The rate of transient beta frequency events predicts behavior across tasks and species

Abstract

Beta oscillations (15-29Hz) are among the most prominent signatures of brain activity. Beta power is predictive of healthy and abnormal behaviors, including perception, attention and motor action. In non-averaged signals, beta can emerge as transient high-power 'events'. As such, functionally relevant differences in averaged power across time and trials can reflect changes in event number, power, duration, and / or frequency span. We show that functionally relevant differences in averaged beta power in primary somatosensory neocortex reflect a difference in the number of high-power beta events per trial, i.e. event rate. Further, beta events occurring close to the stimulus were more likely to impair perception. These results are consistent across detection and attention tasks in human magnetoencephalography, and in local field potentials from mice performing a detection task. These results imply that an increased propensity of beta events predicts the failure to effectively transmit information through specific neocortical representations.

Data availability

The following data sets were generated

Article and author information

Author details

  1. Hyeyoung Shin

    Department of Neuroscience, Brown University, Providence, United States
    For correspondence
    shinehyeyoung@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7587-8577
  2. Robert Law

    Department of Neuroscience, Brown University, Providence, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Shawn Tsutsui

    Department of Neuroscience, Brown University, Providence, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3805-1519
  4. Christopher I Moore

    Department of Neuroscience, Brown University, Providence, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Stephanie R Jones

    Department of Neuroscience, Brown University, Providence, United States
    For correspondence
    Stephanie_Jones@brown.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6760-5301

Funding

National Institute of Mental Health (R01MH106174)

  • Stephanie R Jones

Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Rehabilitation Research and Development Service (N9228-C)

  • Stephanie R Jones

National Institute of Neurological Disorders and Stroke (R01NS045130)

  • Christopher I Moore

Brown Institute for Brain Science

  • Hyeyoung Shin

Fulbright Association

  • Hyeyoung Shin

National Science Foundation Collaborative Research in Computational Neuroscience (NSF CRCNS-1131850)

  • Stephanie R Jones

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

Ethics

Animal experimentation: All experimental procedures and animal care protocols were approved by Brown University Institutional Animal Care and Use Committees and were in accordance with US National Institutes of Health guidelines. All surgery was performed under isofluorane anesthesia, and every effort was made to minimize suffering.

Human subjects: All MEG experimental protocols were approved by the Massachusetts General Hospital Internal Review Board, and each subject gave informed consent before data acquisition.

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Metrics

  • 7,555
    views
  • 1,232
    downloads
  • 231
    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. Hyeyoung Shin
  2. Robert Law
  3. Shawn Tsutsui
  4. Christopher I Moore
  5. Stephanie R Jones
(2017)
The rate of transient beta frequency events predicts behavior across tasks and species
eLife 6:e29086.
https://doi.org/10.7554/eLife.29086

Share this article

https://doi.org/10.7554/eLife.29086

Further reading

    1. Neuroscience
    Francesco Longo
    Insight

    The neurotransmitter dopamine helps form long-term memories by increasing the production of proteins through a unique signaling pathway.

    1. Neuroscience
    Julien Rossato, François Hug ... Simon Avrillon
    Tools and Resources

    Decoding the activity of individual neural cells during natural behaviours allows neuroscientists to study how the nervous system generates and controls movements. Contrary to other neural cells, the activity of spinal motor neurons can be determined non-invasively (or minimally invasively) from the decomposition of electromyographic (EMG) signals into motor unit firing activities. For some interfacing and neuro-feedback investigations, EMG decomposition needs to be performed in real time. Here, we introduce an open-source software that performs real-time decoding of motor neurons using a blind-source separation approach for multichannel EMG signal processing. Separation vectors (motor unit filters) are optimised for each motor unit from baseline contractions and then re-applied in real time during test contractions. In this way, the firing activity of multiple motor neurons can be provided through different forms of visual feedback. We provide a complete framework with guidelines and examples of recordings to guide researchers who aim to study movement control at the motor neuron level. We first validated the software with synthetic EMG signals generated during a range of isometric contraction patterns. We then tested the software on data collected using either surface or intramuscular electrode arrays from five lower limb muscles (gastrocnemius lateralis and medialis, vastus lateralis and medialis, and tibialis anterior). We assessed how the muscle or variation of contraction intensity between the baseline contraction and the test contraction impacted the accuracy of the real-time decomposition. This open-source software provides a set of tools for neuroscientists to design experimental paradigms where participants can receive real-time feedback on the output of the spinal cord circuits.