© 2006 Nature Publishing Group
Statistical analysis. Pearson’s correlations between IQ and cortical thickness
were estimated at each cortical point. Each subject contributed only one scan to
maintain independence of data, and efforts were made to ensure a wide age range
was covered. Developmental effects were explored by dividing the sample
equally into four age groups (called early childhood (age range 3.8–8.4 yr),
late childhood (range 8.6–11.7yr), adolescence (11.8–16.9 yr) and early adulthood
(17–29 yr)). Correlations for each of 56 brain subregions were Z-transformed,
and the difference between the Z scores for each age group, and its signiﬁcance,
was calculated. To correct for the large number of comparisons, a false discovery
rate of 0.05 was applied
. Gender effects were examined for the entire sample in
a similar manner.
To exploit the longitudinal nature of our data set, we used linear mixed-model
regression, as this technique permits the inclusion of multiple measurements per
person, missing data, and irregular intervals between measurements, thereby
increasing statistical power while controlling for within-individual variation
Polynomial models for age effects were compared throughout the cerebral cortex
and a cubic model found to provide the best ﬁt, with the exception of anterior
temporal cortices where a linear model was appropriate. A cubic model was
therefore used to model age effects in the analyses presented. We ﬁrst examined
whether the relationship between IQ and cortical thickness differs with age by
regressing cortical thickness at every vertex against IQ, age terms, and the
interaction of IQ and age terms. For further exploration of the interaction, we
divided the subjects into three IQ groups. This approach loses some power by
categorizing a continuous variable, but has the advantage of rendering the results
readily interpretable, allowing comparisons between highly intelligent and less
intelligent groups. The resulting statistical maps were thresholded to control for
multiple comparisons using the false discovery rate (FDR) procedure with
q ¼ 0.05 (refs 28, 30). An FDR threshold was determined for the statistical
model using all P values pooled across all effects included in the model. At every
cortical point, t-statistics were visualized through projection onto a standard
brain template (the map shows the results of the interaction between the cubic
age term and IQ groups). Such visualization showed clusters of cortical points
that had a signiﬁcant difference between the intelligence groups in the trajectory
of cortical growth. The longitudinal analyses selected and averaged all cortical
points within each of these clusters. Graphs illustrating the trajectories were
generated using ﬁxed-effects parameter estimates.
To illustrate differences in cortical thickness between the superior and average
intelligence groups at different ages, linear mixed-models were run at different
centred ages. For example, for age seven years, seven was subtracted from the age
at scan acquisition, and this value entered as the age term. t-statistics represent-
ing the differences in cortical thickness between the two intelligence groups at
each age were projected onto brain templates. This analysis represents group
differences at each age based on values estimated from developmental curves
modelled on all data.
Received 25 October; accepted 29 November 2005.
1. Spearman, C. ‘General intelligence’ objectively determined and measured. Am.
J. Psychol. 15, 201–-293 (1904).
2. Gottfredson, L. S. Why g matters: The complexity of everyday life. Intelligence
24, 79–-132 (1997).
3. Deary, I. J., Whalley, L. J., Lemmon, H., Crawford, J. R. & Starr, J. M. The
stability of individual differences in mental ability from childhood to old age:
Follow-up of the 1932 Scottish Mental Survey. Intelligence 28, 49–-55 (2000).
4. Booth, J. R. et al. Neural development of selective attention and response
inhibition. Neuroimage 20, 737–-751 (2003).
5. Sowell, E. R. et al. Longitudinal mapping of cortical thickness and brain growth
in normal children. J. Neurosci. 24, 8223–-8231 (2004).
6. McDaniel, M. Big-brained people are smarter. Intelligence 33, 337–-346 (2005).
7. Wilke, M., Sohn, J. H., Byars, A. W. & Holland, S. K. Bright spots: correlations of
gray matter volume with IQ in a normal pediatric population. Neuroimage 20,
8. Frangou, S., Chitins, X. & Williams, S. C. Mapping IQ and gray matter density in
healthy young people. Neuroimage 23, 800–-805 (2004).
9. Haier, R. J., Jung, R. E., Yeo, R. A., Head, K. & Alkire, M. T. Structural brain
variation and general intelligence. Neuroimage 23, 425–-433 (2004).
10. Kraemer, H. C., Yesavage, J. A., Taylor, J. L. & Kupfer, D. How can we learn
about developmental processes from cross-sectional studies, or can we? Am.
