A statistical framework to assess cross-frequency coupling while accounting for confounding analysis effects
- Boston University, United States
- Massachusetts General Hospital, United States
- University of Minnesota, United States
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 how CFC evolves during seizure and is affected by electrical stimuli.
Data availability
In vivo human data available at https://github.com/Eden-Kramer-Lab/GLM-CFCIn vivo rat data available at https://github.com/tne-lab/cl-example-data
Article and author information
Author details
Funding
National Science Foundation (NSF DMS #1451384)
- Jessica K Nadalin
- Mark A Kramer
National Science Foundation (GRFP)
- Jessica K Nadalin
National Institutes of Health (R21 MH109722)
- Alik S Widge
National Institutes of Health (R01 EB026938)
- Alik S Widge
- Uri T Eden
- Mark A Kramer
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: The animal experimentation received IACUC approval from the University of Minnesota (IACUC Protocol # 1806-36024A).
Human subjects: All patients were enrolled after informed consent, and consent to publish, was obtained and approval was granted by local Institutional Review Boards at Massachusetts General Hospital and Brigham Women's Hospitals (Partners Human Research Committee), and at Boston University according to National Institutes of Health guidelines (IRB Protocol # 1558X).
Copyright
© 2019, Nadalin et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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A statistical framework to assess cross-frequency coupling while accounting for confounding analysis effects
eLife 8:e44287.
https://doi.org/10.7554/eLife.44287
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