Remote Neurostimulation with Ultrasound-activated Piezoelectric Nanoparticles

项目摘要 在组织中触发高分辨率毫米到厘米深的神经活动的能力仍然是 神经科学研究中难以实现的目标。目前的研究依赖于使用侵入性电极、光遗传学或 药理刺激。然而，这些技术都不能提供大规模的神经 高空间分辨率的刺激。在这个项目中，我们建议将压电钛酸钡 纳米颗粒与超声波激发，以触发神经活动。超声波能量可以紧密地集中在 具有很高时空分辨率的大脑。然而，单独使用超声波并不是激活心脏功能的有效方法。 一组特定的神经元。因此，我们将使用钛酸钡纳米颗粒作为嵌入式换能器 将超声波转化为电能。我们将用神经束素186的抗体将纳米粒作为靶点 大鼠海马膜上的受体，使轴突起始段的神经元特异性标记成为可能。然后, 高聚焦超声能量将被用来去极化具有高空间特异性的神经元。这些方法 将通过用QUASAR标记的培养大鼠海马神经元的光学成像来验证，QUASAR是一种遗传学上的 编码的荧光电压传感器。最后，我们将探讨动作电位产生的机制 用压电纳米粒子。这些结果将为体内超声刺激群体铺平道路 在小空间尺度上的神经元。总体而言，拟议的技术具有显著改进的潜力 学习复杂神经网络的能力。

Project Summary The ability to trigger neural activity with high resolution millimeters to centimeters deep in tissue remains an elusive goal in neuroscience research. Current research relies on using invasive electrodes, optogenetics, or pharmacological stimulation. None of these technologies, however, is capable of providing large-scale neural stimulation with high spatial resolution. In this project, we propose to combine piezoelectric barium titanate nanoparticles with ultrasound excitation to trigger neural activity. Ultrasound energy can be tightly focused in the brain with very high spatiotemporal resolution. However, ultrasound alone is not an efficient way to activate a specific set of neurons. Thus, we will use barium titanate nanoparticles to act as an embedded transducer to convert ultrasound to electrical energy. We will target the nanoparticles with antibodies to Neurofascin 186 receptors on rat hippocampal membranes, enabling neuron specific labeling at the axon initial segment. Then, highly focused ultrasound energy will be used to depolarize neurons with high spatial specificity. These methods will be validated with optical imaging of cultured rat hippocampal neurons labeled with Quasar, a genetically encoded fluorescent voltage sensor. Finally, we will investigate the mechanisms for action potential generation with the piezoelectric nanoparticles. These results will pave the way for in vivo ultrasound stimulation of groups of neurons at small spatial scales. Overall, the proposed technology has the potential to dramatically improve the ability to study complex neural networks.