Predicting intelligence from brain gray matter volume
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ORIGINAL ARTICLE
Predicting intelligence from brain gray matter volume Kirsten Hilger1,2,3,6 · Nils R. Winter4 · Ramona Leenings4 · Jona Sassenhagen1 · Tim Hahn4 · Ulrike Basten1 · Christian J. Fiebach1,3,5 Received: 5 December 2019 / Accepted: 4 July 2020 © The Author(s) 2020
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 significant prediction results questionable. Analyses based on the gray matter of functional brain networks yielded significant predictions for the fronto-parietal network and the cerebellum. However, the mean absolute errors were not reduced in contrast to the global models, suggesting that general intelligence may be related more to global than region-specific differences in gray matter volume. More generally, our study highlights the importance of predictive statistical analysis approaches for clarifying the neurobiological bases of intelligence and provides important suggestions for future research using predictive modeling. Keywords Intelligence · Gray matter volume · Voxel-based morphometry (VBM) · Machine learning · Prediction · Brain size
Introduction
Kirsten Hilger and Nils R. Winter share first authorship. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00429-020-02113-7) contains supplementary material, which is available to authorized users. * Kirsten Hilger kirsten.hilger@uni‑wuerzburg.de 1
Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany
2
Department of Psychology, Julius Maximilian University Würzburg, Würzburg, Germany
3
IDeA Center for Individual Development and Adaptive Education, Frankfurt am Main, Germany
Intelligence describes an individual’s ability to understand complex ideas, to adapt effectively to the environment, to learn from experience, and to engage in various forms of reasoning
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