The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Total and Regional Brain Volumes in a Population-Based Normative Sample from 4 to 18 Years: The NIH MRI Study of Normal Brain Development
Open accessAbstract
Using a population-based sampling strategy, the National Institutes of Health (NIH) Magnetic Resonance Imaging Study of Normal Brain Development compiled a longitudinal normative reference database of neuroimaging and correlated clinical/behavioral data from a demographically representative sample of healthy children and adolescents aged newborn through early adulthood. The present paper reports brain volume data for 325 children, ages 4.5-18 years, from the first cross-sectional time point. Measures included volumes of whole-brain gray matter (GM) and white matter (WM), left and right lateral ventricles, frontal, temporal, parietal and occipital lobe GM and WM, subcortical GM (thalamus, caudate, putamen, and globus pallidus), cerebellum, and brainstem. Associations with cross-sectional age, sex, family income, parental education, and body mass index (BMI) were evaluated. Key observations are: 1) age-related decreases in lobar GM most prominent in parietal and occipital cortex; 2) age-related increases in lobar WM, greatest in occipital, followed by the temporal lobe; 3) age-related trajectories predominantly curvilinear in females, but linear in males; and 4) small systematic associations of brain tissue volumes with BMI but not with IQ, family income, or parental education. These findings constitute a normative reference on regional brain volumes in children and adolescents.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Kessler, R. C. (1994) · Archives of General Psychiatry · doi:10.1001/archpsyc.1994.03950010008002 · Cited by 12,370
Random-Effects Models for Longitudinal Data
Laird, N. M., & Ware, J. H. (1982) · Biometrics · doi:10.2307/2529876 · Cited by 8,923
A nonparametric method for automatic correction of intensity nonuniformity in MRI data
Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.668698 · Cited by 4,731
Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space
Collins, D. L., Neelin, P., Peters, T. M., & Evans, A. C. (1994) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-199403000-00005 · Cited by 3,431 · cited 2×
“All data were normalized into the Talairach-like MNI stereotaxic space (Collins et al. 1994) in order to account for differences in position, orientation, and size of each subject’s brain in the native scans.”
MethodMeta-analysis of structural imaging findings in attention-deficit/hyperactivity disorder
Valera, E. M., Faraone, S. V., Murray, K. E., & Seidman, L. J. (2007) · Biological Psychiatry · doi:10.1016/j.biopsych.2006.06.011 · Cited by 891
Sowell, E. R., Trauner, D. A., Gamst, A., & Jernigan, T. L. (2002) · Developmental Medicine and Child Neurology · doi:10.1017/s0012162201001591 · Cited by 701
Jack, C. R., Twomey, C. K., Zinsmeister, A. R., Sharbrough, F. W., Petersen, R. C., & Cascino, G. D. (1989) · Radiology · doi:10.1148/radiology.172.2.2748838 · Cited by 592 · cited 1×
“findings in adults (Jack et al. 1989) and the tendency of the Sylvian fissure to course upward posteriorly at a steeper angle on the right than on the left, although this asymmetry may be”
BackgroundSchumann, C. M., Bloss, C. S., Barnes, C. C., Wideman, G. M., Carper, R. A., Akshoomoff, N., Pierce, K., Hagler, D., Schork, N., Lord, C., et al. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.5714-09.2010 · Cited by 554
Brain abnormalities in human obesity: A voxel-based morphometric study
Pannacciulli, N., Del Parigi, A., Chen, K., Le, D. S. N. T., Reiman, E. M., & Tataranni, P. A. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.047 · Cited by 523
Cited by
Is Adolescence a Sensitive Period for Sociocultural Processing?
Blakemore, S.-J., & Mills, K. L. (2014) · Annual Review of Psychology · doi:10.1146/annurev-psych-010213-115202 · Cited by 2,264
A meta-analysis of sex differences in human brain structure
Ruigrok, A. N. V., Salimi-Khorshidi, G., Lai, M.-C., Baron-Cohen, S., Lombardo, M. V., Tait, R. J., & Suckling, J. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2013.12.004 · Cited by 1,192
Decision-making in the adolescent brain
Blakemore, S.-J., & Robbins, T. W. (2012) · Nature Neuroscience · doi:10.1038/nn.3177 · Cited by 711
Metabolic costs and evolutionary implications of human brain development
Kuzawa, C. W., Chugani, H. T., Grossman, L. I., Lipovich, L., Muzik, O., Hof, P. R., Wildman, D. E., Sherwood, C. C., Leonard, W. R., & Lange, N. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1323099111 · Cited by 585
Socioeconomic status and structural brain development
Brito, N. H., & Noble, K. G. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00276 · Cited by 525
Metadata
- Authors
- Brain Development Cooperative Group
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-05-25
- Volume
- 22
- Issue
- 1
- Pages
- 1-12
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- OpenAlex
- W2990728208
- Cited by
- 377
- References
- 15
- FWCI
- 19.76
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Total and Regional Brain Volumes in a Population-Based Normative Sample from 4 to 18 Years: The NIH MRI Study of Normal Brain Development. https://app.cochat.ai/papers/10.1093/cercor/bhr018
- The paper
- Brain Development Cooperative Group. (2012). Total and Regional Brain Volumes in a Population-Based Normative Sample from 4 to 18 Years: The NIH MRI Study of Normal Brain Development. Cerebral Cortex. https://doi.org/10.1093/cercor/bhr018
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
