Neural and Behavioral Predictors of Naming Therapy Outcomes in Chronic Post-Stroke Aphasia

慢性中风后失语症命名治疗结果的神经和行为预测因素

• 批准号: 10186557
• 负责人: Jeffrey P Johnson
• 金额: --
• 依托单位: VETERANS HEALTH ADMINISTRATION
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10186557
• 关键词: Achievement; Address; Affect; Aftercare; American; Aphasia; Award; Base of the Brain; Behavioral; Brain; Brain Injuries; Brain region; Characteristics; Chronic; Cognitive; Communication; Communication impairment; Data; Equilibrium; Evaluation; Functional Magnetic Resonance Imaging; Goals; Graph; Honey; Hour; Impairment; Individual; Intervention; Knowledge; Language; Language Disorders; Language Therapy; Left; Lesion; Linguistics; Link; Magnetic Resonance Imaging; Maps; Measures; Mentorship; Methods; Names; National Institute on Deafness and Other Communication Disorders; Network-based; Neurophysiology - biologic function; Outcome; Participant; Patients; Pattern; Population; Preparation; Production; Property; Randomized Clinical Trials; Recovery; Research; Rest; Scanning; Scheme; Scientist; Semantics; Severities; Signal Transduction; Speech; Stroke; Structure; Theoretical model; Time; Training; Training Activity; Treatment outcome; United States; United States National Institutes of Health; Update; Veterans; Work; aphasia recovery; base; brain behavior; career; clinical trial analysis; cognitive recovery; demographics; expectation; experience; functional independence; graph theory; health related quality of life; implementation design; improved; interest; language outcome; neuroimaging; patient response; post stroke; predictive modeling; prognostic; prognostic model; programs; relating to nervous system; response; segregation; service delivery; skills; statistics; stroke survivor; stroke-induced aphasia; theories; therapy outcome; transmission process; treatment planning; virtual

More than 2 million people in the U.S. have aphasia, a language disorder most often caused by stroke that reduces participation in preferred activities, functional independence, and health-related quality of life. Language therapy for aphasia is efficacious, but outcomes vary across patients, presenting challenges for treatment-planning and prognostication for recovery. Recent evidence suggests brain network properties derived from functional connectivity data and quantified via graph theory may help explain this variability and predict treatment outcomes. However, only a few studies have used graph theory to investigate aphasia and the relationships between graph metrics, stroke- related brain damage, and patients’ response to specific types of intervention remain unclear. This study seeks to address these knowledge gaps by leveraging two potentially informative graph metrics, modularity and global efficiency, which characterize the brain’s segregation into functionally distinct subsystems and its capacity to integrate information among separate regions, respectively. To advance knowledge of the relationship between brain damage and neural function in aphasia, this study will determine the association between lesion size and modularity and global efficiency in Veterans with chronic aphasia (Aim 1). Additionally, to inform predictive models of recovery, the study will determine if pre-treatment modularity and/or global efficiency are associated with outcomes from semantic feature analysis (SFA), a popular intervention for naming impairments (Aim 2a), and whether they provide unique predictive information relative to other neural and behavioral predictors (e.g., lesion size, pre-treatment aphasia severity, demographics) (Aim 2b). This study will include 10 Veterans with chronic aphasia due to left-hemisphere stroke, all of whom will undergo neuroimaging and treatment in a larger randomized clinical trial of SFA therapy. Specifically, participants will complete a language evaluation, structural MRI, and resting-state fMRI (RSfMRI) prior to receiving 60 hours of SFA therapy over 15 days. Treatment outcomes will be derived from pre- and post-treatment naming assessments of trained items. Lesion volume will be calculated from lesion maps drawn on participants’ structural scans. Functional connectivity-based brain graphs (i.e., network representations) consisting of nodes (i.e., 264 brain regions, per a parcellation scheme from Power et al., 2011) and edges (i.e., pairwise correlations in the BOLD signal over time between nodes) will be constructed from participants’ RSfMRI scans, and the modularity and global efficiently of each participant’s graph will subsequently be computed using the Brain Connectivity Toolbox. Aim 1 will be addressed by correlating lesion volume with modularity and global efficiency. Aim 2 will be addressed by regressing treatment outcomes on modularity and global efficiency (Aim 2a), as well as other predictive variables (Aim 2b). If successful, this study will inform theoretical models of the association between brain damage and neural function and support new or updated predictive models of treatment-related language recovery in aphasia. Mentorship and structured training activities in RCT design/implementation, advanced statistics, and neuroimaging methods and analysis will facilitate execution and completion of the proposed project and achievement of the applicant’s career goals. These goals include completing a CDA-1 and pursuing a CDA-2 in the short-term, and becoming an independent VA clinician-scientist supported by VA Merit Review and NIH/NIDCD award mechanisms in the long-term, with a research program focused on improving service delivery and maximizing treatment outcomes for Veterans and others with aphasia.

