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.

尽管被认为是反诊断表型，但Anhedonia可以从动机的缺陷中出现，但估值或享乐主义欣赏，每种都反映了不同的神经过程跨个人表达。为了改善Anhedonia的结构，迫切需要提高构造治疗。我们的长期目标是结合计算，成像和因果操作以定义估值下降的转化生物标志物。在此提案中，我们确定了脑电图称为奖励积极性（REWP）的响应是针对基于价值缺陷的候选生物标志物在阿内迪尼亚。 REWP仅通过呈现奖励结果而引起抑郁症，并取决于增强学习模型的主要特征，积极的奖励预测错误（+RPE）。重要的是，可以在啮齿动物中使用相同的神经反应引起相同的神经反应像人类一样学习任务。该建议的目的是测试感应情绪，沮丧情绪，学习的无助（在小鼠中）直接减少了REWP中的 +RPE编码。理由方法是电生理学是识别复杂行为机制的高度有前途的工具并在物种之间翻译这些机制。在AIM 1中，我们将确定是否感应情绪和 +RPE 对REWP具有独立或互动效果。在AIM 2中，我们将招募沮丧的参与者和确定Anhedonia和 +RPE是否对源级发电机具有独立或交互式影响 REWP的基础（使用MEG）。在AIM 3中，我们将在Infralimbic的鼠标模型中使用相同的任务录音；然后，我们将测试这一点的因果关系减少（学习的无助性）和恢复（氟西汀）机制。这项提出的研究具有创新性，因为我们已经紧密地确定了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能够在啮齿动物模型中评估，操纵和恢复。这项贡献有望要重要的是因为它将推进不足的估值的转化机制。之上这些目标的完成，预期的结果将验证REWP为敏感和特定的机制阿内多尼亚的异常估值。与RDOC框架一致，我们对计算建模的使用将允许我们在积极价上对算法对比多个子构建的方法系统域。转化计算精神病学方法在这里链接电路级别功能障碍，异常计算和反诊断行为表型。成功完成这里提出的目标将创造我们认为是将这些优势结合到中的最有希望的途径在甲壳虫菌中，经计算启发的，机械测试的，可翻译的异常估值模型。这新型候选生物标志物将在物种之间转换，并且在门诊诊所中可检测。