Mapping from functional connectivity (FC) to structural connectivity (SC) can facilitate multimodal brain network fusion and discover potential biomarkers for clinical implications. However, it is challenging to directly bridge the reliable non-linear mapping relations between SC and functional magnetic resonance imaging (fMRI). In this paper, a novel diffusision generative adversarial network-based fMRI-to-SC (DiffGAN-F2S) model is proposed to predict SC from brain fMRI in an end-to-end manner. To be specific, the proposed DiffGAN-F2S leverages denoising diffusion probabilistic models (DDPMs) and adversarial learning to efficiently generate high-fidelity SC through a few steps from fMRI. By designing the dual-channel multi-head spatial attention (DMSA) and graph convolutional modules, the symmetric graph generator first captures global relations among direct and indirect connected brain regions, then models the local brain region interactions. It can uncover the complex mapping relations between fMRI and structural connectivity. Furthermore, the spatially connected consistency loss is devised to constrain the generator to preserve global-local topological information for accurate intrinsic SC prediction. Testing on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the proposed model can effectively generate empirical SC-preserved connectivity from four-dimensional imaging data and shows superior performance in SC prediction compared with other related models. Furthermore, the proposed model can identify the vast majority of important brain regions and connections derived from the empirical method, providing an alternative way to fuse multimodal brain networks and analyze clinical disease.

翻译：从功能连接映射到结构连接有助于多模态脑网络融合，并发现具有临床意义的潜在生物标志物。然而，直接建立SC与功能磁共振成像之间可靠的非线性映射关系仍具挑战性。本文提出一种新颖的基于扩散生成对抗网络的fMRI-to-SC模型，以端到端方式从脑fMRI预测SC。具体而言，所提出的DiffGAN-F2S利用去噪扩散概率模型与对抗学习，通过少量步骤从fMRI高效生成高保真SC。通过设计双通道多头空间注意力和图卷积模块，对称图生成器首先捕获直接与间接连接脑区间的全局关系，进而建模局部脑区相互作用，从而揭示fMRI与结构连接之间的复杂映射关系。此外，还设计了空间连接一致性损失函数以约束生成器保留全局-局部拓扑信息，实现精确的内在SC预测。在公开的阿尔茨海默病神经影像学倡议数据集上测试，所提模型能有效从四维成像数据生成经验性SC保留连接，且在SC预测方面展现出优于其他相关模型的性能。进一步地，该模型可识别经验方法衍生出的绝大多数关键脑区与连接，为融合多模态脑网络及分析临床疾病提供替代方案。