Applicability of independent component analysis on high-density microelectrode array recordings.
Applicability of independent component analysis on high-density microelectrode array recordings.
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DOI：
10.1152/jn.01106.2011
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发表时间：
2012-07
影响因子：
2.5
通讯作者：
Hierlemann A
中科院分区：
文献类型：
--
作者：
Jäckel D;Frey U;Fiscella M;Franke F;Hierlemann A
新兴的基于互补金属氧化物半导体(CMOS)的高密度微电极阵列(HD-MEA)器件提供了亚细胞水平的高空间分辨率和大量的读出通道。这些设备允许同时记录大量神经元的细胞外活动，每个神经元都被多个电极检测到。为了分析记录的信号，必须将放电事件分配给单独的神经元，这一过程被称为“放电分类”。对于一组观测信号，它构成了一组源信号的线性混合，可以使用独立分量分析(ICA)对数据进行盲解分并提取单独的源信号。这项技术提供了巨大的潜力来缓解HD-MEA记录中的棘波分类问题，因为它代表了一种分离神经元来源的无监督方法。然后，分离的源或IC构成单个神经元信号的估计，IC上的阈值检测产生排序的尖峰时间。然而，目前尚不清楚细胞外神经元记录在多大程度上符合ICA的要求。在本文中，我们评估了独立分量分析在HD-MEA记录的尖峰分类中的适用性。对以高时空分辨率记录的细胞外神经元信号的分析表明，记录的数据不能被建模为纯粹的线性混合。因此，ICA不能完全分离神经元信号，不能作为HD-MEA记录中棘波分类的独立方法。我们使用模拟数据集评估了ICA的分离性能，发现其性能强烈依赖于神经元密度和棘波幅度。此外，我们还展示了如何使用后处理技术来克服ICA最严重的限制。与这些后处理技术相结合，ICA代表了一种可行的方法来促进多维神经元记录的快速棘波分类。
Emerging complementary metal oxide semiconductor (CMOS)-based, high-density microelectrode array (HD-MEA) devices provide high spatial resolution at subcellular level and a large number of readout channels. These devices allow for simultaneous recording of extracellular activity of a large number of neurons with every neuron being detected by multiple electrodes. To analyze the recorded signals, spiking events have to be assigned to individual neurons, a process referred to as “spike sorting.” For a set of observed signals, which constitute a linear mixture of a set of source signals, independent component (IC) analysis (ICA) can be used to demix blindly the data and extract the individual source signals. This technique offers great potential to alleviate the problem of spike sorting in HD-MEA recordings, as it represents an unsupervised method to separate the neuronal sources. The separated sources or ICs then constitute estimates of single-neuron signals, and threshold detection on the ICs yields the sorted spike times. However, it is unknown to what extent extracellular neuronal recordings meet the requirements of ICA. In this paper, we evaluate the applicability of ICA to spike sorting of HD-MEA recordings. The analysis of extracellular neuronal signals, recorded at high spatiotemporal resolution, reveals that the recorded data cannot be modeled as a purely linear mixture. As a consequence, ICA fails to separate completely the neuronal signals and cannot be used as a stand-alone method for spike sorting in HD-MEA recordings. We assessed the demixing performance of ICA using simulated data sets and found that the performance strongly depends on neuronal density and spike amplitude. Furthermore, we show how postprocessing techniques can be used to overcome the most severe limitations of ICA. In combination with these postprocessing techniques, ICA represents a viable method to facilitate rapid spike sorting of multi-dimensional neuronal recordings.