Neuro-computational Approach to Determine a Neurochemical Basis of Mood and Depression

摘要：抑郁症是全球残疾的主要原因，影响着3亿多人， 以及大约20%的美国人口。这种脑部疾病的发病率几乎翻了一番。 患有帕金森氏症(PD)。抑郁症患者的特征是一种令人衰弱的负面情绪。 国家和不能寻找积极的经验。不幸的是，潜在的机制是 尚不清楚，但现有的治疗方法表明，多巴胺(DA)和5-羟色胺(SE)系统起着关键作用。 众所周知，DA和SE系统是正常学习、奖励处理和选择的关键 行为。更具体地说，间接证据和混合证据支持DA和SE作为 人脑中的对手过程，DA发出奖励预测错误的信号，SE充当 对方发出信号。这些基本观点与抑郁症的复杂病因之间的关系尚不清楚。 然而，NIMH的研究领域标准(RDoC)框架与计算 强化学习理论为阻碍进一步研究的理论障碍提供了一种潜在的解决方案。 在这项提议中，我们将使用选择行为与一种新的神经化学传感器配对来验证两个关键 RDoC矩阵中的区域：(1)负价系统和(2)正价系统。目标将是 为了更好地理解支持自适应选择行为的计算是如何在亚秒级执行的 人类DA和SE的波动以及抑郁症患者这些信号是如何改变的。 对于人类体内DA和SE的快速微波动或这些信号是如何在大脑中改变的知之甚少。 抑郁症和帕金森氏症等脑部疾病的背景。由于缺乏技术，进展受到了阻碍 允许直接实时测量人体内DA和SE的释放。为了弥合这一差距，这项提议将 利用我们集团最近的技术创新，这导致了世界上第一个同时和 在人类大脑中以亚秒的时间分辨率共同测量DA和SE的释放。 在这里，我们追求两个具体目标，这两个目标将我们的技术进步与计算相结合 方法，以验证RDoC子结构，因为它们可能与亚秒DA中的变化有关，也可能不与亚秒DA和 伴抑郁与不伴抑郁的帕金森病患者的SE信号转导。在目标1中，我们将检查选择行为(在 包含关于主观情绪的主观自我报告的三个任务)以及相关的DA和SE 无抑郁的帕金森病患者纹状体内信号传导。在目标2中，我们将重复相同的措施，但在 并比较两个队列的结果。这些实验 这一提议可能会对人类DA和SE系统的功能产生前所未有的洞察力；但同时， 直接评估这些信号在患有抑郁症的人身上可能会发生什么变化。

SUMMARY: 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.