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11 - Functional magnetic resonance imaging

Published online by Cambridge University Press:  05 October 2012

By
Andreas Bartels ,
Jozien Goense  and
Nikos Logothetis
Edited by
Romain Brette  and
Alain Destexhe
Andreas Bartels
Affiliation:
Max Planck Institute for Biological Cybernetics, Germany
Jozien Goense
Affiliation:
Max Planck Institute for Biological Cybernetics, Germany
Nikos Logothetis
Affiliation:
Max Planck Institute for Biological Cybernetics, Germany
Romain Brette
Affiliation:
Ecole Normale Supérieure, Paris
Alain Destexhe
Affiliation:
Centre National de la Recherche Scientifique (CNRS), Paris
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Summary

Introduction

Functional magnetic resonance imaging (fMRI) allows the non-invasive measurement of neural activity nearly everywhere in the brain. The structural predecessor, MRI, was invented in the early 1970s (Lauterbur, 1973) and has been used clinically since the mid-1980s to provide high-resolution structural images of body parts, including rapid successions of images for example of the beating heart. However, it was the advent of blood oxygenation level dependent (BOLD) functional imaging developed first by Ogawa et al. (1990) that made the method crucial especially for the human neurosciences, leading to a vast expansion of both the method of fMRI as well as the field of human neurosciences. fMRI is now a mainstay of neuroscience research and by far the most widespread method for investigations of neural function in the human brain as it is entirely harmless, relatively easy to use, and the data are relatively straightforward to analyze. It is therefore no surprise that fMRI has provided a wealth of information about the functional organization of the human brain. While many publications initially confirmed knowledge derived from invasive animal experiments or from clinical studies, it is now frequently fMRI that opens up a new field of investigation that is then later followed up by invasive methods.

It is important to note that fMRI does not measure electrical or neurochemical activity directly. Physically, it relies on decay time-constants of water protons, which are affected by brain tissue and the concentration of deoxyhemoglobin.

Type
Chapter
Information
Handbook of Neural Activity Measurement , pp. 410 - 469
Publisher: Cambridge University Press
Print publication year: 2012

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References

Ackermann, R. F., Finch, D. M., Babb, T. L. and Engel, J. Jr., (1984). Increased glucose metabolism during long-duration recurrent inhibition of hippocampal pyramidal cells. J. Neurosci., 4, 251–264.CrossRefGoogle ScholarPubMed
Adams, D. L., Sincich, L. C. and Horton, J. C. (2007). Complete pattern of ocular dominance columns in human primary visual cortex. J. Neurosci., 27, 10391–10403.CrossRefGoogle ScholarPubMed
Akgoren, N., Fabricius, M. and Lauritzen, M. (1994). Importance of nitric oxide for local increases of blood flow in rat cerebellar cortex during electrical stimulation. Proc. Natl. Acad. Sci. USA, 91, 5903–5907.CrossRefGoogle ScholarPubMed
Ames, A. III, (2000). CNS energy metabolism as related to function. Brain Res. Rev., 34, 42–68.CrossRefGoogle Scholar
Attwell, D. and Iadecola, C. (2002). The neural basis of functional brain imaging signals. Trends Neurosci., 25, 621–625.CrossRefGoogle ScholarPubMed
Attwell, D. and Laughlin, S. B. (2001). An energy budget for signaling in the grey matter of the brain. J. Cereb. Blood Flow Metab., 21, 1133–1145.CrossRefGoogle Scholar
Bartels, A. and Zeki, S. (2003). Functional brain mapping during free viewing of natural scenes. Human Brain Mapping, 21, 75–83.Google Scholar
Bartels, A. and Zeki, S. (2005a). Brain dynamics during natural viewing conditions – a new guide for mapping connectivity in vivo. NeuroImage, 24, 339–349.CrossRefGoogle ScholarPubMed
Bartels, A. and Zeki, S. (2005b). The chronoarchitecture of the cerebral cortex. Philos. Trans. R. Soc. London, Ser. B, 360, 733–750.Google ScholarPubMed
Bartels, A., Zeki, S. and Logothetis, N. K. (2008a). Natural vision reveals regional specialization to local motion and to contrast-invariant, global flow in the human brain. Cereb. Cortex, 18, 705–717.CrossRefGoogle ScholarPubMed
Bartels, A., Logothetis, N. K. and Moutoussis, K. (2008b). fMRI and its interpretations: an illustration on directional selectivity in area V5/MT. Trends Neurosci., 31, 444–453.CrossRefGoogle ScholarPubMed
Basar, E. (1980). EEG-Brain Dynamics: Relation between EEG and Brain Evoked Potentials. Amsterdam: Elsevier.Google Scholar
Belitski, A., Gretton, A., Magri, C., Murayama, Y., Montemurro, M. A., Logothetis, N. K. and Panzeri, S. (2008). Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information. J. Neurosci., 28, 5696–5709.CrossRefGoogle ScholarPubMed
Bender, D. B. and Youakim, M. (2001). Effect of attentive fixation in macaque thalamus and cortex. J. Neurophysiol., 85, 219–234.CrossRefGoogle ScholarPubMed
Belliveau, J. W., Rosen, B. R., Kantor, H. L., Rzedzian, R. R., Kennedy, D. N., McKinstry, R. C., Vevea, J. M., Cohen, M. S., Pykett, I. L. and Brady, T. J. (1990). Functional cerebral imaging by susceptibility-contrast NMR. Magn. Reson. Med., 14, 538–546.CrossRefGoogle ScholarPubMed
Belliveau, J. W., Kennedy, D. N. Jr., McKinstry, R. C., Buchbinder, B. R., Weisskoff, R. M., Cohen, M. S., Vevea, J. M., Brady, T. J. and Rosen, B. R. (1991). Functional mapping of the human visual cortex by magnetic resonance imaging. Science, 254, 716–719.CrossRefGoogle ScholarPubMed
Biswal, B., Yetkin, F. Z., Haughton, V. M. and Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med., 34, 537–541.CrossRefGoogle ScholarPubMed
Blake, R. and Logothetis, N. K. (2002). Visual competition. Nature Rev. Neurosci., 3, 13–23.CrossRefGoogle ScholarPubMed
Boxerman, J. L., Hamberg, L. M., Rosen, B. R. and Weisskoff, R. M. (1995). MR contrast due to intravascular magnetic susceptibility perturbations. Magn. Reson. Med., 34, 555–566.CrossRefGoogle ScholarPubMed
Braitenberg, V. and Schüz, A. (1998). Cortex: Statistics and Geometry of Neuronal Connectivity. Berlin: Springer.CrossRefGoogle Scholar
Buchwald, J. S. and Grover, F. S. (1970). Amplitudes of background fast activity characteristic of specific brain sites. J. Neurophysiol., 33, 148–159.CrossRefGoogle ScholarPubMed
Buchwald, J. S., Halas, E. S. and Schramm, S. (1965). A comparison of multi-unit activity and EEG activity recorded from the same brain sites during behavioral conditioning. Nature, 205, 1012–1014.CrossRefGoogle Scholar
Buracas, G. T. and Boynton, G. M. (2002). Efficient design of event-related fMRI experiments using M-sequences. NeuroImage, 16, 801–813.CrossRefGoogle ScholarPubMed
