Multi level Analysis of Positive Valence Systems Across Mood Disorders

描述（由申请人提供）：RDoC倡议的指导原则是基于DSM的精神疾病概念化可能无法捕获生物维度，RFA-MH-12-100的目标是使用转化神经科学方法来测试与基础生物学一致的维度域。正效价系统（PVS）矩阵中的奖励处理域代表了临床研究的一个很好的重点领域，因为有大量的基础科学文献可以借鉴。PVS异常，特别是奖励学习，在情绪障碍中特别突出，其中抑郁和躁狂状态都与异常奖励行为相关，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影像学措施来区分单极和双相抑郁症，以前的工作的一个核心限制是依赖DSM诊断，未能捕捉奖励学习结构的全维度范围。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衡量标准，即概率奖励任务（PRT），它允许评估参与者调节行为的倾向作为强化的函数。PRT已在全世界900多个个体中使用，并提供了估计正常奖励学习的群体规范的机会。在拟议的研究中，我们计划招募160名在三个情绪障碍诊所寻求情绪障碍治疗的人，他们将接受PRT的筛查。将相对于标准化对照数据对患者表现进行分类，并将返回50%（n=80）的筛选样本进行进一步检测。重要的是，将选择这一子样本中的参与者， PRT正态参考分布具有同等代表性。然后，我们将通过四个分析单元来研究奖励学习的生物机制：分子，电路，生理学和行为。还将收集32名健康对照的数据。每个单元的测量将被整合到奖励学习网络综合得分（RLN综合得分）中。在目标1中，我们假设RLN综合评分在预测奖励处理症状（例如，快感缺乏、冲动、躁狂的测量）与DSM诊断相比。在目标2中，我们将测试RLN综合评分在3个月和6个月随访时间点预测症状特征的能力。具体来说，我们假设，相对于DSM诊断，RLN综合评分将有更大的阳性和阴性预测能力为快感缺失，躁狂，冲动和自杀相关的症状，以及在后续评估的整体功能。总之，该提案将在表现出广泛症状和障碍（从严重抑郁到轻躁狂/躁狂）的个体中，通过多个层次的分析来研究奖励学习的全维度范围。这是一项研究计划的第一步，该计划有望重塑精神疾病的概念化和治疗方式。

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.