With the increasing popularity of calcium imaging data in neuroscience research, methods for analyzing calcium trace data are critical to address various questions. 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. Our simulations and applications to real data suggest that the estimated spike data outperform calcium trace data for both clustering and PCA. Although calcium trace data show higher predictability than spike data at each time point, spike history or cumulative spike counts is comparable to or better than calcium traces in population decoding.

翻译：随着钙成像数据在神经科学研究中越来越受欢迎,分析钙痕量数据的方法对于解决各种问题至关重要。观察到的钙痕量要么直接分析,要么分解成加注列列以推导神经活动。当这两种方法都适用时,尚不清楚分离钙痕量是否是一个必要步骤。在本篇文章中,我们比较了使用钙痕量或其分解峰量列的性能,以进行三项共同分析:集成、主要成分分析(PCA)和人口解码。我们对真实数据的模拟和应用表明,估计的加注数据超过聚合物和五氯苯甲醚的钙痕量数据。虽然钙痕量数据显示在每一时间点比加注数据具有更高的可预测性,但加注历史或累积的峰值与人口解码中的钙痕量相当或更好。