DiffGAN-F2S: Symmetric and Efficient Denoising Diffusion GANs for
  Structural Connectivity Prediction from Brain fMRI
Qiankun Zuo Ruiheng Li Yi Di Hao Tian Changhong Jing Xuhang Chen Shuqiang Wang
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.