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Protocol for detecting neuronal subnetworks from in vivo calcium imaging data using multiple clustering algorithms in
Jui-Yen Huang1, Gautam Chauhan2, Pei-Ying Chen3
1The Gill Institute for Neuroscience, Indiana University, Bloomington, IN 47405, USA; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Program in Neuroscience, Indiana University, Bloomington, IN 47405, USA.
STAR Protocols
|August 15, 2025
Summary
This study presents a workflow for detecting neuronal subnetworks from brain imaging data. The method uses clustering in MATLAB and statistical analysis in R for robust results.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Data Science
Background:
- Neuronal subnetworks are key to brain computation, identified through co-activation patterns.
- Detecting these subnetworks from in vivo calcium imaging data is crucial for understanding brain function.
Purpose of the Study:
- To provide a detailed, step-by-step workflow for identifying neuronal subnetworks.
- To enable robust statistical evaluation and reporting of subnetwork analysis.
Main Methods:
- Utilizing multiple clustering algorithms in MATLAB for subnetwork detection from calcium imaging data.
- Applying statistical modeling, model selection, and bootstrapping in R for group-level analysis.
- Generating structured statistical reports using R Markdown templates.
Main Results:
- A reproducible workflow for detecting and analyzing neuronal subnetworks.
- Robust statistical methods for evaluating group differences and ensuring reliable estimations.
- Standardized reporting of findings through R Markdown.
Conclusions:
- The presented workflow facilitates the identification and statistical analysis of neuronal subnetworks.
- This protocol enhances the rigor and reproducibility of neuroimaging data analysis.
- The integrated approach aids in understanding brain computation through subnetwork dynamics.
