  Graph neural networks (GNNs) have demonstrated success in learning
representations of brain graphs derived from functional magnetic resonance
imaging (fMRI) data. However, existing GNN methods assume brain graphs are
static over time and the graph adjacency matrix is known prior to model
training. These assumptions contradict evidence that brain graphs are
time-varying with a connectivity structure that depends on the choice of
functional connectivity measure. Incorrectly representing fMRI data with noisy
brain graphs can adversely affect GNN performance. To address this, we propose
DynDepNet, a novel method for learning the optimal time-varying dependency
structure of fMRI data induced by downstream prediction tasks. Experiments on
real-world fMRI datasets, for the task of sex classification, demonstrate that
DynDepNet achieves state-of-the-art results, outperforming the best baseline in
terms of accuracy by approximately 8 and 6 percentage points, respectively.
Furthermore, analysis of the learned dynamic graphs reveals prediction-related
brain regions consistent with existing neuroscience literature.
