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:

The brainweb: Phase synchronization and large-scale integration

Key Points

  • The large-scale integration problem refers to the series of processes whereby the nervous system coordinates activity that is distributed over distant brain regions to produce a unified cognitive moment. One of the most plausible candidate mechanisms behind large-scale integration is the formation of dynamic links among brain regions, links that are mediated by phase synchronization. Phase synchronization refers to the relation between the temporal structures of the neural signals regardless of signal amplitude. Two signals are said to be synchronous if their rhythms coincide.

  • Neural assemblies — distributed networks of neurons linked by reciprocal connections — provide a conceptual framework for the integration of distributed neural activity. Large-scale integration commonly involves neural assemblies separated by long distances.

  • Converging evidence indicates that phase synchrony is probably involved in brain integration. Electrophysiological analyses in cats and primates have shown that the emergence of phase synchrony over widespread cortical domain correlates with the occurrence of attentive and perceptuomotor behaviours, as well as during the execution of a learning task. Analogous findings have been made in humans using electroencephalographic and magnetoencephalographic techniques.

  • Although the evidence for phase synchronization as a mechanism for large-scale integration is well grounded, it is only correlative. Direct proof that changes in synchronous activity can affect behaviour remains to be established in most cases. Similarly, the cellular mechanisms of synchronization, the interplay between slow and fast brain rhythms, and the role of parallel phase synchronization over different frequency bands constitute topics for future research.

Abstract

The emergence of a unified cognitive moment relies on the coordination of scattered mosaics of functionally specialized brain regions. Here we review the mechanisms of large-scale integration that counterbalance the distributed anatomical and functional organization of brain activity to enable the emergence of coherent behaviour and cognition. Although the mechanisms involved in large-scale integration are still largely unknown, we argue that the most plausible candidate is the formation of dynamic links mediated by synchrony over multiple frequency bands.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

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

Figure 1
Figure 2: Neural synchrony as a multiscale phenomenon.
Figure 3: Long-range integration studies I.
Figure 4: Long-range integration studies II.
Figure 5: Interdependence between different frequency components from local field potentials recorded from the cortex of a behaving cat.

Similar content being viewed by others

References

  1. Abeles, M. Local cortical circuits (Springer, Berlin, 1982).

    Google Scholar 

  2. Palm, G. Cell assemblies as a guideline for brain research. Concepts Neurosci. 1, 133–147 ( 1990).

    Google Scholar 

  3. Eichenbaum, H. Thinking about brain cell assemblies. Science 261, 993–994 (1993).

    CAS  PubMed  Google Scholar 

  4. Damasio, A. Synchronous activation in multiple cortical areas: a mechanism for recall . Sem. Neurosci. 2, 287– 296 (1990).

    Google Scholar 

  5. Llinas, R., Ribary, U., Contreras, D. & Pedroarena, C. The neuronal basis for consciousness. Phil. Trans. R. Soc. Lond. B 353, 1841–1849 ( 1998).

    CAS  Google Scholar 

  6. Edelman, G. Neural Darwinism (Basic Books, New York, 1987).

    Google Scholar 

  7. Varela, F. J. Resonant cell assemblies: a new approach to cognitive functions and neuronal synchrony. Biol. Res. 28, 81– 95 (1995).

    CAS  PubMed  Google Scholar 

  8. Goldman-Rakic, P. S. Topography of cognition: parallel distributed networks in primate association cortex. Annu. Rev. Neurosci. 11, 137– 156 (1988).

    Article  CAS  PubMed  Google Scholar 

  9. Goldman-Rakic, P., Chafee, M. & Friedman, H. in Brain Mechanism of Perception and Memory: from Neuron to Behavior (eds Ono, T. et al.) 445–456 (Oxford Univ. Press, New York, 1992).

    Google Scholar 

  10. Mesulam, M. M. Large-scale neurocognitive networks and distributed processing for attention, language, and memory. Ann. Neurol. 28, 597 –613 (1990).

