  Fluid intelligence (Gf) has been defined as the ability to reason and solve
previously unseen problems. Links to Gf have been found in magnetic resonance
imaging (MRI) sequences such as functional MRI and diffusion tensor imaging. As
part of the Adolescent Brain Cognitive Development Neurocognitive Prediction
Challenge 2019, we sought to predict Gf in children aged 9-10 from T1-weighted
(T1W) MRIs. The data included atlas-aligned volumetric T1W images,
atlas-defined segmented regions, age, and sex for 3739 subjects used for
training and internal validation and 415 subjects used for external validation.
We trained sex-specific convolutional neural net (CNN) and random forest models
to predict Gf. For the convolutional model, skull-stripped volumetric T1W
images aligned to the SRI24 brain atlas were used for training. Volumes of
segmented atlas regions along with each subject's age were used to train the
random forest regressor models. Performance was measured using the mean squared
error (MSE) of the predictions. Random forest models achieved lower MSEs than
CNNs. Further, the external validation data had a better MSE for females than
males (60.68 vs. 80.74), with a combined MSE of 70.83. Our results suggest that
predictive models of Gf from volumetric T1W MRI features alone may perform
better when trained separately on male and female data. However, the
performance of our models indicates that more information is necessary beyond
the available data to make accurate predictions of Gf.
