Intelligent diagnosis of major depression disease based on multi-layer brain network
IntroductionResting-state brain network with physiological and pathological basis has always been the ideal data for intelligent diagnosis of major depression disease (MDD). Brain networks are divided into low-order networks and high-order networks. Most of the studies only use a single-level networ...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1126865/full |
| _version_ | 1797869608640708608 |
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| author | Dan Long Mengda Zhang Jing Yu Qi Zhu Fengnong Chen Fangyin Li |
| author_facet | Dan Long Mengda Zhang Jing Yu Qi Zhu Fengnong Chen Fangyin Li |
| author_sort | Dan Long |
| collection | DOAJ |
| description | IntroductionResting-state brain network with physiological and pathological basis has always been the ideal data for intelligent diagnosis of major depression disease (MDD). Brain networks are divided into low-order networks and high-order networks. Most of the studies only use a single-level network to classify while ignoring that the brain works cooperatively with different levels of networks. This study hopes to find out whether varying levels of networks will provide complementary information in the process of intelligent diagnosis and what impact will be made on the final classification results by combining the characteristics of different networks.MethodsOur data are from the REST-meta-MDD project. After the screening, 1,160 subjects from ten sites were included in this study (597 MDD and 563 normal controls). For each subject, we constructed three different levels of networks according to the brain atlas: the traditional low-order network based on Pearson’s correlation (low-order functional connectivity, LOFC), the high-order network based on topographical profile similarity (topographical information-based high-order functional connectivity, tHOFC) and the associated network between them (aHOFC). Two sample t-test is used for feature selection, and then features from different sources are fused. Finally, the classifier is trained by a multi-layer perceptron or support vector machine. The performance of the classifier was evaluated using the leave-one-site cross-validation method.ResultsThe classification ability of LOFC is the highest among the three networks. The classification accuracy of the three networks combined is similar to the LOFC network. These are seven features chosen in all networks. In the aHOFC classification, six features were selected in each round but not seen in other classifications. In the tHOFC classification, five features were selected in each round but were unique. These new features have crucial pathological significance and are essential supplements to LOFC.ConclusionA high-order network can provide auxiliary information for low-order networks but cannot improve classification accuracy. |
| first_indexed | 2024-04-10T00:15:18Z |
| format | Article |
| id | doaj.art-3c8d8828ec2045ceb7fd8e96342aad4b |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-10T00:15:18Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-3c8d8828ec2045ceb7fd8e96342aad4b2023-03-16T05:20:57ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-03-011710.3389/fnins.2023.11268651126865Intelligent diagnosis of major depression disease based on multi-layer brain networkDan Long0Mengda Zhang1Jing Yu2Qi Zhu3Fengnong Chen4Fangyin Li5Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, ChinaSchool of Automation, Hangzhou Dianzi University, Hangzhou, ChinaThe College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaThe College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaSchool of Automation, Hangzhou Dianzi University, Hangzhou, ChinaZhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, ChinaIntroductionResting-state brain network with physiological and pathological basis has always been the ideal data for intelligent diagnosis of major depression disease (MDD). Brain networks are divided into low-order networks and high-order networks. Most of the studies only use a single-level network to classify while ignoring that the brain works cooperatively with different levels of networks. This study hopes to find out whether varying levels of networks will provide complementary information in the process of intelligent diagnosis and what impact will be made on the final classification results by combining the characteristics of different networks.MethodsOur data are from the REST-meta-MDD project. After the screening, 1,160 subjects from ten sites were included in this study (597 MDD and 563 normal controls). For each subject, we constructed three different levels of networks according to the brain atlas: the traditional low-order network based on Pearson’s correlation (low-order functional connectivity, LOFC), the high-order network based on topographical profile similarity (topographical information-based high-order functional connectivity, tHOFC) and the associated network between them (aHOFC). Two sample t-test is used for feature selection, and then features from different sources are fused. Finally, the classifier is trained by a multi-layer perceptron or support vector machine. The performance of the classifier was evaluated using the leave-one-site cross-validation method.ResultsThe classification ability of LOFC is the highest among the three networks. The classification accuracy of the three networks combined is similar to the LOFC network. These are seven features chosen in all networks. In the aHOFC classification, six features were selected in each round but not seen in other classifications. In the tHOFC classification, five features were selected in each round but were unique. These new features have crucial pathological significance and are essential supplements to LOFC.ConclusionA high-order network can provide auxiliary information for low-order networks but cannot improve classification accuracy.https://www.frontiersin.org/articles/10.3389/fnins.2023.1126865/fullmulti-layer brain function networkmajor depression disease (MDD)intelligent diagnosisthe pathological basisdeep learning |
| spellingShingle | Dan Long Mengda Zhang Jing Yu Qi Zhu Fengnong Chen Fangyin Li Intelligent diagnosis of major depression disease based on multi-layer brain network Frontiers in Neuroscience multi-layer brain function network major depression disease (MDD) intelligent diagnosis the pathological basis deep learning |
| title | Intelligent diagnosis of major depression disease based on multi-layer brain network |
| title_full | Intelligent diagnosis of major depression disease based on multi-layer brain network |
| title_fullStr | Intelligent diagnosis of major depression disease based on multi-layer brain network |
| title_full_unstemmed | Intelligent diagnosis of major depression disease based on multi-layer brain network |
| title_short | Intelligent diagnosis of major depression disease based on multi-layer brain network |
| title_sort | intelligent diagnosis of major depression disease based on multi layer brain network |
| topic | multi-layer brain function network major depression disease (MDD) intelligent diagnosis the pathological basis deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1126865/full |
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