Dynamic functional network connectivity and neuroplasticity in post-stroke aphasia

中风后失语症的动态功能网络连接和神经可塑性

• 批准号: 10826465
• 负责人: Isaac B Falconer
• 金额: $ 5.27万
• 依托单位: BOSTON UNIVERSITY MEDICAL CAMPUS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-18 至 2027-08-17
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10826465
• 关键词: Adult; Aftercare; Age; Aphasia; Basic Science; Boston; Brain; Brain Injuries; Brain region; Chronic; Chronic Phase; Communication; Computer Models; Data; Development; Experimental Designs; Fellowship; Functional Magnetic Resonance Imaging; Goals; Graph; Individual; Language; Language Therapy; Lead; Measurement; Measures; Mediating; Mental Health; Mentors; Modeling; Names; Neuronal Plasticity; Outcome; Patients; Pattern; Persons; Physicians; Prediction of Response to Therapy; Prognosis; Prognostic Marker; Quality of life; Recovery; Rest; Scanning; Scientist; Signal Transduction; Stroke; Structure-Activity Relationship; Techniques; Testing; Time; Training; Treatment outcome; Universities; Variant; analytical tool; aphasia recovery; career; clinical practice; diagnostic biomarker; graph theory; improved; insight; language impairment; neuroimaging; organizational structure; post stroke; pre-doctoral; prognostication; sex; stroke patient; stroke recovery; stroke-induced aphasia; targeted treatment; theories; tool; treatment response

PROJECT SUMMARY/ABSTRACT Language impairment, known as aphasia, is very common after stroke and the extent to which patients recover and respond to language therapy varies greatly. Functional MRI, including resting-state functional connectivity, is a widely used tool to study reorganization in the brain after an acquired brain injury such as stroke. Specifically, resting-state functional connectivity measurements can provide important information on the connectivity and organization of functional networks in the brain and changes in that connectivity over time (i.e., functional network plasticity). This project will make use of both static and dynamic resting-state functional connectivity to identify relationships between the short-term (seconds to minutes) temporal dynamics of functional network connectivity and long-term (months) functional network plasticity in people with post-stroke aphasia. We have previously shown that baseline measures of both static functional connectivity and variability in dynamic functional network connectivity are predictive of treatment response in people with post-stroke aphasia. However, it is not known whether this association is mediated by differences in functional network plasticity. For the studies proposed in this application, we will test the following hypotheses in three aims: (1) that patients with post-stroke aphasia have altered dynamic functional connectivity compared to healthy controls characterized by decreased temporal variability and (2) that higher baseline temporal variability of dynamic functional connectivity (i.e., more like that of healthy controls) is related to greater treatment-induced changes in functional network topology. For the first aim, dynamic functional connectivity will be computed from resting state functional MRI data for 40 individuals with post-stroke aphasia and 40 healthy controls, currently being collected as part of a larger ongoing study. Temporal variability will then be compared between the two groups. For the second aim, the temporal variability of dynamic functional network connectivity computed from existing resting-state functional MRI data from 30 individuals with post-stroke aphasia will be related to changes in graph theory measures of static functional connectivity from pre- to post-treatment. For the third aim computational modeling will be used to test the following proposed mechanism: (1) Transient synchronization between brain regions facilitates activity dependent plasticity and (2) greater diversity of transient connectivity configurations provides a wider range of opportunities for this facilitation of plasticity resulting in greater treatment-induced reorganization of functional networks. The results will yield insights into the factors and mechanisms underlying variation in language recovery after stroke and may inform the development of new or improved tools for prognostication in these patients. These studies will be carried out in the Center for Brain Recovery at Boston University as a part of pre-doctoral training towards a physician- scientist career. As part of the fellowship training, emphasis is placed on hypothesis-driven experimental design, integration of basic research with clinical practice, scientific communication, and mentoring.

项目摘要/摘要 语言障碍，称为失语症，在中风后非常常见，患者在多大程度上 语言治疗的恢复和反应差异很大。功能磁共振成像，包括静息状态功能 连接性是一种广泛使用的工具，用于研究后天脑损伤后大脑的重组，例如 卒中。具体地说，静息状态的功能连通性测量可以提供有关 大脑中功能网络的连通性和组织以及这种连通性随时间的变化 (即功能网络可塑性)。本项目将同时使用静态和动态静态功能 连接性，以确定短期(秒到分钟)时间动态之间的关系 卒中后患者的功能网络连通性和长期(月)功能网络可塑性 失语症。我们之前已经表明，静态功能连通性和可变性的基线测量 动态功能网络连接性可预测卒中后患者的治疗反应 失语症。然而，目前还不知道这种联系是否由功能网络的差异所调节 可塑性。对于本申请中提出的研究，我们将在三个目标上检验以下假设：(1) 中风后失语症患者与健康人相比动态功能连接发生了变化 控制的特点是时间变异性降低和(2)较高的基线时间变异性 动态功能连接(即，更像健康对照组)与更大的治疗诱导相关 运行正常的网络拓扑中的变化。对于第一个目标，动态功能连通性将从 40例卒中后失语症患者和40例健康对照的静息状态功能磁共振数据 是作为一项更大的正在进行的研究的一部分收集的。然后将两者之间的时间变化进行比较 组。对于第二个目标，动态功能网络连通性的时间变异性计算如下 现有的30名中风后失语症患者的静息状态功能磁共振数据将与 图论测量静态功能连接性从治疗前到治疗后的变化。对于第三个 AIM计算模型将被用来测试以下提出的机制：(1)瞬变 大脑区域之间的同步有助于活动依赖的可塑性和(2)更大的多样性 瞬时连通性配置为这种可塑性的促进提供了更广泛的机会 从而导致更大的治疗诱导的功能网络的重组。这些结果将为我们提供关于 中风后语言恢复的潜在因素和机制，并可能告诉我们 开发新的或改进的工具来预测这些患者的预后。这些研究将在#年进行。 波士顿大学的大脑恢复中心是对一名医生进行博士前培训的一部分- 科学家生涯。作为团契培训的一部分，重点放在假设驱动的实验上 设计、基础研究与临床实践的结合、科学交流和指导。