Evaluating overlap and distinctiveness in neurocomputational loss and reward elements of the RDoC matrix

项目摘要/摘要 有证据表明，在传统的精神病学诊断中，存在损失和回报估值的中断 类别，这些元素在NIMH研究领域标准矩阵中有特色。然而， 验证RDoC矩阵的这些功能，并确定损失和奖励的转换效用 估值至少需要三个关键进展：i)了解要素的关系结构(即 损失和报酬估值在多大程度上有联系或不同)，ii)建立 估值措施(即，损失和回报估值的哪些特征与哪些症状有关)，以及 确定元素的稳定性或缺乏稳定性以及元素之间的关系(即， 确定哪些估值特征是状态特征，而不是特征特征)。致力于验证估值元素 以及它们与精神病理学的相关性，我们对RFA-MH-19-242(计算方法，用于 验证与精神病理学相关的维度结构)。具体地说，我们采用数据驱动的 计算精神病学方法融合临床和实验数据以描绘 通过计算得出的损失和回报价值的组成部分，并在大样本中出现症状 有临床显著情绪、焦虑或快感缺乏的参与者(目标1)。在目标2和目标3中，我们加入了 评估损失和报酬估值的组成部分及其之间关系的机械性试验 对变化敏感a)随时间变化，b)经过12次指导估值(目标2)，或c)以下 认知行为疗法(目标3)。如果成功，我们相信有巨大的机会架起桥梁 面向行为的临床医生和计算(神经)科学家，并通过映射推动该领域的发展 症状转化为神经机制疾病过程，并刺激新的神经行为的发展- 引导治疗方法。按照RFA的要求，此应用程序评估多个结构(丢失和 奖励估值结构和学习子结构)， 使用多个任务和数据级别。

PROJECT SUMMARY/ABSTRACT Evidence indicates that disruptions in loss and reward valuation exist across traditional psychiatric diagnostic categories, and these elements are featured in the NIMH Research Domain Criteria matrix. However, validating these features of the RDoC matrix and determining the translational utility of loss and reward valuation requires at least three critical advances: i) understanding the elements’ relational structure (i.e., to what extent are loss and reward valuation linked or distinct), ii) establishing the functional relevance of valuation measures (i.e., which features of loss and reward valuation are related to which symptoms), and iii) determining the stability or lack thereof of the elements and relationships between the elements (i.e., determining which valuation features are state-like vs trait-like). To work toward validating valuation elements and their relevance to psychopathology, we respond to RFA-MH-19-242 (Computational Approaches for Validating Dimensional Constructs of Relevance to Psychopathology). Specifically, we take a data-driven, computational psychiatry approach merging clinical and experimental data to delineate relationships among computationally derived components of loss and reward valuation and with symptoms in a large sample of participants with clinically significant mood, anxiety, or anhedonia (Aim 1). In Aims 2 and 3, we incorporate a mechanistic trial to assess whether components of and relationships between loss and reward valuation are sensitive to change a) over time, b) following 12 sessions of instructed valuation (Aim 2), or c) following cognitive behavioral therapy (Aim 3). If successful, we believe there is immense opportunity to bridge behaviorally-oriented clinicians and computational (neuro)scientists and advance the field by mapping symptoms to neuromechanistic disease processes and spurring the development of new neurobehaviorally- guided treatment approaches. As required by the RFA, this application assesses multiple constructs (loss and reward valuation constructs and learning subconstructs) in the Negative and Positive Valence RDoC domains, using multiple tasks and levels of data.