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Whole‐volume clustering of time series data from zebrafish brain calcium images via mixture modeling

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 for clustering of data from such visualizations is proposed in this paper. The methodology is theoretically justified, and a computationally efficient approach to estimation is suggested. An example analysis of a zebrafish imaging experiment is presented.