The heritability of multi-modal connectivity in human brain activity

  1. Giles L Colclough  Is a corresponding author
  2. Stephen M Smith
  3. Tom E Nichols
  4. Anderson M Winkler
  5. Stamatios N Sotiropoulos
  6. Matthew F Glasser
  7. David C Van Essen
  8. Mark W Woolrich
  1. University of Oxford, United Kingdom
  2. University of Warwick, United Kingdom
  3. Washington University, United States
  4. Washington University in St Louis, United States

Abstract

Patterns of intrinsic human brain activity exhibit a profile of functional connectivity that is associated with behaviour and cognitive performance, and deteriorates with disease. This paper investigates the relative importance of genetic factors and the common environment between twins in determining this functional connectivity profile. Using functional magnetic resonance imaging (fMRI) on 820 subjects from the Human Connectome Project, and magnetoencephalographic (MEG) recordings from a subset, the heritability of connectivity between 39 cortical regions was estimated. On average over all connections, genes account for about 15% of the observed variance in fMRI connectivity (and about 10% in alpha-band and 20% in beta-band oscillatory power synchronisation), which substantially exceeds the contribution from the environment shared between twins. Therefore, insofar as twins share a common upbringing, it appears that genes, rather than the developmental environment, play a dominant role in determining the coupling of neuronal activity.

Data availability

The following previously published data sets were used
    1. Van Essen DC et al.
    (2013) The Human Connectome Project HCP500 data release
    Open access dataset available from ConnectomeDB (https://db.humanconnectome.org/app/template/Login.vm). Account registration is required and access to certain data elements such as family structure is subject to restricted use terms (please see http://www.humanconnectome.org/study/hcp-young-adult/data-use-terms).
    1. Larson-Prior LJ
    (2013) The Human Connectome Project MEG2 data release
    Open access dataset available from ConnectomeDB (https://db.humanconnectome.org/app/template/Login.vm). Account registration is required and access to certain data elements such as family structure is subject to restricted use terms (please see http://www.humanconnectome.org/study/hcp-young-adult/data-use-terms).

Article and author information

Author details

  1. Giles L Colclough

    Oxford Centre for Human Brain Activity, University of Oxford, Oxford, United Kingdom
    For correspondence
    giles.colclough@ohba.ox.ac.uk
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1074-7186
  2. Stephen M Smith

    Oxford Centre for Human Brain Activity, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
  3. Tom E Nichols

    Department of Statistics and WMG, University of Warwick, Coventry, United Kingdom
    Competing interests
    No competing interests declared.
  4. Anderson M Winkler

    Oxford Centre for Human Brain Activity, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
  5. Stamatios N Sotiropoulos

    Oxford Centre for Human Brain Activity, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
  6. Matthew F Glasser

    School of Medicine, Washington University, St. Louis, United States
    Competing interests
    No competing interests declared.
  7. David C Van Essen

    School of Medicine, Washington University in St Louis, St. Louis, United States
    Competing interests
    David C Van Essen, Senior editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7044-4721
  8. Mark W Woolrich

    Oxford Centre for Human Brain Activity, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.

Funding

Research Councils UK (Digital Economy Programme (EP/G036861/1,Centre for Doctoral Training in Healthcare Innovation))

  • Giles L Colclough

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq,211534/2013-7)

  • Anderson M Winkler

Engineering and Physical Sciences Research Council (EP/L023067)

  • Stamatios N Sotiropoulos

National Institutes of Health (R01EB015611-01; NRSA fellowship (F30-MH097312); 1U54MH091657)

  • Tom E Nichols
  • Matthew F Glasser
  • David C Van Essen

National Institute for Health Research (NIHR Oxford Biomedical Research Centre)

  • Mark W Woolrich

Medical Research Council (MRC UK MEG Partnership Grant (MR/K005464/1))

  • Giles L Colclough
  • Mark W Woolrich

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

Reviewing Editor

  1. Jack L Gallant, University of California, Berkeley, United States

Ethics

Human subjects: HCP data were acquired using protocols approved by the Washington University institutional review board. Informedconsent was obtained from subjects. Anonymised data are publicly available from ConnectomeDB (db.humanconnectome.org; Hodge et al. (2016)). Certain parts of the dataset used in this study, such as the family structures of the subjects, are available subject to restricted data usage terms, requiring researchers to ensure that the anonymity of subjects is protected (Van Essen et al., 2013).

Version history

  1. Received: August 3, 2016
  2. Accepted: July 13, 2017
  3. Accepted Manuscript published: July 26, 2017 (version 1)
  4. Accepted Manuscript updated: August 1, 2017 (version 2)
  5. Version of Record published: September 29, 2017 (version 3)

Copyright

© 2017, Colclough 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

  • 4,128
    views
  • 628
    downloads
  • 102
    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. Giles L Colclough
  2. Stephen M Smith
  3. Tom E Nichols
  4. Anderson M Winkler
  5. Stamatios N Sotiropoulos
  6. Matthew F Glasser
  7. David C Van Essen
  8. Mark W Woolrich
(2017)
The heritability of multi-modal connectivity in human brain activity
eLife 6:e20178.
https://doi.org/10.7554/eLife.20178

Share this article

https://doi.org/10.7554/eLife.20178

Further reading

    1. Neuroscience
    Max Schulz, Malte Wöstmann
    Insight

    Asymmetries in the size of structures deep below the cortex explain how alpha oscillations in the brain respond to shifts in attention.

    1. Neuroscience
    Tara Ghafari, Cecilia Mazzetti ... Ole Jensen
    Research Article

    Evidence suggests that subcortical structures play a role in high-level cognitive functions such as the allocation of spatial attention. While there is abundant evidence in humans for posterior alpha band oscillations being modulated by spatial attention, little is known about how subcortical regions contribute to these oscillatory modulations, particularly under varying conditions of cognitive challenge. In this study, we combined MEG and structural MRI data to investigate the role of subcortical structures in controlling the allocation of attentional resources by employing a cued spatial attention paradigm with varying levels of perceptual load. We asked whether hemispheric lateralization of volumetric measures of the thalamus and basal ganglia predicted the hemispheric modulation of alpha-band power. Lateral asymmetry of the globus pallidus, caudate nucleus, and thalamus predicted attention-related modulations of posterior alpha oscillations. When the perceptual load was applied to the target and the distractor was salient caudate nucleus asymmetry predicted alpha-band modulations. Globus pallidus was predictive of alpha-band modulations when either the target had a high load, or the distractor was salient, but not both. Finally, the asymmetry of the thalamus predicted alpha band modulation when neither component of the task was perceptually demanding. In addition to delivering new insight into the subcortical circuity controlling alpha oscillations with spatial attention, our finding might also have clinical applications. We provide a framework that could be followed for detecting how structural changes in subcortical regions that are associated with neurological disorders can be reflected in the modulation of oscillatory brain activity.