Robust prediction of individual creative ability from brain functional connectivity

Roger E. Beaty(Harvard University), Yoed N. Kenett(University of Pennsylvania), Alexander P. Christensen(University of North Carolina at Greensboro), Monica D. Rosenberg(Yale University), Mathias Benedek(University of Graz), Qunlin Chen(Southwest University), Andréas Fink(University of Graz), Jiang Qiu(Southwest University), Thomas R. Kwapil(University of Illinois Urbana-Champaign), Michael J. Kane(University of North Carolina at Greensboro), Paul J. Silvia(University of North Carolina at Greensboro)
Proceedings of the National Academy of Sciences
January 16, 2018
Cited by 841Open Access
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Abstract

= 0.54). At the neural level, we found a pattern of functional brain connectivity related to high-creative thinking ability consisting of frontal and parietal regions within default, salience, and executive brain systems. In a leave-one-out cross-validation analysis, we show that this neural model can reliably predict the creative quality of ideas generated by novel participants within the sample. Furthermore, in a series of external validation analyses using data from two independent task fMRI samples and a large task-free resting-state fMRI sample, we demonstrate robust prediction of individual creative thinking ability from the same pattern of brain connectivity. The findings thus reveal a whole-brain network associated with high-creative ability comprised of cortical hubs within default, salience, and executive systems-intrinsic functional networks that tend to work in opposition-suggesting that highly creative people are characterized by the ability to simultaneously engage these large-scale brain networks.


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