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

用于在青年中实证评估和推导可复制和可转移 RDoC 结构的数据科学框架

• 批准号: 10058921
• 负责人: SEONJOO LEE
• 金额: $ 71.01万
• 依托单位: NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10058921
• 关键词: 11 year old; Accounting; Adolescent; Age; Algorithmic Software; Algorithms; Attention; Attention Deficit Disorder; Base of the Brain; Behavioral; Brain; Characteristics; Child; Chronology; Clinical; Clinical Data; Communities; Data; Data Reporting; Data Science; Data Set; Development; Dimensions; Ensure; Functional Magnetic Resonance Imaging; Gaussian model; Goals; Heterogeneity; Image; Knowledge; Learning; Link; Measurement; Measures; Mental Health; Methodology; Methods; Modality; Modeling; Multimodal Imaging; Obsessive-Compulsive Disorder; Participant; Pathway Analysis; Patient Self-Report; Phenotype; Population Heterogeneity; Prediction of Response to Therapy; Psychological Transfer; Psychopathology; Reproducibility; Reproducibility of Results; Research Domain Criteria; Sampling; Source; Statistical Methods; Structure; Subgroup; Symptoms; Time; Variant; Youth; age effect; analytical tool; autoencoder; base; biological sex; cognitive control; cognitive development; deep learning; design; follow up assessment; follow-up; high dimensionality; independent component analysis; insight; learning algorithm; learning strategy; machine learning algorithm; multimodality; network models; neuroimaging; novel; psychologic; response; sex; tool; unsupervised learning

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.

该项目提供了一个数据科学框架和系统化最佳实践工具箱 以及可重复的数据驱动方法，用于验证和导出 RDoC 结构 与精神病理学的相关性。尽管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通常很难重现结果。缺乏 系统的统计方法和分析设计，以提高再现性。为了填补这个 差距，我们将开发一个数据科学框架，包括新颖的可扩展算法和 软件，派生并验证 RDoC 结构。尽管所提出的方法将 通常适用于所有 RDoC 领域和结构，我们特别关注进一步 了解认知控制 (CC) 和注意力 (ATT) 结构的 RDoC 领域 与注意力缺陷障碍（ADHD）和强迫症（OCD）有关。我们的 应用程序将使用多模式神经影像、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来构建新的 来自多模态图像数据的计算驱动的、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 测量值的相互关系。我们 将进一步对由于未观察到的特征而导致的群体异质性进行建模 介绍数据驱动的精确表型，这是参与者的亚组 类似的 RDoC 尺寸。我们提出了分层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。型号 还可以作为将信息从社区样本 ABCD 传输到当地临床的工具 丰富了学习。在目标3中，我们将利用ABCD和当地临床的后续样本 丰富的数据集来验证目标 1 和 2 的结果并评估该方法的临床效用 预测随访时间心理发展的精确表型。我们的项目 将提供一套分析工具来验证现有的 RDoC 构造并得出新的、 通过考虑异质性的各种来源来构建可重复的结构。