Evolution of the Linked Architecture of Network Control and Executive Function in Adolescence

青春期网络控制和执行功能链接架构的演变

• 批准号: 9242703
• 负责人: Danielle Smith Bassett
• 金额: $ 20.13万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-15 至 2019-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9242703
• 关键词: Adolescence; Age; Aircraft; Architecture; Area; Automation; Automobiles; Behavior; Biological; Biological Markers; Brain; Brain imaging; Brain region; Cognition; Cognitive; Communities; Data; Data Set; Development; Diagnostic; Diffusion; Early treatment; Engineering; Epidemic; Evolution; Exploratory/Developmental Grant; Failure; Familiarity; Fire - disasters; Functional Magnetic Resonance Imaging; Graph; Human; Image; Individual; Individual Differences; Interdisciplinary Study; Intervention; Intuition; Knowledge; Link; Measurement; Measures; Mental disorders; Methods; Modeling; Morbidity - disease rate; Neuropsychology; Pattern; Performance; Philadelphia; Population; Population Study; Positioning Attribute; Property; Psychiatric Diagnosis; Recruitment Activity; Regulation; Reproducibility; Resources; Risk; Sampling; Scanning; Science; Short-Term Memory; Signal Transduction; Social Network; Source; Structure; System; Systems Theory; Techniques; Testing; Vaccines; Work; Youth; cognitive function; cohort; connectome; control theory; cost; cyber physical; design; dynamic system; executive function; graph theory; improved; innovation; mortality; network architecture; neural circuit; neuroimaging; neuropsychiatry; novel; predictive modeling; public health relevance; statistics; tool

 DESCRIPTION (provided by applicant): The rapid development of executive function is a hallmark of adolescence, and requires the integrated recruitment of large-scale neural circuitry spanning multiple brain regions. Here we propose to develop and apply methods from network control theory to brain imaging data in order to allow us to understand how executive function develops in youth. Recent advances in control and dynamical systems theory have provided quantitative diagnostics of network controllability, which collectively define how external input t network nodes (in this case brain areas) can move the entire system (in this case cognitive function). Further, these methods allow quantitative estimation of the costs of disparate control structures. While these techniques have not previously been applied to brain imaging data, they provide an intuitive mechanism for executive function, and therefore have the potential to be putative biomarkers of executive capability. In this proposal, we capitalize upon existing diffusion imaging and working-memory task fMRI data acquired in a large sample of youth ages 8-22 imaged as part of the Philadelphia Neurodevelopmental Cohort (PNC). In Aim 1, we will describe the network control structure of the brain's structural connectome by (i) identifying driver nodes of network control and mapping their relationship to known executive networks and (ii) quantifying test-retest reliability of network control diagnostics. In Aim 2, we will chart th evolution of network control in adolescence by characterizing the development of network control diagnostics using cross-sectional (n=968) and longitudinal data (n=350) PNC data. In Aim 3, we will determine how network control is associated with executive function by examining whether individuals with higher levels of network control demonstrate better executive functioning, and investigating if baseline controllability predicts subsequent longitudinal improvement of executive function. In Aim 4, we will provide a publically available toolbox for measurement of network controllability as a resource to the neuroimaging community. This multi-disciplinary research has the potential to yield high-impact discoveries, and therefore represents a good fit for the R21 mechanism. Risks associated with the innovation in this project are tempered by the strength of our team (expertise in network science, developmental neuroimaging, and neuropsychiatry), the multiple levels of hypotheses to be tested, convergent preliminary data, and our intimate familiarity with the PNC dataset.

 描述（由申请人提供）：执行功能的快速发展是青春期的标志，需要跨越多个大脑区域的大规模神经回路的综合募集。在这里，我们建议开发和应用从网络控制理论到大脑成像数据的方法，以便让我们了解青少年的执行功能是如何发展的。控制和动力系统理论的最新进展提供了网络可控性的定量诊断，这些诊断共同定义了网络节点（在本例中为大脑区域）的外部输入如何移动整个系统（在本例中为认知功能）。此外，这些方法允许对不同控制结构的成本进行定量估计。虽然这些技术之前尚未应用于大脑成像数据，但它们为执行功能提供了直观的机制，因此有可能成为执行能力的假定生物标志物。在这项提案中，我们利用了现有的扩散成像和工作记忆任务 fMRI 数据，这些数据是在费城神经发育队列 (PNC) 的 8-22 岁青少年的大样本中获得的。在目标 1 中，我们将通过以下方式描述大脑结构连接组的网络控制结构：(i) 识别网络控制的驱动节点并将它们的关系映射到已知的执行网络；(ii) 量化网络控制诊断的重测可靠性。在目标 2 中，我们将通过使用横截面数据 (n=968) 和纵向数据 (n=350) PNC 数据来描述网络控制诊断的发展，从而绘制青春期网络控制的演变。在目标 3 中，我们将通过检查网络控制水平较高的个体是否表现出更好的执行功能，并调查基线可控性是否预测执行功能的后续纵向改善，来确定网络控制如何与执行功能相关。在目标 4 中，我们将提供一个公开可用的工具箱，用于测量网络可控性，作为神经影像学界的资源。这项多学科研究有可能产生高影响力的发现，因此非常适合 R21 机制。我们团队的实力（网络科学、发育神经影像学和神经精神病学方面的专业知识）、待测试的多层次假设、收敛的初步数据以及我们对 PNC 数据集的熟悉程度，缓和了与该项目创新相关的风险。