Task-Based Core-Periphery Organization of Human Brain Dynamics
Open accessAbstract
As a person learns a new skill, distinct synapses, brain regions, and circuits are engaged and change over time. In this paper, we develop methods to examine patterns of correlated activity across a large set of brain regions. Our goal is to identify properties that enable robust learning of a motor skill. We measure brain activity during motor sequencing and characterize network properties based on coherent activity between brain regions. Using recently developed algorithms to detect time-evolving communities, we find that the complex reconfiguration patterns of the brain's putative functional modules that control learning can be described parsimoniously by the combined presence of a relatively stiff temporal core that is composed primarily of sensorimotor and visual regions whose connectivity changes little in time and a flexible temporal periphery that is composed primarily of multimodal association regions whose connectivity changes frequently. The separation between temporal core and periphery changes over the course of training and, importantly, is a good predictor of individual differences in learning success. The core of dynamically stiff regions exhibits dense connectivity, which is consistent with notions of core-periphery organization established previously in social networks. Our results demonstrate that core-periphery organization provides an insightful way to understand how putative functional modules are linked. This, in turn, enables the prediction of fundamental human capacities, including the production of complex goal-directed behavior.
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These highly stable modules form a stable task-based core that supports higher order cognitive functions.
“suggesting that they form a stable task-based core that supports higher order cognitive functions (40).”
From Dynamic reconfiguration of frontal brain networks during executive cognition in humans (2015) · cites this paper as [40]
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- Authors
- Bassett, D. S., Wymbs, N. F., Rombach, M. P., Porter, M. A., Mucha, P. J., & Grafton, S. T.
- Affiliations
- University of California, Santa Barbara (US)
- University of Oxford (GB)
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Bassett, Danielle S.
- Wymbs, Nicholas F.
- Rombach, M. Puck
- Porter, Mason A.
- Mucha, Peter J.
- Grafton, Scott T.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-09-26
- Volume
- 9
- Issue
- 9
- Pages
- e1003171
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC3784512
- OpenAlex
- W2962725064
- Cited by
- 356
- References
- 124
- Citations by year
- 2026: 12 · 2025: 15 · 2024: 24 · 2023: 23 · 2022: 28 · 2021: 32 · 2020: 29 · 2019: 31 · 2018: 37 · 2017: 51 · 2016: 34 · 2015: 21 · 2014: 15 · 2013: 4
- FWCI
- 13.29
- Type
- journal article
- Language
- en
- Update notices
- correction (2014-04-11)
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Oxford University Research Archive (ORA) (University of Oxford) (acceptedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- UNC Libraries
- Funders
- David and Lucile Packard Foundation (W911NF-09-D-0001)
- James S. McDonnell Foundation (220020177)
- John Templeton Foundation
- Errett Fisher Foundation
- European Commission (FP7 ICT, 317614, ICT-2011, FP7‐ICT‐2011‐8, FP7‐ICT‐2011)
- Engineering and Physical Sciences Research Council (EP/J001759/1, 317614, 220020177)
- National Institute of General Medical Sciences (R21GM099493, D-0001)
- National Institute of Neurological Disorders and Stroke (NS 44393, P01NS044393)
- Army Research Office (#W911NF-09-D-0001, W911NF)
- FP7 Information and Communication Technologies (FP7-ICT-2011-8)
- Institute for Collaborative Biotechnologies (W911NF‐09‐D‐0001)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- brain dynamics, motor skill learning, motor sequencing, sensorimotor regions, individual differences in learning, goal-directed behavior, brain connectivity
- MeSH terms
- Brain, Humans, Learning, Task Performance and Analysis
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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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