Neuromodulatory connectivity defines the structure of a behavioral neural network

  1. Feici Diao
  2. Amicia D Elliott
  3. Fengqiu Diao
  4. Sarav Shah
  5. Benjamin H White  Is a corresponding author
  1. National Institute of Mental Health, United States
  2. National Institute of General Medical Sciences, United States

Abstract

Neural networks are typically defined by their synaptic connectivity, yet synaptic wiring diagrams often provide limited insight into network function. This is due partly to the importance of non-synaptic communication by neuromodulators, which can dynamically reconfigure circuit activity to alter its output. Here we systematically map the patterns of neuromodulatory connectivity in a network that governs a developmentally critical behavioral sequence in Drosophila. This sequence, which mediates pupal ecdysis, is governed by the serial release of several key factors, which act both somatically as hormones and within the brain as neuromodulators. By identifying and characterizing the functions of the neuronal targets of these factors, we find that they define hierarchically organized layers of the network controlling the pupal ecdysis sequence: a modular input layer, an intermediate central pattern generating layer, and a motor output layer. Mapping neuromodulatory connections in this system thus defines the functional architecture of the network.

Article and author information

Author details

  1. Feici Diao

    Laboratory of Molecular Biology, National Institute of Mental Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Amicia D Elliott

    National Institute of General Medical Sciences, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Fengqiu Diao

    Laboratory of Molecular Biology, National Institute of Mental Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Sarav Shah

    Laboratory of Molecular Biology, National Institute of Mental Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Benjamin H White

    Laboratory of Molecular Biology, National Institute of Mental Health, Bethesda, United States
    For correspondence
    benjaminwhite@mail.nih.gov
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0612-8075

Funding

National Institute of Mental Health (MH002800-15)

  • Benjamin H White

National Institute of General Medical Sciences (FI2 GM117582)

  • Amicia D Elliott

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

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Metrics

  • 3,031
    views
  • 431
    downloads
  • 30
    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. Feici Diao
  2. Amicia D Elliott
  3. Fengqiu Diao
  4. Sarav Shah
  5. Benjamin H White
(2017)
Neuromodulatory connectivity defines the structure of a behavioral neural network
eLife 6:e29797.
https://doi.org/10.7554/eLife.29797

Share this article

https://doi.org/10.7554/eLife.29797

Further reading

    1. Neuroscience
    Damian Koevoet, Laura Van Zantwijk ... Christoph Strauch
    Research Article

    What determines where to move the eyes? We recently showed that pupil size, a well-established marker of effort, also reflects the effort associated with making a saccade (‘saccade costs’). Here, we demonstrate saccade costs to critically drive saccade selection: when choosing between any two saccade directions, the least costly direction was consistently preferred. Strikingly, this principle even held during search in natural scenes in two additional experiments. When increasing cognitive demand experimentally through an auditory counting task, participants made fewer saccades and especially cut costly directions. This suggests that the eye-movement system and other cognitive operations consume similar resources that are flexibly allocated among each other as cognitive demand changes. Together, we argue that eye-movement behavior is tuned to adaptively minimize saccade-inherent effort.

    1. Neuroscience
    Yisi Liu, Pu Wang ... Hongwei Zhou
    Short Report

    The increasing use of tissue clearing techniques underscores the urgent need for cost-effective and simplified deep imaging methods. While traditional inverted confocal microscopes excel in high-resolution imaging of tissue sections and cultured cells, they face limitations in deep imaging of cleared tissues due to refractive index mismatches between the immersion media of objectives and sample container. To overcome these challenges, the RIM-Deep was developed to significantly improve deep imaging capabilities without compromising the normal function of the confocal microscope. This system facilitates deep immunofluorescence imaging of the prefrontal cortex in cleared macaque tissue, extending imaging depth from 2 mm to 5 mm. Applied to an intact and cleared Thy1-EGFP mouse brain, the system allowed for clear axonal visualization at high imaging depth. Moreover, this advancement enables large-scale, deep 3D imaging of intact tissues. In principle, this concept can be extended to any imaging modality, including existing inverted wide-field, confocal, and two-photon microscopy. This would significantly upgrade traditional laboratory configurations and facilitate the study of connectomes in the brain and other tissues.