MHSA: A Multi-scale Hypergraph Network for Mild Cognitive Impairment Detection via Synchronous and Attentive Fusion

2024-12-03

IEEE International Conference on Bioinformatics and Biomedicine

AI Summary

Early identification of mild cognitive impairment (MCI) remains challenging due to its subtle clinical manifestations and the limited representational capacity of single-scale functional brain network modeling. To address these issues, this paper proposes a phase-synchronization-driven multi-scale dynamic hypergraph learning framework. We introduce the phase-locking value (PLV) — for the first time — into fMRI-based functional connectivity modeling to capture dynamic, time-frequency-domain synchronization among brain regions. A dynamic PLV coefficient adaptation strategy is designed, and a spatiotemporal-spectral multi-scale hypergraph network is constructed, integrated with an attention mechanism to identify discriminative subnetworks. Evaluated on a real-world MCI dataset, our model achieves significant performance gains, improving classification accuracy by an average of 3.2% over state-of-the-art methods. This validates the efficacy of phase-synchronization features and multi-scale hypergraph representation for MCI detection. The source code is publicly available.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Multi-modal Vision

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Mild Cognitive Impairment
• Early Detection
• Intervention

Innovation

• Multiscale Hypergraph Network (MHSA)
• Phase Locking Value (PLV)
• Dynamic Hypergraph for Mild Cognitive Impairment (MCI) Detection