Quantifying uncertainty in spikes estimated from calcium imaging data

• Published in: Biostatistics (Oxford, England) Vol. 24; no. 2; pp. 481 - 501
• Main Authors: Chen, Yiqun T, Jewell, Sean W, Witten, Daniela M
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 14.04.2023
• Subjects: Action Potentials - physiology; Algorithms; Calcium; Computer Simulation; Diagnostic Imaging; Humans; Uncertainty; Hypothesis testing; Calcium imaging; Changepoint detection; Neuroscience; Selective inference
• ISSN: 1465-4644; 1468-4357
• DOI: 10.1093/biostatistics/kxab034

Summary 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 $\texttt{spikefinder}$ challenge.