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Cited by 406

The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.

Mind wandering away from pain dynamically engages antinociceptive and default mode brain networks

Open access
Kucyi, Aaron, Salomons, Tim V., Davis, Karen D.·2013·Proceedings of the National Academy of Sciences

Abstract

Significance: The mind easily wanders away from mundane tasks, but pain is presumed to automatically capture attention. We demonstrate that individuals differ in how often their minds spontaneously wander away from pain and that these differences are associated with the disruptive effect of pain on cognitive performance. Brain–behavior relationships underscore these individual differences. When people’s minds wander away from pain, there are increased activations of the default mode network (DMN) and strong interactions between the DMN and periaqueductal gray (PAG), an opiate-rich region mediating pain suppression. Individuals with greater tendencies to mind wander from pain have stronger anatomical links and dynamic functional communication between PAG and DMN. These findings provide clinically important clues about why some individuals cannot disengage from pain.

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Metadata

Authors
Kucyi, A., Salomons, T. V., & Davis, K. D.
Venue
Proceedings of the National Academy of Sciences
Published
2013-10-28
Volume
110
Issue
46
Pages
18692-18697
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2107754415
Cited by
406
References
51
FWCI
13.80
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Mind wandering away from pain dynamically engages antinociceptive and default mode brain networks. https://app.cochat.ai/papers/10.1073/pnas.1312902110
The paper
Kucyi, A., Salomons, T. V., & Davis, K. D. (2013). Mind wandering away from pain dynamically engages antinociceptive and default mode brain networks. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1312902110

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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.