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，被称为失语症，中风后非常普遍，患者的程度很高 恢复并应对语言疗法的差异很大。功能性MRI，包括静止状态功能 连接性是一种在获得后，用于研究大脑重组的一种广泛使用的工具，例如 中风。具体而言，静止状态的功能连接度量可以提供有关的重要信息 大脑功能网络的连通性和组织，随着时间的流逝，连通性的变化 （即功能网络可塑性）。该项目将利用静态和动态静止状态功能 连通性以识别短期（秒至分钟）时间动态之间的关系 中风的人的功能网络连通性和长期（月）功能网络可塑性 失语症。我们先前已经表明，静态功能连接性和可变性的基线度量 在动态功能网络中，连通性可预测中风后患者的治疗反应 失语症。但是，尚不清楚该关联是否是由功能网络差异介导的 可塑性。对于本应用程序提出的研究，我们将以三个目的测试以下假设：（1） 与健康相比 对照以降低时间变异性和（2）为特征的控件。 动态功能连通性（即更像健康对照组）与更大的治疗诱导有关 功能网络拓扑的变化。对于第一个目的，将根据 静止状态功能性MRI数据，用于40名中风后失语症和40个健康对照的人，目前 作为一项正在进行的较大研究的一部分收集。然后将比较两者之间的时间变异性 组。为了第二个目标，动态功能网络连接的时间变化是根据 现有的静止状态功能性MRI数据来自30名中风后失语症的人将与 图理论的变化测量静态功能连通性从治疗前到处理后的变化。第三 AIM计算建模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和（2）更大的多样性 瞬态连通性配置为可塑性提供了更大的机会 导致更大的治疗引起的功能网络重组。结果将产生洞察力 中风后语言恢复的因素和机制的因素和机制，并可能告知 开发这些患者预测的新工具或改进的工具。这些研究将在 波士顿大学的大脑恢复中心是医师前培训前培训的一部分 - 科学家职业。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，科学交流和指导的整合。