Scientific article
OA Policy
English

Sensory-specific predictive models in the human anterior insula

Published inF1000Research, vol. 8, 164
Publication date2019
Abstract

Expectations affect the subjective experience of pain by increasing sensitivity to noxious events, an effect underlain by brain regions such as the insula. However, it has been debated whether these neural processes operate on pain-specific information or on more general signals encoding expectation of unpleasant events. To dissociate these possibilities, two independent studies (Sharvit et al., 2018, Pain; Fazeli and Büchel, 2018, J. Neurosci) implemented a cross-modal expectancy paradigm, testing whether responses to pain could also be modulated by the expectation of similarly unpleasant, but painless, events. Despite their differences, the two studies report remarkably convergent (and in some cases complementary) findings. First, the middle-anterior insula response to noxious stimuli is modulated only by expectancy of pain but not of painless adverse events, suggesting coding of pain-specific information. Second, sub-portions of the middle-anterior insula mediate different aspects of pain predictive coding, related to expectancy and prediction error. Third, complementary expectancy effects are also observed for other negative experiences (i.e., disgust), suggesting that the insular cortex holds prospective models of a wide range of events concerning their sensory-specific features. Taken together, these studies have strong theoretical implications on the functional properties of the insular cortex.

Keywords
  • Pain
  • Expectancy
  • Nocebo
  • Bayesian Coding
  • Unpleasantness
Funding
  • Swiss National Science Foundation - PP00O1_157424/1
Citation (ISO format)
SHARVIT, Gil Shlomo, VUILLEUMIER, Patrik, CORRADI DELL’ACQUA, Corrado. Sensory-specific predictive models in the human anterior insula. In: F1000Research, 2019, vol. 8, p. 164. doi: 10.12688/f1000research.17961.1
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Article (Published version)
Identifiers
Additional URL for this publicationhttps://f1000research.com/articles/8-164/v1
Journal ISSN2046-1402
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289downloads

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