MHSA: A Multi-scale Hypergraph Network for Mild Cognitive Impairment Detection via Synchronous and Attentive Fusion
Manman Yuan, Weiming Jia, Xiong Luo, Jiazhen Ye, Peican Zhu, Junlin, Li

TL;DR
This paper introduces MHSA, a novel multi-scale hypergraph network that models synchronized brain region interactions using phase-locking values and spectrum-temporal fusion to improve mild cognitive impairment detection.
Contribution
It proposes a new hypergraph modeling framework that captures synchronized brain activity across multiple scales using spectrum and temporal domain fusion.
Findings
Effective in real-world MCI detection datasets
Improves modeling of brain region synchronization
Demonstrates superior performance over existing methods
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…
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Taxonomy
TopicsFunctional Brain Connectivity Studies · EEG and Brain-Computer Interfaces
