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，美国每年中风的费用为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高。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，在南卡罗来纳州，至少一半的中风患者年龄在60岁以下（2）。大约四分之一的慢性中风幸存者患有失语症，这是一种由大脑言语和语言区域损伤引起的语言障碍（3，4）。据估计，美国慢性失语症的患病率为100万。失语症的严重程度可以从非常严重的损害，使患者哑巴，没有能力理解他人的讲话，以较轻的形式，患者有很大的困难，检索特定的话。在中风的慢性阶段，失语症已被确定为生活质量差的最强预测因子。失语症不仅影响与家人和朋友沟通的能力，还大大减少了教育和就业机会。虽然在中风后的最初几周和几个月内，失语症会有一定程度的自然恢复，但许多患者仍会留下毁灭性的沟通问题。一旦失语症成为一种慢性疾病，唯一的康复之路就是通过失语症治疗。一些荟萃分析研究表明，失语症治疗是有效的。尽管进行了数十年的研究，但人们对哪些患者从治疗中获益最多以及对具有不同损伤特征的患者应给予何种治疗知之甚少。本文提出的研究的总体目标是提高失语症治疗的有效性，并确定可用于改善语言障碍诊断、指导失语症治疗和预测预后的患者因素。具体来说，我们中心（失语症恢复研究中心; C-STAR）的重点是检查行为失语症治疗、脑电刺激和残余脑功能等因素对失语症恢复的影响程度。为了实现我们的研究目标，该项目将依赖于四个主要研究人员之间的合作：Julius Fridriksson，Argye Hillis，Chris Rorden和Greg Hickok博士。由Fridriksson（慢性患者）和Hillis（急性患者）领导的项目将关注可能促进失语症治疗效果改善的因素。这两个项目将产生一个巨大的，独特的数据集，包括大脑状态和失语症治疗反应的措施。依靠这个数据集，Rorden的项目将使用机器学习方法预测失语症的恢复，而Hickok将利用相同的数据来更好地了解与当代语音和语言处理模型相关的失语症障碍。

 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.