Engineering the Neuronal Response to Electrical Microstimulation

项目总结/摘要 我们提出的努力与RFA-NS-18-019的目标直接一致：优化神经系统调制的变革性技术。具体来说，我们寻求优化微电极阵列（MEA）和超微电极阵列（UMEA）的大规模电路操作，将控制神经活动的细胞分辨率与高时间分辨率。我们的目标是：1）通过测试MEA和UMEA可以提供安全，有效水平的皮层电刺激的单独假设来推进CNS MEA和UMEA电微刺激; 2）通过生成将在整个研究界广泛使用的变革性工具和技术来推进研究。在这里，我们建议结合计算建模，工程优化和体内测量来解决这些挑战，并为社区提供微刺激和工具。我们的目标是：1）设计出非破坏性充电的方法，2）设计出能够选择性和分级激活目标神经元的方法，3）记录目标1和目标2的创新性能。通过一个优秀的团队共同努力解决这个跨学科的问题，我们的创新方法将导致1）模型，以提供非破坏性的电流从MEA和UMEA; 2）评估模型，以优化MEA和UMEA设计的微刺激; 3）实验评估的结果，我们的设计，无论是在我们的团队和我们的合作者。我们的变革性成果将导致基于模型的可靠和高保真多通道微刺激技术的优化，从而实现可持续的，广泛的传播和用户友好的纳入常规神经科学实践。

PROJECT SUMMARY/ABSTRACT Our proposed efforts align directly with a goal of RFA-NS-18-019: optimization of transformative technologies for modulation in the nervous system. Specifically, we seek to optimize microelectrode arrays (MEAs) and ultra-microelectrode arrays (UMEAs) for large-scale circuit manipulation that will control neural activity at cellular resolution with high temporal resolution. Our goals are to 1) advance CNS MEA and UMEA electrical microstimulation by testing the separate hypotheses that MEAs and UMEAs can deliver safe, effective levels of cortical electrical stimulation and 2) advance research by generating transformative tools and technologies that will be widely used throughout the research community. Here we propose combining computational modeling, engineering optimization, and in vivo measurement to address these challenges and produce advances in microstimulation and tools for the community. Our Aims are to 1) engineer approaches to non-damaging charge, 2) engineer approaches to enable selective and graded activation of targeted neural elements, and 3) document the performance of the innovations from Aim 1 and Aim 2. via an outstanding team working together to address this interdisciplinary problem, our innovative approach will result in 1) models to deliver non- damaging currents from MEAs and UMEAs; 2) evaluation of the models to optimize MEA and UMEA design for microstimulation; and 3) experimental assessment of the outcomes of our designs, both within our team and with our collaborators. Our transformative results will lead to model-based optimization of reliable and high-fidelity multichannel microstimulation technologies enabling sustainable, broad dissemination and user-friendly incorporation into regular neuroscience practice.