  44. Boynton, G. M., Demb, J. B., Glover, G. H. & Heeger, D. J. Neuronal basis of contrast discrimination. Vision Res. 39, 257–269 (1999).

    Article  CAS  PubMed  Google Scholar 

  45. Geisler, W. S. & Albrecht, D. G. Visual cortex neurons in monkeys and cats: detection, discrimination, and identification. Vis. Neurosci. 14, 897–919 (1997).

    Article  CAS  PubMed  Google Scholar 

  46. Brinker, G. et al. Simultaneous recording of evoked potentials and T2*-weighted MR images during somatosensory stimulation of rat. Magn. Reson. Med. 41, 469–473 (1999).

    Article  CAS  PubMed  Google Scholar 

  47. 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).A compelling demonstration that blood flow can be decoupled from spiking activity.

    Article  CAS  Google Scholar 

  48. Ances, B. M., Zarahn, E., Greenberg, J. H. & Detre, J. A. Coupling of neural activation to blood flow in the somatosensory cortex of rats is time-intensity separable, but not linear. J. Cereb. Blood Flow Metab. 20, 921–930 (2000).

    Article  CAS  PubMed  Google Scholar 

  49. Nielsen, A. N. & Lauritzen, M. Coupling and uncoupling of activity-dependent increases of neuronal activity and blood flow in rat somatosensory cortex. J. Physiol. (Lond.) 553, 773–785 (2001). | PubMed |

    Article  Google Scholar 

  50. 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).Evaluated the linear transform model by comparing simultaneously recorded fMRI and neuronal signals in the anaesthetized monkey.

    Article  CAS  PubMed  Google Scholar 

  51. Burock, M. A., Buckner, R. L., Woldorff, M. G., Rosen, B. R. & Dale, A. M. Randomized event-related experimental designs allow for extremely rapid presentation rates using functional MRI. Neuroreport 9, 3735–3739 (1998).

    Article  CAS  PubMed  Google Scholar 

  52. Puce, A. et al. Functional magnetic resonance imaging of sensory and motor cortex: comparison with electrophysiological localization. J. Neurosurg. 83, 262–270 (1995).

    Article  CAS  PubMed  Google Scholar 

  53. Yousry, T. A. et al. Topography of the cortical motor hand area: prospective study with functional MR imaging and direct motor mapping at surgery. Radiology 195, 23–29 (1995).

    Article  CAS  PubMed  Google Scholar 

  54. Schulder, M. et al. Functional image-guided surgery of intracranial tumors located in or near the sensorimotor cortex. J. Neurosurg. 89, 412–418 (1998).

    Article  CAS  PubMed  Google Scholar 

  55. Ruge, M. I. et al. Concordance between functional magnetic resonance imaging and intraoperative language mapping. Stereotact. Funct. Neurosurg. 72, 95–102 (1999).

    Article  CAS  PubMed  Google Scholar 

  56. Cannestra, A. F. et al. Temporal spatial differences observed by functional MRI and human intraoperative optical imaging. Cereb. Cortex 11, 773–782 (2001).

    Article  CAS  PubMed  Google Scholar 

  57. Disbrow, E. A., Slutsky, D. A., Roberts, T. P. & Krubitzer, L. A. Functional MRI at 1.5 Tesla: a comparison of the blood oxygenation level-dependent signal and electrophysiology. Proc. Natl Acad. Sci. USA 97, 9718–9723 (2000).Reported a profound discordance between fMRI and electrophysiological recordings of somatosensory cortical maps in the anaesthetized monkey.

    Article  CAS  PubMed  Google Scholar 

  58. Wandell, B., Press, W., Brewer, A. & Logothetis, N. fMRI measurements of visual areas and retinotopic maps in monkey. Soc. Neurosci. Abstr. 30, 309.9 (2000).

  59. Cheng, K., Waggoner, R. A. & Tanaka, K. Human ocular dominance columns as revealed by high-field functional magnetic resonance imaging. Neuron 32, 359–374 (2001).The most compelling demonstration so far of the use of fMRI to image human ocular dominance.

    Article  CAS  PubMed  Google Scholar 

  60. Menon, R. S., Ogawa, S., Strupp, J. P. & Ugurbil, K. Ocular dominance in human V1 demonstrated by functional magnetic resonance imaging. J. Neurophysiol. 77, 2780–2787 (1997).

    Article  CAS  PubMed  Google Scholar 

  61. Goodyear, B. G. & Menon, R. S. Brief visual stimulation allows mapping of ocular dominance in visual cortex using fMRI. Hum. Brain Mapp. 14, 210–217 (2001).

    Article  CAS  PubMed  Google Scholar 

  62. Menon, R. S. & Goodyear, B. G. Submillimeter functional localization in human striate cortex using BOLD contrast at 4 Tesla: implications for the vascular point-spread function. Magn. Reson. Med. 41, 230–235 (1999).

    Article  CAS  PubMed  Google Scholar 

  63. Kim, D. S., Duong, T. Q. & Kim, S. G. High-resolution mapping of iso-orientation columns by fMRI. Nature Neurosci. 3, 164–169 (2000).

    Article  CAS  PubMed  Google Scholar 

  64. Logothetis, N. Can current fMRI techniques reveal the micro-architecture of cortex? Nature Neurosci. 3, 413–414 (2000).

    Article  CAS  PubMed  Google Scholar 

  65. Kim, D. S., Duong, T. Q. & Kim, S. G. Reply to: Can current fMRI techniques reveal the micro-architecture of cortex? Nature Neurosci. 3, 414 (2000). | PubMed |

  66. 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  PubMed  Google Scholar 

  67. Malonek, D. et al. Vascular imprints of neuronal activity: relationships between the dynamics of cortical blood flow, oxygenation, and volume changes following sensory stimulation. Proc. Natl Acad. Sci. USA 94, 14826–14831 (1997).

    Article  CAS  PubMed  Google Scholar 

  68. Vanzetta, I. & Grinvald, A. Increased cortical oxidative metabolism due to sensory stimulation: implications for functional brain imaging. Science 286, 1555–1558 (1999).The most compelling in a series of influential papers about the initial dip, the proposed initial increase in oxygen consumption that occurs immediately after the onset of neuronal activity. See also references 66, 67 and 87.

    Article  CAS  PubMed  Google Scholar 

  69. Fox, P. T. & Raichle, M. E. Focal physiological uncoupling of cerebral blood flow and oxidative metabolism during somatosensory stimulation in human subjects. Proc. Natl Acad. Sci. USA 83, 1140–1144 (1986).

    Article  CAS  PubMed  Google Scholar 

  70. Fox, P. T., Raichle, M. E., Mintun, M. A. & Dence, C. Nonoxidative glucose consumption during focal physiologic neural activity. Science 241, 462–464 (1988).A classic study showing that oxygen consumption did not match blood flow and glucose consumption, indicating that increases in brain activation are supported by non-oxidative glucose metabolism. See also reference 69.

    Article  CAS  PubMed  Google Scholar 

  71. Buxton, R. B., Wong, E. C. & Frank, L. R. Dynamics of blood flow and oxygenation changes during brain activation: the balloon model. Magn. Reson. Med. 39, 855–864 (1998).An influential model of blood flow, volume and oxygenation that explains many of the properties of the haemodynamic response. See also references 72 and 73.

    Article  CAS  PubMed  Google Scholar 

  72. Friston, K. J., Mechelli, A., Turner, R. & Price, C. J. Nonlinear responses in fMRI: the balloon model, volterra kernels, and other hemodynamics. Neuroimage 12, 466–477 (2000).

    Article  CAS  PubMed  Google Scholar 

  73. Mandeville, J. B. et al. Evidence of a cerebrovascular postarteriole windkessel with delayed compliance. J. Cereb. Blood Flow Metab. 19, 679–689 (1999).

    Article  CAS  PubMed  Google Scholar 

  74. Mandeville, J. B. et al. MRI measurement of the temporal evolution of relative CMRO2 during rat forepaw stimulation. Magn. Reson. Med. 42, 944–951 (1999).

    Article  CAS  PubMed  Google Scholar 

  75. Kim, S. G., Rostrup, E., Larsson, H. B., Ogawa, S. & Paulson, O. B. Determination of relative CMRO2 from CBF and BOLD changes: significant increase of oxygen consumption rate during visual stimulation. Magn. Reson. Med. 41, 1152–1161 (1999).

    Article  CAS  PubMed  Google Scholar 

  76. Kim, S. G. & Ugurbil, K. Comparison of blood oxygenation and cerebral blood flow effects in fMRI: estimation of relative oxygen consumption change. Magn. Reson. Med. 38, 59–65 (1997).

    Article  CAS  PubMed  Google Scholar 

  77. Kennan, R. P., Scanley, B. E., Innis, R. B. & Gore, J. C. Physiological basis for BOLD MR signal changes due to neuronal stimulation: separation of blood volume and magnetic susceptibility effects. Magn. Reson. Med. 40, 840–846 (1998).

    Article  CAS  PubMed  Google Scholar 

  78. Lee, S. P., Duong, T. Q., Yang, G., Iadecola, C. & Kim, S. G. Relative changes of cerebral arterial and venous blood volumes during increased cerebral blood flow: implications for BOLD fMRI. Magn. Reson. Med. 45, 791–800 (2001).

    Article  CAS  PubMed  Google Scholar 

  79. Bandettini, P. A. et al. Characterization of cerebral blood oxygenation and flow changes during prolonged brain activation. Hum. Brain Mapp. 5, 93–109 (1997).

    Article  CAS  PubMed  Google Scholar 

  80. Mandeville, J. B. et al. Regional sensitivity and coupling of BOLD and CBV changes during stimulation of rat brain. Magn. Reson. Med. 45, 443–447 (2001).

    Article  CAS  PubMed  Google Scholar 

  81. Marota, J. J. et al. Investigation of the early response to rat forepaw stimulation. Magn. Reson. Med. 41, 247–252 (1999).

    Article  CAS  PubMed  Google Scholar 

  82. Jones, M., Berwick, J., Johnston, D. & Mayhew, J. Concurrent optical imaging spectroscopy and laser-Doppler flowmetry: the relationship between blood flow, oxygenation, and volume in rodent barrel cortex. Neuroimage 13, 1002–1015 (2001).

    Article  CAS  PubMed  Google Scholar 

  83. Hu, X., Le, T. H. & Ugurbil, K. Evaluation of the early response in fMRI in individual subjects using short stimulus duration. Magn. Reson. Med. 37, 877–884 (1997).

    Article  CAS  PubMed  Google Scholar 

  84. Logothetis, N. K., Guggenberger, H., Peled, S. & Pauls, J. Functional imaging of the monkey brain. Nature Neurosci. 2, 555–562 (1999).

    Article  CAS  PubMed  Google Scholar 

  85. Menon, R. S. et al. BOLD based functional MRI at 4 Tesla includes a capillary bed contribution: echo-planar imaging correlates with previous optical imaging using intrinsic signals. Magn. Reson. Med. 33, 453–459 (1995).

    Article  CAS  PubMed  Google Scholar 

  86. Ernst, T. & Hennig, J. Observation of a fast response in functional MR. Magn. Reson. Med. 32, 146–149 (1994).

    Article  CAS  PubMed  Google Scholar 

  87. Buxton, R. B. The elusive initial dip. Neuroimage 13, 953–958 (2001).

    Article  CAS  PubMed  Google Scholar 

  88. Schwartz, W. J. et al. Metabolic mapping of functional activity in the hypothalamo–neurohypophysial system of the rat. Science 205, 723–725 (1979).A classic study linking glucose consumption with synaptic activity.

