MVMGNN: Multi-View Masked Graph Neural Network for Alzheimer's Disease Diagnosis using Structural MRI

Chen Zhao, Yanting Li, Chuang Han, Ni Yao, Zhenxu Wang, Danyang Sun, Jiaofen Nan, Fubao Zhu, Weihua Zhou

arXiv · PDF · Open in the Atlas

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• chenzhao2023/MVMGNN_AD

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Abstract

Alzheimer's disease (AD) is a common neurodegenerative disorder, and early diagnosis is of great significance for delaying disease progression and enabling timely intervention. Mild cognitive impairment (MCI), which represents an intermediate clinical stage between cognitively normal aging and AD, is also an important target for early identification because individuals with MCI are at an increased risk of progressing to AD. Structural magnetic resonance imaging (sMRI) provides detailed characterization of anatomical structures and plays an important role in AD-related brain analysis. However, existing sMRI-based brain network methods typically rely on a single graph construction strategy, limiting their ability to jointly capture spatial relationships and morphological similarities between brain regions. While multi-view approaches can incorporate complementary information, they are often affected by redundant features and noisy connections without effective selection and fusion mechanisms. To address these issues, this paper proposes an sMRI-based multi-view masked graph neural network model (MVMGNN) for AD diagnosis. Brain regions are regarded as nodes with and radiomics features, and two complementary graph views are constructed based on spatial proximity and feature similarity. A joint node-edge masking mechanism is proposed to simultaneously select radiomics feature dimensions and structural connections, reducing redundancy during graph learning. Furthermore, a patient-level cross-view gated fusion mechanism is proposed to integrate multi-view representations. Experimental results on the ADNI dataset demonstrate that MVMGNN outperforms several competing approaches in AD classification. Interpretability analysis further demonstrates that MVMGNN is able to identify key brain regions associated with AD, providing useful insights into discriminative patterns in sMRI-based brain networks.Our implementation is publicly available at https://github.com/chenzhao2023/MVMGNN_AD

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