Explaining aphasic impairment in the context of contemporary models of speech and language processing

在当代言语和语言处理模型的背景下解释失语症障碍

• 批准号: 9083048
• 负责人: Gregory Hickok
• 金额: $ 32.74万
• 依托单位: UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9083048
• 关键词: Accounting; Affect; Aphasia; Area; Assessment tool; Auditory; Back; Basic Science; Behavior; Brain; Brain region; Clinical; Complex; Computer Simulation; Data; Data Set; Development; Diagnostic; Dorsal; Evaluation; Foundations; Generations; Goals; Impairment; Individual; Investigation; Knowledge; Language; Lead; Lesion; Location; Maps; Measures; Minor; Modeling; Modification; Motor; Names; Neurobiology; Outcome Measure; Patients; Pattern; Process; Production; Proxy; Research; Role; Science; Self-Correction; Self-Examination; Semantics; Speech; Stream; Stroke; Symptoms; TNFRSF5 gene; Testing; Theoretical model; Treatment outcome; Weight; Work; acute stroke; aphasia recovery; aphasic; base; chronic stroke; feeding; improved; individual patient; language processing; lexical; motor control; phonology; predicting response; relating to nervous system; response; speech processing; targeted treatment; theories; tool; treatment effect; treatment response

Project Summary: Project 4 Model-driven therapies are only as effective as the models that drive them. The goal of Project 4 is to establish a synergistic interaction between clinical and theoretical progress in language science. Data collected as part of Projects 1 and 2 will serve as input to basic science research that will lead to new knowledge regarding the computational and neural foundation of speech production. And feeding back in the other direction, basic science models of speech production will be explored as possible diagnostic and treatment outcome measure tools, opening the door to a new era in model-driven aphasia treatment. Aim 1: Map the neural organization of computational subcomponents of naming. Naming is a multistep process. A complete understanding of its neural basis, and how it can breakdown in aphasia, depends on our ability to map the computational subcomponents of the process. Using both VLSM (error type maps) and VLPM (parameter maps), in this aim we investigate the neural organization of the subcomponents of naming in chronic and acute stroke. Aim 2: Improve and extend the model and the neurobiological mappings. While existing computational models are successful in accounting for the range of naming response types that have been fed into the models, aphasic production deficits are rather more complex than standard operational definitions admit. For this reason, and in keeping with the overall goal of the P50 to bring together theory and practice, Aim 2 seeks to push the computational models more toward clinical reality. We will use our recently-developed SLAM model to simulate and map speech repetition behavior and we will map the neural basis of a common behavior in both naming and connected speech context, self-correction. Aim 3: Assess the utility of model-driven diagnostics. The premise of Project 1 is that identifying functional deficits to the dorsal or ventral streams— i.e., dorsal stream aphasia (DSA) vs. ventral stream aphasia (VSA) vs. dual stream aphasia (DuSA)—provides useful information for aphasia treatment. In Project 1, DSA, VSA, and DuSA are classified on the basis of lesion location, which is a good (arguably the best available) proxy for the functional deficit that is targeted by the subsequent treatment. The possibility we will assess in this aim is whether we can do better at classifying patients pretreatment by measuring their functional deficit “directly” using functional/computational models rather than relying on a lesion-based proxy. This investigation could result in the development of new, clinically available assessment tools for aphasia.

项目摘要：项目4 模型驱动疗法的有效性取决于驱动它们的模型。项目4的目标是建立 语言科学的临床和理论进展之间的协同作用。作为部分收集的数据 项目1和2将作为对基础科学研究的投入，这些研究将导致关于 语音产生的计算和神经基础。从另一个方向反馈，基本 将探索语音产生的科学模型，作为可能的诊断和治疗结果衡量标准 工具，开启了模型驱动失语症治疗的新纪元。目标1：映射神经组织 命名的计算子组件。命名是一个多步骤的过程。对…的全面了解 它的神经基础，以及它如何在失语症中分解，取决于我们映射计算的能力 流程子组件。使用VLSM(错误类型映射)和VLPM(参数映射)来实现此目标 我们研究了慢性卒中和急性卒中命名亚成分的神经组织。目标2： 改进和扩展模型和神经生物学图谱。虽然现有的计算模型 成功地解释了已被馈送到模型中的命名响应类型的范围， 失语症生产赤字比标准运营定义所承认的要复杂得多。为了这个 理性，并与P50将理论和实践结合在一起的总体目标保持一致，目标2寻求 推动计算模型更接近临床实际。我们将使用我们最近开发的SLAM模型来 模拟和映射语音重复行为，我们将映射两者中共同行为的神经基础 命名和连接的语音上下文，自我纠正。目标3：评估模型驱动的效用 诊断。项目1的前提是识别背侧或腹侧流的功能缺陷- 即背流性失语(DSA)与腹流性失语(VSA)与双流性失语(DUSA)--提供 失语症治疗的有用信息。在项目1中，DSA、VSA和DUSA根据以下各项进行分类 病变位置，这是一个很好的(可以说是最好的可用的)替代功能缺陷的目标 随后的治疗。我们在这个目标中评估的可能性是，我们是否可以在分类方面做得更好 通过使用功能/计算模型“直接”测量患者的功能缺陷进行预处理 而不是依赖基于病变的代理。这项研究可能导致新的、临床上的 失语症的可用评估工具。