PT  - JOURNAL ARTICLE
AU  - Andrea Giovannucci
AU  - Johannes Friedrich
AU  - Pat Gunn
AU  - Jérémie Kalfon
AU  - Sue Ann Koay
AU  - Jiannis Taxidis
AU  - Farzaneh Najafi
AU  - Jeffrey L. Gauthier
AU  - Pengcheng Zhou
AU  - David W. Tank
AU  - Dmitri Chklovskii
AU  - Eftychios A. Pnevmatikakis
TI  - CalmAn: An open source tool for scalable Calcium Imaging data Analysis
AID  - 10.1101/339564
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 339564
4099  - http://biorxiv.org/content/early/2018/06/05/339564.short
4100  - http://biorxiv.org/content/early/2018/06/05/339564.full
AB  - 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. Here 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 performance 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 a corpus of ground truth annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.