ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression
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; ), 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.
Keywords
Cite
@article{arxiv.1905.10831,
title = {ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression},
author = {Agoston Mihalik and Mikael Brudfors and Maria Robu and Fabio S. Ferreira and Hongxiang Lin and Anita Rau and Tong Wu and Stefano B. Blumberg and Baris Kanber and Maira Tariq and Maria Del Mar Estarellas Garcia and Cemre Zor and Daniil I. Nikitichev and Janaina Mourao-Miranda and Neil P. Oxtoby},
journal= {arXiv preprint arXiv:1905.10831},
year = {2019}
}
Comments
Winning entry in the ABCD Neurocognitive Prediction Challenge at MICCAI 2019. 7 pages plus references, 3 figures, 1 table