Major depressive disorder ranks among the most significant causes of mortality and disability in the world. Recent data from our group and others highlight that impairments in reward and loss learning are central to depression, have distinct neural substrates, and improve with successful treatment. Together, these findings suggest an urgent need to delineate the relationships among neurobehavioral reward and loss learning impairments and depression. Equally important, these insights suggest novel targets for intervention such that manipulating the neurobehavioral substrates of reward and loss learning may facilitate symptom change in depression. To address these issues, we use functional neuroimaging and a quantitative reinforcement learning framework to i) systematically characterize the neural and behavioral substrates that attend reward- and loss- learning impairments in depression (Aim 1), and ii) assess the degree to which these impairments respond to two methods of training that directly target reward and loss learning from different angles (Aims 2 and 3).
In Aims 2 and 3, we capitalize on extant data from our group and others showing that explicit and covert task modifications, respectively, lead to adaptive neural and behavioral learning changes in controls. Here we extend this work to individuals with depression and test the broad hypotheses that i) that depression may be characterized by distinct neurobehavioral impairments in reward- and loss- learning, and ii) these deficits may be normalized through targeted behavioral training. Recent advances in computational model-based analyses of reinforcement learning provide a robust neuromechanistic framework within which to delineate the nature and trajectory of the suggested reward- and loss- learning impairments and their amelioration.
Major depression affects as much as 20% of the American population and impairs more than 100 million individuals worldwide (WHO, 2008). To better understand the basic science and treatment of depression, we study the brain processes associated with poor decision-making in depression and how these processes may be repaired over time. Understanding different methods of how these changes occur may in the future assist with novel treatments that contribute to recovery in people with depression.
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| Wang, John M; Zhu, Lusha; Brown, Vanessa M et al. (2018) In Cocaine Dependence, Neural Prediction Errors During Loss Avoidance Are Increased With Cocaine Deprivation and Predict Drug Use. Biol Psychiatry Cogn Neurosci Neuroimaging : |
| Chung, Dongil; Kadlec, Kelly; Aimone, Jason A et al. (2017) Valuation in major depression is intact and stable in a non-learning environment. Sci Rep 7:44374 |
