SFC-GAN: 一种用于脑功能与结构连接组转换的生成对抗网络
SFC-GAN: A Generative Adversarial Network for Brain Functional and Structural Connectome Translation
AI总结：
针对脑成像中SC与FC模态难以同时获取的问题，提出基于CycleGAN的SFC-GAN框架，实现脑结构与功能连接组的双向转换，通过结构保持损失保留拓扑完整性，性能优于基线且可用于下游分类。
摘要：
现代脑成像技术已能实现人脑连接组的精细重建，可从弥散磁共振成像（diffusion MRI）获取结构连接（SC），从功能磁共振成像（functional MRI）获取功能连接（FC）。理解SC与FC之间的复杂关系，对于深入洞察大脑的功能及组织机制至关重要。然而，同时获取SC和FC两种模态仍存在挑战，阻碍了综合分析的开展。现有深度生成模型通常聚焦于单模态合成或FC与SC之间的单向转换，因而错失了双向转换的潜在优势，尤其是在仅能获取单一连接组的场景下。为此，我们提出结构-功能连接生成对抗网络（Structural-Functional Connectivity GAN，SFC-GAN），这是一种实现SC与FC双向转换的新型框架。该方法基于CycleGAN架构，引入卷积层以有效捕捉脑连接组的空间结构。为保留这些连接组的拓扑完整性，我们采用结构保持损失，引导模型同时捕捉连接组的全局和局部模式，同时维持对称性。我们的框架在SC与FC的转换任务中展现出优异性能，在与真实数据的相似度评估和图属性评估中均优于基线模型，且每种转换后的模态都能有效用于下游分类任务。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
