  Brain graph synthesis marked a new era for predicting a target brain graph
from a source one without incurring the high acquisition cost and processing
time of neuroimaging data. However, existing multi-modal graph synthesis
frameworks have several limitations. First, they mainly focus on generating
graphs from the same domain (intra-modality), overlooking the rich multimodal
representations of brain connectivity (inter-modality). Second, they can only
handle isomorphic graph generation tasks, limiting their generalizability to
synthesizing target graphs with a different node size and topological structure
from those of the source one. More importantly, both target and source domains
might have different distributions, which causes a domain fracture between them
(i.e., distribution misalignment). To address such challenges, we propose an
inter-modality aligner of non-isomorphic graphs (IMANGraphNet) framework to
infer a target graph modality based on a given modality. Our three core
contributions lie in (i) predicting a target graph (e.g., functional) from a
source graph (e.g., morphological) based on a novel graph generative
adversarial network (gGAN); (ii) using non-isomorphic graphs for both source
and target domains with a different number of nodes, edges and structure; and
(iii) enforcing the predicted target distribution to match that of the ground
truth graphs using a graph autoencoder to relax the designed loss oprimization.
To handle the unstable behavior of gGAN, we design a new Ground
Truth-Preserving (GT-P) loss function to guide the generator in learning the
topological structure of ground truth brain graphs. Our comprehensive
experiments on predicting functional from morphological graphs demonstrate the
outperformance of IMANGraphNet in comparison with its variants. This can be
further leveraged for integrative and holistic brain mapping in health and
disease.
