1 citations · 1 across the 5 of their papers we have counts for
5 papers
Spatio-Temporal Graph Deep Learning with Stochastic Differential Equations for Uncovering Alzheimer's Disease Progression
Houliang Zhou, Rong Zhou, Yangying Liu +6
Identifying objective neuroimaging biomarkers to forecast Alzheimer's disease (AD) progression is crucial for timely intervention. However, this task remains challenging due to the…
A Brain Graph Foundation Model: Pre-Training and Prompt-Tuning across Broad Atlases and Disorders
Xinxu Wei, Kanhao Zhao, Yong Jiao +2
As large language models (LLMs) continue to revolutionize AI research, there is a growing interest in building large-scale brain foundation models to advance neuroscience. While mo…
Pre-Training Graph Contrastive Masked Autoencoders are Strong Distillers for EEG
Xinxu Wei, Kanhao Zhao, Yong Jiao +3
Effectively utilizing extensive unlabeled high-density EEG data to improve performance in scenarios with limited labeled low-density EEG data presents a significant challenge. In t…
Multi-modal Cross-domain Self-supervised Pre-training for fMRI and EEG Fusion
Xinxu Wei, Kanhao Zhao, Yong Jiao +4
Neuroimaging techniques including functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) have shown promise in detecting functional abnormalities in various br…
Normative Modeling via Conditional Variational Autoencoder and Adversarial Learning to Identify Brain Dysfunction in Alzheimer's Disease
Xuetong Wang, Kanhao Zhao, Rong Zhou +4
Normative modeling is an emerging and promising approach to effectively study disorder heterogeneity in individual participants. In this study, we propose a novel normative modelin…