CaImAn an open source tool for scalable calcium imaging data analysis

Giovannucci, A., Friedrich, J., Gunn, P., Kalfon, J., Brown, B. L., Koay, S. A., Taxidis, J., Najafi, F., Gauthier, J. L., Zhou, P., Khakh, B. S., Tank, D. W., Chklovskii, D. B., Pnevmatikakis, E. A. (January 2019) CaImAn an open source tool for scalable calcium imaging data analysis. Elife, 8. ISSN 2050-084x

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URL: https://www.ncbi.nlm.nih.gov/pubmed/30652683
DOI: 10.7554/eLife.38173

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.

Item Type: Paper
Subjects: bioinformatics > genomics and proteomics > computers > computer software
Investigative techniques and equipment > microscopy > flourescence microscopy > two-photon excitation microscopy
organism description > animal > fish > zebrafish
CSHL Authors:
Communities: CSHL labs > Churchland lab
Depositing User: Matthew Dunn
Date: 17 January 2019
Date Deposited: 28 Jan 2019 21:24
Last Modified: 28 Jan 2019 21:24
PMCID: PMC6342523
Related URLs:
URI: http://repository.cshl.edu/id/eprint/37670

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