Representation of aversive prediction errors in the human periaqueductal gray

Mathieu Roy, Daphna Shohamy, Nathaniel Daw, Marieke Jepma, G. Elliott Wimmer, Tor D. Wager

Research output: Contribution to journalArticle

106 Scopus citations

Abstract

Pain is a primary driver of learning and motivated action. It is also a target of learning, as nociceptive brain responses are shaped by learning processes. We combined an instrumental pain avoidance task with an axiomatic approach to assessing fMRI signals related to prediction errors (PEs), which drive reinforcement-based learning. We found that pain PEs were encoded in the periaqueductal gray (PAG), a structure important for pain control and learning in animal models. Axiomatic tests combined with dynamic causal modeling suggested that ventromedial prefrontal cortex, supported by putamen, provides an expected value-related input to the PAG, which then conveys PE signals to prefrontal regions important for behavioral regulation, including orbitofrontal, anterior mid-cingulate and dorsomedial prefrontal cortices. Thus, pain-related learning involves distinct neural circuitry, with implications for behavior and pain dynamics.

Original languageEnglish (US)
Pages (from-to)1607-1612
Number of pages6
JournalNature neuroscience
Volume17
Issue number11
DOIs
StatePublished - Oct 28 2014

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)

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    Roy, M., Shohamy, D., Daw, N., Jepma, M., Wimmer, G. E., & Wager, T. D. (2014). Representation of aversive prediction errors in the human periaqueductal gray. Nature neuroscience, 17(11), 1607-1612. https://doi.org/10.1038/nn.3832