The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Age-Related Effects and Sex Differences in Gray Matter Density, Volume, Mass, and Cortical Thickness from Childhood to Young Adulthood
Open accessAbstract
Developmental structural neuroimaging studies in humans have long described decreases in gray matter volume (GMV) and cortical thickness (CT) during adolescence. Gray matter density (GMD), a measure often assumed to be highly related to volume, has not been systematically investigated in development. We used T1 imaging data collected on the Philadelphia Neurodevelopmental Cohort to study age-related effects and sex differences in four regional gray matter measures in 1189 youths ranging in age from 8 to 23 years. Custom T1 segmentation and a novel high-resolution gray matter parcellation were used to extract GMD, GMV, gray matter mass (GMM; defined as GMD × GMV), and CT from 1625 brain regions. Nonlinear models revealed that each modality exhibits unique age-related effects and sex differences. While GMV and CT generally decrease with age, GMD increases and shows the strongest age-related effects, while GMM shows a slight decline overall. Females have lower GMV but higher GMD than males throughout the brain. Our findings suggest that GMD is a prime phenotype for the assessment of brain development and likely cognition and that periadolescent gray matter loss may be less pronounced than previously thought. This work highlights the need for combined quantitative histological MRI studies. SIGNIFICANCE STATEMENT This study demonstrates that different MRI-derived gray matter measures show distinct age and sex effects and should not be considered equivalent but complementary. It is shown for the first time that gray matter density increases from childhood to young adulthood, in contrast with gray matter volume and cortical thickness, and that females, who are known to have lower gray matter volume than males, have higher density throughout the brain. A custom preprocessing pipeline and a novel high-resolution parcellation were created to analyze brain scans of 1189 youths collected as part of the Philadelphia Neurodevelopmental Cohort. A clear understanding of normal structural brain development is essential for the examination of brain–behavior relationships, the study of brain disease, and, ultimately, clinical applications of neuroimaging.
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“Women also appear to have lower gray matter volume,50”
From Sex differences in cognitive decline among US adults (2021) · cites this paper as [50]
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Cites
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002) · NeuroImage · doi:10.1006/nimg.2001.0978 · Cited by 16,319
Wood, S. N. (2010) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2010.00749.x · Cited by 7,538
N4ITK: Improved N3 Bias Correction
Tustison, N. J., Avants, B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2046908 · Cited by 6,413
Self-citationAvants, B., Epstein, C. L., Grossman, M., & Gee, J. C. (2007) · Medical Image Analysis · doi:10.1016/j.media.2007.06.004 · Cited by 5,924
Self-citationDynamic mapping of human cortical development during childhood through early adulthood
Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C., Nugent, T. F., Herman, D. H., Clasen, L. S., Toga, A. W., et al. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0402680101 · Cited by 5,604 · cited 3×
“Several developmental neuroimaging studies have described modest decreases in gray matter during adolescence using measures derived from gray matter volume and cortical thickness (Sowell et al., 2003, 2004; Gogtay et al., 2004; Shaw et al., 2008; Brain Development Cooperative Group, 2012).”
BackgroundMethodResultInfluentialA multi-modal parcellation of human cerebral cortex
Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., Ugurbil, K., Andersson, J., Beckmann, C. F., Jenkinson, M., et al. (2016) · Nature · doi:10.1038/nature18933 · Cited by 5,111
NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain
Zhang, H., Schneider, T., Wheeler-Kingshott, C. A., & Alexander, D. C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.072 · Cited by 3,288
Neurodegenerative Diseases Target Large-Scale Human Brain Networks
Seeley, W. W., Crawford, R. K., Zhou, J., Miller, B. L., & Greicius, M. D. (2009) · Neuron · doi:10.1016/j.neuron.2009.03.024 · Cited by 2,350
Mapping cortical change across the human life span
Sowell, E. R., Peterson, B. S., Thompson, P. M., Welcome, S. E., Henkenius, A. L., & Toga, A. W. (2003) · Nature Neuroscience · doi:10.1038/nn1008 · Cited by 2,328
Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration
Klein, A., Andersson, J., Ardekani, B. A., Ashburner, J., Avants, B., Chiang, M.-C., Christensen, G. E., Collins, D. L., Gee, J., Hellier, P., et al. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.12.037 · Cited by 2,227
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Harmonization of cortical thickness measurements across scanners and sites
Fortin, J.-P., Cullen, N., Sheline, Y. I., Taylor, W. D., Aselcioglu, I., Cook, P. A., Adams, P., Cooper, C., Fava, M., McGrath, P. J., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.024 · Cited by 1,580
Sex Differences in the Adult Human Brain: Evidence from 5216 UK Biobank Participants
Ritchie, S. J., Cox, S. R., Shen, X., Lombardo, M. V., Reus, L. M., Alloza, C., Harris, M. A., Alderson, H. L., Hunter, S., Neilson, E., et al. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy109 · Cited by 855
“2008; note that the insula showing the largest sex difference is consistent with a recent large-scale study of children and adolescents (Gennatas et al. 2017)—it appears this region retains its substantial sex difference into later life), but also areas such as the right fusiform gyrus.”
BackgroundResultSex differences in cognitive decline among US adults
Levine, D. A., Gross, A. L., Briceño, E. M., Tilton, N., Giordani, B. J., Sussman, J. B., Hayward, R. A., Burke, J. F., Hingtgen, S., Elkind, M. S. V., et al. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.0169 · Cited by 440
Linked dimensions of psychopathology and connectivity in functional brain networks
Xia, C. H., Ma, Z., Ciric, R., Gu, S., Betzel, R. F., Kaczkurkin, A. N., Calkins, M. E., Cook, P. A., García de la Garza, A., Vandekar, S. N., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05317-y · Cited by 437
Self-citationQuantitative Assessment of Structural Image Quality
Rosen, A. F. G., Roalf, D., Ruparel, K., Blake, J., Seelaus, K., Villa, L., Ciric, R., Cook, P. A., Davatzikos, C., Elliott, M., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.059 · Cited by 443
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Metadata
- Authors
- Gennatas, E. D., Avants, B. B., Wolf, D. H., Satterthwaite, T. D., Ruparel, K., Ciric, R., Hakonarson, H., Gur, R. E., & Gur, R. C.
- Affiliations
- University of Pennsylvania (US)
- Children's Hospital of Philadelphia (US)
- ORCID
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2017-04-21
- Volume
- 37
- Issue
- 20
- Pages
- 5065-5073
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC5444192
- OpenAlex
- W2605712228
- Cited by
- 302
- References
- 37
- Citations by year
- 2026: 23 · 2025: 28 · 2024: 34 · 2023: 37 · 2022: 38 · 2021: 51 · 2020: 38 · 2019: 35 · 2018: 14 · 2017: 4
- FWCI
- 19.10
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5r01mh107235-03, MH-089983, MH-096891, MH107235)
- National Institute of Mental Health (RC2MH089983, P50MH096891, R01-MH-107235)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- gray matter density, gray matter volume, cortical thickness, brain development, sex differences, age-related changes, adolescence, Philadelphia Neurodevelopmental Cohort, structural MRI
- MeSH terms
- Adolescent, Aging, Brain, Child, Female, Humans, Magnetic Resonance Imaging, Male, Organ Size, Sensitivity and Specificity, Sex Characteristics, Reproducibility of Results, Young Adult, Connectome, Gray Matter
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