    CAS  PubMed  Google Scholar 

  11. Van Essen, D. C., Anderson, C. H. & Felleman, D. J. Information processing in the primate visual system: an integrated systems perspective. Science 255, 419–423 (1992).

    CAS  PubMed  Google Scholar 

  12. Van Essen, D., Anderson, C. & Olshausen, B. in Large–scale Neuronal Theories of the Brain (ed. Koch, C.) 271–299 (MIT Press, Cambridge, 1994).

    Google Scholar 

  13. Phillips, W. & Singer, W. In search of common foundations for cortical computation. Behav. Brain. Sci. 20, 657–722 (1997).

    CAS  PubMed  Google Scholar 

  14. Saper, C., Iversen, S. & Frackowiak, R. in Principles of Neuroscience 4th edn (eds Kandel, E. R., Scvhwartz, J. H. & Jessell, T. M.) (McGraw–Hill, New York, 2000).

    Google Scholar 

  15. Roskies, A. The binding problem: special issue. Neuron 24, 7–125 (1999).A special issue of Neuron with contributions from many researchers who have worked extensively on the binding problem. It can serve as a sourcebook and contains a thorough bibliography.

    CAS  PubMed  Google Scholar 

  16. Gray, C. M. The temporal correlation hypothesis of visual feature integration: still alive and well. Neuron 24, 31– 47 (1999).

    CAS  PubMed  Google Scholar 

  17. Traub, R., Whittington, M. & Jeffreys, J. Fast Oscillations in Cortical Networks (MIT Press, Cambridge, 1999).

    Google Scholar 

  18. Destexhe, A., Contreras, D. & Steriade, M. Spatiotemporal analysis of local field potentials and unit discharges in cat cerebral cortex during natural wake and sleep states . J. Neurosci. 19, 4595– 4608 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Girard, P., Hupé, J.-M. & Bullier, J. Feedforward and feedback connections between areas V1 and V2 of the monkey have similar rapid conduction velocities. J. Neurophysiol. 85, 1328–1331 (2001).

    CAS  PubMed  Google Scholar 

  20. Bressler, S. L. Large-scale cortical networks and cognition. Brain Res. 20, 288–304 (1995).

    CAS  Google Scholar 

  21. Engel, A. K., Kreiter, A. K., König, P. & Singer, W. Synchronization of oscillatory neuronal responses between striate and extrastriate visual cortical areas of the cat. Proc. Natl Acad. Sci. USA 88, 6048–6052 (1991).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Frien, A., Eckhorn, R., Bauer, R., Woelbern, T. & Kehr, H. Stimulus-specific fast oscillations at zero phase between visual areas V1 and V2 of awake monkey. Neuroreport 5, 2273–2277 (1994).

    CAS  PubMed  Google Scholar 

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

    Google Scholar 

  24. Mountcastle, V. in The mindful brain (eds Edelman, G. & Mountcastle, V.) (MIT Press, Cambridge, 1978).

    Google Scholar 

  25. Kelso, J. Dynamic patterns: The self-organization of brain and behavior (MIT Press, Cambridge, 1995).

    Google Scholar 

  26. Bressler, S. L. & Kelso, J. S. A. Cortical coordination dynamics and cognition. Trends Cogn. Sci. 5, 26–36 (2001).

    PubMed  Google Scholar 

  27. Tononi, G., Sporns, O. & Edelman, G. M. Reentry and the problem of integrating multiple cortical areas: simulation of dynamic integration in the visual system. Cereb. Cortex 2, 310–335 (1992).

    CAS  PubMed  Google Scholar 

  28. Abeles, M. Corticonics: Neural Circuits of the Cerebral Cortex (Cambridge Univ. Press, Cambridge, 1991).

    Google Scholar 

  29. Roland, P. & Seitz, R. in The Principles of Design and Operation of the Brain (ed. Eccles, J. C.) 161–177 (Springer, Berlin, 1990).

