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.

Metrics

  • 3,491
    Page views
  • 531
    Downloads
  • 4
    Citations

Article citation count generated by polling the highest count across the following sources: PubMed Central, Crossref, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  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

Further reading

    1. Neuroscience
    Dongwon Lee, Wu Chen ... Mingshan Xue
    Research Article Updated

    UBE3A encodes ubiquitin protein ligase E3A, and in neurons its expression from the paternal allele is repressed by the UBE3A antisense transcript (UBE3A-ATS). This leaves neurons susceptible to loss-of-function of maternal UBE3A. Indeed, Angelman syndrome, a severe neurodevelopmental disorder, is caused by maternal UBE3A deficiency. A promising therapeutic approach to treating Angelman syndrome is to reactivate the intact paternal UBE3A by suppressing UBE3A-ATS. Prior studies show that many neurological phenotypes of maternal Ube3a knockout mice can only be rescued by reinstating Ube3a expression in early development, indicating a restricted therapeutic window for Angelman syndrome. Here, we report that reducing Ube3a-ATS by antisense oligonucleotides in juvenile or adult maternal Ube3a knockout mice rescues the abnormal electroencephalogram (EEG) rhythms and sleep disturbance, two prominent clinical features of Angelman syndrome. Importantly, the degree of phenotypic improvement correlates with the increase of Ube3a protein levels. These results indicate that the therapeutic window of genetic therapies for Angelman syndrome is broader than previously thought, and EEG power spectrum and sleep architecture should be used to evaluate the clinical efficacy of therapies.

    1. Neuroscience
    2. Physics of Living Systems
    Xiaowen Chen, Faustine Ginoux ... Claire Wyart
    Tools and Resources

    One challenge in neuroscience is to understand how information flows between neurons in vivo to trigger specific behaviors. Granger causality (GC) has been proposed as a simple and effective measure for identifying dynamical interactions. At single-cell resolution however, GC analysis is rarely used compared to directionless correlation analysis. Here, we study the applicability of GC analysis for calcium imaging data in diverse contexts. We first show that despite underlying linearity assumptions, GC analysis successfully retrieves non-linear interactions in a synthetic network simulating intracellular calcium fluctuations of spiking neurons. We highlight the potential pitfalls of applying GC analysis on real in vivo calcium signals, and offer solutions regarding the choice of GC analysis parameters. We took advantage of calcium imaging datasets from motoneurons in embryonic zebrafish to show how the improved GC can retrieve true underlying information flow. Applied to the network of brainstem neurons of larval zebrafish, our pipeline reveals strong driver neurons in the locus of the mesencephalic locomotor region (MLR), driving target neurons matching expectations from anatomical and physiological studies. Altogether, this practical toolbox can be applied on in vivo population calcium signals to increase the selectivity of GC to infer flow of information across neurons.