Buxton, R. B. (2009). Introduction to Functional Magnetic Resonance Imaging: Principles and Techniques (2nd edition). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Buxton, R. B., Uludag, K., Dubowitz, D. J. and Liu, T. T. (2004). Modeling the hemodynamic response to brain activation. NeuroImage, 23 (Suppl 1), S220–S233.CrossRefGoogle ScholarPubMed
Buzsaki, G. and Chrobak, J. J. (1995). Temporal structure in spatially organized neuronal ensembles: a role for interneuronal networks. Curr. Opinion Neurobiol., 5, 504–510.CrossRefGoogle ScholarPubMed
Buzsaki, G., Bickford, R. G., Ponomareff, G., Thal, L. J., Mandel, R. and Gage, F. H. (1988). Nucleus basalis and thalamic control of neocortical activity in the freely moving rat. J. Neurosci., 8, 4007–4026.CrossRefGoogle ScholarPubMed
Buzsaki, G., Kaila, K. and Raichle, M. (2007). Inhibition and brain work. Neuron, 56, 771–783.CrossRefGoogle ScholarPubMed
Cauli, B., Tong, X. K., Rancillac, A., Serluca, N., Lambolez, B., Rossier, J. and Hamel, E. (2004). Cortical GABA interneurons in neurovascular coupling: relays for subcortical vasoactive pathways. J. Neurosci., 24, 8940–8949.CrossRefGoogle ScholarPubMed
Chaigneau, E., Oheim, M., Audinat, E. and Charpak, S. (2003). Two-photon imaging of capillary blood flow in olfactory bulb glomeruli. Proc. Natl. Acad. Sci. USA, 100, 13081–13086.CrossRefGoogle ScholarPubMed
Cheng, K., Waggoner, R. A. and Tanaka, K. (2001). Human ocular dominance columns as revealed by high-field functional magnetic resonance imaging. Neuron, 32, 359–374.CrossRefGoogle ScholarPubMed
Chih, C. P., and Roberts, E. L. Jr., (2003). Energy substrates for neurons during neural activity: a critical review of the astrocyte-neuron lactate shuttle hypothesis. J. Cereb. Blood Flow Metab., 23, 1263–1281.CrossRefGoogle ScholarPubMed
Cox, D. D. and Savoy, R. L. (2003). Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex. NeuroImage, 19, 261–270.CrossRefGoogle ScholarPubMed
Dechent, P. and Frahm, J. (2000). Direct mapping of ocular dominance columns in human primary visual cortex. NeuroReport, 11, 3247–3249.CrossRefGoogle ScholarPubMed
Dennie, J., Mandeville, J. B., Boxerman, J. L., Packard, S. D., Rosen, B. R. and Weisskoff, R. M. (1998). NMR imaging of changes in vascular morphology due to tumor angiogenesis. Magn. Reson. Med., 40, 793–799.CrossRefGoogle ScholarPubMed
Denys, K., VanDuffel, W., Fize, D., Nelissen, K., Peuskens, H., E. D., and Orban, G. A. (2004). The processing of visual shape in the cerebral cortex of human and nonhuman primates: a functional magnetic resonance imaging study. J. Neurosci., 24, 2551–2565.CrossRefGoogle ScholarPubMed
Disbrow, E. A., Slutsky, D. A., Roberts, T. P. and Krubitzer, L. A. (2000). 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.CrossRefGoogle ScholarPubMed
Douglas, R. J. and Martin, K. A. (2007). Mapping the matrix: the ways of neocortex. Neuron, 56, 226–238.CrossRefGoogle ScholarPubMed
Duong, T. Q., Kim, D. S., Ugurbil, K. and Kim, S. G. (2001). Localized cerebral blood flow response at submillimeter columnar resolution. Proc. Natl. Acad. Sci. USA, 98, 10904–10909.CrossRefGoogle ScholarPubMed
Duong, T. Q., Yacoub, E., Adriany, G., Hu, X. P., Ugurbil, K. and Kim, S. G. (2003). Microvascular BOLD contribution at 4 and 7T in the human brain: gradient-echo and spin-echo fMRI with suppression of blood effects. Magn. Reson. Medi., 49, 1019–1027.Google Scholar
Duvernoy, H. M., Delon, S. and Vannson, J. L. (1981). Cortical blood vessels of the human brain. Brain Res. Bull., 7, 519–579.CrossRefGoogle ScholarPubMed
Engel, A. K., Moll, C. K., Fried, I. and Ojemann, G. A. (2005). Invasive recordings from the human brain: clinical insights and beyond. Nature. Rev. Neurosci., 6, 35–47.CrossRefGoogle ScholarPubMed
Erecinska, M. and Silver, I. A. (1994). Ions and energy in mammalian brain. Prog. Neurobiol., 43, 37–71.CrossRefGoogle ScholarPubMed
Faraci, F. M. and Heistad, D. D. (1998). Regulation of the cerebral circulation: role of endothelium and potassium channels. Physiol. Rev., 78, 53–97.CrossRefGoogle ScholarPubMed
Fergus, A. and Lee, K. S. (1997). GABAergic regulation of cerebral microvascular tone in the rat. J. Cereb. Blood Flow Metab., 17, 992–1003.CrossRefGoogle ScholarPubMed
Fox, P. T. and Raichle, M. E. (1986). Focal physiological uncoupling of cerebral blood-flow and oxidative-metabolism during somatosensory stimulation in human-subjects. Proc. Natl. Acad. Sci. USA, 83, 1140–1144.CrossRefGoogle ScholarPubMed
Fox, P. T., Raichle, M. E., Mintun, M. A. and Dence, C. (1988). Nonoxidative glucose consumption during focal physiologic neural activity. Science, 241, 462–464.CrossRefGoogle ScholarPubMed
Friston, K. J., Frith, C. D., Liddle, P. F. and Frackowiak, R. S. J. (1993). Functional connectivity – the principal-component analysis of large (Pet) data sets. J. Cereb. Blood Flow Metab., 13, 5–14.CrossRefGoogle ScholarPubMed
Friston, K. J., Holmes, A. P., Poline, J. B., Grasby, P. J., Williams, S. C., Frackowiak, R. S. and Turner, R. (1995). Analysis of fMRI time-series revisited. NeuroImage, 2, 45–53.CrossRefGoogle ScholarPubMed
Friston, K. J., Holmes, A., Poline, J. B., Price, C. J. and Frith, C. D. (1996). Detecting activations in PET and fMRI: levels of inference and power. NeuroImage, 4, 223–235.CrossRefGoogle ScholarPubMed
Friston, K. J., Buechel, C., Fink, G. R., Morris, J., Rolls, E. and Dolan, R. J. (1997). Psychophysiological and modulatory interactions in neuroimaging. NeuroImage, 6, 218–229.CrossRefGoogle ScholarPubMed
Friston, K. J., Harrison, L. and Penny, W. (2003). Dynamic causal modeling. NeuroImage, 19, 1273–1302.CrossRefGoogle Scholar
Friston, K. J., Ashburner, J. T., Kiebel, S. J., Nichols, T. E. and Penny, W. D. (editors). (2007). Statistical Parametric Mapping. London: Academic Press.CrossRef
Fromm, G. H. and Bond, H. W. (1964). Slow changes in the electrocorticogram and the activity of cortical neurons. Electroencephalogr. Clin. Neurophysiol., 17, 520–523.CrossRefGoogle ScholarPubMed
Fromm, G. H. and Bond, H. W. (1967). The relationship between neuron activity and cortical steady potentials. Electroencephalogr. Clini. Neurophysiol., 22, 159–166.Google ScholarPubMed
Gail, A., Brinksmeyer, H. J. and Eckhorn, R. (2004). Perception-related modulations of local field potential power and coherence in primary visual cortex of awake monkey during binocular rivalry. Cereb. Cortex, 14, 300–313.CrossRefGoogle ScholarPubMed
Gasser, H. S. and Grundfest, H. (1939). Axon diameters in relation to the spike dimensions and the conduction velocity in mammalian A fibers. Am. J. Physiol., 127, 393–414.Google Scholar
