A connectomics-based taxonomy of mammals

  1. Laura E Suarez  Is a corresponding author
  2. Yossi Yovel
  3. Martijn P van den Heuvel
  4. Olaf Sporns
  5. Yaniv Assaf
  6. Guillaume Lajoie
  7. Bratislav Misic  Is a corresponding author
  1. McGill University, Canada
  2. Tel Aviv University, Israel
  3. Vrije Universiteit Amsterdam, Netherlands
  4. Indiana University, United States
  5. Mila - Quebec Artificial Intelligence Institute, Canada

Abstract

Mammalian taxonomies are conventionally defined by morphological traits and genetics. How species differ in terms of neural circuits and whether inter-species differences in neural circuit organization conform to these taxonomies is unknown. The main obstacle for the comparison of neural architectures have been differences in network reconstruction techniques, yielding species-specific connectomes that are not directly comparable to one another. Here we comprehensively chart connectome organization across the mammalian phylogenetic spectrum using a common reconstruction protocol. We analyze the mammalian MRI (MaMI) data set, a database that encompasses high-resolution ex vivo structural and diffusion magnetic resonance imaging (MRI) scans of 124 species across 12 taxonomic orders and 5 superorders, collected using a unified MRI protocol. We assess similarity between species connectomes using two methods: similarity of Laplacian eigenspectra and similarity of multiscale topological features. We find greater inter-species similarities among species within the same taxonomic order, suggesting that connectome organization reflects established taxonomic relationships defined by morphology and genetics. While all connectomes retain hallmark global features and relative proportions of connection classes, inter-species variation is driven by local regional connectivity profiles. By encoding connectomes into a common frame of reference, these findings establish a foundation for investigating how neural circuits change over phylogeny, forging a link from genes to circuits to behaviour.

Data availability

The MaMI data set was originally collected and analyzed by Assaf and colleagues in Assaf, Y. et al., 2020 , Nat. Neurosci. (doi: https://doi.org/10.1038/s41593-020-0641-7). We have included the connectivity matrices used in this study in a public repository available at \url{https://doi.org/10.5281/zenodo.7143143}.

The following data sets were generated

Article and author information

Author details

  1. Laura E Suarez

    Montréal Neurological Institute, McGill University, Montreal, Canada
    For correspondence
    laura.suarez@mail.mcgill.ca
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0700-1500
  2. Yossi Yovel

    School of Neurobiology, Biochemistry and Biophysics, Tel Aviv University, Tel Aviv, Israel
    Competing interests
    The authors declare that no competing interests exist.
  3. Martijn P van den Heuvel

    Vrije Universiteit Amsterdam, Amsterdam, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
  4. Olaf Sporns

    Psychological and Brain Sciences, Indiana University, Indiana, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Yaniv Assaf

    School of Neurobiology, Biochemistry and Biophysics, Tel Aviv University, Tel Aviv, Israel
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6941-1535
  6. Guillaume Lajoie

    Mila - Quebec Artificial Intelligence Institute, Montreal, Canada
    Competing interests
    The authors declare that no competing interests exist.
  7. Bratislav Misic

    Montréal Neurological Institute, McGill University, Montreal, Canada
    For correspondence
    bratislav.misic@mcgill.ca
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0307-2862

Funding

Natural Sciences and Engineering Research Council of Canada

  • Bratislav Misic

National Science Foundation - BSF

  • Yaniv Assaf

Canadian Institutes of Health Research

  • Bratislav Misic

Fondation Brain Canada (Future Leaders Fund)

  • Bratislav Misic

Canada Research Chairs

  • Bratislav Misic

Michael J. Fox Foundation for Parkinson's Research

  • Bratislav Misic

Healthy Brains for Healthy Lives

  • Bratislav Misic

Natural Sciences and Engineering Research Council of Canada

  • Guillaume Lajoie

Canada Research Chairs

  • Guillaume Lajoie

Canadian Institute for Advanced Research

  • Guillaume Lajoie

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

Reviewing Editor

  1. Chris I Baker, National Institute of Mental Health, National Institutes of Health, United States

Version history

  1. Preprint posted: March 12, 2022 (view preprint)
  2. Received: March 14, 2022
  3. Accepted: November 4, 2022
  4. Accepted Manuscript published: November 7, 2022 (version 1)
  5. Version of Record published: November 22, 2022 (version 2)

Copyright

© 2022, Suarez 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.

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  1. Laura E Suarez
  2. Yossi Yovel
  3. Martijn P van den Heuvel
  4. Olaf Sporns
  5. Yaniv Assaf
  6. Guillaume Lajoie
  7. Bratislav Misic
(2022)
A connectomics-based taxonomy of mammals
eLife 11:e78635.
https://doi.org/10.7554/eLife.78635

Share this article

https://doi.org/10.7554/eLife.78635

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