Engineering the Neuronal Response to Electrical Microstimulation

项目摘要/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。具体地说，我们寻求优化微电极阵列(MEA)和超微电极阵列(UMEA)，以用于大规模电路操作，以高时间分辨率控制细胞分辨率的神经活动。我们的目标是1)通过测试MEAS和UMEA可以提供安全、有效水平的皮质电刺激的不同假设来推进CNS MEA和UMEA电微刺激，以及2)通过产生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，我们建议将计算建模、工程优化和活体测量结合起来，以应对这些挑战，并为社区在微刺激和工具方面取得进展。我们的目标是1)设计非破坏性电荷的方法，2)设计方法以实现选择性和分级激活目标神经元件，以及3)记录来自目标1和目标2的创新的性能。通过一个共同解决这一跨学科问题的杰出团队，我们的创新方法将产生1)模型，以提供来自MEA和UMEA的非破坏性电流；2)评估模型，以优化MEA和UMEA的微刺激设计；以及3)对我们的设计结果进行实验评估，包括在我们的团队内和与我们的合作伙伴。我们的变革性成果将导致可靠和高保真的多通道微刺激技术的基于模型的优化，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.