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Journal of Computational Neuroscience

, Volume 34, Issue 2, pp 337–343 | Cite as

Power and phase properties of oscillatory neural responses in the presence of background activity

  • Nai Ding
  • Jonathan Z. Simon
Article

Abstract

Natural sensory inputs, such as speech and music, are often rhythmic. Recent studies have consistently demonstrated that these rhythmic stimuli cause the phase of oscillatory, i.e. rhythmic, neural activity, recorded as local field potential (LFP), electroencephalography (EEG) or magnetoencephalography (MEG), to synchronize with the stimulus. This phase synchronization, when not accompanied by any increase of response power, has been hypothesized to be the result of phase resetting of ongoing, spontaneous, neural oscillations measurable by LFP, EEG, or MEG. In this article, however, we argue that this same phenomenon can be easily explained without any phase resetting, and where the stimulus-synchronized activity is generated independently of background neural oscillations. It is demonstrated with a simple (but general) stochastic model that, purely due to statistical properties, phase synchronization, as measured by ‘inter-trial phase coherence’, is much more sensitive to stimulus-synchronized neural activity than is power. These results question the usefulness of analyzing the power and phase of stimulus-synchronized activity as separate and complementary measures; particularly in the case of attempting to demonstrate whether stimulus-synchronized neural activity is generated by phase resetting of ongoing neural oscillations.

Keywords

Phase resetting Neural oscillations Phase coherence Entrainment 

Notes

Acknowledgements

We are grateful to Mary F. Howard and David Poeppel for insightful comments and discussion. This research was supported by the National Institute of Deafness and Other Communication Disorders Grant R01-DC-05660.

References

  1. Ding, N., & Simon, J. Z. (2012). Neural coding of continuous speech in auditory cortex during monaural and dichotic listening. Journal of Neurophysiology, 107, 78–89.Google Scholar
  2. Fisher, N. I. (1993). Statistical analysis of circular data. Cambridge: Cambridge Univ Press.CrossRefGoogle Scholar
  3. Howard, M. F., & Poeppel, D. (2010). Discrimination of speech stimuli based on neuronal response phase patterns depends on acoustics but not comprehension. Journal of Neurophysiology, 104, 2500–2511.PubMedCrossRefGoogle Scholar
  4. Johnson, N. L., Kotz, S., & Balakrishnan, N. (1995). Continuous univariate distributions. New York: John Wiley and Sons Inc.Google Scholar
  5. Kayser, C., Montemurro, M. A., Logothetis, N. K., & Panzeri, S. (2009). Spike-phase coding boosts and stabilizes information carried by spatial and temporal spike patterns. Neuron, 61, 597–608.PubMedCrossRefGoogle Scholar
  6. Lakatos, P., Karmos, G., Mehta, A. D., Ulbert, I., & Schroeder, C. E. (2008). Entrainment of neuronal oscillations as a mechanism of attentional selection. Science, 320, 110–113.PubMedCrossRefGoogle Scholar
  7. Luo, H., & Poeppel, D. (2007). Phase patterns of neuronal responses reliably discriminate speech in human auditory cortex. Neuron, 54, 1001–1010.PubMedCrossRefGoogle Scholar
  8. Mallat, S. G. (1999). A wavelet tour of signal processing. San Diego: Academic.Google Scholar
  9. Miller, J., & Thomas, J. (1972). Detectors for discrete-time signals in non-Gaussian noise. IEEE Transcations on Information Theory, 18(2), 241–250.Google Scholar
  10. Poor, H. V. (1994). An introduction to signal detection and estimation. New York: Springer.CrossRefGoogle Scholar
  11. Sahani, M., & Linden, J. F. (2003). How linear are auditory cortical responses? In S. Becker, S. Thrun, & K. Obermeyer (Eds.), Advances in neural information processing systems (Vol. 15, pp. 109–116). Cambridge: MIT Press.Google Scholar
  12. Sauseng, P., Klimesch, W., Gruber, W. R., Hanslmayr, S., Freunbergera, R., & Doppelmayr, M. (2007). Are event-related potential components generated by phase resetting of brain oscillations? A critical discussion. Neuroscience, 146, 1435–1444.PubMedCrossRefGoogle Scholar
  13. Schroeder, C. E., & Lakatos, P. (2009). Low-frequency neuronal oscillations as instruments of sensory selection. Trends in Neurosciences, 32, 9–18.PubMedCrossRefGoogle Scholar
  14. Shah, A. S., Bressler, S. L., Knuth, K. H., Ding, M., Mehta, A. D., Ulbert, I., & Schroeder, C. E. (2004). Neural dynamics and the fundamental mechanisms of event-related brain potentials. Cerebral Cortex, 14, 476–483.PubMedCrossRefGoogle Scholar
  15. Telenczuk, B., Nikulin, V. V., & Curio, G. (2010). Role of neuronal synchrony in the generation of evoked EEG/MEG responses. Journal of Neurophysiology, 104, 3557–3567.Google Scholar
  16. Yeung, N., Bogacz, R., Holroyd, C. B., & Cohen, J. D. (2004). Detection of synchronized oscillations in the electroencephalogram: an evaluation of methods. Psychophysiology, 41, 822–832.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of MarylandCollege ParkUSA
  2. 2.Department of BiologyUniversity of MarylandCollege ParkUSA

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