Abstract
Patterns in external sensory stimuli can rapidly entrain neuronally generated oscillations observed in electrophysiological data. Here, we manipulated the temporal dynamics of visual stimuli with cross-frequency coupling (CFC) characteristics to generate steady-state visual evoked potentials (SSVEPs). Although CFC plays a pivotal role in neural communication, some cases reporting CFC may be false positives due to non-sinusoidal oscillations that can generate artificially inflated coupling values. Additionally, temporal characteristics of dynamic and non-linear neural oscillations cannot be fully derived with conventional Fourier-based analyses mainly due to trade off of temporal resolution for frequency precision. In an attempt to resolve these limitations of linear analytical methods, Holo-Hilbert Spectral Analysis (HHSA) was investigated as a potential approach for examination of non-linear and non-stationary CFC dynamics in this study. Results from both simulation and SSVEPs demonstrated that temporal dynamic and non-linear CFC features can be revealed with HHSA. Specifically, the results of simulation showed that the HHSA is less affected by the non-sinusoidal oscillation and showed possible cross frequency interactions embedded in the simulation without any a priori assumptions. In the SSVEPs, we found that the time-varying cross-frequency interaction and the bidirectional coupling between delta and alpha/beta bands can be observed using HHSA, confirming dynamic physiological signatures of neural entrainment related to cross-frequency coupling. These findings not only validate the efficacy of the HHSA in revealing the natural characteristics of signals, but also shed new light on further applications in analysis of brain electrophysiological data with the aim of understanding the functional roles of neuronal oscillation in various cognitive functions.
Highlights
BackgroundNeural activity related to human behaviors are prominently implemented in a dynamic and non-linear manner (Buzsáki and Mizuseki, 2014; Clarke et al, 2015)
Oscillatory neural dynamics have been commonly considered to be categorized into multiple frequency bands that interact with each other
By using HHSA, we found a full dimensional frequency representation of these features from the signals
Summary
BackgroundNeural activity related to human behaviors are prominently implemented in a dynamic and non-linear manner (Buzsáki and Mizuseki, 2014; Clarke et al, 2015). Various forms of neural oscillations may play a critical role in these processes (e.g., Buzsaki, 2006) and these are typically categorized into activity in different frequency bands (i.e., delta, theta, alpha, beta, gamma, and high gamma) and commonly reported for magneto/electroencephalograms (MEG and EEG) and local field potentials (LFP) (e.g., Cole and Voytek, 2017). Neural crossfrequency coupling (CFC) represents the interactions between two neuronal oscillations of different frequencies and can occur both within a neural area or as inter-area communication (Singer, 1999; Salinas and Sejnowski, 2001; Varela et al, 2001; Fries, 2005; Canolty et al, 2006; Jensen and Colgin, 2007; Roach and Mathalon, 2008; Canolty and Knight, 2010; Giraud and Poeppel, 2012; Siegel et al, 2012; Hsu et al, 2014; Lopes-dosSantos et al, 2018; Cole and Voytek, 2019; Hanslmayr et al, 2019; Nguyen et al, 2019; Siebenhühner et al, 2020; Giehl et al, 2021; Liang et al, 2021). Multiple competing algorithms and approaches have been proposed for conducting PAC analysis, such methods may still result in false positives due to suboptimal analysis practices and/or the presence of artifacts within the data (Aru et al, 2015; Hyafil, 2015)
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