TY - JOUR
T1 - Minian, an open-source miniscope analysis pipeline
AU - Dong, Zhe
AU - Mau, William
AU - Feng, Yu
AU - Pennington, Zachary T.
AU - Chen, Lingxuan
AU - Zaki, Yosif
AU - Rajan, Kanaka
AU - Shuman, Tristan
AU - Aharoni, Daniel
AU - Cai, Denise J.
N1 - Funding Information:
We thank Eftychios A Pnevmatikakis, Andrea Giovannucci, and Liam Paninski for establishing the theoretical foundation and providing helpful insights for the pipeline. We thank Pat Gunn for helping with benchmarking with CaImAn pipeline. We thank Taylor Francisco and Denisse Morales-Rodriguez for helping with data analysis and revision. We thank MetaCell (Stephen Larson, Giovanni Idili, Zoran Sinnema, Dan Knudsen, and Paolo Bazzigaluppi) for contributing to the documentation and continuous integration of the pipeline. We thank Stellate Communications for assistance with the preparation of this manuscript. We thank Brandon Wei, Mimi La-Vu, and Christopher Lee for contributing to the dataset used in Minian development and testing. The authors acknowledge support from the following funding sources: WM is supported by NIH F32AG067640. KR is supported by NIH BRAIN Initiative (R01EB028166), James S. McDonnell Foundation’s Understanding Human Cognition Scholar Award (220020466), NSF Award (NSF1926800 and NSF2046583), Simons Foundation (891834) and Alfred P. Sloan Foundation (FG-2019- 12027). TS is supported by CURE Epilepsy Taking Flight Award, American Epilepsy Society Junior investigator Award, R03 NS111493, R21 DA049568, and R01 NS116357. DA is supported by NIH U01NS094286-01, and NSF Award (NSF1700408 Neurotech Hub). DJC is supported by NIH DP2MH122399, R01 MH120162, One Mind Otsuka Rising Star Award, McKnight Memory and Cognitive Disorders Award, Klingenstein-Simons Fellowship Award in Neuroscience, Mount Sinai Distinguished Scholar Award, Brain Research Foundation Award, and NARSAD Young Investigator Award.
Funding Information:
We thank Eftychios A Pnevmatikakis, Andrea Giovannucci, and Liam Paninski for establishing the theoretical foundation and providing helpful insights for the pipeline. We thank Pat Gunn for helping with benchmarking with CaImAn pipeline. We thank Taylor Francisco and Denisse Morales-Rodriguez for helping with data analysis and revision. We thank MetaCell (Stephen Larson, Giovanni Idili, Zoran Sinnema, Dan Knudsen, and Paolo Bazzigaluppi) for contributing to the documentation and continuous integration of the pipeline. We thank Stellate Communications for assistance with the preparation of this manuscript. We thank Brandon Wei, Mimi La-Vu, and Christopher Lee for contributing to the dataset used in Minian development and testing. The authors acknowledge support from the following funding sources: WM is supported by NIH F32AG067640. KR is supported by NIH BRAIN Initiative (R01EB028166), James S. McDonnell Foundation’s Understanding Human Cognition Scholar Award (220020466), NSF Award (NSF1926800 and NSF2046583), Simons Foundation (891834) and Alfred P. Sloan Foundation (FG-2019-12027). TS is supported by CURE Epilepsy Taking Flight Award, American Epilepsy Society Junior investigator Award, R03 NS111493, R21 DA049568, and R01 NS116357. DA is supported by NIH U01NS094286-01, and NSF Award (NSF1700408 Neuro-tech Hub). DJC is supported by NIH DP2MH122399, R01 MH120162, One Mind Otsuka Rising Star Award, McKnight Memory and Cognitive Disorders Award, Klingenstein-Simons Fellowship Award in Neuroscience, Mount Sinai Distinguished Scholar Award, Brain Research Foundation Award, and NARSAD Young Investigator Award.
Publisher Copyright:
© Dong et al.
PY - 2022/6
Y1 - 2022/6
N2 - Miniature microscopes have gained considerable traction for in vivo calcium imaging in freely behaving animals. However, extracting calcium signals from raw videos is a computationally complex problem and remains a bottleneck for many researchers utilizing single-photon in vivo calcium imaging. Despite the existence of many powerful analysis packages designed to detect and extract calcium dynamics, most have either key parameters that are hard-coded or insufficient step-by-step guidance and validations to help the users choose the best parameters. This makes it difficult to know whether the output is reliable and meets the assumptions necessary for proper analysis. Moreover, large memory demand is often a constraint for setting up these pipelines since it limits the choice of hardware to specialized computers. Given these difficulties, there is a need for a low memory demand, user-friendly tool offering interactive visualizations of how altering parameters at each step of the analysis affects data output. Our open-source analysis pipeline, Minian (miniscope analysis), facilitates the transparency and accessibility of single-photon calcium imaging analysis, permitting users with little computational experience to extract the location of cells and their corresponding calcium traces and deconvolved neural activities. Minian contains interactive visualization tools for every step of the analysis, as well as detailed documentation and tips on parameter exploration. Furthermore, Minian has relatively small memory demands and can be run on a laptop, making it available to labs that do not have access to specialized computational hardware. Minian has been validated to reliably and robustly extract calcium events across different brain regions and from different cell types. In practice, Minian provides an open-source calcium imaging analysis pipeline with user-friendly interactive visualizations to explore parameters and validate results.
AB - Miniature microscopes have gained considerable traction for in vivo calcium imaging in freely behaving animals. However, extracting calcium signals from raw videos is a computationally complex problem and remains a bottleneck for many researchers utilizing single-photon in vivo calcium imaging. Despite the existence of many powerful analysis packages designed to detect and extract calcium dynamics, most have either key parameters that are hard-coded or insufficient step-by-step guidance and validations to help the users choose the best parameters. This makes it difficult to know whether the output is reliable and meets the assumptions necessary for proper analysis. Moreover, large memory demand is often a constraint for setting up these pipelines since it limits the choice of hardware to specialized computers. Given these difficulties, there is a need for a low memory demand, user-friendly tool offering interactive visualizations of how altering parameters at each step of the analysis affects data output. Our open-source analysis pipeline, Minian (miniscope analysis), facilitates the transparency and accessibility of single-photon calcium imaging analysis, permitting users with little computational experience to extract the location of cells and their corresponding calcium traces and deconvolved neural activities. Minian contains interactive visualization tools for every step of the analysis, as well as detailed documentation and tips on parameter exploration. Furthermore, Minian has relatively small memory demands and can be run on a laptop, making it available to labs that do not have access to specialized computational hardware. Minian has been validated to reliably and robustly extract calcium events across different brain regions and from different cell types. In practice, Minian provides an open-source calcium imaging analysis pipeline with user-friendly interactive visualizations to explore parameters and validate results.
UR - http://www.scopus.com/inward/record.url?scp=85132453968&partnerID=8YFLogxK
U2 - 10.7554/eLife.70661
DO - 10.7554/eLife.70661
M3 - Article
C2 - 35642786
AN - SCOPUS:85132453968
VL - 11
JO - eLife
JF - eLife
SN - 2050-084X
M1 - e70661
ER -