Neuroimaging of Speech Motor Control

描述（由申请人提供）：由于沟通对世界的运作至关重要，言语产生障碍是最令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言语）。一种建模这种二元性的方法是采用基于感觉反馈控制的言语运动控制的现有模型，并用单独的前馈控制器对其进行增强。这是DIVA中采用的方法，DIVA是目前占主导地位的言语运动控制模型，其中反馈和前馈控制子系统联合收割机在运动皮层中组合它们的输出以控制声道。然而，在我们的实验室中，我们一直在研究另一种建模语音前馈和反馈特性的方法，称为基于前馈的状态反馈控制（SFC）。在这里，言语控制完全基于反馈控制，但是反馈来自于一个被称为观察者的代理人，该代理人仅间接地受到真实的感觉反馈的影响。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检验的预测。在这里，我们将测试这两个模型的不同预测，干扰受试者的听觉反馈，因为他们说话，并检查他们的神经反应，这些反馈扰动使用几种不同的功能性神经成像方法：脑磁图成像（MEG-I）和皮层脑电图（ECoG）。在言语运动研究之外，其他运动行为（例如，伸手、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移动（即，最小化消耗的能量，仅控制它们运动的与任务相关的方面）和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基础，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.