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

  • 6,478
    views
  • 644
    downloads
  • 78
    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
    Olga Kepinska, Josue Dalboni da Rocha ... Narly Golestani
    Research Article

    This study examines whether auditory cortex anatomy reflects multilingual experience, specifically individuals’ phonological repertoire. Using data from over 200 participants exposed to 1–7 languages across 36 languages, we analyzed the role of language experience and typological distances between languages they spoke in shaping neural signatures of multilingualism. Our findings reveal a negative relationship between the thickness of the left and right second transverse temporal gyrus (TTG) and participants’ degree of multilingualism. Models incorporating phoneme-level information in the language experience index explained the most variance in TTG thickness, suggesting that a more extensive and more phonologically diverse language experience is associated with thinner cortices in the second TTG. This pattern, consistent across two datasets, supports the idea of experience-driven pruning and neural efficiency. Our findings indicate that experience with typologically distant languages appear to impact the brain differently than those with similar languages. Moreover, they suggest that early auditory regions seem to represent phoneme-level cross-linguistic information, contrary to the most established models of language processing in the brain, which suggest that phonological processing happens in more lateral posterior superior temporal gyrus (STG) and superior temporal sulcus (STS).

    1. Neuroscience
    Sara A Nolin, Mary E Faulkner ... Kristina Visscher
    Research Article

    The brain is organized into systems and networks of interacting components. The functional connections among these components give insight into the brain's organization and may underlie some cognitive effects of aging. Examining the relationship between individual differences in brain organization and cognitive function in older adults who have reached oldest old ages with healthy cognition can help us understand how these networks support healthy cognitive aging. We investigated functional network segregation in 146 cognitively healthy participants aged 85+ in the McKnight Brain Aging Registry. We found that the segregation of the association system and the individual networks within the association system [the fronto-parietal network (FPN), cingulo-opercular network (CON) and default mode network (DMN)], has strong associations with overall cognition and processing speed. We also provide a healthy oldest-old (85+) cortical parcellation that can be used in future work in this age group. This study shows that network segregation of the oldest-old brain is closely linked to cognitive performance. This work adds to the growing body of knowledge about differentiation in the aged brain by demonstrating that cognitive ability is associated with differentiated functional networks in very old individuals representing successful cognitive aging.