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

项目简介：Project 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步之间的协同作用。收集的数据 项目1和项目2中的每一个项目都将作为基础科学研究的投入，这将导致有关 语音产生的计算和神经基础。从另一个方向反馈， 将探讨语言产生的科学模型作为可能的诊断和治疗结果的衡量标准 工具，为模型驱动的失语症治疗打开了新时代的大门。目标1：映射神经组织 命名的计算子组件。这是一个多步骤的过程。完全理解 它的神经基础，以及它如何在失语症中崩溃，取决于我们映射计算能力的能力。 过程的子组件。使用VLSM（错误类型映射）和VLPM（参数映射）， 我们研究了慢性和急性中风中命名亚成分的神经组织。目标二： 改进和扩展模型和神经生物学映射。虽然现有的计算模型 成功地解释了已经输入模型的命名响应类型的范围， 失语症的产生缺陷比标准的操作定义所承认的要复杂得多。为此 根据P50将理论与实践结合起来的总体目标，目标2旨在 将计算模型推向临床现实。我们将使用我们最近开发的SLAM模型， 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。 命名与关联语境、自我纠错。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- 也就是说，背流失语症（DSA）与腹流失语症（VSA）与双流失语症（DuSA）-提供 失语症治疗的有用信息。在项目1中，DSA、VSA和DuSA基于以下分类： 病变位置，这是一个很好的（可以说是最好的）代理的功能缺陷，目标是 后续治疗。我们在这方面评估的可能性是，我们能否在分类方面做得更好 患者通过使用功能/计算模型“直接”测量其功能缺陷进行治疗前 而不是依赖于基于病变的代理。这项研究可能会导致新的，临床上 失语症的评估工具。

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.