1. Neuroscience

Striatal adenosine A2A receptor neurons control active-period sleep via parvalbumin neurons in external globus pallidus

  1. Xiang-Shan Yuan
  2. Lu Wang
  3. Hui Dong
  4. Wei-Min Qu
  5. Su-Rong Yang
  6. Yoan Cherasse
  7. Michael Lazarus
  8. Serge N Schiffmann
  9. Alban de Kerchove d'Exaerde
  10. Rui-Xi Li  Is a corresponding author
  11. Zhi-Li Huang  Is a corresponding author
  1. Fudan University, China
  2. University of Tsukuba, Japan
  3. Université Libre de Bruxelles, Belgium
https://doi.org/10.7554/eLife.29055
Share this article
Cite this article
  1. Xiang-Shan Yuan
  2. Lu Wang
  3. Hui Dong
  4. Wei-Min Qu
  5. Su-Rong Yang
  6. Yoan Cherasse
  7. Michael Lazarus
  8. Serge N Schiffmann
  9. Alban de Kerchove d'Exaerde
  10. Rui-Xi Li
  11. Zhi-Li Huang
(2017)
Striatal adenosine A2A receptor neurons control active-period sleep via parvalbumin neurons in external globus pallidus
eLife 6:e29055.
https://doi.org/10.7554/eLife.29055
  2. Download BibTeX
  3. Download .RIS

Abstract

Dysfunction of the striatum is frequently associated with sleep disturbances. However, its role in sleep-wake regulation has been paid little attention even though the striatum densely expresses adenosine A2A receptors (A2ARs), which are essential for adenosine-induced sleep. Here we showed that chemogenetic activation of A2AR neurons in specific subregions of the striatum induced a remarkable increase in non-rapid eye movement (NREM) sleep. Anatomical mapping and immunoelectron microscopy revealed that striatal A2AR neurons innervated the external globus pallidus (GPe) in a topographically organized manner and preferentially formed inhibitory synapses with GPe parvalbumin (PV) neurons. Moreover, lesions of GPe PV neurons abolished the sleep-promoting effect of striatal A2AR neurons. In addition, chemogenetic inhibition of striatal A2AR neurons led to a significant decrease of NREM sleep at active period, but not inactive period of mice. These findings reveal a prominent contribution of striatal A2AR neuron/GPe PV neuron circuit in sleep control.

Article and author information

Author details

  1. Xiang-Shan Yuan

    Department of Pharmacology, Fudan University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.

  2. Lu Wang

    Department of Pharmacology, Fudan University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.

  3. Hui Dong

    Department of Pharmacology, Fudan University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.

  4. Wei-Min Qu

    Department of Pharmacology, Fudan University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.

  5. Su-Rong Yang

    Department of Pharmacology, Fudan University, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.

  6. Yoan Cherasse

    International Institute for Integrative Sleep Medicine, University of Tsukuba, Tsukuba, Japan
    Competing interests
    The authors declare that no competing interests exist.

  7. Michael Lazarus

    International Institute for Integrative Sleep Medicine, University of Tsukuba, Tsukuba, Japan
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3863-4474

  8. Serge N Schiffmann

    Laboratory of Neurophysiology, ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
    Competing interests
    The authors declare that no competing interests exist.

  9. Alban de Kerchove d'Exaerde

    Laboratory of Neurophysiology, ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
    Competing interests
    The authors declare that no competing interests exist.

  10. Rui-Xi Li

    Department of Pharmacology, Fudan University, Shanghai, China
    For correspondence
    ruixilee@shmu.edu.cn
    Competing interests
    The authors declare that no competing interests exist.

  11. Zhi-Li Huang

    Department of Pharmacology, Fudan University, Shanghai, China
    For correspondence
    huangzl@fudan.edu.cn
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9359-1150

Funding

National Natural Science Foundation of China (81420108015)

  • Zhi-Li Huang

National Natural Science Foundation of China (31671099)

  • Wei-Min Qu

National Natural Science Foundation of China (81271466)

  • Rui-Xi Li

National Natural Science Foundation of China (31571103)

  • Lu Wang

National Natural Science Foundation of China (81571296)

  • Su-Rong Yang

National Basic Research Program of China (2015CB856401)

  • Zhi-Li Huang

Shanghai Committee of Science and Technology (14JC1400900)

  • Zhi-Li Huang

National Natural Science Foundation of China (31471064)

  • Wei-Min Qu

National Natural Science Foundation of China (31530035)

  • Zhi-Li Huang

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

Ethics

Animal experimentation: All animal studies were performed in accordance with protocols approved by the Committee on the Ethics of Animal Experiments of Fudan University Shanghai Medical College (permit number: 20110307-049). Every effort was made to minimize the number of animals used and any pain and discomfort experienced by the subjects.

