  Brain graphs (i.e, connectomes) constructed from medical scans such as
magnetic resonance imaging (MRI) have become increasingly important tools to
characterize the abnormal changes in the human brain. Due to the high
acquisition cost and processing time of multimodal MRI, existing deep learning
frameworks based on Generative Adversarial Network (GAN) focused on predicting
the missing multimodal medical images from a few existing modalities. While
brain graphs help better understand how a particular disorder can change the
connectional facets of the brain, synthesizing a target brain multigraph (i.e,
multiple brain graphs) from a single source brain graph is strikingly lacking.
Additionally, existing graph generation works mainly learn one model for each
target domain which limits their scalability in jointly predicting multiple
target domains. Besides, while they consider the global topological scale of a
graph (i.e., graph connectivity structure), they overlook the local topology at
the node scale (e.g., how central a node is in the graph). To address these
limitations, we introduce topology-aware graph GAN architecture (topoGAN),
which jointly predicts multiple brain graphs from a single brain graph while
preserving the topological structure of each target graph. Its three key
innovations are: (i) designing a novel graph adversarial auto-encoder for
predicting multiple brain graphs from a single one, (ii) clustering the encoded
source graphs in order to handle the mode collapse issue of GAN and proposing a
cluster-specific decoder, (iii) introducing a topological loss to force the
prediction of topologically sound target brain graphs. The experimental results
using five target domains demonstrated the outperformance of our method in
brain multigraph prediction from a single graph in comparison with baseline
approaches.
