A Data Science Framework for Empirically Evaluating and Deriving Reproducible and Transferrable RDoC Constructs in Youth

该项目提供了数据科学框架和系统化最佳实践工具箱 和可重复使用的数据驱动的方法，用于验证和派生RDoC构造 与精神变态有关。尽管用于数据驱动构造的方法最近取得了进展， 使用其他研究的样本，结果往往很难重现。缺少这样一个人 系统的统计方法和分析设计，以提高重复性。为了填满这个 GAP，我们将开发一个数据科学框架，包括新的可伸缩算法和 软件，以派生和验证RDoC构造。尽管提议的方法将 一般适用于所有RDoC域和构造，我们特别关注进一步 对认知控制(CC)和注意(ATT)结构的RDoC域的理解 与注意力缺陷障碍(ADHD)和强迫症(OCD)有关。我们的 应用程序将使用来自以下各项的多模式神经成像、行为和临床/自我报告数据 《青少年脑认知发展研究》中具有全国代表性的大样本 (ABCD)研究和多个患有ADHD和OCD的当地临床样本。具体地说，使用 在目标1中，我们将应用和开发方法来评估和验证 使用验证性潜变量建模的CC和ATT的RDOC当前配置。我们 将实施和开发新的无监督学习方法，以构建新的 从多模式图像数据计算驱动的、基于大脑的领域。在目标2中，我们将 引入网络分析(通过高斯图形模型)来表征 由于观察到的特征(即年龄和性别)而导致的RDoC测量的相互关系。我们 将进一步模拟由于未观察到的特征而导致的种群的异质性 介绍数据驱动的精度表型，这是参与者的子组，具有 相似的RDOC尺寸。提出了一种层次化贝叶斯生成模型，该模型具有可扩展性 同时进行降维和识别精确表型的算法。模型 还用作将信息从社区样本ABCD传输到当地诊所的工具 丰富的研究。在目标3中，我们将利用ABCD和当地临床的随访样本 丰富的数据集，以验证目标1和目标2的结果，并评估 在随访期预测心理发育的精确表型。我们的项目 将提供一套分析工具来验证现有的RDoC结构并派生新的、 通过考虑各种异质性来源的可重复性的结构。

This project provides a data science framework and a toolbox of best practices for systematic and reproducible data-driven methods for validating and deriving RDoC constructs with relevance to psychopathology. Despite recent advances in methods for data-driven constructs, results are often hard to reproduce using samples from other studies. There is a lack of systematic statistical methods and analytical design for enhancing reproducibility. To fill this gap, we will develop a data science framework, including novel scalable algorithms and software, to derive and validate RDoC constructs. Although the proposed methods will generally apply to all RDoC domains and constructs, we focus specifically on furthering understanding of the RDoC domains of cognitive control (CC) and attention (ATT) constructs implicated in attention deficit disorder (ADHD) and obsessive-compulsive disorder (OCD). Our application will use multi-modal neuroimaging, behavioral, and clinical/self-report data from large, nationally representative samples from the on Adolescent Brain Cognitive Development (ABCD) study and multiple local clinical samples with ADHD and OCD. Specifically, using the baseline ABCD samples, in aim 1, we will apply and develop methods to assess and validate the current configuration of RDoC for CC and ATT using confirmatory latent variable modeling. We will implement and develop new unsupervised learning methods to construct new computational-driven, brain-based domains from multi-modal image data. In Aim 2, We will introduce network analysis (via Gaussian graphical models) to characterize heterogeneity in the interrelationship of RDoC measurements due to observed characteristics (i.e., age and sex). We will further model the heterogeneity of the population due to unobserved characteristics by introducing the data-driven precision phenotypes, which are the subgroup of participants with similar RDoC dimensions. We propose a Hierarchical Bayesian Generative Model and scalable algorithm for simultaneous dimension reduction and identify precision phenotypes. The model also serves as a tool to transfer information from the community sample ABCD to local clinical enriched studies. In aim 3, we will utilize the follow-up samples from ABCD and local clinical enriched data sets to validate the results from Aims 1 and 2 and assess the clinical utility of the precision phenotypes in predicting psychological development in follow-up time. Our project will provide a suite of analytical tools to validate existing RDoC constructs and derive new, reproducible constructs by accounting for various sources of heterogeneity.