CaImAn an open source tool for scalable calcium imaging data analysis

eLife

Dmitri B Chklovskii¹  Jérémie Kalfon¹  Pat Gunn²  Andrea Giovannucci³  Johannes Friedrich³  Brandon L Brown³  Pengcheng Zhou⁴  Jeffrey L Gauthier⁵  Eftychios A Pnevmatikakis⁶  Jiannis Taxidis⁶  David W Tank⁷  Sue Ann Koay⁸  Baljit S Khakh⁹  Farzaneh Najafi⁹ 

[1] Center for Theoretical Neuroscience, Columbia University, New York, United States;Department of Statistics, Columbia University, New York, United States;Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, United States;Cold Spring Harbor Laboratory, New York, United States;Department of Neurology, University of California, Los Angeles, Los Angeles, United States;Department of Physiology, University of California, Los Angeles, Los Angeles, United States;Department of Statistics, Columbia University, New York, United States;ECE Paris, Paris, France;Princeton Neuroscience Institute, Princeton University, Princeton, United States;

关键词: calcium imaging;    open source;    software;    two-photon;    one-photon;    data analysis;   

DOI  :  10.7554/eLife.38173

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown