Inter-intra High-Order Brain Network for ASD Diagnosis via Functional MRIs

Currently in the field of computer-aided diagnosis, graph or hypergraph-based methods are widely used in the diagnosis of neurological diseases. However, existing graph-based work primarily focuses on pairwise correlations, neglecting high-order correlations. Additionally, existing hypergraph methods can only explore the commonality of high-order representations at a single scale, resulting in the lack of a framework that can integrate multi-scale high-order correlations. To address the above issues, we propose an Inter-Intra High-order Brain Network ( \(\mathrm {I^{2}}\) HBN) framework for ASD-assisted diagnosis, which is divided into two parts: intra-hypergraph computation and inter-hypergraph computation. Specifically, the intra-hypergraph computation employs the hypergraph to represent high-order correlations among different brain regions based on fMRI signal, generating intra-embeddings and intra-results. Subsequently, inter-hypergraph computation utilizes these intra-embeddings as features of inter-vertices to model inter-hypergraph that captures the inter-correlations among individuals at the population level. Finally, the intra-results and the inter-results are weighted to perform brain disease diagnosis. We demonstrate the potential of this method on two ABIDE datasets (NYU and UCLA), the results show that the proposed method for ASD diagnosis has superior performance, compared with existing state-of-the-art methods.