To deconvolve, or not to deconvolve: Inferences of neuronal activities using calcium imaging data.
To deconvolve, or not to deconvolve: Inferences of neuronal activities using calcium imaging data.
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DOI：
10.1016/j.jneumeth.2021.109431
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发表时间：
2022-01-15
影响因子：
3
通讯作者：
Yu, Zhaoxia
中科院分区：
文献类型：
--
作者：
Shen, Tong;Lur, Gyorgy;Xu, Xiangmin;Yu, Zhaoxia
关键词：
随着钙成像在神经科学研究中的日益普及，选择正确的方法来分析钙成像数据对于解决各种科学问题至关重要。与使用电极测量的尖峰序列不同，荧光强度迹线提供了对潜在神经元活动的间接和嘈杂测量。观察到的钙痕迹要么直接分析，要么反卷积到尖峰序列来推断神经元活动。当两种方法都适用时，尚不清楚反褶积钙痕迹是否是必要的步骤。在本文中，我们比较了使用钙痕迹或其反卷积尖峰序列进行三种常见分析的性能：聚类、主成分分析（PCA）和种群解码。我们发现：(1)两种方法导致了不同的结果；(2)对估计的峰值序列进行适当的平滑或分类，通常会产生令人满意的性能，例如更准确地估计集群的隶属度；(3)虽然估计峰值序列产生的结果比跟踪数据更接近真实峰值数据，但我们发现来自跟踪数据的主成分分析结果可能更好地反映潜在的神经元集合（簇）；(4)对于这两种方法，可通过使用去噪或平滑方法来提高可解码性。我们对真实数据的模拟和应用表明，估计的峰值数据在聚类分析中优于跟踪数据，并为群体解码提供了可比较的结果。此外，通过适当的滤波/平滑方法，估计的峰值数据的可解解性可以略好于钙痕量数据。我们得出结论，尖峰检测可能是一个有用的预处理步骤，为某些问题，如聚类；然而，钙成像数据的连续性提供了一种自然的平滑性，这可能有助于诸如降维之类的问题。
With the increasing popularity of calcium imaging in neuroscience research, choosing the right methods to analyze calcium imaging data is critical to address various scientific questions. Unlike spike trains measured using electrodes, fluorescence intensity traces provide an indirect and noisy measurement of the underlying neuronal activities. The observed calcium traces are either analyzed directly or deconvolved to spike trains to infer neuronal activities. When both approaches are applicable, it is unclear whether deconvolving calcium traces is a necessary step. In this article, we compare the performance of using calcium traces or their deconvolved spike trains for three common analyses: clustering, principal component analysis (PCA), and population decoding. We found that (1) the two approaches lead to diverging results; (2) estimated spike trains, when smoothed or binned appropriately, usually lead to satisfactory performances, such as more accurate estimation of cluster membership; (3) although estimate spike train produce results more similar to true spike data than trace data, we found that the PCA results from trace data might better reflect the underlying neuronal ensembles (clusters); and (4) for both approaches, decobability can be improved by using denoising or smoothing methods. Our simulations and applications to real data suggest that estimated spike data outperform trace data in cluster analysis and give comparable results for population decoding. In addition, the decobability of estimated spike data can be slightly better than that of calcium trace data with appropriate filtering / smoothing methods. We conclude that spike detection might be a useful pre-processing step for certain problems such as clustering; however, the continuous nature of calcium imaging data provides a natural smoothness that might be helpful for problems such as dimensional reduction.
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DOI：
10.1088/1742-5468/2005/09/p09008
发表时间：
2005-09-01
影响因子：
2.4
作者：
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影响因子：
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影响因子：
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