Using support vector machine to explore the difference of function connection between deficit and non-deficit schizophrenia based on gray matter volume
ObjectiveSchizophrenia can be divided into deficient schizophrenia (DS) and non-deficient schizophrenia (NDS) according to the presence of primary and persistent negative symptoms. So far, there are few studies that have explored the differences in functional connectivity (FC) between the different...
| Main Authors: | , , , , |
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| Format: | Article |
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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.2023.1132607/full |
| _version_ | 1797859710036082688 |
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| author | Wenjing Zhu Wenjing Zhu Zan Wang Miao Yu Xiangrong Zhang Zhijun Zhang Zhijun Zhang |
| author_facet | Wenjing Zhu Wenjing Zhu Zan Wang Miao Yu Xiangrong Zhang Zhijun Zhang Zhijun Zhang |
| author_sort | Wenjing Zhu |
| collection | DOAJ |
| description | ObjectiveSchizophrenia can be divided into deficient schizophrenia (DS) and non-deficient schizophrenia (NDS) according to the presence of primary and persistent negative symptoms. So far, there are few studies that have explored the differences in functional connectivity (FC) between the different subtypes based on the region of interest (ROI) from GMV (Gray matter volume), especially since the characteristics of brain networks are still unknown. This study aimed to investigate the alterations of functional connectivity between DS and NDS based on the ROI obtained by machine learning algorithms and differential GMV. Then, the relationships between the alterations and the clinical symptoms were analyzed. In addition, the thalamic functional connection imbalance in the two groups was further explored.MethodsA total of 16 DS, 31 NDS, and 38 health controls (HC) underwent resting-state fMRI scans, patient group will further be evaluated by clinical scales including the Brief Psychiatric Rating Scale (BPRS), the Scale for the Assessment of Negative Symptoms (SANS), and the Scale for the Assessment of Positive Symptoms (SAPS). Based on GMV image data, a support vector machine (SVM) is used to classify DS and NDS. Brain regions with high weight in the classification were used as seed points in whole-brain FC analysis and thalamic FC imbalance analysis. Finally, partial correlation analysis explored the relationships between altered FC and clinical scale in the two subtypes.ResultsThe relatively high classification accuracy is obtained based on the SVM. Compared to HC, the FC increased between the right inferior parietal lobule (IPL.R) bilateral thalamus, and lingual gyrus, and between the right inferior temporal gyrus (ITG.R) and the Salience Network (SN) in NDS. The FC between the right thalamus (THA.R) and Visual network (VN), between ITG.R and right superior occipital gyrus in the DS group was higher than that in HC. Furthermore, compared with NDS, the FC between the ITG.R and the left superior and middle frontal gyrus decreased in the DS group. The thalamic FC imbalance, which is characterized by frontotemporal-THA.R hypoconnectivity and sensory motor network (SMN)-THA.R hyperconnectivity was found in both subtypes. The FC value of THA.R and SMN was negatively correlated with the SANS score in the DS group but positively correlated with the SAPS score in the NDS group.ConclusionUsing an SVM classification method and based on an ROI from GMV, we highlighted the difference in functional connectivity between DS and NDS from the local to the brain network, which provides new information for exploring the neural physiopathology of the two subtypes of schizophrenic. |
| first_indexed | 2024-04-09T21:35:00Z |
| format | Article |
| id | doaj.art-ae2099bb17384e9e966a916898ae33f3 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-09T21:35:00Z |
| publishDate | 2023-03-01 |
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| series | Frontiers in Neuroscience |
| spelling | doaj.art-ae2099bb17384e9e966a916898ae33f32023-03-27T04:57:35ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-03-011710.3389/fnins.2023.11326071132607Using support vector machine to explore the difference of function connection between deficit and non-deficit schizophrenia based on gray matter volumeWenjing Zhu0Wenjing Zhu1Zan Wang2Miao Yu3Xiangrong Zhang4Zhijun Zhang5Zhijun Zhang6Department of Neurology, School of Medicine, Affiliated Zhongda Hospital, Research Institution of Neuropsychiatry, Southeast University, Nanjing, ChinaAffiliated Mental Health Center, Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, ChinaDepartment of Neurology, School of Medicine, Affiliated Zhongda Hospital, Research Institution of Neuropsychiatry, Southeast University, Nanjing, ChinaDepartment of