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

确定情绪和抑郁的神经化学基础的神经计算方法

• 批准号: 10415066
• 负责人: Kenneth Tucker Kishida
• 金额: $ 38.37万
• 依托单位: WAKE FOREST UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10415066
• 关键词: Affect; American; Behavior; Behavioral; Brain; Brain Diseases; Choice Behavior; Clinic; Complex; Computer Models; Conscious; Corpus striatum structure; Decision Making; Diagnosis; Dimensions; Dopamine; Electrodes; Etiology; Feeling; Gambling; Goals; Human; Incentives; Individual; Investigation; Learning; Link; Mathematics; Measurement; Measures; Mental Depression; Monitor; Moods; Movement Disorders; National Institute of Mental Health; Negative Valence; Operative Surgical Procedures; Outcome; Parkinson Disease; Participant; Patient Self-Report; Patients; Persons; Play; Population; Positive Valence; Process; Psychological reinforcement; Punishment; Research; Research Domain Criteria; Rewards; Role; Serotonergic System; Serotonin; Signal Transduction; Symptoms; System; Technology; Testing; Time; Validation; Work; associated symptom; base; behavior prediction; classical conditioning; cohort; comorbidity; depressive symptoms; disability; dopamine system; expectation; experience; experimental study; extracellular; financial incentive; human model; human subject; implantation; insight; negative affect; neural correlate; neurochemistry; novel; reward processing; sensor; technological innovation; temporal measurement; theories; volunteer

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.

摘要：抑郁症是全球残疾的主要原因，影响了3亿多人， 约占美国人口的20％。这种脑部疾病的发生率几乎使患者增加一倍 帕金森氏病（PD）。抑郁症患者的特征是令人衰弱的阴性情感 声明和无法寻求积极的经验。不幸的是，基本机制是 未知，但额外的治疗表明多巴胺（DA）和5-羟色胺（SE）系统至关重要。 DA和SE系统对于正常学习，奖励处理和选择至关重要 行为。更具体地说，间接和混合证据支持DA和SE充当的假设 对手在人脑中的处理，具有DA信号奖励预测错误，而SE充当 附属信号。这些基本思想与抑郁症复杂的病因的关系尚不清楚。 但是，NIMH的研究领域标准（RDOC）框架与计算结合 强化学习理论为理论障碍提供了潜在的解决方案，阻碍了进一步的研究。 在此提案中，我们将使用与新型神经化学传感器配对的选择行为来验证两个键 RDOC矩阵中的域：（1）负价系统和（2）正价系统。目标意志 要更好地了解支持自适应选择行为的计算如何由子秒执行 DA和SE在人类中的波动以及抑郁症患者的这些信号如何改变。 关于人类中DA和SE的快速微透明或这些信号如何改变的迅速微透明知之甚少 抑郁症和PD等脑部疾病的背景。缺乏技术阻碍了进步 允许在人类中直接对DA和SE释放的实时测量。为了弥合这一差距，该提议将 利用我们小组最近的技术创新，这导致了世界上第一个同时的创新 DA和SE释放的共定位测量在人脑中以下临时分辨率释放。 在此，我们购买了两个具体目标，它们将我们的技术进步与计算相结合 方法，以验证RDOC子构造，因为它们可能与以下DA和 PD患者与无抑郁症患者的SE信号传导。在AIM 1中，我们将检查选择行为（ON 三个任务结合了主观情绪的主观自我报告）以及相关的DA和SE 没有抑郁症的PD患者的纹状体信号。在AIM 2中，我们将重复相同的测量，但 患有抑郁症状并在两个队列中进行比较结果的患者。实验 提出的可能会对人类DA和SE系统的功能产生前所未有的见解。但是也， 直接评估患有抑郁症的人类可能如何改变这些信号。