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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.
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“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]
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“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]
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“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]
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“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
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“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
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“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]
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“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
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“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)
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Developmental changes in brain network hub connectivity in late adolescence
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Exploring early human brain development with structural and physiological neuroimaging
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Microstructural abnormalities of short-distance white matter tracts in autism spectrum disorder
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Prediction of brain maturity in infants using machine-learning algorithms
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Self-citationMechanisms of Action and Persistent Neuroplasticity by Drugs of Abuse
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Functional interactions between intrinsic brain activity and behavior
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Autism-Associated Promoter Variant in MET Impacts Functional and Structural Brain Networks
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The brain matures with stronger functional connectivity and decreased randomness of its network
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Large-scale brain network dynamics supporting adolescent cognitive control
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Predicting individual brain maturity using dynamic functional connectivity
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“…work identifying ICNs in infants (Fransson et al., 2007) and numerous subsequent studies of resting state connectivity have provided greater understanding of the functional architecture of the human brain during early development (Doria et al., 2010; Dosenbach et al., 2010; Gao et al., 2009).”
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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
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