English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The cross-modal synthesis between structural magnetic resonance imaging(sMRI) and functional network connectivity (FNC) is a relatively unexploredarea in medical imaging, especially with respect to schizophrenia. This studyemploys conditional Vision Transformer Generative Adversarial Networks(cViT-GANs) to generate FNC data based on sMRI inputs. After training on acomprehensive dataset that included both individuals with schizophrenia andhealthy control subjects, our cViT-GAN model effectively synthesized the FNCmatrix for each subject, and then formed a group difference FNC matrix,obtaining a Pearson correlation of 0.73 with the actual FNC matrix. Inaddition, our FNC visualization results demonstrate significant correlations inparticular subcortical brain regions, highlighting the model's capability ofcapturing detailed structural-functional associations. This performancedistinguishes our model from conditional CNN-based GAN alternatives such asPix2Pix. Our research is one of the first attempts to link sMRI and FNCsynthesis, setting it apart from other cross-modal studies that concentrate onT1- and T2-weighted MR images or the fusion of MRI and CT scans.

Cross-Modal Synthesis of Structural MRI and Functional Connectivity Networks via Conditional ViT-GANs