6 citations · 26 across the 11 of their papers we have counts for
6 papers · 1 filter
BDHT: Generative AI Enables Causality Analysis for Mild Cognitive Impairment
Qiankun Zuo, Ling Chen, Yanyan Shen +3
Effective connectivity estimation plays a crucial role in understanding the interactions and information flow between different brain regions. However, the functional time series u…
Alzheimer's Disease Prediction via Brain Structural-Functional Deep Fusing Network
Qiankun Zuo, Junren Pan, Shuqiang Wang
Fusing structural-functional images of the brain has shown great potential to analyze the deterioration of Alzheimer's disease (AD). However, it is a big challenge to effectively f…
Generative AI Enables Structural Brain Network Construction from fMRI via Symmetric Diffusion Learning
Qiankun Zuo, Bangjun Lei, Wanyu Qiu +3
Mapping from functional connectivity (FC) to structural connectivity (SC) can facilitate multimodal brain network fusion and discover potential biomarkers for clinical implications…
Fusing Structural and Functional Connectivities using Disentangled VAE for Detecting MCI
Qiankun Zuo, Yanfei Zhu, Libin Lu +3
Brain network analysis is a useful approach to studying human brain disorders because it can distinguish patients from healthy people by detecting abnormal connections. Due to the…
Brain Structure-Function Fusing Representation Learning using Adversarial Decomposed-VAE for Analyzing MCI
Qiankun Zuo, Baiying Lei, Ning Zhong +2
Integrating the brain structural and functional connectivity features is of great significance in both exploring brain science and analyzing cognitive impairment clinically. Howeve…
Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis
Qiankun Zuo, Hao Tian, Chi-Man Pun +3
Effective connectivity can describe the causal patterns among brain regions. These patterns have the potential to reveal the pathological mechanism and promote early diagnosis and…