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，有助于或受许多精神和神经疾病的影响。了解大脑网络动力学如何支持认知控制功能，对于澄清损伤恢复力的基础和确定干预的实质性进展机会至关重要。虽然网络科学（例如，图论）对大脑的组织和人类认知基础的启示，阐明翻译的含义需要明确的焦点。我提议这样做。我的目标是应用动态网络分析（图论的最新扩展）和网络控制理论在神经影像学数据的最新创新，以检查在健康和中风功能障碍的认知控制功能的基础。该计划整合了认知神经科学，网络科学和控制理论的方法。其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）量化脑网络的结构和动态特性，这些特性是脑卒中后健康和功能障碍中认知控制功能的基础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）分析网络结构和功能的适应性认知控制和（2）使用网络控制理论应用于扩散纤维束成像数据（a）区分认知控制的网络机制和（B）促进患有中风的个体的认知控制恢复。这将为我们了解认知控制过程如何在大脑网络中发挥影响力提供实质性的进步。虽然一些研究已经开始出现在这一领域，我建议使用动态网络分析中的最先进的技术，结合有效的行为措施。这将作为当前应用程序之外的工作的重要基准。它也将开始表征参考状态的适应性任务的性能，将用于指导以后的控制理论为基础的方法，以脑刺激。在这里，网络控制理论将被用于在个体基础上进行无创脑刺激。这可能导致我们对非侵入性脑刺激反应性差异的理解取得实质性进展，并导致基于控制理论的认知控制功能障碍干预框架。更广泛地说，这项工作的结果将为神经和精神病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和与物理科学的整合，以概念化有效的干预机会。该项目提供了第一个机会，建立这种交叉点，并促进所代表的领域之间的新的跨学科对话。