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: 10652559
• 负责人: Kenneth Tucker Kishida
• 金额: $ 38.36万
• 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 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10652559
• 关键词: Affect; American; Association Learning; Behavior; Behavioral; Brain; Brain Diseases; Choice Behavior; Clinic; Complex; Computer Models; Conscious; Corpus striatum structure; Decision Making; Deep Brain Stimulation; 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; behavior prediction; 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，影响超过3亿人， 和大约20%的美国人口。这种脑部疾病的发病率在病人中几乎翻了一番 帕金森病（PD）抑郁症患者的特征是一种使人衰弱的消极情感， 状态和无法寻求积极的经验。不幸的是，潜在的机制是 未知，但现存的治疗表明多巴胺（DA）和血清素（SE）系统的关键作用。 DA和SE系统被认为是正常学习，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 在人脑中，DA信号奖励预测错误，而SE则作为一种 对手信号这些基本概念与抑郁症复杂病因学的关系仍不清楚。 然而，NIMH的研究领域标准（RDoC）框架结合计算 强化学习理论为解决阻碍进一步研究的理论障碍提供了一个潜在的解决方案。 在这个建议中，我们将使用选择行为与一种新的神经化学传感器配对，以验证两个关键 RDoC矩阵中的域：（1）负价系统和（2）正价系统。进球会 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的波动以及这些信号在抑郁症患者中如何改变。 关于人类DA和SE的快速微波动或这些信号如何在大脑中改变， 抑郁症和帕金森病等脑部疾病的背景下。由于缺乏技术， 允许直接实时测量人体中DA和SE的释放。为了弥合这一差距，本提案将 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， 在人脑中以亚秒级时间分辨率对DA和SE释放进行共定位测量。 在这里，我们追求两个具体的目标，这联合收割机结合我们的技术进步与计算 方法，以验证RDoC子结构，因为它们可能与亚秒DA的变化有关，也可能无关， SE信号在PD患者与无抑郁症。在目标1中，我们将研究选择行为（在 三个任务，包括主观自我报告的主观情绪）和相关的DA和SE 没有抑郁症的PD患者纹状体中的信号传导。在目标2中，我们将重复同样的措施，但在 患有抑郁症的共病症状的患者，并比较两个队列的结果。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洞察力;但是， 直接评估这些信号如何在患有抑郁症的人中改变。