Remote Neurostimulation with Ultrasound-activated Piezoelectric Nanoparticles

项目摘要 在组织中以毫米到厘米深的高分辨率触发神经活动的能力仍然是一个挑战。 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网络。 具有高空间分辨率的刺激。在这个项目中，我们提出将联合收割机压电钛酸钡 纳米粒子与超声波激发，以触发神经活动。超声能量可以紧密地集中在 具有非常高的时空分辨率。然而，单独的超声波不是一种有效的方式来激活一个细胞。 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。我们将用抗神经成束蛋白186的抗体靶向纳米颗粒 受体的大鼠海马膜，使神经元特异性标记在轴突起始段。然后， 高度聚焦的超声能量将用于以高空间特异性去神经元。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进行验证，Quasar是一种遗传学标记， 编码荧光电压传感器。最后，我们将研究动作电位产生的机制 与压电纳米粒子。这些结果将为在体超声刺激群体铺平道路 神经元在小空间尺度上的变化。总的来说，所提出的技术有可能大大改善 研究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.