A critical note on the definition of phase–amplitude cross-frequency coupling

Özkurt, Tolga Esat; Schnitzler, Alfons

doi:10.1016/j.jneumeth.2011.08.014

Cited by 113 publications

(157 citation statements)

References 20 publications

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“…As there is no direct measure available to establish the significance of an estimated PAC score, one often has to rely on surrogate data, i.e., the bootstrapping approach (Özkurt and Schnitzler, 2011; Dvorak and Fenton, 2014; Aru et al, 2015). In the present study, we generated various kinds of surrogate data having a null hypothesis of no neurophysiological PAC phenomenon.…”

Section: Methodsmentioning

confidence: 99%

“…Modulation index (MI) is a direct computational approach for PAC scoring that has been frequently used in various studies (Özkurt and Schnitzler, 2011; Florin and Baillet, 2015). In the present study, we also used it as one of the PAC score computation approaches (Equation 5).…”

Section: Methodsmentioning

confidence: 99%

“…Indeed, in an earlier study (Penny et al, 2008), PAC scores were derived by computing the correlation between the absolute HF time course and the real component of the LF time course. However, this process also includes underlying AAC that is inappropriate with regard to the PAC phenomenon (Özkurt and Schnitzler, 2011). Thus, we computed the correlation-based PAC score in two ways.…”

Section: Methodsmentioning

confidence: 99%

“…This process will destroy the specific PAC, but will have a minimal detrimental effect on the cyclostationarity of the phase or amplitude components. Moreover, across the studies, a wide range of computational methods, such as modulation index (Özkurt and Schnitzler, 2011; Florin and Baillet, 2015), general linear model (Penny et al, 2008; Özkurt and Schnitzler, 2011; van Wijk et al, 2015), and oscillation-triggered coupling based PAC analysis (Dvorak and Fenton, 2014) have been used to capture the PAC phenomenon. Calculating Kullback–Leibler divergence between the phase-amplitude coupled distribution and the uniform distribution (Tort et al, 2010), and correlation-based PAC (Bruns and Eckhorn, 2004; Penny et al, 2008) have also been used to capture this phenomenon.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Phase-Amplitude Coupling in Resting State Magnetoencephalographic Signals: Effect of Surrogates and Evaluation Approach

Gohel

Lim

Kim

et al. 2016

Front. Comput. Neurosci.

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Phase-amplitude coupling (PAC) plays an important role in neural communication and computation. Interestingly, recent studies have indicated the presence of ubiquitous PAC phenomenon even during the resting state. Despite the importance of PAC phenomenon, estimation of significant physiological PAC is challenging because of the lack of appropriate surrogate measures to control false positives caused by non-physiological PAC. Therefore, in the present study, we evaluated PAC phenomenon during resting-state magnetoencephalography (MEG) signal and considered various surrogate measures and computational approaches widely used in the literature in addition to proposing new ones. We evaluated PAC phenomenon over the entire length of the MEG signal and for multiple shorter time segments. The results indicate that the extent of PAC phenomenon mainly depends on the surrogate measures and PAC computational methods used, as well as the evaluation approach. After a careful and critical evaluation, we found that resting-state MEG signals failed to exhibit ubiquitous PAC phenomenon, contrary to what has been suggested previously.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of Phase-Amplitude Coupling in Resting State Magnetoencephalographic Signals: Effect of Surrogates and Evaluation Approach

Gohel

Lim

Kim

et al. 2016

Front. Comput. Neurosci.

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“…This occurs in both hippocampus [9] and neocortex [73], allowing information processing and transfer across a hierarchy of frequency bands (time-scales) [17, 33] (Figure 2). For example, the phase of delta or theta fluctuations determines gamma amplitude, during both ongoing activity and entrainment to external rhythms [16, 74-77]. A recent study provided evidence that cross frequency coupling influences perception, showing that beta phase predicted detection of visual stimuli, but the strength of this relationship was modulated by the phase of theta and delta oscillations [78].…”

Section: Box 1: Mechanisms Of Oscillatory Entrainmentmentioning

confidence: 99%

Entrainment of neural oscillations as a modifiable substrate of attention

Calderone¹,

Lakatos

Butler

et al. 2014

Trends in Cognitive Sciences

237

195

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Brain operation is profoundly rhythmic. Oscillations of neural excitability shape sensory, motor, and cognitive processes. Intrinsic oscillations also entrain to external rhythms, allowing the brain to optimize the processing of predictable events such as speech. Moreover, selective attention to a particular rhythm in a complex environment entails entrainment of neural oscillations to its temporal structure. Entrainment appears to form one of the core mechanisms of selective attention, likely relevant to certain psychiatric disorders. Deficient entrainment has been found in schizophrenia and dyslexia, and mounting evidence also suggests that it may be abnormal in Attention-Deficit/Hyperactivity Disorder. Accordingly, we suggest that studying entrainment in selective attention paradigms will likely reveal mechanisms underlying deficits across multiple disorders.

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Data‐driven MEG analysis to extract fMRI resting‐state networks

Pelzer,

Sharma,

Florin

2024

Human Brain Mapping

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The electrophysiological basis of resting‐state networks (RSN) is still under debate. In particular, no principled mechanism has been determined that is capable of explaining all RSN equally well. While magnetoencephalography (MEG) and electroencephalography are the methods of choice to determine the electrophysiological basis of RSN, no standard analysis pipeline of RSN yet exists. In this article, we compare the two main existing data‐driven analysis strategies for extracting RSNs from MEG data and introduce a third approach. The first approach uses phase–amplitude coupling to determine the RSN. The second approach extracts RSN through an independent component analysis of the Hilbert envelope in different frequency bands, while the third new approach uses a singular value decomposition instead. To evaluate these approaches, we compare the MEG‐RSN to the functional magnetic resonance imaging (fMRI)‐RSN from the same subjects. Overall, it was possible to extract RSN with MEG using all three techniques, which matched the group‐specific fMRI‐RSN. Interestingly the new approach based on SVD yielded significantly higher correspondence to five out of seven fMRI‐RSN than the two existing approaches. Importantly, with this approach, all networks—except for the visual network—had the highest correspondence to the fMRI networks within one frequency band. Thereby we provide further insights into the electrophysiological underpinnings of the fMRI‐RSNs. This knowledge will be important for the analysis of the electrophysiological connectome.

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A critical note on the definition of phase–amplitude cross-frequency coupling

Cited by 113 publications

References 20 publications

Evaluation of Phase-Amplitude Coupling in Resting State Magnetoencephalographic Signals: Effect of Surrogates and Evaluation Approach

Evaluation of Phase-Amplitude Coupling in Resting State Magnetoencephalographic Signals: Effect of Surrogates and Evaluation Approach

Entrainment of neural oscillations as a modifiable substrate of attention

Data‐driven MEG analysis to extract fMRI resting‐state networks

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