The main objective of this study was to examine how the brain represents future painful outcomes when making decisions about pain. The research team further aimed to test whether this representation was similar or distinct to the representation of monetary rewards, experienced pain and negative emotions elicited by aversive pictures.<\/p>\n\n\n\n
The researchers designed a functional magnetic resonance study in which participants were asked to choose between one of 10 levels of pain induced by electrical stimulations and one of 10 levels of monetary rewards. In order to obtain the rewards, participants had to experience the pain. Machine learning was used to identify a multivariate brain signature predicting the intensity of the potential future pain involved in each decision. The research team then compared this representation to the multivariate signatures predicting the amount of monetary reward, the intensity of experienced shocks and the intensity of aversive pictures.<\/p>\n\n\n\n
Results show that a distributed pattern of activity encodes the value of future pain comprising both unsigned saliency signals (positively related to more pain and more money) and signed valuation signals (positively related to money and negatively related to pain). This pattern can predict the intensity of future pain involved in economic decisions and participants\u2019 decisions to accept or avoid the pain stimulation. This representation is partly related to the representation of monetary reward and negative valence but distinct from the representation of physical pain.<\/p>\n\n\n\n
These results identify a specific representation of the value of future pain in the brain and significantly contribute to further our understanding of the cerebral mechanisms responsible for making decisions about future pain. These findings could have potentially important implications for our understanding of disorders characterized by excessive or insufficient pain avoidance.<\/p>\n\n\n\n