A zero-inflated gamma model for deconvolved calcium imaging traces

TL;DR

This paper introduces a zero-inflated gamma model for better statistical representation of deconvolved calcium imaging signals, improving neural data analysis by capturing zero responses and activity variability more accurately.

Contribution

The paper proposes a novel zero-inflated gamma model for deconvolved calcium signals, enhancing the statistical modeling of neural activity data.

Findings

ZIG model outperforms Poisson and Bernoulli models in neural encoding tasks.

The model effectively captures zero responses in calcium imaging data.

Application to real data demonstrates improved decoding accuracy.

Abstract

Calcium imaging is a critical tool for measuring the activity of large neural populations. Much effort has been devoted to developing "pre-processing" tools for calcium video data, addressing the important issues of e.g., motion correction, denoising, compression, demixing, and deconvolution. However, statistical modeling of deconvolved calcium signals (i.e., the estimated activity extracted by a pre-processing pipeline) is just as critical for interpreting calcium measurements, and for incorporating these observations into downstream probabilistic encoding and decoding models. Surprisingly, these issues have to date received significantly less attention. In this work we examine the statistical properties of the deconvolved activity estimates, and compare probabilistic models for these random signals. In particular, we propose a zero-inflated gamma (ZIG) model, which characterizes the calcium responses as a mixture of a gamma distribution and a point mass that serves to model zero responses. We apply the resulting models to neural encoding and decoding problems. We find that the ZIG model outperforms simpler models (e.g., Poisson or Bernoulli models) in the context of both simulated and real neural data, and can therefore play a useful role in bridging calcium imaging analysis methods with tools for analyzing activity in large neural populations.