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IEEE Trans Biomed Eng. 2012 Jan;59(1):8-11. doi: 10.1109/TBME.2011.2172439. Epub 2011 Oct 18.

Multivariate phase-amplitude cross-frequency coupling in neurophysiological signals.

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Department of Electrical Engineering and Computer Sciences and the Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA 94720, USA.


Phase-amplitude cross-frequency coupling (CFC)-where the phase of a low-frequency signal modulates the amplitude or power of a high-frequency signal-is a topic of increasing interest in neuroscience. However, existing methods of assessing CFC are inherently bivariate and cannot estimate CFC between more than two signals at a time. Given the increase in multielectrode recordings, this is a strong limitation. Furthermore, the phase coupling between multiple low-frequency signals is likely to produce a high rate of false positives when CFC is evaluated using bivariate methods. Here, we present a novel method for estimating the statistical dependence between one high-frequency signal and N low-frequency signals, termed multivariate phase-coupling estimation (PCE). Compared to bivariate methods, the PCE produces sparser estimates of CFC and can distinguish between direct and indirect coupling between neurophysiological signals-critical for accurately estimating coupling within multiscale brain networks.

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