  Analyzing the relation between intelligence and neural activity is of the
utmost importance in understanding the working principles of the human brain in
health and disease. In existing literature, functional brain connectomes have
been used successfully to predict cognitive measures such as intelligence
quotient (IQ) scores in both healthy and disordered cohorts using machine
learning models. However, existing methods resort to flattening the brain
connectome (i.e., graph) through vectorization which overlooks its topological
properties. To address this limitation and inspired from the emerging graph
neural networks (GNNs), we design a novel regression GNN model (namely RegGNN)
for predicting IQ scores from brain connectivity. On top of that, we introduce
a novel, fully modular sample selection method to select the best samples to
learn from for our target prediction task. However, since such deep learning
architectures are computationally expensive to train, we further propose a
\emph{learning-based sample selection} method that learns how to choose the
training samples with the highest expected predictive power on unseen samples.
For this, we capitalize on the fact that connectomes (i.e., their adjacency
matrices) lie in the symmetric positive definite (SPD) matrix cone. Our results
on full-scale and verbal IQ prediction outperforms comparison methods in autism
spectrum disorder cohorts and achieves a competitive performance for
neurotypical subjects using 3-fold cross-validation. Furthermore, we show that
our sample selection approach generalizes to other learning-based methods,
which shows its usefulness beyond our GNN architecture.
