Reward processing and depression: Current findings and future directions


Depressive disorders, including major depressive disorder (MDD) and persistent depressive disorder, are common and impairing. Worldwide, more than 300 million people suffer from depression (WHO, 2012) and depressive disorders are the leading contributor to the global disease burden (Ferrari et al., 2013). Over the past decade, dysfunction in neural processing of rewards has emerged as one of the most promising biological markers for the development of depressive disorders due to the role of reward processing in learning and in emotions central to depressive disorders. Despite this, depressive disorders are still defined by self-reported symptoms and behavior, and research has begun to focus on identifying the pathophysiology of the disorder. These findings have implications for etiological theories of depressive disorders while providing important targets for interventions. In this chapter, we provide a selected review of theory and research on the association between reward processing and depression. Because adolescence is a critical period in the development of depressive disorders and understanding risk factors that predate the onset of psychopathology is crucial for understanding etiology, we review the rewardprocessing literature as it relates to depression in childhood and adolescence, as well as adulthood. Although we review behavioral studies, we emphasize studies using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). We also provide an overview of a more recent line of research examining the influence of stressful life events on the link between reward processing and depression. Finally, we provide suggestions for the direction of future research in the area of reward processing and depression.

In The Neuroscience of Depression - Genetics, Cell Biology, Neurology, Behaviour, and Diet
Daniel M. Mackin, Ph.D.
Daniel M. Mackin, Ph.D.
Postdoctoral Fellow in Biomedical Data Science

Psychologist and data scientist interested in the intersection of technology and mental health. I apply traditional and advanced quantitative methods to understand the development and course of psychopathology.