5 papers
Adaptive Clinical-Aware Latent Diffusion for Multimodal Brain Image Generation and Missing Modality Imputation
Rong Zhou, Houliang Zhou, Yao Su +4
Multimodal neuroimaging provides complementary insights for Alzheimer's disease diagnosis, yet clinical datasets frequently suffer from missing modalities. We propose ACADiff, a fr…
Diffusion-Guided Pretraining for Brain Graph Foundation Models
Xinxu Wei, Rong Zhou, Lifang He +1
With the growing interest in foundation models for brain signals, graph-based pretraining has emerged as a promising paradigm for learning transferable representations from connect…
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…
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…
Normative Modeling for AD Diagnosis and Biomarker Identification
Songlin Zhao, Rong Zhou, Yu Zhang +2
In this paper, we introduce a novel normative modeling approach that incorporates focal loss and adversarial autoencoders (FAAE) for Alzheimer's Disease (AD) diagnosis and biomarke…