在美国有超过200万人患有失语症，这是一种语言障碍，最常见的原因是 中风减少了对首选活动的参与，功能独立性和健康相关性 生活质量失语症的语言治疗是有效的，但结果因患者而异， 这对治疗计划和恢复的实施提出了挑战。最近的证据 提出了从功能连接数据中获得的大脑网络特性，并通过图表进行量化 理论可能有助于解释这种变异性并预测治疗结果。然而，只有少数研究 已经使用图论来研究失语症以及图形度量，中风， 相关的脑损伤，以及患者对特定类型干预的反应仍不清楚。这 研究试图通过利用两个潜在的信息图表指标来解决这些知识差距， 模块化和全球效率，这表明大脑的分离成功能不同的 这两个方面分别涉及信息和通信技术、信息和通信技术子系统及其在不同区域之间整合信息的能力。 为了进一步了解脑损伤和神经功能之间的关系， 失语症，这项研究将确定病变大小和模块化和全球之间的关联 慢性失语症退伍军人的疗效（目标1）。此外，为了告知预测模型 恢复，研究将确定治疗前模块化和/或整体效率是否相关 语义特征分析（SFA）的结果， (Aim 2a），以及它们是否提供相对于其他神经和 行为预测器（例如，病变大小、治疗前失语症严重程度、人口统计学）（目标2b）。 这项研究将包括10名退伍军人与慢性失语症由于左半球中风，所有的 他们将在一项更大的SFA治疗随机临床试验中接受神经成像和治疗。 具体来说，参与者将完成语言评估，结构MRI和静息状态fMRI （RSfMRI），然后在15天内接受60小时的SFA治疗。治疗结果将是 来自训练项目的治疗前和治疗后命名评估。病变体积将为 根据参与者的结构扫描绘制的病变图计算。基于功能连接 脑图（即，网络表示）包括节点（即，264个大脑区域， 来自Power等人的分块方案，2011）和边缘（即，BOLD信号中的成对相关性 随着时间的推移节点之间）将从参与者的RSfMRI扫描构建， 随后将使用Brain计算每个参与者的图的全局效率 连通性目标1将通过将病变体积与组配性相关联来解决， 全球效率。目标2将通过回归模块化和整体治疗结局来解决 效率（目标2a）以及其他预测变量（目标2b）。如果成功，这项研究将为 脑损伤和神经功能之间联系的理论模型，并支持新的或 失语症治疗相关语言恢复的最新预测模型。 RCT设计/实施方面的指导和结构化培训活动，高级 统计学、神经影像学方法和分析将有助于执行和完成 申请人的职业目标的实现情况。这些目标包括： CDA-1和追求CDA-2在短期内，并成为一个独立的VA临床医生，科学家 由VA Merit Review和NIH/NIDCD奖励机制长期支持，研究 该计划的重点是改善服务提供和最大限度地提高退伍军人的治疗效果， 其他人患有失语症。