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.
Reviewing Editor
- Frances K Skinner, Krembil Research Institute, University Health Network, Canada
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).
Version history
- Received: December 12, 2018
- Accepted: October 6, 2019
- Accepted Manuscript published: October 16, 2019 (version 1)
- Version of Record published: October 30, 2019 (version 2)
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
Further reading
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Probing memory of a complex visual image within a few hundred milliseconds after its disappearance reveals significantly greater fidelity of recall than if the probe is delayed by as little as a second. Classically interpreted, the former taps into a detailed but rapidly decaying visual sensory or ‘iconic’ memory (IM), while the latter relies on capacity-limited but comparatively stable visual working memory (VWM). While iconic decay and VWM capacity have been extensively studied independently, currently no single framework quantitatively accounts for the dynamics of memory fidelity over these time scales. Here, we extend a stationary neural population model of VWM with a temporal dimension, incorporating rapid sensory-driven accumulation of activity encoding each visual feature in memory, and a slower accumulation of internal error that causes memorized features to randomly drift over time. Instead of facilitating read-out from an independent sensory store, an early cue benefits recall by lifting the effective limit on VWM signal strength imposed when multiple items compete for representation, allowing memory for the cued item to be supplemented with information from the decaying sensory trace. Empirical measurements of human recall dynamics validate these predictions while excluding alternative model architectures. A key conclusion is that differences in capacity classically thought to distinguish IM and VWM are in fact contingent upon a single resource-limited WM store.
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Our ability to recall details from a remembered image depends on a single mechanism that is engaged from the very moment the image disappears from view.
