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 ).

Reviewing Editor

  1. David C Van Essen, Washington University in St. Louis, United States

Publication history

  1. Received: May 19, 2017
  2. Accepted: February 20, 2018
  3. Accepted Manuscript published: February 22, 2018 (version 1)
  4. Version of Record published: March 27, 2018 (version 2)

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

  • 22,258
    Page views
  • 3,163
    Downloads
  • 175
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, Scopus, PubMed Central.

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

Further reading

    1. Neuroscience
    Arefeh Sherafati et al.
    Research Article Updated

    Cochlear implants are neuroprosthetic devices that can restore hearing in people with severe to profound hearing loss by electrically stimulating the auditory nerve. Because of physical limitations on the precision of this stimulation, the acoustic information delivered by a cochlear implant does not convey the same level of acoustic detail as that conveyed by normal hearing. As a result, speech understanding in listeners with cochlear implants is typically poorer and more effortful than in listeners with normal hearing. The brain networks supporting speech understanding in listeners with cochlear implants are not well understood, partly due to difficulties obtaining functional neuroimaging data in this population. In the current study, we assessed the brain regions supporting spoken word understanding in adult listeners with right unilateral cochlear implants (n=20) and matched controls (n=18) using high-density diffuse optical tomography (HD-DOT), a quiet and non-invasive imaging modality with spatial resolution comparable to that of functional MRI. We found that while listening to spoken words in quiet, listeners with cochlear implants showed greater activity in the left prefrontal cortex than listeners with normal hearing, specifically in a region engaged in a separate spatial working memory task. These results suggest that listeners with cochlear implants require greater cognitive processing during speech understanding than listeners with normal hearing, supported by compensatory recruitment of the left prefrontal cortex.

    1. Neuroscience
    Mohammad Ali Salehinejad et al.
    Research Article Updated

    Sleep strongly affects synaptic strength, making it critical for cognition, especially learning and memory formation. Whether and how sleep deprivation modulates human brain physiology and cognition is not well understood. Here we examined how overnight sleep deprivation vs overnight sufficient sleep affects (a) cortical excitability, measured by transcranial magnetic stimulation, (b) inducibility of long-term potentiation (LTP)- and long-term depression (LTD)-like plasticity via transcranial direct current stimulation (tDCS), and (c) learning, memory, and attention. The results suggest that sleep deprivation upscales cortical excitability due to enhanced glutamate-related cortical facilitation and decreases and/or reverses GABAergic cortical inhibition. Furthermore, tDCS-induced LTP-like plasticity (anodal) abolishes while the inhibitory LTD-like plasticity (cathodal) converts to excitatory LTP-like plasticity under sleep deprivation. This is associated with increased EEG theta oscillations due to sleep pressure. Finally, we show that learning and memory formation, behavioral counterparts of plasticity, and working memory and attention, which rely on cortical excitability, are impaired during sleep deprivation. Our data indicate that upscaled brain excitability and altered plasticity, due to sleep deprivation, are associated with impaired cognitive performance. Besides showing how brain physiology and cognition undergo changes (from neurophysiology to higher-order cognition) under sleep pressure, the findings have implications for variability and optimal application of noninvasive brain stimulation.