Quantifying uncertainty in spikes estimated from calcium imaging data

TL;DR

This paper introduces a method to accurately quantify the uncertainty of estimated neuronal spikes from calcium imaging data, addressing the challenge of controlling false positives in spike detection.

Contribution

It proposes a selective inference approach with an efficient algorithm to compute valid p-values and confidence intervals for estimated spikes.

Findings

The method controls selective Type I error in spike detection.

Simulation and real data demonstrate improved uncertainty quantification.

The approach is applicable to existing spike estimation methods.

Abstract

In recent years, a number of methods have been proposed to estimate the times at which a neuron spikes on the basis of calcium imaging data. However, quantifying the uncertainty associated with these estimated spikes remains an open problem. We consider a simple and well-studied model for calcium imaging data, which states that calcium decays exponentially in the absence of a spike, and instantaneously increases when a spike occurs. We wish to test the null hypothesis that the neuron did not spike -- i.e., that there was no increase in calcium -- at a particular timepoint at which a spike was estimated. In this setting, classical hypothesis tests lead to inflated Type I error, because the spike was estimated on the same data used for testing. To overcome this problem, we propose a selective inference approach. We describe an efficient algorithm to compute finite-sample p-values that control selective Type I error, and confidence intervals with correct selective coverage, for spikes estimated using a recent proposal from the literature. We apply our proposal in simulation and on calcium imaging data from the spikefinder challenge.