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Background <p>Analyzing calcium imaging data to understand complex functional networks can be challenging, often requiring multiple tools, custom scripts, and some coding expertise. To address these challenges, we present CalciumNetExploreR (CNER), an R package designed to streamline and standardize the analysis of time-series data from neuronal populations.</p> Results <p>CNER integrates essential steps-normalization, binarization, population activity visualization, network construction, degree distribution analysis, principal component analysis, power spectral density evaluation, and event frequency calculations-into a single, cohesive pipeline. This comprehensive approach enables users to efficiently extract and compare network metrics, including clustering coefficients, global efficiency, community structures, and principal component variances. By offering a flexible and customizable framework, CNER simplifies the examination of functional connectivity and network topology, effectively providing the means to characterize a cellular functional network or analogous structures in other modalities.</p> Conclusion <p>Designed as a user-friendly package, CNER allows both experimental and computational neuroscientists to incorporate robust statistical and graphical analyses into their workflows without extensive coding knowledge. By unifying key analytical components into one pipeline, CNER reduces barriers associated with large-scale data analyses, ultimately facilitating deeper insights into the functional organization and dynamic properties of neuronal networks across diverse recording techniques.</p>

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Calciumnetexplorer: an R package for network analysis of calcium imaging data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月26日

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  • Simone Lenci,
  • Dirk Sieger

摘要

Background

Analyzing calcium imaging data to understand complex functional networks can be challenging, often requiring multiple tools, custom scripts, and some coding expertise. To address these challenges, we present CalciumNetExploreR (CNER), an R package designed to streamline and standardize the analysis of time-series data from neuronal populations.

Results

CNER integrates essential steps-normalization, binarization, population activity visualization, network construction, degree distribution analysis, principal component analysis, power spectral density evaluation, and event frequency calculations-into a single, cohesive pipeline. This comprehensive approach enables users to efficiently extract and compare network metrics, including clustering coefficients, global efficiency, community structures, and principal component variances. By offering a flexible and customizable framework, CNER simplifies the examination of functional connectivity and network topology, effectively providing the means to characterize a cellular functional network or analogous structures in other modalities.

Conclusion

Designed as a user-friendly package, CNER allows both experimental and computational neuroscientists to incorporate robust statistical and graphical analyses into their workflows without extensive coding knowledge. By unifying key analytical components into one pipeline, CNER reduces barriers associated with large-scale data analyses, ultimately facilitating deeper insights into the functional organization and dynamic properties of neuronal networks across diverse recording techniques.