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.
“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]
The paper’s discussion of fcMVPA feature weights (indexed under Abstract; page unavailable) reports strengthening of long-range connections and greater segregation of functional areas during maturation. It also reports stronger within-network and weaker between-network connections, but does not report an increase in modularity over time; modularity optimization was used to identify networks in an average adult connectivity matrix. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMachine-learning algorithms predicted brain maturation patterns with 92% accuracy based on characteristic patterns of development.
“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]
The paper reports 92% accuracy for binary adult-versus-child classification in its second data set (passage 7); the corresponding figure for its first data set is 91% (passage 4). It predicts individual functional brain maturity, but reports that continuous prediction using variance explained, not 92% accuracy. The claim conflates the classification result with maturation prediction. (LLM, conf=0.88)
Experimental — accuracy not yet measuredOverall this produced modules that were more segregated from each other but more densely connected within themselves.
“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]
Weakening of short-range functional connections between major brain functional modules was the most important factor in the model.
“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]
Prior to 2012, multiple studies found that functional connectivity between anatomically proximate ROIs gradually decreased, while long-range functional connections, especially anterior-posterior links, gradually increased until adulthood.
“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
Distance-dependent effects were strong enough to enable reasonably accurate predictions of an individual subject's age from a single resting-state scan.
“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
MVPA is still rare in developmental and clinical populations.
“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]
Recent research indicates functional connectivity increases steadily throughout adolescence, reaching a plateau around age 22, around the time rates of most risky behavior have begun to decline.
“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
Over the course of adolescence, anatomical and functional connections between the striatum and prefrontal cortex increase.
“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
Functional connectivity patterns can predict age during development with lower accuracy (subjects aged 7-30, r2 = 0.553).
“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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Self-citation
- 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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