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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. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 373 (1756). Art. No. 20170284. ISSN 0962-8436. http://resolver.caltech.edu/CaltechAUTHORS:20180620-135756318

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Abstract

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, because it is the single best predictor of long-term life success. The most replicated neural correlate of human intelligence to date is total brain volume; however, this coarse morphometric correlate 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 (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 connectivity matrices. 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:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1098/rstb.2017.0284DOIArticle
https://doi.org/10.1101/257865DOIDiscussion Paper
https://doi.org/10.6084/m9.figshare.c.4150940DOISupplemental Material
ORCID:
AuthorORCID
Dubois, Julien0000-0002-3029-173X
Galdi, Paola0000-0003-4556-6799
Paul, Lynn K.0000-0002-3128-8313
Adolphs, Ralph0000-0002-8053-9692
Additional Information:© 2018 The Author(s) Published by the Royal Society. Data Sharing: The Young Adult HCP dataset is publicly available at https://www.humanconnectome.org/study/hcp-young-adult. Analysis scripts are available in the following public repository: https://github.com/adolphslab/HCP_MRI-behavior. Accepted: 30 April 2018. One contribution of 15 to a theme issue ‘Causes and consequences of individual differences in cognitive abilities’. Data accessibility: The Young Adult HCP dataset is publicly available at https://www.humanconnectome.org/study/hcp-young-adult. Analysis scripts are available in the following public repository: https://github.com/adolphslab/HCP_MRI-behavior. Authors' contributions: J.D. and P.G. developed the overall general analysis framework and conducted some of the initial analyses for the paper. J.D. conducted all final analyses and produced all figures. L.P. helped with literature search, analysis of behavioural data and interpretation of the results. J.D. and R.A. 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. This work was supported by NIMH grant no. 2P50MH094258 (PI: R.A.), the Chen Neuroscience Institute, the Carver Mead Seed Fund, and a NARSAD Young Investigator Grant from the Brain and Behavior Research Foundation (PI: J.D.). We thank Stuart Ritchie, Gilles Gignac, William Revelle and Ruben Gur for invaluable advice on the behavioural side of the analyses—though the final analytical choices rest solely with the authors.
Group:Tianqiao and Chrissy Chen Institute for Neuroscience
Funders:
Funding AgencyGrant Number
NIH2P50MH094258
Tianqiao and Chrissy Chen Institute for NeuroscienceUNSPECIFIED
Carver Mead Seed FundUNSPECIFIED
Brain and Behavior Research FoundationUNSPECIFIED
Subject Keywords:neuroscience, cognition
Record Number:CaltechAUTHORS:20180620-135756318
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20180620-135756318
Official Citation:A distributed brain network predicts general intelligence from resting-state human neuroimaging data Julien Dubois, Paola Galdi, Lynn K. Paul, Ralph Adolphs Phil. Trans. R. Soc. B 2018 373 20170284; DOI: 10.1098/rstb.2017.0284. Published 13 August 2018
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:87270
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:20 Jun 2018 21:31
Last Modified:30 Aug 2018 17:24

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