Chronic pain is the greatest cause of disability worldwide, it is also the most economically costly neurological conditions in western societies, because of its impact on the working population and its care requirements. The sustained failure of pharmaceutical therapies (through inefficacy or intolerance) has led to growing interest in technology-based treatments, such as neuromodulation. However, little is known about the underlying mechanism by which pain-related brain activity reacts and adapts to these external interventions, nor do we have reliable information on subjective pain experience and its relief except for self-reports.
My PhD aims to understand how the transition from pain to relief is encoded by the brain, and whether this information can be decoded and implemented as feedback for therapeutic systems. My research combines functional neuroimaging and physiological data with computational modelling and information engineering to test these hypotheses. More specifically, I am adopting animal learning experiment paradigms to study human pain relief from a learning and motivational perspective. Preliminary results are encouraging, revealing a specific role for striatal-medial prefrontal brain regions in instrumental learning of pain relief. It is hoped that better understanding of the encoding of human pain relief in the brain will lead to improvements in current technology-based chronic pain treatments.