A positive-negative mode of population covariation links brain connectivity, demographics and behavior | Nature Neuroscience
Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Brief Communication
  • Published:

A positive-negative mode of population covariation links brain connectivity, demographics and behavior

Nature Neuroscience volume 18, pages 1565–1567 (2015)Cite this article

Subjects

Abstract

We investigated the relationship between individual subjects' functional connectomes and 280 behavioral and demographic measures in a single holistic multivariate analysis relating imaging to non-imaging data from 461 subjects in the Human Connectome Project. We identified one strong mode of population co-variation: subjects were predominantly spread along a single 'positive-negative' axis linking lifestyle, demographic and psychometric measures to each other and to a specific pattern of brain connectivity.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: CCA mode subject measure weights, connectome weights and data variance explained.
Figure 2: CCA mode connectome weights and associated spatial maps.

Similar content being viewed by others

References

  1. Van Essen, D.C. et al. Neuroimage 80, 62–79 (2013).

    Article  Google Scholar 

  2. Beckmann, C.F. & Smith, S.M. IEEE Trans. Med. Imaging 23, 137–152 (2004).

    Article  Google Scholar 

  3. Hotelling, H. Biometrika 28, 321–377 (1936).

    Article  Google Scholar 

  4. Winkler, A. et al. Neuroimage published online, 10.1016/j.neuroimage.2015.05.092 (11 June 2015).

  5. Spearman, C. Am. J. Psychol. 15, 201–292 (1904).

    Article  Google Scholar 

  6. Fox, M.D. et al. Proc. Natl. Acad. Sci. USA 102, 9673–9678 (2005).

    Article  CAS  Google Scholar 

  7. Binder, J.R. et al. Cereb. Cortex 19, 2767–2796 (2009).

    Article  Google Scholar 

  8. Schacter, D.L. et al. Neuron 76, 677–694 (2012).

    Article  CAS  Google Scholar 

  9. Clithero, J.A. & Rangel, A. Soc. Cogn. Affect. Neurosci. 9, 1289–1302 (2014).

    Article  Google Scholar 

  10. Kable, J.W. & Glimcher, P.W. Nat. Neurosci. 10, 1625–1633 (2007).

    Article  CAS  Google Scholar 

  11. Doeller, C.F. et al. Nature 463, 657–661 (2010).

    Article  CAS  Google Scholar 

  12. Saxe, R. et al. Annu. Rev. Psychol. 55, 87–124 (2004).

    Article  CAS  Google Scholar 

  13. Poldrack, R.A. Trends Cogn. Sci. 10, 59–63 (2006).

    Article  Google Scholar 

  14. Cole, M.W. et al. J. Neurosci. 32, 8988–8999 (2012).

    Article  CAS  Google Scholar 

  15. Woolgar, A. et al. Proc. Natl. Acad. Sci. USA 107, 14899–14902 (2010).

    Article  CAS  Google Scholar 

  16. Thomson, G.H. Br. J. Psychol. 8, 271–281 (1916).

    Google Scholar 

  17. Harrison, S.J. et al. Neuroimage 109, 217–231 (2015).

    Article  Google Scholar 

  18. Ugˇurbil, K. et al. Neuroimage 80, 80–104 (2013).

    Article  Google Scholar 

  19. Jenkinson, M. et al. Neuroimage 62, 782–790 (2012).

    Article  Google Scholar 

  20. Fischl, B. et al. Neuroimage 9, 195–207 (1999).

    Article  CAS  Google Scholar 

  21. Marcus, D.S. et al. Neuroimage 80, 202–219 (2013).

    Article  Google Scholar 

  22. Smith, S.M. et al. Neuroimage 80, 144–168 (2013).

    Article  Google Scholar 

  23. Glasser, M.F. et al. Neuroimage 80, 105–124 (2013).

    Article  Google Scholar 

  24. Salimi-Khorshidi, G. et al. Neuroimage 90, 449–468 (2014).

    Article  Google Scholar 

  25. Griffanti, L. et al. Neuroimage 95, 232–247 (2014).

    Article  Google Scholar 

  26. Smith, S.M. et al. Neuroimage 101, 738–749 (2014).

    Article  Google Scholar 

  27. Filippini, N. et al. Proc. Natl. Acad. Sci. USA 106, 7209–7214 (2009).

    Article  CAS  Google Scholar 

  28. Marrelec, G. et al. Neuroimage 32, 228–237 (2006).

    Article  Google Scholar 

  29. Van der Waerden, B.L. Proc. K. Ned. Akad. Wet. A. 55, 453–458 (1952).

    Google Scholar 

Download references

Acknowledgements

We thank our many colleagues in the WU-Minn HCP Consortium for their invaluable contributions in generating the publicly available HCP data and implementing the procedures needed to acquire, analyze, visualize and share these data sets. We are grateful for funding from the US National Institutes of Health (grants 1U54MH091657, P30-NS057091, P41-RR08079/EB015894 and F30-MH097312) and the Wellcome Trust (grants 098369/Z/12/Z and 091509/Z/10/Z).

Author information

Authors and Affiliations

  1. Oxford University Centre for Functional MRI of the Brain (FMRIB), Oxford, UK

    Stephen M Smith, Anderson M Winkler, Timothy E J Behrens & Karla L Miller

  2. Department of Statistics and Warwick Manufacturing Group, University of Warwick, Coventry, UK

    Thomas E Nichols

  3. Oxford University Centre for Human Brain Activity (OHBA), Oxford, UK

    Diego Vidaurre

  4. Washington University School of Medicine, Washington University, St. Louis, Missouri, USA

    Matthew F Glasser, Deanna M Barch & David C Van Essen

  5. Center for Magnetic Resonance Research, University of Minnesota, Minnesota, USA

    Kamil Ugurbil

Authors
  1. Stephen M Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thomas E Nichols
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Diego Vidaurre
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anderson M Winkler
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Timothy E J Behrens
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Matthew F Glasser
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kamil Ugurbil
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Deanna M Barch
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. David C Van Essen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Karla L Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.V.E., K.U., D.B., T.B., M.G. and S.S. contributed to the data acquisition and image processing. S.S., K.M., T.N., D.V., A.W. and T.B. designed and carried out the statistical analyses. All of the authors contributed to writing the paper.

Corresponding author

Correspondence to Stephen M Smith.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4 and Supplementary Tables 1 and 2 (PDF 4287 kb)

Supplementary Methods Checklist (PDF 384 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Smith, S., Nichols, T., Vidaurre, D. et al. A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nat Neurosci 18, 1565–1567 (2015). https://doi.org/10.1038/nn.4125

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.4125

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing