Carbonero Daniel, Noueihed Jad, Kramer Mark A, White John A
Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America.
Center for Systems Neuroscience, Boston University, Boston, Massachusetts, United States of America.
bioRxiv. 2024 Apr 30:2023.10.11.561797. doi: 10.1101/2023.10.11.561797.
Calcium imaging allows recording from hundreds of neurons with the ability to resolve single cell activity. Evaluating and analyzing neuronal responses, while also considering all dimensions of the data set to make specific conclusions, is extremely difficult. Often, descriptive statistics are used to analyze these forms of data. These analyses, however, remove variance by averaging the responses of single neurons across recording sessions, or across combinations of neurons, to create single quantitative metrics, losing the temporal dynamics of neuronal activity, and their responses relative to each other. Dimensionally Reduction (DR) methods serve as a good foundation for these analyses because they reduce the dimensions of the data into components, while still maintaining the variance. Non-negative Matrix Factorization (NMF) is an especially promising DR analysis method for analyzing activity recorded in calcium imaging because of its mathematical constraints, which include positivity and linearity. We adapt NMF for our analyses and compare its performance to alternative dimensionality reduction methods on both artificial and data. We find that NMF is well-suited for analyzing calcium imaging recordings, accurately capturing the underlying dynamics of the data, and outperforming alternative methods in common use.
钙成像技术能够记录数百个神经元的活动，并分辨单个细胞的活动。评估和分析神经元反应，同时还要考虑数据集的所有维度以得出具体结论，这极其困难。通常，描述性统计用于分析这些数据形式。然而，这些分析通过对单个神经元在不同记录时段或不同神经元组合的反应进行平均来消除方差，以创建单一的定量指标，从而失去了神经元活动的时间动态以及它们彼此之间的反应。降维（DR）方法为这些分析提供了良好的基础，因为它们将数据维度减少为组件，同时仍保留方差。非负矩阵分解（NMF）是一种特别有前景的DR分析方法，用于分析钙成像记录中的活动，因为其数学约束包括非负性和线性。我们将NMF应用于我们的分析，并在人工数据和实际数据上，将其性能与其他降维方法进行比较。我们发现NMF非常适合分析钙成像记录，能够准确捕捉数据的潜在动态，并且在性能上优于常用的其他方法。
