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Bayesian nonparametric analysis for the detection of spikes in noisy calcium imaging data
Author(s) -
D'Angelo Laura,
Canale Antonio,
Yu Zhaoxia,
Guindani Michele
Publication year - 2023
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.13626
Subject(s) - bayesian probability , calcium imaging , computer science , prior probability , spike (software development) , cluster analysis , pattern recognition (psychology) , artificial intelligence , amplitude , nonparametric statistics , bayesian inference , biological neural network , biological system , machine learning , mathematics , calcium , statistics , physics , biology , chemistry , software engineering , organic chemistry , quantum mechanics
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 intracellular calcium signals. An ongoing challenge is deconvolving the temporal signals to extract the spike trains from the noisy calcium signals' time series. In this article, 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 dataset from the Allen Brain Observatory show the effectiveness of the method in clustering and detecting neuronal activities.