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.

项目概要/摘要 失语症是中风最常见且使人衰弱的后果之一。失语症是由脑损伤引起的。 幸运的是，大多数人的大脑语言区域通常位于左半球。 中风后失语症患者的语言功能随着时间的推移会出现一定程度的恢复。 在最初的几周和几个月内恢复得最好，但具有临床意义的语言功能增益是 失语症的恢复甚至可能取决于神经可塑性，即 幸存大脑区域的功能重组，使其在语言中发挥新的或扩展的作用 然而，尽管进行了大量研究，但这一功能过程的机制仍然存在。 该项目的总体目标是更好地表征神经网络。 中风后失语症恢复的相关性，并确定功能重组的模式 与或多或少有利的语言结果相关。为了解决先前研究的主要局限性， 我们将使用一系列创新方法来识别自适应语言映射范例。 以可靠和有效的方式划分语言区域，同时最大限度地减少性能混乱。 两个既定地点的患者数量，支持严格的统计方法，例如线性混合 模型和排列测试将使我们能够解开复杂的问题。 我们将研究结构损伤、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纵向补充患者队列（急性和慢性） 和结构 MRI 方案，以及相同的语言评估 在急性队列中，我们将调查 早期恢复和功能重组的动态，而在慢性队列中，我们将确定 在更大的样本中重组模式和语言结果之间的关系，并跟踪 我们的第一个具体目标是建立慢性期恢复的神经相关性。 基于结构神经影像的失语症谱的演变轨迹，包括病变位置和范围， 以及存活组织完整性的多种测量，以及其他临床变量。 具体目标是表征失语症患者用于语言处理的大脑区域，以及 重要的是，关键分析将确定与或多或少有利的结果相关的模式。 使用目标 1 中的预测模型来确定哪些功能重组模式会带来更好的效果或 与仅根据结构和临床因素预期的结果相比，结果更糟。 第三个目标是再次确定与语言功能增益相关的功能活动的变化 在目标 1 中开发的预测模型的背景下。更好地理解生物学 失语症恢复的机制将有助于神经调节和功能的发展 治疗干预措施，并将改善失语症患者的临床管理。