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A distributed brain network predicts general intelligence from resting-state human neuroimaging data

Dubois, Julien and Galdi, Paola and Paul, Lynn K. and Adolphs, Ralph (2018) A distributed brain network predicts general intelligence from resting-state human neuroimaging data. . (Unpublished)

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Individual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, since it is the single best predictor of long-term life success, and since individual differences in a similar broad ability are found across animal species. The most replicated neural correlate of human intelligence to date is total brain volume. However, this coarse morphometric correlate gives no insights into mechanisms; it says little about function. Here we ask whether measurements of the activity of the resting brain (resting-state fMRI) might also carry information about intelligence. We used the final release of the Young Adult Human Connectome Project dataset (N=884 subjects after exclusions), providing a full hour of resting-state fMRI per subject; controlled for gender, age, and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks. Using a cross-validated predictive framework, we predicted 20% of the variance in general intelligence in the sampled population from their resting-state fMRI data. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Dubois, Julien0000-0002-3029-173X
Adolphs, Ralph0000-0002-8053-9692
Additional Information:The copyright holder for this preprint is the author/funder. This work was supported by NIMH grant 2P50MH094258 (PI: RA), the Chen Neuroscience Institute, the Carver Mead Seed Fund, and a NARSAD Young Investigator Grant from the Brain and Behavior Research Foundation (PI: JD). We thank Stuart Ritchie, Gilles Gignac, William Revelle, and Ruben Gur for invaluable advice on the behavioral side of the analyses -- though the final analytical choices rest solely with the authors. Author contributions: J. Dubois and P. Galdi developed the overall general analysis framework and conducted some of the initial analyses for the paper. J. Dubois conducted all final analyses and produced all figures. L. Paul helped with literature search, analysis of behavioral data, and interpretation of the results. J. Dubois and R. Adolphs wrote the initial manuscript and all authors contributed to the final manuscript. All authors contributed to planning and discussion on this project. The authors declare no conflict of interest. Data Sharing: The Young Adult HCP dataset is publicly available at Analysis scripts are available in the following public repository:
Group:Tianqiao and Chrissy Chen Institute for Neuroscience
Funding AgencyGrant Number
Tianqiao and Chrissy Chen Institute for NeuroscienceUNSPECIFIED
Carver Mead Seed FundUNSPECIFIED
Brain and Behavior Research FoundationUNSPECIFIED
Record Number:CaltechAUTHORS:20180620-135756318
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:87270
Deposited By: George Porter
Deposited On:20 Jun 2018 21:31
Last Modified:20 Jun 2018 21:59

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