Multimodal brain graph learning improves tracking of neurodegenerative stages
Stage-aware Multimodal Brain Graph Contrastive Learning for Neurodegenerative Disorder Diagnosis
Artificial Intelligence
Summary
Neurodegenerative disorders like Alzheimer's get worse in stages, but current diagnostic methods struggle to tell those stages apart well. The authors developed a new machine learning method called smBGC that looks at brain data from multiple sources and learns patterns specific to each disease stage. This helps create brain graph models that better recognize the progression of these disorders. Tests on real patient data showed this method performs better than existing techniques for diagnosing different stages of neurodegenerative diseases.
What this means in practice
- •For medical imaging developers: Develop diagnostic software that distinguishes neurodegenerative disorder stages by integrating multiple brain imaging modalities with stage-aware brain graphs.$Commercial implications: Enables creation of advanced clinical tools that provide accurate stage-specific diagnoses for neurologists managing neurodegenerative diseases.
- •For clinical data analysts: Improve analysis pipelines for patient brain data by incorporating stage-adaptive graph learning techniques to better understand disease progression.
Authors
Jingjing Zhou, Chenxuan Meng, Ciyuan Peng, Jing Ren, Feng Xia
Abstract
Neurodegenerative disorders are characterized by their progressive nature, with distinct stages that require precise identification and tracking of progression trajectories for accurate diagnosis. However, existing diagnostic methods often fail to fully capture the unique brain patterns associated with different stages, limiting their ability to effectively target specific stages of the disorders. To address this issue, we propose a Stage-aware Multimodal Brain Graph Contrastive Learning (smBGC) method. Specifically, smBGC introduces a stage-aware feature enhancement strategy to capture stage-specific features across all progression stages, enabling the generation of stage-aware multimodal brain graph representations. In addition, smBGC incorporates a stage-adaptive brain graph augmentation approach within a graph contrastive learning framework, facilitating multimodal fusion and improving the model's capability to distinguish between different disorder stages. Experiments on two real-world datasets demonstrate the superior performance of smBGC in various neurodegenerative disorder diagnosis tasks compared with state-of-the-art methods.