Data-driven validation of cognitive RDoC dimensions using deep phenotyping

使用深度表型分析对认知 RDoC 维度进行数据驱动验证

基本信息

批准号：
10686101
负责人：
Russell A Poldrack
金额：
$ 77.41万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-19 至 2027-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10686101
关键词：
Address Algorithms Animal Experimentation Animals Atlases Attention Automobile Driving Behavior Behavioral Brain Brain imaging Cognitive Collection Complement Consultations Data Dimensions Disease Education Equation Experimental Designs Exposure to Factor Analysis Functional Magnetic Resonance Imaging Functional disorder Human Individual Informal Social Control Link Literature Logic MRI Scans Maps Measurement Measures Mental Health Mental Processes Mental disorders Methods Modeling Multivariate Analysis National Institute of Mental Health Outcome Patient Self-Report Performance Phenotype Play Psychopathology Recording of previous events Research Research Domain Criteria Research Personnel Research Project Grants Sampling Scanning Series Short-Term Memory Specific qualifier value Structural Models Structure Support System Surveys System Techniques Testing Training Validation behavior measurement behavior test behavioral study cognitive control cognitive neuroscience cognitive system cognitive task experience large scale data mental set neural neural circuit neuroimaging prevent programs psychologic response substance use

项目摘要

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.

项目概要 NIMH 研究领域标准 (RDoC) 根据一系列要点重新概念化了心理健康研究然而，这些维度是通过自上而下的方式确定的。我们提出了一种数据驱动的方法来测试关键 RDoC 的有效性。我们将使用多种方法来评估这些结构。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最后，我们建议对 RDoC 框架进行增强。添加新的分析单元：对比和实践。当前的 RDoC 矩阵直接从任务范式映射到构造和子构造，即这是有问题的，因为所谓的不同构造有时可以映射到完全相同的一组任务。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我们将通过实验设计来识别认知系统构造和对比之间的映射。然后，我们将获取大规模数据集来测试探索性和测试性。最后，我们将评估这些 RDoC 认知系统构造的验证模型。认知系统构造可以预测相关的现实世界结果。 RDoC 矩阵将构造链接到行为测量和神经回路，但目前的映射认知系统结构和大脑系统之间是稀疏且不一致的。对 65 名受试者进行 fMRI 采集采样，每名受试者完成 10 次扫描任务，并完成与行为研究，开发一个精确的数据驱动的矩阵每个级别的神经参与图谱，然后，我们将使用神经网络验证行为衍生模型。我们还将进行全面的探索性分析来识别受试者之间和受试者内部的数据。这些任务的数据驱动神经回路结构是否偏离 RDoC 矩阵。对动物和动物的长期研究表明，对一项任务的重复练习会改变方法我们将利用我们的行为和支持执行任务的大脑系统。脑成像样本来评估认知系统域的结构是否保持不变与此同时，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，以及跨行为和神经验证注意力、认知控制和工作记忆的结构电路。