Graph Residual Noise Learner Network for Brain Connectivity Graph Prediction
This work is significant for neurologists and researchers in medical imaging, as it aims to improve the diagnosis of neurological disorders by enabling brain graph prediction from incomplete data, representing an incremental improvement over existing generative models.
The paper addresses the problem of predicting a target brain connectivity graph from a source graph, which is crucial for diagnosing neurological disorders when data is incomplete. The authors propose Grenol-Net, the first graph diffusion model for this task, to overcome limitations of GAN-based methods like mode collapse and large data requirements, while maintaining topological symmetry.
A morphological brain graph depicting a connectional fingerprint is of paramount importance for charting brain dysconnectivity patterns. Such data often has missing observations due to various reasons such as time-consuming and incomplete neuroimage processing pipelines. Thus, predicting a target brain graph from a source graph is crucial for better diagnosing neurological disorders with minimal data acquisition resources. Many brain graph generative models were proposed for promising results, yet they are mostly based on generative adversarial networks (GAN), which could suffer from mode collapse and require large training datasets. Recent developments in diffusion models address these problems by offering essential properties such as a stable training objective and easy scalability. However, applying a diffusion process to graph edges fails to maintain the topological symmetry of the brain connectivity matrices. To meet these challenges, we propose the Graph Residual Noise Learner Network (Grenol-Net), the first graph diffusion model for predicting a target graph from a source graph.