The cross-modal synthesis between structural magnetic resonance imaging (sMRI) and functional network connectivity (FNC) is a relatively unexplored area in medical imaging, especially with respect to schizophrenia. This study employs conditional Vision Transformer Generative Adversarial Networks (cViT-GANs) to generate FNC data based on sMRI inputs. After training on a comprehensive dataset that included both individuals with schizophrenia and healthy control subjects, our cViT-GAN model effectively synthesized the FNC matrix for each subject, and then formed a group difference FNC matrix, obtaining a Pearson correlation of 0.73 with the actual FNC matrix. In addition, our FNC visualization results demonstrate significant correlations in particular subcortical brain regions, highlighting the model's capability of capturing detailed structural-functional associations. This performance distinguishes our model from conditional CNN-based GAN alternatives such as Pix2Pix. Our research is one of the first attempts to link sMRI and FNC synthesis, setting it apart from other cross-modal studies that concentrate on T1- and T2-weighted MR images or the fusion of MRI and CT scans.

翻译：结构磁共振成像（sMRI）与功能网络连接（FNC）之间的跨模态合成是医学影像领域一个相对未充分探索的方向，尤其是针对精神分裂症的研究。本研究采用条件视觉Transformer生成对抗网络（cViT-GANs），基于sMRI输入生成FNC数据。在包含精神分裂症患者和健康对照受试者的综合性数据集上训练后，我们的cViT-GAN模型有效生成了每个受试者的FNC矩阵，进而构建出组间差异FNC矩阵，该矩阵与实际FNC矩阵的皮尔逊相关系数达到0.73。此外，FNC可视化结果显示出特定皮层下脑区存在显著相关性，充分体现了模型捕捉详细结构-功能关联的能力。这一性能使我们的模型区别于基于条件CNN的GAN替代方案（如Pix2Pix）。本研究是首次尝试建立sMRI与FNC合成关联的探索之一，与其他聚焦于T1/T2加权MRI图像或MRI与CT融合的跨模态研究形成鲜明对比。