@article{33d09ba59a8f4a1eba95179dbdb2deda,
title = "Caiman an open source tool for scalable calcium imaging data analysis",
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.",
author = "Andrea Giovannucci and Johannes Friedrich and Pat Gunn and J{\'e}r{\'e}mie Kalfon and Brown, {Brandon L.} and Koay, {Sue Ann} and Jiannis Taxidis and Farzaneh Najafi and Gauthier, {Jeffrey L.} and Pengcheng Zhou and Khakh, {Baljit S.} and Tank, {David W.} and Chklovskii, {Dmitri B.} and Pnevmatikakis, {Eftychios A.}",
note = "Funding Information: We thank B Cohen, L Myers, N Roumelioti, and S Villani for providing us with manual annotations. We thank V Staneva and B Deverett for contributing to the early stages of CAIMAN, M Schachter for his insight and contributions, and L Paninski for numerous useful discussions. We thank N Carriero, I Fisk, and D Simon from the Flatiron Institute (Simons Foundation) for useful discussions and suggestions to optimize High Performance Computing code. We thank T Kawashima and M Ahrens for sharing the whole brain zebrafish dataset. Last but not least, we thank the active community of users for their great help in terms of code/method contributions, bug reporting, code testing and suggestions that have led to the growth of into a widely used open source package. A partial list of contributors (in the form of GitHub usernames) can be found in https://github.com/flatironinstitute/ CaImAn/graphs/contributors (Python) and https://github.com/flatironinstitute/CaImAn-MATLAB/ graphs/contributors (MATLAB). The authors acknowledge support from following funding sources: AG, EAP, JF, PG (Simons Foundation, internal funding). JG, SAK, DWT (NIH NRSA F32NS077840-01,5U01NS090541, 1U19NS104648 and Simons Foundation SCGB), PZ (NIH NIBIB R01EB022913, NSF NeuroNex DBI-1707398, Gatsby Foundation), JT (NIH R01-MH101198), FN (MURI, Simons Collaboration on the Global Brain and Pew Foundation). Funding Information: ?JK contributed to this work during an internship at the Flatiron InstituteWe thank B Cohen, L Myers, N Roumelioti, and S Villani for providing us with manual annotations. We thank V Staneva and B Deverett for contributing to the early stages of CAIMAN, M Schachter for his insight and contributions, and L Paninski for numerous useful discussions. We thank N Carriero, I Fisk, and D Simon from the Flatiron Institute (Simons Foundation) for useful discussions and suggestions to optimize High Performance Computing code. We thank T Kawashima and M Ahrens for sharing the whole brain zebrafish dataset. Last but not least, we thank the active community of users for their great help in terms of code/method contributions, bug reporting, code testing and suggestions that have led to the growth of into a widely used open source package. A partial list of contributors (in the form of GitHub usernames) can be found in https://github.com/flatironinstitute/ CaImAn/graphs/contributors (Python) and https://github.com/flatironinstitute/CaImAn-MATLAB/ graphs/contributors (MATLAB). The authors acknowledge support from following funding sources: AG, EAP, JF, PG (Simons Foundation, internal funding). JG, SAK, DWT (NIH NRSA F32NS077840-01,5U01NS090541, 1U19NS104648 and Simons Foundation SCGB), PZ (NIH NIBIB R01EB022913, NSF NeuroNex DBI-1707398, Gatsby Foundation), JT (NIH R01-MH101198), FN (MURI, Simons Collaboration on the Global Brain and Pew Foundation). Publisher Copyright: {\textcopyright} Giovannucci et al.",
year = "2019",
doi = "10.7554/eLife.38173",
language = "English (US)",
volume = "8",
journal = "eLife",
issn = "2050-084X",
publisher = "eLife Sciences Publications",
}