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

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

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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).

抽象的被熟悉的语言障碍的农业失语症（农业症）会影响产生的能力并理解句子并显着影响交流的能力。选择针对特定语言障碍的最有效，最具成本效益的治疗方法（包括农业主义是临床医生的主要挑战，表达预后的能力也是如此基于个体的语言和神经谱的治疗影响的陈述。因此，了解治疗对句子处理缺陷的影响的进步是提供最佳干预措施很重要。目前的提案以先前的工作为基础显示了基础形式的处理（TUF，[1-2]），这是一种属金属语言方法明确地提高了农业失语症中的句子处理能力，导致强大获取和概括效应（请参阅[1]，有关审查），在线句子处理的变化如在视觉世界的眼球研究研究[3-6]和自适应神经可塑性中所确定的。通过从功能神经影像学得出的粗体信号中的移位[7-8]。总体目标拟议的研究旨在完善TUF，以最大化其功效并提高治疗结果最终增加其临床用途。我们提出了一组研究，以测试早期的影响 TUF的治疗成分以识别那些对成功结果最重要的人（AIM 1，研究1），比较TUF和动词网络加强治疗的相对效应（vnest）在离线，在线和神经处理（AIM 2，研究2）上，并检查非侵入性神经刺激（即高定义，经颅直流刺激（HD-- TDCS））作为TUF的辅助（AIM 3，研究3）。此外，我们将使用多模式神经影像学和行为数据，以检查成功治疗结果的基本因素和开发算法以根据这些变量预测对治疗的反应性（AIM 4，研究4）。