NATUREVol 45312 June 2008 REVIEWS cen function-specific procesing 9-180990) 6 and ed m-uph 78 (09900 nitudr mryiporspur ly sot tsnnot Ee qantifed to reflutely 8 9. responses are sensitive to the size of the activated population,whi 10 0e497-10 review of PET studies of pho blogical processing.Brain Lan 12 tion s.⊥D&Ree ally t stabe5 17 gnetic re sonance tech statistica d xperimental protocol, eof)probablybe the best ng ar eses form ated on d hierarchical processing in primat matri:the MRI is not the methodolog that ha surements 02644952 8rtotaodmgrei 07200l systemn.Nature Re out ne ork activity.Singl uni of int nd ficd and ch ed in the context of the fMRI signal 23 (2006) ergic axo-axonic cells in cortical ry than vrfor the ez,H.H.Recurren ical circuits e26 Nois y ass the brain's neural basis of hacm odynamic re ng of thean and us to thon 63 M.Timofeev.L.Gn a view nentation)should be sufficient to ent exci tion an (that is excluding animal exp eh T 200 tion an dis 3 B.Duq funct we cannot af d to card any releva 6.4 the the no 391245- 01998 ke E M ML&Weh F.V he [C]o 3rdedn (.D.)Mob e ut hetio 37 1R6 functional organization of the brain, as well as to inappropriate experimental protocols that ignore this organization. The fMRI sig￾nal cannot easily differentiate between function-specific processing and neuromodulation, between bottom-up and top-down signals, and it may potentially confuse excitation and inhibition. The mag￾nitude of the fMRI signal cannot be quantified to reflect accurately differences between brain regions, or between tasks within the same region. The origin of the latter problem is not due to our current inability to estimate accurately cerebral metabolic rate of oxygen (CMRO2) from the BOLD signal, but to the fact that haemodynamic responses are sensitive to the size of the activated population, which may change as the sparsity of neural representations varies spatially and temporally. In cortical regions in which stimulus- or task-related perceptual or cognitive capacities are sparsely represented (for example, instantiated in the activity of a very small number of neu￾rons), volume transmission (see Supplementary Information)— which probably underlies the altered states of motivation, attention, learning and memory—may dominate haemodynamic responses and make it impossible to deduce the exact role of the area in the task at hand. Neuromodulation is also likely to affect the ultimate spatiotemporal resolution of the signal. This having been said, and despite its shortcomings, fMRI is cur￾rently the best tool we have for gaining insights into brain function and formulating interesting and eventually testable hypotheses, even though the plausibility of these hypotheses critically depends on used magnetic resonance technology, experimental protocol, statistical analysis and insightful modelling. Theories on the brain’s functional organization (not just modelling of data) will probably be the best strategy for optimizing all of the above. Hypotheses formulated on the basis of fMRI experiments are unlikely to be analytically tested with fMRI itself in terms of neural mechanisms, and this is unlikely to change any time in the near future. Of course, fMRI is not the only methodology that has clear and serious limitations. Electrical measurements of brain activity, includ￾ing invasive techniques with single or multiple electrodes, also fall short of affording real answers about network activity. Single-unit recordings and firing rates are better suited to the study of cellular properties than of neuronal assemblies, and field potentials share much of the ambiguity discussed in the context of the fMRI signal. None of the above techniques is a substitute for the others. Today, a multimodal approach is more necessary than ever for the study of the brain’s function and dysfunction. Such an approach must include further improvements to MRI technology and its combination with other non-invasive techniques that directly assess the brain’s elec￾trical activity, but it also requires a profound understanding of the neural basis of haemodynamic responses and a tight coupling of human and animal experimentation that will allow us to fathom the homologies between humans and other primates that are amen￾able to invasive electrophysiological and pharmacological testing. Claims that computational methods and non-invasive neuroimaging (that is, excluding animal experimentation) should be sufficient to understand brain function and disorders are, in my opinion, naive and utterly incorrect. If we really wish to understand how our brain functions, we cannot afford to discard any relevant methodology, much less one providing direct information from the actual neural elements that underlie all our cognitive capacities. 1. Wandell, B. A., Brewer, A. A. & Dougherty, R. F. Visual field map clusters in human cortex. Phil. Trans. R. Soc. Lond. B 360, 693–707 (2005). This paper provides a description of human visual field maps and the rationale generating and naming them. 2. Haacke, E. M. et al. Magnetic Resonance Imaging: Principles and Sequence Design (John Wiley & Son, New York, 1999). 3. Wood, M. L. & Wehrli, F. W. Principles of magnetic resonance imaging. In Magnetic Resonance Imaging 3rd edn (eds Stark, D. D. & Bradley,W.) 1–14 (Mosby, St Louis/Baltimore/Boston/London/Tokyo, 1999). 