Mechanisms of endogenous analgesia. (360G-Wellcome-097490_Z_11_A)

The programme will address how two distinct mechanisms of endogenous analgesia control activity in the brainstem periaqueductal grey. I aim to provide an account in computational terms, in order to specify the precise decision algorithms (rules) by which they operate. First I address the mechanism by which predictions reduce pain, by exploring precisely what information within a prediction controls pain perception. Specifically, I will test Bayesian models against competing accounts. Brain i maging will be used to determine how this is implemented in forebrain structures, and if/how they modulate activity in the periaqueductal grey. Second, I address the mechanism by which escape under threat reduces pain. Here I propose a theoretical analysis of controllability in escape/avoidance learning, building on my existing Reinforcement Learning framework. I will implement evolutionary simulations designed to show whether pain modulation emerges as an intrinsic reward to guide escape. On the basis of this, I will test model predictions (behaviourally) and their implementation (using fMRI), using an active versus passive escape learning paradigm. Finally, I will use deep brain stimulation to provide convergent evidence of how these mechanisms depend on the periaqueductal grey, looking for a statistical interaction between stimulation and endogenous analgesia.