The control of tonic pain by active relief learning

  1. Suyi Zhang  Is a corresponding author
  2. Hiroaki Mano
  3. Michael Lee
  4. Wako Yoshida
  5. Mitsuo Kawato
  6. Trevor W Robbins
  7. Ben Seymour  Is a corresponding author
  1. University of Cambridge, United Kingdom
  2. Advanced Telecommunications Research Institute International, Japan

Abstract

Tonic pain after injury characterises a behavioural state that prioritises recovery. Although generally suppressing cognition and attention, tonic pain needs to allow effective relief learning to reduce the cause of the pain. Here, we describe a central learning circuit that supports learning of relief and concurrently suppresses the level of ongoing pain. We used computational modelling of behavioural, physiological and neuroimaging data in two experiments in which subjects learned to terminate tonic pain in static and dynamic escape-learning paradigms. In both studies, we show that active relief-seeking involves a reinforcement learning process manifest by error signals observed in the dorsal putamen. Critically, this system uses an uncertainty ('associability') signal detected in pregenual anterior cingulate cortex that both controls the relief learning rate, and endogenously and parametrically modulates the level of tonic pain. The results define a self-organising learning circuit that reduces ongoing pain when learning about potential relief.

Article and author information

Author details

  1. Suyi Zhang

    Department of Engineering, University of Cambridge, Cambridge, United Kingdom
    For correspondence
    sz321@cam.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9028-6265
  2. Hiroaki Mano

    Department of Engineering, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. Michael Lee

    Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Wako Yoshida

    Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9273-1617
  5. Mitsuo Kawato

    Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
    Competing interests
    The authors declare that no competing interests exist.
  6. Trevor W Robbins

    Department of Psychology, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  7. Ben Seymour

    Department of Engineering, University of Cambridge, Cambridge, United Kingdom
    For correspondence
    bjs49@cam.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1724-5832

Funding

Wellcome

  • Trevor W Robbins
  • Ben Seymour

National Institute of Information and Communications Technology

  • Suyi Zhang
  • Hiroaki Mano
  • Ben Seymour

Japan Society for the Promotion of Science

  • Hiroaki Mano
  • Wako Yoshida
  • Ben Seymour

Japan Agency for Medical Research and Development

  • Wako Yoshida
  • Mitsuo Kawato
  • Ben Seymour

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

Ethics

Human subjects: The two experiments were performed in different institutes, and approved by their relevant ethics and Safety committees: for the National Institute of Information and Communications Technology, Japan (Expt 1), and the Advanced Telecommunications Research Institute, Japan (Expt 2). All subjects gave informed consent prior to participation

Reviewing Editor

  1. Tor Wager, 1Institute of Cognitive Science, University of Colorado Boulder, United States

Publication history

  1. Received: September 12, 2017
  2. Accepted: February 8, 2018
  3. Accepted Manuscript published: February 27, 2018 (version 1)
  4. Version of Record published: March 8, 2018 (version 2)
  5. Version of Record updated: April 11, 2018 (version 3)

Copyright

© 2018, Zhang 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. Suyi Zhang
  2. Hiroaki Mano
  3. Michael Lee
  4. Wako Yoshida
  5. Mitsuo Kawato
  6. Trevor W Robbins
  7. Ben Seymour
(2018)
The control of tonic pain by active relief learning
eLife 7:e31949.
https://doi.org/10.7554/eLife.31949

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