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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The relationship between brain structure and function is critical forrevealing the pathogenesis of brain disease, including Alzheimer's disease(AD). However, it is a great challenge to map brain structure-functionconnections due to various reasons. In this work, a bidirectional graphgenerative adversarial networks (BGGAN) is proposed to represent brainstructure-function connections. Specifically, by designing a moduleincorporating inner graph convolution network (InnerGCN), the generators ofBGGAN can employ features of direct and indirect brain regions to learn themapping function between structural domain and functional domain. Besides, anew module named Balancer is designed to counterpoise the optimization betweengenerators and discriminators. By introducing the Balancer into BGGAN, both thestructural generator and functional generator can not only alleviate the issueof mode collapse but also learn complementarity of structural and functionalfeatures. Experimental results using ADNI datasets show that the both thegenerated structure connections and generated function connections can improvethe identification accuracy of AD. More importantly, based the proposed model,it is found that the relationship between brain structure and function is not acomplete one-to-one correspondence. Brain structure is the basis of brainfunction. The strong structural connections are almost accompanied by strongfunctional connections.

BGGAN: Generative AI Enables Representing Brain Structure-Function Connections for Alzheimer's Disease