Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks
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METHODOLOGY
Open Access
Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks Jin Liu1 , Guanxin Tan1 , Wei Lan2 and Jianxin Wang1* From 15th International Symposium on Bioinformatics Research and Applications (ISBRA’19) Barcelona, Spain. 3-6 June 2019 *Correspondence: [email protected] 1 Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, 932 Lushan South Road, 410083, Changsha, China Full list of author information is available at the end of the article
Abstract Background: The identification of early mild cognitive impairment (EMCI), which is an early stage of Alzheimer’s disease (AD) and is associated with brain structural and functional changes, is still a challenging task. Recent studies show great promises for improving the performance of EMCI identification by combining multiple structural and functional features, such as grey matter volume and shortest path length. However, extracting which features and how to combine multiple features to improve the performance of EMCI identification have always been a challenging problem. To address this problem, in this study we propose a new EMCI identification framework using multi-modal data and graph convolutional networks (GCNs). Firstly, we extract grey matter volume and shortest path length of each brain region based on automated anatomical labeling (AAL) atlas as feature representation from T1w MRI and rs-fMRI data of each subject, respectively. Then, in order to obtain features that are more helpful in identifying EMCI, a common multi-task feature selection method is applied. Afterwards, we construct a non-fully labelled subject graph using imaging and non-imaging phenotypic measures of each subject. Finally, a GCN model is adopted to perform the EMCI identification task. Results: Our proposed EMCI identification method is evaluated on 210 subjects, including 105 subjects with EMCI and 105 normal controls (NCs), with both T1w MRI and rs-fMRI data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that our proposed framework achieves an accuracy of 84.1% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.856 for EMCI/NC classification. In addition, by comparison, the accuracy and AUC values of our proposed framework are better than those of some existing methods in EMCI identification. Conclusion: Our proposed EMCI identification framework is effective and promising for automatic diagnosis of EMCI in clinical practice. Keywords: Early mild cognitive impairment, Multi-modal MRI data, Graph convolutional networks, Identification © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and
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