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

美国有200多万人患有失语症，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、功能独立性和健康相关活动的参与 生活质量。语言疗法对失语症是有效的，但结果因患者而异， 提出了治疗规划和康复预测的挑战。最近的证据 建议从功能连接性数据派生并通过图形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。然而，只有少数几项研究 使用图论来研究失语症以及图形指标与中风之间的关系 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 这项研究试图通过利用两个潜在的信息性图表度量来解决这些知识差距， 模块化和全局效率，这是大脑分离为不同功能的特征 各分系统及其在不同区域之间整合信息的能力。 提高对脑损伤与神经功能关系的认识 失语症，这项研究将确定病变大小和模块化之间的联系以及 慢性失语症退伍军人的疗效(目标1)。此外，为了让预测模型了解 恢复后，研究将确定前处理模块化和/或全局效率是否相关 根据语义特征分析(SFA)的结果，这是一种流行的命名障碍干预方法 (目标2a)，以及它们是否提供相对于其他神经和 行为预测因素(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名因左半球中风而患有慢性失语的退伍军人，所有 他们将在一项更大规模的SFA治疗随机临床试验中接受神经成像和治疗。 具体地说，参与者将完成语言评估、结构磁共振和静息状态功能磁共振 (RSfMRI)，在接受为期15天的SFA治疗60小时之前。治疗结果将是 来自治疗前和治疗后对训练项目的命名评估。病变体积将是 根据参与者的结构扫描绘制的病变地图进行计算。基于功能连接 由节点组成的脑图(即，网络表示)(即，每小时264个脑区 来自Power等人的分割方案，2011)和边(即，粗体信号中的成对相关 节点之间的时间)将从参与者的RSfMRI扫描中构建，并且模块化 每个参与者的图形的全局效率随后将使用大脑来计算 连接工具箱。目标1将通过将病变体积与模块化和 全球效率。目标2将通过将治疗结果倒退到模块化和全局性来实现 效率(目标2a)以及其他预测变量(目标2b)。如果成功，这项研究将告知 脑损伤与神经功能关系的理论模型及其支持 失语症治疗相关语言恢复的最新预测模型。 RCT设计/实施方面的指导和有组织的培训活动，高级 统计、神经成像方法和分析将有助于执行和完成 提出的项目和申请人职业目标的实现情况。这些目标包括完成 CDA-1和短期内追求CDA-2，并成为独立的VA临床医生-科学家 长期得到退伍军人事务部荣誉审查和NIH/NIDCD奖励机制的支持，并进行了一项研究 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和 其他人患有失语症。

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.