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.

Gender differences in cortical complexity

Nature Neuroscience volume 7, pages 799–800 (2004)Cite this article

Abstract

Cortical complexity, a measure that quantifies the spatial frequency of gyrification and fissuration of the brain surface, has not been thoroughly characterized with respect to gender differences in the human brain. Using a new three-dimensional (3D) analytic technique with magnetic resonance imaging, we found greater gyrification in women than men in frontal and parietal regions. Increased complexity implies more cortical surface area, which may offset gender differences in brain volume and account for behavioral gender differences.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

Buy

All prices are NET prices.

Figure 1: The cerebral cortex.

References

  1. Rabinowicz, T. et al. J. Neuropathol. Exp. Neurol. 61, 46–57 (2002).

    Article  PubMed  Google Scholar 

  2. Witelson, S.F., Glezer, I.I. & Kigar, D.L. J. Neurosci. 15, 3418–3428 (1995).

    CAS  Article  PubMed  Google Scholar 

  3. Gur, R.C. et al. J. Neurosci. 19, 4065–4072 (1999).

    CAS  Article  PubMed  Google Scholar 

  4. Van Essen, D.C. Nature 385, 313–318 (1997).

    CAS  Article  PubMed  Google Scholar 

  5. Sled, J.G., Zijdenbos, A.P. & Evans, A.C. IEEE Trans. Med. Imaging 17, 87–97 (1998).

    CAS  Article  PubMed  Google Scholar 

  6. Woods, R.P., Grafton, S.T., Watson, J.D., Sicotte, N.L. & Mazziotta, J.C. J. Comput. Assist. Tomogr. 22, 153–165 (1998).

    CAS  Article  Google Scholar 

  7. MacDonald, D., Avis, D. & Evans, A. Proc. Int. Soc. Opt. Eng. 2359, 160–169 (1994).

    Google Scholar 

  8. Sowell, E.R. et al. Cereb. Cortex 12, 17–26 (2002).

    Article  Google Scholar 

  9. Thompson, P.M., Woods, R.P., Mega, M.S. & Toga, A.W. Hum. Brain Mapp. 9, 81–92 (2000).

    CAS  Article  Google Scholar 

  10. Thompson, P.M., Schwartz, C., Lin, R.T., Khan, A.A. & Toga, A.W. J. Neurosci. 16, 4261–4274 (1996).

    CAS  Article  PubMed  Google Scholar 

  11. Nopoulos, P., Flaum, M., O'Leary, D. & Andreasen, N.C. Psychiatry Res. 98, 1–13 (2000).

    CAS  Article  PubMed  Google Scholar 

  12. Zilles, K., Armstrong, E., Schleicher, A. & Kretschmann, H.J. Anat. Embryol. (Berl.) 179, 173–179 (1988).

    CAS  Article  Google Scholar 

  13. Roland, P.E. & Zilles, K. Trends Neurosci. 17, 458–467 (1994).

    CAS  Article  Google Scholar 

  14. Caviness, V.S. Science 189, 18–21 (1975).

    Article  PubMed  Google Scholar 

  15. Cohen, J. Statistical Power Analysis for the Behavioral Sciences pp. 25–27 (Hillsdale, New York, 1988).

    Google Scholar 

Download references

Acknowledgements

This work was supported by Deutsche Forschungsgemeinschaft (DFG, JA 737/8-1), research grants from the National Library of Medicine and National Institute on Aging (R01 LM05639), National Institute of Mental Health and National Institute of Neurological Disorders and Stroke (P20 MH065166), resource grants from the National Center for Research Resources (P41 RR013642 & M01 RR000865), an NIMH NRSA Training Grant (MH14584) and NARSAD Young Investigator Award (to K.L.N.), and R21 grants RR19771 and EB01561 (to P.T.). Additional support was provided by the National Institute of Biomedical Imaging and Bioengineering, National Institute of Neurological Disorders and Stroke, and National Institute of Mental Health (P01 EB001955).

Author information

Authors and Affiliations

  1. Department of Neurology, Brain Mapping Division, Laboratory of Neuroimaging, UCLA School of Medicine, 710 Westwood Plaza, 4238 Reed, Los Angeles, 90095, California, USA

    Eileen Luders, Katherine L Narr, Paul M Thompson, David E Rex & Arthur W Toga

  2. Department of Neuropsychology, University of Zurich, Treichlerstrasse 10, Zurich, 8032, Switzerland

    Lutz Jancke

  3. Department of Neurology, Johann-Wolfgang-Goethe University, Schleusenweg 2-16, Frankfurt am Main, 60528, Germany

    Eileen Luders & Helmuth Steinmetz

Authors
  1. Eileen Luders
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Katherine L Narr
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paul M Thompson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David E Rex
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lutz Jancke
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Helmuth Steinmetz
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Arthur W Toga
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arthur W Toga.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Figure 1

Calculating cortical complexity. In our analyses (1) ordered hierarchies of parametric meshes with variable resolutions were generated for each lobar region of interest, as shown here for the right superior-frontal region. (2) Subsequently, the logarithmic least squares regression of the measured surface area was plotted against the logarithm of the spatial frequency of the mesh. This dependence of the area measurement on the spatial resolution of the parametric grid is often termed the 'fractal dimension' and reflects the rate at which the measured surface area increases as the spatial detail in the surface representation is increased. For example, the area measured for a flat surface would always be the same at different spatial frequencies; the area of a convoluted surface will increase with increasing spatial resolution. (3) Finally, the slope of the regression plot was derived and added to the number 2, resulting in complexity values between 2 (for a planar or flat surface) and 3 (for a surface with immense numbers of convolutions and extensive folding). Although the slope of the regression plot would provide an equally useful complexity measure, adding the number 2 results in fractal dimension values that agree with the true physical dimension of the surface in the case of a 2D plane (where the fractal dimension turns out to be 2), and a surface that fills all of 3D space (where it is 3). (GIF 53 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Luders, E., Narr, K., Thompson, P. et al. Gender differences in cortical complexity. Nat Neurosci 7, 799–800 (2004). https://doi.org/10.1038/nn1277

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn1277

Further reading

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