Proj 4: Genetics (197-223)

项目 4：遗传学 (197-223)

• 批准号: 8078164
• 负责人: JOHN K. HEWITT
• 金额: $ 20.7万
• 依托单位: UNIVERSITY OF COLORADO
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-02-03 至 2013-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8078164
• 关键词: Adult; Age; Attention; Behavioral; Brain; Clinical; Cognition; Cognitive; Complex; Computer Simulation; Data; Data Set; Dopamine; Equation; Etiology; Factor Analysis; Foundations; Future; Genes; Genetic; Genetic Polymorphism; Goals; Human; Individual; Individual Differences; Intelligence; Lead; Learning; Literature; Measures; Modeling; Molecular Genetics; Multivariate Analysis; Neurobiology; Performance; Population; Process; Production; Public Health; Regulation; Research; Research Personnel; Role; Sampling; Short-Term Memory; Sorting - Cell Movement; Source; Specific qualifier value; Structure; Study Subject; Testing; To specify; Translational Research; Twin Multiple Birth; Update; Wisconsin; cognitive function; dopamine system; executive function; frontal lobe; genetic analysis; indexing; mental set; neuropsychological; operation; psychologic; response; young adult

Molecular genetic studies of executive functions (EFs) examining the roles of dopamine-(DA) related genes have provided some support for the widely held view that DA modulates executive control. However, these studies have had little impact on current understanding of EFs, because they have usually only examined very few DA genes and have focused on gross executive control as measured by individual frontal-lobe tests, rather than separable, theoretically defined EFs identified in the cognitive literature. The goal of this project is to use molecular genetic analyses in concert with computational modeling to specify in more detail how the DA system regulates three correlated but separable EFs - inhibiting prepotent responses, updating working memory, and shifting mental sets. In doing so, it will begin to bridge current understanding of EFs across the psychological, computational, and neurobiological levels. The primary dataset will be existing EF data from 814 individual twins aged 17. Our multivariate analyses indicate that the three EF latent variables are highly heritable and reflect both common and unique genetic influences. This project will take the important next step of specifying how different DA-related genes influence the common and unique genetic variance in EFs. Specifically, we will (1) use computational models of EF tasks to both generate predictions and incorporate results for genetic analyses, with the goal of developing more detailed models of how the DA system dynamically regulates executive control; (2) examine the extent to which polymorphisms in a large set of DA-related genes contribute to variance in each EF, and whether the contributions differ for the three EFs; and (3) conduct parallel analyses on data from identical or comparable EF tasks collected as part of three ongoing studies of subjects selected for learning, conduct, and/or attention problems. Comparison of the results for the clinically extreme groups versus unselected samples will provide an additional source of information about how DA modulates EFs and serve as a foundation for future translational research in these and other clinical samples that are currently being collected by Center investigators and their collaborators. Hence, the results of the proposed study will contribute to a better understanding of how DA modulates different EFs in normal young adults, with broad implications for public health issues related to everyday cognitive functioning and EF deficits in both healthy and clinical populations.

执行功能（EFs）的分子遗传学研究考察了多巴胺（DA）相关基因的作用，为DA调节执行控制的广泛观点提供了一些支持。然而，这些研究对目前对脑电图的理解影响不大，因为它们通常只检查了很少的DA基因，并且主要关注通过个体额叶测试测量的总执行控制，而不是认知文献中确定的可分离的、理论上定义的脑电图。这个项目的目标是使用分子遗传分析和计算模型来更详细地说明DA系统如何调节三种相关但可分离的EFs -抑制优势反应，更新工作记忆和转移心理集。在这样做的过程中，它将开始跨越心理学、计算学和神经生物学水平，将目前对电磁场的理解连接起来。主要数据集将是来自814名17岁双胞胎的现有EF数据。我们的多变量分析表明，三个EF潜在变量具有高度遗传性，反映了共同和独特的遗传影响。该项目将采取重要的下一步，确定不同的da相关基因如何影响EFs中常见和独特的遗传变异。具体来说，我们将(1)使用EF任务的计算模型来生成预测并将结果纳入遗传分析，目的是开发更详细的数据处理系统如何动态调节执行控制的模型；(2)研究大量da相关基因的多态性在多大程度上导致了每种EF的变异，以及三种EF的贡献是否不同；(3)对来自相同或可比较的EF任务的数据进行并行分析，这些数据是三个正在进行的研究的一部分，这些研究选择了学习、行为和/或注意力问题。将临床极端组与未选择样本的结果进行比较，将为DA如何调节EFs提供额外的信息来源，并为中心研究人员及其合作者目前收集的这些和其他临床样本的未来转化研究奠定基础。因此，拟议研究的结果将有助于更好地理解DA如何调节正常年轻人的不同脑电图，对健康和临床人群中与日常认知功能和脑电图缺陷相关的公共卫生问题具有广泛的意义。