Genovese, C. R., Lazar, N. A. and Nichols, T. (2002). Thresholding of statistical maps in functional neuroimaging using the false discovery rate. NeuroImage, 15, 870–878.CrossRefGoogle ScholarPubMed
Gilbert, C. D. and Sigman, M. (2007). Brain states: top-down influences in sensory processing. Neuron, 54, 677–696.CrossRefGoogle ScholarPubMed
Gjedde, A. and Marrett, S. (2001). Glycolysis in neurons, not astrocytes, delays oxidative metabolism of human visual cortex during sustained checkerboard stimulation in vivo. J. Cereb. Blood Flow Metab., 21, 1384–1392.CrossRefGoogle Scholar
Gladden, L. B. (2004). Lactate metabolism: a new paradigm for the third millennium. J. Physiol., 558, 5–30.CrossRefGoogle ScholarPubMed
Goebel, R., Roebroeck, A., Kim, D. S. and Formisano, E. (2003). Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping. Magn. Reson. Imaging, 21, 1251–1261.CrossRefGoogle ScholarPubMed
Goense, J. B. and Logothetis, N. K. (2006). Laminar specificity in monkey V1 using high-resolution SE-fMRI. Magn. Reson. Imaging, 24, 381–392.CrossRefGoogle ScholarPubMed
Goense, J. B. and Logothetis, N. K. (2008). Neurophysiology of the BOLD fMRI signal in awake monkeys. Curr. Biol., 18, 631–640.CrossRefGoogle ScholarPubMed
Goense, J. B. and Logothetis, N. K. (2010). Physiological basis of the BOLD signal. In: M., Ullsperger (editor), Integrating EEG and fMRI: Recording, Analysis and Integration. Oxford: Oxford University Press, pp. 21–46.Google Scholar
Goense, J. B., Zappe, A. C. and Logothetis, N. K. (2007). High-resolution fMRI of macaque V1. Magn. Reson. Imaging,. 25, 740–747.CrossRefGoogle ScholarPubMed
Gray, C. M., Maldonado, P. E., Wilson, M. and McNaughton, B. (1995). Tetrodes markedly improve the reliability and yield of multiple single-unit isolation from multi-unit recordings in cat striate cortex. J. Neurosci. Methods, 63, 43–54.CrossRefGoogle ScholarPubMed
Grill-Spector, K., Henson, R. and Martin, A. (2006). Repetition and the brain: neural models of stimulus-specific effects. Trends Cogn. Sci., 10, 14–23.CrossRefGoogle ScholarPubMed
Grover, F. S. and Buchwald, J. S. (1970). Correlation of cell size with amplitude of background fast activity in specific brain nuclei. J. Neurophysiol., 33, 160–171.CrossRefGoogle ScholarPubMed
Gsell, W., Burke, M., Wiedermann, D., Bonvento, G., Silva, A. C., Dauphin, F., Buhrle, C., Hoehn, M. and Schwindt, W. (2006). Differential effects of NMDA and AMPA glutamate receptors on functional magnetic resonance imaging signals and evoked neuronal activity during forepaw stimulation of the rat. J. Neurosci., 26, 8409–8416.CrossRefGoogle ScholarPubMed
Gur, M., Beylin, A. and Snodderly, D. M. (1999). Physiological properties of macaque V1 neurons are correlated with extracellular spike amplitude, duration, and polarity. J. Neurophysiol., 82, 1451–1464.CrossRefGoogle ScholarPubMed
Haacke, M. E., Brown, R. W., Thompson, M. R. and Venkatesan, R. (1999). Magnetic Resonance Imaging: Physical Principles and Sequence Design. New York: Wiley-Liss.Google Scholar
Haacke, E. M., Lin, W. L., Hu, X. P. and Thulborn, K. (2001). A current perspective of the status of understanding BOLD imaging and its use in studying brain function: a summary of the workshop at the University of North Carolina in Chapel Hill, 26–28 October, 2000. Nucl. Magn. Reson. Biomed., 14, 384–388.Google Scholar
Hamel, E. (2006). Perivascular nerves and the regulation of cerebrovascular tone. J. Appl. Physiol., 100, 1059–1064.CrossRefGoogle ScholarPubMed
Harada, Y. and Takahashi, T. (1983). The calcium component of the action potential in spinal motoneurones of the rat. J. Physiol., 335, 89–100.CrossRefGoogle ScholarPubMed
Harel, N., Lin, J., Moeller, S., Ugurbil, K. and Yacoub, E. (2006a). Combined imaging-histological study of cortical laminar specificity of fMRI signals. NeuroImage, 29, 879–887.CrossRefGoogle ScholarPubMed
Harel, N., Ugurbil, K., Uludag, K. and Yacoub, E. (2006b). Frontiers of brain mapping using MRI. J. Magn. Reson. Imaging, 23, 945–957.CrossRefGoogle ScholarPubMed
Haynes, J. D. and Rees, G. (2006). Decoding mental states from brain activity in humans. Rev. Neurosci., 7, 523–534.Google ScholarPubMed
Haynes, J. D., Deichmann, R. and Rees, G. (2005). Eye-specific effects of binocular rivalry in the human lateral geniculate nucleus. Nature, 438, 496–499.CrossRefGoogle ScholarPubMed
Henze, D. A., Borhegyi, Z., Csicsvari, J., Mamiya, A., Harris, K. D. and Buzsaki, G. (2000). Intracellular features predicted by extracellular recordings in the hippocampus in vivo. J. Neurophysiol., 84, 390–400.CrossRefGoogle ScholarPubMed
Hevner, R. F., Duff, R. S. and Wong-Riley, M. T. (1992). Coordination of ATP production and consumption in brain: parallel regulation of cytochrome oxidase and Na+, K(+)-ATPase. Neurosci. Lett., 138, 188–192.CrossRefGoogle ScholarPubMed
Hirase, H., Creso, J., Singleton, M., Bartho, P. and Buzsaki, G. (2004). Two-photon imaging of brain pericytes in vivo using dextran-conjugated dyes. Glia, 46, 95–100.CrossRefGoogle ScholarPubMed
Hoffmeyer, H. W., Enager, P., Thomsen, K. J. and Lauritzen, M. J. (2007). Nonlinear neurovascular coupling in rat sensory cortex by activation of transcallosal fibers. J. Cereb. Blood Flow Metab., 27, 575–587.CrossRefGoogle ScholarPubMed
Hopfinger, J. B., Buchel, C., Holmes, A. P. and Friston, K. J. (2000). A study of analysis parameters that influence the sensitivity of event- related fMRI analyses. NeuroImage, 11, 326–333.CrossRefGoogle ScholarPubMed
Huettel, S. A., Song, A. W. and McCarthy, G. (2008). Functional Magnetic Resonance Imaging (2nd edition). Sutherland, MA: Sinauer Associates.Google Scholar
Huk, A. C., Ress, D. and Heeger, D. J. (2001). Neuronal basis of the motion aftereffect reconsidered. Neuron, 32, 161–172.CrossRefGoogle ScholarPubMed
Hunt, C. (1951). The reflex activity of mammalian small-nerve fibers. J. Physiol., 115, 456–469.CrossRefGoogle Scholar
Hyder, F., Patel, A. B., Gjedde, A., Rothman, D. L., Behar, K. L. and Shulman, R. G. (2006). Neuronal-glial glucose oxidation and glutamatergic-GABAergic function. J. Cereb. Blood Flow Metab., 26, 865–877.CrossRefGoogle ScholarPubMed
Iadecola, C. (2004). Neurovascular regulation in the normal brain and in Alzheimer's disease. Nature Rev. Neurosci., 5, 347–360.CrossRefGoogle ScholarPubMed
Iadecola, C. and Nedergaard, M. (2007). Glial regulation of the cerebral microvasculature. Nature Neurosci., 10, 1369–1376.CrossRefGoogle ScholarPubMed
Ido, Y., Chang, K., Woolsey, T. A. and Williamson, J. R. (2001). NADH: sensor of blood flow need in brain, muscle, and other tissues. FASEB J. 15, 1419–1421.CrossRefGoogle ScholarPubMed
Ido, Y., Chang, K. and Williamson, J. R. (2004). NADH augments blood flow in physiologically activated retina and visual cortex. Proc. Natl. Acad. Sci. USA, 101, 653–658.CrossRefGoogle ScholarPubMed
