Bayesian analysis of phase data in EEG and MEG

  1. Sydney Dimmock  Is a corresponding author
  2. Cian O'Donnell
  3. Conor J Houghton
  1. University of Bristol, United Kingdom
  2. University of Ulster, United Kingdom

Abstract

Electroencephalography and magnetoencephalography recordings are non-invasive and temporally precise, making them invaluable tools in the investigation of neural responses in humans. However, these recordings are noisy, both because the neuronal electrodynamics involved produces a muffled signal and because the neuronal processes of interest compete with numerous other processes, from blinking to day-dreaming. One fruitful response to this noisiness has been to use stimuli with a specific frequency and to look for the signal of interest in the response at that frequency. Typically this signal involves measuring the coherence of response phase: here a Bayesian approach to measuring phase coherence is described. This Bayesian approach is illustrated using an example from neurolinguistics and is more descriptive and more data-efficient than the traditional statistical approaches.

Data availability

This manuscript is a computational study, so no data have been generated. All modelling code for this study is available from the GitHub link provided in appendix 2. The statistical learning dataset used as a case study in this paper is not publicly available.

The following previously published data sets were used

Article and author information

Author details

  1. Sydney Dimmock

    Department of Computer Science, University of Bristol, Bristol, United Kingdom
    For correspondence
    sd14814@bristol.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0163-2048
  2. Cian O'Donnell

    School of Computing, Engineering abd Intelligent Systems, University of Ulster, Londonderry, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. Conor J Houghton

    Department of Computer Science, University of Bristol, Bristol, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5017-9473

Funding

Leverhulme Trust (RF-2021-533)

  • Conor J Houghton

Medical Research Council (MR/S026630/1)

  • Cian O'Donnell

Engineering and Physical Sciences Research Council (EP/R513179/1)

  • Sydney Dimmock

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Andrea E Martin, Max Planck Institute for Psycholinguistics, Netherlands

Version history

  1. Preprint posted: October 17, 2022 (view preprint)
  2. Received: October 31, 2022
  3. Accepted: September 11, 2023
  4. Accepted Manuscript published: September 12, 2023 (version 1)
  5. Accepted Manuscript updated: September 20, 2023 (version 2)
  6. Version of Record published: October 20, 2023 (version 3)

Copyright

© 2023, Dimmock 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

  • 1,141
    views
  • 254
    downloads
  • 0
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

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. Sydney Dimmock
  2. Cian O'Donnell
  3. Conor J Houghton
(2023)
Bayesian analysis of phase data in EEG and MEG
eLife 12:e84602.
https://doi.org/10.7554/eLife.84602

Share this article

https://doi.org/10.7554/eLife.84602

Further reading

    1. Immunology and Inflammation
    2. Neuroscience
    Irini Papazian, Maria Kourouvani ... Lesley Probert
    Research Article

    Autoimmune diseases of the central nervous system (CNS) such as multiple sclerosis (MS) are only partially represented in current experimental models and the development of humanized immune mice is crucial for better understanding of immunopathogenesis and testing of therapeutics. We describe a humanized mouse model with several key features of MS. Severely immunodeficient B2m-NOG mice were transplanted with peripheral blood mononuclear cells (PBMCs) from HLA-DRB1-typed MS and healthy (HI) donors and showed rapid engraftment by human T and B lymphocytes. Mice receiving cells from MS patients with recent/ongoing Epstein–Barr virus reactivation showed high B cell engraftment capacity. Both HLA-DRB1*15 (DR15) MS and DR15 HI mice, not HLA-DRB1*13 MS mice, developed human T cell infiltration of CNS borders and parenchyma. DR15 MS mice uniquely developed inflammatory lesions in brain and spinal cord gray matter, with spontaneous, hCD8 T cell lesions, and mixed hCD8/hCD4 T cell lesions in EAE immunized mice, with variation in localization and severity between different patient donors. Main limitations of this model for further development are poor monocyte engraftment and lack of demyelination, lymph node organization, and IgG responses. These results show that PBMC humanized mice represent promising research tools for investigating MS immunopathology in a patient-specific approach.

    1. Neuroscience
    Ju-Young Lee, Dahee Jung, Sebastien Royer
    Research Article

    Animals can use a repertoire of strategies to navigate in an environment, and it remains an intriguing question how these strategies are selected based on the nature and familiarity of environments. To investigate this question, we developed a fully automated variant of the Barnes maze, characterized by 24 vestibules distributed along the periphery of a circular arena, and monitored the trajectories of mice over 15 days as they learned to navigate towards a goal vestibule from a random start vestibule. We show that the patterns of vestibule visits can be reproduced by the combination of three stochastic processes reminiscent of random, serial, and spatial strategies. The processes randomly selected vestibules based on either uniform (random) or biased (serial and spatial) probability distributions. They closely matched experimental data across a range of statistical distributions characterizing the length, distribution, step size, direction, and stereotypy of vestibule sequences, revealing a shift from random to spatial and serial strategies over time, with a strategy switch occurring approximately every six vestibule visits. Our study provides a novel apparatus and analysis toolset for tracking the repertoire of navigation strategies and demonstrates that a set of stochastic processes can largely account for exploration patterns in the Barnes maze.