A Study on the Classification of Schizophrenia Based on Single Gray Matter Networks
In order to study the covariance of subtle differences in gray matter in schizophrenic patients, single subject brain network model was constructed by using voxel similarity index. The gray matter was divided into fixed size cubes, the single subject gray matter covariant network was constructed by...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Editorial Office of Journal of Taiyuan University of Technology
2021-05-01
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| Series: | Taiyuan Ligong Daxue xuebao |
| Subjects: | |
| Online Access: | https://tyutjournal.tyut.edu.cn/englishpaper/show-117.html |
| _version_ | 1797216690740658176 |
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| author | Yunxiao MA Jiarui LIANG Nan ZHANG Jie SUN Bin WANG |
| author_facet | Yunxiao MA Jiarui LIANG Nan ZHANG Jie SUN Bin WANG |
| author_sort | Yunxiao MA |
| collection | DOAJ |
| description | In order to study the covariance of subtle differences in gray matter in schizophrenic patients, single subject brain network model was constructed by using voxel similarity index. The gray matter was divided into fixed size cubes, the single subject gray matter covariant network was constructed by means of Pearson correlation between voxels, and the network was mode sparse by empirical null model method and finally normalized to a 90×90 gray matter covariant network. The results show that the topological properties of the single subject gray matter network of schizophrenia patients were significantly changed, the node efficiency and degree of the parietal lobe and frontal lobe were different, and the efficiency of the right precuneus was significantly correlated with the scale. Support vector machine, KNN, Naive Bayes and random forest algorithm were used to establish the classification model of schizophrenia patients, whose classification accuracy was significantly higher than the classification results based on the brain region. In conclusion, the personal gray matter network based on the gray matter similarity of voxels can better detect the abnormal brain network in schizophrenia, and can be applied to the early diagnosis of schizophrenia. |
| first_indexed | 2024-04-24T11:49:58Z |
| format | Article |
| id | doaj.art-3c7727a48a9b49b69bfe74f7e2733152 |
| institution | Directory Open Access Journal |
| issn | 1007-9432 |
| language | English |
| last_indexed | 2024-04-24T11:49:58Z |
| publishDate | 2021-05-01 |
| publisher | Editorial Office of Journal of Taiyuan University of Technology |
| record_format | Article |
| series | Taiyuan Ligong Daxue xuebao |
| spelling | doaj.art-3c7727a48a9b49b69bfe74f7e27331522024-04-09T08:03:59ZengEditorial Office of Journal of Taiyuan University of TechnologyTaiyuan Ligong Daxue xuebao1007-94322021-05-0152342442910.16355/j.cnki.issn1007-9432tyut.2021.03.0141007-9432(2021)03-0424-06A Study on the Classification of Schizophrenia Based on Single Gray Matter NetworksYunxiao MA0Jiarui LIANG1Nan ZHANG2Jie SUN3Bin WANG4College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Software, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, ChinaIn order to study the covariance of subtle differences in gray matter in schizophrenic patients, single subject brain network model was constructed by using voxel similarity index. The gray matter was divided into fixed size cubes, the single subject gray matter covariant network was constructed by means of Pearson correlation between voxels, and the network was mode sparse by empirical null model method and finally normalized to a 90×90 gray matter covariant network. The results show that the topological properties of the single subject gray matter network of schizophrenia patients were significantly changed, the node efficiency and degree of the parietal lobe and frontal lobe were different, and the efficiency of the right precuneus was significantly correlated with the scale. Support vector machine, KNN, Naive Bayes and random forest algorithm were used to establish the classification model of schizophrenia patients, whose classification accuracy was significantly higher than the classification results based on the brain region. In conclusion, the personal gray matter network based on the gray matter similarity of voxels can better detect the abnormal brain network in schizophrenia, and can be applied to the early diagnosis of schizophrenia.https://tyutjournal.tyut.edu.cn/englishpaper/show-117.htmlsupport vector machineclassificationschizophreniavoxelbrain networkgray matter |
| spellingShingle | Yunxiao MA Jiarui LIANG Nan ZHANG Jie SUN Bin WANG A Study on the Classification of Schizophrenia Based on Single Gray Matter Networks Taiyuan Ligong Daxue xuebao support vector machine classification schizophrenia voxel brain network gray matter |
| title | A Study on the Classification of Schizophrenia Based on Single Gray Matter Networks |
| title_full | A Study on the Classification of Schizophrenia Based on Single Gray Matter Networks |
| title_fullStr | A Study on the Classification of Schizophrenia Based on Single Gray Matter Networks |
| title_full_unstemmed | A Study on the Classification of Schizophrenia Based on Single Gray Matter Networks |
| title_short | A Study on the Classification of Schizophrenia Based on Single Gray Matter Networks |
| title_sort | study on the classification of schizophrenia based on single gray matter networks |
| topic | support vector machine classification schizophrenia voxel brain network gray matter |
| url | https://tyutjournal.tyut.edu.cn/englishpaper/show-117.html |
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