Ito, H., Ibaraki, M., Kanno, I., Fukuda, H. and Miura, S. (2005). Changes in the arterial fraction of human cerebral blood volume during hypercapnia and hypocapnia measured by positron emission tomography. J. Cereb. Blood Flow Metab., 25, 852–857.CrossRefGoogle ScholarPubMed
Jin, T., Zhao, F. and Kim, S. G. (2006). Sources of functional apparent diffusion coefficient changes investigated by diffusion-weighted spin-echo fMRI. Magn. Reson. Med., 56, 1283–1292.CrossRefGoogle ScholarPubMed
Juergens, E., Eckhorn, R., Frien, A. and Woelbern, T. (1996). Restricted coupling range of fast oscillations in striate cortex of awake monkey. In: Brain and Evolution. Berlin: Thieme, p. 418.Google Scholar
Juergens, E., Guettler, A. and Eckhorn, R. (1999). Visual stimulation elicits locked and induced gamma oscillations in monkey intracortical- and EEG-potentials, but not in human EEG. Exp. Brain Res., 129, 247–259.CrossRefGoogle ScholarPubMed
Kamitani, Y. and Tong, F. (2006). Decoding seen and attended motion directions from activity in the human visual cortex. Curr. Biol., 16, 1096–1102.CrossRefGoogle ScholarPubMed
Kamondi, A., Acsady, L., Wang, X. J. and Buzsaki, G. (1998). Theta oscillations in somata and dendrites of hippocampal pyramidal cells in vivo: activity-dependent phase-precession of action potentials. Hippocampus, 8, 244–261.3.0.CO;2-J>CrossRefGoogle ScholarPubMed
Kandel, A. and Buzsaki, G. (1997). Cellular-synaptic generation of sleep spindles, spike-and-wave discharges, and evoked thalamocortical responses in the neocortex of the rat. J. Neurosci., 17, 6783–6797.CrossRefGoogle ScholarPubMed
Katzner, S., Nauhaus, I., Benucci, A., Bonin, V., Ringach, D. L. and Carandini, M. (2009). Local origin of field potentials in visual cortex. Neuron, 61, 35–41.CrossRefGoogle ScholarPubMed
Kennan, R. P., Zhong, J. and Gore, J. C. (1994). Intravascular susceptibility contrast mechanisms in tissues. Magn. Reson. Med., 31, 9–21.CrossRefGoogle ScholarPubMed
Kennedy, C., Des Rosiers, M. H., Sakurada, O., Shinohara, M., Reivich, M., Jehle, J. W. and Sokoloff, L. (1976). Metabolic mapping of the primary visual system of the monkey by means of the autoradiographic [14C]deoxyglucose technique. Proc. Natl. Acad. Sci. USA, 73, 4230–4234.CrossRefGoogle ScholarPubMed
Kiebel, S. J., Poline, J. B., Friston, K. J., Holmes, A. P. and Worsley, K. J. (1999). Robust smoothness estimation in statistical parametric maps using standardized residuals from the general linear model. NeuroImage, 10, 756–766.CrossRefGoogle ScholarPubMed
Kim, T., Hendrich, K. S., Masamoto, K. and Kim, S. G. (2007). Arterial versus total blood volume changes during neural activity-induced cerebral blood flow change: implication for BOLD fMRI. J. Cereb. Blood Flow Metab., 27,1235–1247.CrossRefGoogle ScholarPubMed
Kocsis, B., Bragin, A. and Buzsaki, G. (1999). Interdependence of multiple theta generators in the hippocampus: a partial coherence analysis. J. Neurosci., 19, 6200–6212.CrossRefGoogle ScholarPubMed
Koehler, R. C., Gebremedhin, D. and Harder, D. R. (2006). Role of astrocytes in cerebrovascular regulation. J. Appl. Physiol., 100, 307–317.CrossRefGoogle ScholarPubMed
Kohn, A. and Movshon, J. A. (2003). Neuronal adaptation to visual motion in area MT of the macaque. Neuron, 39, 681–691.CrossRefGoogle ScholarPubMed
Koyama, M., Hasegawa, I., Osada, T., Adachi, Y., Nakahara, K. and Miyashita, Y. (2004). Functional magnetic resonance imaging of macaque monkeys performing visually guided saccade tasks: comparison of cortical eye fields with humans. Neuron, 41, 795–807.CrossRefGoogle ScholarPubMed
Kreiman, G., Hung, C. P., Kraskov, A., Quiroga, R. Q., Poggio, T. and DiCarlo, J. J. (2006). Object selectivity of local field potentials and spikes in the macaque inferior temporal cortex. Neuron, 49, 433–445.CrossRefGoogle ScholarPubMed
Krekelberg, B., Boynton, G. M. and van Wezel, R. J. (2006). Adaptation: from single cells to BOLD signals. Trends Neurosci., 29, 250–256.CrossRefGoogle ScholarPubMed
Kwong, K. K., Belliveau, J. W., Chesler, D. A., Goldberg, I. E., Weisskoff, R. M., Poncelet, B. P., Kennedy, D. N., Hoppel, B. E., Cohen, M. S. and Turner, R. (1992). Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc. Natl. Acad. Sci. USA, 89, 5675–5679.CrossRefGoogle ScholarPubMed
Lauritzen, M. (2005). Reading vascular changes in brain imaging: is dendritic calcium the key?Nature Rev. Neurosci., 6, 77–85.CrossRefGoogle ScholarPubMed
Lauterbur, P. C. (1973). Image formation by induced local interactions – examples employing nuclear magnetic-resonance. Nature, 242, 190–191.CrossRefGoogle Scholar
Lauwers, F., Cassot, F., Lauwers-Cances, V., Puwanarajah, P. and Duvernoy, H. (2008). Morphometry of the human cerebral cortex microcirculation: general characteristics and space-related profiles. NeuroImage, 39, 936–948.CrossRefGoogle ScholarPubMed
Legatt, A. D., Arezzo, J. and Vaughan, H. G. J. (1980). 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.CrossRefGoogle ScholarPubMed
Lehky, S. R. and Maunsell, J. H. (1996). No binocular rivalry in the LGN of alert macaque monkeys. Vision Res., 36, 1225–1234.CrossRefGoogle ScholarPubMed
Lennie, P. (2003). The cost of cortical computation. Curr. Biol., 13, 493–497.CrossRefGoogle ScholarPubMed
Leopold, D. A. and Logothetis, N. K. (1996). Activity changes in early visual cortex reflect monkeys' percepts during binocular rivalry. Nature, 379, 549–553.CrossRefGoogle ScholarPubMed
Li, J. and Iadecola, C. (1994). Nitric oxide and adenosine mediate vasodilation during functional activation in cerebellar cortex. Neuropharmacology, 33, 1453–1461.CrossRefGoogle ScholarPubMed
Li, W., Piech, V. and Gilbert, C. D. (2004). Perceptual learning and top-down influences in primary visual cortex. Nature Neurosci., 7, 651–657.CrossRefGoogle ScholarPubMed
Liang, Z. P. and Lauterbur, P. C. (1999). Principles of Magnetic Resonance Imaging: A Signal Processing Perspective. New York: Wiley.CrossRefGoogle Scholar
Lindsley, D. B. and Wicke, J. D. (1974). The electroencephalogram: autonomous electrical activity in man and animals. In: R. F., Thomson, and M. M., Patterson (editors), Electroencephalography and Human Brain Potentials. New York: Academic Press, pp. 3–83.Google Scholar
Lipton, M. L., Fu, K. M. G., Branch, C. A. and Schroeder, C. E. (2006). Ipsilateral hand input to area 3b revealed by converging hemodynamic and electrophysiological analyses in macaque monkeys. J. Neurosci., 26, 180–185.CrossRefGoogle ScholarPubMed
Liu, T. T. (2004). Efficiency, power, and entropy in event-related fMRI with multiple trial types. Part II: design of experiments. NeuroImage, 21, 401–413.CrossRefGoogle ScholarPubMed
