Examination of Neurobehavioral Development Using the PING Data Resource

描述（由申请人提供）：这个为期三年的项目将利用一组独特的、最近收集的数据，名为儿科影像学、神经认知和遗传学（PING），其中包括神经影像学、认知、人口统计学、行为和全基因组基因分型数据，涉及1200多名3至20岁的儿童和青少年。这是迄今为止最大的神经影像学、基因组学和认知数据的来源，横跨发育年龄跨度，并被创建为科学界的资源。我们将在一个典型的发育中的儿童样本中进行第一次全面和有力的认知发展和最先进的神经结构表型和全基因组基因分型研究，以促进对执行功能的神经行为发展的理解。我们的初步研究表明，在该样本中，脑形态学、扩散率和信号强度的测量对不同年龄的发育阶段的预测有不同的贡献，反映了不同组织类型内生物变化的动态级联。美国国立卫生研究院工具箱认知电池的结果表明，这些措施对神经发育的影响很敏感，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，旨在将执行功能表现的个体差异与典型的神经系统和遗传影响联系起来，使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体遗传影响。我们预测，这些结果将增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的理解，以及个体社会人口变化的影响。这对于理解行为和神经精神结果的可变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.