Abstract

Comparing the brains of related species faces the challenges of establishing homologies whilst accommodating evolutionary specializations. Here we propose a general framework for understanding similarities and differences between the brains of primates. The approach uses white matter blueprints of the whole cortex based on a set of white matter tracts that can be anatomically matched across species. The blueprints provide a common reference space that allows us to navigate between brains of different species, identify homologous cortical areas, or to transform whole cortical maps from one species to the other. Specializations are cast within this framework as deviations between the species' blueprints. We illustrate how this approach can be used to compare human and macaque brains.

Data availability

The human diffusion MRI data was obtained from the Human Connectome Project (www.humanconnectome.org.). Tractography protocols for building the blueprints, code, and results are available for download from Gitlab at https://git.fmrib.ox.ac.uk/rmars/comparing-connectivity-blueprints.git.

The following previously published data sets were used

Article and author information

Author details

  1. Rogier B Mars

    Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
    For correspondence
    rogier.mars@psy.ox.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6302-8631
  2. Stamatios N Sotiropoulos

    Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4735-5776
  3. Richard E Passingham

    Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Jerome Sallet

    Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7878-0209
  5. Lennart M Verhagen

    Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. Alexandre A Khrapitchev

    Department of Oncology, University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7616-6635
  7. Nicola Sibson

    Department of Oncology, University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  8. Saad Jbabdi

    Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

Funding

Biotechnology and Biological Sciences Research Council (BB/N019814/1)

  • Rogier B Mars

Medical Research Council (MR/L009013/1)

  • Saad Jbabdi

Wellcome (203139/Z/16/Z)

  • Rogier B Mars
  • Jerome Sallet
  • Saad Jbabdi

Nederlandse Organisatie voor Wetenschappelijk Onderzoek (452-­13-­015)

  • Rogier B Mars

Cancer Research UK (C5255/A15935)

  • Alexandre A Khrapitchev
  • Nicola Sibson

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

Copyright

© 2018, Mars 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

  • 6,759
    views
  • 855
    downloads
  • 153
    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. Rogier B Mars
  2. Stamatios N Sotiropoulos
  3. Richard E Passingham
  4. Jerome Sallet
  5. Lennart M Verhagen
  6. Alexandre A Khrapitchev
  7. Nicola Sibson
  8. Saad Jbabdi
(2018)
Whole brain comparative anatomy using connectivity blueprints
eLife 7:e35237.
https://doi.org/10.7554/eLife.35237

Share this article

https://doi.org/10.7554/eLife.35237

Further reading

    1. Neuroscience
    Simonas Griesius, Amy Richardson, Dimitri Michael Kullmann
    Research Article

    Non-linear summation of synaptic inputs to the dendrites of pyramidal neurons has been proposed to increase the computation capacity of neurons through coincidence detection, signal amplification, and additional logic operations such as XOR. Supralinear dendritic integration has been documented extensively in principal neurons, mediated by several voltage-dependent conductances. It has also been reported in parvalbumin-positive hippocampal basket cells, in dendrites innervated by feedback excitatory synapses. Whether other interneurons, which support feed-forward or feedback inhibition of principal neuron dendrites, also exhibit local non-linear integration of synaptic excitation is not known. Here, we use patch-clamp electrophysiology, and two-photon calcium imaging and glutamate uncaging, to show that supralinear dendritic integration of near-synchronous spatially clustered glutamate-receptor mediated depolarization occurs in NDNF-positive neurogliaform cells and oriens-lacunosum moleculare interneurons in the mouse hippocampus. Supralinear summation was detected via recordings of somatic depolarizations elicited by uncaging of glutamate on dendritic fragments, and, in neurogliaform cells, by concurrent imaging of dendritic calcium transients. Supralinearity was abolished by blocking NMDA receptors (NMDARs) but resisted blockade of voltage-gated sodium channels. Blocking L-type calcium channels abolished supralinear calcium signalling but only had a minor effect on voltage supralinearity. Dendritic boosting of spatially clustered synaptic signals argues for previously unappreciated computational complexity in dendrite-projecting inhibitory cells of the hippocampus.

    1. Neuroscience
    Christine Ahrends, Mark W Woolrich, Diego Vidaurre
    Tools and Resources

    Predicting an individual’s cognitive traits or clinical condition using brain signals is a central goal in modern neuroscience. This is commonly done using either structural aspects, such as structural connectivity or cortical thickness, or aggregated measures of brain activity that average over time. But these approaches are missing a central aspect of brain function: the unique ways in which an individual’s brain activity unfolds over time. One reason why these dynamic patterns are not usually considered is that they have to be described by complex, high-dimensional models; and it is unclear how best to use these models for prediction. We here propose an approach that describes dynamic functional connectivity and amplitude patterns using a Hidden Markov model (HMM) and combines it with the Fisher kernel, which can be used to predict individual traits. The Fisher kernel is constructed from the HMM in a mathematically principled manner, thereby preserving the structure of the underlying model. We show here, in fMRI data, that the HMM-Fisher kernel approach is accurate and reliable. We compare the Fisher kernel to other prediction methods, both time-varying and time-averaged functional connectivity-based models. Our approach leverages information about an individual’s time-varying amplitude and functional connectivity for prediction and has broad applications in cognitive neuroscience and personalised medicine.