TY  - JOUR
A1  - Hilger, Kirsten
A1  - Winter, Nils R.
A1  - Leenings, Ramona
A1  - Sassenhagen, Jona
A1  - Hahn, Tim
A1  - Basten, Ulrike
A1  - Fiebach, Christian
T1  - Predicting intelligence from brain gray matter volume
T2  - Brain structure & function
N2  - 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.
KW  - Intelligence
KW  - Gray matter volume
KW  - Voxel-based morphometry (VBM)
KW  - Machine learning
KW  - Prediction
KW  - Brain size I
Y1  - 2020
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/69228
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-692283
SN  - 1863-2661
N1  - Open Access funding provided by Projekt DEAL.
N1  - The research leading to these results has received funding from the German Research Foundation (DFG Grant FI 848/6-1) and from the European Community's Seventh Framework Programme (FP7/2013) under Grant agreement n° 617891.
VL  - 225.2020
IS  - 7
SP  - 2111
EP  - 2129
PB  - Springer
CY  - Berlin ; Heidelberg
ER  -