Examining the hierarchical structure of the RDoC framework using large-scale data-driven computational approaches

使用大规模数据驱动的计算方法检查 RDoC 框架的层次结构

• 批准号: 10306101
• 负责人: Manish Saggar
• 金额: $ 67.83万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-22 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10306101
• 关键词: Address; Adolescent; Adult; Advocate; Affect; Age; Architecture; Attention; Base of the Brain; Behavior; Behavior assessment; Behavioral; Behavioral Paradigm; Biological; Bipolar Disorder; Brain; Child; Clinical; Cognitive; Communities; Costs and Benefits; Data; Data Aggregation; Data Analyses; Data Analytics; Data Set; Development; Diagnosis; Dimensions; Effectiveness; General Population; Genes; Goals; Hyperactivity; Individual Differences; Instruction; Maps; Measures; Methods; National Institute of Mental Health; Negative Valence; Neuropsychology; Neurosciences; Participant; Pathway interactions; Patients; Population; Positive Valence; Psychopathology; Public Health; Research; Research Domain Criteria; Research Project Summaries; Rest; Sample Size; Sampling; Scanning; Schizophrenia; Site; Structure; Syndrome; System; Task Performances; Translations; Validation; Vertebral column; base; clinical translation; cognitive development; cohort; computer framework; connectome; disease classification; disorder control; efficacy research; functional MRI scan; insight; large scale data; neuroimaging; patient population; secondary analysis; sex; spatiotemporal

Project Summary The Research Domain Criteria (RDoC) applies an integrative, dimensional approach anchored in circuit neuroscience, genes, molecules, and behaviors. The RDoC framework, currently only for research, ultimately aims at facilitating the development of psychiatric nosology (disorder-classification system) based upon primary behavioral functions and their associated biological features that the brain has evolved to carry out. Although the impetus behind RDoC is in the right direction, for greater efficacy of RDoC in clinical translation, a data-driven examination is needed to validate and refine the architecture of RDoC. Further, several key questions remain unanswered. First, as noted in the current RFA (RFA-MH-19-242), since the inception of RDoC, a thorough data-driven validation that broadly explores, compares, and validates the constructs within the framework has not been performed. Second, to increase clinical translation of the RDoC framework, it is essential to assess whether constructs within a domain consistently relate to similar dimensions of psychopathology. Thus, providing data-driven evidence for the convergent and discriminant validity of the RDoC framework in predicting psychopathology. Lastly, and perhaps more fundamentally, it is unclear whether carefully crafted behavioral paradigms are required to examine domain-specific features (behavioral or circuit- level) or task-free paradigms (e.g., resting-state) can be computationally employed to extract similar domain- specific features. The lack of task instructions in resting-state paradigms enhances compliance in clinical populations, makes data aggregation across sites straightforward, and could provide a higher cost-benefit ratio if a single resting-state scan can provide information that would otherwise require multiple, carefully crafted, domain-specific neuroimaging task scans. Here, we propose to mine, systemically and computationally, three large-scale datasets from the general population and diagnosed patient populations to answer critical questions regarding the validity of the RDoC framework. Specifically, we aim to examine whether: (1) within- domain constructs overlap more than do between-domain constructs; (2) within-domain constructs relate to similar dimensions of psychopathology; and (3) task-free paradigms (e.g., resting-state) can be mined to extract similar domain-specific information that is usually extracted using specific task-based paradigms. By addressing these three key questions, our central goal is to provide the much-needed bottom-up examination of the RDoC framework to pave a pathway for its refinement and translation. Our long-term goal is to develop new computational frameworks to generate converging insights for grounding psychiatric nosology in biological features. Altogether, without careful data-driven validation, the RDoC framework remains theoretical. Hence, we advocate for developing a computational backbone for the RDoC framework to validate the assumptions underlying RDoC and facilitate framework refinement for greater clinical translation.

项目摘要 研究域标准（RDOC）应用了锚定在电路中的集成，维度方法 神经科学，基因，分子和行为。 RDOC框架目前仅用于研究 旨在促进基于精神病学疾病学（疾病分类系统）的发展 大脑已经进化为执行的主要行为功能及其相关的生物学特征。 尽管RDOC背后的动力朝着正确的方向 需要进行数据驱动的检查以验证和完善RDOC的体系结构。此外，有几个钥匙 问题仍未得到答复。首先，如当前RFA（RFA-MH-19-242）所述，自成立以来 RDOC是一种彻底探索，比较和验证内部构造的彻底验证验证 该框架尚未执行。第二，为了增加RDOC框架的临床翻译，这是 必须评估域内的构造是否始终与类似的维度有关 心理病理学。因此，提供数据驱动的证据，以证明 RDOC框架预测心理病理学。最后，也许从根本上讲，尚不清楚是否清楚 精心制作的行为范例需要检查特定领域特异性特征（行为或电路 - 级别）或无任务范例（例如，静止状态）可以用于提取类似的域 - 特定功能。静止状态范式缺乏任务说明可以增强临床的依从性 种群，使跨站点的数据聚集直接，并且可以提供更高的成本效益比 如果单个静止状态扫描可以提供否则需要多个精心设计的信息， 域特异性神经成像任务扫描。在这里，我们建议在系统和计算上开采三个 来自一般人群的大规模数据集和诊断出患者人群以回答关键 有关RDOC框架有效性的问题。具体来说，我们旨在检查：（1） 域构造重叠的重叠量超过域间结构。 （2）域内结构与 类似的心理病理学方面； （3）可以将无任务范式（例如，静止状态）开采到 提取通常使用基于特定任务的范例提取的类似域特异性信息。经过 解决这三个关键问题，我们的中心目标是提供急需的自下而上检查 RDOC框架为其改进和翻译铺平了途径。我们的长期目标是发展 新的计算框架，以生成融合见解，以扎根生物学中的精神病学 特征。总体而言，如果没有仔细的数据驱动验证，RDOC框架仍然是理论上的。因此， 我们主张为RDOC框架开发一个计算主链来验证假设 基础RDOC并促进框架改进，以进行更大的临床翻译。