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: 10441499
• 负责人: SEONJOO LEE
• 金额: $ 66.03万
• 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/10441499
• 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; 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; multimodal neuroimaging; multimodality; network models; neuroimaging; novel; psychologic; response; sex; tool; transfer learning; 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的最佳实践工具箱 以及可重现的数据驱动方法，用于验证和得出使用RDOC构建体 与心理病理学有关。尽管最近的数据驱动结构方法取得了进步，但 使用其他研究的样品通常很难繁殖结果。缺乏 系统的统计方法和分析设计，以增强可重复性。填写这个 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，以得出和验证RDOC构建体。虽然提出的方法将 通常适用于所有RDOC域和构造，我们专门关注进一步 了解认知控制（CC）和注意力（ATT）构建的RDOC领域 与注意力缺陷障碍（ADHD）和强迫症（OCD）有关。我们的 应用将使用多模式的神经影像学，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）研究和多种多动症和强迫症的多个局部临床样本。具体来说，使用 基线ABCD样品，在AIM 1中，我们将应用和开发方法来评估和验证 使用确认潜在可变建模的RDOC当前对CC和ATT的配置。我们 将实施和开发新的无监督学习方法来构建新的 来自多模式图像数据的计算驱动，基于大脑的域。在AIM 2中，我们将 介绍网络分析（通过高斯图形模型）以表征异质性 由于观察到的特征（即年龄和性别）引起的RDOC测量的相互关系。我们 由于未观察到的特征，将进一步建模人口的异质性 引入数据驱动的精确表型，该表型是参与者的子组 类似的RDOC尺寸。我们提出了一个分层的贝叶斯生成模型和可扩展的 同时降低维度并识别精确表型的算法。模型 还可以作为将信息从社区样本ABCD传输到本地临床的工具 丰富的研究。在AIM 3中，我们将利用来自ABCD和本地临床的后续样本 丰富的数据集以验证目标1和2的结果，并评估 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，以验证现有的RDOC构建体并得出新的构造， 可再现的结构通过考虑各种异质性来源。