Accurate detection of low signal-to-noise ratio neuronal calcium transient waves using a matched filter

•We develop a matched filter for multi-unit calcium event detection in neurons.•We tested the detector on simulated and experimentally recorded calcium imaging data.•The detector had near perfect performance on simulated data with SNR as low as 0.2.•The detector also performed well on experimentally recorded data.•The detector is written in MATLAB and freely available. Calcium imaging has become a fundamental modality for studying neuronal circuit dynamics both in vitro and in vivo. However, identifying calcium events (CEs) from spectral data remains laborious and difficult, especially since the signal-to-noise ratio (SNR) often falls below 2. Existing automated signal detection methods are generally applied at high SNRs, leaving a large need for an automated algorithm that can accurately extract CEs from fluorescence intensity data of SNR 2 and below. In this work we develop a Matched filter for Multi-unit Calcium Event (MMiCE) detection to extract CEs from fluorescence intensity traces of simulated and experimentally recorded neuronal calcium imaging data. MMiCE reached perfect performance on simulated data with SNR ≥ 2 and a true positive (TP) rate of 98.27% (± 1.38% with a 95% confidence interval), and a false positive(FP) rate of 6.59% (± 2.56%) on simulated data with SNR 0.2. On real data, verified by patch-clamp recording, MMiCE performed with a TP rate of 100.00% (± 0.00) and a FP rate of 2.04% (± 4.10). This high level of performance exceeds existing methods at SNRs as low as 0.2, which are well below those used in previous studies (SNR ≃ 5–10). Overall, the MMiCE detector performed exceptionally well on both simulated data, and experimentally recorded neuronal calcium imaging data. The MMiCE detector is accurate, reliable, well suited for wide-spread use, and freely available at sites.uci.edu/aggies or from the corresponding author.