Predicting Fluid Intelligence from Structural MRI Using Random Forest regression
Fluid intelligence (FI) indicates a set of general abilities like pattern recognition, abstract thinking, and problem-solving. FI is related to inherent, biological factors. We present a method to predict the fluid intelligence score in children (9–10 y/o) from their structural brain scans. For the purposes of this work, we used features derived from the T1-weighted Magnetic Resonance scans from the ABCD study. We used data from 3739 subjects for training and 415 for validation of the model. As features we used the volumes of gray matter regions of interest provided by the challenge organizers, as well as three additional groups of features. These include signal intensity features based on the ROIs, as well as shape-based features derived from the anterior and posterior cross sectional area of the corpus callosum. We used the random forest regressor model for prediction. We compare its performance to other regression-based models (XGBoost Regression and Support Vector Regression). Additionally, we ran a mean decrease accuracy (MDA) algorithm to select features that had high influence on the prediction results. The results we have obtained for the validation set were as follows: MSE = 67.39, R-squared = 0.0762. The proposed method showed promising results and has the potential to provide a good prediction of fluid intelligence based on structural brain scans.
KeywordsAdolescence development Fluid intelligence Random Forest Regressor
The authors would like to thank Professor Mark Jenkinson for his helpful comments regarding the manuscript.
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