    Google Scholar 

  30. Eckhorn, R. Information Processing in the Cortex (ed. Aertsen, A.) 385– 420 (Springer, Berlin, 1992).

    Google Scholar 

  31. 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).This careful study provides direct evidence of synchrony over cortical regions involved in a visuomotor task, by using multiple recordings in a behaving animal.

    CAS  PubMed  Google Scholar 

  32. Munk, M., Roelfsema, P., Fries, P., Kreiter, A. & Singer, W. Rapidly changing synchronization of gamma-frequency oscillations across visual, parietal, and motor areas of the macaque monkeys performing a visuo-motor task. Soc. Neurosci. Abstr. 777, 6 (2000).

    Google Scholar 

  33. von Stein, A., Chiang, C. & Konig, P. Top-down processing mediated by interareal synchronization . Proc. Natl Acad. Sci. USA 97, 14748– 14753 (2000).This paper highlights the top-down influences of behavioural expectation at various levels of the visual stream, and examines the differences in synchronization at long distances. The role of multifrequency interaction is also examined.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Bressler, S. L., Coppola, R. & Nakamura, R. Episodic multiregional cortical coherence at multiple frequencies during visual task performance. Nature 366, 153–156 (1993). This pioneer paper examined for the first time frequency coherence between multiple electrodes implanted on the cortical surface of a behaving monkeys. The results highlight long-distance integration between many regions. The coherence measures need to be refined using present methods.

    CAS  PubMed  Google Scholar 

  35. Bressler, S. L. Interareal synchronization in the visual cortex. Behav. Brain Res. 76, 37–49 ( 1996).

    CAS  PubMed  Google Scholar 

  36. Bressler, S., Ding, M., Liang, H., Viana di Prisco, G. & Nakamura, R. From anticipation to perception:Dynamic reorganization of visual large-scale networks. Cogn. Brain Res. (submitted).

  37. Kalaska, J. F. & Crammond, D. J. Cerebral cortical mechanisms of reaching movements. Science 255, 1517–1523 (1992).

    CAS  PubMed  Google Scholar 

  38. Burnod, Y. et al. Parieto-frontal coding of reaching: an integrated framework . Exp. Brain Res. 129, 325– 346 (1999).

    CAS  PubMed  Google Scholar 

  39. Wise, S. P., Boussaoud, D., Johnson, P. B. & Caminiti, R. Premotor and parietal cortex: corticocortical connectivity and combinatorial computations. Annu. Rev. Neurosci. 20, 25 –42 (1997).

    CAS  PubMed  Google Scholar 

  40. Chafee, M. V. & Goldman-Rakic, P. S. Inactivation of parietal and prefrontal cortex reveals interdependence of neural activity during memory-guided saccades. J. Neurophysiol. 83, 1550– 1566 (2000).

    CAS  PubMed  Google Scholar 

  41. Classen, J., Gerloff, C., Honda, M. & Hallett, M. Integrative visuomotor behavior is associated with interregionally coherent oscillations in the human brain. J. Neurophysiol. 79, 1567– 1573 (1998).

    CAS  PubMed  Google Scholar 

  42. Aoki, F., Fetz, E. E., Shupe, L., Lettich, E. & Ojemann, G. A. Increased gamma-range activity in human sensorimotor cortex during performance of visuomotor tasks. Clin. Neurophysiol. 110, 524–537 ( 1999).

    CAS  PubMed  Google Scholar 

  43. Riehle, A., Grun, S., Diesmann, M. & Aertsen, A. Spike synchronization and rate modulation differentially involved in motor cortical function. Science 278, 1950–1953 ( 1997).

    CAS  PubMed  Google Scholar 

  44. Nelson, J. I., Salin, P. A., Munk, M. H., Arzi, M. & Bullier, J. Spatial and temporal coherence in cortico-cortical connections: a cross–correlation study in areas 17 and 18 in the cat. Vis. Neurosci. 9, 21– 37 (1992).

