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

By J. Dubois, Paola Galdi, Lynn K. Paul, Ralph Adolphs. bioRxiv, 2018

305 citations recorded by the source corpus.

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 longterm 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.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/721403ddba361c57cb0f27f8c3019e8ba82418d8

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Benchmarking functional connectome‐based predictive models for resting‐state fMRI (2019, unknown): “The weights of the corresponding brain functional connectome are used to characterize individual subjects behavior, cognition, andmental health (Craddock et al., 2009; Richiardi et al., 2010; Milazzo et al., 2014; Smith et al., 2015; Miller et al., 2016; Colclough et al., 2017; Dubois et al., 2018),”
Ten simple rules for predictive modeling of individual differences in neuroimaging (2019, background): “For a discussion on the reliability of phenotypic measures in the context of predictive modeling, we point the interested reader to: (Dubois et al., 2018a, 2018b; Gignac and Bates, 2017).”
Task‐specific topology of brain networks supporting working memory and inhibition (2024, background): “…efficient network reconfiguration with fluid intelligence, suggesting that the brain's ability to switch between different network states in response to cognitive demands is a basis of cognitive ability (Anderson & Barbey, 2023; Bassett et al., 2011; Dubois et al., 2018; Wen et al., 2015).”