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Enhanced pain-related conditioning for face compared to hand pain.

Katharina SchmidtKatarina ForkmannSigrid ElsenbruchUlrike Bingel
Published in: PloS one (2020)
Pain is evolutionarily hardwired to signal potential danger and threat. It has been proposed that altered pain-related associative learning processes, i.e., emotional or fear conditioning, might contribute to the development and maintenance of chronic pain. Pain in or near the face plays a special role in pain perception and processing, especially with regard to increased pain-related fear and unpleasantness. However, differences in pain-related learning mechanisms between the face and other body parts have not yet been investigated. Here, we examined body-site specific differences in associative emotional conditioning using electrical stimuli applied to the face and the hand. Acquisition, extinction, and reinstatement of cue-pain associations were assessed in a 2-day emotional conditioning paradigm using a within-subject design. Data of 34 healthy subjects revealed higher fear of face pain as compared to hand pain. During acquisition, face pain (as compared to hand pain) led to a steeper increase in pain-related negative emotions in response to conditioned stimuli (CS) as assessed using valence ratings. While no significant differences between both conditions were observed during the extinction phase, a reinstatement effect for face but not for hand pain was revealed on the descriptive level and contingency awareness was higher for face pain compared to hand pain. Our results indicate a stronger propensity to acquire cue-pain-associations for face compared to hand pain, which might also be reinstated more easily. These differences in learning and resultant pain-related emotions might play an important role in the chronification and high prevalence of chronic facial pain and stress the evolutionary significance of pain in the head and face.
Keyphrases
  • chronic pain
  • pain management
  • neuropathic pain
  • gene expression
  • machine learning
  • spinal cord
  • high resolution
  • deep learning
  • big data
  • optical coherence tomography