SFC-GAN: A Generative Adversarial Network for Brain Functional and Structural Connectome Translation
Journal:
arXiv
Published Date:
Jan 13, 2025
Abstract
Modern brain imaging technologies have enabled the detailed reconstruction of
human brain connectomes, capturing structural connectivity (SC) from diffusion
MRI and functional connectivity (FC) from functional MRI. Understanding the
intricate relationships between SC and FC is vital for gaining deeper insights
into the brain's functional and organizational mechanisms. However, obtaining
both SC and FC modalities simultaneously remains challenging, hindering
comprehensive analyses. Existing deep generative models typically focus on
synthesizing a single modality or unidirectional translation between FC and SC,
thereby missing the potential benefits of bi-directional translation,
especially in scenarios where only one connectome is available. Therefore, we
propose Structural-Functional Connectivity GAN (SFC-GAN), a novel framework for
bidirectional translation between SC and FC. This approach leverages the
CycleGAN architecture, incorporating convolutional layers to effectively
capture the spatial structures of brain connectomes. To preserve the
topological integrity of these connectomes, we employ a structure-preserving
loss that guides the model in capturing both global and local connectome
patterns while maintaining symmetry. Our framework demonstrates superior
performance in translating between SC and FC, outperforming baseline models in
similarity and graph property evaluations compared to ground truth data, each
translated modality can be effectively utilized for downstream classification.