4. Buxton, R. B. Introduction to Functional Magnetic Resonance Imaging: Principles and Techniques (Cambridge Univ. Press, Cambridge, UK, 2002). 5. Ogawa, S. & Lee, T. M. Magnetic resonance imaging of blood vessels at high fields: in vivo and in vitro measurements and image simulation. Magn. Reson. Med. 16, 9–18 (1990). 6. Ogawa, S. et al. Oxygenation-sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields. Magn. Reson. Med. 14, 68–78 (1990). 7. Motter, B. C. Focal attention produces spatially selective processing in visual cortical areas V1, V2, and V4 in the presence of competing stimuli. J. Neurophysiol. 70, 909–919 (1993). 8. 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). 9. Petersen, S. E., Fox, P. T., Posner, M. I., Mintun, M. & Raichle, M. E. Positron emission tomographic studies of the processing of single words. J. Cogn. Neurosci. 1, 153–170 (1989). 10. Friston, K. J. et al. The trouble with cognitive subtraction. Neuroimage 4, 97–104 (1996). 11. Poeppel, D. A critical review of PET studies of phonological processing. Brain Lang. 55, 317–351 (1996). 12. Buckner, R. L. et al. Detection of cortical activation during averaged single trials of a cognitive task using functional magnetic resonance imaging. Proc. Natl Acad. Sci. USA 93, 14878–14883 (1996). 13. Krekelberg, B., Boynton, G. M. & van Wezel, R. J. Adaptation: from single cells to BOLD signals. Trends Neurosci. 29, 250–256 (2006). This paper discusses fMRI adaptation designs. 14. Friston, K. J. et al. Analysis of fMRI time-series revisited. Neuroimage 2, 45–53 (1995). 15. Haynes, J. D. & Rees, G. Decoding mental states from brain activity in humans. Nature Rev. Neurosci. 7, 523–534 (2006). 16. Braitenberg, V. & Schuez, A. Cortex: Statistics and Geometry of Neuronal Connectivity 2nd edn (Springer, Berlin, 1998). 17. Douglas, R. J. & Martin, K. A. Neuronal circuits of the neocortex. Annu. Rev. Neurosci. 27, 419–451 (2004). This paper provides a review of cortical microcircuits. 18. Ullman, S. Sequence seeking and counter streams: A computational model for bidirectional information flow in the visual cortex. Cereb. Cortex 5, 1–11 (1995). 19. Friston, K. A theory of cortical responses. Phil. Trans. R. Soc. B 360, 815–836 (2005). 20. Felleman, D. J. & Van Essen, D. C. Distributed hierarchical processing in primate cerebral cortex. Cereb. Cortex 1, 1–47 (1991). 21. Douglas, R. J. & Martin, K. A. Mapping the matrix: the ways of neocortex. Neuron 56, 226–238 (2007). 22. Freund, T. F. Interneuron diversity series: Rhythm and mood in perisomatic inhibition. Trends Neurosci. 26, 489–495 (2003). 23. Markram, H. et al. Interneurons of the neocortical inhibitory system. Nature Rev. Neurosci. 5, 793–807 (2004). This paper is a review on the various types of interneuron. 24. DeFelipe, J. Types of neurons, synaptic connections and chemical characteristics of cells immunoreactive for calbindin-D28K, parvalbumin and calretinin in the neocortex. J. Chem. Neuroanat. 14, 1–19 (1997). 25. Szabadics, J. et al. Excitatory effect of GABAergic axo-axonic cells in cortical microcircuits. Science 311, 233–235 (2006). 26. Douglas, R. J., Koch, C., Mahowald, M., Martin, K. A. & Suarez, H. H. Recurrent excitation in neocortical circuits. Science 269, 981–985 (1995). 27. Shadlen, M. N. & Newsome, W. T. Noise, neural codes and cortical organization. Curr. Opin. Neurobiol. 4, 569–579 (1994). 28. Chance, F. S., Abbott, L. F. & Reyes, A. D. Gain modulation from background synaptic input. Neuron 35, 773–782 (2002). 29. Brunel, N. & Wang, X. J. Effects of neuromodulation in a cortical network model of object working memory dominated by recurrent inhibition. J. Comput. Neurosci. 11, 63–85 (2001). 30. Steriade, M., Timofeev, I. & Grenier, F. Natural waking and sleep states: a view from inside neocortical neurons. J. Neurophysiol. 85, 1969–1985 (2001). 31. McCormick, D. A., Shu, Y. S. & Hasenstaub, A. Balanced recurrent excitation and inhibition in local cortical networks. in Excitatory-Inhibitory Balance: Synapses, Circuits, Systems (ed. Hensch, T.) (Kluver Academic Press, New York, 2003). 32. Haider, B., Duque, A., Hasenstaub, A. R. & McCormick, D. A. Neocortical network activity in vivo is generated through a dynamic balance of excitation and inhibition. J. Neurosci. 26, 4535–4545 (2006). This paper provides a demonstration of the regulation of excitation–inhibition balance changes in vivo. 33. Sherman, S. M. & Guillery, R. W. Exploring the Thalamus and its Role in Cortical Function 2nd edn (MIT Press, Cambridge, Massachusetts, 2006). 34. Crick, F. & Koch, C. Constraints on cortical and thalamic projections: the no￾strong-loops hypothesis. Nature 391, 245–250 (1998). 35. Jueptner, M. & Weiller, C. Review: does measurement of regional cerebral blood flow reflect synaptic activity? Implications for PET and fMRI. Neuroimage 2, 148–156 (1995). 36. Sokoloff, L. et al. 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, 897–916 (1977). 37. Nudo, R. J. & Masterton, R. B. Stimulation-induced [14C]2-deoxyglucose labeling of synaptic activity in the central auditory system. J. Comp. Neurol. 245, 553–565 (1986). NATUREjVol 453j12 June 2008 REVIEWS 877 ©2008 Macmillan Publishers Limited. All rights reserved