Multi level Analysis of Positive Valence Systems Across Mood Disorders

描述（由申请人提供）：RDoC计划的指导原则是基于dsm的精神疾病概念化可能无法捕获生物学维度，RFA-MH-12-100的目标是使用转化神经科学方法来测试与潜在生物学一致的维度域。正价系统（PVS）矩阵中的奖励处理域代表了临床研究的一个极好的重点领域，因为有大量的基础科学文献可供借鉴。PVS异常，尤其是奖励学习，在情绪障碍中尤为突出，抑郁和躁狂状态都与异常的奖励行为有关，尽管方向相反。虽然过去的研究试图使用奖励处理的行为和神经成像措施来区分单相和双相抑郁症，但先前工作的一个主要限制是依赖DSM诊断，未能捕捉到奖励学习结构的完整维度范围。为了应对这一挑战，PI开发了一种广泛使用的奖励学习的客观测量方法，即概率奖励任务（PRT），它允许评估参与者作为强化函数调节行为的倾向。PRT已在世界各地的900多人中使用，并提供了估计正常奖励学习的总体规范的机会。在拟议的研究中，我们计划在三个情绪障碍诊所招募160名寻求情绪障碍治疗的个体，他们将接受PRT筛查。患者的表现将相对于规范对照数据进行分类，50% （n=80）的筛查样本将返回进一步检测。重要的是，该子样本中的参与者将被选择，以便每个五分位数的

DESCRIPTION (provided by applicant): The guiding principle of the RDoC initiative is that DSM-based conceptualizations of psychiatric illness may fail to capture biological dimensionality, and the goal of RFA-MH-12-100 is to use translational neuroscience approaches to test dimensional domains consistent with underlying biology. Reward-processing domains within the Positive Valence Systems (PVS) matrix represent an excellent area of focus for clinical research, given the vast basic science literature on which to draw upon. PVS abnormalities, especially Reward Learning, are particularly salient in mood disorders, where both depressive and manic states are associated with aberrant reward behavior, albeit in opposite directions. While past studies have sought to use behavioral and neuroimaging measures of reward processing to discriminate between unipolar and bipolar depression, a central limitation of prior work is the reliance on DSM diagnoses that fail to capture the full dimensional range of the reward learning construct. To address this challenge, the PI developed a widely-used objective measure of reward learning, the Probabilistic Reward Task (PRT), which allow the assessment of participants' propensity to modulate behavior as a function of reinforcements. The PRT has been used in over 900 individuals across the world and provides the opportunity to estimate population norms for normal reward learning. In the proposed research, we plan to recruit 160 individuals seeking treatment for mood disorders at three mood disorder clinics who will be screened with the PRT. Patient performance will be classified relative to normed control data, and 50% (n=80) of the screening sample will return for further testing. Importantly, participants in this sub-sample will be selected so that each quintile of the PRT normative-reference distribution is equally represented. We will then investigate biological mechanisms of reward learning across four units of analysis: molecules, circuitry, physiology, and behavior. Data on 32 healthy controls will also be collected. Measures from each unit will be integrated into a Reward Learning Network composite score (RLN Composite). In Aim 1, we hypothesize that the RLN Composite score will show superior ability in predicting reward-processing symptoms (e.g., measures of anhedonia, impulsivity, mania) compared to DSM diagnoses. In Aim 2, we will test how well the RLN Composite score predicts symptom profiles at 3- and 6-month follow-up time-points. Specifically, we hypothesize that, relative to DSM diagnoses, the RLN composite score will have greater positive and negative predictive power for anhedonic, manic, impulsive and suicide-related symptoms as well as overall functioning assessed at follow-up. In sum, this proposal would study the full dimensional range of reward learning across multiple levels of analysis in individuals exhibiting a wide range of symptoms and impairments (from severe depression to hypomania/mania). This constitutes the first step in a research program that has the promise to reshape how mental illness is conceptualized and treated.