Pain-related learning signals in the human insula

Abstract

Pain is not only a perceptual phenomenon, but also a preeminent learning signal. In reinforcement learning models, prediction errors (PEs) play a crucial role, i.e. the mismatch between expectation and sensory input. In particular, advanced learning models require the representation of different types of PEs, namely signed PEs (whether more or less pain was expected) to specify the direction of learning, and unsigned PEs (the absolute deviation from an expectation) to adapt the learning rate. The insula has been shown to play an important role in pain intensity coding and in signaling surprise. However, mainly unsigned PEs could be identified in the anterior insula. It remains an open question whether these PEs are specific to pain, and whether signed PEs are also represented in the insula. To answer these questions, 47 subjects learned associations of two conditioned stimuli (CS) with four unconditioned stimuli (US; painful heat or loud sound, of one low and one high intensity each) while undergoing functional magnetic resonance imaging (fMRI) and skin conductance response (SCR) measurements. CS-US associations reversed multiple times between intensities and between sensory modalities, generating frequent PEs. SCRs indicated comparable nonspecific characteristics of the two modalities. fMRI analyses focusing on the insular and opercular cortices contralateral to painful stimulation showed that activation in the anterior insula correlated with unsigned intensity PEs. Importantly, this unsigned PE signal was similar for pain and aversive sounds and also modality PEs, indicating an unspecific aversive surprise signal. Conversely, signed pain intensity PE signals were modality-specific and located in the dorsal posterior insula, an area previously implicated in pain intensity processing. Previous studies have identified abnormal insula function and abnormal learning as potential causes of pain chronification. Our findings link these results and suggest one potential mechanism, namely a misrepresentation of learning relevant prediction errors in the insular cortex.