Maximizing and predicting sentence processing treatment outcomes in aphasia

摘要 语法缺失性失语症（语法缺失症），一种获得性语言障碍， 和理解句子，并显著影响沟通能力。的选择 针对特定语言障碍的最有效和最具成本效益的治疗方法（包括 语法缺失）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概况。 因此，理解句子处理缺陷治疗效果的进展 这对于提供最佳干预至关重要。本提案是在以往工作的基础上提出的 这表明，底层形式的处理（TUF，[1-2]），一种元语言学方法， 明确提高失语症患者的句子加工能力，结果显示， 习得和泛化效应（参见[1]，用于审查），在线句子处理的变化 如视觉世界眼动追踪研究[3-6]所确定的， 通过从功能性神经成像中导出的BOLD信号的变化[7-8]。的首要目标 拟议的研究是完善TUF，以最大限度地提高其疗效，并提高治疗效果， 最终提高其临床应用。我们提出了一组研究，测试早期的影响， TUF的治疗成分，以确定那些对成功结局最关键的治疗成分（Aim 研究1）比较TUF和动词网络强化处理的相对效果 （VNeST）对离线，在线和神经处理的影响（目标2，研究2），并检查 非侵入性神经刺激（即，高清晰度，经颅直流电刺激（HD- tDCS））作为TUF的辅助治疗（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的潜在因素， 基于这些变量开发用于预测对治疗的反应性的算法（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).