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: 9604631
• 负责人: John Medaglia
• 金额: $ 40.87万
• 依托单位: DREXEL UNIVERSITY
• 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/9604631
• 关键词: Dynamic; Network; Neuroscience; Control; Theory

 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。了解脑网络动力学如何支持认知控制功能，对于澄清损伤弹性的基础和确定干预措施取得实质性进展的机会至关重要。虽然网络科学(例如，图论)在大脑的组织和人类认知的基础上带来了启发，但阐明翻译的含义需要明确的重点。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)和网络控制理论中的最新创新应用于神经成像数据，以检验认知控制功能在健康和中风功能障碍中的基础。该项目整合了认知神经科学、网络科学和控制理论的方法。其目标是在使用非侵入性脑刺激治疗认知功能障碍方面取得理论进展。本项目的具体目标是：1)量化中风后健康和功能障碍认知控制功能潜在的结构和动态的脑网络属性2)使用网络控制理论干预支持认知控制的大脑网络本项目的两个主要组成部分：(1)分析潜在的适应性认知控制的网络结构和功能，(2)使用应用于扩散束成像数据的网络控制理论来(A)区分认知控制的网络机制，(B)促进中风患者的认知控制恢复。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提供实质性的进步。虽然在这一领域已经开始出现一些研究，但我建议在动态网络分析中使用最先进的技术，并结合经过充分验证的行为测量。这将作为当前应用程序之外的工作的重要基准。它还将开始表征潜在的自适应任务表现的参考状态，这些参考状态将被用来指导后来基于控制理论的脑刺激方法。在这里，网络控制理论将被用于针对个体的非侵入性脑刺激。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，这项工作的结果将在神经学和精神病学人群中提供网络神经科学和基于系统工程的翻译之间的真正整合的一步。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融合，以概念化潜在的干预机会。这个项目提供了建立这种交叉的第一次机会，并促进了所代表的领域之间的新的跨学科对话。