A Heterogeneous Graph Neural Network Fusing Functional and Structural Connectivity for MCI Diagnosis

• URL: http://arxiv.org/abs/2411.08424v1
• Date: Wed, 13 Nov 2024 08:17:52 GMT
• Title: A Heterogeneous Graph Neural Network Fusing Functional and Structural Connectivity for MCI Diagnosis
• Authors: Feiyu Yin, Yu Lei, Siyuan Dai, Wenwen Zeng, Guoqing Wu, Liang Zhan, Jinhua Yu,
• Abstract summary: We propose a novel method that integrates functional and structural connectivity based on heterogeneous graph neural networks (HGNNs) Experimental results indicate the proposed method is effective and superior to other algorithms, with a mean classification accuracy of 93.3%.
• Score: 5.626542453309023
• License:
• Abstract: Brain connectivity alternations associated with brain disorders have been widely reported in resting-state functional imaging (rs-fMRI) and diffusion tensor imaging (DTI). While many dual-modal fusion methods based on graph neural networks (GNNs) have been proposed, they generally follow homogenous fusion ways ignoring rich heterogeneity of dual-modal information. To address this issue, we propose a novel method that integrates functional and structural connectivity based on heterogeneous graph neural networks (HGNNs) to better leverage the rich heterogeneity in dual-modal images. We firstly use blood oxygen level dependency and whiter matter structure information provided by rs-fMRI and DTI to establish homo-meta-path, capturing node relationships within the same modality. At the same time, we propose to establish hetero-meta-path based on structure-function coupling and brain community searching to capture relations among cross-modal nodes. Secondly, we further introduce a heterogeneous graph pooling strategy that automatically balances homo- and hetero-meta-path, effectively leveraging heterogeneous information and preventing feature confusion after pooling. Thirdly, based on the flexibility of heterogeneous graphs, we propose a heterogeneous graph data augmentation approach that can conveniently address the sample imbalance issue commonly seen in clinical diagnosis. We evaluate our method on ADNI-3 dataset for mild cognitive impairment (MCI) diagnosis. Experimental results indicate the proposed method is effective and superior to other algorithms, with a mean classification accuracy of 93.3%.

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