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

项目摘要 研究领域标准(RDoC)采用以电路为基础的综合维度方法 神经科学、基因、分子和行为。RDoC框架，目前仅用于研究，最终 旨在促进精神病因学(障碍-分类系统)的发展 大脑进化来执行的主要行为功能及其相关的生物特征。 尽管RDoC背后的推动力是正确的，但为了使RDoC在临床翻译中更有效，a 需要数据驱动的检查来验证和细化RDoC的体系结构。此外，还有几个关键 问题仍然没有得到回答。第一，如目前的RFA(RFA-MH-19-242)所述，自成立以来 RDoC是一种全面的数据驱动的验证，它广泛地探索、比较和验证 该框架尚未执行。第二，增加RDoC框架的临床翻译， 对于评估域中的构造是否一致地与 精神变态学。因此，提供了数据驱动的证据，证明了 RDoC框架在预测精神病理学中的作用。最后，或许更根本的是，目前还不清楚 需要精心设计的行为范例来检查特定于领域的特征(行为或电路- 级别)或无任务范例(例如，休眠状态)可以被计算地用来提取相似的领域- 特定功能。静息状态范式中缺乏任务指导可提高临床依从性 人口，使得跨站点的数据聚合变得简单，并可以提供更高的成本效益比 如果单个休眠状态扫描能够提供原本需要多个、精心设计 特定领域的神经成像任务扫描。在这里，我们建议从系统和计算上挖掘三个 来自普通人群和确诊患者群体的大规模数据集，以回答关键问题 关于RDoC框架有效性的问题。具体来说，我们的目标是研究：(1)在以下范围内- 域构造比域间构造重叠更多；(2)域内构造涉及 精神病理学的相似维度；以及(3)可以挖掘无任务范式(例如，休息状态)以 提取通常使用基于特定任务的范例提取的类似领域特定信息。通过 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 为RDoC框架的完善和翻译铺平了道路。我们的长远目标是发展 新的计算框架，以产生融合的洞察力，使精神病学病因学在生物学中扎根 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， 我们主张为RDoC框架开发一个计算骨干来验证这些假设 为RDoC提供基础，并促进框架改进，以实现更好的临床翻译。

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.