Calcium is a ubiquitous messenger in neural signaling events. An increasing number of techniques are enabling visualization of neurological activity in animal models via luminescent proteins that bind to calcium ions. These techniques generate large volumes of spatially correlated time series. A model‐based functional data analysis methodology via Gaussian mixtures for clustering of data from such visualizations is proposed in this paper. The methodology is theoretically justified, and a computationally efficient approach to estimation is suggested. An example analysis of a zebrafish imaging experiment is presented.

中文翻译：

---

通过混合建模从斑马鱼脑钙图像中对时间序列数据进行全量聚类。

钙是神经信号事件中普遍存在的信使。越来越多的技术通过结合钙离子的发光蛋白使动物模型中的神经活动可视化。这些技术生成了大量的空间相关时间序列。本文提出了一种基于模型的功能数据分析方法，该方法通过高斯混合来对来自这些可视化的数据进行聚类。该方法在理论上是合理的，并提出了一种计算上有效的估计方法。提出了斑马鱼成像实验的示例分析。