Neural Correlates of Recovery from Aphasia After Stroke

中风后失语症恢复的神经相关性

• 批准号: 10407501
• 负责人: Stephen M Wilson
• 金额: $ 53.71万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10407501
• 关键词: Acute; Address; Aphasia; Area; Behavior Therapy; Biological; Brain region; Chronic; Chronic Phase; Clinical; Clinical Data; Clinical Management; Cognitive; Complement; Complex; Development; Evaluation; Functional Imaging; Goals; Image; Individual; Intervention; Knowledge; Language; Language Disorders; Left; Lesion; Location; Longitudinal Studies; Magnetic Resonance Imaging; Measures; Modeling; Neuronal Plasticity; Outcome; Participant; Patients; Pattern; Performance; Persons; Phase; Play; Process; Prognosis; Protocols documentation; Recovery; Rehabilitation therapy; Reproducibility; Research; Resolution; Resources; Role; Sample Size; Sampling; Site; Stimulus; Stroke; Task Performances; Testing; Therapeutic Intervention; Time; Tissues; Validity and Reliability; Work; acute stroke; aphasia recovery; base; chronic stroke; clinical infrastructure; cohort; disability; experience; follow-up; high dimensionality; improved; innovation; language impairment; language outcome; language processing; machine learning algorithm; multimodality; neural correlate; neuroimaging; neuroregulation; post stroke; predictive modeling; recruit; stroke patient; support vector machine

Project Summary/Abstract Aphasia is one of the most common and debilitating consequences of stroke. Aphasia is caused by damage to language regions of the brain, which are usually localized to the left hemisphere. Fortunately, most individuals with aphasia after a stroke experience some degree of recovery of language function over time. The pace of recovery is greatest in the first weeks and months, but clinically meaningful gains in language function are possible even years after stroke. Recovery from aphasia is thought to depend on neural plasticity, that is, functional reorganization of surviving brain regions such that they take on new or expanded roles in language processing. However, despite much research, the mechanisms that underlie this process of functional reorganization remain poorly understood. The overall goals of this project are to better characterize the neural correlates of recovery from aphasia after stroke, and to determine which patterns of functional reorganization are associated with more or less favorable language outcomes. To address major limitations of prior studies, we will use a range of innovative approaches. Adaptive language mapping paradigms will be used to identify language regions in a reliable and valid manner, while minimizing performance confounds. We will recruit large numbers of patients at two established sites, enabling rigorous statistical approaches such as linear mixed models and permutation testing. Advanced machine learning algorithms will allow us to disentangle complex relationships between structural damage, neurofunctional changes, and language outcomes. We will study two complementary cohorts of patients—acute and chronic—longitudinally using the same multimodal functional and structural MRI protocol, and the same language evaluations. In the acute cohort, we will investigate the dynamics of early recovery and functional reorganization, while in the chronic cohort, we will identify relationships between patterns of reorganization and language outcomes in a larger sample, and track the neural correlates of recovery in the chronic phase. Our first specific aim is to build predictive models of the trajectory of evolving aphasia profiles based on structural neuroimaging, including lesion location and extent, as well as multiple measures of the integrity of surviving tissue, and other clinical variables. Our second specific aim is to characterize the brain regions recruited for language processing in people with aphasia, and to identify patterns that are associated with more or less favorable outcomes. Critically, the key analyses will use the predictive models from Aim 1 to determine which patterns of functional reorganization result in better or worse outcomes relative to what would be expected on the basis of structural and clinical factors alone. Our third aim is to identify changes in functional activity associated with gains in language function over time, again in the context of the predictive models developed in Aim 1. A better understanding of the biological mechanisms that underlie recovery from aphasia will contribute to the development of neuromodulatory and therapeutic interventions, and will improve the clinical management of individuals with aphasia.

项目摘要/摘要 失语症是com的，中风的衰弱是由对 大脑的语言区域被局部到左半球 随着时间的流逝，随着时间的流逝，语言的努力 在头几周和几个月中，恢复最大，但语言功能的临床意义有意义是 甚至是中风后的几年。 生存的大脑区域的功能重组。 但是，尽管有很多研究，但功能的过程 重组仍然是糟糕的联盟。 从中风后的失语症失语症恢复的相关性，并确定功能转化的模式 与或多或少的语言结果相关联。 我们将使用一系列创新的方法。 语言区域以可靠和有效的方式，同时最小化绩效。 两个已建立的患者人数坐着，实现了严格的统计方法，例如线性混合 模型和排列测试。 结构性损害，神经功能变化和语言结果之间的关系。 使用相同的多模式功能 和结构性MRI协议，以及同样的语言评估。 早期恢复和功能重组的动态，而在慢性队列中，我们将确定 重组模式与较大样本中语言结果之间的关系，并跟踪您 慢性阶段恢复的神经相关性。 基于结构性神经影像的进化失语概况的轨迹，包括病变位置和程度， 以及幸存组织的完整性和其他临床变异性的多种测量。 具体的目的是表征招聘的大脑区域以进行失语症患者的语言处理 为了确定与良好结果相关的模式。 使用AIM 1中的预测模型来确定哪种功能转录模式会导致更好或或 仅根据我们的结构和临床因素，相对于预期的结果较差。 第三个目的是识别功能活动的变化随着时间的流逝而随着语言功能的提高而变化，再次 在目标1中开发的预测模型的背景下。对生物学有更好的了解 从失语症中恢复的基础的机制将有助于神经调节的发展和 治疗性干预措施，并将改善失语症患者的临床管理。