CaImAn an open source tool for scalable calcium imaging data analysis

TLDR

Advances in fluorescence microscopy enable monitoring larger brain areas in vivo with finer time resolution, generating data rates that demand reproducible, fully automated, and scalable analysis pipelines. The authors present CaImAn, an open‑source library for calcium imaging data analysis. CaImAn provides automatic, scalable methods for motion correction, neural activity identification, and cross‑session registration on two‑photon and one‑photon data, supports real‑time streaming, requires minimal user intervention, scales from laptops to HPC clusters, and was benchmarked using a corpus of manual annotations from nine mouse two‑photon datasets. CaImAn achieves near‑human performance in detecting locations of active neurons.

Abstract

Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. We present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good scalability on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CaImAn we collected and combined a corpus of manual annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.