Task-induced brain state manipulation improves prediction of individual traits

Abigail S. Greene (), Siyuan Gao, Dustin Scheinost and R. Todd Constable
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Abigail S. Greene: Yale School of Medicine
Siyuan Gao: Yale School of Engineering and Applied Science
Dustin Scheinost: Yale School of Medicine
R. Todd Constable: Yale School of Medicine

Nature Communications, 2018, vol. 9, issue 1, 1-13

Abstract: Abstract Recent work has begun to relate individual differences in brain functional organization to human behaviors and cognition, but the best brain state to reveal such relationships remains an open question. In two large, independent data sets, we here show that cognitive tasks amplify trait-relevant individual differences in patterns of functional connectivity, such that predictive models built from task fMRI data outperform models built from resting-state fMRI data. Further, certain tasks consistently yield better predictions of fluid intelligence than others, and the task that generates the best-performing models varies by sex. By considering task-induced brain state and sex, the best-performing model explains over 20% of the variance in fluid intelligence scores, as compared to

Date: 2018
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DOI: 10.1038/s41467-018-04920-3

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