1. Computational and Systems Biology
  2. Neuroscience

A hardware system for real time decoding of in vivo calcium imaging data

  1. Zhe Chen
  2. Garrett J Blair
  3. Changliang Guo
  4. Jim Zhou
  5. Juan-Luis Romero-Sosa
  6. Alicia Izquierdo
  7. Peyman Golshani
  8. Jason Cong
  9. Daniel Aharoni
  10. Hugh T Blair  Is a corresponding author
  1. University of California, Los Angeles, United States
https://doi.org/10.7554/eLife.78344
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Cite this article
  1. Zhe Chen
  2. Garrett J Blair
  3. Changliang Guo
  4. Jim Zhou
  5. Juan-Luis Romero-Sosa
  6. Alicia Izquierdo
  7. Peyman Golshani
  8. Jason Cong
  9. Daniel Aharoni
  10. Hugh T Blair
(2023)
A hardware system for real time decoding of in vivo calcium imaging data
eLife 12:e78344.
https://doi.org/10.7554/eLife.78344
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Abstract

Epifluorescence miniature microscopes ('miniscopes') are widely used for in vivo calcium imaging of neural population activity. Imaging data is typically collected during a behavioral task and stored for later offline analysis, but emerging techniques for online imaging can support novel closed-loop experiments in which neural population activity is decoded in real time to trigger neurostimulation or sensory feedback. To achieve short feedback latencies, online imaging systems must be optimally designed to maximize computational speed and efficiency while minimizing errors in population decoding. Here we introduce DeCalciOn, an open-source device for real-time imaging and population decoding of in vivo calcium signals that is hardware compatible with all miniscopes that use the UCLA Data Acquisition (DAQ) interface. DeCalciOn performs online motion stabilization, neural enhancement, calcium trace extraction, and decoding of up to 1024 traces per frame at latencies of <50 ms after fluorescence photons arrive at the miniscope image sensor. We show that DeCalciOn can accurately decode the position of rats (n=12) running on a linear track from calcium fluorescence in the hippocampal CA1 layer, and can categorically classify behaviors performed by rats (n=2) during an instrumental task from calcium fluorescence in orbitofrontal cortex (OFC). DeCalciOn achieves high decoding accuracy at short latencies using innovations such as field-programmable gate array (FPGA) hardware for real time image processing and contour-free methods to efficiently extract calcium traces from sensor images. In summary, our system offers an affordable plug-and-play solution for real-time calcium imaging experiments in behaving animals.

Data availability

All hardware, software, and firmware are openly available through miniscope.org and at github.com/zhe-ch/ACTEV.

The following data sets were generated

Article and author information

Author details

  1. Zhe Chen

    Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    No competing interests declared.

  2. Garrett J Blair

    Department of Psychology, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2724-8914

  3. Changliang Guo

    David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    No competing interests declared.

  4. Jim Zhou

    Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    No competing interests declared.

  5. Juan-Luis Romero-Sosa

    Department of Psychology, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    No competing interests declared.

  6. Alicia Izquierdo

    Department of Psychology, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    Alicia Izquierdo, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9897-2091

  7. Peyman Golshani

    David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    No competing interests declared.

  8. Jason Cong

    Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    No competing interests declared.

  9. Daniel Aharoni

    Department of Neurology, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4931-8514

  10. Hugh T Blair

    Department of Psychology, University of California, Los Angeles, Los Angeles, United States
    For correspondence
    tadblair@g.ucla.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8256-5109

Funding

NSF NeuroNex (1707408)

  • Peyman Golshani
  • Jason Cong
  • Daniel Aharoni
  • Hugh T Blair

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

Reviewing Editor

  1. Brice Bathellier, CNRS, France

Ethics

Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols (#2017-038) of the University of California Los Angeles. The protocol was approved by the Committee on the Ethics of Animal Experiments of UCLA. All surgery was performed under deep isoflurane anesthesia, and every effort was made to minimize suffering, including administration of pre- and post-surgical analgesia.

Version history

  1. Preprint posted: February 2, 2022 (view preprint)
  2. Received: March 3, 2022
  3. Accepted: January 23, 2023
  4. Accepted Manuscript published: January 24, 2023 (version 1)
  5. Version of Record published: February 8, 2023 (version 2)

Copyright

© 2023, Chen 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.

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  1. Zhe Chen
  2. Garrett J Blair
  3. Changliang Guo
  4. Jim Zhou
  5. Juan-Luis Romero-Sosa
  6. Alicia Izquierdo
  7. Peyman Golshani
  8. Jason Cong
  9. Daniel Aharoni
  10. Hugh T Blair
(2023)
A hardware system for real time decoding of in vivo calcium imaging data
eLife 12:e78344.
https://doi.org/10.7554/eLife.78344

Share this article

https://doi.org/10.7554/eLife.78344

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