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

该项目提供了一个数据科学框架和一个最佳实践工具箱， 和可重复的数据驱动的方法，用于验证和推导RDoC构建体， 与精神病理学有关尽管最近在数据驱动构造的方法方面取得了进展， 结果往往很难用其他研究的样本重现。都缺乏 系统的统计方法和分析设计，以提高再现性。填补这一 gap，我们将开发一个数据科学框架，包括新颖的可扩展算法， 软件，以推导和验证RDoC结构。虽然所提出的方法将 通常适用于所有RDoC域和结构，我们特别关注进一步 认知控制（CC）和注意力（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 应用程序将使用多模态神经成像、行为和临床/自我报告数据， 来自青少年大脑认知发展的大型全国代表性样本 （ABCD）研究和多个患有ADHD和OCD的当地临床样本。具体来说，使用 基线ABCD样本，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 计算驱动的，基于大脑的多模态图像数据域。目标2：我们将 引入网络分析（通过高斯图形模型）来表征 由于观察到的特性而引起的RDoC测量的相互关系（即，年龄和性别）。我们 将进一步模拟由于未观察到的特征而导致的群体异质性， 引入数据驱动的精确表型，这是参与者的亚组， 类似的RDoC尺寸。我们提出了一个层次贝叶斯生成模型和可扩展的 同时降维和识别精确表型的算法。模型 也可作为一种工具，将社区样本ABCD的信息传输到当地临床 丰富的研究。在目标3中，我们将利用来自ABCD和当地临床的随访样本， 丰富的数据集，以验证目标1和2的结果，并评估 精确的表型在预测心理发展的后续时间。我们的项目 将提供一套分析工具来验证现有的RDoC结构， 通过解释异质性的各种来源来构建可重复的构建体。

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.