Ensemble of SVM, Random-Forest and the BSWiMS Method to Predict and Describe Structural Associations with Fluid Intelligence Scores from T1-Weighed MRI

Abstract

The degree of association between fluid intelligence and neuroanatomy is important in refining our understanding of brain development. The primary goal of this work is twofold: to predict fluid intelligence from T1-weighed MRI, and to describe the MRI features that are associated with fluid intelligence. In this paper, we propose to ensemble the predictions of three machine learning strategies: Support Vector Machine (SVM), Random Forest (RF), and Bootstrapped Step Wise Model Selection (BSWiMS). Gender-stratified SVM was trained on children using age (ages 9–10), plus 122 volumetric scores provided by the ABCD challenge team. RF and BSWiMS were gender-stratified and trained using cubic root transformed data, summarized by left-right mean and relative absolute differences, and augmented by 19 volumetric statistical descriptors of major anatomical regions. Then, the transformed-augmented feature set was adjusted by age and the mean volume of the training set. The predictions of the three models were averaged to get the final prediction on each one of the test subjects. The Mean Squared Error (MSE) of MRI-predicted fluid intelligence on the test subjects was 100.89. The top features associated with fluid intelligence were the volume of the pons white mater and the volume of the parahippocampal gyrus.