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.

认知控制是引导我们的思想和行为与我们的内在意图保持一致的能力。它使我们能够做出正确的决定，平衡各种选择，选择适当的行为，并抑制不适当的行为。然而，我们对认知控制如何在大脑中发挥作用的理解严重不足。这项提案中概述的研究将通过开发和验证一个机械模型来解释使认知控制成为可能的基本原则，从而解决这一突出问题。这一问题具有迫切的国家利益和临床意义：更好地了解大脑结构如何引起认知控制，可能对于开发许多认知控制缺陷的神经精神障碍的早期和更有效的治疗方法至关重要。此外，该项目将为神经科学、网络理论、数据科学和数学领域的本科生和研究生创造新的研究机会。研究人员将把这项研究整合到本科生和研究生的教学活动中，为宾夕法尼亚大学和加州大学河滨分校的学生提供一座强大的理论和实验应用之间的桥梁。这两所大学是美国种族差异最大的研究密集型机构之一。调查人员还将把这些材料纳入广泛的社区和教育推广工作中，此外，还将把这些知识转化为精神健康诊所。在这项研究项目中，研究人员试图开发、验证和测试认知控制的机械理论。他们假设，认知功能的调节是由网络层面的控制过程驱动的，类似于技术、网络物理和社会系统中使用的控制过程。他们的方法以网络控制理论为基础，这是控制和动力系统的一个相对较新的分支学科。与图论的描述性统计不同，网络控制理论提供了一个原则性的数学建模框架，以向网络系统注入能量，从而导致系统动态的可预测变化。传统上应用于机械和技术系统，该领域建立在结构可控性的概念基础上，以提出关于控制任务的难度以及如何在有限的时间内在有限的能源资源下设计现实的控制策略的具体问题。这项工作将(I)发展基于神经成像数据的认知控制网络理论，(Ii)使用数据信息计算模型验证基于网络的认知控制理论，(Iii)定义网络结构如何影响正在接受认知训练的成年人的认知控制表现的个体差异，以及(Iv)发布一个公开可用的网络可控性分析工具箱。这些理论和工具是认知控制的真正集成和跨学科方法的结果，这种方法将工程学和数据科学与神经科学中的经验方法相结合。

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.