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Detection of Phase Synchronization in Multivariate Single Brain Signals by a Clustering Approach

  • Axel Hutt
  • Matthias H.J. Munk
Chapter
Part of the Springer Series in Computational Neuroscience book series (NEUROSCI, volume 2)

Abstract

Analog signals of the cerebral cortex in behaving subjects frequently express strong oscillatory components. To investigate functional interactions among different areas of the cortex, it is biologically plausible to determine dependencies of oscillatory signals such as their phase relation both within and across areas. The chapter introduces a cluster approach algorithm to detect phase synchronization in single brain signals. The introduced synchronization index allows for the extraction of time windows, which exhibit strong phase synchronization in all examined time series. This kind of phase synchronization is highly non-stationary and is called mutual phase synchronization. Further the assessment of single trials with respect to the trial average revealed that a number of features in time–frequency space are common to different trials.

Keywords

Phase Difference Cluster Centre Cluster Result Single Trial Phase Synchronization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Allefeld, C., Kurths, J.: Multivariate phase synchronization analysis of EEG data. IEICE Trans. Fundam. E86-A(9), 2218–2221 (2003)Google Scholar
  2. 2.
    Arieli, A., Shoham, D., Hildesheim, R., Grinvald, A.: Coherent spatio-temporal pattern of on-going activity revealed by real-time optical imaging coupled with single unit recording in the cat visual cortex. J. Neurophysiol. 73, 2072–2093 (1995)PubMedGoogle Scholar
  3. 3.
    beim Graben, P., Saddy, J.D., Schlesewsky, J.D., Kurths, J.: Symbolic dynamics of event-related brain potentials. Phys. Rev. E 62(4), 5518–5541 (2000)CrossRefGoogle Scholar
  4. 4.
    Boashash, B.: Estimating and interpreting the instantaneous frequency of a signal – part 1: Fundamentals. Proc. IEEE 80(4), 520–538 (1992)CrossRefGoogle Scholar
  5. 5.
    Breakspear, M., Terry, J.: Detection and description of nonlinear interdependence in normal multichannel human EEG. Clin. Neurophysiol. 113, 735–753 (2002)PubMedCrossRefGoogle Scholar
  6. 6.
    Bressler, S.: Interareal synchronization in the visual cortex. Beh. Brain Res. 76, 37–49 (1996)CrossRefGoogle Scholar
  7. 7.
    Castelo-Branco, M., Neuenschwander, S., Singer, W.: Synchronization of visual response between the cortex, lateral geniculate nucleus, and retina in the anesthetized cat. J. Neurosci. 18(16), 6395–6410 (1998)PubMedGoogle Scholar
  8. 8.
    DeShazer, D., Breban, R., Ott, E., Roy, R.: Detecting phase synchronization in a chaotic laser array. Phys. Rev. Lett. 87(4), 044101 (2001)Google Scholar
  9. 9.
    Duda, R., Hart, P.: Pattern Classification and Scene Analysis. Wiley, New York (1973)Google Scholar
  10. 10.
    Eckhorn, R., Bauer, R., Jordan, W., Brosch, M., Kruse, W., Munk, M., Reitboeck, H.: Coherent oscillations: a mechanism of feature linking in the visual cortex? multiple electrode and correlation analyses in the cat. Biol. Cybern. 60, 121–130 (1988)PubMedCrossRefGoogle Scholar
  11. 11.
    Effern, A., Lehnertz, K., Fernandez, G., Grunwald, T., David P., Elger, C.: Single trial analysis of event related potentials: non-linear de-noising with wavelets. Clin. Neurophysiol. 111, 2255–2263 (2000)PubMedCrossRefGoogle Scholar
  12. 12.
    Engel, A., Koenig, O., Kreiter, A., Singer, W.: Interhemispheric synchronization of oscillatory neuronal response in cat visual cortex. Science 252, 1177–1179 (1991)CrossRefGoogle Scholar
  13. 13.
    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)PubMedCrossRefGoogle Scholar
  14. 14.
