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 研究领域标准（RDoC）采用了一种基于电路的综合性、维度性方法 神经科学、基因、分子和行为。RDoC框架目前仅用于研究， 旨在促进精神疾病分类学（疾病分类系统）的发展， 主要的行为功能及其相关的生物特征，大脑已经进化到执行。 尽管RDoC背后的动力是正确的，但为了提高RDoC在临床翻译中的功效， 需要数据驱动的检查来验证和改进RDoC的体系结构。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）中所指出的， RDoC，一种全面的数据驱动验证，广泛地探索、比较和验证 该框架尚未执行。其次，为了增加RDoC框架的临床翻译， 关键是要评估一个领域内的结构是否始终与相似的维度相关， 精神病理学因此，提供数据驱动的证据的收敛和判别效度的 RDoC框架预测精神病理学。最后，也许更根本的是，不清楚是否 需要精心制作的行为范例来检查特定于域的特征（行为或电路， 级别）或无任务范例（例如，静止状态）可以计算地用于提取类似的域- 具体特点。静息态范式中任务指令的缺乏增强了临床研究中的依从性 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 如果单个休眠状态扫描可以提供信息，而这些信息在其他情况下需要多个精心制作的， 特定领域的神经成像任务扫描在这里，我们提出挖掘，系统和计算，三个 来自普通人群和确诊患者人群的大规模数据集， 关于RDoC框架有效性的问题。具体而言，我们的目的是研究：（1）在- 域结构比域间结构重叠更多;（2）域内结构与 精神病理学的相似维度;和（3）无任务范例（例如，休眠状态）可以被挖掘以 提取通常使用特定的基于任务的范例提取的类似的领域特定信息。通过 为了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 的RDoC框架，铺平道路，其完善和翻译。我们的长期目标是发展 新的计算框架，以产生融合的见解，为生物学中的精神病疾病分类学奠定基础 功能.总而言之，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，我们认为， 我们主张为RDoC框架开发一个计算骨干，以验证这些假设 作为RDoC的基础，并促进框架完善，以实现更大的临床翻译。