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Investigating the Response of CNS Neurons to Electric and Magnetic Stimulation

研究中枢神经系统神经元对电和磁刺激的反应

基本信息

批准号：
10673590
负责人：
Shelley Fried
金额：
$ 59.83万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-06-15 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10673590
关键词：
Action Potentials Anatomy Axon Biophysics Blinded Calibration Cell membrane Cell model Cell physiology Cells Cellular Morphology Communities Complex Computer Models Dendrites Development Devices Distal Effectiveness Electric Stimulation Fire - disasters Germ Cells Goals Implant Individual Ion Channel Ion Channel Gating Knowledge Location Macular degeneration Magnetism Maps Measurement Measures Microelectrodes Modeling Morphology Motor Cortex Mus Neocortex Neurons Oryctolagus cuniculus Performance Physiological Physiology Population Prefrontal Cortex Process Property Prosthesis Recommendation Research Retina Retinal Degeneration Retinal Ganglion Cells Retinitis Pigmentosa Series Shapes Signal Transduction Sodium Channel Stimulus Testing Tissues Training Translations Vision Work area striata cell type density effectiveness evaluation electric field experimental study hippocampal pyramidal neuron implantable device improved luminance neocortical nervous system disorder neural neural patterning neural prosthesis neuronal cell body neurotransmission next generation nonhuman primate novel predicting response predictive modeling response retinal neuron retinal prosthesis voltage

项目摘要

Our long-term goals are to better understand the response of neurons to artificial stimulation, and, to use this knowledge to develop new and more effective strategies for stimulating non- or improperly-functioning neurons of the CNS. The development of models that comprehensively and accurately predict the response of neural populations to electric stimulation has proven challenging, in part because of the significant morphological differences that can exist even between nearby cells, and, a lack of understanding as to how such differences shape each cell’s response to stimulation. A comprehensive understanding of the activation process would not only allow the development of models that would more accurately predict population responses but would also support the development of more effective stimulation strategies. In the retina for example, cells that respond to increases in luminance (ON cells) typically lie adjacent to cells that respond to luminance decreases (OFF cells); the two do not typically fire action potentials in response to the same stimulus and therefore, a prosthesis that activates both simultaneously creates a pattern of neural activity that is non-physiological. Mis- match between natural and artificial signals limits the quality of vision that can be obtained by a retinal prosthesis and similarly limits the effectiveness of other CNS-based prostheses as well. Here, we propose to comprehensively study how individual cellular properties each influence the response to artificial stimulation. Our approach will be to map sensitivity across a cell, and then compare physiological maps to cellular morphology, including the expression of voltage-gated ion channels; this will allow us to identify the specific cellular regions that have the strongest influence on responsivity. Computational models based on our precise anatomical measurements can be calibrated from the physiological maps to optimize the accuracy of the models; they will also help to unequivocally identify the relative sensitivity of individual features. Comparison of multiple cells within the same cell type will help to further identify the features that have the strongest influence on threshold and repeating the process across multiple cell types, different CNS regions and multiple species will lead to a comprehensive understanding of the activation process, along with the concurrent development of models that accurately predict the response of large populations of neurons to many different forms of stimulation. The inclusion of non-human primate tissue in the study will enhance the translation value of our findings. Validated models will be used to study responses to more advanced stimulating strategies, e.g. the high-rate stimulus trains that produce selective activation in ON vs. OFF cell types of the retina, and, the use of magnetic stimulation from implantable micro-coils to selectively target pyramidal neurons in the cortex while avoiding nearby passing axons from distal neurons. Models will be further enhanced from each new set of experiments and the comprehensive set (of models) will be made widely available to the research community.

我们的长期目标是更好地了解神经元对人工刺激的反应，并利用这一点，开发新的和更有效的刺激非或功能不正常的神经元的策略的知识的CNS。全面准确地预测神经系统反应的模型的发展人群的电刺激已被证明是具有挑战性的，部分原因是由于显着的形态即使在附近的细胞之间也可能存在差异，而且，缺乏对这种差异如何存在的理解，塑造每个细胞对刺激的反应。对激活过程的全面理解不会只允许开发更准确地预测人口反应的模型，支持制定更有效的刺激战略。例如在视网膜中，响应于亮度增加的电池（ON电池）通常与响应于亮度降低的电池（OFF电池）相邻细胞）;两者通常不会对相同的刺激做出反应，因此，同时激活两者的假体会产生非生理性的神经活动模式。错了自然信号和人工信号之间的匹配限制了通过视网膜成像系统可以获得的视觉质量。并且同样限制了其他基于CNS的假体的有效性。在此，我们建议全面研究单个细胞特性如何影响对人工刺激的反应。我们的方法将是绘制整个细胞的敏感性，然后将生理图与细胞图进行比较。形态学，包括电压门控离子通道的表达;这将使我们能够识别特定的对响应性有最强影响的细胞区域。计算模型基于我们精确的可以从生理图校准解剖测量结果，以优化测量结果的准确性。模型;它们还将有助于明确地确定个别特征的相对敏感性。比较同一像元类型中的多个像元将有助于进一步识别具有最强影响的要素并在多种细胞类型、不同CNS区域和多种物种中重复该过程沿着，这些模型可以准确预测大量神经元对许多不同形式的神经元的反应。刺激.在研究中纳入非人类灵长类动物组织将提高我们的翻译价值调查结果。经验证的模型将用于研究对更先进的刺激策略的反应，例如高速率刺激训练，其在视网膜的ON与OFF细胞类型中产生选择性激活，并且，使用来自可植入微线圈的磁刺激以选择性地靶向皮质中的锥体神经元，避免从远端神经元附近经过的轴突。模型将从每一套新的将向研究界广泛提供实验和一套全面的（模型）。