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.

项目概要/摘要 语言障碍（称为失语症）在中风后很常见，患者的语言障碍程度 恢复和对语言治疗的反应差异很大。功能性 MRI，包括静息态功能性 连接性是一种广泛使用的工具，用于研究获得性脑损伤后的大脑重组，例如 中风。具体来说，静息态功能连接测量可以提供以下重要信息： 大脑中功能网络的连接和组织以及该连接随时间的变化 （即功能网络可塑性）。该项目将利用静态和动态静息态功能 连接性来识别短期（秒到分钟）时间动态之间的关系 中风后患者的功能网络连接和长期（数月）功能网络可塑性 失语症。我们之前已经表明，静态功能连接性和变异性的基线测量 动态功能网络连接可预测中风后患者的治疗反应 失语症。然而，尚不清楚这种关联是否是由功能网络的差异介导的 可塑性。对于本申请中提出的研究，我们将在三个目标上测试以下假设：（1） 与健康人相比，中风后失语症患者的动态功能连接发生了改变 控制的特点是时间变异性降低，并且（2）基线时间变异性较高 动态功能连接（即更像健康对照）与更大的治疗诱导相关 功能网络拓扑的变化。对于第一个目标，动态功能连接将通过以下方式计算 目前，40 名中风后失语症患者和 40 名健康对照者的静息态功能 MRI 数据 被收集作为正在进行的更大研究的一部分。然后将比较两者之间的时间变异性 组。对于第二个目标，动态功能网络连接的时间变化计算如下： 来自 30 名中风后失语症患者的现有静息态功能 MRI 数据将与 从治疗前到治疗后静态功能连接的图论测量的变化。对于第三个 目标计算模型将用于测试以下提出的机制：（1）瞬态 大脑区域之间的同步促进了活动依赖性可塑性，并且（2）更大的多样性 瞬态连接配置为这种可塑性的促进提供了更广泛的机会 导致治疗引起的功能网络更大程度的重组。结果将产生以下见解： 中风后语言恢复变化的因素和机制，可能会告诉 开发新的或改进的工具来预测这些患者。这些研究将在 波士顿大学脑恢复中心作为医生博士前培训的一部分- 科学家的职业生涯。作为奖学金培训的一部分，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。