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

Mihalik, Agoston; Brudfors, Mikael; Robu, Maria; Ferreira, Fabio S.; Lin, Hongxiang; Rau, Anita; Wu, Tong; Blumberg, Stefano B.; Kanber, Baris; Tariq, Maira; Garcia, Mar Estarellas; Zor, Cemre; Nikitichev, Daniil I.; Mourão-Miranda, Janaina; Oxtoby, Neil P.

doi:10.1007/978-3-030-31901-4_16

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

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Mihalik, Agoston
Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany, and London, UK, Max Planck Institute for Human Development, Max Planck Society;

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Ferreira, Fabio S.
Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany, and London, UK, Max Planck Institute for Human Development, Max Planck Society;

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Wu, Tong
Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany, and London, UK, Max Planck Institute for Human Development, Max Planck Society;

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Zor, Cemre
Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany, and London, UK, Max Planck Institute for Human Development, Max Planck Society;

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Mourão-Miranda, Janaina
Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany, and London, UK, Max Planck Institute for Human Development, Max Planck Society;
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https://doi.org/10.1007/978-3-030-31901-4_16
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Citation

Mihalik, A., Brudfors, M., Robu, M., Ferreira, F. S., Lin, H., Rau, A., et al. (2019). ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and Kernel ridge regression. In K. M. Pohl, W. K. Thompson, E. Adeli, & M. G. Linguraru (Eds.), Adolescent brain cognitive development neurocognitive prediction (pp. 133-142). Cham: Springer.

Cite as: https://hdl.handle.net/21.11116/0000-0005-5F3C-F

Abstract

We applied several regression and deep learning methods to predict fluid intelligence scores from T1-weighted MRI scans as part of the ABCD Neurocognitive Prediction Challenge 2019. We used voxel intensities and probabilistic tissue-type labels derived from these as features to train the models. The best predictive performance (lowest mean-squared error) came from kernel ridge regression ($$\lambda =10$$), which produced a mean-squared error of 69.7204 on the validation set and 92.1298 on the test set. This placed our group in the fifth position on the validation leader board and first place on the final (test) leader board.