The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Robust prediction of individual creative ability from brain functional connectivity
Open accessAbstract
Significance: People’s capacity to generate creative ideas is central to technological and cultural progress. Despite advances in the neuroscience of creativity, the field lacks clarity on whether a specific neural architecture distinguishes the highly creative brain. Using methods in network neuroscience, we modeled individual creative thinking ability as a function of variation in whole-brain functional connectivity. We identified a brain network associated with creative ability comprised of regions within default, salience, and executive systems—neural circuits that often work in opposition. Across four independent datasets, we show that a person’s capacity to generate original ideas can be reliably predicted from the strength of functional connectivity within this network, indicating that creative thinking ability is characterized by a distinct brain connectivity profile.
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The human brain is intrinsically organized into dynamic, anticorrelated functional networks
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Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity
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Self-citationSalience processing and insular cortical function and dysfunction
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“Nevertheless, the high-creative network showed stable prediction of creative ability across datasets, and the neuroanatomy of this network mirrors previous fMRI studies of creative cognition reporting functional interactions among default, executive, and salience networks (1), suggesting that it may be a more reliable…”
BackgroundResultInfluentialSelf-citationUsing connectome-based predictive modeling to predict individual behavior from brain connectivity
Shen, X., Finn, E. S., Scheinost, D., Rosenberg, M. D., Chun, M. M., Papademetris, X., & Constable, R. T. (2017) · Nature Protocols · doi:10.1038/nprot.2016.178 · Cited by 1,093
Self-citationReliability, Validity, and Factor Structure of the Creative Achievement Questionnaire
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Cited by
Sui, J., Jiang, R., Bustillo, J., & Calhoun, V. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.02.016 · Cited by 309
Automating creativity assessment with SemDis: An open platform for computing semantic distance
Beaty, R. E., & Johnson, D. R. (2021) · Behavior Research Methods · doi:10.3758/s13428-020-01453-w · Cited by 341
Self-citationOrganisciak, P., Acar, S., Dumas, D., & Berthiaume, K. (2023) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2023.101356 · Cited by 202
Humans as Creativity Gatekeepers: Are We Biased Against AI Creativity?
Magni, F., Park, J., & Chao, M. M. (2024) · Journal of Business and Psychology · doi:10.1007/s10869-023-09910-x · Cited by 201
Safron, A. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.00030 · Cited by 133
“…DMN are ideally suited to serve these functions, having both high centrality—and so high potential for integrating information and exerting control (Kenett et al., 2018)—while also being located distally from primary modalities, and so being capable of supporting dynamics that are more decoupled…”
Background
Metadata
- Authors
- Beaty, R. E., Kenett, Y. N., Christensen, A. P., Rosenberg, M. D., Benedek, M., Chen, Q., Fink, A., Qiu, J., Kwapil, T. R., Kane, M. J., & Silvia, P. J.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2018-01-16
- Volume
- 115
- Issue
- 5
- Pages
- 1087-1092
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2782673913
- Cited by
- 820
- References
- 40
- FWCI
- 116.30
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Beaty, R. E., Kenett, Y. N., Christensen, A. P., Rosenberg, M. D., Benedek, M., Chen, Q., Fink, A., Qiu, J., Kwapil, T. R., Kane, M. J., & Silvia, P. J. (2018). Robust prediction of individual creative ability from brain functional connectivity. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1713532115
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