Neuroimaging of Speech Motor Control

描述（由申请人提供）：由于沟通对世界上的功能至关重要，语言产生障碍是最使人衰弱的神经系统疾病之一。开发有效的治疗方法需要精确的、可解释的控制说话的神经过程模型。在定义这些模型时，感觉反馈的作用一直是一个关键问题：说话似乎既是一个前馈过程（你可以在感觉反馈受阻的情况下说话），也是一个反馈过程（感觉反馈的改变会改变语言）。建模这种二元性的一种方法是采用基于感官反馈控制的现有语音运动控制模型，并用单独的前馈控制器对其进行增强。这是DIVA所采用的方法，DIVA是目前占主导地位的语音运动控制模型，其中反馈和前馈控制子系统将它们在运动皮层的输出结合起来控制声道。然而，在我们的实验室里，我们一直在研究另一种模拟语音前馈和反馈特征的方法，称为基于观测器的状态反馈控制（SFC）。在这里，语言控制完全基于反馈控制，但反馈来自一个被称为观察者的替代物，它只间接受到真实感官反馈的影响。这两种模型都可以解释说话的行为特征，但它们对负责这些行为的潜在神经过程做出了非常不同且可测试的预测。在这里，我们将通过干扰受试者说话时的听觉反馈来测试这两种模型的不同预测，并使用几种不同的功能性神经成像方法（脑磁成像（MEG-I）和皮质电成像（ECoG））检查他们对这些反馈扰动的神经反应。在言语运动研究之外，其他运动行为（如到达、眼球运动）的SFC模型正变得越来越普遍，这在很大程度上是因为人们似乎以最佳方式运动（即，最小化消耗的能量，只控制与任务相关的运动方面），而SFC是现代最优控制理论的基础。如果说话的神经控制被证明与SFC模型一致，我们就可以将其与运动控制研究的其他领域联系起来，并利用广泛的理论知识库，使我们能够对模型的行为做出强有力的预测。

DESCRIPTION (provided by applicant): Because communicating is so critical to functioning in the world, disorders of speech production are among the most debilitating neurological conditions. Developing effective treatments will require accurate, interpretable models of the neural processes controlling speaking. In defining such models, the role of sensory feedback has been a key issue: speaking appears to be both a feedforward process (you can speak with sensory feedback blocked) and a feedback process (alteration of sensory feedback modifies speech). One way to model this duality is to take an existing model of speech motor control based on sensory feedback control and augment it with a separate feedforward controller. This is the approach taken in DIVA, a currently dominant model of speech motor control, where feedback and feedforward control subsystems combine their outputs in motor cortex to control the vocal tract. In our lab, however, we have been investigating another way of modeling the feedforward and feedback characteristics of speech called observer-based, state feedback control (SFC). Here, control of speech is based entirely on feedback control, but the feedback comes from a surrogate called an observer that is only indirectly affected by real sensory feedback. Both models can account for the behavioral characteristics of speaking, but they make very different and testable predictions about the underlying neural processes responsible for those behaviors. Here, we will test the differing predictions of these two models by perturbing the auditory feedback of subjects as they speak and examining their neural responses to these feedback perturbations using several different functional neuroimaging methods: magnetoencephalographic imaging (MEG-I) and electrocorticography (ECoG). Outside of speech motor research, SFC models of other motor behaviors (e.g. reaching, eye movements) are becoming more prevalent, in large part because people appear to move in optimal ways (i.e., minimizing expended energy, only controlling task-relevant aspects of their movements) and SFC is the foundation of modern optimal control theory. If the neural control of speaking were shown to be consistent with an SFC model, we could relate it to other domains of motor control research and leverage an extensive theoretical knowledge base, allowing us to make powerful predictions of the model's behavior. PUBLIC HEALTH RELEVANCE: Because communicating is so critical to functioning in the world, disorders of speech production are among the most debilitating neurological conditions. In order to develop effective treatments for speech dysfunctions, such as those in stuttering, apraxia of speech, dysarthria, spasmodic dysphonia, and Parkinson's disease, we need accurate models of the neural processes controlling speaking. In this project, we will use functional neuroimaging to test how well a promising new model of speech motor control predicts the neural activity associated with speaking.