One paper been accepted by IEEE TNNLS

Title: Hypergraph Foundation Model for Brain Disease Diagnosis

The goal of the hypergraph foundation model is to learn an encoder based on the hypergraph computation paradigm through self-supervised pre-training on high-order correlation structures, enabling the encoder to rapidly adapt to various downstream tasks in scenarios where no labeled data or only a small amount of labeled data is available. Initial exploratory work has been applied to brain disease diagnosis tasks. However, existing methods primarily rely on graph-based approaches to learn low-order correlation patterns between brain regions in brain networks, neglecting the modeling and learning of complex correlations between different brain diseases and patients.

This paper proposes a hypergraph foundation model (HGFM) for brain disease diagnosis, which conducts multi-dimensional pre-training tasks to explore latent cross-dimensional high-order correlation patterns on various brain disease datasets. HGFM is a high-order correlation-driven foundation model for brain disease diagnosis and effectively improves prediction performance. Specifically, HGFM first performs brain functional network link prediction tasks on individual brain networks and group interaction network link prediction tasks on group brain networks, constructing a hypergraph foundation model for brain disease diagnosis. In downstream tasks, it achieves predictions for different brain disease diagnosis tasks through few-shot learning fine-tuning methods.

The proposed method is evaluated on functional magnetic resonance imaging data from 4,409 patients across four brain diseases. Results show that it outperforms existing state-of-the-art methods in all brain disease diagnosis tasks, demonstrating its potential value in clinical applications.