Validation of independent component analysis for rapid spike sorting of optical recording data.
Validation of independent component analysis for rapid spike sorting of optical recording data.
复制标题
验证独立组件分析的光学记录数据的快速尖峰分类。
DOI：
10.1152/jn.00691.2010
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发表时间：
2010-12
影响因子：
2.5
通讯作者：
Frost WN
中科院分区：
文献类型：
--
作者：
Hill ES;Moore-Kochlacs C;Vasireddi SK;Sejnowski TJ;Frost WN
独立分量分析（伊卡）是一种从未知源信号的混合信号中提取源信号的技术。用快速电压敏感染料对无脊椎动物神经元网络的光学记录进行分析可以大大受益于伊卡。这些实验可以生成数百个电压轨迹，其中包含源自未知数量神经元的动作电位的冗余和混合记录。伊卡可以作为一种将复杂数据集转换为单神经元轨迹的方法，但其准确性从未进行过经验评估。在这里，我们测试了伊卡的准确性，这样的盲源分离，同时进行尖锐的电极细胞内记录和快速电压敏感染料成像的神经元位于中央神经节的Tritonia diomedea和Aaplasia californica，使用464元件的光电二极管阵列。在光学数据集上运行伊卡后，我们发现34例中的34例细胞内记录的动作电位100%对应于伊卡返回的独立成分之一的尖峰活动。我们还表明，伊卡可以准确地排序动作电位到单个神经元的痕迹，从一系列的光学数据文件在不同的时间从相同的准备，允许一个监测网络参与大量的个别可识别的神经元在几个记录事件。我们的伊卡提取的神经活动的许多个人的神经元从嘈杂的，混合的，和冗余的光学记录数据集的准确性的验证应该使使用这种强大的大规模成像方法的无脊椎动物和合适的脊椎动物神经元网络的研究。
Independent component analysis (ICA) is a technique that can be used to extract the source signals from sets of signal mixtures where the sources themselves are unknown. The analysis of optical recordings of invertebrate neuronal networks with fast voltage-sensitive dyes could benefit greatly from ICA. These experiments can generate hundreds of voltage traces containing both redundant and mixed recordings of action potentials originating from unknown numbers of neurons. ICA can be used as a method for converting such complex data sets into single-neuron traces, but its accuracy for doing so has never been empirically evaluated. Here, we tested the accuracy of ICA for such blind source separation by simultaneously performing sharp electrode intracellular recording and fast voltage-sensitive dye imaging of neurons located in the central ganglia of Tritonia diomedea and Aplysia californica, using a 464-element photodiode array. After running ICA on the optical data sets, we found that in 34 of 34 cases the intracellularly recorded action potentials corresponded 100% to the spiking activity of one of the independent components returned by ICA. We also show that ICA can accurately sort action potentials into single neuron traces from a series of optical data files obtained at different times from the same preparation, allowing one to monitor the network participation of large numbers of individually identifiable neurons over several recording episodes. Our validation of the accuracy of ICA for extracting the neural activity of many individual neurons from noisy, mixed, and redundant optical recording data sets should enable the use of this powerful large-scale imaging approach for studies of invertebrate and suitable vertebrate neuronal networks.
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影响因子：
5.3
作者：
Briggman, Kevin L.;Kristan, William B., Jr.
通讯作者：
Kristan, William B., Jr.
DOI：
10.1007/978-1-4419-6558-5_5
发表时间：
2010-01-01
期刊：
MEMBRANE POTENTIAL IMAGING IN THE NERVOUS SYSTEMS: METHODS AND APPLICATIONS
影响因子：
--
作者：
Frost, William N.;Wang, Jean;Hill, Evan S.
通讯作者：
Hill, Evan S.
影响因子：
2.8
作者：
Obaid, AL;Nelson, ME;Salzberg, BM
通讯作者：
Salzberg, BM
影响因子：
16.2
作者：
Mukamel, Eran A.;Nimmerjahn, Axel;Schnitzer, Mark J.
通讯作者：
Schnitzer, Mark J.
影响因子：
20.6
作者：
Jung, TP;Makeig, S;Sejnowski, TJ
通讯作者：
Sejnowski, TJ