Liu, T. T. and Frank, L. R. (2004). Efficiency, power, and entropy in event-related FMRI with multiple trial types. Part I: theory. NeuroImage, 21, 387–400.CrossRefGoogle ScholarPubMed
Logothetis, N. K. (2003). MR imaging in the non-human primate: studies of function and of dynamic connectivity. Curr. Opinion Neurobiolo., 13, 630–642.CrossRefGoogle ScholarPubMed
Logothetis, N. K. (2008). What we can do and what we cannot do with fMRI. Nature, 453, 869–878.CrossRefGoogle Scholar
Logothetis, N. K. and Schall, J. D. (1989). Neuronal correlates of subjective visual perception. Science, 245, 761–763.CrossRefGoogle ScholarPubMed
Logothetis, N. K., Guggenberger, H., Peled, S. and Pauls, J. (1999). Functional imaging of the monkey brain. Nature Neurosci., 2, 555–562.CrossRefGoogle ScholarPubMed
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T. and Oeltermann, A. (2001). Neurophysiological investigation of the basis of the fMRI signal. Nature, 412, 150–157.CrossRefGoogle ScholarPubMed
Logothetis, N. K., Merkle, H., Augath, M., Trinath, T. and Ugurbil, K. (2002). Ultra high-resolution fMRI in monkeys with implanted RF coils. Neuron, 35, 227–242.CrossRefGoogle ScholarPubMed
Logothetis, N. K., Kayser, C. and Oeltermann, A. (2007). In vivo measurement of cortical impedance spectrum in monkeys: implications for signal propagation. Neuron, 55, 809–823.CrossRefGoogle ScholarPubMed
Lorente, de Nó R. (1947). Analysis of the distribution of action currents of nerve in volume conductors. In: Studies from the Rockefeller Institute Medical Research, Vol 132, A Study of Nerve Physiology, pp. 384–477.Google Scholar
Lowel, S., Freeman, B. and Singer, W. (1987). Topographic organization of the orientation column system in large flat-mounts of the cat visual cortex: a 2-deoxyglucose study. J. Comput. Neurol., 255, 401–415.CrossRefGoogle ScholarPubMed
Lu, H., Zuo, Y., Gu, H., Waltz, J. A., Zhan, W., Scholl, C. A., Rea, W., Yang, Y. and Stein, E. A. (2007). Synchronized delta oscillations correlate with the resting-state functional MRI signal. Proc. Natl. Acad. Sci. USA, 104, 18265–18269.CrossRefGoogle ScholarPubMed
Maandag, N. J., Coman, D., Sanganahalli, B. G., Herman, P., Smith, A. J., Blumenfeld, H., Shulman, R. G. and Hyder, F. (2007). Energetics of neuronal signaling and fMRI activity. Proc. Natl. Acad. Sci. USA, 104, 20546–20551.CrossRefGoogle ScholarPubMed
Mangia, S., Tkac, I., Gruetter, R., Van de Moortele, P. F., Maraviglia, B. and Ugurbil, K. (2007). Sustained neuronal activation raises oxidative metabolism to a new steady-state level: evidence from 1H NMR spectroscopy in the human visual cortex. J. Cereb. Blood Flow Metab., 27, 1055–1063.CrossRefGoogle ScholarPubMed
Mansfield, P. (1977). Multi-planar image-formation using NMR spin echoes. J. Phys. C, 10, L55–L58.CrossRefGoogle Scholar
Mata, M., Fink, D. J., Gainer, H., Smith, C. B., Davidsen, L., Savaki, H., Schwartz, W. J. and Sokoloff, L. (1980). Activity-dependent energy metabolism in rat posterior pituitary primarily reflects sodium pump activity. J. Neurochem., 34, 213–215.CrossRefGoogle ScholarPubMed
Mathiesen, C., Caesar, K., Akgoren, N. and Lauritzen, M. (1998). Modification of activity-dependent increases of cerebral blood flow by excitatory synaptic activity and spikes in rat cerebellar cortex. J. Physiol., 512 (2), 555–566.CrossRefGoogle ScholarPubMed
McAdams, C. J. and Maunsell, J. H. (1999). Effects of attention on orientation-tuning functions of single neurons in macaque cortical area V4. J. Neurosci., 19, 431–441.CrossRefGoogle ScholarPubMed
McAlonan, K., Cavanaugh, J. and Wurtz, R. H. (2006). Attentional modulation of thalamic reticular neurons. J. Neurosci., 26, 4444–4450.CrossRefGoogle ScholarPubMed
McCasland, J. S. and Hibbard, L. S. (1997). GABAergic neurons in barrel cortex show strong, whisker-dependent metabolic activation during normal behavior. J. Neurosci., 17, 5509–5527.CrossRefGoogle ScholarPubMed
McKeown, M. J., Makeig, S., Brown, G. G., Jung, T. P., Kindermann, S. S., Bell, A. J. and Sejnowski, T. J. (1998). Analysis of fMRI data by blind separation into independent spatial components. Human Brain Mapping, 6, 160–188.Google ScholarPubMed
Mehta, A. D., Ulbert, I. and Schroeder, C. E. (2000). Intermodal selective attention in monkeys. I: distribution and timing of effects across visual areas. Cereb. Cortex, 10, 343–358.CrossRefGoogle ScholarPubMed
Menon, R. S. and Goodyear, B. G. (1999). Submillimeter functional localization in human striate cortex using BOLD contrast at 4 Tesla: implications for the vascular point-spread function. Magn. Reson. Medi., 41, 230–235.Google ScholarPubMed
Metea, M. R. and Newman, E. A. (2006). Glial cells dilate and constrict blood vessels: a mechanism of neurovascular coupling. J. Neurosci., 26, 2862–2870.CrossRefGoogle ScholarPubMed
Mintun, M. A., Vlassenko, A. G., Shulman, G. L. and Snyder, A. Z. (2002). Time-related increase of oxygen utilization in continuously activated human visual cortex. NeuroImage, 16, 531–537.CrossRefGoogle ScholarPubMed
Mitzdorf, U. (1985). Current source-density method and application in cat cerebral cortex: investigation of evoked potentials and EEG phenomena. Physiol. Rev., 65, 37–100.CrossRefGoogle ScholarPubMed
Mitzdorf, U. (1987). Properties of the evoked potential generators: current source-density analysis of visually evoked potentials in the cat cortex. Int. J. Neurosci., 33, 33–59.CrossRefGoogle ScholarPubMed
Moon, C. H., Fukuda, M., Park, S. H. and Kim, S. G. (2007). Neural interpretation of blood oxygenation level-dependent fMRI maps at submillimeter columnar resolution. J. Neurosci., 27, 6892–6902.CrossRefGoogle ScholarPubMed
Moutoussis, K. and Zeki, S. (2006). Seeing invisible motion: a human FMRI study. Curr. Biol., 16, 574–579.CrossRefGoogle ScholarPubMed
Moutoussis, K., Keliris, G., Kourtzi, Z. and Logothetis, N. (2005). A binocular rivalry study of motion perception in the human brain. Vision Res., 45, 2231–2243.CrossRefGoogle ScholarPubMed
Mukamel, R., Gelbard, H., Arieli, A., Hasson, U., Fried, I. and Malach, R. (2005). Coupling between neuronal firing, field potentials, and fMRI in human auditory cortex. Science, 309, 951–954.CrossRefGoogle ScholarPubMed
Mulligan, S. J. and Macvicar, B. A. (2004). Calcium transients in astrocyte endfeet cause cerebrovascular constrictions. Nature, 431, 195–199.CrossRefGoogle ScholarPubMed
Nakada, T., Nabetani, A., Kabasawa, H., Nozaki, A. and Matsuzawa, H. (2005). The passage to human MR microscopy: a progress report from Niigata on April 2005. Magn. Reson. Med. Sci., 4, 83–87.CrossRefGoogle ScholarPubMed
Nakahara, K., Hayashi, T., Konishi, S. and Miyashita, Y. (2002). Functional MRI of macaque monkeys performing a cognitive set-shifting task. Science, 295, 1532–1536.CrossRefGoogle ScholarPubMed
