The neurocomputational basis of mood-reward dynamics

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Most, if not all of us, go through periods of feeling happy or blue. Mood swings are a core feature of everyday life and not tantamount to the existence of mental illness. However, excessive or prolonged moodiness can lead to a serious mood disorder, above all depression, which is one of modern society’s most challenging and costly health burdens. Mood may be shaped by experience, such as reward or punishment. For instance, a smile from a stranger on the bus may brighten our day, whilst losing a tennis match may make us feel low. Our mood, however, may also influence how we perceive an experience. For example, imagine getting ticked off by your boss when beaming with joy, e.g., right after winning the lottery, or alternatively, in the middle of a bad day, e.g., after a grant rejection. In other words, outcomes, such as reward and punishment, impact on mood, and mood, in turn, shapes sensitivity to future outcomes. Yet, despite the importance of bidirectional feedback dynamics between mood and reward sensitivity, we know little about their neurobiological underpinnings and their role in the treatment of mental illness. The overarching goal of this project is to provide a mechanistic account of the neurobiology of such mood-reward dynamics in humans – with a focus on the treatment of mood disorders, such as depression. Applying state-of-the-art methods in cognitive computational neuroscience, I will answer the following research questions: (1) How, and where, are bidirectional influences between mood and reward sensitivity represented in the healthy brain? (2) Are these neurobiological processes altered in mood disorders, and does this relate to symptoms of depression? (3) How can we modulate these processes with different types of drugs, and can we use this knowledge to improve depression therapy? To address these questions, I will use a multimodal approach of cognitive experiments, functional neuroimaging, psychopharmacology, and computational modelling. This project will provide a neurocomputational framework for understanding mood swings in health and psychiatric illness. Further, it promises to provide neurocognitive markers for identifying individuals at-risk for depression, and antidepressant treatment response, that can be tested in large-scale clinical trials in the future. Ultimately, by bridging a gap between basic neuroscience research and clinical psychiatry, this project has the potential to transform how we diagnose, and treat, the most common form of mental illness.