J. Psychiatry 157, 163–-171 (2000).
11. Giedd, J. N. et al. Brain development during childhood and adolescence: a
longitudinal MRI study. Nature Neurosci. 2, 861–-863 (1999).
12. Wechsler, D. Manual for the Wechsler Intelligence Scale for Children
(The Psychological Corporation, New York, 1974).
13. O’Donnell, S., Noseworthy, M. D., Levine, B. & Dennis, M. Cortical thickness of
the frontopolar area in typically developing children and adolescents.
Neuroimage 24, 948–-954 (2005).
14. Kabani, N., Le Goualher, G., MacDonald, D. & Evans, A. C. Measurement of
cortical thickness using an automated 3-D algorithm: a validation study.
Neuroimage 13, 375–-380 (2001).
15. Lerch, J. P. & Evans, A. C. Cortical thickness analysis examined through power
analysis and a population simulation. Neuroimage 24, 163–-173 (2005).
16. Gray, J. R., Chabris, C. F. & Braver, T. S. Neural mechanisms of general ﬂuid
intelligence. Nature Neurosci. 6, 316–-322 (2003).
17. Duncan, J. et al. A neural basis for general intelligence. Science 289, 457–-460
18. Kostovic, I., Judas, M., Rados, M. & Hrabac, P. Laminar organization of the
human fetal cerebrum revealed by histochemical markers and magnetic
resonance imaging. Cereb. Cortex 12, 536–-544 (2002).
19. Kostovic, I. & Rakic, P. Developmental history of the transient subplate zone in
the visual and somatosensory cortex of the macaque monkey and human
brain. J. Comp. Neurol. 297, 441–-470 (1990).
20. Yakovlev, P. I. & Lecours, A. R. in Regional Development of the Brain in Early Life
(ed. Minokowski, A.) (Blackwell Scientiﬁc, Oxford, 1967).
21. Huttenlocher, P. R. & Dabholkar, A. S. Regional differences in synaptogenesis in
human cerebral cortex. J. Comp. Neurol. 387, 167–-178 (1997).
22. Hensch, T. K. Critical period regulation. Annu. Rev. Neurosci. 27, 549–-579
23. Chugani, H. T., Phelps, M. E. & Mazziotta, J. C. Positron emission tomography
study of human brain functional development. Ann. Neurol. 22, 487–-497
24. Collins, D. L., Neelin, P., Peters, T. M. & Evans, A. C. Automatic 3D intersubject
registration of MR volumetric data in standardized Talairach space. J. Comput.
Assist. Tomogr. 18, 192–-205 (1994).
25. Sled, J. G., Zijdenbos, A. P. & Evans, A. C. A nonparametric method for
automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med.
Imaging 17, 87–-97 (1998).
26. Zijdenbos, A. P., Forghani, R. & Evans, A. C. Automatic “pipeline” analysis of
3-D MRI data for clinical trials: application to multiple sclerosis. IEEE Trans.
Med. Imaging 21, 1280–-1291 (2002).
27. MacDonald, D., Kabani, N., Avis, D. & Evans, A. C. Automated 3-D extraction
of inner and outer surfaces of cerebral cortex from MRI. Neuroimage 12,
28. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical
and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–-300
29. Pinheiro, J. C. & Bates, D. M. Mixed-effects Models in S and S-PLUS (Springer,
New York, 2000).
30. Genovese, C. R., Lazar, N. A. & Nichols, T. Thresholding of statistical maps in
functional neuroimaging using the false discovery rate. Neuroimage 15,
Supplementary Information is linked to the online version of the paper at
Acknowledgements This research was supported by the Intramural Research
Program of the National Institutes of Health. We acknowledge the statistical
advice of G. Chen and technical assistance from T. Nugent III. The authors thank
the children who participated in the study and their families.
Author Contributions P.S. designed and wrote the study with J.R. and J.G., and
conducted neuroimaging analyses. J.G. and J.R. directed the project. D.G.
conducted longitudinal analyses. L.C. was data manager, and R.L. and N.G.
advised on interpretation and analysis. J.L. and A.E. developed cortical thickness
analytic tools and J.L. developed software for longitudinal neuroimaging
Author Information Reprints and permissions information is available at
npg.nature.com/reprintsandpermissions. The authors declare no competing
ﬁnancial interests. Correspondence and requests for materials should be
addressed to P.S. (firstname.lastname@example.org).
NATURE|Vol 440|30 March 2006 LETTERS