Nauhaus, I., Busse, L., Carandini, M. and Ringach, D. L. (2009). Stimulus contrast modulates functional connectivity in visual cortex. Nature Neurosci., 12, 70–76.CrossRefGoogle ScholarPubMed
Nedergaard, M., Ransom, B. and Goldman, S. A. (2003). New roles for astrocytes: redefining the functional architecture of the brain. Trends Neurosci., 26, 523–530.CrossRefGoogle Scholar
Nelson, P. G. (1966). Interaction between spinal motoneurons of the cat.J. Neurophysiol., 29, 275–287.CrossRefGoogle ScholarPubMed
Newman, E. A. (2003). New roles for astrocytes: regulation of synaptic transmission. Trends Neurosci., 26, 536–542.CrossRefGoogle ScholarPubMed
Nicholson, C. and Llinas, R. (1971). Field potentials in the alligator cerebellum and theory of their relationship to Purkinje cell dendritic spikes. J. Neurophysiol., 34, 509–531.CrossRefGoogle ScholarPubMed
Niessing, J., Ebisch, B., Schmidt, K. E., Niessing, M., Singer, W. and Galuske, R. A. (2005). Hemodynamic signals correlate tightly with synchronized gamma oscillations. Science, 309, 948–951.CrossRefGoogle ScholarPubMed
Nudo, R. J. and Masterton, R. B. (1986). Stimulation-induced [14C]2-deoxyglucose labeling of synaptic activity in the central auditory system. J. Comput. Neurol., 245, 553–565.CrossRefGoogle ScholarPubMed
O'Connor, D. H., Fukui, M. M., Pinsk, M. A. and Kastner, S. (2002). Attention modulates responses in the human lateral geniculate nucleus. Nature Neurosci., 5, 1203–1209.CrossRefGoogle ScholarPubMed
Oeltermann, A., Augath, M. A. and Logothetis, N. K. (2007). Simultaneous recording of neuronal signals and functional NMR imaging. Magn. Reson. Imaging, 25, 760–774.CrossRefGoogle ScholarPubMed
Ogawa, S., Lee, T. M., Kay, A. R. and Tank, D. W. (1990). Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc. Natl. Acad. Sci. USA, 87, 9868–9872.CrossRefGoogle ScholarPubMed
Ogawa, S., Tank, D. W., Menon, R., Ellermann, J. M., Kim, S. G., Merkle, H. and Ugurbil, K. (1992). Intrinsic signal changes accompanying sensory stimulation – functional brain mapping with magnetic-resonance-imaging. Proc. Natl. Acad. Sci. USA, 89, 5951–5955.CrossRefGoogle ScholarPubMed
Patel, A. B., de Graaf, R. A., Mason, G. F., Rothman, D. L., Shulman, R. G. and Behar, K. L. (2005). The contribution of GABA to glutamate/glutamine cycling and energy metabolism in the rat cortex in vivo. Proc. Natl. Acad. Sci. USA, 102, 5588–5593.CrossRefGoogle ScholarPubMed
Pellerin, L. and Magistretti, P. J. (1994). Glutamate uptake into astrocytes stimulates aerobic glycolysis: a mechanism coupling neuronal activity to glucose utilization. Proc. Natl. Acad. Sci. USA, 91, 10625–10629.CrossRefGoogle ScholarPubMed
Pellerin, L. and Magistretti, P. J. (2003). Food for thought: challenging the dogmas. J. Cereb. Blood Flow Metab., 23, 1282–1286.CrossRefGoogle ScholarPubMed
Pellerin, L., Bouzier-Sore, A. K., Aubert, A., Serres, S., Merle, M., Costalat, R. and Magistretti, P. J. (2007). Activity-dependent regulation of energy metabolism by astrocytes: an update. Glia, 55, 1251–1262.CrossRefGoogle ScholarPubMed
Peppiatt, C. and Attwell, D. (2004). Neurobiology: feeding the brain. Nature, 431, 137–138.CrossRefGoogle ScholarPubMed
Pessoa, L., Kastner, S. and Ungerleider, L. G. (2003). Neuroimaging studies of attention: from modulation of sensory processing to top-down control. J. Neurosci., 23, 3990–3998.CrossRefGoogle ScholarPubMed
Peters, A. and Payne, B. R. (1993). Numerical relationships between geniculocortical afferents and pyramidal cell modules in cat primary visual cortex. Cereb. Cortex, 3, 69–78.CrossRefGoogle ScholarPubMed
Peters, A., Payne, B. R. and Budd, J. (1994). A numerical analysis of the geniculocortical input to striate cortex in the monkey. Cereb. Cortex, 4, 215–229.CrossRefGoogle ScholarPubMed
Petzold, G. C., Albeanu, D. F., Sato, T. F. and Murthy, V. N. (2008). Coupling of neural activity to blood flow in olfactory glomeruli is mediated by astrocytic pathways. Neuron, 58, 897–910.CrossRefGoogle ScholarPubMed
Pfeuffer, J., Van de Moortele, P. F., Yacoub, E., Shmuel, A., Adriany, G., Andersen, P., Merkle, H., Garwood, M., Ugurbil, K. and Hu, X. P. (2002). Zoomed functional imaging in the human brain at 7 Tesla with simultaneous high spatial and high temporal resolution. NeuroImage, 17, 272–286.CrossRefGoogle Scholar
Pfeuffer, J., Merkle, H., Beyerlein, M., Steudel, T. and Logothetis, N. K. (2004). Anatomical and functional MR imaging in the macaque monkey using a vertical large-bore 7 Tesla setup. Magn. Reson. Imaging, 22, 1343–1359.CrossRefGoogle ScholarPubMed
Poline, J. B., Worsley, K. J., Evans, A. C. and Friston, K. J. (1997). Combining spatial extent and peak intensity to test for activations in functional imaging. NeuroImage, 5, 83–96.CrossRefGoogle ScholarPubMed
Prichard, J., Rothman, D., Novotny, E., Petroff, O., Kuwabara, T., Avison, M., Howseman, A., Hanstock, C. and Shulman, R. (1991). Lactate rise detected by 1H NMR in human visual cortex during physiologic stimulation. Proc. Natl. Acad. Sci. USA, 88, 5829–5831.CrossRefGoogle ScholarPubMed
Privman, E., Nir, Y., Kramer, U., Kipervasser, S., Andelman, F., Neufeld, M. Y., Mukamel, R., Yeshurun, Y., Fried, I. and Malach, R. (2007). Enhanced category tuning revealed by intracranial electroencephalograms in high-order human visual areas. J. Neurosci., 27, 6234–6242.CrossRefGoogle ScholarPubMed
Raichle, M. E. and Mintun, M. A. (2006). Brain work and brain imaging. Annu. Rev. Neurosci., 29, 449–476.CrossRefGoogle ScholarPubMed
Rajimehr, R., Young, J. C. and Tootell, R. B. (2009). An anterior temporal face patch in human cortex, predicted by macaque maps. Proc. Natl. Acad. Sci. USA, 106, 1995–2000.CrossRefGoogle ScholarPubMed
Rauch, A., Rainer, G. and Logothetis, N. K. (2008). The effect of a serotonin-induced dissociation between spiking and perisynaptic activity on BOLD functional MRI. Proc. Natl. Acad. Sci. USA, 105, 6759–6764.CrossRefGoogle ScholarPubMed
Riera, J. J., Schousboe, A., Waagepetersen, H. S., Howarth, C. and Hyder, F. (2008). The micro-architecture of the cerebral cortex: functional neuroimaging models and metabolism. NeuroImage, 40, 1436–1459.CrossRefGoogle ScholarPubMed
Roberts, M. J., Zinke, W., Guo, K., Robertson, R., McDonald, J. S. and Thiele, A. (2005). Acetylcholine dynamically controls spatial integration in marmoset primary visual cortex. J. Neurophysiol, 93, 2062–2072.CrossRefGoogle ScholarPubMed
Roelfsema, P. R., Lamme, V. A. and Spekreijse, H. (1998). Object-based attention in the primary visual cortex of the macaque monkey. Nature, 395, 376–381.CrossRefGoogle ScholarPubMed
