Data-driven validation of cognitive RDoC dimensions using deep phenotyping

项目摘要 NIMH研究领域标准（RDoC）沿着一系列关键指标重新定义了心理健康研究。 交叉无序的维度结构然而，这些尺寸是以自上而下的方式确定的， 研究人员相对较少。我们提出了一个数据驱动的方法，测试的有效性的关键RDoC 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，我们提出了一个增强RDoC框架， 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结构和子结构， 这是有问题的，因为所谓的不同结构有时可以映射到完全相同的任务集。到 为了解决这个问题，我们提出了一个新的RDoC分析单元，称为“对比”，它更好地反映了通常的逻辑， 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，我们将获得一个大规模的数据集来测试探索性和 RDoC认知系统结构的验证模型。最后，我们将评估这些RDoC是否 认知系统构造可预测相关的现实世界结果。 RDoC矩阵将结构与行为测量和神经回路联系起来，但目前的映射 认知系统结构和大脑系统之间的联系是稀疏和不一致的。我们会用一个高密度的- 对65名受试者进行fMRI采集，每个受试者完成10次扫描，完成相同的任务， 行为研究，开发一个精确的数据驱动的地图集的神经参与在每一个层次的矩阵， 从对比到子结构再到结构。然后，我们将使用神经网络验证行为衍生模型。 受试者之间和受试者内部的数据。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。 对人和动物的长期研究表明，对一项任务的重复练习会改变 任务的执行和支持执行的大脑系统。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， 实践与此同时，我们还将应用探索性方法来评估结构模型的一致性 在训练初期或广泛练习后估计。 总体而言，该项目通过两个新的分析单元（对比和实践）扩展了RDoC矩阵，并且 验证了注意力、认知控制和工作记忆的结构， 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.