Abstract
The brain's default network (DN) has been a topic of considerable empirical interest. In fMRI research, DN activity is associated with spontaneous and self-generated cognition, such as mind-wandering, episodic memory retrieval, future thinking, mental simulation, theory of mind reasoning, and creative cognition. Despite large literatures on developmental and disease-related influences on the DN, surprisingly little is known about the factors that impact normal variation in DN functioning. Using structural equation modeling and graph theoretical analysis of resting-state fMRI data, we provide evidence that Openness to Experience-a normally distributed personality trait reflecting a tendency to engage in imaginative, creative, and abstract cognitive processes-underlies efficiency of information processing within the DN. Across two studies, Openness predicted the global efficiency of a functional network comprised of DN nodes and corresponding edges. In Study 2, Openness remained a robust predictor-even after controlling for intelligence, age, gender, and other personality variables-explaining 18% of the variance in DN functioning. These findings point to a biological basis of Openness to Experience, and suggest that normally distributed personality traits affect the intrinsic architecture of large-scale brain systems. Hum Brain Mapp 37:773-779, 2016.
Original language | English |
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Pages (from-to) | 773-779 |
Number of pages | 7 |
Journal | Human Brain Mapping |
Volume | 37 |
Issue number | 2 |
DOIs | |
State | Published - 1 Feb 2016 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2015 Wiley Periodicals, Inc.
Funding
R.E.B. was supported by grant RFP-15-12, funded by the John Templeton Foundation. This research was also supported in part by a grant from the Austrian Science Fund (FWF): P23914.
Funders | Funder number |
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John Templeton Foundation | |
Austrian Science Fund | P23914 |
Keywords
- Default mode network
- Individual differences
- Network science
- Personality
- Structural equation modeling