NCS-FO: Collaborative Research: A Mechanistic Model of Cognitive Control

NCS-FO：协作研究：认知控制的机制模型

• 批准号: 1631550
• 负责人: Danielle Bassett
• 金额: $ 54.42万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1631550&HistoricalAwards=false
• 关键词: NCS; FO; Collaborative; Research; Mechanistic

Cognitive control is the ability to guide our thoughts and actions in accord with our internal intentions. It enables us to make good decisions, balance options, choose appropriate behaviors and inhibit inappropriate behaviors. Yet our understanding of how cognitive control works in the brain is critically lacking. The research outlined in this proposal will address this outstanding problem by developing and validating a mechanistic model to explain the fundamental principles enabling cognitive control. This problem is of urgent national interest and clinical relevance: greater understanding of how brain structure gives rise to cognitive control may be critical for the development of earlier and more effective treatments of the many neuropsychiatric disorders where cognitive control deficits are present. In addition, this project will create new research opportunities for undergraduate and graduate students in neuroscience, network theory, data sciences, and mathematics. The investigators will integrate the research into undergraduate and graduate teaching activities, providing a powerful bridge between theoretical and experimental applications for students at the University of Pennsylvania and the University of California at Riverside, one of America's most ethnically diverse research-intensive institutions. The investigators will also incorporate this material in extensive community and educational outreach efforts, in addition to translating this knowledge to mental health clinics. In this research project, the investigators seek to develop, validate, and test a mechanistic theory of cognitive control. They postulate that the regulation of cognitive function is driven by a network-level control process akin to those utilized in technological, cyberphysical, and social systems. Their approach is grounded in network control theory, a relatively new subdiscipline of control and dynamical systems. In contrast to the descriptive statistics of graph theory, network control theory offers a principled mathematical modeling framework to inject energy into a networked system leading to a predictable alteration in the system's dynamics. Traditionally applied to mechanical and technological systems, this field builds on notions of structural controllability to ask specific questions about the difficulty of the control task and how to design realistic control strategies in finite time, with limited energy resources. The work will (i) develop a network-based theory of cognitive control informed by neuroimaging data, (ii) validate a network-based theory of cognitive control using data-informed computational models, (iii) define how network structure impacts individual differences in cognitive control performance in adults undergoing cognitive training, and (iv) release a publicly available toolbox for network controllability analysis. These theories and tools are the result of a truly integrated and cross-disciplinary approach to cognitive control, which blends the engineering and data sciences with empirical methodologies in neuroscience.

认知控制是根据我们的内部意图指导我们的思想和行动的能力。它使我们能够做出良好的决定，平衡选择，选择适当的行为并抑制不当行为。然而，我们对认知控制如何在大脑中起作用的理解是严重缺乏的。该提案中概述的研究将通过开发和验证机械模型来解释实现认知控制的基本原理来解决这个杰出的问题。这个问题具有紧急的国家利益和临床相关性：对大脑结构如何产生认知控制可能对早期，更有效的治疗方法的发展至关重要，对存在认知控制缺陷的许多神经精神疾病至关重要。此外，该项目将为神经科学，网络理论，数据科学和数学的本科生和研究生创造新的研究机会。研究人员将将研究纳入本科和研究生教学活动，为宾夕法尼亚大学的学生和加利福尼亚大学Riverside的学生提供强大的桥梁，Riverside是美国最多样化的研究密集型机构之一。调查人员还将将这些材料纳入广泛的社区和教育外展工作中，除了将这些知识转化为心理健康诊所。在该研究项目中，研究人员试图开发，验证和测试认知控制的机理理论。他们假定认知功能的调节是由类似于技术，网络物理和社会系统的网络级控制过程驱动的。他们的方法基于网络控制理论，这是一个相对较新的控制和动态系统的细分。与图理论的描述性统计相反，网络控制理论提供了一个原则上的数学建模框架，可以将能量注入网络系统，从而导致系统动力学的可预测变化。该领域传统上应用于机械和技术系统，基于结构可控性的概念，以提出有关控制任务难度以及如何在有限时间内使用有限的能源资源的特定问题以及如何在有限的时间内设计现实的控制策略。这项工作将（i）开发一种基于网络的认知控制理论，该理论是通过神经影像学数据告知的，（ii）使用数据知识计算模型验证基于网络的认知控制理论，（iii）定义了网络结构如何影响成人认知培训中认知培训的认知控制性能中的个体差异，以及（IV）释放一个公开可用的网络控制工具。这些理论和工具是一种真正综合和跨学科的认知控制方法的结果，该方法将工程学和数据科学与神经科学的经验方法融合在一起。

项目成果

Sex differences in network controllability as a predictor of executive function in youth

• DOI: 10.1016/j.neuroimage.2018.11.048
• 发表时间: 2019-03-01
• 期刊: NEUROIMAGE
• 影响因子: 5.7
• 作者: Cornblath, Eli J.;Tang, Evelyn;Bassett, Danielle S.
• 通讯作者: Bassett, Danielle S.

Temporal sequences of brain activity at rest are constrained by white matter structure and modulated by cognitive demands

• DOI: 10.1038/s42003-020-0961-x
• 发表时间: 2020-05-22
• 期刊: COMMUNICATIONS BIOLOGY
• 影响因子: 5.9
• 作者: Cornblath, Eli J.;Ashourvan, Arian;Bassett, Danielle S.
• 通讯作者: Bassett, Danielle S.

Benchmarking Measures of Network Controllability on Canonical Graph Models

• DOI: 10.1007/s00332-018-9448-z
• 发表时间: 2017-06
• 期刊: Journal of Nonlinear Science
• 影响因子: 3
• 作者: Elena Wu-Yan;Richard F. Betzel;Evelyn Tang;Shi Gu;F. Pasqualetti;D. Bassett

Multimodal network dynamics underpinning working memory

• DOI: 10.1038/s41467-020-15541-0
• 发表时间: 2020-06-15
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Murphy, Andrew C.;Bertolero, Maxwell A.;Bassett, Danielle S.
• 通讯作者: Bassett, Danielle S.