Cognitive neural prosthetics for clinical applications

 描述(由申请人提供)：最近的研究表明，用于神经假体应用的神经植入物可以通过允许控制外部设备来帮助瘫痪患者群体。我们最近已经证明，从四肢瘫痪受试者的后顶叶皮质(PPC)记录的神经信号为假肢控制提供了一个有价值的神经信号来源。以前的研究已经证明了使用来自人类运动皮质(M1)的信号的实用性。根据这些发现，我们建议同时在M1和PPC植入，以回答这两个大脑区域在帮助患者群体方面如何比较的重要问题。为了比较大脑区域，我们认为使用严格的测试范式是必要的，这些范式能够解决意图在两个大脑区域如何编码的基本问题。此外，拟议中的实验在许多情况下将是检验意图表征的性质和复杂性的第一次研究。因此，在目标1a中，我们将比较两个大脑区域如何编码高级目标和瞬时执行信号。目标1b将测试这些意图信号如何在多个上下文中概括。目标2将测试意图信号的参考帧，例如目标信号是否用 关于受试者正在看的地方，效应器的当前位置，或者身体或世界上有意义的行为，最常见的是按时间顺序的运动组合的结果，因此我们将在目标3中测试意图的神经表征是如何组合和排序的。对这些特性的基本科学探索将被用来实现打字界面和对平板电脑的控制。我们的建议不仅允许我们测试大脑中的一个区域是否比另一个区域更适合特定的运动变量，而且还可以测试它们是否提供了补充类型的信息。解码算法通过解释神经活动来产生动作。拟议的研究将提供关于意图在这两个区域如何编码的开创性数据，从而通过更好地理解神经信号应该如何解释来指导下一代解码算法的设计。临床相关性：据估计，仅在美国，患有某种形式瘫痪的患者数量就高达560万。瘫痪可由脊髓损伤、创伤性脑损伤、中风、周围神经病以及神经退行性疾病引起，如肌萎缩侧索硬化症和多发性硬化症。另有200万名患者因截肢而导致运动障碍。目前的应用将对记录假体控制信号的两个突出区域进行比较，以确定它们在假体适用性方面的异同。这项研究将使神经假体的改进设计能够最大限度地利用在这两个领域发现的互补信号。

 DESCRIPTION (provided by applicant): Recent studies have demonstrated that neural implants for neural prosthetic applications can help paralyzed patient populations by allowing control of external devices. We have recently demonstrated that neural signals recorded from the posterior parietal cortex (PPC) of a tetraplegic subject provides a valuable source of neural signals for prosthetic control. Previous studies have demonstrated the utility of using signals from human motor cortex (M1). In light of these findings, we propose simultaneous implants in M1 and PPC to answer the important question of how these two brain areas compare in helping the patient population. To compare brain areas, we believe it is essential to use rigorous testing paradigms that are able to address fundamental questions of how intentions are coded in the two brain areas. Moreover, the experiments proposed will be in many cases the first studies examining the properties and complexities of the representation of intentions. In Aim 1a we will thus compare how the two brain areas code high-level goals and instantaneous execution signals. Aim 1b will test how these intention signals generalize across multiple contexts. Aim 2 will test the reference frames of intention signals, e.g. whether goal signals are represented with respect to where the subject is looking, the current location of the effector, or the body or world Meaningful behaviors are most often the result of combinations of movements sequenced in time, and therefore we will test in Aim 3 how neural representations of intentions are combined and sequenced. Basic scientific explorations of these properties will be leveraged to enable typing interfaces and the control of a tablet computer. Our proposal not only allows us to test whether one brain area is better than the other for particular motor variables but also whether they provide complimentary types of information. Decoding algorithms produce actions by interpreting neural activity. The proposed studies will provide seminal data on how intentions are coded in the two areas, thus informing how the next generation of decoding algorithms should be designed by providing a better understanding of how neural signals should be interpreted. Clinical relevance: the number of patients suffering from some form of paralysis in the United States alone has been estimated to be as high as 5.6 million. Paralysis can result from spinal cord injury, traumatic brain injury, stroke, peripheral neuropathies, and neurodegenerative disorders such as amyotrophic lateral sclerosis and multiple sclerosis. Another 2 million patients have motor disabilities due to limb amputation. The current application will compare two prominent areas for recording prosthetic controls signals to determine their similarities and differences in applicability to prosthetics. This research will enable improved design of neuroprosthetics that can use the complementary signals found in these two areas to maximum advantage.