Whole-Volume Clustering of Time Series Data from Zebrafish Brain Calcium Images via Mixture Modeling

TL;DR

This paper introduces a Gaussian mixture model-based method for clustering large volumes of spatially correlated calcium imaging time series data from zebrafish brains, providing a theoretically justified and computationally efficient approach.

Contribution

It proposes a novel model-based clustering methodology specifically designed for large-scale calcium imaging data, with theoretical validation and efficient estimation techniques.

Findings

Successfully applied to zebrafish brain data

Demonstrates computational efficiency and theoretical soundness

Effective clustering of neural activity patterns

Abstract

Calcium is a ubiquitous messenger in neural signaling events. An increasing number of techniques are enabling visualization of neurological activity in animal models via luminescent proteins that bind to calcium ions. These techniques generate large volumes of spatially correlated time series. A model-based functional data analysis methodology via Gaussian mixtures is suggested for the clustering of data from such visualizations is proposed. The methodology is theoretically justified and a computationally efficient approach to estimation is suggested. An example analysis of a zebrafish imaging experiment is presented.