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

## TL;DR

This study applied various regression and deep learning methods to predict fluid intelligence scores from structural MRI scans, achieving top performance in a large neurocognitive challenge.

## Contribution

The paper introduces the use of probabilistic tissue segmentation features combined with kernel ridge regression for neurocognitive prediction from MRI.

## Key findings

- Kernel Ridge Regression achieved the best predictive accuracy.
- The model ranked first on the challenge's test leaderboard.
- Voxel intensity and tissue-type features were effective for prediction.

## 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.