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Cited by 477

The DOI matches its Crossref record; 106 of the 106 references it cites were found in a registry or an index.

Reward and motivation in pain and pain relief

Open access
Navratilova, Edita, Porreca, Frank·2014·Nature Neuroscience

Abstract

Pain is fundamentally unpleasant, a feature that protects the organism by promoting motivation and learning. Relief of aversive states, including pain, is rewarding. The aversiveness of pain, as well as the reward from relief of pain, is encoded by brain reward/motivational mesocorticolimbic circuitry. In this Review, we describe current knowledge of the impact of acute and chronic pain on reward/motivation circuits gained from preclinical models and from human neuroimaging. We highlight emerging clinical evidence suggesting that anatomical and functional changes in these circuits contribute to the transition from acute to chronic pain. We propose that assessing activity in these conserved circuits can offer new outcome measures for preclinical evaluation of analgesic efficacy to improve translation and speed drug discovery. We further suggest that targeting reward/motivation circuits may provide a path for normalizing the consequences of chronic pain to the brain, surpassing symptomatic management to promote recovery from chronic pain.

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Metadata

Authors
Navratilova, E., & Porreca, F.
Venue
Nature Neuroscience
Published
2014-09-25
Volume
17
Issue
10
Pages
1304-1312
ISSN
  • 1097-6256 (ISSN-L)
  • 1546-1726
OpenAlex
W2085234856
Cited by
477
References
106
FWCI
21.57
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Reward and motivation in pain and pain relief. https://app.cochat.ai/papers/10.1038/nn.3811
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Navratilova, E., & Porreca, F. (2014). Reward and motivation in pain and pain relief. Nature Neuroscience. https://doi.org/10.1038/nn.3811

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.