Abstract
We propose an AutoML approach for the prediction of fluid intelligence from
T1-weighted magnetic resonance images. We extracted 122 features from MRI
scans and employed Sequential Model-based Algorithm Configuration to search
for the best prediction pipeline, including the best data pre-processing and
regression model. In total, we evaluated over 2600 prediction pipelines. We
studied our final model by employing results from game theory in the form of
Shapley values. Results indicate that predicting fluid intelligence from
volume measurements is a challenging task with many challenges. We found that
our final ensemble of 50 prediction pipelines associated larger
parahippocampal gyrus volumes with lower fluid intelligence, and higher pons
white matter volume with higher fluid intelligence.
Publication
Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge (ABCD-NP-Challenge)