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，从后顶叶皮层（PPC）的四肢瘫痪的主题记录的神经信号提供了一个有价值的来源，假肢控制的神经信号。以前的研究已经证明了使用来自人类运动皮层（M1）的信号的实用性。鉴于这些发现，我们建议在M1和PPC中同时植入，以回答这两个大脑区域在帮助患者人群方面如何比较的重要问题。为了比较大脑区域，我们认为必须使用严格的测试范式，这些范式能够解决意图如何在两个大脑区域中编码的基本问题。此外，在许多情况下，所提出的实验将是第一次研究意图表征的性质和复杂性。在目标1a中，我们将比较这两个大脑区域如何编码高级目标和即时执行信号。目标1b将测试这些意图信号如何在多个上下文中泛化。目标2将测试意图信号的参考系，例如，目标信号是否用 有意义的行为通常是按时间顺序排列的动作组合的结果，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平板电脑的控制。我们的建议不仅可以让我们测试一个大脑区域是否比另一个更适合特定的运动变量，而且还可以测试它们是否提供了互补的信息。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如何在这两个领域编码的开创性数据，从而通过更好地理解神经信号应如何解释，为下一代解码算法的设计提供信息。临床相关性：据估计，仅在美国，患有某种形式瘫痪的患者人数就高达560万。瘫痪可由脊髓损伤、创伤性脑损伤、中风、周围神经病和神经退行性疾病如肌萎缩性侧索硬化和多发性硬化引起。另有200万患者因截肢而出现运动残疾。本申请将比较用于记录假体控制信号的两个突出区域，以确定它们在假体适用性方面的相似性和差异。这项研究将使神经修复术的设计得到改进，可以利用这两个领域中发现的互补信号来最大限度地发挥优势。

 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.