Dynamic reconfiguration of human brain networks during learning
Open accessAbstract
Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes—flexibility and selection—must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we investigate the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.
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This temporal instability property is important for executive functioning, learning, and switching between challenging task demands.
“There is evidence that this property is important for executive functioning, learning, and switching between challenging task demands (Bassett et al., 2011; Braun et al., 2015; Chen et al., 2016).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Bassett et al., 2011
Brain areas and associated circuits or networks may be engaged in tasks differently over time: some transiently and some consistently.
“Brain areas and associated circuits or networks may be engaged in tasks differently over time: some transiently and some consistently (2, 3).”
From Dynamic reconfiguration of frontal brain networks during executive cognition in humans (2015) · cites this paper as [3]
The modular structure of the brain is not static but undergoes changes in response to external and internal drivers, ranging from cognitive processes to disease.
“However, this modular structure is not static but undergoes changes in response to external and internal drivers, ranging from cognitive processes (3, 31) to disease (32).”
From Dynamic reconfiguration of frontal brain networks during executive cognition in humans (2015) · cites this paper as [3]
Dynamic community detection methods have been used in the context of human motor skill learning and lexical processing.
“Finally, dynamic community detection methods have further been used in the context of human motor skill learning (3) and lexical processing (59).”
From Dynamic reconfiguration of frontal brain networks during executive cognition in humans (2015) · cites this paper as [3]
Momentary neural coalitions underlie complex cognitive functions, learning, and consciousness.
“the emergence of momentary neural coalitions forms the basis for complex cognitive functions (Bassett et al., 2015; Cole et al., 2014), learning (Bassett et al., 2011) and consciousness (Barttfeld et al., 2015; Godwin et al., 2015)”
From The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance (2016) · cites this paper as Bassett et al., 2011
Flexibility of nodes within the fronto-parietal attention network predicts subjects' learning of a simple motor task with visual cues at a future time point.
“Specifically, Bassett et al.77 showed that flexibility of nodes within this network predicts subjects’ learning of a simple motor task with visual cues at a future time point”
From Paying attention to attention in depression (2019) · cites this paper as [77]
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Learning-induced autonomy of sensorimotor systems
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Robust detection of dynamic community structure in networks
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“The authors contend that these changes potentially support the ability to traverse through more diverse cognitive states in order to accomplish increasingly complex task demands (Bassett et al., 2011; Braun et al., 2015).”
BackgroundCognitive Network Neuroscience
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Understanding brain networks and brain organization
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“For example, Bassett and colleagues [51] studied the dynamic reconfiguration of human brain networks during learning and uncovered several clusters of brain regions that remained integrated with one another by a complex pattern of weak functional interconnections.”
BackgroundAn anatomical substrate for integration among functional networks in human cortex
van den Heuvel, M. P., & Sporns, O. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.2128-13.2013 · Cited by 404 · see citing works
Task-Based Core-Periphery Organization of Human Brain Dynamics
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Self-citationDefault Mode Dynamics for Global Functional Integration
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Optimal trajectories of brain state transitions
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- Authors
- Bassett, D. S., Wymbs, N. F., Porter, M. A., Mucha, P. J., Carlson, J. M., & Grafton, S. T.
- Affiliations
- University of California, Santa Barbara (US)
- University of Oxford (GB)
- University of North Carolina at Chapel Hill (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2011-04-18
- Volume
- 108
- Issue
- 18
- Pages
- 7641-7646
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3088578
- OpenAlex
- W2142029338
- Cited by
- 1,705
- References
- 109
- Citations by year
- 2026: 90 · 2025: 118 · 2024: 108 · 2023: 109 · 2022: 124 · 2021: 166 · 2020: 147 · 2019: 139 · 2018: 167 · 2017: 146 · 2016: 110 · 2015: 81 · 2014: 80 · 2013: 66 · 2012: 47
- FWCI
- 32.49
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Oxford University Research Archive (ORA) (University of Oxford) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- arXiv (Cornell University)
- UNC Libraries
- Funders
- National Science Foundation (0645369)
- David and Lucile Packard Foundation (W911NF-09-D-0001)
- James S. McDonnell Foundation (220020177)
- National Institute of Neurological Disorders and Stroke (NS 44393, P01NS044393)
- Division of Mathematical Sciences
- Army Research Office (#W911NF-09-D-0001, W911NF)
- Institute for Collaborative Biotechnologies (W911NF‐09‐D‐0001)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- brain network modularity, functional connectivity, motor skill learning, learning flexibility, dynamic network reconfiguration, time scales, evolving systems, human learning
- MeSH terms
- Adaptation, Physiological, Adult, Brain, Female, Humans, Learning, Magnetic Resonance Imaging, Male, Models, Neurological, Nerve Net, Neuronal Plasticity, Psychomotor Performance
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.
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