Dataset Open Access

CaImAn: An open source tool for scalable Calcium Imaging data Analysis

Andrea Giovannucci; Johannes Friedrich; Pat Gunn; Brandon L. Brown; Sue Ann Koay; Jiannis Taxidis; Farzaneh Najafi; Jeffrey L. Gauthier; Pengcheng Zhou; Baljit S. Khakh; David W. Tank; Dmitri Chklovskii; Eftychios A. Pnevmatikakis

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 preprocessing, 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, that are contained in this open access repository. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.

In order to reproduce the results of the paper or download the annotations and the raw movies, please refer to the readme.md at:

https://github.com/flatironinstitute/CaImAn/blob/master/use_cases/eLife_scripts/README.md

 

V1.3 of CaImAn should be used to reproduce the results of the paper
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