Mild cognitive impairment (MCI), an early stage of Alzheimer's disease, is marked by subtle cognitive decline. While graph neural networks (GNNs) have shown promise in modeling neuroimaging-based brain networks for MCI detection, most treat the brain as a homogeneous graph, ignoring hemispheric structural and functional heterogeneity. To address this limitation, we propose a Hemispheric Heterogeneity-aware Graph Neural Network (HemiHeter-GNN) to explicitly model hemispheric structural and functional heterogeneity for improved MCI detection. Multimodal brain networks are constructed by integrating diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), and decomposed into left-, right, and inter-hemispheric subnetworks to capture hemispherespecific patterns. Each subnetwork is encoded by a dedicated graph encoder and refined by heat-kernel diffusion for topological regularity, while a cross-subnetwork attention mechanism then fuses them into a unified whole-brain embedding. Experiments on the ADNI dataset show that HemiHeter-GNN consistently outperforms state-of-the-art baselines, demonstrating the effectiveness of modeling hemispheric asymmetry for MCI detection.

Hypergraph-based models have shown great potential in capturing high-order interactions within brain functional networks for disease identification. However, most hypergraph neural networks (HGNNs) depend on static and heuristically constructed hypergraphs, which fail to model the brain's dynamic connectivity patterns. In this paper, we propose a Cluster-Aware Hypergraph Network that jointly learns functional clusters and adaptive high-order connections, enhanced by contrastive learning to preserve topological consistency. Comprehensive experiments conducted on three real-world datasets demonstrate the effectiveness of our proposed methods.

Hypergraphs provide excellent modeling ability for brain disorder identification, especially in capturing high-order interactions among regions of interest (ROIs). Nevertheless, existing methods overlook the impact of directional hyperedges learning on the brain network, leading to wasteful functional connectivity and poor identification performance. To address the above issue, this paper proposes a novel Brain Disorder Identification method via Directed Hypergraph Networks (D-HyperNet). Specifically, our methodology employs an Effective Network Construction module to capture causal dependencies and infer directional functional connectivity among ROIs. Followed by the Flow-aware Feature Aggregation module, which designs a novel directed hypergraph encoder that directionally aggregates node features, effectively improving the accuracy and reliability of brain network representations. Additionally, we are integrating the proposed encoder into a contrastive learning program to obtain a more robust whole-brain representation. Extensive experiments demonstrate the efficacy of our D-HyperNet approach. The code is available at https://github.com/Jia-Weiming/D-HyperNet.

The precise detection of mild cognitive impairment (MCI) is of significant importance in preventing the deterioration of patients in a timely manner. Although hypergraphs have enhanced performance by learning and analyzing brain networks, they often only depend on vector distances between features at a single scale to infer interactions. In this paper, we deal with a more arduous challenge, hypergraph modelling with synchronization between brain regions, and design a novel framework, i.e., A Multi-scale Hypergraph Network for MCI Detection via Synchronous and Attentive Fusion (MHSA), to tackle this challenge. Specifically, our approach employs the Phase-Locking Value (PLV) to calculate the phase synchronization relationship in the spectrum domain of regions of interest (ROIs) and designs a multi-scale feature fusion mechanism to integrate dynamic connectivity features of functional magnetic resonance imaging (fMRI) from both the temporal and spectrum domains. To evaluate and op-timize the direct contribution of each ROI to phase synchronization in the temporal domain, we structure the PLV coefficients dynamically adjust strategy, and the dynamic hypergraph is modelled based on a comprehensive temporal-spectrum fusion matrix. Experiments on the real-world dataset indicate the effectiveness of our strategy. The code is available at https://github.com/Jia-Weiming/MHSA.