Examination of Neurobehavioral Development Using the PING Data Resource

描述(申请人提供)：这个为期三年的项目将利用最近收集的一组独特的数据，名为儿科成像、神经认知和遗传学(PING)，其中包括1,200多名3至20岁的儿童和青少年的神经成像、认知、人口统计学、行为和基因组范围的基因分型数据。这是迄今为止收集的整个发育年龄段的神经成像、基因组学和认知数据的最大来源，是作为科学界的资源创建的。我们将对认知发展、最先进的神经结构表型和全基因组基因分型进行第一次全面和有力的研究，在一个典型的儿科大样本中进行，以促进对执行功能神经行为发展的理解。我们的初步研究表明，在这个样本中，脑形态、扩散率和信号强度的测量对预测不同年龄的发育阶段显示出不同的贡献，反映了不同组织类型内生物变化的动态级联。来自NIH工具箱认知电池的结果表明，这些测量方法对神经发育影响很敏感，并提供了关于各种重要认知功能的丰富信息。我们的研究项目有四个具体目标，旨在利用尖端的非线性多维统计模型和一种新的多基因风险评分方法来评估总的遗传影响，将执行功能表现的个体变异性与典型的神经系统和遗传影响联系起来。我们预测，这些结果将加强我们对认知维度如何随着神经表型的变化而变化和出现，以及个体社会人口统计学差异的影响的理解。这对于了解行为和神经精神病学结果的变异性以及制定重要的预防和干预措施至关重要。

DESCRIPTION (provided by applicant): This three-year project will leverage a unique, recently collected set of data entitled Pediatric Imaging, Neurocognition, and Genetics (PING), which includes neuroimaging, cognitive, demographic, behavioral, and genome-wide genotyping data for over 1,200 children and adolescents ranging from 3 to 20 years of age. This is the largest source of neuroimaging, genomics, and cognitive data across the developmental age span assembled to date and was created as a resource to the scientific community. We will conduct the first comprehensive and well-powered study of cognitive development and state-of-the-art neural architectural phenotypes and genome-wide genotyping in a large typically developing pediatric sample to advance the understanding of the neurobehavioral development of executive functions. Our preliminary studies have demonstrated that the measures of brain morphology, diffusivity, and signal intensity show varying contributions to the prediction of developmental phase at different ages in this sample, reflecting a dynamic cascade of biological changes within different tissue types. Results from the NIH Toolbox Cognitive Battery show that these measures are sensitive to neurodevelopmental effects and provide rich information about a variety of important cognitive functions. Our research project has four Specific Aims designed to link individual variability in executive function performance to typically developing neural systems and genetic influences, using cutting-edge nonlinear multidimensional statistical modeling and a novel polygenic risk scores approach for assessing aggregate genetic influence. We predict that these results will enhance our understanding of how cognitive dimensions change and emerge with changing neural phenotypes, and the impact of individual sociodemographic variation. This is critical to understanding variability in behavioral and neuropsychiatric outcomes and developing important prevention and intervention efforts.