Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data

  1. Pengcheng Zhou  Is a corresponding author
  2. Shanna L Resendez
  3. Jose Rodriguez-Romaguera
  4. Jessica C Jimenez
  5. Shay Q Neufeld
  6. Andrea Giovannucci
  7. Johannes Friedrich
  8. Eftychios A Pnevmatikakis
  9. Garret D Stuber
  10. Rene Hen
  11. Mazen A Kheirbek
  12. Bernardo L Sabatini
  13. Robert E Kass
  14. Liam Paninski
  1. Carnegie Mellon University, United States
  2. University of North Carolina at Chapel Hill, United States
  3. Columbia University, United States
  4. Harvard Medical School, United States
  5. Flatiron Institute, Simons Foundation, United States
  6. University of California, San Francisco, United States

Abstract

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.

Data availability

The following data sets were generated

Article and author information

Author details

  1. Pengcheng Zhou

    Center for the Neural Basis of Cognition and Machine Learning Department, Carnegie Mellon University, Pittsburgh, United States
    For correspondence
    zhoupc1988@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1237-3931
  2. Shanna L Resendez

    Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Jose Rodriguez-Romaguera

    Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Jessica C Jimenez

    Department of Neuroscience, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Shay Q Neufeld

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Andrea Giovannucci

    Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Johannes Friedrich

    Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1321-5866
  8. Eftychios A Pnevmatikakis

    Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Garret D Stuber

    Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1730-4855
  10. Rene Hen

    Department of Neuroscience, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Mazen A Kheirbek

    Department of Psychiatry, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  12. Bernardo L Sabatini

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  13. Robert E Kass

    Center for the Neural Basis of Cognition and Machine Learning Department, Carnegie Mellon University, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.
  14. Liam Paninski

    Department of Statistics, Columbia University, Columbia, United States
    Competing interests
    The authors declare that no competing interests exist.

Funding

National Institute of Mental Health

  • Pengcheng Zhou
  • Jessica C Jimenez
  • Rene Hen
  • Mazen A Kheirbek
  • Robert E Kass

New York State Stem Cell Science

  • Jessica C Jimenez
  • Rene Hen

Hope for Depression Research Foundation

  • Jessica C Jimenez
  • Rene Hen

Canadian Institutes of Health Research

  • Shay Q Neufeld

Simons Foundation

  • Andrea Giovannucci
  • Johannes Friedrich
  • Eftychios A Pnevmatikakis
  • Garret D Stuber
  • Liam Paninski

International Mental Health Research Organization

  • Mazen A Kheirbek

National Institute of Neurological Disorders and Stroke

  • Bernardo L Sabatini

National Institute on Drug Abuse

  • Pengcheng Zhou
  • Jose Rodriguez-Romaguera
  • Garret D Stuber

Intelligence Advanced Research Projects Activity

  • Pengcheng Zhou
  • Liam Paninski

Defense Advanced Research Projects Agency

  • Liam Paninski

Army Research Office

  • Liam Paninski

National Institute of Biomedical Imaging and Bioengineering

  • Liam Paninski

Eunice Kennedy Shriver National Institute of Child Health and Human Development

  • Shanna L Resendez
  • Garret D Stuber

Howard Hughes Medical Institute

  • Jessica C Jimenez

National Institute on Aging

  • Jessica C Jimenez
  • Rene Hen

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: These procedures were conducted in accordance with the Guide for the Care and Use of Laboratory Animals, as adopted by the NIH, and with approval from the Harvard Standing Committee on Animal Care (protocol number: IS00000571 ), or the University of North Carolina Institutional Animal Care and Use Committee (UNC IACUC, protocol number: 16-075.0), or the New York State Psychiatric Institutional Animal Care and Use Committee (protocol number: NYSPI-1412 ).

Copyright

© 2018, Zhou et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 30,663
    views
  • 4,041
    downloads
  • 583
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Pengcheng Zhou
  2. Shanna L Resendez
  3. Jose Rodriguez-Romaguera
  4. Jessica C Jimenez
  5. Shay Q Neufeld
  6. Andrea Giovannucci
  7. Johannes Friedrich
  8. Eftychios A Pnevmatikakis
  9. Garret D Stuber
  10. Rene Hen
  11. Mazen A Kheirbek
  12. Bernardo L Sabatini
  13. Robert E Kass
  14. Liam Paninski
(2018)
Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data
eLife 7:e28728.
https://doi.org/10.7554/eLife.28728

Share this article

https://doi.org/10.7554/eLife.28728

Further reading

    1. Neuroscience
    Pál Barzó, Ildikó Szöts ... Gábor Tamás
    Research Article

    The basic excitatory neurons of the cerebral cortex, the pyramidal cells, are the most important signal integrators for the local circuit. They have quite characteristic morphological and electrophysiological properties that are known to be largely constant with age in the young and adult cortex. However, the brain undergoes several dynamic changes throughout life, such as in the phases of early development and cognitive decline in the aging brain. We set out to search for intrinsic cellular changes in supragranular pyramidal cells across a broad age range: from birth to 85 y of age and we found differences in several biophysical properties between defined age groups. During the first year of life, subthreshold and suprathreshold electrophysiological properties changed in a way that shows that pyramidal cells become less excitable with maturation, but also become temporarily more precise. According to our findings, the morphological features of the three-dimensional reconstructions from different life stages showed consistent morphological properties and systematic dendritic spine analysis of an infantile and an old pyramidal cell showed clear significant differences in the distribution of spine shapes. Overall, the changes that occur during development and aging may have lasting effects on the properties of pyramidal cells in the cerebral cortex. Understanding these changes is important to unravel the complex mechanisms underlying brain development, cognition, and age-related neurodegenerative diseases.

    1. Neuroscience
    Elissa Sutlief, Charlie Walters ... Marshall G Hussain Shuler
    Research Article

    Reward-rate maximization is a prominent normative principle in behavioral ecology, neuroscience, economics, and AI. Here, we identify, compare, and analyze equations to maximize reward rate when assessing whether to initiate a pursuit. In deriving expressions for the value of a pursuit, we show that time’s cost consists of both apportionment and opportunity cost. Reformulating value as a discounting function, we show precisely how a reward-rate-optimal agent’s discounting function (1) combines hyperbolic and linear components reflecting apportionment and opportunity costs, and (2) is dependent not only on the considered pursuit’s properties but also on time spent and rewards obtained outside the pursuit. This analysis reveals how purported signs of suboptimal behavior (hyperbolic discounting, and the Delay, Magnitude, and Sign effects) are in fact consistent with reward-rate maximization. To better account for observed decision-making errors in humans and animals, we then analyze the impact of misestimating reward-rate-maximizing parameters and find that suboptimal decisions likely stem from errors in assessing time’s apportionment—specifically, underweighting time spent outside versus inside a pursuit—which we term the ‘Malapportionment Hypothesis’. This understanding of the true pattern of temporal decision-making errors is essential to deducing the learning algorithms and representational architectures actually used by humans and animals.