Geriatric Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, ChinaDepartment of Geriatric Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, ChinaDepartment of Neurology, School of Medicine, Affiliated Zhongda Hospital, Research Institution of Neuropsychiatry, Southeast University, Nanjing, ChinaAffiliated Mental Health Center, Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, ChinaObjectiveSchizophrenia can be divided into deficient schizophrenia (DS) and non-deficient schizophrenia (NDS) according to the presence of primary and persistent negative symptoms. So far, there are few studies that have explored the differences in functional connectivity (FC) between the different subtypes based on the region of interest (ROI) from GMV (Gray matter volume), especially since the characteristics of brain networks are still unknown. This study aimed to investigate the alterations of functional connectivity between DS and NDS based on the ROI obtained by machine learning algorithms and differential GMV. Then, the relationships between the alterations and the clinical symptoms were analyzed. In addition, the thalamic functional connection imbalance in the two groups was further explored.MethodsA total of 16 DS, 31 NDS, and 38 health controls (HC) underwent resting-state fMRI scans, patient group will further be evaluated by clinical scales including the Brief Psychiatric Rating Scale (BPRS), the Scale for the Assessment of Negative Symptoms (SANS), and the Scale for the Assessment of Positive Symptoms (SAPS). Based on GMV image data, a support vector machine (SVM) is used to classify DS and NDS. Brain regions with high weight in the classification were used as seed points in whole-brain FC analysis and thalamic FC imbalance analysis. Finally, partial correlation analysis explored the relationships between altered FC and clinical scale in the two subtypes.ResultsThe relatively high classification accuracy is obtained based on the SVM. Compared to HC, the FC increased between the right inferior parietal lobule (IPL.R) bilateral thalamus, and lingual gyrus, and between the right inferior temporal gyrus (ITG.R) and the Salience Network (SN) in NDS. The FC between the right thalamus (THA.R) and Visual network (VN), between ITG.R and right superior occipital gyrus in the DS group was higher than that in HC. Furthermore, compared with NDS, the FC between the ITG.R and the left superior and middle frontal gyrus decreased in the DS group. The thalamic FC imbalance, which is characterized by frontotemporal-THA.R hypoconnectivity and sensory motor network (SMN)-THA.R hyperconnectivity was found in both subtypes. The FC value of THA.R and SMN was negatively correlated with the SANS score in the DS group but positively correlated with the SAPS score in the NDS group.ConclusionUsing an SVM classification method and based on an ROI from GMV, we highlighted the difference in functional connectivity between DS and NDS from the local to the brain network, which provides new information for exploring the neural physiopathology of the two subtypes of schizophrenic.https://www.frontiersin.org/articles/10.3389/fnins.2023.1132607/fulldeficit schizophreniafunctional connectivityresting-state fMRIgray matter volumesupport vector machine |
| spellingShingle | Wenjing Zhu Wenjing Zhu Zan Wang Miao Yu Xiangrong Zhang Zhijun Zhang Zhijun Zhang Using support vector machine to explore the difference of function connection between deficit and non-deficit schizophrenia based on gray matter volume Frontiers in Neuroscience deficit schizophrenia functional connectivity resting-state fMRI gray matter volume support vector machine |
| title | Using support vector machine to explore the difference of function connection between deficit and non-deficit schizophrenia based on gray matter volume |
| title_full | Using support vector machine to explore the difference of function connection between deficit and non-deficit schizophrenia based on gray matter volume |
| title_fullStr | Using support vector machine to explore the difference of function connection between deficit and non-deficit schizophrenia based on gray matter volume |
| title_full_unstemmed | Using support vector machine to explore the difference of function connection between deficit and non-deficit schizophrenia based on gray matter volume |
| title_short | Using support vector machine to explore the difference of function connection between deficit and non-deficit schizophrenia based on gray matter volume |
| title_sort | using support vector machine to explore the difference of function connection between deficit and non deficit schizophrenia based on gray matter volume |
| topic | deficit schizophrenia functional connectivity resting-state fMRI gray matter volume support vector machine |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1132607/full |
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