Carbonero Daniel, Noueihed Jad, Gabel Christopher V, Kramer Mark A, White John A
bioRxiv. 2025 Aug 1:2025.08.01.668030. doi: 10.1101/2025.08.01.668030.
The widespread use of calcium imaging has produced large-scale datasets capturing neuronal population activity across diverse experimental contexts, posing challenges for analyzing complex, high-dimensional data. Dimensionality reduction (DR) methods have been pivotal in addressing these challenges by simplifying data into interpretable, low-dimensional structures, while capturing essential network dynamics. Among DR methods, Nonnegative Matrix Factorization (NMF) can produce biologically meaningful representations through its nonnegativity constraint and parts-based decomposition, making it especially suited for analyzing neuronal calcium signals. To enhance accessibility and standardization in the analysis of state-dependent neuronal dynamics, we introduce Calcium Network dynamiCs (CaNetiCs), an open-source toolbox centered on NMF, integrating standardized DR methods (PCA, ICA, UMAP), geometric low-dimensional component space analyses, and neuronal network simulation modules. We validate our toolbox by applying it to two diverse experimental datasets that describe responses to graded anesthesia: whole-ganglion cellular calcium imaging of C. elegans, and two-photon imaging of murine somatosensory cortex. Our analyses recapitulate previously observed trends, such as network suppression and decorrelation with anesthesia, while uncovering novel insights into neuronal activity under differing contexts. CaNetiCs provides an accessible, modular, and interpretable framework, facilitating broader adoption of standardized dimensionality reduction methodologies for deeper exploration of neuronal network dynamics across experimental paradigms. The open-source code, along with documentation, is available at https://github.com/dannycarbonero/CaNetiCs.
钙成像的广泛应用产生了大规模数据集，这些数据集捕捉了不同实验背景下的神经元群体活动，为分析复杂的高维数据带来了挑战。降维（DR）方法通过将数据简化为可解释的低维结构，同时捕捉基本的网络动态，在应对这些挑战方面发挥了关键作用。在DR方法中，非负矩阵分解（NMF）通过其非负性约束和基于部分的分解，可以产生具有生物学意义的表示，使其特别适合分析神经元钙信号。为了提高对状态依赖神经元动态分析的可及性和标准化，我们引入了钙网络动力学（CaNetiCs），这是一个以NMF为中心的开源工具箱，集成了标准化的DR方法（主成分分析、独立成分分析、均匀流形近似与投影）、几何低维成分空间分析和神经元网络模拟模块。我们将该工具箱应用于两个描述对分级麻醉反应的不同实验数据集来验证它：秀丽隐杆线虫的全神经节细胞钙成像和小鼠体感皮层的双光子成像。我们的分析重现了先前观察到的趋势，如网络抑制和与麻醉的去相关，同时揭示了不同背景下神经元活动的新见解。CaNetiCs提供了一个可及、模块化且可解释的框架，促进了标准化降维方法的更广泛采用，以便在不同实验范式中更深入地探索神经元网络动态。开源代码以及文档可在https://github.com/dannycarbonero/CaNetiCs获取。
