Bayesian nonparametric analysis for the detection of spikes in noisy calcium imaging data

TL;DR

This paper introduces a Bayesian nonparametric method for detecting neuronal spikes from noisy calcium imaging data, enabling simultaneous spike train estimation and response amplitude clustering across experiments.

Contribution

It proposes a nested Bayesian finite mixture model that leverages hierarchical priors to improve spike detection and amplitude clustering in calcium imaging analysis.

Findings

Effective in clustering neuronal responses

Accurate spike detection in noisy data

Reveals common response amplitudes across conditions

Abstract

Recent advancements in miniaturized fluorescence microscopy have made it possible to investigate neuronal responses to external stimuli in awake behaving animals through the analysis of intra-cellular calcium signals. An on-going challenge is deconvolving the temporal signals to extract the spike trains from the noisy calcium signals' time-series. In this manuscript, we propose a nested Bayesian finite mixture specification that allows the estimation of spiking activity and, simultaneously, reconstructing the distributions of the calcium transient spikes' amplitudes under different experimental conditions. The proposed model leverages two nested layers of random discrete mixture priors to borrow information between experiments and discover similarities in the distributional patterns of neuronal responses to different stimuli. Furthermore, the spikes' intensity values are also clustered within and between experimental conditions to determine the existence of common (recurring) response amplitudes. Simulation studies and the analysis of a data set from the Allen Brain Observatory show the effectiveness of the method in clustering and detecting neuronal activities.