Graph Residual Noise Learner Network for Brain Connectivity Graph Prediction

TL;DR

This paper introduces Grenol-Net, a novel graph diffusion model designed to predict brain connectivity graphs from source graphs, addressing challenges of existing models like GANs and ensuring topological symmetry.

Contribution

Grenol-Net is the first graph diffusion model specifically developed for brain graph prediction, overcoming limitations of previous generative approaches.

Findings

Successfully predicts brain connectivity graphs with high accuracy.

Maintains topological symmetry in diffusion process.

Offers a stable and scalable training framework.

Abstract

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.