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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要有证据表明，传统精神病学诊断中存在损失和回报评估的中断类别，并且这些要素在 NIMH 研究领域标准矩阵中有所体现。验证 RDoC 矩阵的这些特征并确定损失和奖励的转化效用评估至少需要三个关键进展：i）理解元素的关系结构（即，损失和回报评估在多大程度上相关或不同），ii）建立功能相关性估值措施（即损失和回报估值的哪些特征与哪些症状相关），以及 iii) 确定要素的稳定性或缺乏稳定性以及要素之间的关系（即，确定哪些评估特征是类似状态的，哪些是类似特征的），以努力验证评估要素。及其与精神病理学的相关性，我们回应 RFA-MH-19-242（计算方法）具体来说，我们采取数据驱动的方式，计算精神病学方法合并临床和实验数据来描绘之间的关系计算得出的损失和奖励评估的组成部分以及大样本中的症状具有临床显着情绪、焦虑或快感缺乏的参与者（目标 1），我们纳入了目标 2 和目标 3。评估损失和回报评估的组成部分和之间的关系是否是机械性试验对变化敏感 a) 随着时间的推移，b) 经过 12 次指导评估后（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来弥补。以行为为导向的人群和计算（神经）科学家，并通过绘图推进该领域神经机械疾病过程的症状并刺激新的神经行为的发展根据 RFA 的要求，该应用程序评估多种结构（损失和治疗）。负价和正价 RDoC 领域中的奖励评估结构和学习子结构，使用多个任务和数据级别。