Multi-scale Graph Convolutional Network for Mild Cognitive Impairment Detection

• Published in: Graph Learning in Medical Imaging Vol. 11849; pp. 79 - 87
• Main Authors: Yu, Shuangzhi, Yue, Guanghui, Elazab, Ahmed, Song, Xuegang, Wang, Tianfu, Lei, Baiying
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Dynamic high-order brain functional connectivity network; Mild cognitive impairment detection; Multi-scale graph convolutional network; rs-fMRI
• ISBN: 303035816X; 9783030358167
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-35817-4_10

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.