RESUMO
BACKGROUND: Cross-frequency coupling (CFC) occurs when non-identical frequency components entrain one another. A ubiquitous example from neuroscience is low frequency phase to high frequency amplitude coupling in electrophysiological signals. Seminal work by Canolty revealed CFC in human ECoG data. Established methods band-pass the data into component frequencies then convert the band-passed signals into the analytic representation, from which we infer the instantaneous amplitude and phase of each component. Though powerful, such methods resolve signals with respect to time and frequency without addressing the multiresolution problem. NEW METHOD: We build upon the ground-breaking work of Canolty and others and derive a wavelet-based CFC detection algorithm that efficiently searches a range of frequencies using a sequence of filters with optimal trade-off between time and frequency resolution. We validate our method using simulated data and analyze CFC within and between the primary motor cortex and dorsal striatum of rats under ketamine-xylazine anesthesia. RESULTS: Our method detects the correct CFC in simulated data and reveals CFC between frequency bands that were previously shown to participate in corticostriatal effective connectivity. COMPARISON WITH EXISTING METHODS: Other CFC detection methods address the need to increase bandwidth when analyzing high frequency components but none to date permit rigorous bandwidth selection with no a priori knowledge of underlying CFC. Our method is thus particularly useful for exploratory studies. CONCLUSIONS: The method developed here permits rigorous and efficient exploration of a hypothesis space and is particularly useful when the frequencies participating in CFC are unknown.
