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We applied several regression and deep learning methods to predict fluidintelligence scores from T1-weighted MRI scans as part of the ABCDNeurocognitive Prediction Challenge (ABCD-NP-Challenge) 2019. We used voxelintensities and probabilistic tissue-type labels derived from these as featuresto train the models. The best predictive performance (lowest mean-squarederror) came from Kernel Ridge Regression (KRR; $\lambda=10$), which produced amean-squared error of 69.7204 on the validation set and 92.1298 on the testset. This placed our group in the fifth position on the validation leader boardand first place on the final (test) leader board.

ABCD Neurocognitive Prediction Challenge 2019: Predicting individual  fluid intelligence scores from structural MRI using probabilistic  segmentation and kernel ridge regression