    CAS  PubMed  Google Scholar 

  45. Bullier, J., Hupe, J. M., James, A. & Girard, P. Functional interactions between areas V1 and V2 in the monkey. J. Physiol. (Paris) 90, 217–220 (1996).

    CAS  Google Scholar 

  46. Engel, A. K., Konig, P., Kreiter, A. K. & Singer, W. Interhemispheric synchronization of oscillatory neuronal responses in cat visual cortex. Science 252, 1177– 1179 (1991).

    CAS  PubMed  Google Scholar 

  47. Buzsaki, G., Leung, L. W. & Vanderwolf, C. H. Cellular bases of hippocampal EEG in the behaving rat. Brain Res. 287, 139– 171 (1983).

    CAS  PubMed  Google Scholar 

  48. Murthy, V. N. & Fetz, E. E. Coherent 25- to 35-Hz oscillations in the sensorimotor cortex of awake behaving monkeys. Proc. Natl Acad. Sci. USA 89, 5670–5674 (1992).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Sanes, J. N. & Donoghue, J. P. Oscillations in local field potentials of the primate motor cortex during voluntary movement. Proc. Natl Acad. Sci. USA 90, 4470– 4474 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Maynard, E. M. et al. Neuronal interactions improve cortical population coding of movement direction. J. Neurosci. 19, 8083 –8093 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Prut, Y. et al. Spatiotemporal structure of cortical activity: properties and behavioral relevance. J. Neurophysiol. 79, 2857–2874 (1998).

    CAS  PubMed  Google Scholar 

  52. Vaadia, E. et al. Dynamics of neuronal interactions in monkey cortex in relation to behavioural events. Nature 373, 515– 518 (1995).

    CAS  PubMed  Google Scholar 

  53. Lachaux, J. P., Rodriguez, E., Martinerie, J. & Varela, F. J. Measuring phase synchrony in brain signals. Hum. Brain Mapp. 8, 194–208 (1999). This paper introduces in detail the methods for synchrony analysis of non-unitary brain signals. The importance of separating amplitude and phase is emphasized as well as the use of surrogate methods for statistical validation.

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Lachaux, J. P. et al. A quantitative study of gamma-band activity in human intracranial recordings triggered by visual stimuli. Eur. J. Neurosci. 12, 2608–2622 (2000).

    CAS  PubMed  Google Scholar 

  55. Rodriguez, E. et al. Perception's shadow: long-distance synchronization of human brain activity. Nature 397, 430– 433 (1999).This study provides direct evidence for the role of large-scale synchrony using scalp electroencephalographic measures during a perceptual task. The existence of a period of phase scattering is first described. The question of the level of resolution of surface recordings needs now to be refined.

    CAS  PubMed  Google Scholar 

  56. Cardoso de Oliveira, S., Donchin, O., Gribova, A., Bergman, H. & Vaadia, E. Dynamic interactions between and within cortical hemispheres during bilateral and unilateral arm movements. (submitted).

  57. Freiwald, W., Kreiter, A. & Singer, W. in 27th Göttingen Neurobiology Conference 491 (1999).

    Google Scholar 

  58. Gramont, F. Role Functionel de la Cooperativité impliqué dans la Preparation à l'Action Thesis, Universite de Provence, Marseille ( 2000).

    Google Scholar 

  59. Srinivasan, R., Russell, D. P., Edelman, G. M. & Tononi, G. Increased synchronization of neuromagnetic responses during conscious perception . J. Neurosci. 19, 5435– 5448 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Von Stein, A., Rappelsberger, P., Sarnthein, J. & Petsche, H. Synchronization between temporal and parietal cortex during multimodal object processing in man. Cereb. Cortex 9, 137– 150 (1999).

    CAS  PubMed  Google Scholar 

  61. Miltner, W. H., Braun, C., Arnold, M., Witte, H. & Taub, E. Coherence of gamma-band EEG activity as a basis for associative learning. Nature 397, 434–436 ( 1999).

