The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Developmental differences in white matter architecture between boys and girls
Open accessAbstract
Previous studies have found developmental differences between males and females in brain structure. During childhood and adolescence, relative white matter volume increases faster in boys than in girls. Sex differences in the development of white matter microstructure were investigated in a cohort of normal children ages 5–18 in a cross‐sectional diffusion tensor imaging (DTI) study. Greater fractional anisotropy (FA) in boys was shown in associative white matter regions (including the frontal lobes), while greater FA in girls was shown in the splenium of the corpus callosum. Greater mean diffusivity (MD) in boys was shown in the corticospinal tract and in frontal white matter in the right hemisphere; greater MD in girls was shown in occipito‐parietal regions and the most superior aspect of the corticospinal tract in the right hemisphere. Significant sex–age interactions on FA and MD were also shown. Girls displayed a greater rate of fiber density increase with age when compared with boys in associative regions (reflected in MD values). However, girls displayed a trend toward increased organization with age (reflected in FA values) only in the right hemisphere, while boys displayed this trend only in the left hemisphere. These results indicate differing developmental trajectories in white matter for boys and girls and the importance of taking sex into account in developmental DTI studies. The results also may have implications for the study of the relationship of brain architecture with intelligence. Hum Brain Mapp, 2008. © 2007 Wiley‐Liss, Inc.
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Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007) · Behavior Research Methods · doi:10.3758/bf03193146 · Cited by 65,971
Gur, R. C., Turetsky, B. I., Matsui, M., Yan, M., Bilker, W., Hughett, P., & Gur, R. E. (1999) · The Journal of Neuroscience · doi:10.1523/jneurosci.19-10-04065.1999 · Cited by 909
Sex Differences in Brain Maturation during Childhood and Adolescence
De Bellis, M. D. (2001) · Cerebral Cortex · doi:10.1093/cercor/11.6.552 · Cited by 728 · cited 6×
“…size, a significant sex–age interaction has been demonstrated, with boys displaying greater rates of increase with age of normalized white matter volume, and greater rates of decrease with age of normalized gray matter volume, relative to girls between the ages of 7–17 [De Bellis et al., 2001].”
BackgroundResultInfluentialThe Human Brain Age 7–11 Years: A Volumetric Analysis Based on Magnetic Resonance Images
Caviness, V. S., Kennedy, D. N., Richelme, C., Rademacher, J., & Filipek, P. A. (1996) · Cerebral Cortex · doi:10.1093/cercor/6.5.726 · Cited by 509
Gender differences in the left inferior frontal gyrus in normal children
Blanton, R. E., Levitt, J. G., Peterson, J. R., Fadale, D., Sporty, M. L., Lee, M., To, D., Mormino, E. C., Thompson, P. M., McCracken, J. T., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.01.010 · Cited by 90
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Microstructural maturation of the human brain from childhood to adulthood
Lebel, C., Walker, L., Leemans, A., Phillips, L., & Beaulieu, C. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.12.053 · Cited by 1,398
Sex differences in the structural connectome of the human brain
Ingalhalikar, M., Smith, A., Parker, D., Satterthwaite, T. D., Elliott, M. A., Ruparel, K., Hakonarson, H., Gur, R. E., Gur, R. C., & Verma, R. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1316909110 · Cited by 1,223
Diffusion tensor imaging of white matter tract evolution over the lifespan
Lebel, C., Gee, M., Camicioli, R., Wieler, M., Martin, W., & Beaulieu, C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.094 · Cited by 1,100
Tamnes, C. K., Østby, Y., Fjell, A. M., Westlye, L. T., Due-Tønnessen, P., & Walhovd, K. B. (2010) · Cerebral Cortex · doi:10.1093/cercor/bhp118 · Cited by 779
The development of brain white matter microstructure
Lebel, C., & Deoni, S. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.097 · Cited by 602
Metadata
- Authors
- Schmithorst, V. J., Holland, S. K., & Dardzinski, B. J.
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2007-06-27
- Volume
- 29
- Issue
- 6
- Pages
- 696-710
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- OpenAlex
- W2106533387
- Cited by
- 244
- References
- 5
- FWCI
- 11.86
- Type
- journal article
- Language
- en
- Open access
- green
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