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

  1. Jessica K Nadalin
  2. Louis-Emmanuel Martinet
  3. Ethan B Blackwood
  4. Meng-Chen Lo
  5. Alik S Widge
  6. Sydney S Cash
  7. Uri T Eden
  8. Mark A Kramer  Is a corresponding author
  1. Boston University, United States
  2. Massachusetts General Hospital, United States
  3. 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

  1. Jessica K Nadalin

    Department of Mathematics and Statistics, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Louis-Emmanuel Martinet

    Department of Neurology, Massachusetts General Hospital, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Ethan B Blackwood

    Department of Psychiatry, University of Minnesota, Minneapolis, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3049-0640
  4. Meng-Chen Lo

    Department of Psychiatry, University of Minnesota, Minneapolis, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3913-3233
  5. Alik S Widge

    Department of Psychiatry, University of Minnesota, Minneapolis, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8510-341X
  6. Sydney S Cash

    Department of Neurology, Massachusetts General Hospital, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Uri T Eden

    Department of Mathematics and Statistics, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Mark A Kramer

    Department of Mathematics and Statistics, Boston University, Boston, United States
    For correspondence
    mak@bu.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9979-7202

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).

Reviewing Editor

  1. Frances K Skinner, Krembil Research Institute, University Health Network, Canada

Publication history

  1. Received: December 12, 2018
  2. Accepted: October 6, 2019
  3. Accepted Manuscript published: October 16, 2019 (version 1)
  4. 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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  1. Jessica K Nadalin
  2. Louis-Emmanuel Martinet
  3. Ethan B Blackwood
  4. Meng-Chen Lo
  5. Alik S Widge
  6. Sydney S Cash
  7. Uri T Eden
  8. Mark A Kramer
(2019)
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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    Funding: Deutsche Forschungsgesellschaft (410169619, 424778381), Deutsches Zentrum für Luftund Raumfahrt (DynaSti), National Institutes of Health (2R01 MH113929), Foundation for OCD Research (FFOR).