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.

Metrics

  • 1,342
    Page views
  • 217
    Downloads
  • 4
    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)

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. 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

Further reading

    1. Neuroscience
    Stijn A Nuiten, Jan Willem de Gee ... Simon van Gaal
    Research Article

    Perceptual decisions about sensory input are influenced by fluctuations in ongoing neural activity, most prominently driven by attention and neuromodulator systems. It is currently unknown if neuromodulator activity and attention differentially modulate perceptual decision-making and/or whether neuromodulatory systems in fact control attentional processes. To investigate the effects of two distinct neuromodulatory systems and spatial attention on perceptual decisions, we pharmacologically elevated cholinergic (through donepezil) and catecholaminergic (through atomoxetine) levels in humans performing a visuo-spatial attention task, while we measured electroencephalography (EEG). Both attention and catecholaminergic enhancement improved decision-making at the behavioral and algorithmic level, as reflected in increased perceptual sensitivity and the modulation of the drift rate parameter derived from drift diffusion modeling. Univariate analyses of EEG data time-locked to the attentional cue, the target stimulus, and the motor response further revealed that attention and catecholaminergic enhancement both modulated pre-stimulus cortical excitability, cue- and stimulus-evoked sensory activity, as well as parietal evidence accumulation signals. Interestingly, we observed both similar, unique, and interactive effects of attention and catecholaminergic neuromodulation on these behavioral, algorithmic, and neural markers of the decision-making process. Thereby, this study reveals an intricate relationship between attentional and catecholaminergic systems and advances our understanding about how these systems jointly shape various stages of perceptual decision-making.

    1. Neuroscience
    Manfred G Kitzbichler, Daniel Martins ... Neil A Harrison
    Research Article Updated

    The relationship between obesity and human brain structure is incompletely understood. Using diffusion-weighted MRI from ∼30,000 UK Biobank participants, we test the hypothesis that obesity (waist-to-hip ratio, WHR) is associated with regional differences in two micro-structural MRI metrics: isotropic volume fraction (ISOVF), an index of free water, and intra-cellular volume fraction (ICVF), an index of neurite density. We observed significant associations with obesity in two coupled but distinct brain systems: a prefrontal/temporal/striatal system associated with ISOVF and a medial temporal/occipital/striatal system associated with ICVF. The ISOVF~WHR system colocated with expression of genes enriched for innate immune functions, decreased glial density, and high mu opioid (MOR) and other neurotransmitter receptor density. Conversely, the ICVF~WHR system co-located with expression of genes enriched for G-protein coupled receptors and decreased density of MOR and other receptors. To test whether these distinct brain phenotypes might differ in terms of their underlying shared genetics or relationship to maps of the inflammatory marker C-reactive Protein (CRP), we estimated the genetic correlations between WHR and ISOVF (rg = 0.026, P = 0.36) and ICVF (rg = 0.112, P < 9×10−4) as well as comparing correlations between WHR maps and equivalent CRP maps for ISOVF and ICVF (P<0.05). These correlational results are consistent with a two-way mechanistic model whereby genetically determined differences in neurite density in the medial temporal system may contribute to obesity, whereas water content in the prefrontal system could reflect a consequence of obesity mediated by innate immune system activation.