    Article  CAS  PubMed  Google Scholar 

  89. Magistretti, P. J. & Pellerin, L. Cellular mechanisms of brain energy metabolism and their relevance to functional brain imaging. Phil. Trans. R. Soc. Lond. B 354, 1155–1163 (1999).An excellent review of the literature on neurotransmitter recycling and its relationship to neuroimaging measurements. | PubMed |

    Article  CAS  Google Scholar 

  90. Sibson, N. R. et al. Stoichiometric coupling of brain glucose metabolism and glutamatergic neuronal activity. Proc. Natl Acad. Sci. USA 95, 316–321 (1998).

    Article  CAS  PubMed  Google Scholar 

  91. Shulman, R. G. & Rothman, D. L. Interpreting functional imaging studies in terms of neurotransmitter cycling. Proc. Natl Acad. Sci. USA 95, 11993–11998 (1998).

    Article  CAS  PubMed  Google Scholar 

  92. Buxton, R. B. & Frank, L. R. A model for the coupling between cerebral blood flow and oxygen metabolism during neural stimulation. J. Cereb. Blood Flow Metab. 17, 64–72 (1997).A model showing that the apparent mismatch between blood flow and oxygen consumption might be required to support a change in oxygen metabolism, because extraction (through passive diffusion) of oxygen from the blood is less efficient at higher flow rates. See also references 93 and 94.

    Article  CAS  PubMed  Google Scholar 

  93. Hyder, F., Shulman, R. G. & Rothman, D. L. A model for the regulation of cerebral oxygen delivery. J. Appl. Physiol. 85, 554–564 (1998).

    Article  CAS  PubMed  Google Scholar 

  94. Vafaee, M. S. & Gjedde, A. Model of blood-brain transfer of oxygen explains nonlinear flow–metabolism coupling during stimulation of visual cortex. J. Cereb. Blood Flow Metab. 20, 747–754 (2000).

    Article  CAS  PubMed  Google Scholar 

  95. Hoge, R. D. et al. Investigation of BOLD signal dependence on cerebral blood flow and oxygen consumption: the deoxyhemoglobin dilution model. Magn. Reson. Med. 42, 849–863 (1999).

    Article  CAS  PubMed  Google Scholar 

  96. Attwell, D. & Laughlin, S. B. An energy budget for signaling in the grey matter of the brain. J. Cereb. Blood Flow Metab. 21, 1133–1145 (2001).An excellent review of energy consumption in the brain, which concludes that action potentials and postsynaptic effects of glutamate consume much of the energy (47% and 34%, respectively), with the resting potential consuming a smaller amount (13%) and glutamate recycling using only 3%.

    Article  CAS  PubMed  Google Scholar 

  97. Shulman, R. G., Hyder, F. & Rothman, D. L. Cerebral energetics and the glycogen shunt: neurochemical basis of functional imaging. Proc. Natl Acad. Sci. USA 98, 6417–6422 (2001).

    Article  CAS  PubMed  Google Scholar 

  98. Villringer, A. & Dirnagl, U. Coupling of brain activity and cerebral blood flow: basis of functional neuroimaging. Cerebrovasc. Brain Metab. Rev. 7, 240–276 (1995).

    CAS  PubMed  Google Scholar 

  99. Mintun, M. A. et al. Blood flow and oxygen delivery to human brain during functional activity: theoretical modeling and experimental data. Proc. Natl Acad. Sci. USA 98, 6859–6864 (2001).

    Article  CAS  PubMed  Google Scholar 

  100. Powers, W. J., Hirsch, I. B. & Cryer, P. E. Effect of stepped hypoglycemia on regional cerebral blood flow response to physiological brain activation. Am. J. Physiol. 270, H554–H559 (1996).

