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.

项目概要 研究领域标准 (RDoC) 采用基于电路的综合维度方法 神经科学、基因、分子和行为。 RDoC框架，目前仅用于研究，最终 旨在促进基于精神科分类学（疾病分类系统）的发展 大脑进化出的主要行为功能及其相关的生物学特征。 尽管 RDoC 背后的推动力是正确的，但为了提高 RDoC 在临床转化中的功效， 需要数据驱动的检查来验证和完善 RDoC 的架构。此外，几个关键 问题仍然没有答案。首先，正如当前 RFA (RFA-MH-19-242) 中所述，自 RFA 成立以来 RDoC，一种彻底的数据驱动验证，广泛探索、比较和验证内部的结构 该框架尚未执行。其次，为了增加RDoC框架的临床转化， 对于评估一个领域内的构造是否始终与相似的维度相关至关重要 精神病理学。因此，为收敛和判别有效性提供数据驱动的证据 预测精神病理学的 RDoC 框架。最后，也许更根本的是，尚不清楚是否 需要精心设计的行为范例来检查特定领域的特征（行为或电路） 水平）或无任务范式（例如，静息状态）可以通过计算来提取相似的域 具体功能。静息态范例中缺乏任务指令增强了临床的依从性 人口，使跨站点的数据聚合变得简单，并且可以提供更高的成本效益比 如果单次静息态扫描可以提供需要多次精心设计的信息 特定领域的神经影像任务扫描。在这里，我们建议系统地和计算地挖掘三个 来自一般人群和诊断患者群体的大规模数据集，以回答关键问题 有关 RDoC 框架有效性的问题。具体来说，我们的目标是检查是否：(1) 域结构的重叠比域间结构的重叠更多； (2) 域内结构涉及 精神病理学的相似维度； （3）可以挖掘无任务范式（例如，静息状态） 提取通常使用特定的基于任务的范例提取的类似的特定领域信息。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的检查 RDoC 框架，为其完善和翻译铺平道路。我们的长期目标是发展 新的计算框架可以产生汇聚的见解，为生物学中的精神病学分类学奠定基础 特征。总而言之，如果没有仔细的数据驱动验证，RDoC 框架仍然停留在理论上。因此， 我们主张为 RDoC 框架开发计算主干来验证假设 基础 RDoC 并促进框架细化以实现更大的临床转化。