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arXiv · 1905.10831

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

Agoston Mihalik·Mikael Brudfors·Maria Robu·Fabio S. Ferreira·Hongxiang Lin·Anita Rau·Tong Wu·Stefano B. Blumberg·Baris Kanber·Maira Tariq·Maria Del Mar Estarellas Garcia·Cemre Zor·Daniil I. Nikitichev·Janaina Mourao-Miranda·Neil P. Oxtoby
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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 (ABCD-NP-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 (KRR; $\lambda=10$), 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.

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Agoston Mihalik, Mikael Brudfors, Maria Robu, Fabio S. Ferreira, Hongxiang Lin, Anita Rau, Tong Wu, Stefano B. Blumberg, Baris Kanber, Maira Tariq, Maria Del Mar Estarellas Garcia, Cemre Zor, Daniil I. Nikitichev, Janaina Mourao-Miranda, Neil P. Oxtoby. 2019-05-26. ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression. https://arxiv.org/abs/1905.10831

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