  Functional magnetic resonance imaging (fMRI) is one of the most common
imaging modalities to investigate brain functions. Recent studies in
neuroscience stress the great potential of functional brain networks
constructed from fMRI data for clinical predictions. Traditional functional
brain networks, however, are noisy and unaware of downstream prediction tasks,
while also incompatible with the deep graph neural network (GNN) models. In
order to fully unleash the power of GNNs in network-based fMRI analysis, we
develop FBNETGEN, a task-aware and interpretable fMRI analysis framework via
deep brain network generation. In particular, we formulate (1) prominent region
of interest (ROI) features extraction, (2) brain networks generation, and (3)
clinical predictions with GNNs, in an end-to-end trainable model under the
guidance of particular prediction tasks. Along with the process, the key novel
component is the graph generator which learns to transform raw time-series
features into task-oriented brain networks. Our learnable graphs also provide
unique interpretations by highlighting prediction-related brain regions.
Comprehensive experiments on two datasets, i.e., the recently released and
currently largest publicly available fMRI dataset Adolescent Brain Cognitive
Development (ABCD), and the widely-used fMRI dataset PNC, prove the superior
effectiveness and interpretability of FBNETGEN. The implementation is available
at https://github.com/Wayfear/FBNETGEN.
