We study predicting fluid intelligence of 9–10 year old children from
T1-weighted magnetic resonance images. We extract volume and thickness
measurements from MRI scans using FreeSurfer and the SRI24 atlas. We
empirically compare two predictive models: (i) an ensemble of gradient
boosted trees and (ii) a linear ridge regression model. For both, a Bayesian
black-box optimizer for finding the best suitable prediction model is used.
To systematically analyze feature importance our model, we employ results
from game theory in the form of Shapley values. Our model with gradient
boosting and FreeSurfer measures ranked third place among 24 submissions to
the ABCD Neurocognitive Prediction Challenge. Our results on feature
importance could be used to guide future research on the neurobiological
mechanisms behind fluid intelligence in children.
Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge (ABCD-NP-Challenge)