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.
Highlights
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 (Neisser et al 1996)
The lack of consistent Voxel-based morphometric methods (VBM) findings may result from the widespread use of rather limited sample sizes, and this situation is further complicated by the fact that not all VBM studies of regional gray matter correlates of intelligence differences controlled for the effect of individual differences in total brain size
This is further supported by the fact that the mean absolute error (MAE = 11.35, Table 1, Fig. S3A) and root mean squared error (RMSE = 14.05, Table 1, Fig. S3C) were only slightly improved compared to the PCA-based analysis approach, and a similar correlation coefficient was obtained (r = 0.11)
Summary
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 (Neisser et al 1996) It is the best predictor of educational and occupational success (Neisser et al 1996), relates closely to positive life outcomes like health and. The lack of consistent VBM findings may result from the widespread use of rather limited sample sizes (i.e., between 30 and 104 participants in studies included in the meta-analysis of Basten et al 2015), and this situation is further complicated by the fact that not all VBM studies of regional gray matter correlates of intelligence differences controlled for the effect of individual differences in total brain size (see, e.g., Lee et al 2005, as an example of a VBM study based on uncorrected gray matter volume data). Whether relative gray matter volumes, i.e., local deviations in gray matter volume beyond the global influence of total brain size, are correlated with intelligence is still an open question
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