1. Neuroscience
  2. Physics of Living Systems
Download icon

Fast deep neural correspondence for tracking and identifying neurons in C. elegans using semi-synthetic training

  1. Xinwei Yu
  2. Matthew S Creamer
  3. Francesco Randi
  4. Anuj Kumar Sharma Ph.D.
  5. Scott W Linderman
  6. Andrew Michael Leifer  Is a corresponding author
  1. Princeton University, United States
  2. Stanford University, United States
Tools and Resources
  • Cited 0
  • Views 282
  • Annotations
Cite this article as: eLife 2021;10:e66410 doi: 10.7554/eLife.66410

Abstract

We present an automated method to track and identify neurons in C. elegans, called 'fast Deep Neural Correspondence' or fDNC, based on the transformer network architecture. The model is trained once on empirically derived semi-synthetic data and then predicts neural correspondence across held-out real animals. The same pre-trained model both tracks neurons across time and identifies corresponding neurons across individuals. Performance is evaluated against hand-annotated datasets, including NeuroPAL [1]. Using only position information, the method achieves 79.1% accuracy at tracking neurons within an individual and 64.1% accuracy at identifying neurons across individuals. Accuracy at identifying neurons across individuals is even higher (78.2%) when the model is applied to a dataset published by another group [2]. Accuracy reaches 74.7% on our dataset when using color information from NeuroPAL. Unlike previous methods, fDNC does not require straightening or transforming the animal into a canonical coordinate system. The method is fast and predicts correspondence in 10ms making it suitable for future real-time applications.

Data availability

All datasets generated as part of this work have been deposited in a public Open Science Foundation repository DOI:10.17605/OSF.IO/T7DZU.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Xinwei Yu

    Department of Physics, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Matthew S Creamer

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Francesco Randi

    Department of Physics, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Anuj Kumar Sharma Ph.D.

    Department of Physics, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5061-9731
  5. Scott W Linderman

    Department of Statistics, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3878-9073
  6. Andrew Michael Leifer

    Department of Physics and Princeton Neuroscience Institute, Princeton University, Princeton, United States
    For correspondence
    leifer@princeton.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5362-5093

Funding

Simons Foundation (543003)

  • Andrew Michael Leifer

Simons Foundation (697092)

  • Scott W Linderman

National Science Foundation (IOS-184537)

  • Andrew Michael Leifer

National Science Foundation (PHY-1734030)

  • Andrew Michael Leifer

National Institutes of Health (R21NS101629)

  • Andrew Michael Leifer

National Institutes of Health (1R01NS113119)

  • Scott W Linderman

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

Reviewing Editor

  1. Gordon J Berman, Emory University, United States

Publication history

  1. Received: January 9, 2021
  2. Accepted: July 13, 2021
  3. Accepted Manuscript published: July 14, 2021 (version 1)

Copyright

© 2021, Yu 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

  • 282
    Page views
  • 50
    Downloads
  • 0
    Citations

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

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)

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

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

Further reading

    1. Genetics and Genomics
    2. Neuroscience
    Li Hou et al.
    Research Article Updated

    Long-term flight depends heavily on intensive energy metabolism in animals; however, the neuroendocrine mechanisms underlying efficient substrate utilization remain elusive. Here, we report that the adipokinetic hormone/corazonin-related peptide (ACP) can facilitate muscle lipid utilization in a famous long-term migratory flighting species, Locusta migratoria. By peptidomic analysis and RNAi screening, we identified brain-derived ACP as a key flight-related neuropeptide. ACP gene expression increased notably upon sustained flight. CRISPR/Cas9-mediated knockout of ACP gene and ACP receptor gene (ACPR) significantly abated prolonged flight of locusts. Transcriptomic and metabolomic analyses further revealed that genes and metabolites involved in fatty acid transport and oxidation were notably downregulated in the flight muscle of ACP mutants. Finally, we demonstrated that a fatty-acid-binding protein (FABP) mediated the effects of ACP in regulating muscle lipid metabolism during long-term flight in locusts. Our results elucidated a previously undescribed neuroendocrine mechanism underlying efficient energy utilization associated with long-term flight.

    1. Neuroscience
    Krishna N Badhiwala et al.
    Research Article

    Hydra vulgaris is an emerging model organism for neuroscience due to its small size, transparency, genetic tractability, and regenerative nervous system; however, fundamental properties of its sensorimotor behaviors remain unknown. Here, we use microfluidic devices combined with fluorescent calcium imaging and surgical resectioning to study how the diffuse nervous system coordinates Hydra's mechanosensory response. Mechanical stimuli cause animals to contract, and we find this response relies on at least two distinct networks of neurons in the oral and aboral regions of the animal. Different activity patterns arise in these networks depending on whether the animal is contracting spontaneously or contracting in response to mechanical stimulation. Together, these findings improve our understanding of how Hydra’s diffuse nervous system coordinates sensorimotor behaviors. These insights help reveal how sensory information is processed in an animal with a diffuse, radially symmetric neural architecture unlike the dense, bilaterally symmetric nervous systems found in most model organisms.