Maximizing and predicting sentence processing treatment outcomes in aphasia

最大化和预测失语症的句子处理治疗结果

基本信息

批准号：
10412434
负责人：
CYNTHIA K THOMPSON
金额：
$ 67.93万
依托单位：
NORTHWESTERN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2022-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10412434
关键词：
Acquired Language Disorders Address Affect Agrammatism Alzheimer&apos s Disease Anodes Aphasia Behavioral Clinical Clinical Trials Complex Comprehension Data Disease Early treatment Effectiveness Functional Magnetic Resonance Imaging Goals Individual Inferior Intervention Language Learning Lesion Linguistics Magnetic Resonance Imaging Malignant Neoplasms Measures Methods Neuronal Plasticity Outcome Parietal Pattern Positioning Attribute Probability Production Prognosis Protocols documentation Quality of life Research Rest Role Semantics Series Signal Transduction Site Step training Structure Testing Tissues Training Treatment Efficacy Treatment outcome Visual Work aphasia rehabilitation base clinical practice cost effective treatment efficacy evaluation improved language impairment multimodality neural network neural recruitment neural stimulation neuroimaging predict responsiveness prediction algorithm predictive modeling prognostic relating to nervous system relative effectiveness specific language impairment success syntax theories treatment effect

项目摘要

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).

抽象的语法失语症（agrammatism）是一种后天性语言障碍，影响生产能力和理解句子并显着影响沟通能力。的选择针对特定语言障碍（包括语法错误）对于临床医生来说是一个重大挑战，制定预后的能力也是如此根据个人的语言和神经特征对治疗效果进行陈述。因此，在理解句子处理缺陷治疗效果方面取得的进展是对于提供最佳干预非常重要。本提案建立在以前的工作基础上表明底层形式的处理（TUF，[1-2]），一种元语言方法，重点关注明确地提高语法失语症的句子处理能力，结果是强有力的习得和泛化效应（参见[1]，以供审查），在线句子处理的变化正如视觉世界眼球追踪研究[3-6]中所确定的，以及测量的适应性神经可塑性通过来自功能神经影像的 BOLD 信号的变化 [7-8]。该组织的总体目标是拟议的研究旨在改进 TUF 以最大限度地发挥其功效并提高治疗效果最终提高其临床应用。我们提出了一系列研究来测试早期的影响 TUF 的治疗组成部分，以确定对成功结果最关键的治疗组成部分（目标 1、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（目标 2，研究 2）的影响，并检查无创神经刺激（即高清经颅直流电刺激 (HD- tDCS)) 作为 TUF 的辅助手段（目标 3，研究 3）。此外，我们将使用多模式神经影像和行为数据来检查成功治疗结果的因素开发基于这些变量预测治疗反应的算法（目标 4，研究4）。