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

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

Fingerprint

Periaqueductal Gray
Learning
Pain
Prefrontal Cortex
Putamen
Gyrus Cinguli
Animal Models
Magnetic Resonance Imaging
Brain

ASJC Scopus subject areas

  • Neuroscience(all)
  • Medicine(all)

Cite this

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

Representation of aversive prediction errors in the human periaqueductal gray. / Roy, Mathieu; Shohamy, Daphna; Daw, Nathaniel; Jepma, Marieke; Wimmer, G. Elliott; Wager, Tor D.

In: Nature Neuroscience, Vol. 17, No. 11, 28.10.2014, p. 1607-1612.

Research output: Contribution to journalArticle

Roy, M, Shohamy, D, Daw, N, Jepma, M, Wimmer, GE & Wager, TD 2014, 'Representation of aversive prediction errors in the human periaqueductal gray', Nature Neuroscience, vol. 17, no. 11, pp. 1607-1612. https://doi.org/10.1038/nn.3832
Roy M, Shohamy D, Daw N, Jepma M, Wimmer GE, Wager TD. Representation of aversive prediction errors in the human periaqueductal gray. Nature Neuroscience. 2014 Oct 28;17(11):1607-1612. https://doi.org/10.1038/nn.3832
Roy, Mathieu ; Shohamy, Daphna ; Daw, Nathaniel ; Jepma, Marieke ; Wimmer, G. Elliott ; Wager, Tor D. / Representation of aversive prediction errors in the human periaqueductal gray. In: Nature Neuroscience. 2014 ; Vol. 17, No. 11. pp. 1607-1612.
@article{576a28424c404ddfba45cae38d8d192a,
title = "Representation of aversive prediction errors in the human periaqueductal gray",
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.",
author = "Mathieu Roy and Daphna Shohamy and Nathaniel Daw and Marieke Jepma and Wimmer, {G. Elliott} and Wager, {Tor D.}",
year = "2014",
month = "10",
day = "28",
doi = "10.1038/nn.3832",
language = "English (US)",
volume = "17",
pages = "1607--1612",
journal = "Nature Neuroscience",
issn = "1097-6256",
publisher = "Nature Publishing Group",
number = "11",

}

TY - JOUR

T1 - Representation of aversive prediction errors in the human periaqueductal gray

AU - Roy, Mathieu

AU - Shohamy, Daphna

AU - Daw, Nathaniel

AU - Jepma, Marieke

AU - Wimmer, G. Elliott

AU - Wager, Tor D.

PY - 2014/10/28

Y1 - 2014/10/28

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84908521188&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84908521188&partnerID=8YFLogxK

U2 - 10.1038/nn.3832

DO - 10.1038/nn.3832

M3 - Article

C2 - 25282614

AN - SCOPUS:84908521188

VL - 17

SP - 1607

EP - 1612

JO - Nature Neuroscience

JF - Nature Neuroscience

SN - 1097-6256

IS - 11

ER -