Depression is the leading cause of disability worldwide, affecting more than 300 million people, and approximately 20% of the American population. The rate of this brain disorder nearly doubles in patients with Parkinson?s disease (PD). Patients with depression are characterized by a debilitating negative affective state and an inability to seek out positive experiences. Unfortunately, the underlying mechanisms are unknown, but extant treatments suggest a critical role for the dopamine (DA) and serotonin (SE) systems. The DA and SE systems are known to be a critical for normal learning, reward processing, and choice behavior. More specifically, circumstantial and mixed evidence supports the hypotheses that DA and SE act as opponent processes in the human brain, with DA signaling reward prediction errors and SE acting as an opponent signal. The relationship of these basic ideas to the complex etiology of depression remains unclear. However, the NIMH?s Research Domain Criteria (RDoC) framework in combination with computational reinforcement learning theory provides a potential solution to theoretical barriers hindering further investigation. In this proposal, we will use choice behavior paired with a novel neurochemical sensor to validate two key domains in the RDoC Matrix: (1) Negative Valence Systems and (2) Positive Valence Systems. The goal will be to better understand how computations supporting adaptive choice behavior are executed by sub-second fluctuations in DA and SE in humans and how these signals are altered in patients with depression. Little is known about rapid microfluctuations in DA and SE in humans or how these signals are altered in the context of brain disorders like depression and PD. Progress has been hindered by the lack of technology that permits direct real-time measurements of DA and SE release in humans. To bridge this gap, this proposal will capitalize on our group?s recent technological innovation, which resulted in the world?s first simultaneous and co-localized measurements of DA and SE release with sub-second temporal resolution in the human brain. Herein, we pursue two specific aims, which combine our technological advance with computational approaches, to validate RDoC subconstructs as they may or may not relate to changes in sub-second DA and SE signaling in PD patients with versus without depression.
In Aim 1, we will examine choice behavior (on three tasks that incorporate subjective self-reports about subjective mood) and associated DA and SE signaling in the striatum in PD patients without depression.
In Aim 2, we will repeat the same measures, but in patients with co-morbid symptoms of depression and compare results across the two cohorts. The experiments proposed may yield unprecedented insight into the function of the DA and SE systems in humans; but, also, directly assess how these signals may be altered in humans afflicted with depression.
Dopamine and serotonin are key neurochemical signals underlying the etiology of depression, but whose actions at sub-second timescales have previously been impossible to monitor in humans. This proposal brings together (1) a first-of-its-kind approach, which permits simultaneous and co-localized sub-second measurements of dopamine and serotonin microfluctuations in the brains of consciously behaving human subjects, with (2) computational reinforcement learning theory, and (3) monetarily incentivized instrumental- and passive Pavlovian conditioning tasks. We propose to use our unique experimental platform to test mathematically framed hypotheses about dopamine and serotonin?s role in processing rewards and punishments during adaptive decision-making behavior and real-time changes in subjective mood states in humans with and without depression.
