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.

美国有超过200万人患有失语症，这种语言障碍通常是由 中风可以减少参与首选活动，功能独立性和与健康有关的参与 生活质量。失语症的语言疗法是有效的，但是患者的结果各不相同， 提出了治疗计划和促使康复的挑战。最近的证据 建议从功能连接数据得出并通过图量化的大脑网络属性 理论可能有助于解释这种变异性并预测治疗结果。但是，只有少数研究 已经使用图论来研究失语症和图指标之间的关系，中风 - 相关的脑损伤以及患者对特定类型干预的反应尚不清楚。这 研究旨在通过利用两个潜在信息图指标来解决这些知识差距， 模块化和全球效率，将大脑的隔离特征在功能上不同 子系统及其在单独区域之间分别集成信息的能力。 促进对脑损伤与神经功能之间关系的了解 失语症，这项研究将确定病变大小与模块化与全球的关联 患有慢性失语症的退伍军人的效率（AIM 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/或全球效率是否相关 随着语义特征分析的结果（SFA），这是一种流行的命名障碍的干预措施 （AIM 2A），以及它们是否提供有关其他神经和 行为预测因素（例如，病变大小，治疗前的失语症严重程度，人口统计学）（AIM 2B）。 这项研究将包括10名因左脊椎动用术而引起的慢性失语症的退伍军人 在SFA治疗的更大的随机临床试验中，他们将接受神经影像学和治疗。 具体而言，参与者将完成语言评估，结构性MRI和静止状态fMRI （RSFMRI）在15天内接受60小时的SFA治疗之前。治疗结果将是 源自对训练有素的项目的治疗前和治疗后命名评估。病变量将是 根据参与者的结构扫描绘制的病变图计算得出。基于功能连接 大脑图（即网络表示）由节点组成（即264个大脑区域， Power等，2011）和边缘（即粗体信号中的成对相关性 随着节点之间的时间）将由参与者的RSFMRI扫描和模块化构建 随后将使用大脑计算每个参与者的图表的全球效果 连接工具箱。 AIM 1将通过将病变量与模块化和模块化和 全球效率。 AIM 2将通过回归模块化和全球的治疗结果来解决 效率（AIM 2A）以及其他预测变量（AIM 2B）。如果成功，这项研究将告知 脑损伤与神经功能之间的关联的理论模型，并支持新的或 失语症中与治疗相关语言恢复的更新预测模型。 RCT设计/实施中的指导和结构化培训活动，高级 统计数据，神经影像学方法和分析将有助于执行和完成 拟议项目和申请人职业目标的实现。这些目标包括完成 CDA-1并在短期内追求CDA-2，并成为独立的VA临床科学家 从长远来看，由VA优异审查和NIH/NIDCD奖励机制支持 计划致力于改善服务提供和最大化退伍军人的治疗结果 其他患有失语症的人。