Diagnosing Alzheimer's disease using hypergraph neural networks with prompt tuning

The accurate diagnosis of Alzheimer's disease (AD) and prognosis of mild cognitive impairment (MCI) conversion are crucial for early intervention. However, existing multimodal methods face several challenges, from the heterogeneity of input data, to underexplored modality interactions, missing data due to patient dropouts, and limited data caused by the time-consuming and costly data collection process. In this paper, we propose a novel Prompted Hypergraph Neural Network (PHGNN) framework that addresses these limitations by integrating hypergraph based learning with prompt learning. Hypergraphs capture higher-order relationships between different modalities, while our prompt learning approach for hypergraphs, adapted from NLP, enables efficient training with limited data. Our model is validated through extensive experiments on the ADNI dataset as well as cross-domain validations using the OASIS-3 and NACC datasets. The results demonstrate that PHGNN outperforms SOTA methods in both AD diagnosis and MCI conversion prediction, showing superior cross-domain generalization capabilities. At the same time, it uses only a fraction (6%) of the tunable parameters of traditional fine-tuning and maintains a low computational load compared to alternative tuning strategies.