Sanchez-Vives, M. V., Nowak, L. G. and McCormick, D. A. (2000). Membrane mechanisms underlying contrast adaptation in cat area 17 in vivo. J. Neurosci., 20, 4267–4285.CrossRefGoogle ScholarPubMed
Sappey-Marinier, D., Calabrese, G., Fein, G., Hugg, J. W., Biggins, C. and Weiner, M. W. (1992). Effect of photic stimulation on human visual cortex lactate and phosphates using 1H and 31P magnetic resonance spectroscopy. J. Cereb. Blood Flow Metab., 12, 584–592.CrossRefGoogle ScholarPubMed
Sawamura, H., Georgieva, S., Vogels, R., VanDuffel W. and Orban, G. A. (2005). Using functional magnetic resonance imaging to assess adaptation and size invariance of shape processing by humans and monkeys. J. Neurosci., 25, 4294–4306.CrossRefGoogle ScholarPubMed
Sawamura, H., Orban, G. A. and Vogels, R. (2006). Selectivity of neuronal adaptation does not match response selectivity: a single-cell study of the FMRI adaptation paradigm. Neuron, 49, 307–318.CrossRefGoogle Scholar
Schummers, J., Yu, H. and Sur, M. (2008). Tuned responses of astrocytes and their influence on hemodynamic signals in the visual cortex. Science, 320, 1638–1643.CrossRefGoogle ScholarPubMed
Schwartz, W. J., Smith, C. B., Davidsen, L., Savaki, H., Sokoloff, L., Mata, M., Fink, D. J. and Gainer, H. (1979). Metabolic mapping of functional activity in the hypothalamoneurohypophysial system of the rat. Science, 205, 723–725.CrossRefGoogle Scholar
Shmuel, A., Augath, M., Oeltermann, A. and Logothetis, N. K. (2006). Negative functional MRI response correlates with decreases in neuronal activity in monkey visual area V1. Nature Neurosci., 9, 569–577.CrossRefGoogle ScholarPubMed
Shoham, S., O'Connor, D. H., Segev, R. (2006). How silent is the brain: is there a “dark matter” problem in neuroscience?J. Comp. Physiol., 192, 777–784.CrossRefGoogle Scholar
Shulman, R. G., Rothman, D. L., Behar, K. L., Hyder, F. (2004). Energetic basis of brain activity: implications for neuroimaging. Trends Neurosci., 27, 489–495.CrossRefGoogle ScholarPubMed
Silva, A. C. and Koretsky, A. P. (2002). Laminar specificity of functional MRI onset times during somatosensory stimulation in rat. Proc. Natl. Acad. Sci. USA, 99, 15182–15187.CrossRefGoogle ScholarPubMed
Simard, M. and Nedergaard, M. (2004). The neurobiology of glia in the context of water and ion homeostasis. Neuroscience, 129, 877–896.CrossRefGoogle ScholarPubMed
Smith, A. J., Blumenfeld, H., Behar, K. L., Rothman, D. L., Shulman, R. G. and Hyder, F. (2002). Cerebral energetics and spiking frequency: the neurophysiological basis of fMRI. Proc. Natl. Acad. Sci. USA, 99, 10765–10770.CrossRefGoogle ScholarPubMed
Sokoloff, L. (1960). The metabolism of the central nervous system in vivo. In: J., Field, H. W., Magoun and V. E., Hall (editors), Handbook of Physiology–Neurophysiology. Washington, DC: American Physiological Society, pp. 1843–1864.Google Scholar
Sokoloff, L. (1999). Energetics of functional activation in neural tissues. Neurochem Res., 24, 321–329.CrossRefGoogle ScholarPubMed
Steriade, M. (1991). Alertness, quiet sleep, dreaming. In: Cerebral Cortex. New York: Plenum Press, pp. 279–357.Google Scholar
Steriade, M. and Hobson, J. (1976). Neuronal activity during the sleep-waking cycle. Prog. Neurobiol., 6, 155–376.CrossRefGoogle ScholarPubMed
Stone, J. (1973). Sampling properties of microelectrodes assessed in the cat's retina. J. Neurophysiol., 36, 1071–1079.CrossRefGoogle ScholarPubMed
Sun, P., Ueno, K., Waggoner, R. A., Gardner, J. L., Tanaka, K. and Cheng, K. (2007). A temporal frequency-dependent functional architecture in human V1 revealed by high-resolution fMRI. Nature Neurosci., 10, 1404–1406.CrossRefGoogle ScholarPubMed
Szentagothai, J. (1978). The Ferrier Lecture, 1977. The neuron network of the cerebral cortex: a functional interpretation. Proc. R. Soc. London, Sci. B, 201, 219–248.CrossRefGoogle ScholarPubMed
Takano, T., Tian, G. F., Peng, W., Lou, N., Libionka, W., Han, X. and Nedergaard, M. (2006). Astrocyte-mediated control of cerebral blood flow. Nature Neurosci., 9, 260–267.CrossRefGoogle ScholarPubMed
Talairach, J. and Tournoux, P. (1988). Co-planar Stereotaxic Atlas of the Human Brain. Stuttgart: Thieme.Google Scholar
Tolias, A. S., Keliris, G. A., Smirnakis, S. M. and Logothetis, N. K. (2005a). Neurons in macaque area V4 acquire directional tuning after adaptation to motion stimuli. Nature Neurosci., 8, 591–593.CrossRefGoogle ScholarPubMed
Tolias, A. S., Sultan, F., Augath, M., Oeltermann, A., Tehovnik, E. J., Schiller, P. H. and Logothetis, N. K. (2005b). Mapping cortical activity elicited with electrical microstimulation using FMRI in the macaque. Neuron, 48, 901–911.CrossRefGoogle ScholarPubMed
Towe, A. L. and Harding, G. W. (1970). Extracellular microelectrode sampling bias. Exp. Neurol., 29, 366–381.CrossRefGoogle ScholarPubMed
Ts'o, D. Y., Frostig, R. D., Lieke, E. E. and Grinvald, A. (1990). Functional organization of primate visual cortex revealed by high resolution optical imaging. Science, 249, 417–420.CrossRefGoogle ScholarPubMed
Tsao, D. Y., VanDuffel, W., Sasaki, Y., Fize, D., Knutsen, T. A., Mandeville, J. B., Wald, L. L., Dale, A. M., Rosen, B. R., Van Essen, D. C., Livingstone, M. S., Orban, G. A. and Tootell, R. B. (2003). Stereopsis activates V3A and caudal intraparietal areas in macaques and humans. Neuron, 39, 555–568.CrossRefGoogle ScholarPubMed
Tsao, D. Y., Freiwald, W. A., Tootell, R. B. and Livingstone, M. S. (2006). A cortical region consisting entirely of face-selective cells. Science, 311, 670–674.CrossRefGoogle ScholarPubMed
Tsao, D. Y., Moeller, S. and Freiwald, W. A. (2008). Comparing face patch systems in macaques and humans. Proc. Natl. Acad. Sci. USA, 105, 19514–19519.CrossRefGoogle ScholarPubMed
Ugurbil, K., Adriany, G., Andersen, P., Chen, W., Gruetter R., Hu, X. P., Merkle, H., Kim, D. S., Kim, S. G., Strupp, J., Zhu, X. H. and Ogawa, S. (2000). Magnetic resonance studies of brain function and neurochemistry. Annu. Rev. Biomed. Eng., 2, 633–660.CrossRefGoogle ScholarPubMed
Ugurbil, K., Toth, L. and Kim, D. S. (2003). How accurate is magnetic resonance imaging of brain function?Trends Neurosci., 26, 108–114.CrossRefGoogle ScholarPubMed
Ugurbil, K., Adriany, G., Akgun, C., Andersen, P., Chen, W., Garwood, M., Gruetter, R., Henry, P. G., Marjanska, M., Moeller, S., Van de Moortele, P. F., Pruessmann, K. P., Tkac, I., Vaughan, J. T., Wiesinger, F., Yacoub, E. and Zhu, X. H. (2006). High magnetic fields for imaging cerebral morphology, function, and biochemistry. In: P. M., Robitaille and L. J., Berliner (editors), Ultra High Field Magnetic Resonance Imaging. New York: Springer, pp. 285–342.Google Scholar
VanDuffel, W., Fize, D., Peuskens, H., Denys, K., Sunaert, S., Todd, J. T. and Orban, G. A. (2002). Extracting 3D from motion: differences in human and monkey intraparietal cortex. Science, 298, 413–415.CrossRefGoogle ScholarPubMed
