CaNetiCs - An Open-Source Toolbox for Standardized Dimensionality Reduction of Neuronal Calcium Activity

Area of Science:

• Neuroscience
• Computational Neuroscience
• Data Science

Background:

• Calcium imaging generates large, high-dimensional datasets of neuronal activity.
• Analyzing complex neuronal population dynamics requires effective dimensionality reduction (DR).
• Nonnegative Matrix Factorization (NMF) offers biologically meaningful decomposition for neuronal signals.

Purpose of the Study:

• Introduce CaNetiCs, an open-source toolbox for standardized analysis of neuronal network dynamics.
• Integrate NMF with other DR methods (PCA, ICA, UMAP) and analysis modules.
• Facilitate deeper exploration of state-dependent neuronal activity.

Main Methods:

• Developed CaNetiCs toolbox integrating NMF, PCA, ICA, UMAP, and simulation modules.
• Applied CaNetiCs to calcium imaging data from C. elegans and murine somatosensory cortex.
• Analyzed neuronal responses to graded anesthesia to assess network dynamics.

Main Results:

• CaNetiCs successfully recapitulated known anesthesia-induced network suppression and decorrelation.
• Uncovered novel insights into neuronal activity patterns in different experimental contexts.
• Demonstrated the toolbox's utility in analyzing diverse neuronal datasets.

Conclusions:

• CaNetiCs provides an accessible, modular, and interpretable framework for neuronal data analysis.
• Promotes broader adoption of standardized DR methodologies in neuroscience.
• Enables deeper investigation of neuronal network dynamics across experimental paradigms.