The development of human functional brain networks
Open accessAbstract
Recent advances in MRI technology have enabled precise measurements of correlated activity throughout the brain, leading to the first comprehensive descriptions of functional brain networks in humans. This article reviews the growing literature on the development of functional networks, from infancy through adolescence, as measured by resting-state functional connectivity MRI. We note several limitations of traditional approaches to describing brain networks and describe a powerful framework for analyzing networks, called graph theory. We argue that characterization of the development of brain systems (e.g., the default mode network) should be comprehensive, considering not only relationships within a given system, but also how these relationships are situated within wider network contexts. We note that, despite substantial reorganization of functional connectivity, several large-scale network properties appear to be preserved across development, suggesting that functional brain networks, even in children, are organized in manners similar to other complex systems.
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In childhood, regional modular assignments appeared to strongly depend on local anatomy.
“In childhood, regional assignments appeared to strongly depend on local anatomy.”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Power et al., 2010
With age, regions gradually became more defined by their respective functional roles.
“With age, regions gradually became more defined by their respective functional roles (Power et al., 2011; Power 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 Power et al., 2010
Prior work in fMRI using this modularity algorithm required that the researcher set an arbitrary threshold for similarity between individuals.
“Prior work in fMRI using this modularity algorithm required that the researcher set an arbitrary threshold, or cutoff point, for what constitutes similarity between any two given individuals. Researchers would then arrive at thresholds by looking for consistency in community detection results at multiple thresholds (e.g., [2], [21], [22]).”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [21]
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- Authors
- Power, J. D., Fair, D. A., Schlaggar, B. L., & Petersen, S. E.
- Affiliations
- Washington University in St. Louis (US)
- Oregon Health & Science University (US)
- Corresponding author
- Power, Jonathan D.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2010-09
- Volume
- 67
- Issue
- 5
- Pages
- 735-748
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC2941973
- OpenAlex
- W1996954170
- Cited by
- 751
- References
- 99
- Citations by year
- 2026: 22 · 2025: 34 · 2024: 36 · 2023: 40 · 2022: 48 · 2021: 38 · 2020: 103 · 2019: 48 · 2018: 49 · 2017: 42 · 2016: 52 · 2015: 54 · 2014: 43 · 2013: 54 · 2012: 48
- FWCI
- 3.30
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS046424, K02NS053425, R01NS032979, R21NS041255)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD057076)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- functional brain networks, brain development, resting-state functional connectivity, graph theory, default mode network, network reorganization, children, adolescence, infant brain, complex systems
- MeSH terms
- Age Factors, Brain, Brain Mapping, Humans, Models, Neurological, Nerve Net, Neural Pathways
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