Abstract

Sustained changes in mood or action require persistent changes in neural activity, but it has been difficult to identify the neural circuit mechanisms that underlie persistent activity and contribute to long-lasting changes in behavior. Here, we show that a subset of Doublesex+ pC1 neurons in the Drosophila female brain, called pC1d/e, can drive minutes-long changes in female behavior in the presence of males. Using automated reconstruction of a volume electron microscopic (EM) image of the female brain, we map all inputs and outputs to both pC1d and pC1e. This reveals strong recurrent connectivity between, in particular, pC1d/e neurons and a specific subset of Fruitless+ neurons called aIPg. We additionally find that pC1d/e activation drives long-lasting persistent neural activity in brain areas and cells overlapping with the pC1d/e neural network, including both Doublesex+ and Fruitless+ neurons. Our work thus links minutes-long persistent changes in behavior with persistent neural activity and recurrent circuit architecture in the female brain.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files.

Article and author information

Author details

  1. David S Deutsch

    Princeton Neuroscience Institute, 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:" 0000-0002-8587-2435
  2. Diego A Pacheco

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Lucas Encarnacion-Rivera

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Talmo D Pereira

    Princeton Neuroscience Institute, 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:" 0000-0001-9075-8365
  5. Ramie Fathy

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Jan Clemens

    European Neuroscience Institute, Göttingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4200-8097
  7. Cyrille Girardin

    Neuroscience, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Adam J Calhoun

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Elise C Ireland

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Austin T Burke

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Sven Dorkenwald

    Princeton Neuroscience Institute and Department of Computer Science, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  12. Claire E McKellar

    Princeton Neuroscience Institute, 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:" 0000-0002-3580-7336
  13. Thomas Macrina

    Princeton Neuroscience Institute and Department of Computer Science, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  14. Ran Lu

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  15. Kisuk Lee

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  16. Nico Kemnitz

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  17. Dodham Ih

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  18. Manuel Castro

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  19. Akhilesh Halageri

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  20. Chris Jordan

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  21. William Silversmith

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  22. Jingpeng Wu

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  23. H Sebastian Seung

    Princeton Neuroscience Institute and Department of Computer Science, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  24. Mala Murthy

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    For correspondence
    mmurthy@princeton.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3063-3389

Funding

National Institutes of Health (RF1 MH117815-01)

  • Mala Murthy

National Institutes of Health (R01 NS104899)

  • Mala Murthy

Howard Hughes Medical Institute (Faculty Scholar)

  • Mala Murthy

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

Copyright

© 2020, Deutsch 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

  • 6,194
    views
  • 631
    downloads
  • 69
    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. David S Deutsch
  2. Diego A Pacheco
  3. Lucas Encarnacion-Rivera
  4. Talmo D Pereira
  5. Ramie Fathy
  6. Jan Clemens
  7. Cyrille Girardin
  8. Adam J Calhoun
  9. Elise C Ireland
  10. Austin T Burke
  11. Sven Dorkenwald
  12. Claire E McKellar
  13. Thomas Macrina
  14. Ran Lu
  15. Kisuk Lee
  16. Nico Kemnitz
  17. Dodham Ih
  18. Manuel Castro
  19. Akhilesh Halageri
  20. Chris Jordan
  21. William Silversmith
  22. Jingpeng Wu
  23. H Sebastian Seung
  24. Mala Murthy
(2020)
The neural basis for a persistent internal state in Drosophila females
eLife 9:e59502.
https://doi.org/10.7554/eLife.59502

Share this article

https://doi.org/10.7554/eLife.59502

Further reading

    1. Neuroscience
    Li-Wen Huang, Derek LF Garden ... Matthew F Nolan
    Research Article

    Interactions between excitatory and inhibitory neurons are critical to computations in cortical circuits but their organization is difficult to assess with standard electrophysiological approaches. Within the medial entorhinal cortex, representation of location by grid and other spatial cells involves circuits in layer 2 in which excitatory stellate cells interact with each other via inhibitory parvalbumin expressing interneurons. Whether this connectivity is structured to support local circuit computations is unclear. Here, we introduce strategies to address the functional organization of excitatory-inhibitory interactions using crossed Cre- and Flp-driver mouse lines to direct targeted presynaptic optogenetic activation and postsynaptic cell identification. We then use simultaneous patch-clamp recordings from postsynaptic neurons to assess their shared input from optically activated presynaptic populations. We find that extensive axonal projections support spatially organized connectivity between stellate cells and parvalbumin interneurons, such that direct connections are often, but not always, shared by nearby neurons, whereas multisynaptic interactions coordinate inputs to neurons with greater spatial separation. We suggest that direct excitatory-inhibitory synaptic interactions may operate at the scale of grid cell clusters, with local modules defined by excitatory-inhibitory connectivity, while indirect interactions may coordinate activity at the scale of grid cell modules.

    1. Neuroscience
    Giordano de Guglielmo, Lieselot Carrette ... Olivier George
    Research Article

    Addiction is commonly characterized by escalation of drug intake, compulsive drug seeking, and continued use despite harmful consequences. However, the factors contributing to the transition from moderate drug use to these problematic patterns remain unclear, particularly regarding the role of sex. Many preclinical studies have been limited by small sample sizes, low genetic diversity, and restricted drug access, making it challenging to model significant levels of intoxication or dependence and translate findings to humans. To address these limitations, we characterized addiction-like behaviors in a large sample of >500 outbred heterogeneous stock (HS) rats using an extended cocaine self-administration paradigm (6 hr/daily). We analyzed individual differences in escalation of intake, progressive ratio (PR) responding, continued use despite adverse consequences (contingent foot shocks), and irritability-like behavior during withdrawal. Principal component analysis showed that escalation of intake, progressive ratio responding, and continued use despite adverse consequences loaded onto a single factor that was distinct from irritability-like behaviors. Categorizing rats into resilient, mild, moderate, and severe addiction-like phenotypes showed that females exhibited higher addiction-like behaviors, with a lower proportion of resilient individuals compared to males. These findings suggest that, in genetically diverse rats with extended drug access, escalation of intake, continued use despite adverse consequences, and PR responding are highly correlated measures of a shared underlying construct. Furthermore, our results highlight sex differences in resilience to addiction-like behaviors.