Copyright

© 2017, Yuan 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

  • 3,101
    views
  • 635
    downloads
  • 86
    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. Xiang-Shan Yuan
  2. Lu Wang
  3. Hui Dong
  4. Wei-Min Qu
  5. Su-Rong Yang
  6. Yoan Cherasse
  7. Michael Lazarus
  8. Serge N Schiffmann
  9. Alban de Kerchove d'Exaerde
  10. Rui-Xi Li
  11. Zhi-Li Huang
(2017)
Striatal adenosine A2A receptor neurons control active-period sleep via parvalbumin neurons in external globus pallidus
eLife 6:e29055.
https://doi.org/10.7554/eLife.29055

Share this article

https://doi.org/10.7554/eLife.29055

Categories and tags

Research organism

Further reading

    1. Neuroscience

    Evaluating hippocampal replay without a ground truth

    Masahiro Takigawa, Marta Huelin Gorriz ... Daniel Bendor
    Research Article

    During rest and sleep, memory traces replay in the brain. The dialogue between brain regions during replay is thought to stabilize labile memory traces for long-term storage. However, because replay is an internally-driven, spontaneous phenomenon, it does not have a ground truth - an external reference that can validate whether a memory has truly been replayed. Instead, replay detection is based on the similarity between the sequential neural activity comprising the replay event and the corresponding template of neural activity generated during active locomotion. If the statistical likelihood of observing such a match by chance is sufficiently low, the candidate replay event is inferred to be replaying that specific memory. However, without the ability to evaluate whether replay detection methods are successfully detecting true events and correctly rejecting non-events, the evaluation and comparison of different replay methods is challenging. To circumvent this problem, we present a new framework for evaluating replay, tested using hippocampal neural recordings from rats exploring two novel linear tracks. Using this two-track paradigm, our framework selects replay events based on their temporal fidelity (sequence-based detection), and evaluates the detection performance using each event's track discriminability, where sequenceless decoding across both tracks is used to quantify whether the track replaying is also the most likely track being reactivated.

    1. Neuroscience

    A deep learning approach for automated scoring of the Rey–Osterrieth complex figure

    Nicolas Langer, Maurice Weber ... Ce Zhang
    Tools and Resources

    Memory deficits are a hallmark of many different neurological and psychiatric conditions. The Rey–Osterrieth complex figure (ROCF) is the state-of-the-art assessment tool for neuropsychologists across the globe to assess the degree of non-verbal visual memory deterioration. To obtain a score, a trained clinician inspects a patient’s ROCF drawing and quantifies deviations from the original figure. This manual procedure is time-consuming, slow and scores vary depending on the clinician’s experience, motivation, and tiredness. Here, we leverage novel deep learning architectures to automatize the rating of memory deficits. For this, we collected more than 20k hand-drawn ROCF drawings from patients with various neurological and psychiatric disorders as well as healthy participants. Unbiased ground truth ROCF scores were obtained from crowdsourced human intelligence. This dataset was used to train and evaluate a multihead convolutional neural network. The model performs highly unbiased as it yielded predictions very close to the ground truth and the error was similarly distributed around zero. The neural network outperforms both online raters and clinicians. The scoring system can reliably identify and accurately score individual figure elements in previously unseen ROCF drawings, which facilitates explainability of the AI-scoring system. To ensure generalizability and clinical utility, the model performance was successfully replicated in a large independent prospective validation study that was pre-registered prior to data collection. Our AI-powered scoring system provides healthcare institutions worldwide with a digital tool to assess objectively, reliably, and time-efficiently the performance in the ROCF test from hand-drawn images.

    1. Neuroscience

    Brain Activity: Unifying networks of a rhythm

    Mohsen Alavash
    Insight

    Combining electrophysiological, anatomical and functional brain maps reveals networks of beta neural activity that align with dopamine uptake.