    CAS  PubMed  Google Scholar 

  62. Sarnthein, J., Petsche, H., Rappelsberger, P., Shaw, G. L. & von Stein, A. Synchronization between prefrontal and posterior association cortex during human working memory. Proc. Natl Acad. Sci. USA 95, 7092– 7096 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Buzsaki, G. The hippocampo–neocortical dialogue. Cereb. Cortex 6, 81–92 (1996).

    CAS  PubMed  Google Scholar 

  64. Bouyer, J. J., Montaron, M. F. & Rougeul, A. Fast fronto-parietal rhythms during combined focused attentive behaviour and immobility in cat: cortical and thalamic localizations . Electroencephalogr. Clin. Neurophysiol. 51, 244–252 (1981).

    CAS  PubMed  Google Scholar 

  65. John, E. R. et al. Invariant reversible QEEG effects of anesthetics. Conscious Cogn. (in the press).

  66. Munk, M. H., Roelfsema, P. R., Konig, P., Engel, A. K. & Singer, W. Role of reticular activation in the modulation of intracortical synchronization. Science 272, 271–274 (1996).

    CAS  PubMed  Google Scholar 

  67. Herculano-Houzel, S., Munk, M. H., Neuenschwander, S. & Singer, W. Precisely synchronized oscillatory firing patterns require electroencephalographic activation. J. Neurosci. 19, 3992– 4010 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Steinmetz, P. N. et al. Attention modulates synchronized neuronal firing in primate somatosensory cortex. Nature 404, 187– 190 (2000).

    CAS  PubMed  Google Scholar 

  69. Mackey, M. C. & Glass, L. Oscillation and chaos in physiological control systems. Science 197, 287– 289 (1977).

    CAS  PubMed  Google Scholar 

  70. Llinas, R. R., Ribary, U., Jeanmonod, D., Kronberg, E. & Mitra, P. P. Thalamocortical dysrhythmia: a neurological and neuropsychiatric syndrome characterized by magnetoencephalography . Proc. Natl Acad. Sci. USA 96, 15222– 15227 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Martinerie, J. et al. Epileptic seizures can be anticipated by nonlinear analysis . Nature Med. 4, 1173–1176 (1998).

    CAS  PubMed  Google Scholar 

  72. Le Van Quyen, M. et al. Anticipation of epileptic seizures from standard EEG recordings . Lancet 357, 183–188 (2001).

    CAS  PubMed  Google Scholar 

  73. Hurtado, J. M., Lachaux, J. P., Beckley, D. J., Gray, C. M. & Sigvardt, K. A. Inter- and intralimb oscillator coupling in parkinsonian tremor. Mov. Disord. 15, 683–691 (2000).

    CAS  PubMed  Google Scholar 

  74. Hoffman, R. E. & McGlashan, T. H. Parallel distributed processing and the emergence of schizophrenic symptoms. Schizophr. Bull. 19, 119–140 (1993).

    CAS  PubMed  Google Scholar 

  75. Tononi, G. & Edelman, G. M. Schizophrenia and the mechanisms of conscious integration. Brain Res. Rev. 31, 391–400 (2000).

    CAS  PubMed  Google Scholar 

  76. Stopfer, M., Bhagavan, S., Smith, B. H. & Laurent, G. Impaired odour discrimination on desynchronization of odour-encoding neural assemblies. Nature 390, 70– 74 (1997).The only study that gives direct proof of the functional role of synchrony by showing that odour discrimination in an insect deteriorates if the synchronization patterns among olfactory bulb cells are disturbed while leaving their rate modulation unaltered. Similar experiments have yet to be done in vertebrates.

    CAS  PubMed  Google Scholar 

  77. Tass, P. et al. Detection of n:m phase locking from noisy data: application to magnetoencephalography. Phys. Rev. Lett. 81, 3291–3294 (1998).

