IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v607y2022i7920d10.1038_s41586-022-04909-5.html
   My bibliography  Save this article

The neuronal logic of how internal states control food choice

Author

Listed:
  • Daniel Münch

    (Champalimaud Foundation)

  • Dennis Goldschmidt

    (Champalimaud Foundation)

  • Carlos Ribeiro

    (Champalimaud Foundation)

Abstract

When deciding what to eat, animals evaluate sensory information about food quality alongside multiple ongoing internal states1–10. How internal states interact to alter sensorimotor processing and shape decisions such as food choice remains poorly understood. Here we use pan-neuronal volumetric activity imaging in the brain of Drosophila melanogaster to investigate the neuronal basis of internal state-dependent nutrient appetites. We created a functional atlas of the ventral fly brain and find that metabolic state shapes sensorimotor processing across large sections of the neuropil. By contrast, reproductive state acts locally to define how sensory information is translated into feeding motor output. These two states thus synergistically modulate protein-specific food intake and food choice. Finally, using a novel computational strategy, we identify driver lines that label neurons innervating state-modulated brain regions and show that the newly identified ‘borboleta’ region is sufficient to direct food choice towards protein-rich food. We thus identify a generalizable principle by which distinct internal states are integrated to shape decision making and propose a strategy to uncover and functionally validate how internal states shape behaviour.

Suggested Citation

  • Daniel Münch & Dennis Goldschmidt & Carlos Ribeiro, 2022. "The neuronal logic of how internal states control food choice," Nature, Nature, vol. 607(7920), pages 747-755, July.
  • Handle: RePEc:nat:nature:v:607:y:2022:i:7920:d:10.1038_s41586-022-04909-5
    DOI: 10.1038/s41586-022-04909-5
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41586-022-04909-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1038/s41586-022-04909-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shivesh Chaudhary & Sihoon Moon & Hang Lu, 2022. "Fast, efficient, and accurate neuro-imaging denoising via supervised deep learning," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    2. Mami Nakamizo-Dojo & Kenichi Ishii & Jiro Yoshino & Masato Tsuji & Kazuo Emoto, 2023. "Descending GABAergic pathway links brain sugar-sensing to peripheral nociceptive gating in Drosophila," Nature Communications, Nature, vol. 14(1), pages 1-18, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:607:y:2022:i:7920:d:10.1038_s41586-022-04909-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.