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Philos Trans R Soc Lond B Biol Sci. 2018 Sep 26;373(1756). pii: 20170284. doi: 10.1098/rstb.2017.0284.

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

Dubois J1,2, Galdi P3,4, Paul LK5,6, Adolphs R5,7,6.

Author information

1
Division of Humanities and Social Sciences, Pasadena, CA 91125, USA jcrdubois@gmail.com.
2
Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
3
Department of Management and Innovation Systems, University of Salerno, Fisciano Salerno, Italy.
4
MRC Centre for Reproductive Health, University of Edinburgh, EH16 4TJ, UK.
5
Division of Humanities and Social Sciences, Pasadena, CA 91125, USA.
6
Chen Neuroscience Institute, California Institute of Technology, Pasadena, CA 91125, USA.
7
Division of Biology and Biological Engineering, Pasadena, CA 91125, USA.

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.This article is part of the theme issue 'Causes and consequences of individual differences in cognitive abilities'.

KEYWORDS:

brain–behaviour relationship; functional connectivity; general intelligence; individual differences; prediction; resting-state fMRI

PMID:
30104429
PMCID:
PMC6107566
[Available on 2019-09-26]
DOI:
10.1098/rstb.2017.0284

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