    Gratton, G., Coles, M., Donchin, E.: A procedure for using multi-electrode information in the analysis of components of the event-related potential: vector filter. Psychophysiology. 26, 222–232 (1989)PubMedCrossRefGoogle Scholar
  15. 15.
    Grindvald, A., Arieli, A., Tsodyks, M., Kenet, T.: Neuronal assemblies: single cortical neurons are obedient members of a huge orchestra. Biopolymers 68, 422–436 (2002)CrossRefGoogle Scholar
  16. 16.
    Haig, A., Gordon, E., Wright, J., Meares, R., Bahramali, H.: Synchronous cortical gamma-band activity in task-relevant cognition. Neuroreport 11, 669–675 (2000)PubMedCrossRefGoogle Scholar
  17. 17.
    Hutt, A.: Methoden zur Untersuchung der Dynamik raumzeitlicher Signale. MPI Series in Cognitive Neuroscience, vol. 15. PhD thesis. Max Planck-Institute of Cognitive Neuroscience, Leipzig (2001).Google Scholar
  18. 18.
    Hutt, A.: Spatiotemporal modelling of EEG/MEG. Brain Topogr. 14(4) (2002)Google Scholar
  19. 19.
    Hutt, A.: An analytical framework for modeling evoked and event-related potentials. Int. J. Bif. Chaos 14(2), 653–666 (2004)CrossRefGoogle Scholar
  20. 20.
    Hutt, A., Daffertshofer, A., Steinmetz, U.: Detection of mutual phase synchronization in multivariate signals and application to phase ensembles and chaotic data. Phys. Rev. E 68, 036219 (2003)Google Scholar
  21. 21.
    Hutt, A., Munk, M.H.: Mutual phase synchronization in single trial data. Chaos Complex. Lett. 2(2), 6 (2006)Google Scholar
  22. 22.
    Hutt, A., Riedel, H.: Analysis and modeling of quasi-stationary multivariate time series and their application to middle latency auditory evoked potentials. Phys. D 177, 203 (2003)Google Scholar
  23. 23.
    Hutt, A., Schrauf, M.: Detection of transient synchronization in multivariate brain signals, application to event-related potentials. Chaos Complex. Lett. 3(1), 1–24 (2007)Google Scholar
  24. 24.
    Hutt, A., Svensen, M., Kruggel, F., Friedrich, R.: Detection of fixed points in spatiotemporal signals by a clustering method. Phys. Rev. E 61(5), R4691–R4693 (2000)CrossRefGoogle Scholar
  25. 25.
    Hutt, A., Uhl, C., Friedrich, R.: Analysis of spatio-temporal signals: a method based on perturbation theory. Phys. Rev. E 60(2), 1350–1358 (1999)CrossRefGoogle Scholar
  26. 26.
    Ioannides, A., Kostopoulos, G., Laskaris, N., Liu, L., Shibata, T., Schellens, M., Poghosyan, V., Khurshudyan, A.: Timing and connectivity in the human somatosensory cortex from single trial mass electrical activity. Hum. Brain Mapp. 15, 231–246 (2002)PubMedCrossRefGoogle Scholar
  27. 27.
    Karjalainen, P.A., Kaipio, J.P.: Subspace regularization method for the single-trial estimation of evoked potentials. IEEE Trans. Biomed. Eng. 46(7), 849–859 (1999)PubMedCrossRefGoogle Scholar
  28. 28.
    Koch, C.: Biophysics of Computation. Oxford University Press, Oxford (1999)Google Scholar
  29. 29.
    Kuramoto, Y.: Chemical Oscillations, Waves, and Turbulence. Springer, Berlin (1984)CrossRefGoogle Scholar
  30. 30.
    Knig, P., Engel, A., Singer, W.: Relation between oscillatory activity and long-range synchronization in cat visual cortex. Proc Natl Acad Sci USA 92, 290–294 (1995)CrossRefGoogle Scholar
  31. 31.
    Lachaux, J.P., Rodriguez, E., Martinerie, J., Varela, F.: Measuring phase synchrony in brain signals. Hum. Brain Mapp. 8, 194–208 (1999)PubMedCrossRefGoogle Scholar
  32. 32.
    Laskaris, N., Ioannides, A.: Semantic geodesic maps: a unifying geometrical approach for studying the structure and dynamics of single trial evoked responses. Clin. Neurophysiol. 113, 1209–1226 (2002)PubMedCrossRefGoogle Scholar
  33. 33.
    Le Van Quyen, M., Foucher, J., Lachaux, J., Rodriguez, E., Lutz, A., Martinerie, J., Varela, F.: Comparison of hilbert transform and wavelet methods for the analysis of neuronal synchrony. J. Neurosci. Methods 111(2), 83–98 (2001)CrossRefGoogle Scholar
