Oleg Zabluda's blog
Wednesday, July 11, 2018
 
A distributed brain network predicts general intelligence from resting-state human neuroimaging data
A distributed brain network predicts general intelligence from resting-state human neuroimaging data
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The most replicated neural correlate of human intelligence to date is total brain volume. [...] 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 dataset (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 fMRI data. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.
[...]
Structural studies

The best replicated brain correlate of intelligence is [...] brain volume derived from structural MRI scans; the correlation coefficient is about r=0.24 [...] or maybe as much as r=0.40). [...] The volume of gray-matter seems slightly more strongly related to intelligence than the volume of white matter.
[...]
Brain size, gender, and motion are correlated with g

There are known effects of gender [107,108], age [109,110], in-scanner motion [111–113] and brain size [114] on the functional connectivity patterns measured in the resting-state with fMRI. It is thus necessary to control for these variables [115]

Resting-state FC predicts 20% of the variance in g across subjects
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https://authors.library.caltech.edu/87270/
https://authors.library.caltech.edu/87270/

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