“Predicting Intelligence from Brain Gray Matter Volume”, 2020-07-21 (; similar):
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 morphometry 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 statistically-significant prediction, while absolute gray matter (uncorrected) allowed for above-chance prediction with both approaches. Importantly, in all statistically-significant predictions, the absolute error was relatively high, ie. greater than 10 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 statistically-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]
…The currently available evidence from prediction-based studies, thus, seems to suggest that brain function (ie. resting-state functional connectivity or task-induced brain activation) may be more important than brain structure in determining individual differences in general cognitive ability—at least when operationalizing brain structure exclusively as regional gray matter volume differences. Highest prediction accuracies have so far been reported with respect to intrinsic functional connectivity, ie. correlated neural activation patterns measured in the absence of any task demand ( et al 2018; et al 2017; et al 2015; but note also et al 2018 for task-based prediction models). As the organization of intrinsic brain networks is assumed to be closely related to the underlying anatomical connectivity backbone, ie. the strongest structural connections between different brain regions ( et al 2009), we speculate that measures of structural connectivity (as assessed, eg. with diffusion tensor imaging) may allow for a more accurate prediction of general intelligence than volumetric indices of regional gray matter volume (for correlative support of this assumption, see, eg. et al 2018). On the other hand, intelligence has also been linked to other regionally specific morphometric properties of the brain such as cortical surface area (eg. et al 2014), gyrification (eg. et al 2016), or cortical thickness (eg. et al 2011). Future predictive work, in our view, should thus aim at more strongly integrating the different functional and neuroanatomical characteristics of the brain, to better understand their respective roles for general cognitive abilities.