Power and phase properties of oscillatory neural responses in the presence of background activity
- 552 Downloads
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.
KeywordsPhase resetting Neural oscillations Phase coherence Entrainment
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.
- 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
- Johnson, N. L., Kotz, S., & Balakrishnan, N. (1995). Continuous univariate distributions. New York: John Wiley and Sons Inc.Google Scholar
- Mallat, S. G. (1999). A wavelet tour of signal processing. San Diego: Academic.Google Scholar
- 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
- 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
- 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