Predicting Fluid Intelligence in Adolescent Brain MRI Data: An Ensemble Approach

Abstract

Decoding fluid intelligence from brain MRI data in adolescents is a highly challenging task. In this study, we took part in the ABCD Neurocognitive Prediction (NP) Challenge 2019, in which a large set of T1-weighted magnetic resonance imaging (MRI) data and pre-residualized fluid intelligence scores (corrected for brain volume, data collection site and sociodemographic variables) of children between 9–11 years were provided ($N=3739$ for training, $N=415$ for validation and $N=4516$ for testing). We propose here the Caruana Ensemble Search method to choose best performing models over a large and diverse set of candidate models. These candidate models include convolutional neural networks (CNNs) applied to brain areas considered to be relevant in fluid intelligence (e.g. frontal and parietal areas) and high-performing standard machine learning methods (namely support vector regression, random forests, gradient boosting and XGBoost) applied to region-based scores including volume, mean intensity and count of gray matter voxels. To further create diversity and increase robustness, a wide set of hyperparameter configurations for each of the models was used. On the validation and the hold out test data, we obtained a mean squared error (MSE) of 71.15 and 93.68 respectively (rank 12 out of 24, MSE range 92.13–102.25). Among most selected models were XGBoost together with the three region-based scores, the other regression models together with volume or CNNs based on the middle frontal gyrus. We discuss these results in light of previous research findings on fluid intelligence.