Predicting intelligence from brain gray matter volume.

Brain Structure & Function
Kirsten HilgerChristian J Fiebach

Abstract

A positive association between brain size and intelligence is firmly established, but whether region-specific anatomical differences contribute to general intelligence remains an open question. Results from voxel-based morphometry (VBM) - one of the most widely used morphometric methods - have remained inconclusive so far. Here, we applied cross-validated machine learning-based predictive modeling to test whether out-of-sample prediction of individual intelligence scores is possible on the basis of voxel-wise gray matter volume. Features were derived from structural magnetic resonance imaging data (N = 308) using (a) a purely data-driven method (principal component analysis) and (b) a domain knowledge-based approach (atlas parcellation). When using relative gray matter (corrected for total brain size), only the atlas-based approach provided significant prediction, while absolute gray matter (uncorrected) allowed for above-chance prediction with both approaches. Importantly, in all significant predictions, the absolute error was relatively high, i.e., greater than ten IQ points, and in the atlas-based models, the predicted IQ scores varied closely around the sample mean. This renders the practical value even of statistically sig...Continue Reading

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Methods Mentioned

BETA
PCA

Software Mentioned

NIH Toolbox
SPM12 Statistic Parametric Mapping
statsmodels
Scikit Optimize
PHOTON
Python
ABCD Neurocognitive Prediction
CAT12 SPM
Scikit Learn
CAT12 Computational Anatomy Toolbox

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