SFC-GAN: A Generative Adversarial Network for Brain Functional and Structural Connectome Translation

TL;DR

SFC-GAN is a novel bidirectional generative model that translates between structural and functional brain connectomes, enabling comprehensive analysis of brain connectivity with improved accuracy and utility for downstream tasks.

Contribution

This work introduces a cycle-consistent GAN framework for simultaneous bidirectional translation of brain connectomes, preserving topological features and outperforming existing models.

Findings

Superior translation accuracy between SC and FC

Effective preservation of connectome topology

Enhanced downstream classification performance

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.