Center for the Study of Aphasia Recovery (C-STAR)

描述(申请人提供的描述)：中风是美国成年人残疾的主要原因，使其成为一个主要的公共卫生问题(1)。疾病控制中心(CDC)估计，美国每年中风的成本为365亿美元(1)。因此，很明显，中风对个人和社会的负面影响非常大。中风通常被认为影响老年人；然而，许多年轻人也患有中风。例如，南卡罗来纳州至少有一半的中风患者年龄在60岁以下(2)。在所有慢性中风幸存者中，约有四分之一患有失语症，这是一种由大脑语言和语言区域受损引起的语言障碍(3，4)。据估计，美国慢性失语症的流行率为100万。失语症的严重程度不一，从使患者失声并无法理解他人语言的非常严重的损害，到较轻的形式，患者在回忆特定单词方面存在很大困难。在中风的慢性期，失语症被认为是生活质量差的最强预测因素。失语症不仅影响与家人和朋友交流的能力，还会极大地减少教育和就业机会。虽然在中风后的头几周和几个月内，失语症的某种程度的自然恢复是典型的，但许多患者留下了毁灭性的沟通问题。一旦失语症成为一种慢性病，恢复的唯一途径就是通过失语症治疗。几项荟萃分析研究表明，失语症治疗是有效的。尽管进行了数十年的研究，但对于哪些患者从治疗中受益最大，以及应该对具有不同损害特征的患者进行何种治疗，人们知之甚少。本研究的总体目标是提高失语症的治疗效果，并找出可用于改善语言障碍的诊断、指导失语症治疗和预测预后的患者因素。具体地说，我们中心(失语症康复研究中心；C-STAR)的重点是检查行为失语症治疗、脑电刺激和残留脑功能等因素对失语症康复的影响程度。为了实现我们的研究目标，这个项目将依靠四位主要研究人员的合作：朱利叶斯·弗里德里克森博士、阿尔盖·希利斯博士、克里斯·罗登博士和格雷格·希科克博士。由Fridriksson(慢性患者)和Hillis(急性患者)领导的项目将重点关注可能促进失语症治疗结果改善的因素。这两个项目都将产生一个庞大的、独特的数据集，包括衡量大脑状态和对失语症治疗的反应。根据这个数据集，罗登的项目将使用机器学习方法预测失语症的康复，而希科克将利用相同的数据来更好地理解与当代言语和语言处理模式有关的失语症损害。

DESCRIPTION (Description as provided by applicant): Stroke is the leading cause of adult disability in the United States, making it a major public health concern (1). The Centers for Disease Control (CDC) estimates the annual cost of stroke in the United States to be $36.5 billion (1). Accordingly, it is clear that the negative personal and societal impact of stroke is vry high. Stroke is typically thought to affect older persons; however, many younger individuals also suffer strokes. For example, at least half of all stroke patients in the state of South Carolina ar under the age of 60 (2). Approximately a quarter of all chronic stroke survivors present with aphasia, a language disorder caused by damage to the speech and language areas of the brain (3, 4). The prevalence of chronic aphasia in the United States is estimated to be one million. Aphasia can vary in severity from very profound impairment that renders patients mute and without the ability to understand others' speech, to milder forms where patients have great difficulty retrieving specific words. In the chronic stage of stroke, aphasia has been identified a the strongest predictor of poor quality of life. Aphasia not only influences the ability to communicate with family and friends, but also drastically decreases education and employment opportunities. Although some degree of spontaneous recovery from aphasia is typical in the first weeks and months following stroke, many patients are left with devastating communication problems. Once aphasia has become a chronic condition, the only road to recovery is through aphasia therapy. Several meta-analysis studies suggest that aphasia therapy is effective. In spite of decades of research, very little is known about which patients benefit the most from treatment and what kind of treatment should be administered to patients with different impairment profiles. The overarching goal of the research proposed here is to improve aphasia treatment effectiveness as well as identify patient factors that can be used to improve diagnosis of language impairment, guide aphasia treatment, and predict prognosis. Specifically, the focus of our center (Center for the Study of Aphasia Recovery; C-STAR) is to examine the extent to which factors such as behavioral aphasia treatment, electrical brain stimulation, and residual brain function influence aphasia recovery. To accomplish our research goals, this project will rely on collaboration among four main investigators: Drs. Julius Fridriksson, Argye Hillis, Chris Rorden, and Greg Hickok. Projects led by Fridriksson (chronic patients) and Hillis (acute patients) will focus on factors that may promote improved outcome of aphasia therapy. Both projects will yield a vast, unique dataset including measures of brain status and response to aphasia treatment. Relying on this dataset, Rorden's project will predict recovery from aphasia using machine learning approaches whereas Hickok will utilize the same data to better understand aphasic impairment in relation to contemporary models of speech and language processing.