Literature DB >> 29482716

The control of tonic pain by active relief learning.

Suyi Zhang1,2, Hiroaki Mano1,2,3, Michael Lee4, Wako Yoshida2, Mitsuo Kawato2, Trevor W Robbins5, Ben Seymour1,2,3.   

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.
© 2018, Zhang et al.

Entities:  

Keywords:  basal ganglia; cingulate cortex; endogenous analgesia; human; neuroscience; pain; reinforcement learning; relief

Mesh:

Year:  2018        PMID: 29482716      PMCID: PMC5843408          DOI: 10.7554/eLife.31949

Source DB:  PubMed          Journal:  Elife        ISSN: 2050-084X            Impact factor:   8.140


  74 in total

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  4 in total

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4.  Pain Control by Co-adaptive Learning in a Brain-Machine Interface.

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  4 in total

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