Influence of Neuronal Morphology on the Shape of Extracellular Recordings With Microelectrode Arrays: A Finite Element Analysis
Influence of Neuronal Morphology on the Shape of Extracellular Recordings With Microelectrode Arrays: A Finite Element Analysis
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DOI：
10.1109/tbme.2020.3026635
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发表时间：
2021-04-01
影响因子：
4.6
通讯作者：
Appali, Revathi
中科院分区：
文献类型：
--
作者：
Bestel, Robert;van Rienen, Ursula;Appali, Revathi
目的：使用微电极阵列测量神经元细胞活动，揭示细胞外记录中多种衍生信号形状。然而，造成这种多样性的可能机制尚未完全确定，这可能会妨碍对记录的神经元数据的任何后续分析。方法：为了研究这个问题，我们提出了一种基于有限元方法的计算模型，详细描述了电活性神经元和细胞外记录电极之间的电耦合。这允许对可能的参数进行系统研究，这些参数可能在定义或改变测量的电极电势的形状中发挥重要作用。结果：我们的结果表明，神经元几何形状、神经突结构以及引起动作电位产生的输入电位的实际路径，对所得细胞外电极记录的形状有显着影响，并解释了大多数已知的信号形状变化。结论：所提出的模型提供了对几何和形态因素对所得电极信号的影响的全面了解。意义：计算模型与实验测量相结合，有望对神经网络的电活动产生有意义的见解。
Objective: Measuring neuronal cell activity using microelectrode arrays reveals a great variety of derived signal shapes within extracellular recordings. However, possible mechanisms responsible for this variety have not yet been entirely determined, which might hamper any subsequent analysis of the recorded neuronal data. Methods: To investigate this issue, we propose a computational model based on the finite element method describing the electrical coupling between an electrically active neuron and an extracellular recording electrode in detail. This allows for a systematic study of possible parameters that may play an essential role in defining or altering the shape of the measured electrode potential. Results: Our results indicate that neuronal geometry, neurite structure, as well as the actual pathways of input potentials that evoke action potential generation, have a significant impact on the shape of the resulting extracellular electrode recording and explain most of the known variations of signal shapes. Conclusion: The presented models offer a comprehensive insight into the effect of geometrical and morphological factors on the resulting electrode signal. Significance: Computational modeling complemented with experimental measurements shows much promise to yield meaningful insights into the electrical activity of a neuronal network.