  We propose a four-layer fully-connected neural network (FNN) for predicting
fluid intelligence scores from T1-weighted MR images for the ABCD-challenge. In
addition to the volumes of brain structures, the FNN uses cortical WM/GM
contrast and cortical thickness at 78 cortical regions. These last two
measurements were derived from the T1-weighted MR images using cortical
surfaces produced by the CIVET pipeline. The age and gender of the subjects and
the scanner manufacturer are also used as features for the learning algorithm.
This yielded 283 features provided to the FNN with two hidden layers of 20 and
15 nodes. The method was applied to the data from the ABCD study. Trained with
a training set of 3736 subjects, the proposed method achieved a MSE of 71.596
and a correlation of 0.151 in the validation set of 415 subjects. For the final
submission, the model was trained with 3568 subjects and it achieved a MSE of
94.0270 in the test set comprised of 4383 subjects.
