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The Control of Tonic Pain by Active Relief Learning

By Suyi Zhang, Hiroaki Mano, Michael Lee, Wako Yoshida, Mitsuo Kawato, Trevor Robbins, Ben Seymour

Posted 21 Nov 2017
bioRxiv DOI: 10.1101/222653 (published DOI: 10.7554/eLife.31949)

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 relief-seeking involves a reinforcement learning process manifest by error signals in the dorsal putamen. Critically, this system also uses an uncertainty signal detected in pregenual anterior cingulate cortex that both controls the relief learning rate, and endogenously and parametrically modulates tonic pain. The results define a self-organising learning circuit that allows reduction of ongoing pain when learning about potential relief.

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