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A statistical framework to assess cross-frequency coupling while accounting for confounding analysis effects.

ABSTRACT: Cross frequency coupling (CFC) is emerging as a fundamental feature of brain activity, correlated with brain function and dysfunction. Many different types of CFC have been identified through application of numerous data analysis methods, each developed to characterize a specific CFC type. Choosing an inappropriate method weakens statistical power and introduces opportunities for confounding effects. To address this, we propose a statistical modeling framework to estimate high frequency amplitude as a function of both the low frequency amplitude and low frequency phase; the result is a measure of phase-amplitude coupling that accounts for changes in the low frequency amplitude. We show in simulations that the proposed method successfully detects CFC between the low frequency phase or amplitude and the high frequency amplitude, and outperforms an existing method in biologically-motivated examples. Applying the method to in vivo data, we illustrate examples of CFC during a seizure and in response to electrical stimuli.

SUBMITTER: Nadalin JK

PROVIDER: S-EPMC6821458 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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A statistical framework to assess cross-frequency coupling while accounting for confounding analysis effects.

Nadalin Jessica K JK Martinet Louis-Emmanuel LE Blackwood Ethan B EB Lo Meng-Chen MC Widge Alik S AS Cash Sydney S SS Eden Uri T UT Kramer Mark A MA

eLife 20191016

Cross frequency coupling (CFC) is emerging as a fundamental feature of brain activity, correlated with brain function and dysfunction. Many different types of CFC have been identified through application of numerous data analysis methods, each developed to characterize a specific CFC type. Choosing an inappropriate method weakens statistical power and introduces opportunities for confounding effects. To address this, we propose a statistical modeling framework to estimate high frequency amplitud ...[more]

PMID: 31617848

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Project description:Neuroimaging studies have identified networks of brain areas and oscillations associated with tinnitus perception. However, how these regions relate to perceptual characteristics of tinnitus, and how oscillations in various frequency bands are associated with communications within the tinnitus network is still incompletely understood. Recent evidence suggests that apart from changes of the tinnitus severity the changes of tinnitus dominant pitch also have modulating effect on the underlying neuronal activity in a number of brain areas within the tinnitus network. Therefore, in a re-analysis of an existing dataset, we sought to determine how the oscillations in the tinnitus network in the various frequency bands interact. We also investigate how changes of tinnitus loudness, annoyance and pitch affect cross-frequency interaction both within and between nodes of the tinnitus network. Results of this study provide, to our knowledge, the first evidence that in tinnitus patients, aside from the previously described changes of oscillatory activity, there are also changes of cross-frequency coupling (CFC); phase-amplitude CFC was increased in tinnitus patients within the auditory cortex and the dorsolateral prefrontal regions between the phase of delta-theta and the amplitude of gamma oscillations (Modulation Index [MI] 0.17 in tinnitus patients vs. 0.08 in tinnitus free controls). Moreover, theta phase in the anterior cingulate region modulated gamma in the auditory (MI 0.1) and dorsolateral prefrontal regions (MI 0.19). Reduction of tinnitus severity after acoustic coordinated reset therapy led to a partial normalization of abnormal CFC. Also treatment induced changes in tinnitus pitch significantly modulated changes in CFC. Thus, tinnitus perception is associated with a more pronounced CFC within and between nodes of the tinnitus network. CFC can coordinate tinnitus-relevant activity in the tinnitus network providing a mechanism for effective communication between nodes of this network.

Dataset Information

A statistical framework to assess cross-frequency coupling while accounting for confounding analysis effects.

Publications

A statistical framework to assess cross-frequency coupling while accounting for confounding analysis effects.

Similar Datasets

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