6 citations · 26 across the 11 of their papers we have counts for
11 papers
A New Brain Network Construction Paradigm for Brain Disorder via Diffusion-based Graph Contrastive Learning
Yongcheng Zong, Shuqiang Wang
Brain network analysis plays an increasingly important role in studying brain function and the exploring of disease mechanisms. However, existing brain network construction tools h…
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…
Multiscale Autoencoder with Structural-Functional Attention Network for Alzheimer's Disease Prediction
Yongcheng Zong, Changhong Jing, Qiankun Zuo
The application of machine learning algorithms to the diagnosis and analysis of Alzheimer's disease (AD) from multimodal neuroimaging data is a current research hotspot. It remains…
Cross-Modal Transformer GAN: A Brain Structure-Function Deep Fusing Framework for Alzheimer's Disease
Junren Pan, Shuqiang Wang
Cross-modal fusion of different types of neuroimaging data has shown great promise for predicting the progression of Alzheimer's Disease(AD). However, most existing methods applied…
A Prior Guided Adversarial Representation Learning and Hypergraph Perceptual Network for Predicting Abnormal Connections of Alzheimer's Disease
Qiankun Zuo, Baiying Lei, Shuqiang Wang +3
Alzheimer's disease is characterized by alterations of the brain's structural and functional connectivity during its progressive degenerative processes. Existing auxiliary diagnost…
Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer's Disease Prediction
Qiankun Zuo, Baiying Lei, Yanyan Shen +3
Multimodal neuroimage can provide complementary information about the dementia, but small size of complete multimodal data limits the ability in representation learning. Moreover,…