Multivariate cross-frequency coupling via generalized eigendecomposition

Abstract

This paper presents a new framework for analyzing cross-frequency coupling in multichannel electrophysiological recordings. The generalized eigendecomposition-based cross-frequency coupling framework (gedCFC) is inspired by source-separation algorithms combined with dynamics of mesoscopic neurophysiological processes. It is unaffected by factors that confound traditional CFC methods—such as non-stationarities, non-sinusoidality, and non-uniform phase angle distributions—attractive properties considering that brain activity is neither stationary nor perfectly sinusoidal. The gedCFC framework opens new opportunities for conceptualizing CFC as network interactions with diverse spatial/topographical distributions. Five specific methods within the gedCFC framework are detailed, these are validated in simulated data and applied in several empirical datasets. gedCFC accurately recovers physiologically plausible CFC patterns embedded in noise that causes traditional CFC methods to perform poorly. The paper also demonstrates that spike-field coherence in multichannel local field potential data can be analyzed using the gedCFC framework, which provides significant advantages over traditional spike-field coherence analyses. Null-hypothesis testing is also discussed.DOI: http://dx.doi.org/10.7554/eLife.21792.001

Synchronization between keyboard typing and neural oscillations

“…The resulting component time-series is then the row vector y on which all subsequent analyses can be performed. This spatial filter method has the advantage of increasing signal-to-noise ratio, taking into account inter-individual topographical differences, and avoiding electrode selection bias (Nikulin et al, 2011;Cohen, 2017).…”

“…In order to increase the signal-to-noise ratio, to avoid bias in electrode selection, and to account for variability in subject's topography, we chose to apply a multivariate guided source separation method, namely, generalized-eigen-decomposition (Nikulin et al, 2011;de Cheveigné and Arzounian, 2015). This method allows to create a single component that best reflects target features of the signal (in this case, a narrow-band frequency-specific signal) and has been shown to be very helpful in maximizing low-frequency features in the EEG signal (Cohen, 2017). Our results showed that this method successfully isolated different narrowband frequency-specific components.…”

“…To this end we used a typing task that required participants to type both real words/sentences, or pseudo-words/sentences, in which case the need for cognitive control would be greater. We first focused on showing rhythmicity in typing, and then used frequency-specific spatial filters based on a multivariate guided source separation method (generalized eigendecomposition; Cohen, 2017) to investigate synchronization between typing and neuronal oscillations.…”

Theta-Gamma Cross-Frequency Analyses (Hippocampus)

“…As an active field, new CFC metrics are continuously being elaborated; there are certainly far more CFC metrics than the ones summarized in this chapter. Important advances include metrics designed to infer coupling directionality (Paluš 2014;Jiang et al 2015;Li et al 2016), to improve time-resolution (Voytek et al 2013;Dvorak and Fenton 2014;Samiee and Baillet 2017), to provide confidence intervals and higher statistical rigor (Kramer and Eden 2013;van Wijk et al 2015), to avoid standard filters by using autoregressive models (Tour et al 2017) or empirical mode decomposition (Pittman-Polletta et al 2014), and to cope with multiple channel data and spatial filtering (Cohen 2017).…”

Nonlinear Interaction Decomposition (NID): A Method for Separation of Cross-frequency Coupled Sources in Human Brain

“…While most of the multivariate source-separation methods focus on the extraction of independent sources (e.g. independent component analysis -ICA), there are only a few studies utilizing multivariate methods to extract dependent sources from the electrophysiological recordings of the human brain (Chella et al;Cohen, 2017;Dähne et al, 2014;Nikulin et al, 2012;Volk et al, 2018). These methods optimize a contrast function of the desired type of coupling.…”