Cross-frequency decomposition: a novel technique for studying interactions between neuronal oscillations with
Vadim V Nikulin1, Guido Nolte, Gabriel Curio
1Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charité - University Medicine Berlin, D-12200 Berlin, Germany. vadim.nikulin@charite.de
Objective:
We present a novel method for the extraction of neuronal components showing cross-frequency phase synchronization.
Methods:
In general the method can be applied for the detection of phase interactions between components with frequencies f1 and f2, where f2 ≈ rf1 and r is some integer. We refer to the method as cross-frequency decomposition (CFD), which consists of the following steps: (a) extraction of f1-oscillations with the spatio-spectral decomposition algorithm (SSD); (b) frequency modification of the f1-oscillations obtained with SSD; and (c) finding f2-oscillations synchronous with f1-oscillations using least-squares estimation.
Results:
Our simulations showed that CFD was capable of recovering interacting components even when the signal-to-noise ratio was as low as 0.01. An application of CFD to the real EEG data demonstrated that cross-frequency phase synchronization between alpha and beta oscillations can originate from the same or remote neuronal populations.
Conclusions:
CFD allows a compact representation of the sets of interacting components. The application of CFD to EEG data allows differentiating cross-frequency synchronization arising due to genuine neurophysiological interactions from interactions occurring due to quasi-sinusoidal waveform of neuronal oscillations.
Significance:
CFD is a method capable of extracting cross-frequency coupled neuronal oscillations even in the presence of strong noise.
