Maximizing and predicting sentence processing treatment outcomes in aphasia

摘要 语法失语症(语法失语症)是一种后天语言障碍，会影响语言产生的能力 和理解句子，并显著影响沟通能力。选择 针对特定语言障碍的最有效和最具成本效益的治疗(包括 语法错误)是临床医生面临的主要挑战，制定预后的能力也是如此 关于治疗效果的陈述，基于个人的语言和神经特征。 因此，在理解治疗句子加工缺陷的效果方面取得了进展 对于提供最佳干预很重要。本提案是在以前工作的基础上提出的 显示了对潜在形式的处理(TUF，[1-2])，这是一种侧重于元语言的方法 明确提高失语症患者的句子加工能力，效果显著 习得和泛化效应(见[1]，复习)，在线句子处理的变化 如视觉世界眼睛跟踪研究[3-6]所确定的，以及测量的适应性神经可塑性 通过来自功能神经成像的BOLD信号的变化[7-8]。的首要目标是 拟议的研究是改进TUF，以最大限度地提高其疗效，并提高治疗结果 最终增加其临床应用。我们提出了一系列研究来测试早期治疗的效果 TUF的治疗部分，以确定对成功结果最关键的部分(目标 1、研究1)、比较TUF和动词网络强化治疗的相对效果 (VNeST)关于离线、在线和神经处理(目标2，研究2)，并检查 无创神经刺激(即高清晰度经颅直流电刺激(HD- Tdcs))作为TUF的附属机构(目标3，研究3)。此外，我们将使用多模式神经成像 和行为数据，以检查成功治疗结果和 基于这些变量开发用于预测治疗应答的算法(目标4， 研究4)。

Abstract Agrammatic aphasia (agrammatism), an acquired language disorder, affects the ability to produce and comprehend sentences and significantly impacts the ability to communicate. The choice of the most efficacious and cost-effective treatment for specific language impairments (including agrammatism) is a major challenge for clinicians, as is the ability to formulate prognostic statements about the effects of treatment, based on individuals' language and neural profiles. Advances in understanding the effects of treatment for sentence processing deficits are therefore important for providing optimal intervention. The present proposal builds on previous work showing that Treatment of Underlying Forms (TUF, [1-2]), a metalinguistic approach focused explicitly on improving sentence processing abilities in agrammatic aphasia, results in strong acquisition and generalization effects (see [1], for review), changes in on-line sentence processing as identified in visual world eyetracking studies [3-6], and adaptive neuroplasticity as measured by shifts in BOLD signal derived from functional neuroimaging [7-8]. The overarching goal of the proposed studies is to refine TUF to maximize its efficacy and boost treatment outcomes to ultimately increase its clinical use. We propose a set of studies that test the effects of early treatment components of TUF to identify those that are most critical for successful outcomes (Aim 1, Study 1), compare the relative effects of TUF and Verb Network Strengthening Treatment (VNeST) on offline, online and neural processing (Aim 2, Study 2), and examine the effects of noninvasive neurostimulation (i.e., high-definition, transcranial direct current stimulation (HD- tDCS)) as an adjunct to TUF (Aim 3, Study 3). In addition, we will use multimodal neuroimaging and behavioral data to examine the factors underlying successful treatment outcomes and develop algorithms for predicting responsiveness to treatment based on these variables (Aim 4, Study 4).