Bayesian Nonparametric Analysis for the Detection of Spikes in Noisy Calcium Imaging Data

• Activity Number: 204 - Bayesian Methods for the Analysis of Complex Brain Imaging Data
• Type: Topic-Contributed
• Date/Time: Tuesday, August 10, 2021 : 1:30 PM to 3:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Laura D'Angelo* and Michele Guindani and Antonio Canale and Zhaoxia Yu
• Companies: University of Padova and University of California Irvine and University of Padova and University of California, Irvine
• Keywords: Dirichlet process; Mixtures of finite mixtures; Model-based clustering; Nested Dirichlet process; Spike and slab

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 deconvoluting the temporal signals to extract the spike trains from the noisy calcium signals' time-series. We propose a nested Bayesian finite mixture specification that allows for 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.