Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data
In vivo calcium imaging through microendoscopic lenses enables imaging of previously inaccessible neuronal populations deep within the brains of freely moving animals. However, it is computationally challenging to extract single-neuronal activity from microendoscopic data, because of the very large...
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Format: | Article |
Language: | English |
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eLife Sciences Publications Ltd
2018-02-01
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Series: | eLife |
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Online Access: | https://elifesciences.org/articles/28728 |
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doaj-def92cf5e4324b0fb67089311ced7d1e |
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record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Pengcheng Zhou Shanna L Resendez Jose Rodriguez-Romaguera Jessica C Jimenez Shay Q Neufeld Andrea Giovannucci Johannes Friedrich Eftychios A Pnevmatikakis Garret D Stuber Rene Hen Mazen A Kheirbek Bernardo L Sabatini Robert E Kass Liam Paninski |
spellingShingle |
Pengcheng Zhou Shanna L Resendez Jose Rodriguez-Romaguera Jessica C Jimenez Shay Q Neufeld Andrea Giovannucci Johannes Friedrich Eftychios A Pnevmatikakis Garret D Stuber Rene Hen Mazen A Kheirbek Bernardo L Sabatini Robert E Kass Liam Paninski Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data eLife calcium imaging microendoscope source extraction |
author_facet |
Pengcheng Zhou Shanna L Resendez Jose Rodriguez-Romaguera Jessica C Jimenez Shay Q Neufeld Andrea Giovannucci Johannes Friedrich Eftychios A Pnevmatikakis Garret D Stuber Rene Hen Mazen A Kheirbek Bernardo L Sabatini Robert E Kass Liam Paninski |
author_sort |
Pengcheng Zhou |
title |
Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data |
title_short |
Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data |
title_full |
Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data |
title_fullStr |
Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data |
title_full_unstemmed |
Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data |
title_sort |
efficient and accurate extraction of in vivo calcium signals from microendoscopic video data |
publisher |
eLife Sciences Publications Ltd |
series |
eLife |
issn |
2050-084X |
publishDate |
2018-02-01 |
description |
In vivo calcium imaging through microendoscopic lenses enables imaging of previously inaccessible neuronal populations deep within the brains of freely moving animals. However, it is computationally challenging to extract single-neuronal activity from microendoscopic data, because of the very large background fluctuations and high spatial overlaps intrinsic to this recording modality. Here, we describe a new constrained matrix factorization approach to accurately separate the background and then demix and denoise the neuronal signals of interest. We compared the proposed method against previous independent components analysis and constrained nonnegative matrix factorization approaches. On both simulated and experimental data recorded from mice, our method substantially improved the quality of extracted cellular signals and detected more well-isolated neural signals, especially in noisy data regimes. These advances can in turn significantly enhance the statistical power of downstream analyses, and ultimately improve scientific conclusions derived from microendoscopic data. |
topic |
calcium imaging microendoscope source extraction |
url |
https://elifesciences.org/articles/28728 |
work_keys_str_mv |
AT pengchengzhou efficientandaccurateextractionofinvivocalciumsignalsfrommicroendoscopicvideodata AT shannalresendez efficientandaccurateextractionofinvivocalciumsignalsfrommicroendoscopicvideodata AT joserodriguezromaguera efficientandaccurateextractionofinvivocalciumsignalsfrommicroendoscopicvideodata AT jessicacjimenez efficientandaccurateextractionofinvivocalciumsignalsfrommicroendoscopicvideodata AT shayqneufeld efficientandaccurateextractionofinvivocalciumsignalsfrommicroendoscopicvideodata AT andreagiovannucci efficientandaccurateextractionofinvivocalciumsignalsfrommicroendoscopicvideodata AT johannesfriedrich efficientandaccurateextractionofinvivocalciumsignalsfrommicroendoscopicvideodata AT eftychiosapnevmatikakis efficientandaccurateextractionofinvivocalciumsignalsfrommicroendoscopicvideodata AT garretdstuber efficientandaccurateextractionofinvivocalciumsignalsfrommicroendoscopicvideodata AT renehen efficientandaccurateextractionofinvivocalciumsignalsfrommicroendoscopicvideodata AT mazenakheirbek efficientandaccurateextractionofinvivocalciumsignalsfrommicroendoscopicvideodata AT bernardolsabatini efficientandaccurateextractionofinvivocalciumsignalsfrommicroendoscopicvideodata AT robertekass efficientandaccurateextractionofinvivocalciumsignalsfrommicroendoscopicvideodata AT liampaninski efficientandaccurateextractionofinvivocalciumsignalsfrommicroendoscopicvideodata |
