Prior-Guided Adversarial Learning With Hypergraph for Predicting Abnormal Connections in Alzheimer's Disease

Area of Science:

• Neuroscience
• Artificial Intelligence
• Medical Imaging Analysis

Background:

• Alzheimer's disease (AD) involves progressive changes in brain connectivity.
• Current diagnostic methods struggle to accurately assess these dynamic connectivity alterations.
• There is a need for advanced tools to evaluate brain network changes in AD.

Purpose of the Study:

• To propose a novel prior-guided adversarial learning with hypergraph (PALH) model.
• To accurately predict abnormal brain connections in Alzheimer's disease using triple-modality medical images.
• To evaluate the characteristics of abnormal brain connections at different stages of AD.

Main Methods:

• Developed a PALH model integrating anatomical prior knowledge and adversarial learning.
• Utilized a pairwise collaborative discriminator to refine multimodal representation learning.
• Employed a hypergraph perceptual network for effective fusion of multimodal image data and identification of high-order relations.

Main Results:

• The PALH model significantly outperformed existing methods in analyzing and predicting AD progression.
• Identified abnormal brain connections that show partial consistency with established neuroscience findings.
• Demonstrated the model's capability to assess brain connection abnormalities across various AD stages.

Conclusions:

• The proposed PALH model offers a powerful approach for evaluating brain connectivity alterations in Alzheimer's disease.
• Findings support the potential of the model for early diagnosis and personalized treatment strategies in cognitive diseases.
• The study contributes to a deeper understanding of AD's impact on brain networks.