  101. Cholet, N., Seylaz, J., Lacombe, P. & Bonvento, G. Local uncoupling of the cerebrovascular and metabolic responses to somatosensory stimulation after neuronal nitric oxide synthase inhibition. J. Cereb. Blood Flow Metab. 17, 1191–1201 (1997).

    Article  CAS  PubMed  Google Scholar 

  102. Legatt, A. D., Arezzo, J. & Vaughan, H. G. Jr. Averaged multiple unit activity as an estimate of phasic changes in local neuronal activity: effects of volume-conducted potentials. J. Neurosci. Methods 2, 203–217 (1980).

    Article  CAS  PubMed  Google Scholar 

  103. Freeman, W. Mass Action in the Nervous System (Academic, New York, 1975).

  104. Mitzdorf, U. Properties of the evoked potential generators: current source-density analysis of visually evoked potentials in the cat cortex. Int. J. Neurosci. 33, 33–59 (1987).

    Article  CAS  PubMed  Google Scholar 

  105. Singer, W. Synchronization of cortical activity and its putative role in information processing and learning. Annu. Rev. Physiol. 55, 349–374 (1993).

    Article  CAS  PubMed  Google Scholar 

  106. Singer, W. Neuronal synchrony: a versatile code for the definition of relations? Neuron 24, 49–65; 111–125 (1999). | PubMed |

    Article  CAS  PubMed  Google Scholar 

  107. Shadlen, M. N. & Movshon, J. A. Synchrony unbound: a critical evaluation of the temporal binding hypothesis. Neuron 24, 67–77; 111–125 (1999). | PubMed |

    Article  CAS  PubMed  Google Scholar 

  108. Braitenberg, V. & Schuz, A. Anatomy of the Cortex (Springer, Berlin, 1991).

  109. Peters, A. & Sethares, C. Organization of pyramidal neurons in area 17 of monkey visual cortex. J. Comp. Neurol. 306, 1–23 (1991).

    Article  CAS  PubMed  Google Scholar 

  110. Latawiec, D., Martin, K. A. & Meskenaite, V. Termination of the geniculocortical projection in the striate cortex of macaque monkey: a quantitative immunoelectron microscopic study. J. Comp. Neurol. 419, 306–319 (2000).

    Article  CAS  PubMed  Google Scholar 

  111. Eckhorn, R. et al. Coherent oscillations: a mechanism of feature linking in the visual cortex? Multiple electrode and correlation analyses in the cat. Biol. Cybern. 60, 121–130 (1988).

    Article  CAS  PubMed  Google Scholar 

  112. Roelfsema, P. R., Engel, A. K., Konig, P. & Singer, W. Visuomotor integration is associated with zero time-lag synchronization among cortical areas. Nature 385, 157–161 (1997).

    Article  CAS  PubMed  Google Scholar 

  113. Bressler, S. L., Coppola, R. & Nakamura, R. Episodic multiregional cortical coherence at multiple frequencies during visual task performance. Nature 366, 153–156 (1993).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank W. Newsome, G. DeAngelis, P. Fries, B. Wandell, G. Rees, R. Buxton, P. Bandettini, G. Glover, G. Boynton, M. Raichle and N. Logothetis for detailed comments on this manuscript. The authors are supported by grants from the National Eye Institute and the Human Frontier Science Program.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to David J. Heeger.

Related links

Related links

FURTHER INFORMATION

brain imaging: observing ongoing neural activity

magnetic resonance imaging 

MIT Encyclopedia of Cognitive Sciences

electrophysiology, electric and magnetic evoked fields

magnetic resonance imaging

Glossary

EXTRASTRIATE CORTEX

All visually responsive areas of cortex except the primary visual cortex.

CENTROID

Geometric centre.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Heeger, D., Ress, D. What does fMRI tell us about neuronal activity?. Nat Rev Neurosci 3, 142–151 (2002). https://doi.org/10.1038/nrn730

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrn730

This article is cited by

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