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Related Experiment Video

Updated: Oct 3, 2025

Imaging Local Ca2+ Signals in Cultured Mammalian Cells

Imaging Local Ca2+ Signals in Cultured Mammalian Cells

Published on: March 3, 2015

Detecting cell assemblies by NMF-based clustering from calcium imaging data.

Mizuo Nagayama¹, Toshimitsu Aritake², Hideitsu Hino²

¹Waseda University, 1-104 Totsuka-cho, Shinjuku, Tokyo, 169-8050, Japan.

Neural Networks : the Official Journal of the International Neural Network Society

|February 20, 2022

Summary

This summary is machine-generated.

Researchers developed a new clustering method using non-negative matrix factorization (NMF) to identify neural cell assemblies from calcium imaging data. This approach improves upon traditional methods and reveals how assembly size relates to brain activity during sleep and wakefulness.

Keywords:

Calcium imaging Clustering NMF

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Last Updated: Oct 3, 2025

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Area of Science:

Neuroscience
Computational Biology
Data Science

Background:

Neurons form cell assemblies crucial for brain information processing.
Advancements in calcium imaging enable studying these neural networks.
Extracting meaningful patterns from complex neural data remains a challenge.

Purpose of the Study:

To develop and validate a novel clustering approach for extracting cell assemblies from calcium imaging data.
To address the model selection problem in non-negative matrix factorization (NMF).
To investigate the relationship between cell assembly size and cortical slow-wave activity.

Main Methods:

Utilized non-negative matrix factorization (NMF) to derive a neuron similarity matrix.
Applied spectral clustering on the NMF-derived similarity matrix.
Introduced a model averaging technique with a novel estimator to resolve NMF model selection issues.
Validated the approach using simulated data and real calcium imaging data from mice.

Main Results:

The proposed NMF-based clustering method demonstrated superior performance compared to correlation-based methods across various sampling rates in simulated data.
Analysis of mouse calcium imaging data revealed a correlation between cell assembly size and the degree/spatial extent of cortical slow-wave generation.
The model averaging approach effectively addressed the NMF model selection challenge.

Conclusions:

The NMF-based spectral clustering offers a robust method for cell assembly extraction from calcium imaging.
Cell assembly dynamics are influenced by cortical slow-wave activity during different behavioral states (sleep/wake).
The developed method provides a valuable tool for systems neuroscience research.