Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks
Schizophrenia (SZ) is a functional mental disorder that seriously affects the social life of patients. Therefore, accurate diagnosis of SZ has raised extensive attention of researchers. At present, study of brain network based on resting-state functional magnetic resonance imaging (rs-fMRI) has prov...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-01-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fbioe.2019.00479/full |
| _version_ | 1818910703891775488 |
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| author | Yizhen Xiang Jianxin Wang Jianxin Wang Guanxin Tan Fang-Xiang Wu Jin Liu |
| author_facet | Yizhen Xiang Jianxin Wang Jianxin Wang Guanxin Tan Fang-Xiang Wu Jin Liu |
| author_sort | Yizhen Xiang |
| collection | DOAJ |
| description | Schizophrenia (SZ) is a functional mental disorder that seriously affects the social life of patients. Therefore, accurate diagnosis of SZ has raised extensive attention of researchers. At present, study of brain network based on resting-state functional magnetic resonance imaging (rs-fMRI) has provided promising results for SZ identification by studying functional network alteration. However, previous studies based on brain network analysis are not very effective for SZ identification. Therefore, we propose an improved SZ identification method using multi-view graph measures of functional brain networks. Firstly, we construct an individual functional connectivity network based on Brainnetome atlas for each subject. Then, multi-view graph measures are calculated by the brain network analysis method as feature representations. Next, in order to consider the relationships between measures within the same brain region in feature selection, multi-view measures are grouped according to the corresponding regions and Sparse Group Lasso is applied to identify discriminative features based on this feature grouping structure. Finally, a support vector machine (SVM) classifier is employed to perform SZ identification task. To evaluate our proposed method, computational experiments are conducted on 145 subjects (71 schizophrenic patients and 74 healthy controls) using a leave-one-out cross-validation (LOOCV) scheme. The results show that our proposed method can obtain an accuracy of 93.10% for SZ identification. By comparison, our method is more effective for SZ identification than some existing methods. |
| first_indexed | 2024-12-19T22:47:02Z |
| format | Article |
| id | doaj.art-2f9675703ddb43079d1f3ec7b500bc74 |
| institution | Directory Open Access Journal |
| issn | 2296-4185 |
| language | English |
| last_indexed | 2024-12-19T22:47:02Z |
| publishDate | 2020-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj.art-2f9675703ddb43079d1f3ec7b500bc742022-12-21T20:02:55ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852020-01-01710.3389/fbioe.2019.00479496030Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain NetworksYizhen Xiang0Jianxin Wang1Jianxin Wang2Guanxin Tan3Fang-Xiang Wu4Jin Liu5School of Computer Science and Engineering, Central South University, Changsha, ChinaSchool of Computer Science and Engineering, Central South University, Changsha, ChinaHunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, ChinaSchool of Computer Science and Engineering, Central South University, Changsha, ChinaDivision of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, CanadaSchool of Computer Science and Engineering, Central South University, Changsha, ChinaSchizophrenia (SZ) is a functional mental disorder that seriously affects the social life of patients. Therefore, accurate diagnosis of SZ has raised extensive attention of researchers. At present, study of brain network based on resting-state functional magnetic resonance imaging (rs-fMRI) has provided promising results for SZ identification by studying functional network alteration. However, previous studies based on brain network analysis are not very effective for SZ identification. Therefore, we propose an improved SZ identification method using multi-view graph measures of functional brain networks. Firstly, we construct an individual functional connectivity network based on Brainnetome atlas for each subject. Then, multi-view graph measures are calculated by the brain network analysis method as feature representations. Next, in order to consider the relationships between measures within the same brain region in feature selection, multi-view measures are grouped according to the corresponding regions and Sparse Group Lasso is applied to identify discriminative features based on this feature grouping structure. Finally, a support vector machine (SVM) classifier is employed to perform SZ identification task. To evaluate our proposed method, computational experiments are conducted on 145 subjects (71 schizophrenic patients and 74 healthy controls) using a leave-one-out cross-validation (LOOCV) scheme. The results show that our proposed method can obtain an accuracy of 93.10% for SZ identification. By comparison, our method is more effective for SZ identification than some existing methods.https://www.frontiersin.org/article/10.3389/fbioe.2019.00479/fullSchizophrenia identificationfMRIfunctional brain networksmulti-view graph measuresSVM |
| spellingShingle | Yizhen Xiang Jianxin Wang Jianxin Wang Guanxin Tan Fang-Xiang Wu Jin Liu Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks Frontiers in Bioengineering and Biotechnology Schizophrenia identification fMRI functional brain networks multi-view graph measures SVM |
| title | Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks |
| title_full | Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks |
| title_fullStr | Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks |
| title_full_unstemmed | Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks |
| title_short | Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks |
| title_sort | schizophrenia identification using multi view graph measures of functional brain networks |
| topic | Schizophrenia identification fMRI functional brain networks multi-view graph measures SVM |
| url | https://www.frontiersin.org/article/10.3389/fbioe.2019.00479/full |
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