  34. 34.
    Lehmann, D., Skrandies, W.: Reference-free identification of components of checkerboard-evoked multichannel potential fields. Electroenceph. Clin. Neurophysiol. 48, 609 (1980)Google Scholar
  35. 35.
    Liu, F., Hu, B., Wang, W.: Effects of correlated and independant noise on signal processing in neuronal systems. Phys. Rev. E 63, 031,907 (2001)Google Scholar
  36. 36.
    Makeig, S., Jung, T., Bell, A., Sejnowski, T.: Blind separation of auditory event-related brain responses into independent components. Proc. Natl. Acad. Sci. 94, 10979–10984 (1997)PubMedCrossRefGoogle Scholar
  37. 37.
    Makeig, S., Westerfield, M., Jung, T., Enghoff, S., Townsend, J., Courchesne, E., Sejnowski, T.: Dynamic brain sources of visual evoked responses. Science 295, 690–694 (2002)PubMedCrossRefGoogle Scholar
  38. 38.
    Mardia, K., Jupp, P.: Directional Statistics. Wiley, New York (1999)CrossRefGoogle Scholar
  39. 39.
    Nelson, J., Salin, P., Munk, M., 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)PubMedCrossRefGoogle Scholar
  40. 40.
    Nowak, L., Munk, M., Nelson, J., James, A., Bullier, J.: Structural basis of cortical synchronization. i. three types of interhemispheric coupling. J. Neurophysiol. 74, 2379–2400 (1995)PubMedGoogle Scholar
  41. 41.
    Pikovsky, A., Rosenblum, M., Kurths, J.: Phase synchronization in regular and chaotic systems. Int. J. Bif. Chaos 10(10), 2219 (2000)Google Scholar
  42. 42.
    Pikovsky, A., Rosenblum, M., Kurths, J.: Synchronization: a universal concept in nonlinear sciences. Cambridge University Press, Cambridge (2001)CrossRefGoogle Scholar
  43. 43.
    Prusseit, J., Lehnertz, K.: Stochastic qualifiers of epileptic brain dynamics. Phys. Rev. Lett. 98, 138,103 (2007)Google Scholar
  44. 44.
    Quian Quiroga, R., Garcia, H.: Single-trial event-related potentials with wavelet denoising. Clin. Neurophysiol. 114, 376–390 (2003)PubMedCrossRefGoogle Scholar
  45. 45.
    Salin, P., Bullier, J.: Corticocortical connections in the visual system: structure and function. Physiol. Rev. 75, 107–154 (1995)PubMedGoogle Scholar
  46. 46.
    Salinas, E., Sejnowski, T.: Correlated neuronal activity and the flow of neural information. Nat. Rev. Neurosci. 2, 539–550 (2001)PubMedCrossRefGoogle Scholar
  47. 47.
    Shahl, A., Bressler, S., Knuth, K., Ding, M., Mehta, A., Ulbert, I., Schroeder, C.: Neural dynamics and the fundamental mechanisms of event-related brain potentials. Cereb. Cortex 14, 476–483 (2004)CrossRefGoogle Scholar
  48. 48.
    Singer, W.: Neural synchrony: a versatile code for the definition of relations? Neuron 24, 49–65 (1999)PubMedCrossRefGoogle Scholar
  49. 49.
    Stam, C., Dijk, B.: Synchronization likelihood: an un-biased measure of generalized synchronization in multivariate data sets. Phys. D 163, 236–251 (2002)CrossRefGoogle Scholar
  50. 50.
    Steinmetz, P., Roy, A., Fitzgerald, P., Hsiao, S., Johnson, K., Niebur, E.: Attention modulates synchronized neuronal firing in primate somatosensory cortex. Nature 404, 187–190 (2000)PubMedCrossRefGoogle Scholar
  51. 51.
    Tallon-Baudry, C., Mandon, S., Freiwald, W., Kreiter, A.: Oscillatory synchrony in the monkey temporal lobe correlates with performance in a visual short-term memory task. Cereb. Cortex 14, 713–720 (2004)PubMedCrossRefGoogle Scholar
  52. 52.
    Tass, P., Rosenblum, M., Weule, J., Kurths, J., Pikovsky, A., Volkmann, J., Schnitzler, A., Freund, H.J.: Detection of n:m phase locking from noisy data: application to magnetoencephalography. Phys. Rev. Lett. 81(5), 3291–3294 (1998)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.INRIA CR Nancy – Grand Est, CS20101Villers-ls-Nancy CedexFrance
  2. 2.Max-Planck-Institute for Biological CyberneticsTuebingenGermany

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