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Modeling RDoc Dimensions Across Levels of Analysis

跨分析级别的 RDoc 维度建模

基本信息

批准号：
9261593
负责人：
Ariana Anderson
金额：
$ 7.7万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-04-15 至 2019-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9261593
关键词：
Attention deficit hyperactivity disorder Bayesian Modeling Behavior Behavioral Biological Assay Bipolar Disorder Candidate Disease Gene Cells Clinical Cognitive Consensus Data Data Analytics Data Set Databases Deposition Diagnosis Diagnostic Diffusion Magnetic Resonance Imaging Dimensions Educational workshop Equation Functional Magnetic Resonance Imaging Future Gene Expression Profiling Genes Genetic Genome Genotype International Interview Link MRI Scans Magnetic Resonance Imaging Maintenance Measurable Measurement Measures Mediating Mental disorders Methodology Methods Modeling National Institute of Mental Health Nature Neurobiology Neurocognitive Neuropsychology Patient Self-Report Patients Personality Phenotype Physiology Psychometrics Questionnaires Research Research Domain Criteria Schedule Schizophrenia Short-Term Memory Specific qualifier value Structure Syndrome Taxonomy Testing United States National Institutes of Health Update Validation Work analytical method base behavior measurement causal model clinical diagnostics cognitive control cognitive system cognitive testing genetic variant genome-wide healthy volunteer innovation knowledge base neural circuit neuroimaging neuropsychiatry novel phenomics public health relevance repository response screening stem working group

项目摘要

DESCRIPTION (provided by applicant):The Research Domains Criteria (RDoC) initiative has proposed to overcome limitations in the existing diagnostic taxonomy by investigating "new ways of classifying mental disorders based on dimensions of observable behavior and neurobiological measures." A fundamental challenge is determining the validity of the implied relations among these measures both within and across levels of analysis. Using an existing database, this project aims to implement novel data-analytic strategies to examine the validity of selected RDoC domains: working memory (maintenance, updating), and cognitive (effortful) control (response inhibition/suppression). We will accomplish this in two separate Aims: (1). Examine the cross-level relations of selected genetic variants, self-report, behavioral, and MRI measures using Bayesian network models. We propose related analytic approaches for each construct, first identifying measurement models at each available level, and then using exploratory methods (ESEM, MIMIC) to interrogate relations across dimensions. (2). Examine the cross- level relations of selected genetic variants, self-report, behavioral, and MRI measures using Bayesian network models. In the RDoC framework identified by the NIMH workgroups, there is an implied hierarchical structure among different levels of measurement. Using Bayesian network models, we will create cross-level models to investigate whether the hierarchical structure proposed by the RDoC working group is validated in the data, using both observed and latent measures within each level. The existing database includes extensive phenotyping of these RDoC dimensions at diverse levels of analysis including: self-reports, clinical rating scales, clinical diagnostic interview schedules, neuropsychological measures, experimental cognitive measures, and genome-wide genotyping assays. All these data types were acquired in 153 patients, including those with schizophrenia (SZ, n = 58), bipolar disorder (BP, n = 49) and ADHD (n = 46). Healthy volunteers (n =1,137) received all personality, neurocognitive measures and genotyping, along with the ASRS for ADHD screening and the SCID for diagnosis. Additional fMRI and MRI neuroimaging data were obtained in a subset of 128 healthy people and all 121 patients. Among the deliverables of this research will be objective determination about whether selected measures of neural circuit integrity (from structural and functional MRI methods) are truly intermediate phenotypes (i.e., do they mediate relations from genetic to behavioral measures) for both working memory and cognitive control. We will establish whether the dimensions of working memory and response inhibition are consistent across healthy controls and patient groups (ADHD, BP, SZ).

描述(由申请人提供)：研究领域标准(RDoC)倡议已提议通过调查“基于可观察行为和神经生物学测量的维度对精神障碍进行分类的新方法”来克服现有诊断分类中的局限性。一个根本的挑战是确定这些措施之间的隐含关系的有效性，无论是在分析的内部还是跨分析的层面。利用现有的数据库，该项目旨在实施新的数据分析策略，以检查选定的RDoC领域的有效性：工作记忆(维护、更新)和认知(努力)控制(反应抑制/抑制)。我们将通过两个不同的目标来实现这一目标：(1)。使用贝叶斯网络模型检查选定的遗传变量、自我报告、行为和核磁共振测量之间的跨级别关系。我们为每个结构提出了相关的分析方法，首先确定每个可用水平上的测量模型，然后使用探索性方法(ESEM，MIMIC)来询问维度之间的关系。(2)。使用贝叶斯网络模型检查选定的遗传变量、自我报告、行为和核磁共振测量之间的跨级别关系。在NIMH工作组确定的RDoC框架中，在不同的测量级别之间有一个隐含的层次结构。使用贝叶斯网络模型，我们将创建跨级别模型，以调查RDoC工作组提出的分层结构是否在数据中得到验证，使用每个级别中的观测和潜在测量。现有的数据库包括在不同分析水平上对这些RDoC维度进行广泛的表型分析，包括：自我报告、临床评定量表、临床诊断性面谈日程、神经心理测量、实验认知测量和全基因组基因分型分析。所有这些数据类型在153例患者中获得，包括精神分裂症(SZ，n=58)、双相情感障碍(BP，n=49)和ADHD(n=46)。健康志愿者(n=1137)接受了所有的个性、神经认知测试和基因分型，以及用于ADHD筛查的ASRS和用于诊断的SCID。在128名健康人和所有121名患者的子组中获得了额外的fMRI和MRI神经成像数据。这项研究的成果之一将是客观地确定所选的神经回路完整性指标(来自结构和功能磁共振方法)是否真的是工作记忆和认知控制的中间表型(即，它们是否中介了从遗传到行为指标的关系)。我们将确定工作记忆和反应抑制的维度在健康对照组和患者组(ADHD、BP、SZ)中是否一致。