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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 (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'.

SUBMITTER: Dubois J

PROVIDER: S-EPMC6107566 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Although previous studies have shown that intra-network abnormalities in brain functional networks are correlated with clinical/cognitive impairment in multiple sclerosis (MS), there is little information regarding the pattern of causal interactions among cognition-related resting-state networks (RSNs) in different disease stages of relapsing-remitting MS (RRMS) patients. We hypothesized that abnormalities of causal interactions among RSNs occurred in RRMS patients in the acute and remitting phases. METHODS:Seventeen patients in the acute phases of RRMS, 24 patients in the remitting phases of RRMS, and 23 appropriately matched healthy controls participated in this study. First, we used group independent component analysis to extract the time courses of the spatially independent components from all the subjects. Then, the Granger causality analysis was used to investigate the causal relationships among RSNs in the spectral domain and to identify correlations with clinical indices. RESULTS:Compared with the patients in the acute phase of RRMS, patients in the remitting phase of RRMS showed a significantly lower expanded disability status scale, modified fatigue impact scale scores, and significantly higher paced auditory serial addition test (PASAT) scores. Compared with healthy subjects, during the acute phase, RRMS patients had significantly increased driving connectivity from the right executive control network (rECN) to the anterior salience network (aSN), and the causal coefficient was negatively correlated with the PASAT score. During the remitting phase, RRMS patients had significantly increased driving connectivity from the rECN to the aSN and from the rECN to the visuospatial network. CONCLUSIONS:Together with the disease duration (mean disease duration?<?5 years) and relatively better clinical scores than those in the acute phase, abnormal connections, such as the information flow from the rECN to the aSN and the rECN to the visuospatial network, might provide adaptive compensation in the remitting phase of RRMS.

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

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