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1721459960805588992 |
spelling |
doaj-def92cf5e4324b0fb67089311ced7d1e2021-05-05T15:37:01ZengeLife Sciences Publications LtdeLife2050-084X2018-02-01710.7554/eLife.28728Efficient and accurate extraction of in vivo calcium signals from microendoscopic video dataPengcheng Zhou0https://orcid.org/0000-0003-1237-3931Shanna L Resendez1Jose Rodriguez-Romaguera2Jessica C Jimenez3Shay Q Neufeld4Andrea Giovannucci5https://orcid.org/0000-0002-7850-444XJohannes Friedrich6https://orcid.org/0000-0002-1321-5866Eftychios A Pnevmatikakis7https://orcid.org/0000-0003-1509-6394Garret D Stuber8https://orcid.org/0000-0003-1730-4855Rene Hen9Mazen A Kheirbek10Bernardo L Sabatini11Robert E Kass12Liam Paninski13Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, United States; Department of Statistics, Columbia University, New York, United States; Machine Learning Department, Carnegie Mellon University, Pittsburgh, United States; Grossman Center for the Statistics of Mind, Columbia University, New York, United States; Center for Theoretical Neuroscience, Columbia University, New York, United StatesDepartment of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, United StatesDepartment of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, United StatesDepartment of Neuroscience, Columbia University, New York, United States; Division of Integrative Neuroscience, Department of Psychiatry, New York State Psychiatric Institute, New York, United States; Department of Psychiatry & Pharmacology, Columbia University, New York, United StatesDepartment of Neurobiology, Harvard Medical School, Howard Hughes Medical Institute, Boston, United StatesCenter for Computational Biology, Flatiron Institute, Simons Foundation, New York, United StatesCenter for Computational Biology, Flatiron Institute, Simons Foundation, New York, United StatesCenter for Computational Biology, Flatiron Institute, Simons Foundation, New York, United StatesDepartment of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, United States; Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, United States; Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, United StatesDepartment of Neuroscience, Columbia University, New York, United States; Division of Integrative Neuroscience, Department of Psychiatry, New York State Psychiatric Institute, New York, United States; Department of Psychiatry & Pharmacology, Columbia University, New York, United StatesWeill Institute for Neurosciences, University of California, San Francisco, San Francisco, United States; Neuroscience Graduate Program, University of California, San Francisco, United States; Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, United States; Department of Psychiatry, University of California, San Francisco, San Francisco, United StatesDepartment of Neurobiology, Harvard Medical School, Howard Hughes Medical Institute, Boston, United StatesCenter for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, United States; Machine Learning Department, Carnegie Mellon University, Pittsburgh, United States; Department of Statistics, Carnegie Mellon University, Pittsburgh, United StatesDepartment of Statistics, Columbia University, New York, United States; Grossman Center for the Statistics of Mind, Columbia University, New York, United States; Center for Theoretical Neuroscience, Columbia University, New York, United States; Department of Neuroscience, Columbia University, New York, United States; Kavli Institute for Brain Science, Columbia University, New York, United States; Neurotechnology Center, Columbia University, New York, United StatesIn vivo calcium imaging through microendoscopic lenses enables imaging of previously inaccessible neuronal populations deep within the brains of freely moving animals. However, it is computationally challenging to extract single-neuronal activity from microendoscopic data, because of the very large background fluctuations and high spatial overlaps intrinsic to this recording modality. Here, we describe a new constrained matrix factorization approach to accurately separate the background and then demix and denoise the neuronal signals of interest. We compared the proposed method against previous independent components analysis and constrained nonnegative matrix factorization approaches. On both simulated and experimental data recorded from mice, our method substantially improved the quality of extracted cellular signals and detected more well-isolated neural signals, especially in noisy data regimes. These advances can in turn significantly enhance the statistical power of downstream analyses, and ultimately improve scientific conclusions derived from microendoscopic data.https://elifesciences.org/articles/28728calcium imagingmicroendoscopesource extraction |