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: 10455059
• 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/10455059
• 关键词: Anhedonia; Anxiety; Bayesian Modeling; Behavioral; Categories; Characteristics; 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; Manuals; Measures; Mental Depression; Mental Health; Mental disorders; Modeling; Moods; National Institute of Mental Health; Negative Valence; Neurosciences; Outcome; Participant; Persons; 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; machine learning classification; 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.

项目摘要/摘要 证据表明，传统的精神病诊断中存在损失和奖励价值的中断 类别以及这些元素在NIMH研究领域标准矩阵中列出。然而， 验证RDOC矩阵的这些功能，并确定损失和奖励的翻译效用 价值至少需要三个关键进展：i）了解要素的关系结构（即 损失和奖励价值链接或不同的程度），ii）建立功能相关性 价值度量（即，损失和奖励值的特征与哪些符号有关），以及III） 确定元素之间的元素和关系的稳定性或缺乏（即 确定哪些值特征类似于状态与性状样）。致力于验证价值要素 以及它们与心理病理学的相关性，我们对RFA-MH-19-242做出了回应（计算方法 验证与心理病理学相关的维度结构）。具体来说，我们采用数据驱动 计算精神病学方法合并临床和实验数据以描述之间的关系 损失和奖励价值的计算派生成分，并在大量样本中具有符号 患有临床意义，焦虑或抗痛的参与者（AIM 1）。在目标2和3中，我们合并了 评估损失和奖励价值之间关系的机械试验是否是 敏感更改a）随着时间的流逝，b）在12个会话后的指示值（AIM 2）或c）之后 认知行为疗法（AIM 3）。如果成功，我们相信有巨大的机会桥接 以行为为导向的临床医生和计算（NEURO）科学家，并通过映射推进了现场 神经力学疾病过程的症状，并激发了新的神经行为的发展 指导治疗方法。根据RFA的要求，本申请评估多个结构（损失和 奖励价值构造和学习子构造）在负和正价rdoc域中， 使用多个任务和数据级别。