    CAS  Google Scholar 

  78. Fries, P., Reynolds, J., Rorie, A. & Desimone, R. Modulation of oscillatory neuronal synchronization by selective visual attention. Science 291, 1560–1563 ( 2001).A study of synchronized neuronal activity in attentional selection, showing increased oscillatory synchronized activity in the high frequency range (35–90 Hz) and decreased activity in the low-frequency range (<17 Hz). This phenomenon might serve a fundamental role in enhancing behaviorally relevant signals in the cortex.

    CAS  PubMed  Google Scholar 

  79. Lutz, A., Martinerie, J. & Varela, F. J. Preparation strategies as context for visual perception: a study of endogeneous neural synchronies. (submitted).

  80. Bartos, M., Manor, Y., Nadim, F., Marder, E. & Nusbaum, M. P. Coordination of fast and slow rhythmic neuronal circuits . J. Neurosci. 19, 6650– 6660 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Friston, K. Another neural code? Neuroimage 5, 213– 220 (1997).

    CAS  PubMed  Google Scholar 

  82. Ermentrout, G. B. & Kopell, N. Fine structure of neural spiking and synchronization in the presence of conduction delays . Proc. Natl Acad. Sci. USA 95, 1259– 1564 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Kopell, N., Ermentrout, G. B., Whittington, M. A. & Traub, R. D. Gamma rhythms and beta rhythms have different synchronization properties. Proc. Natl Acad. Sci. USA 97, 1867– 1872 (2000).The different rhythms are based on different firing properties of neurons, in turn depending on the various ion channels used. Using Hodgkin–Huxley equation modelling, this study reproduces the role and interaction of the synchrony over short and long distances, thus illuminating the process of synchrony at the cellular level.

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Steriade, M. & Amzica, F. Intracortical and corticothalamic coherency of fast spontaneous oscillations. Proc. Natl Acad. Sci. USA 93, 2533–2538 ( 1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Llinas, R. R. The intrinsic electrophysiological properties of mammalian neurons: insights into central nervous system function. Science 242, 1654–1664 (1988).

    CAS  PubMed  Google Scholar 

  86. O'Keefe, J. & Burgess, N. Theta activity, virtual navigation and the human hippocampus. Trends Cogn. Sci. 3, 403–406 (1999).

    CAS  PubMed  Google Scholar 

  87. Tesche, C. D. & Karhu, J. Theta oscillations index human hippocampal activation during a working memory task. Proc. Natl Acad. Sci. USA 97, 919–924 ( 2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Dennett, D. & Kinsbourne, M. Time and the observer: the where and when of time in the brain. Behav. Brain Sci. 15 , 183–247 (1991).

    Google Scholar 

  89. Friston, K. Transients, metastability and neuronal dynamics. Neuroimage 5, 164–171 (1997).

    CAS  PubMed  Google Scholar 

  90. Le Van Quyen, M., Martinerie, J., Adam, C., Schuster, H. & Varela, F. Unstable periodic orbits in human epileptic acivity . Physica E. 56, 3401–3411 (1997).

    CAS  Google Scholar 

  91. So, P., Francis, J. T., Netoff, T. I., Gluckman, B. J. & Schiff, S. J. Periodic orbits: a new language for neuronal dynamics. Biophys. J. 74, 2776 –2785 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Friston, K. Functional and effective connectivity: a synthesis. Hum. Brain Mapp. 2, 56–78 ( 1994).

    Google Scholar 

  93. Friston, K. J. The labile brain. I. Neuronal transients and nonlinear coupling. Phil. Trans. R. Soc. Lond. B 355, 215– 236 (2000).

    CAS  Google Scholar 

  94. Tononi, G. & Edelman, G. M. Consciousness and complexity . Science 282, 1846–1851 (1998).

    CAS  PubMed  Google Scholar 

  95. Dehaene, S., Kerszberg, M. & Changeux, J. P. A neuronal model of a global workspace in effortful cognitive tasks. Proc. Natl Acad. Sci. USA 95, 14529–14534 (1998). A detailed analysis of how a cognitive task can be modelled by means of the large-scale integration ('global workspace') of various relevant brain regions starting from local coherence and constituting a global dynamic pattern. A comparison with experimental results is provided.