Viswanathan, A. and Freeman, R. D. (2007). Neurometabolic coupling in cerebral cortex reflects synaptic more than spiking activity. Nature Neurosci., 10, 1308–1312.CrossRefGoogle ScholarPubMed
Vlassenko, A. G., Rundle, M. M. and Mintun, M. A. (2006a). Human brain glucose metabolism may evolve during activation: findings from a modified FDG PET paradigm. NeuroImage, 33, 1036–1041.CrossRefGoogle ScholarPubMed
Vlassenko, A. G., Rundle, M. M., Raichle, M. E. and Mintun, M. A. (2006b). Regulation of blood flow in activated human brain by cytosolic NADH/NAD+ ratio. Proc. Natl. Acad. Sci. USA, 103, 1964–1969.CrossRefGoogle ScholarPubMed
Wager, T. D. and Nichols, T. E. (2003). Optimization of experimental design in fMRI: a general framework using a genetic algorithm. NeuroImage, 18, 293–309.CrossRefGoogle ScholarPubMed
Wald, L. L., Fischl, B. and Rosen, B. R. (2006). High-resolution and microscopic imaging at high field. In: P. M., Robitaille and L. J., Berliner (editors), Ultra High Field Magnetic Resonance Imaging. New York: Springer, pp. 343–371.Google Scholar
Waldvogel, D., Van G. P., , Muellbacher, W., Ziemann, U., Immisch, I. and Hallett, M. (2000). The relative metabolic demand of inhibition and excitation. Nature, 406, 995–998.CrossRefGoogle ScholarPubMed
Wang, H., Hitron, I. M., Iadecola, C. and Pickel, V. M. (2005). Synaptic and vascular associations of neurons containing cyclooxygenase-2 and nitric oxide synthase in rat somatosensory cortex. Cereb. Cortex, 15, 1250–1260.CrossRefGoogle ScholarPubMed
Weber, B., Keller, A. L., Reichold, J. and Logothetis, N. K. (2008). The microvascular system of the striate and extrastriate visual cortex of the macaque. Cereb. Cortex, 18, 2318–2330.CrossRefGoogle ScholarPubMed
Weisskoff, R. M., Zuo, C. S., Boxerman, J. L. and Rosen, B. R. (1994). Microscopic susceptibility variation and transverse relaxation: theory and experiment. Magn. Reson. Med., 31, 601–610.CrossRefGoogle ScholarPubMed
Wilke, M., Logothetis, N. K. and Leopold, D. A. (2006). Local field potential reflects perceptual suppression in monkey visual cortex. Proc. Natl. Acad. Sci. USA, 103, 17507–17512.CrossRefGoogle ScholarPubMed
Wong-Riley, M. T. (1989). Cytochrome oxidase: an endogenous metabolic marker for neuronal activity. Trends Neurosci., 12, 94–101.CrossRefGoogle ScholarPubMed
Woolrich, M. W., Ripley, B. D., Brady, M. and Smith, S. M. (2001). Temporal autocorrelation in univariate linear modeling of FMRI data. NeuroImage, 14, 1370–1386.CrossRefGoogle ScholarPubMed
Worsley, K. J. (2005). Spatial smoothing of autocorrelations to control the degrees of freedom in fMRI analysis. NeuroImage, 26, 635–641.CrossRefGoogle ScholarPubMed
Wu, E. X., Tang, H. and Jensen, J. H. (2004). Applications of ultrasmall superparamagnetic iron oxide contrast agents in the MR study of animal models. Nucl. Magn. Reson. Biomed., 17, 478–483.Google ScholarPubMed
Wunderlich, K., Schneider, K. A. and Kastner, S. (2005). Neural correlates of binocular rivalry in the human lateral geniculate nucleus. Nature Neurosci., 8, 1595–1602.CrossRefGoogle ScholarPubMed
Xu, F., Liu, N., Kida, I., Rothman, D. L., Hyder, F. and Shepherd, G. M. (2003). Odor maps of aldehydes and esters revealed by functional MRI in the glomerular layer of the mouse olfactory bulb. Proc. Natl. Acad. Sci. USA, 100, 11029–11034.CrossRefGoogle ScholarPubMed
Yacoub, E., Shmuel, A., Pfeuffer, J., Van de Moortele, P. F., Adriany, G., Andersen, P., Vaughan, J. T., Merkle, H., Ugurbil, K. and Hu, X. P. (2001). Imaging brain function in humans at 7 Tesla. Magn. Reson. Med., 45, 588–594.CrossRefGoogle ScholarPubMed
Yacoub, E., Van de Moortele, P. F., Shmuel, A. and Ugurbil, K. (2005). Signal and noise characteristics of Hahn SE and GE BOLD fMRI at 7T in humans. NeuroImage, 24, 738–750.CrossRefGoogle Scholar
Yacoub, E., Ugurbil, K. and Harel, N. (2006). The spatial dependence of the poststimulus undershoot as revealed by high-resolution BOLD- and CBV-weighted fMRI. J. Cereb. Blood Flow Metab., 26, 634–644.CrossRefGoogle ScholarPubMed
Yacoub, E., Shmuel, A., Logothetis, N. and Ugurbil, K. (2007). Robust detection of ocular dominance columns in humans using Hahn spin echo BOLD functional MRI at 7 Tesla. NeuroImage, 37, 1161–1177.CrossRefGoogle ScholarPubMed
Yacoub, E., Harel, N. and Ugurbil, K. (2008). High-field fMRI unveils orientation columns in humans. Proc. Natl. Acad. Sci. USA, 105, 10607–10612.CrossRefGoogle ScholarPubMed
Yang, G., Zhang, Y., Ross, M. E. and Iadecola, C. (2003). Attenuation of activity-induced increases in cerebellar blood flow in mice lacking neuronal nitric oxide synthase. Am. J. Physiol. Heart. Circ. Physiol., 285, H298–H304.CrossRefGoogle ScholarPubMed
Yoshor, D., Ghose, G. M., Bosking, W. H., Sun, P. and Maunsell, J. H. (2007). Spatial attention does not strongly modulate neuronal responses in early human visual cortex. J Neurosci., 27, 13205–13209.CrossRefGoogle Scholar
Zappe, A. C., Pfeuffer, J., Merkle, H., Logothetis, N. K. and Goense, J. B. (2008). The effect of labeling parameters on perfusion-based fMRI in nonhuman primates. J. Cereb. Blood Flow Metab., 28, 640–652.CrossRefGoogle ScholarPubMed
Zeki, S. and Ffytche, D. (1998). The Riddoch syndrome: insights into the neurobiology of conscious vision. Brain, 121, 25–45.CrossRefGoogle ScholarPubMed
Zhang, C. and Wong-Riley, M. T. (1996). Do nitric oxide synthase, NMDA receptor subunit R1 and cytochrome oxidase co-localize in the rat central nervous system?Brain Res., 729, 205–215.CrossRefGoogle ScholarPubMed
Zhao, F. Q., Wang, P. and Kim, S. G. (2004). Cortical depth-dependent gradient-echo and spin-echo BOLD fMRI at 9.4T. Magn. Reson. Med., 51, 518–524.CrossRefGoogle ScholarPubMed
Zhao, F., Wang, P., Hendrich, K. and Kim, S. G. (2005). Spatial specificity of cerebral blood volume-weighted fMRI responses at columnar resolution. NeuroImage, 27, 416–424.CrossRefGoogle ScholarPubMed
Zhao, F., Wang, P., Hendrich, K., Ugurbil, K. and Kim, S. G. (2006). Cortical layer-dependent BOLD and CBV responses measured by spin-echo and gradient-echo fMRI: insights into hemodynamic regulation. NeuroImage, 30, 1149–1160.CrossRefGoogle ScholarPubMed
Zhao, F., Jin, T., Wang, P. and Kim, S. G. (2007). Improved spatial localization of post-stimulus BOLD undershoot relative to positive BOLD. NeuroImage, 34, 1084–1092.CrossRefGoogle ScholarPubMed
Zonta, M., Angulo, M. C., Gobbo, S., Rosengarten, B., Hossmann, K. A., Pozzan, T. and Carmignoto, G. (2003). Neuron-to-astrocyte signaling is central to the dynamic control of brain microcirculation. Nature Neurosci., 6, 43–50.CrossRefGoogle ScholarPubMed

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