Multi-relation graph convolutional network for Alzheimer’s disease diagnosis using structural MRI
Structural magnetic resonance imaging (sMRI) is widely applied in Alzheimer’s disease (AD) diagnosis tasks by reflecting structural anomalies of the brain. Currently, most existing methods solely focus on pathological changes in disease-affected brain regions and ignore their potential associations...
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| Vydáno v: | Knowledge-based systems Ročník 270; s. 110546 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
21.06.2023
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| Témata: | |
| ISSN: | 0950-7051, 1872-7409 |
| On-line přístup: | Získat plný text |
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| Abstract | Structural magnetic resonance imaging (sMRI) is widely applied in Alzheimer’s disease (AD) diagnosis tasks by reflecting structural anomalies of the brain. Currently, most existing methods solely focus on pathological changes in disease-affected brain regions and ignore their potential associations and interactions, which provide valuable information for brain investigation. Meanwhile, how to construct effective structural brain graphs composed of nodes and edges remains appealing. To tackle these issues, in this paper, we propose a novel multi-relation reasoning network (MRN) to learn multi-relation-aware representations of brain regions in sMRI data for AD diagnosis, including spatial correlations and topological information. We frame distinguishing different disease statuses as the graph classification problem. Each scan is regarded as a graph, where nodes represent brain regions with abnormal changes selected by group-wise comparison, and edges denote semantic or spatial relations between them. Specifically, the dilated convolution module learns informative features to provide discriminative node representations for constructing brain graphs. Multi-type inter-region relations are then captured by the local reasoning module based on the graph convolutional network to provide a reliable basis for AD diagnosis, including geometric correlations and semantic interactions. Moreover, global reasoning is employed on the learned graph structure to achieve information aggregation and gradually generate the subject-level representation for AD diagnosis. We evaluate the effectiveness of our proposed method on the ADNI dataset, and extensive experiments demonstrate that our MRN achieves competitive performance for multiple AD-related classification tasks, compared to several state-of-the-art methods.
•A novel multi-relation reasoning network for sMRI-based AD diagnosis is proposed.•Brain connectivity graphs are constructed based on disease-related brain regions.•Potential interactions between discriminative brain regions in sMRI are learned.•The diagnosis results achieve promising performance for AD diagnosis tasks. |
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| AbstractList | Structural magnetic resonance imaging (sMRI) is widely applied in Alzheimer’s disease (AD) diagnosis tasks by reflecting structural anomalies of the brain. Currently, most existing methods solely focus on pathological changes in disease-affected brain regions and ignore their potential associations and interactions, which provide valuable information for brain investigation. Meanwhile, how to construct effective structural brain graphs composed of nodes and edges remains appealing. To tackle these issues, in this paper, we propose a novel multi-relation reasoning network (MRN) to learn multi-relation-aware representations of brain regions in sMRI data for AD diagnosis, including spatial correlations and topological information. We frame distinguishing different disease statuses as the graph classification problem. Each scan is regarded as a graph, where nodes represent brain regions with abnormal changes selected by group-wise comparison, and edges denote semantic or spatial relations between them. Specifically, the dilated convolution module learns informative features to provide discriminative node representations for constructing brain graphs. Multi-type inter-region relations are then captured by the local reasoning module based on the graph convolutional network to provide a reliable basis for AD diagnosis, including geometric correlations and semantic interactions. Moreover, global reasoning is employed on the learned graph structure to achieve information aggregation and gradually generate the subject-level representation for AD diagnosis. We evaluate the effectiveness of our proposed method on the ADNI dataset, and extensive experiments demonstrate that our MRN achieves competitive performance for multiple AD-related classification tasks, compared to several state-of-the-art methods.
•A novel multi-relation reasoning network for sMRI-based AD diagnosis is proposed.•Brain connectivity graphs are constructed based on disease-related brain regions.•Potential interactions between discriminative brain regions in sMRI are learned.•The diagnosis results achieve promising performance for AD diagnosis tasks. |
| ArticleNumber | 110546 |
| Author | He, Xiaohai Liu, Yan Zhang, Jin Chen, Honggang Qing, Linbo Chen, Xiang |
| Author_xml | – sequence: 1 givenname: Jin surname: Zhang fullname: Zhang, Jin organization: College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, 610065, China – sequence: 2 givenname: Xiaohai orcidid: 0000-0002-2967-2682 surname: He fullname: He, Xiaohai email: hxh@scu.edu.cn organization: College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, 610065, China – sequence: 3 givenname: Linbo surname: Qing fullname: Qing, Linbo organization: College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, 610065, China – sequence: 4 givenname: Xiang orcidid: 0000-0003-4203-4578 surname: Chen fullname: Chen, Xiang organization: Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK – sequence: 5 givenname: Yan surname: Liu fullname: Liu, Yan organization: Department of Neurology, The Affiliated Hospital of Southwest Jiaotong University, The Third People’s Hospital of Chengdu, Chengdu, Sichuan, 610031, China – sequence: 6 givenname: Honggang surname: Chen fullname: Chen, Honggang organization: College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, 610065, China |
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| Keywords | Deep learning Medical image processing Graph convolutional network Alzheimer’s disease Structural magnetic resonance imaging |
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| SubjectTerms | Alzheimer’s disease Deep learning Graph convolutional network Medical image processing Structural magnetic resonance imaging |
| Title | Multi-relation graph convolutional network for Alzheimer’s disease diagnosis using structural MRI |
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