Controllability of structural brain networks
Open accessAbstract
Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function.
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The fronto-parietal attention network has high modal controllability, meaning that it is capable of affecting a wider range of possible neural states, including harder-to-reach states, than other large-scale networks.
“and Gu et al.78 demonstrated that the fronto-parietal attention network has high modal controllability, meaning that it is capable of affecting a wider range of possible neural states, including harder-to-reach states, than other large-scale networks”
From Paying attention to attention in depression (2019) · cites this paper as [78]
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- Authors
- Gu, S., Pasqualetti, F., Cieslak, M., Telesford, Q. K., Yu, A. B., Kahn, A. E., Medaglia, J. D., Vettel, J. M., Miller, M. B., Grafton, S. T., & Bassett, D. S.
- Affiliations
- University of Pennsylvania (US)
- University of California, Riverside (US)
- University of California, Santa Barbara (US)
- DEVCOM Army Research Laboratory (US)
- Corresponding author
- Bassett, Danielle S.
- ORCID
- Venue
- Nature Communications
- Published
- 2015-10-01
- Volume
- 6
- Issue
- 1
- Pages
- 8414-8414
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC4600713
- OpenAlex
- W2100145912
- Cited by
- 912
- References
- 146
- Citations by year
- 2026: 81 · 2025: 93 · 2024: 106 · 2023: 102 · 2022: 103 · 2021: 88 · 2020: 81 · 2019: 63 · 2018: 79 · 2017: 63 · 2016: 44 · 2015: 7 · 2014: 1
- FWCI
- 38.66
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- National Science Foundation (W911NF-10-2-0022, 1441502, 1430279, W911NF-09-0001)
- Alfred P. Sloan Foundation (W911NF-10-2-0022, BCS-1441502)
- University of Pennsylvania
- Institute for Translational Medicine and Therapeutics
- National Institute of Neurological Disorders and Stroke (NS 44393, P01NS044393)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (T32HD071844)
- Army Research Office (W911NF-09-0001, BCS-1441502, W911NF-10-2-0022, W911NF)
- Army Research Laboratory (W911-NF-10-2-0022, W911NF, W911NF‐09‐0001, BCS-1441502)
- Institute for Collaborative Biotechnologies (W911NF‐09‐0001)
- 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
- structural brain networks, controllability, cognitive states, default mode network, cognitive control, attentional control, white matter microstructure, network dynamics
- MeSH terms
- Adult, Brain, Cognition, Female, Humans, Male, Nerve Net, Young Adult
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