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Cleeremans, A. The Unity of Consciousness: Binding, Integration and Dissociation (Oxford Univ. Press, New York, (2000).

    Google Scholar 

  97. Thompson, E. & Varela, F. Radical embodiment: neural dynamics and conscious experience. Trends Cogn. Sci. (in the press).

  98. Zeki, S. A Vision of the Brain (Blackwell Scientific, Boston, 1993).

  99. Hupé, J. M. et al. Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons. Nature 394, 784–787 (1998).

    PubMed  Google Scholar 

  100. Roelfsema, P. R., Lamme, V. A. F. & Spekreijse, H. Object-based attention in the primary visual cortex of the macaque monkey. Nature 395, 376– 381 (1998).

    CAS  PubMed  Google Scholar 

  101. König, P., Engel, A. K., Roelfsema, P. R. & Singer, W. How precise is neuronal synchronization? Neural Comp. 7, 469–485 (1995).

    Google Scholar 

  102. Gardner, W. A unifying view of coherence in signal processing. Signal Processing 29, 113–140 ( 1992).

    Google Scholar 

  103. MacIntosh, A., Bookstein, F., Haxby, J. & Grady, C. Spatial pattern analysis of functional brain images using partial least squares. Neuroimage 3, 143–157 ( 1996).

    Google Scholar 

  104. Buchel, C. & Friston, K. J. Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI. Cereb. Cortex 7, 768–778 (1997).

    CAS  PubMed  Google Scholar 

  105. Friston, K., Holmes, A., Poline, J., Price, C. & Frith, C. Detecting activations in PET and fMRI: levels of inference and power. Neuroimage 4, 223– 235 (1996).

    CAS  PubMed  Google Scholar 

  106. Strother, S. C. et al. Principal component analysis and the scaled subprofile model compared to intersubject averaging and statistical parametric mapping. I. 'Functional connectivity' of the human motor system studied with [15O]water PET. J. Cereb. Blood Flow Metab. 15, 738–753 (1995).

    CAS  PubMed  Google Scholar 

  107. Worsley, K. J., Poline, J. B., Friston, K. J. & Evans, A. C. Characterizing the response of PET and fMRI data using multivariate linear models. Neuroimage 6, 305– 319 (1997).

    CAS  PubMed  Google Scholar 

  108. Buchel, C., Coull, J. T. & Friston, K. J. The predictive value of changes in effective connectivity for human learning. Science 283, 1538– 1541 (1999).

    CAS  PubMed  Google Scholar 

  109. Logothetis, N. K., Pauls, J., Oeltermann, A., Trinath, T. & Augath, M. The relationship of LFPs, MUA, and SUA to the BOLD fMRI signal. Soc. Neurosci. Abstr. 309, 5 (2000).

    Google Scholar 

  110. David, O., Garnero, L. & Varela, F. Studying the phase synchrony of neural sources estimated from the MEG/EEG inverse problem. IEEE Trans. Biomed. Eng. (submitted).

  111. Gross, J. et al. Dynamic imaging of coherent sources: studying neural interactions in the human brain. Proc. Natl Acad. Sci. USA 98, 694–699 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  112. Cowey, A. & Walsh, V. Magnetically induced phosphenes in sighted, blind and blindsighted observers. Neuroreport 11, 3269–3273 (2000).

    CAS  PubMed  Google Scholar 

  113. Paus, T. et al. Transcranial magnetic stimulation during positron emission tomography: a new method for studying connectivity of the human cerebral cortex. J. Neurosci. 17, 3178–3184 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Perkel, D. H., Gerstein, G. L. & Moore, G. P. Neuronal spike trains and stochastic point processes. II. Simultaneous spike trains. Biophys. J. 7, 419–440 (1967).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Le van Quyen, M. et al. J. Measuring phase synchrony in neural signals: comparing wavelet analysis and the Hilbert transform. J. Neurosci. Meth. (submitted).

