Multivariate phase-amplitude cross-frequency coupling in neurophysiological signals.
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.
Department of Electrical Engineering and Computer Sciences and the Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA 94720, USA. rcanolty@gmail.com
Ryan T
RT
Canolty
Charles F
CF
Cadieu
Kilian
K
Koepsell
Robert T
RT
Knight
Jose M
JM
Carmena
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001683:Biological Clocks / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods; D006801:Humans; D008959:Models, Neurological; D015999:Multivariate Analysis; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted