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/

Zendy Plus

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

Zendy Tools

Zendy Open

Effective connectivity estimation plays a crucial role in understanding theinteractions and information flow between different brain regions. However, thefunctional time series used for estimating effective connectivity is derivedfrom certain software, which may lead to large computing errors because ofdifferent parameter settings and degrade the ability to model complex causalrelationships between brain regions. In this paper, a brain diffuser withhierarchical transformer (BDHT) is proposed to estimate effective connectivityfor mild cognitive impairment (MCI) analysis. To our best knowledge, theproposed brain diffuser is the first generative model to apply diffusion modelsto the application of generating and analyzing multimodal brain networks.Specifically, the BDHT leverages structural connectivity to guide the reverseprocesses in an efficient way. It makes the denoising process more reliable andguarantees effective connectivity estimation accuracy. To improve denoisingquality, the hierarchical denoising transformer is designed to learnmulti-scale features in topological space. By stacking the multi-head attentionand graph convolutional network, the graph convolutional transformer(GraphConformer) module is devised to enhance structure-functioncomplementarity and improve the ability in noise estimation. Experimentalevaluations of the denoising diffusion model demonstrate its effectiveness inestimating effective connectivity. The proposed model achieves superiorperformance in terms of accuracy and robustness compared to existingapproaches. Moreover, the proposed model can identify altered directionalconnections and provide a comprehensive understanding of parthenogenesis forMCI treatment.

BDHT: Generative AI Enables Causality Analysis for Mild Cognitive Impairment