Institut de la Vision, Sorbonne Université, INSERM, Paris, France.
Institut de la Vision, Sorbonne Université, INSERM, Paris, France.
J Neurosci Methods. 2024 Dec;412:110297. doi: 10.1016/j.jneumeth.2024.110297. Epub 2024 Oct 9.
High density microelectrode arrays (HD-MEAs) are now widely used for both in-vitro and in-vivo recordings, as they allow spikes from hundreds of neurons to be recorded simultaneously. Since extracellular recordings do not allow visualization of the recorded neurons, algorithms are needed to estimate their physical positions, especially to track their movements when the are drifting away from recording devices.
The objective of this study was to evaluate the performance of multiple algorithms for neuron localization solely from extracellular traces (MEA recordings), either artificial or obtained from mouse retina. The algorithms compared included center-of-mass, monopolar, and grid-based algorithms. The first method is a barycenter calculation. The second algorithm infers the position of the cell using triangulation with the assumption that the neuron behaves as a monopole. Finally, grid-based methods rely on comparing the recorded spike with a projection of spikes of hypothetical neurons with different positions.
The Grid-Based algorithm yielded the most satisfactory outcomes. The center-of-mass exhibited a minimal computational cost, yet its average localization was suboptimal. Monopolar algorithms gave cell localizations with an average error of less than 10μm, but they had considerable variability and a high computational cost. For the grid-based method, the variability was smaller, with satisfactory performance and low computational cost.
COMPARISON WITH EXISTING METHOD(S): The accuracy of the different localization methods benchmarked in this article had not been properly tested with ground-truth recordings before.
The objective of this article is to provide guidance to researchers on the selection of optimal methods for localizing neurons based on MEA recordings.
高密度微电极阵列（HD-MEAs）现在广泛用于体外和体内记录，因为它们允许同时记录数百个神经元的尖峰。由于细胞外记录不能可视化记录的神经元，因此需要算法来估计它们的物理位置，特别是在记录的神经元漂移出记录设备时跟踪它们的运动。
本研究的目的是评估仅从细胞外轨迹（MEA 记录）评估神经元定位的多种算法的性能，无论是人工的还是从老鼠视网膜获得的。比较的算法包括质心、单极和基于网格的算法。第一种方法是重心计算。第二种算法通过假设神经元表现为单极来进行三角测量，从而推断细胞的位置。最后，基于网格的方法依赖于将记录的尖峰与具有不同位置的假设神经元的尖峰投影进行比较。
基于网格的算法产生了最令人满意的结果。质心表现出最小的计算成本，但平均定位效果不理想。单极算法给出的细胞定位平均误差小于 10μm，但它们具有相当大的可变性和高计算成本。对于基于网格的方法，可变性较小，性能令人满意，计算成本低。
在本文中，不同定位方法的准确性以前没有使用地面真实记录进行适当测试。
本文的目的是为研究人员提供根据 MEA 记录选择最佳神经元定位方法的指导。
