Dynamic Network Neuroscience and Control Theory: Toward Interventions for Cognitive Control Dysfunction

动态网络神经科学与控制理论：认知控制功能障碍的干预措施

• 批准号: 9001622
• 负责人: John Medaglia
• 金额: $ 40万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-19 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9001622
• 关键词: Affect; Area; Base of the Brain; Behavior; Benchmarking; Brain; Brain Injuries; Clinical; Cognition; Cognitive; Communities; Complex; Computer Simulation; Conceptions; Data; Diagnostic; Diffusion; Engineering; Functional Magnetic Resonance Imaging; Functional disorder; Goals; Graph; Health; Human; Impaired cognition; Individual; Injury; Intervention; Knowledge; Lead; Mental disorders; Modeling; Network-based; Neurologic; Neuronal Plasticity; Neurosciences; Outcome; Pathway Analysis; Performance; Population; Process; Property; Recovery; Research; Sampling; Science; Stroke; Structure; System; Task Performances; Techniques; Technology; Testing; Transcranial magnetic stimulation; Translations; Variant; Work; base; behavior measurement; cognitive control; cognitive function; cognitive neuroscience; effective therapy; executive function; improved; innovation; mind control; nervous system disorder; neuroimaging; novel; operation; physical science; programs; public health relevance; research study; resilience; response; theories; tool; translational neuroscience

 DESCRIPTION (provided by applicant): Executive functions, and in particular cognitive control functions, contribute to or are affected by numerous psychiatric and neurological conditions. Understanding how brain network dynamics support cognitive control function is crucial for clarifying the basis of resilience to injury and identifying opportunities for substantive advancements in intervention. While network science (e.g., graph theory) has led to enlightenment in the organization of the brain and basis of human cognition, elucidating translational implications requires an explicit focus. I propose to do so. I aim to apply recent innovations in dynamic network analysis (recent extensions of graph theory) and network control theory in neuroimaging data to examine the basis of cognitive control function in health and dysfunction in stroke. The program integrates approaches from cognitive neuroscience, network science, and control theory. The goal is to produce a theoretical advance in the use of noninvasive brain stimulation treatments for cognitive dysfunction. The specific aims for this project are to: 1) Quantify structural and dynamic brain network properties underlying cognitive control function in health and dysfunction following stroke 2) Use network control theory to intervene in brain networks that support cognitive control There are two main components of this project: (1) the analysis of network structure and function underlying adaptive cognitive control and (2) the use of network control theory applied to diffusion tractography data to (a) discriminate between network mechanisms of cognitive control and (b) facilitate cognitive control recovery in individuals that have suffered from stroke. This would provide a substantial advance in our knowledge of how cognitive control processes exert their influences across brain networks. While some research has begun to emerge in this area, I propose to use state of the art techniques within dynamic network analysis in conjunction with well-validated behavioral measures. This will serve as an important benchmark for work outside of the current application. It will also begin to characterize reference states underlying adaptive task performance that will be used to guide later control theory-based approaches to brain stimulation. Here, network control theory will be used to target noninvasive brain stimulation on an individual basis. This could lead to a substantive advance in our understanding of the variance in responsiveness to noninvasive brain stimulation and lead to a control theory based framework for intervention in cognitive control dysfunction. More broadly, the outcome this work will provide a step toward true integration between network neuroscience and systems engineering-based translation in neurological and psychiatric populations. These fields are developing rapidly, but an explicit focus on cognition and integration with the physical sciences will be required to conceptualize potent opportunities for intervention. This project offers the first opportunity to establish this intersection and promote a new interdisciplinary conversation between the fields represented.

 描述（由申请人提供）：执行功能，特别是认知控制功能，导致许多精神和神经系统疾病或受其影响。了解大脑网络动力学如何支持认知控制功能对于阐明损伤恢复力的基础和确定干预方面取得实质性进展的机会至关重要。虽然网络科学（例如图论）带来了大脑组织和人类认知基础的启蒙，但阐明转化意义需要明确的重点。我建议这样做。我的目标是将动态网络分析（图论的最新扩展）和网络控制理论的最新创新应用于神经影像数据，以研究健康中认知控制功能和中风功能障碍的基础。该项目整合了认知神经科学、网络科学和控制理论的方法。目标是在使用无创脑刺激治疗认知功能障碍方面取得理论进展。该项目的具体目标是： 1) 量化中风后健康和功能障碍中认知控制功能的结构和动态脑网络特性 2) 使用网络控制理论干预支持认知控制的脑网络 该项目有两个主要组成部分：(1) 分析自适应认知控制的网络结构和功能，以及 (2) 将网络控制理论应用于扩散纤维束成像数据，以 (a) 区分认知控制的网络机制，以及（b）促进中风患者认知控制的恢复。这将使我们对认知控制过程如何在大脑网络中发挥影响的认识取得重大进展。虽然该领域已经开始出现一些研究，但我建议将动态网络分析中最先进的技术与经过充分验证的行为测量结合使用。这将作为当前应用程序之外的工作的重要基准。它还将开始描述适应性任务表现背后的参考状态，这些参考状态将用于指导后来基于控制理论的大脑刺激方法。在这里，网络控制理论将用于针对个体的无创脑刺激。这可能会导致我们对非侵入性脑刺激反应差异的理解取得实质性进展，并导致基于控制理论的框架来干预认知控制功能障碍。更广泛地说，这项工作的成果将为神经学和精神病学人群中网络神经科学和基于系统工程的翻译之间的真正整合迈出一步。这些领域正在迅速发展，但需要明确关注认知以及与物理科学的整合，以概念化潜在的干预机会。该项目首次提供了建立这种交叉点并促进所代表领域之间新的跨学科对话的机会。