ArticleeLife2019
A statistical framework to assess cross-frequency coupling while accounting for confounding analysis effects.
Article in eLife, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 17 citations in OpenAlex.
- Article
- Different Methods to Estimate the Phase of Neural Rhythms Agree But Only During Times of Low Uncertainty.eNeuro · 2023Article
- Different methods to estimate the phase of neural rhythms agree, but only during times of low uncertainty.bioRxiv : the preprint server for biology · 2023Article
- Review
- Reduced Cross-Frequency Coupling and Daytime Sleepiness in Obstructive Sleep Apnea Patients.Biology · 2022Article
- Dyconnmap: Dynamic connectome mapping-A neuroimaging python module.Human brain mapping · 2021Article
- Deep brain stimulation for psychiatric disorders: From focal brain targets to cognitive networks.NeuroImage · 2021Review
- Seizure onset location shapes dynamics of initiation.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2020Article
- Prefrontal cortex and cognitive control: new insights from human electrophysiology.F1000Research · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.