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

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 ( = 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'.

A machine learning investigation of volumetric and functional MRI abnormalities in adults born preterm

“…This result highlights a promising future direction for functional MRI predictive analyses because it tentatively suggests that improvements in accuracy may allow the techniques to be used as a proxy for traditional cognitive batteries in combination with birth-related variables (Dubois et al, 2018). This result highlights a promising future direction for functional MRI predictive analyses because it tentatively suggests that improvements in accuracy may allow the techniques to be used as a proxy for traditional cognitive batteries in combination with birth-related variables (Dubois et al, 2018).…”

“…Our further analyses also demonstrated that the prediction of general IQ was improved by the addition of the ALFF decision score. This result highlights a promising future direction for functional MRI predictive analyses because it tentatively suggests that improvements in accuracy may allow the techniques to be used as a proxy for traditional cognitive batteries in combination with birth-related variables (Dubois et al, 2018). Specifically, high prediction error was noted in the high (>110) and low (<90) IQ ranges, thus further research is needed using larger samples to improve predictive capacity (see Data S1).…”

“…We expected to find a volumetric map containing subcortical decreases and cortical increases (Karolis et al, 2017); this signature was hypothesized to spatially overlap with the functional abnormality signature, and the degree to which the individuals loaded on the signatures was expected to correlate with the degree to which they were born prematurely (i.e., earlier birth, higher loading on the map). We also expected that the brain signatures would increase the accuracy of predicting the intelligence quotient over-and-above clinical prematurity data alone due to the close links between intelligence and functional brain networks (Dubois, Galdi, Paul, & Adolphs, 2018).…”

Brain Network Mechanisms of General Intelligence

“…Constructing a General Intelligence Factor We conducted an exploratory factor analysis utilizing the approachß and associated code made available by Dubois and colleagues (https://github.com/adolphslab/HCP_MRI-behavior), who recently investigated prediction of intelligence from resting state fMRI in the HCP dataset 58 .…”

“…In prior work, working memory tasks were often used to investigate brain regions implicated in general intelligence 22,24,29 , due to extensive evidence that working memory and intelligence are closely related [53][54][55][56] . In other lines of research, a number of structural and functional imaging studies have highlighted the involvement of FPN and DMN in general intelligence 14,57,58 . Of particular relevance, we recently showed that cognitive tasks that produce greater separation between FPN and DMN activation are more effective for prediction of intelligence 59 .…”