1. Neuroscience
Download icon

Neural network of social interaction observation in marmosets

  1. Justine C Cléry  Is a corresponding author
  2. Yuki Hori
  3. David J Schaeffer
  4. Ravi S Menon
  5. Stefan Everling  Is a corresponding author
  1. Robarts Research Institute, The University of Western Ontario, Canada
  2. University of Pittsburg, United States
Research Article
  • Cited 0
  • Views 941
  • Annotations
Cite this article as: eLife 2021;10:e65012 doi: 10.7554/eLife.65012

Abstract

A crucial component of social cognition is to observe and understand the social interactions of other individuals. A promising nonhuman primate model for investigating the neural basis of social interaction observation is the common marmoset (Callithrix jacchus), a small New World primate that shares a rich social repertoire with humans. Here we used functional magnetic resonance imaging (fMRI) acquired at 9.4 Tesla to map the brain areas activated by social interaction observation in awake marmosets. We discovered a network of subcortical and cortical areas, predominately in the anterior lateral frontal and medial frontal cortex, that was specifically activated by social interaction observation. This network resembled that recently identified in Old World macaque monkeys (Sliwa and Freiwald, 2017). Our findings suggest that this network is largely conserved between New and Old World primates and support the use of marmosets for studying the neural basis of social cognition.

Data availability

The datasets generated during this study are available at https://github.com/JClery/Social_interaction_paper.

Article and author information

Author details

  1. Justine C Cléry

    Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada
    For correspondence
    jclery@uwo.ca
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1020-1845
  2. Yuki Hori

    Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada
    Competing interests
    The authors declare that no competing interests exist.
  3. David J Schaeffer

    Department of Neurobiology, University of Pittsburg, Pittsburg, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Ravi S Menon

    Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada
    Competing interests
    The authors declare that no competing interests exist.
  5. Stefan Everling

    Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada
    For correspondence
    severlin@uwo.ca
    Competing interests
    The authors declare that no competing interests exist.

Funding

Canadian Institutes of Health Research (FRN 148365)

  • Stefan Everling

Canada First Research Excellence Fund (BrainsCAN)

  • Stefan Everling

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 experimental methods described were performed in accordance with the guidelines of the Canadian Council on Animal Care policy on the care and use of experimental animals and an ethics protocol #2017-114 approved by the Animal Care Committee of the University of Western Ontario.Animals were monitoring during the acquisition sessions by a veterinary technician.

Reviewing Editor

  1. Thorsten Kahnt, Northwestern University, United States

Publication history

  1. Received: November 18, 2020
  2. Accepted: March 29, 2021
  3. Accepted Manuscript published: March 31, 2021 (version 1)
  4. Version of Record published: April 6, 2021 (version 2)

Copyright

© 2021, Cléry 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

  • 941
    Page views
  • 146
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

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)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Neuroscience
    Qiaoli Huang et al.
    Research Article Updated

    In memory experiences, events do not exist independently but are linked with each other via structure-based organization. Structure context largely influences memory behavior, but how it is implemented in the brain remains unknown. Here, we combined magnetoencephalogram (MEG) recordings, computational modeling, and impulse-response approaches to probe the latent states when subjects held a list of items in working memory (WM). We demonstrate that sequence context reorganizes WM items into distinct latent states, that is, being reactivated at different latencies during WM retention, and the reactivation profiles further correlate with recency behavior. In contrast, memorizing the same list of items without sequence task requirements weakens the recency effect and elicits comparable neural reactivations. Computational modeling further reveals a dominant function of sequence context, instead of passive memory decaying, in characterizing recency effect. Taken together, sequence structure context shapes the way WM items are stored in the human brain and essentially influences memory behavior.

    1. Neuroscience
    Sanne ten Oever, Andrea E Martin
    Research Article

    Neuronal oscillations putatively track speech in order to optimize sensory processing. However, it is unclear how isochronous brain oscillations can track pseudo-rhythmic speech input. Here we propose that oscillations can track pseudo-rhythmic speech when considering that speech time is dependent on content-based predictions flowing from internal language models. We show that temporal dynamics of speech are dependent on the predictability of words in a sentence. A computational model including oscillations, feedback, and inhibition is able to track pseudo-rhythmic speech input. As the model processes, it generates temporal phase codes, which are a candidate mechanism for carrying information forward in time. The model is optimally sensitive to the natural temporal speech dynamics and can explain empirical data on temporal speech illusions. Our results suggest that speech tracking does not have to rely only on the acoustics but could also exploit ongoing interactions between oscillations and constraints flowing from internal language models.