The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Prediction of Individual Brain Maturity Using fMRI
Open accessAbstract
Connectivity Map of the Brain: The growing appreciation that clinically abnormal behaviors in children and adolescents may be influenced or perhaps even initiated by developmental miscues has stoked an interest in mapping normal human brain maturation. Several groups have documented changes in gray and white matter using structural and functional magnetic resonance imaging (fMRI) in cross-sectional and longitudinal studies. Dosenbach et al. (p. 1358 ) developed an index of resting-state functional connectivity (that is, how tightly neuronal activities in distinct brain regions are correlated while the subject is at rest or even asleep) from analyses of three independent data sets (each based on fMRI scans of 150 to 200 individuals from ages 6 to 35 years old). Long-range connections increased with age and short-range connections decreased, indicating that networks become sparser and sharper with brain maturation.
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Other identified changes over time included strengthening of long-range connections and increased segregation and modularity.”
From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [31]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Characteristic patterns of development have allowed machine-learning algorithms to predict brain maturation patterns with 92% accuracy.31”
From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [31]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Overall this produced modules that were more segregated from each other, but more densely connected within themselves.”
From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [31]
The cited source says what the claim attributes to it.
In the citing paper
“Weakening of short-range functional connections between major brain functional modules was the most important factor in the model”
From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [31]
In the citing paper
“Prior to 2012, multiple studies had found that functional connectivity between anatomically proximate ROIs gradually decreased, whereas long-range functional connections, especially anterior-posterior links, gradually increased until adulthood (Dosenbach et al., 2010; Fair et al., 2009; Kelly et al., 2009; Supekar et al., 2009).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Dosenbach et al., 2010
In the citing paper
“Distance-dependent effects were even strong enough to enable reasonably accurate predictions of an individual subject’s age on the basis of a single resting-state scan (Dosenbach et al., 2010).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Dosenbach et al., 2010
In the citing paper
“although this methodology is still rare in developmental and clinical populations [82].”
From Arrested development? Reconsidering dual-systems models of brain function in adolescence and disorders (2012) · cites this paper as [82]
In the citing paper
“Recent research indicates that functional connectivity increases steadily throughout adolescence, reaching a plateau around age 22 (Dosenbach et al., 2010), around the time that rates of most forms of risky behavior have already begun to decline.”
From Impact of socio-emotional context, brain development, and pubertal maturation on adolescent risk-taking (2013) · cites this paper as Dosenbach et al., 2010
In the citing paper
“over the course of adolescence anatomical and functional connections between structures within the affective and cognitive brain systems, specifically the striatum and PFC, increase”
From Impact of socio-emotional context, brain development, and pubertal maturation on adolescent risk-taking (2013) · cites this paper as Dosenbach et al., 2010
In the citing paper
“Similarly, Dosenbach et al. (2010) found that the complex patterns of functional connectivity can predict a subject's age during development, although with a somewhat lower degree of accuracy (subjects aged 7-30 years, r 2 = 0.553).”
From Imaging Patterns of Brain Development and their Relationship to Cognition (2015) · cites this paper as Dosenbach et al. (2010)
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 default mode of brain function
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
Mapping the structural core of human cerebral cortex
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060159 · Cited by 4,173
Distributed and overlapping representations of faces and objects in ventral temporal cortex
Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001) · Science · doi:10.1126/science.1063736 · Cited by 3,940
Why do many psychiatric disorders emerge during adolescence?
Paus, T., Keshavan, M., & Giedd, J. N. (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2513 · Cited by 3,123
Toward discovery science of human brain function
Biswal, B. B., Mennes, M., Zuo, X.-N., Gohel, S., Kelly, C., Smith, S. M., Beckmann, C. F., Adelstein, J. S., Buckner, R. L., Colcombe, S., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0911855107 · Cited by 2,970
Self-citationDistinct brain networks for adaptive and stable task control in humans
Dosenbach, N. U. F., Fair, D. A., Miezin, F. M., Cohen, A. L., Wenger, K. K., Dosenbach, R. A. T., Fox, M. D., Snyder, A. Z., Vincent, J. L., Raichle, M. E., et al. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0704320104 · Cited by 2,632
Self-citationBeyond mind-reading: multi-voxel pattern analysis of fMRI data
Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.07.005 · Cited by 2,271
A Core System for the Implementation of Task Sets
Dosenbach, N. U. F., Visscher, K. M., Palmer, E. D., Miezin, F. M., Wenger, K. K., Kang, H. C., Burgund, E. D., Grimes, A. L., Schlaggar, B. L., & Petersen, S. E. (2006) · Neuron · doi:10.1016/j.neuron.2006.04.031 · Cited by 1,772
Self-citationUnconscious determinants of free decisions in the human brain
Soon, C. S., Brass, M., Heinze, H.-J., & Haynes, J.-D. (2008) · Nature Neuroscience · doi:10.1038/nn.2112 · Cited by 1,741
Flagged first, then by citations
Research works printed but not linked: 1
- H. Akaike in Second International Symposium on Inference Theory B. N. Petrov F. Csaki Eds. (Akademiai Kiado Budapest 1973) pp. 267–281.
Cited by
Sorted by citations
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.018 · Cited by 7,429
Self-citationFunctional Network Organization of the Human Brain
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., Vogel, A. C., Laumann, T. O., Miezin, F. M., Schlaggar, B. L., et al. (2011) · Neuron · doi:10.1016/j.neuron.2011.09.006 · Cited by 4,124
Self-citationBrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics
Xia, M., Wang, J., & He, Y. (2013) · PLoS ONE · doi:10.1371/journal.pone.0068910 · Cited by 4,106
Network hubs in the human brain
van den Heuvel, M. P., & Sporns, O. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.012 · Cited by 2,123
Resting-state connectivity biomarkers define neurophysiological subtypes of depression
Drysdale, A. T., Grosenick, L., Downar, J., Dunlop, K., Mansouri, F., Meng, Y., Fetcho, R. N., Zebley, B., Oathes, D. J., Etkin, A., et al. (2017) · Nature Medicine · doi:10.1038/nm.4246 · Cited by 2,170
Metadata
- Authors
- Dosenbach, N. U. F., Nardos, B., Cohen, A. L., Fair, D. A., Power, J. D., Church, J. A., Nelson, S. M., Wig, G. S., Vogel, A. C., Lessov-Schlaggar, C. N., Barnes, K. A., Dubis, J. W., Feczko, E., Coalson, R. S., Pruett, J. R., Barch, D. M., Petersen, S. E., & Schlaggar, B. L.
- Affiliations
- Washington University in St. Louis (US)
- Oregon Health & Science University (US)
- Harvard University (US)
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA.
- Corresponding author
- Dosenbach, Nico U. F.
- Schlaggar, Bradley L.
- ORCID
- Venue
- Science
- Published
- 2010-09-10
- Volume
- 329
- Issue
- 5997
- Pages
- 1358-1361
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC3135376
- OpenAlex
- W2063404606
- Cited by
- 2,183
- References
- 36
- Citations by year
- 2026: 95 · 2025: 135 · 2024: 135 · 2023: 141 · 2022: 188 · 2021: 177 · 2020: 193 · 2019: 155 · 2018: 150 · 2017: 118 · 2016: 138 · 2015: 139 · 2014: 117 · 2013: 128 · 2012: 105
- FWCI
- 58.41
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Wellcome Trust
- National Institute of Mental Health (P20MH062130, MH62130)
- National Institute on Drug Abuse (DA027046, K01DA027046)
- National Eye Institute (K12EY016336, EY16336)
- National Institute of Neurological Disorders and Stroke (NS55582, R01NS046424, K02NS053425, NS51281, NS46424, NS 41255, NS-32979, R01NS032979, R21NS041255, NS053425, NS00169011)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD057076, HD057076)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- functional connectivity, resting-state fMRI, individual differences, brain development, functional brain networks, support vector machine, multivariate pattern analysis, nonlinear growth models
- MeSH terms
- Adolescent, Adult, Aging, Algorithms, Artificial Intelligence, Brain, Brain Mapping, Cerebellum, Child, Female, Frontal Lobe, Humans, Magnetic Resonance Imaging, Male, Neural Pathways, Occipital Lobe, Multivariate Analysis, Young Adult
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
