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

评估 RDoC 矩阵的神经计算损失和奖励元素的重叠和独特性

• 批准号: 10312509
• 负责人: PEARL H CHIU
• 金额: $ 78.89万
• 依托单位: VIRGINIA POLYTECHNIC INST AND ST UNIV
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-21 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10312509
• 关键词: Anhedonia; Anxiety; Bayesian Modeling; Behavioral; Categories; Characteristics; Classification; Clinical; Clinical Data; Cognitive Therapy; Computer Models; Computing Methodologies; Coupled; Data; Depressed mood; Development; Diagnosis; Diagnostic; Dimensions; Disease; Elements; Ensure; Environment; Image; Individual; Lead; Learning; Link; Machine Learning; Manuals; Measures; Mental Depression; Mental Health; Mental disorders; Modeling; Moods; National Institute of Mental Health; Negative Valence; Neurosciences; Outcome; Participant; Positioning Attribute; Positive Valence; Process; Protocols documentation; Psychiatry; Psychopathology; Randomized; Research Domain Criteria; Rewards; Role; Sampling; Scientist; Specificity; Structure; Symptoms; Task Performances; Techniques; Testing; Time; Translating; Validation; Visit; Work; arm; base; clinically relevant; clinically significant; improved; negative mood; neurobehavioral; neuroimaging; novel; symptom treatment; trait

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.

项目总结/摘要 有证据表明，在传统的精神病诊断中， 类别，这些元素在NIMH研究领域标准矩阵中有特色。然而，在这方面， 验证RDoC矩阵的这些特征，并确定损失和回报的转化效用 估价需要至少三个关键的进展：i）理解元素的关系结构（即，到 损失和报酬的估价在多大程度上是联系在一起的或不同的），ii）确定 估值措施（即，损失和奖励评估的哪些特征与哪些症状有关），以及iii） 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类对特质类）。努力验证评估要素 及其与精神病理学的相关性，我们回应RFA-MH-19-242（计算方法， 验证与精神病理学相关的维度结构）。具体来说，我们采取数据驱动， 计算精神病学方法合并临床和实验数据，以描述 计算得出的损失和回报估值的组成部分，并在一个大样本的症状， 具有临床显著情绪、焦虑或快感缺乏的参与者（目标1）。在目标2和3中，我们纳入了 机械试验，以评估损失和回报估值的组成部分和关系是否 对变化敏感a）随着时间的推移，B）经过12次指示估值（目标2），或c） 认知行为疗法（目标3）。如果成功，我们相信有巨大的机会， 行为导向的临床医生和计算（神经）科学家，并通过映射推进该领域 症状到神经机械疾病过程，并刺激新的神经行为的发展， 指导治疗方法。根据RFA的要求，该应用程序评估多个结构（损失和 奖励评价结构和学习子结构）， 使用多个任务和数据级别。