  116. Ding, M., Bressler, S. L., Yang, W. & Liang, H. Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment. Biol. Cybern. 83, 35–45 (2000).

    CAS  PubMed  Google Scholar 

  117. Schack, B. & Krause, W. Dynamic power and coherence analysis of ultra short–term cognitive processes — a methodical study. Brain Topography 8, 127–136 (1995).

    CAS  PubMed  Google Scholar 

  118. Lachaux, J. et al. Studying single-trials of phase-synchronous activity in the brain. Int. J. Bifurcat. Chaos 10, 2429– 2439 (2000).

    Google Scholar 

  119. Kocarev, L., Parlitz, U. & Brown, R. Robust synchronization of chaotic systems. Phys. Rev. E 61, 3716–3720 (2000).

    CAS  Google Scholar 

  120. Nunez, P. et al. EEG coherency I: statistics, reference electrode, volume conduction, Laplacians, cortical imaging and interpretation at multiple scales. Electroencephalogr. Clin. Neurophysiol. 103, 499– 515 (1997).

    CAS  PubMed  Google Scholar 

  121. Friston, K., Stephan, K. & Frackowiak, R. Transient phase-locking and dynamic correlations: are they the same thing? Human Brain Mapp. 5, 48–57 (1997).

    CAS  Google Scholar 

  122. Garnero, L. et al. Introducing priors in the EEG/MEG inverse problem. Electroencephalogr. Clin. Neurophysiol. 50, 183– 189 (1999).

    CAS  Google Scholar 

  123. Muldonado, P., Friedman, H, Il, S. & Gray, C. Temporal dynamics in the striate cortex of alert macaque. Cereb. Cortex (in the press).

  124. Tallon-Baudry, C. & Bertrand, O. Oscillatory gamma activity in humans and its role in object representation. Trends Cogn. Sci. 3, 151–162 ( 1999).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Thanks to Jean-Baptiste Poline for this help concerning metabolic imaging methods. This work was partly supported by the Ministère de l'Education et la Recherche (Action Cognitique) and the Fundacion Puelma (E.R.).

Author information

Authors and Affiliations

Authors

Related links

Related links

ENCYCLOPEDIA OF LIFE SCIENCES

Brain imaging: localization of brain functions

Brain imaging: observing ongoing neural activity

Glossary

INVERSE PROBLEM

Mathematical analysis aimed at localizing the neural sources of the electromagnetic field measured at the scalp surface.

CORTICAL COLUMN

Cylinder of cortex with a diameter up to 1 mm that groups neurons with strong reciprocal connections.

BETA RHYTHM

Neural rhythmic activity (12–25 cycles per second).

GAMMA RHYTHM

Neural rhythmic activity (about 25–70 cycles per second).

TIME–FREQUENCY ANALYSIS

Mathematical techniques used to estimate the spectral components (amplitude, frequency and phase) of short non-stationary signals (for example, Wavelets, ARMA, Hilbert).

CROSS-CORRELATION

Probability for a neuron to spike as a function of the latency of the last spike of a second neuron.

GO–NO-GO PARADIGM

Task in which the subject must produce a motor response for one class of stimulus while ignoring others.

BINOCULAR RIVALRY TASK

Task in which each eye of the subject is shown a different image. This results in a bistable visual experience.

THETA RHYTHM

Neural rhythmic activity (4–8 cycles per second).

PARKINSONIAN TREMOR

Abnormal rhythmic muscular activity (4–8 Hz) observed in Parkinsonian patients.

ALPHA RHYTHM

Neural rhythmic activity (8–12 cycles per second).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Varela, F., Lachaux, JP., Rodriguez, E. et al. The brainweb: Phase synchronization and large-scale integration. Nat Rev Neurosci 2, 229–239 (2001). https://doi.org/10.1038/35067550

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1038/35067550

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