Data-driven validation of cognitive RDoC dimensions using deep phenotyping

项目摘要 NIMH研究领域标准(RDoC)沿着一系列关键因素重新定义了心理健康研究的概念 交叉无序的维度结构。然而，这些维度是由以下因素以自上而下的方式确定的 相对较小的研究人员小组。我们提出了一种数据驱动的方法来测试密钥RDoC的有效性 注意力、认知控制和工作记忆的结构。我们将使用多个 每个结构的认知任务，以检查它们与大脑网络的关系以及它们预测现实世界的能力 与心理健康相关的行为。最后，我们建议通过以下方式对RDoC框架进行增强 增加新的分析单位：对比和实践。 当前的RDoC矩阵直接从任务范例映射到构造和子构造，这是 这是有问题的，因为假定不同的构造有时可以映射到完全相同的一组任务。至 为了解决这一问题，我们提出了一种新的RDoC分析单位，称为“对比”，它更好地反映了 实验设计。我们将通过以下方式识别认知系统结构和对比之间的映射 咨询领域专家。然后，我们将获取一个大规模数据集，以测试探索性和 RDoC认知系统建构的验证性模型。最后，我们将评估这些RDoC是否 认知系统结构对相关的现实世界结果具有预测性。 RDoC矩阵将结构链接到行为测量和神经回路，但当前的映射 认知系统和大脑系统之间的结构是稀疏和不一致的。我们将使用密集的- 对65名受试者进行fMRI采样采集，每个受试者在相同的任务组中完成10次扫描 这项行为研究旨在开发一份精确的数据驱动的矩阵每个层面的神经参与图谱， 从对比到次结构再到结构。然后，我们将使用神经网络验证行为派生的模型 数据，包括对象之间和对象内部的数据。我们还将进行全面的探索性分析，以确定 这些任务上的数据驱动神经电路结构是否偏离RDoC矩阵。 对动物和动物的长期研究表明，在一项任务上反复练习会改变方式 任务已经完成，以及支持性能的大脑系统。我们将利用我们的行为和 脑成像样本，以评估认知系统域的结构是否保持不变 练习一下。同时，我们还将应用探索性方法来评估结构模型的一致性。 在训练早期或在广泛练习后估计。 总体而言，该项目用两个新的分析单元(对比和实践)扩展了RDoC矩阵，以及 验证了注意力、认知控制和工作记忆在行为和神经方面的结构 电路。

Project Summary The NIMH research domain criteria (RDoC) reconceptualizes mental health research along a series of key cross-disorder dimensional constructs. However, these dimensions were determined in a top-down fashion by relatively small groups of researchers. We propose a data-driven approach that tests the validity of the key RDoC constructs of attention, cognitive control, and working memory. We will evaluate these constructs using multiple cognitive tasks per construct to examine their relationship to brain networks and their ability to predict real-world behaviors that are relevant to mental health. Finally, we propose an augmentation to the RDoC framework by adding new units of analysis: contrasts and practice. The current RDoC matrix maps directly from task paradigms to constructs and subconstructs, which is problematic because supposedly distinct constructs can sometimes map to exactly the same set of tasks. To address this, we propose a new RDoC unit of analysis called a “contrast”, which better reflects the usual logic of experimental design. We will identify mappings between cognitive systems constructs and contrasts through consultation with domain experts. We will then acquire a large-scale dataset to test both exploratory and confirmatory models for RDoC cognitive system constructs. Finally, we will evaluate whether these RDoC cognitive systems constructs are predictive of related real-world outcomes. The RDoC matrix links constructs to both behavioral measures and neural circuits, but the present mappings between cognitive systems constructs and brain systems are sparse and inconsistent. We will use a dense- sampling fMRI acquisition of 65 subjects each completing 10 scanning sessions on the same battery of tasks as the behavioral study, to develop a precise data-driven atlas of neural engagement at each level of the matrix, from contrasts to subconstructs to constructs. We will then validate the behaviorally-derived models using neural data, both between subjects and within subjects. We will also perform fully exploratory analyses to identify whether the data-driven neural circuit structure on these tasks diverges from the RDoC matrix. A long history of research in both and animals has shown that repeated practice on a task changes the way that the task is performed and the brain systems that support performance. We will leverage our behavioral and brain imaging samples to evaluate whether the structure of the cognitive systems domain remains constant with practice. In parallel we will also apply exploratory methods to assess the consistency of structural models estimated either early in training or after extensive practice. Overall, this project expands the RDoC matrix with two new units of analysis (contrasts and practice), and validates the constructs of attention, cognitive control, and working memory across both behavior and neural circuits.