A Novel Bench-to-Bedside Translational Model of Anhedonia

一种新颖的快感缺乏从临床到临床的转化模型

基本信息

批准号：
10554166
负责人：
JAMES F CAVANAGH
金额：
$ 19.64万
依托单位：
UNIVERSITY OF NEW MEXICO
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-03-14 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10554166
关键词：
Address Affect Affective Algorithms Ambulatory Care Facilities Anhedonia Animal Model Behavior Biological Markers Chronic Code Complex Computer Models Depressed mood Desire for food Diagnosis Diagnostic Disease Electroencephalography Electrophysiology (science)Emotional Emotions Epilepsy Estimation Techniques Fluoxetine Functional Magnetic Resonance Imaging Functional disorder Goals High Prevalence Hospitals Human Image Individual Learned Helplessness Learning Link Magnetic Resonance Imaging Masks Medication Management Mental Depression Mental Health Mental disorders Mission Modeling Monitor Moods Motivation Mus National Institute of Mental Health Outcome Participant Patients Phenotype Positive Valence Process Prognosis Psychiatry Psychological reinforcement Recovery Research Research Domain Criteria Rewards Risk Rodent Rodent Model Services Signal Transduction Source Statistical Models System Techniques Testing Translating Work analog behavioral phenotyping bench-to-bedside translation candidate marker frontal lobe hedonic improved innovation mouse model nervous system disorder neural novel recruit remediation response reward processing specific biomarkers tool translational model treatment response

项目摘要

Although considered a trans-diagnostic phenotype, anhedonia can emerge from deficits in motivation, valuation, or hedonic appreciation, each of which reflect different neural processes and are differently expressed across individuals. There is a critical need to refine the construct of anhedonia in order to improve treatment. Our long-term goal is to combine computational, imaging, and causal manipulations to define a translational biomarker of diminished valuation in anhedonia. In this proposal we identify how the EEG response known as the Reward Positivity (RewP) is a candidate biomarker specific to value-based deficiencies in anhedonia. The RewP is only elicited by the presentation of a rewarding outcome, it is decreased in depression, and it scales with the central feature of reinforcement learning models, the positive reward prediction error (+RPE). Importantly, this same neural response can be elicited in rodents using the same learning task as in humans. The objective of this proposal is to test whether induced emotion, depressed mood, and learned helplessness (in mice) directly diminish +RPE coding in the RewP. The rationale for this approach is that electrophysiology is a highly promising tool for identifying mechanisms of complex behaviors and translating these mechanisms between species. In Aim 1, we will determine if induced emotion and +RPE have independent or interactive effects on the RewP. In Aim 2 we will recruit depressed participants and determine if anhedonia and +RPE have independent or interactive influences on the source-level generators underlying the RewP (using MEG). In Aim 3 we will use the same task in a mouse model with infralimbic recordings; we will then test the causal diminishment (learned helplessness) and recovery (fluoxetine) of this mechanism. This proposed research is innovative because we have identified a computational function tightly tied to a neural response that directly addresses the disease-specific phenotype in human patients and is capable of being assessed, manipulated, and recovered within a rodent model. This contribution is expected to be significant because it will advance a translational mechanism for deficient valuation in anhedonia. Upon completion of these aims, the expected outcome will validate the RewP as a sensitive and specific mechanism of aberrant valuation in anhedonia. In line with the RDoC framework, our use of computational modeling will allow us to algorithmically contrast multiple sub-constructs of approach motivation in the positive valence systems domain. The translational computational psychiatry approach advanced here links circuit-level dysfunction, aberrant computations, and trans-diagnostic behavioral phenotype. The successful completion of the aims advanced here will create what we think is the most promising path for combining these strengths into a computationally-inspired, mechanistically tested, translatable model of aberrant valuation in anhedonia. This novel candidate biomarker will be translatable between species and testable in an outpatient clinic.

尽管被认为是一种跨诊断表型，但快感缺乏可能是由于动机缺陷而出现的，估价，或享乐欣赏，每一种都反映了不同的神经过程，并且有不同的表现体现在个体之间。迫切需要完善快感缺失的构造，以改善治疗。我们的长期目标是结合计算、成像和因果操作来定义一个快感缺失价值降低的转化生物标志物。在本提案中，我们确定了脑电图如何称为奖励积极性 (RewP) 的反应是针对基于价值的缺陷的候选生物标志物快感缺失。 RewP 仅通过呈现奖励性结果来引发，它会随着奖励结果的增加而减少抑郁症，并且它与强化学习模型的核心特征（积极奖励）成比例预测误差（+RPE）。重要的是，使用相同的方法可以在啮齿动物中引发相同的神经反应与人类一样的学习任务。该提案的目的是测试是否诱发情绪、抑郁情绪和习得性无助（小鼠）直接削弱 RewP 中的 +RPE 编码。这样做的理由方法是电生理学是识别复杂行为机制的非常有前途的工具并在物种之间翻译这些机制。在目标 1 中，我们将确定是否诱发情绪和 +RPE 对 RewP 有独立或交互的影响。在目标 2 中，我们将招募抑郁的参与者并确定快感缺乏和 +RPE 对源级生成器是否具有独立或交互影响 RewP 的基础（使用 MEG）。在目标 3 中，我们将在具有下边缘系统的小鼠模型中使用相同的任务录音；然后我们将测试这种因果关系的减少（习得性无助）和恢复（氟西汀）机制。这项研究是创新的，因为我们已经紧密地确定了一个计算函数与直接解决人类患者疾病特异性表型的神经反应有关，并且是能够在啮齿动物模型中进行评估、操作和恢复。这笔捐款预计将意义重大，因为它将推进针对快感缺失评估不足的转化机制。之上完成这些目标后，预期结果将验证 RewP 作为一种敏感且具体的机制快感缺乏中的异常估价。根据 RDoC 框架，我们对计算建模的使用将允许我们通过算法对比正价接近动机的多个子结构系统域。这里先进的转化计算精神病学方法将电路级联系起来功能障碍、异常计算和跨诊断行为表型。顺利完成这里提出的目标将创造我们认为最有希望的途径，将这些优势结合起来一种受计算启发、经过机械测试、可翻译的快感缺失异常评估模型。这新的候选生物标志物将可在物种之间进行翻译，并可在门诊进行测试。