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Multi-scale Graph Convolutional Network for Mild Cognitive Impairment Detection

  • Shuangzhi Yu
  • Guanghui Yue
  • Ahmed Elazab
  • Xuegang Song
  • Tianfu Wang
  • Baiying LeiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11849)

Abstract

Mild cognitive impairment (MCI) is an early stage of Alzheimer’s disease (AD), which is also the best time for treatment. However, existing methods only consider neuroimaging features learned from group relationships instead of the subjects’ individual features. Such methods ignore demographic relationships (i.e., non-image information). In this paper, we propose a novel method based on multi-scale graph convolutional network (MS-GCN) via inception module, which combines image and non-image information for MCI detection. Specifically, since the brain has the characteristics of high-order interactions, we first analyze the dynamic high-order features of resting functional magnetic resonance imaging (rs-fMRI) time series and construct a dynamic high-order brain functional connectivity network (DH-FCN). To get more effective features and further improve the detection performance, we extract the local weighted clustering coefficients from the original DH-FCN. Then, gender and age information are combined with the neuroimaging data to build a graph. Finally, we perform the detection using the MS-GCN, and validate the proposed method on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The experimental results demonstrate that our proposed method can achieve remarkable MCI detection performance.

Keywords

Multi-scale graph convolutional network Mild cognitive impairment detection rs-fMRI Dynamic high-order brain functional connectivity network 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Shuangzhi Yu
    • 1
  • Guanghui Yue
    • 1
  • Ahmed Elazab
    • 1
  • Xuegang Song
    • 1
  • Tianfu Wang
    • 1
  • Baiying Lei
    • 1
    Email author
  1. 1.National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science CenterShenzhen UniversityShenzhenChina

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