Gamma rhythms and visual information in mouse V1 specifically modulated by somatostatin+ neurons in reticular thalamus

  1. Mahmood S Hoseini
  2. Bryan Higashikubo
  3. Frances S Cho
  4. Andrew H Chang
  5. Alexandra Clemente-Perez
  6. Irene Lew
  7. Agnieszka Ciesielska
  8. Michael P Stryker
  9. Jeanne T Paz  Is a corresponding author
  1. University of California, San Francisco, United States
  2. Gladstone Institutes, United States

Abstract

Visual perception in natural environments depends on the ability to focus on salient stimuli while ignoring distractions. This kind of selective visual attention is associated with gamma activity in the visual cortex. While the nucleus reticularis thalami (nRT) has been implicated in selective attention, its role in modulating gamma activity in the visual cortex remains unknown. Here we show that somatostatin- (SST) but not parvalbumin-expressing (PV) neurons in the visual sector of the nRT preferentially project to the dorsal lateral geniculate nucleus (dLGN), and modulate visual information transmission and gamma activity in primary visual cortex (V1). These findings pinpoint the SST neurons in nRT as powerful modulators of the visual information encoding accuracy in V1, and represent a novel circuit through which the nRT can influence representation of visual information.

Data availability

All data generated or analyzed during this study are included in the manuscript and supporting files. Source data for all figures are available in a spreadsheet format.

Article and author information

Author details

  1. Mahmood S Hoseini

    Department of Physiology, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3139-0561
  2. Bryan Higashikubo

    Neurology, Gladstone Institutes, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Frances S Cho

    Neurology, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Andrew H Chang

    Neurology, Gladstone Institutes, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Alexandra Clemente-Perez

    Neuroscience Graduate Program, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Irene Lew

    Neurology, Gladstone Institutes, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Agnieszka Ciesielska

    Neurology, Gladstone Institutes, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Michael P Stryker

    Center for Integrative Neuroscience, Department of Physiology, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Jeanne T Paz

    Neurology, Gladstone Institutes, San Francisco, United States
    For correspondence
    jeanne.paz@gladstone.ucsf.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6339-8130

Funding

National Institute of Neurological Disorders and Stroke (NS096369)

  • Jeanne T Paz

National Science Foundation (1822598)

  • Michael P Stryker

National Institute for Health Research (EY025174)

  • Michael P Stryker

American Epilepsy Society

  • Bryan Higashikubo

National Institute of Neurological Disorders and Stroke (F31NA111819)

  • Frances S Cho

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

Ethics

Animal experimentation: We performed all experiments in compliance with protocols approved by the Institutional Animal Care and Use Committees at the University of California, San Francisco and Gladstone Institutes (protocol numbers AN180588-02C and AN174396-03E). Precautions were taken to minimize stress and the number of animals used in all experiments. We followed the NIH guidelines for rigor and reproducibility of the research.

Copyright

© 2021, Hoseini 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

  • 3,006
    views
  • 419
    downloads
  • 12
    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. Mahmood S Hoseini
  2. Bryan Higashikubo
  3. Frances S Cho
  4. Andrew H Chang
  5. Alexandra Clemente-Perez
  6. Irene Lew
  7. Agnieszka Ciesielska
  8. Michael P Stryker
  9. Jeanne T Paz
(2021)
Gamma rhythms and visual information in mouse V1 specifically modulated by somatostatin+ neurons in reticular thalamus
eLife 10:e61437.
https://doi.org/10.7554/eLife.61437

Share this article

https://doi.org/10.7554/eLife.61437

Further reading

    1. Developmental Biology
    2. Neuroscience
    Taro Ichimura, Taishi Kakizuka ... Takeharu Nagai
    Tools and Resources

    We established a volumetric trans-scale imaging system with an ultra-large field-of-view (FOV) that enables simultaneous observation of millions of cellular dynamics in centimeter-wide three-dimensional (3D) tissues and embryos. Using a custom-made giant lens system with a magnification of ×2 and a numerical aperture (NA) of 0.25, and a CMOS camera with more than 100 megapixels, we built a trans-scale scope AMATERAS-2, and realized fluorescence imaging with a transverse spatial resolution of approximately 1.1 µm across an FOV of approximately 1.5×1.0 cm2. The 3D resolving capability was realized through a combination of optical and computational sectioning techniques tailored for our low-power imaging system. We applied the imaging technique to 1.2 cm-wide section of mouse brain, and successfully observed various regions of the brain with sub-cellular resolution in a single FOV. We also performed time-lapse imaging of a 1-cm-wide vascular network during quail embryo development for over 24 hr, visualizing the movement of over 4.0×105 vascular endothelial cells and quantitatively analyzing their dynamics. Our results demonstrate the potential of this technique in accelerating production of comprehensive reference maps of all cells in organisms and tissues, which contributes to understanding developmental processes, brain functions, and pathogenesis of disease, as well as high-throughput quality check of tissues used for transplantation medicine.

    1. Neuroscience
    Sean M Perkins, Elom A Amematsro ... Mark M Churchland
    Research Article

    Decoders for brain-computer interfaces (BCIs) assume constraints on neural activity, chosen to reflect scientific beliefs while yielding tractable computations. Recent scientific advances suggest that the true constraints on neural activity, especially its geometry, may be quite different from those assumed by most decoders. We designed a decoder, MINT, to embrace statistical constraints that are potentially more appropriate. If those constraints are accurate, MINT should outperform standard methods that explicitly make different assumptions. Additionally, MINT should be competitive with expressive machine learning methods that can implicitly learn constraints from data. MINT performed well across tasks, suggesting its assumptions are well-matched to the data. MINT outperformed other interpretable methods in every comparison we made. MINT outperformed expressive machine learning methods in 37 of 42 comparisons. MINT’s computations are simple, scale favorably with increasing neuron counts, and yield interpretable quantities such as data likelihoods. MINT’s performance and simplicity suggest it may be a strong candidate for many BCI applications.