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

项目总结 针对严重语音障碍的增强和替代通信(AAC)技术 运动障碍(SSMI)继续改善，最近在 通信设备的神经控制。在之前由NIDCD支持的研究中，我们的研究 该团队开发了一种高性能的皮质内脑机接口(IBCI)，可以解码 手臂的运动意图直接来自大脑的活动。这项技术使人们能够 SSMI以足够的速度和精度控制计算机光标，打字速度高达8 字/分钟，实现了对未修改的消费设备的完全控制，仅使用解码 运动皮质活动。在拟议的U01临床研究中，作为多站点的一部分进行 BrainGate财团，我们将建立在研究发动机系统的几十年经验的基础上 人类和非人类灵长类动物，最终目标是推进IBCI技术。的目标 这个项目是研究演讲是如何在合奏的层面上准备和产生的 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)，并创造一种语音假体，允许以速率进行交流 接近会话演讲(每分钟120-150个单词)。我们将处理这些问题 用一套先进的方法进行研究，包括(1)新开发的动力学 系统计算方法提供了对功能的基本见解 运动系统，以及(2)用于解码运动意图的机器学习算法 语言建模已经构成了迄今为止最快的交流假体的基础 据报道。最后，我们将继续评估基于犹他州阵列的iBCI的安全性 通过正在进行的BrainGate2试点临床试验。建成后，这一项目将向前推进 IBCI的交流能力和我们对详细神经的理解 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.