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Multi-modal graph neural network for early diagnosis of Alzheimer's disease from sMRI and PET scans

计算机科学 卷积神经网络 模态(人机交互) 神经影像学 人工智能 深度学习 图形 情态动词 正电子发射断层摄影术 人工神经网络 机器学习 模式识别(心理学) 医学 放射科 理论计算机科学 精神科 化学 高分子化学
作者
Yanteng Zhang,Xiaohai He,Yi Hao Chan,Qizhi Teng,Jagath C. Rajapakse
出处
期刊:Computers in Biology and Medicine [Elsevier BV]
卷期号:164: 107328-107328 被引量:24
标识
DOI:10.1016/j.compbiomed.2023.107328
摘要

In recent years, deep learning models have been applied to neuroimaging data for early diagnosis of Alzheimer's disease (AD). Structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) images provide structural and functional information about the brain, respectively. Combining these features leads to improved performance than using a single modality alone in building predictive models for AD diagnosis. However, current multi-modal approaches in deep learning, based on sMRI and PET, are mostly limited to convolutional neural networks, which do not facilitate integration of both image and phenotypic information of subjects. We propose to use graph neural networks (GNN) that are designed to deal with problems in non-Euclidean domains. In this study, we demonstrate how brain networks are created from sMRI or PET images and can be used in a population graph framework that combines phenotypic information with imaging features of the brain networks. Then, we present a multi-modal GNN framework where each modality has its own branch of GNN and a technique that combines the multi-modal data at both the level of node vectors and adjacency matrices. Finally, we perform late fusion to combine the preliminary decisions made in each branch and produce a final prediction. As multi-modality data becomes available, multi-source and multi-modal is the trend of AD diagnosis. We conducted explorative experiments based on multi-modal imaging data combined with non-imaging phenotypic information for AD diagnosis and analyzed the impact of phenotypic information on diagnostic performance. Results from experiments demonstrated that our proposed multi-modal approach improves performance for AD diagnosis. Our study also provides technical reference and support the need for multivariate multi-modal diagnosis methods.
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