Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

The control of tonic pain by active relief learning

View ORCID ProfileSuyi Zhang, Hiroaki Mano, Michael Lee, View ORCID ProfileWako Yoshida, Mitsuo Kawato, Trevor W Robbins, View ORCID ProfileBen Seymour
doi: https://doi.org/10.1101/222653
Suyi Zhang
1Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK
2Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Suyi Zhang
  • For correspondence: sz321@cam.ac.uk bjs49@cam.ac.uk
Hiroaki Mano
1Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK
2Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan
3Center for Information and Neural Networks, National Institute for Information and Communications Technology, 1-4 Yamadaoka, Suita, Osaka, Japan, 565-0871
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Michael Lee
4Division of Anaesthesia, University of Cambridge, Box 93, Addenbrooke’s Hospital, Cambridge, CB2 2QQ, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Wako Yoshida
2Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Wako Yoshida
Mitsuo Kawato
2Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Trevor W Robbins
5Department of Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Downing site, Cambridge CB2 3EB, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ben Seymour
1Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK
2Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan
3Center for Information and Neural Networks, National Institute for Information and Communications Technology, 1-4 Yamadaoka, Suita, Osaka, Japan, 565-0871
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Ben Seymour
  • For correspondence: sz321@cam.ac.uk bjs49@cam.ac.uk
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

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 so that the cause of the pain can be reduced if possible. 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 also 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 allows reduction of ongoing pain when learning about potential relief.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted November 21, 2017.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
The control of tonic pain by active relief learning
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
The control of tonic pain by active relief learning
Suyi Zhang, Hiroaki Mano, Michael Lee, Wako Yoshida, Mitsuo Kawato, Trevor W Robbins, Ben Seymour
bioRxiv 222653; doi: https://doi.org/10.1101/222653
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
The control of tonic pain by active relief learning
Suyi Zhang, Hiroaki Mano, Michael Lee, Wako Yoshida, Mitsuo Kawato, Trevor W Robbins, Ben Seymour
bioRxiv 222653; doi: https://doi.org/10.1101/222653

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (3609)
  • Biochemistry (7590)
  • Bioengineering (5533)
  • Bioinformatics (20833)
  • Biophysics (10347)
  • Cancer Biology (7998)
  • Cell Biology (11663)
  • Clinical Trials (138)
  • Developmental Biology (6619)
  • Ecology (10227)
  • Epidemiology (2065)
  • Evolutionary Biology (13647)
  • Genetics (9557)
  • Genomics (12860)
  • Immunology (7931)
  • Microbiology (19575)
  • Molecular Biology (7678)
  • Neuroscience (42192)
  • Paleontology (309)
  • Pathology (1259)
  • Pharmacology and Toxicology (2208)
  • Physiology (3272)
  • Plant Biology (7064)
  • Scientific Communication and Education (1295)
  • Synthetic Biology (1953)
  • Systems Biology (5434)
  • Zoology (1119)