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: 1631112
• 负责人: Fabio Pasqualetti
• 金额: $ 21.01万
• 依托单位: University of California-Riverside
• 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=1631112&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.

认知控制是一种引导我们的思想和行动与我们的内在意图保持雅阁的能力。它使我们能够做出正确的决定，平衡选择，选择适当的行为并抑制不适当的行为。然而，我们对认知控制在大脑中如何运作的理解严重缺乏。本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实现认知控制的基本原理，从而解决这一突出问题。这个问题是迫切的国家利益和临床相关性：更好地了解大脑结构如何引起认知控制可能是至关重要的发展早期和更有效的治疗许多神经精神疾病的认知控制缺陷。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。研究人员将把研究整合到本科和研究生教学活动中，为宾夕法尼亚大学和加州大学滨江分校的学生提供理论和实验应用之间的强大桥梁，这是美国种族最多样化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外展工作，并将这些知识转化为精神健康诊所。在这个研究项目中，研究人员试图发展，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类似于技术，网络物理和社会系统中使用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相反，网络控制理论提供了一个原则性的数学建模框架，将能量注入网络系统，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上，以询问有关控制任务的难度以及如何在有限的时间内设计现实的控制策略的具体问题。这项工作将（i）开发一个基于网络的认知控制理论，通过神经成像数据，（ii）验证基于网络的认知控制理论，使用数据知情的计算模型，（iii）定义网络结构如何影响接受认知训练的成年人认知控制性能的个体差异，以及（iv）发布一个公开的工具箱用于网络可控性分析。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，它将工程和数据科学与神经科学的经验方法相结合。

项目成果

White Matter Network Architecture Guides Direct Electrical Stimulation through Optimal State Transitions

• DOI: 10.1016/j.celrep.2019.08.008
• 发表时间: 2019-09-03
• 期刊: CELL REPORTS
• 影响因子: 8.8
• 作者: Stiso, Jennifer;Khambhati, Ankit N.;Bassett, Danielle S.
• 通讯作者: Bassett, Danielle S.

Exact and Approximate Stability Conditions for Cluster Synchronization of Kuramoto Oscillators

• DOI: 10.23919/acc.2019.8814837
• 发表时间: 2019-07
• 期刊: 2019 American Control Conference (ACC)
• 作者: Tommaso Menara;Giacomo Baggio;D. Bassett;F. Pasqualetti

Structural Controllability of Symmetric Networks

• DOI: 10.1109/tac.2018.2881112
• 发表时间: 2019-09-01
• 期刊: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• 影响因子: 6.8
• 作者: Menara, Tommaso;Bassett, Danielle S.;Pasqualetti, Fabio
• 通讯作者: Pasqualetti, Fabio

Synchronization Patterns in Networks of Kuramoto Oscillators: A Geometric Approach for Analysis and Control

Kuramoto 振荡器网络中的同步模式：分析和控制的几何方法

• 发表时间: 2017
• 期刊: IEEE Conference on Decision and Control
• 作者: Tiberi, L;Favaretto, C;Innocenti, M;Bassett, Danielle S;Pasqualetti, F
• 通讯作者: Pasqualetti, F

Minimum-Gain Pole Placement With Sparse Static Feedback

• DOI: 10.1109/tac.2020.3018615
• 发表时间: 2018-05
• 期刊: IEEE Transactions on Automatic Control
• 影响因子: 6.8
• 作者: Vaibhav Katewa;F. Pasqualetti