Single-neuron population dynamics in human speech motor cortex for a speech prosthesis

项目摘要 增强和替代通信（AAC）技术，用于严重言语患者 和运动障碍（SSMI）继续改善，最近的进展是在 通讯设备的神经控制在先前NIDCD支持的研究中，我们的研究 一个团队开发了一种高性能的皮质内脑机接口（iBCI）， 手臂运动意图直接来自大脑活动。这项技术让人们 SSMI以足够的速度和准确度控制计算机光标，以高达8 字/分钟，并且仅使用解码的 运动皮层活动在拟定的U 01临床研究中，作为多中心研究的一部分进行 BrainGate财团，我们将建立在数十年的经验，在研究电机系统， 人类和非人类灵长类动物，最终目标是推进iBCI技术。的目标 这个项目是研究如何准备和生产的整体水平的讲话， 肌萎缩侧索硬化症患者大脑语言相关运动区的单个神经元 硬化症（ALS），并创建一个语音假体，将允许通信速率 接近会话的演讲（120-150字每分钟）。我们将接近这些 研究与一套先进的方法，包括（1）新开发的动态 系统的计算方法，提供了基本的见解的功能， 运动系统，以及（2）用于解码运动意图的机器学习算法， 语言建模已经形成了最快的通信假体的基础 报道最后，我们将继续评估基于Utah阵列的iBCI的安全性 通过正在进行的BrainGate 2试点临床试验。完成后，该项目将推进 iBCI的通信能力和我们对详细的神经网络的理解 言语产生的机制。

PROJECT SUMMARY Augmentative and alternative communication (AAC) technology for people with severe speech and motor impairment (SSMI) continues to improve, with recent advances being made in the neural control of communication devices. In prior NIDCD-supported research, our research team developed a high-performance intracortical brain-computer interface (iBCI) that decodes arm movement intentions directly from brain activity. This technology has allowed people with SSMI to control a computer cursor with sufficient speed and accuracy to type at up to 8 words/min and has enabled full control of unmodified consumer devices using only decoded motor cortical activity. In the proposed U01 clinical research, performed as part of the multi-site BrainGate consortium, we will build upon decades of experience in studying the motor system in humans and non-human primates, with the end goal of advancing iBCI technology. The goals of this project are to study how speech is prepared and produced at the level of ensembles of single neurons in speech-related motor areas of the brain in people with amyotrophic lateral sclerosis (ALS), and to create a speech prosthesis that will allow communication at rates approaching conversational speech (120-150 words per minute). We will approach these investigations with a suite of advanced methods, including (1) newly-developed dynamical systems computational approaches that have provided fundamental insights into the function of the motor system, and (2) machine learning algorithms for decoding of movement intention and language modeling that have formed the basis of the fastest communication prosthesis yet reported. Finally, we will continue to evaluate the safety profile of Utah-array based iBCIs through the ongoing BrainGate2 pilot clinical trial. Upon completion, this project will advance both the capabilities of iBCIs for communication and our understanding of the detailed neural mechanisms of speech production.