Volume 12 Supplement 1

Twentieth Annual Computational Neuroscience Meeting: CNS*2011

Open Access

Analyzing possible pitfalls of cross-frequency analysis

  • Juhan Aru1,
  • Jaan Aru2, 3,
  • Michael Wibral4,
  • Viola Priessemann5,
  • Wolf Singer2, 3 and
  • Raul Vicente2, 3Email author
BMC Neuroscience201112(Suppl 1):P303

DOI: 10.1186/1471-2202-12-S1-P303

Published: 18 July 2011

One of the central questions in neuroscience is how neural activity is organized across different spatial and temporal scales. As larger populations oscillate and synchronize at lower frequencies and smaller ensembles are active at higher frequencies, a cross-frequency coupling would facilitate flexible coordination of neural activity simultaneously in time and space. Although various experiments have revealed amplitude-to-amplitude and phase-to-phase coupling, the most common and most celebrated result is that the phase of the lower frequency component modulates the amplitude of the higher frequency component. Over the recent 5 years the amount of experimental works finding such phase-amplitude coupling in LFP, ECoG, EEG and MEG has been tremendous (summarized in [1]). We suggest that although the mechanism of cross-frequency-coupling (CFC) is theoretically very tempting, the current analysis methods might overestimate any physiological CFC actually evident in the signals of LFP, ECoG, EEG and MEG. In particular, we point out three conceptual problems in assessing the components and their correlations of a time series. Although we focus on phase-amplitude coupling, most of our argument is relevant for any type of coupling.

1) The first conceptual problem is related to isolating physiological frequency components of the recorded signal. The key point is to notice that there are many different mathematical representations for a time series but the physical interpretation we make out of them is dependent on the choice of the components to be analyzed. In particular, when one isolates the components by Fourier-representation based filtering, it is the width of the filtering bands what defines what we consider as our components and how their power or group phase change in time. We will discuss clear cut examples where the interpretation of the existence of CFC depends on the width of the filtering process. 2) A second problem deals with the origin of spectral correlations as detected by current cross-frequency analysis. It is known that non-stationarities are associated with spectral correlations in the Fourier space. Therefore, there are two possibilities regarding the interpretation of any observed CFC. One scenario is that basic neuronal mechanisms indeed generate an interaction across different time scales (or frequencies) resulting in processes with non-stationary features. The other and problematic possibility is that unspecific non-stationarities can also be associated with spectral correlations which in turn will be detected by cross frequency measures even if physiologically there is no causal interaction between the frequencies. 3) We discuss on the role of non-linearities as generators of cross frequency interactions. As an example we performed a phase-amplitude coupling analysis of two nonlinearly related signals: atmospheric noise and the square of it (Figure 1) observing an enhancement of phase-amplitude coupling in the second signal while no pattern is observed in the first.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2202-12-S1-P303/MediaObjects/12868_2011_Article_2321_Fig1_HTML.jpg
Figure 1

Time-evolution of the amplitudes of high frequency components locked to the trough of a low frequency component (from 4 to 8 Hz) for random noise from an atmospheric source (left) and its square (right).

Finally, we discuss some minimal conditions need to be tested to solve some of the ambiguities here noted. In summary, we simply want to point out that finding a significant cross frequency pattern does not always have to imply that there indeed is physiological cross frequency interaction in the brain.

Authors’ Affiliations

(1)
Center for Research and Interdisciplinarity, Faculty of Medicine, Paris Descartes University
(2)
Dept. Neurophysiology, Max-Planck Institute for Brain Research
(3)
Frankfurt Institute for Advanced Studies
(4)
MEG Unit, Brain Imaging Center, Goethe University
(5)
Neural Systems and Coding, Max-Planck Institute for Brain Research

References

  1. Canolty RT, Knight RT: The functional role of cross-frequency coupling. Trends in Cognitive Sciences. 2010, 11 (14): 506-514. 10.1016/j.tics.2010.09.001.View ArticleGoogle Scholar

Copyright

© Aru et al; licensee BioMed Central Ltd. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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