Abstract
Oscillations have been increasingly recognized as a core property of neural responses that contribute to spontaneous, induced, and evoked activities within and between individual neurons and neural ensembles. They are considered as a prominent mechanism for information processing within and communication between brain areas. More recently, it has been proposed that interactions between periodic components at different frequencies, known as cross-frequency couplings, may support the integration of neuronal oscillations at different temporal and spatial scales. The present study details methods based on an adaptive frequency tracking approach that improve the quantification and statistical analysis of oscillatory components and cross-frequency couplings. This approach allows for time-varying instantaneous frequency, which is particularly important when measuring phase interactions between components. We compared this adaptive approach to traditional band-pass filters in their measurement of phase-amplitude and phase-phase cross-frequency couplings. Evaluations were performed with synthetic signals and EEG data recorded from healthy humans performing an illusory contour discrimination task. First, the synthetic signals in conjunction with Monte Carlo simulations highlighted two desirable features of the proposed algorithm vs. classical filter-bank approaches: resilience to broad-band noise and oscillatory interference. Second, the analyses with real EEG signals revealed statistically more robust effects (i.e. improved sensitivity) when using an adaptive frequency tracking framework, particularly when identifying phase-amplitude couplings. This was further confirmed after generating surrogate signals from the real EEG data. Adaptive frequency tracking appears to improve the measurements of cross-frequency couplings through precise extraction of neuronal oscillations.
Highlights
Oscillatory activity is a key component of brain dynamics and has increasingly been the focus of neuroscientific research
Synthetic Signals Before presenting the results obtained with the signals recorded during the illusory contour (IC) experiment, we present the outcomes of the Monte Carlo simulations with synthetic signals
Without noise the PLV should be equal to one in this scenario as the two oscillatory components were perfectly synchronized with 7:1 coefficients
Summary
Oscillatory activity is a key component of brain dynamics and has increasingly been the focus of neuroscientific research. The ‘‘communication through coherence’’ model [4] suggests that phase synchronization is a binding mechanism through which communication between different cortical areas is established. Synchronization of neuronal oscillations is considered a key mechanism for solving the problem of binding multiple and/or distributed representations This mechanism encompasses interactions between different cortical areas and interactions between classical neuronal frequency bands; so-called cross-frequency couplings [8]. These cross-frequency couplings have been proposed as a framework for unifying the neuronal oscillations at different temporal and spatial scales [9]. The importance of these coupling processes have been demonstrated in recent studies of motor, sensory and cognitive tasks (e.g. [10,11,12,13,14,15,16,17])
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
